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This led to many environmentally beneficial working practices being adopted in Radiology in the South West Peninsula Deanery, and throughout this paper we have evaluated these changes and used our collective experience of these to inform our suggestions on how to improve the environmental sustainability of Medical and Radiological training.Radiology &. ImagingDiagnostic radiologyMedical education &.

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The technique took hold bayer levitra coupons in the U.S. When then-President Richard Nixon opened up relations with China, says Kimberly Henneman, a veterinarian who specializes in performance animals and uses the technique in her practice.Although not every person (or animal) responds to the technique, you’d be hard-pressed to find a condition that hasn’t been studied in connection with acupuncture, including low back pain, neck pain, knee pain from osteoarthritis, carpal tunnel, infertility, migraines, bayer levitra coupons bedwetting, ADHD, nausea and vomiting.The body responds to acupuncture depending on where the needle is placed and how the area is stimulated, says Chi-Tsai Tang, a rehabilitation physician in the department of orthopedics at Washington University School of Medicine in St. Louis, MO.There are also different types of acupuncture.

Some techniques relax the muscle bayer levitra coupons and surrounding fascia, a kind of connective tissue. Electroacupuncture, which is commonly used for pain relief, stimulates your body to bayer levitra coupons release its own pain inhibitors, as well as an immune chemical that's normally released during exercise. Acupuncture also causes the release of local anti-inflammatory chemicals, and some research suggests it might even rewire the brain to produce long-term relief from conditions like carpal tunnel syndrome.All Creatures Great and SmallMany people might be surprised to learn that acupuncture is also sometimes used on animals.

As with bayer levitra coupons people, not all animals respond to the treatment. Likewise, many animals dislike needles so much that it's not an option. But for bayer levitra coupons some cooperative animals, it works well, saya Henneman.

€œYou will see profound relaxation at the time of treatment, or some will have a little check-out moment, and then all of a sudden, they bayer levitra coupons are very energetic."Electroacupuncture in a middle-aged bomb detection dog who was starting to develop back pain and disk degeneration. This was done out on the sidewalk of the handler's agency while they were both on duty (with the dog unrestrained). It was bayer levitra coupons summer and the dog was most comfortable outside.

(Credit. Kimberly Henneman)When she first started using acupuncture, Henneman says she got a lot of grief from local horse vets. Now, it’s common for veterinary schools to have an acupuncture specialist on staff.As with the technique in humans, there’s much discussion over where to place the needles, and whether location really matters.

If you’re familiar with the charts from traditional Chinese medicine showing an outline of the human body with needles jutting out along anatomical markers, veterinary acupuncture uses similar ancient charts.The technique has its skeptics in both human and animal practices. Needle placement is only one of the contentious issues. Researchers still haven’t connected the dots between mechanical stimulation of the needle and response to treatment.Veterinarian Kimberly Henneman performs acupuncture on a Clydesdale named Duffy in 2002.

(Credit. Tracy Turner)Under the MicroscopePrevious clinical trials on acupuncture (in humans) often didn’t include enough people and didn’t last long enough, according to Langevin. Acupuncture is also tricky to study in blinded, randomized controlled trials because designing a sham treatment to use on the control groups hasn’t been easy.

The feel of the needle going into the skin is distinctive, and both patients and practitioners would know the difference if they were being duped. This makes it easy for both parties to figure out whether they’re in the experimental or the sham group, which could influence results. Complicating matters even more, study participants receiving fake treatments also commonly report pain relief.

But whether that’s due to a placebo effect or something else has yet to be sorted out.“Some of the well-done studies don’t show that true acupuncture is better than sham [treatments] and that’s where a lot of issues come in,” says Tang.A 2012 review of many studies did show that people who got acupuncture over a control treatment experienced improvements to pain, but the effect was small. The researchers updated their work in a 2017 analysis based on data for more than 20,000 people and found a statistically significant difference between the acupuncture, sham and usual-care groups."Ten or 15 years ago, I was one of the people who would have said there’s no difference between real and sham acupuncture,” says Langevin. €œSince then, I think what it needed was a lot of data, because the response to acupuncture is variable, and we need big studies to see the effects.”Sticking PointsWhile the practice has won over a few skeptics, acupuncture is still a controversial treatment in the medical community.

Critics say that there aren’t enough solid studies to make the technique scientifically credible and often point to a 2017 review that picked apart a slew of acupuncture trials for a wide variety of conditions. After the review was published, Edzard Ernst — a former professor of complementary medicine at the University of Exeter and critic of the procedure — posted a commentary on his website, writing that “It would be hard to dispute the conclusion that there is no convincing evidence that acupuncture is an effective therapy, I believe.” The website Science-Based Medicine has several posts criticizing the insufficient evidence for the technique, as does Coyne of the Realm.But if acupuncture does work for pain, the benefits likely come from a combination of things — including the specific needling technique used, the amount of pressure applied on the body and the natural analgesic effects produced, along with other factors. There is also a placebo effect, says Tang.As to whether it matters where needles are placed on the body, Tang says this aspect is "less important than what people think it is.” Langevin agrees and says this dogma of acupuncture bugs her.

€œI have been one of the critics of the notion that there are specific points you are supposed to put the needle.” To help resolve the debate, Langevin is advocating for a reliable database that describes the exact anatomical location of the points, and the anatomical features that needles are interacting with. Such information could help researchers sort out whether there’s really something specific about applying needles to a particular location.“This thing about the points is dragging the field down,” says Langevin. €œIt’s been heavily criticized, and if that can get cleaned up it would go a long way to rehabilitate the image of acupuncture as something that’s scientific and rational, as opposed to pre-scientific.”BCAA supplements are considered one of the most essential products for improving muscle growth, recovery, and exercise capacity.

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SHOP HUGE BCAAWhen we consider all criteria, there's no doubt that Huge BCAA by Huge Nutrition is the best BCAA supplement currently available. With a hefty 19.4 grams serving size per scoop, it beats all other alternatives on the market. It has 8000mg of branched-chain amino acids powder in there at the scientifically backed 2:1:1 ratio.

The massive dose in this supplement is what makes it so unique and effective. Since you will be getting considerably large amount of ingredients, you'll notice the effects and results more quickly.Why it's #1 on our list:● Most stacked option on the market.● Nearly 20 grams per scoop.● It keeps you hydrated during workouts.● 8000mg of BCAAs at 2:1:1 ratio.● It contains all the essential ingredients.● Combines BCAAs &. EAAs.● Best bang for your buck supplement.As you can see, there is a lot of reasons why this product has reached the top of our list, and you surely won't be disappointed by it.

Huge BCAA can be bought directly from the official Huge Nutrition site. Click here to visit the product page and check its availability.#2. Alpha Amino by CellucorThe second BCAA supplement we stand by is Cellucor's Alpha Amino.

It's a well-known product that has been designed to help accelerate recovery and muscle growth. Each scoop of Alpha Amino provides a total of 12.8 grams, of which 5 grams are branched-chain amino acids. Not as much as our top pick, but still a substantial amount.Why it's #2 on our list:● Produced by a well-known supplement company.● It contains several types of Electrolytes.● It doesn't have any calories or sugar.● Designed to help optimize your performance.● Provides 5000mg of branched-chain amino acids.● Available in different tasty flavors.With this option, you'll stay hydrated during your workouts, and the product will make sure to maximize your recovery.

You can find Alpha Amino for sale on platforms such as Amazon and other sports supplement retailers.#3. Xtend Sport by ScivationAnother excellent BCAA supplement that has landed the third spot on our list is Xtend Sport by Scivation. Chances are that you've come across this product since it's well-known amongst athletes.

It's often consumed during workouts by athletes to help them stay hydrated, pumped, and on top of their game. Why it's #3 on our list:● It holds 7 grams of BCAAs per serving.● Comes in several flavors.● Tested and trusted by third parties.● Calorie and sugar-free.● Focuses on improving muscle recovery.Xtend Sport contains a solid amount of amino acids that will positively impact your muscles' recovery process. Each tub of this product holds 30 servings.

Since Scivation's XTend is a relatively popular supplement, many online retailers carry it. #4. BCAA5000 by Evlution NutritionThe next product we have lined up is BCAA500 by Evlution Nutrition.

It's often used by athletes looking to improve muscle mass, endurance, and recovery naturally. Simply mix a scoop of this product with water and consume it before or during your workout, and you'll be reaping the benefits.Why it's #4 on our list:● Uses the science-backed 2:1:1 ratio of branched-chain amino acids.● It helps with preserving lean muscle mass.● This supplement contains 5 grams of premium BCAAs per scoop.● It helps fuel the muscles and prevents fatigue.● Free of gluten and tastes excellent. You've probably noticed that there are just 5 grams of amino acids in this product, which is less than the others mentioned on our list.

However, that doesn't mean it's not a good product as it will still help with improving recovery and other aspects of your training.Let's move onto the final product that is featured in our article.#5. Naked BCAAs by Naked NutritionWe're finishing this top 5 with Naked BCAAs by Naked Nutrition. If you're a vegan, this is the perfect option for you.

Holding only pure, vegan branched-chain amino acids, this product doesn't contain any useless fillers whatsoever. You get what you buy.Why it's #5 on our list:● It holds vegan BCAA powder, making it one of a kind.● Five grams of amino acids per 1 serving.● Assists with repairing muscle recovery.● Dissolves fast and absorbed quickly.● It Doesn't contain artificial sweeteners or coloring.And with this product, we finish our top 5 best BCAA supplements on the market. But, we're not done yet and want to give you some additional information about these supplements Please continue reading for more information on the benefits of BCAA supplements.Why BCAA Supplements Are BeneficialIf you're unsure why you should consider adding a BCAA supplement to your routine, allow me to explain.

First, it's essential to know that there's a lot of scientific data available to back up the benefits these amino acids may have. The primary reasons they are being used is because:● They help reduce muscle soreness (1).● They boost muscle recovery.● They stimulate muscle protein synthesis.● They increase time to exhaustion (2).Based on the above benefits, there's no denying that these types of supplements are an excellent addition for anyone. Whether you are new to working out or an experienced athlete, this stuff is great.

We are massive fans of these amino acids simply because they've got a lot of data to prove their effectiveness. It's one of the main reasons we've written this article, to show our audience that there are, in fact, natural supplements that can help.Our recommendation would be to go with Huge BCAA powder since it holds the most aminoacids. It also contains other ingredients that will help with recovery and muscle growth.The Final VerdictThere are many BCAA supplements on the market, but we've managed to bring it down to just a handful of products through extensive research and testing.

Our top 5 best BCAA powders consist of only the best quality products to help take your training and physique to a new level. Of course, there are other useful products out there besides the ones we have mentioned. It's essential to do your research on the ingredients in any supplement to be sure of their efficacy.

But if you want our advice, choose a product from our list as you can't go wrong with them. Our top-rated product can be bought from HugeSupplements..

When Helene Langevin was practicing medicine, many of her patients came to her for pain levitra price comparison relief, and she had little to offer them. Curiosity led her to a nearby school for training in acupuncture.A few years later, Langevin transitioned to full-time research and began to study how acupuncture needles react to connective tissue.“I could feel with my hands that something was levitra price comparison happening. I felt a resistance to the needle manipulation, and there was no explanation,” says Langevin, who’s now director of the National Center for Complementary and Integrative Health (NCCIH) at the National Institutes of Health.Acupuncture has been around for 3,000 years and comes from traditional Chinese medicine, which aims to prevent and treat health issues with mind and body practices. The technique took hold in the levitra price comparison U.S. When then-President Richard Nixon opened up relations with China, says Kimberly Henneman, a veterinarian who specializes in performance animals and uses the technique in her practice.Although not every person (or animal) responds to the technique, you’d be hard-pressed to find a condition that hasn’t been studied in connection with acupuncture, including low back pain, neck pain, knee pain from osteoarthritis, carpal tunnel, infertility, migraines, bedwetting, ADHD, nausea and vomiting.The body responds to acupuncture depending on where the needle is placed and how levitra price comparison the area is stimulated, says Chi-Tsai Tang, a rehabilitation physician in the department of orthopedics at Washington University School of Medicine in St.

Louis, MO.There are also different types of acupuncture. Some techniques relax the muscle and surrounding fascia, levitra price comparison a kind of connective tissue. Electroacupuncture, which is commonly used for pain relief, stimulates your body levitra price comparison to release its own pain inhibitors, as well as an immune chemical that's normally released during exercise. Acupuncture also causes the release of local anti-inflammatory chemicals, and some research suggests it might even rewire the brain to produce long-term relief from conditions like carpal tunnel syndrome.All Creatures Great and SmallMany people might be surprised to learn that acupuncture is also sometimes used on animals. As with people, not all animals respond to levitra price comparison the treatment.

Likewise, many animals dislike needles so much that it's not an option. But for levitra price comparison some cooperative animals, it works well, saya Henneman. €œYou will levitra price comparison see profound relaxation at the time of treatment, or some will have a little check-out moment, and then all of a sudden, they are very energetic."Electroacupuncture in a middle-aged bomb detection dog who was starting to develop back pain and disk degeneration. This was done out on the sidewalk of the handler's agency while they were both on duty (with the dog unrestrained). It was summer levitra price comparison and the dog was most comfortable outside.

(Credit. Kimberly Henneman)When she first started using acupuncture, Henneman says she got a lot of grief from local horse vets. Now, it’s common for veterinary schools to have an acupuncture specialist on staff.As with the technique in humans, there’s much discussion over where to place the needles, and whether location really matters. If you’re familiar with the charts from traditional Chinese medicine showing an outline of the human body with needles jutting out along anatomical markers, veterinary acupuncture uses similar ancient charts.The technique has its skeptics in both human and animal practices. Needle placement is only one of the contentious issues.

Researchers still haven’t connected the dots between mechanical stimulation of the needle and response to treatment.Veterinarian Kimberly Henneman performs acupuncture on a Clydesdale named Duffy in 2002. (Credit. Tracy Turner)Under the MicroscopePrevious clinical trials on acupuncture (in humans) often didn’t include enough people and didn’t last long enough, according to Langevin. Acupuncture is also tricky to study in blinded, randomized controlled trials because designing a sham treatment to use on the control groups hasn’t been easy. The feel of the needle going into the skin is distinctive, and both patients and practitioners would know the difference if they were being duped.

This makes it easy for both parties to figure out whether they’re in the experimental or the sham group, which could influence results. Complicating matters even more, study participants receiving fake treatments also commonly report pain relief. But whether that’s due to a placebo effect or something else has yet to be sorted out.“Some of the well-done studies don’t show that true acupuncture is better than sham [treatments] and that’s where a lot of issues come in,” says Tang.A 2012 review of many studies did show that people who got acupuncture over a control treatment experienced improvements to pain, but the effect was small. The researchers updated their work in a 2017 analysis based on data for more than 20,000 people and found a statistically significant difference between the acupuncture, sham and usual-care groups."Ten or 15 years ago, I was one of the people who would have said there’s no difference between real and sham acupuncture,” says Langevin. €œSince then, I think what it needed was a lot of data, because the response to acupuncture is variable, and we need big studies to see the effects.”Sticking PointsWhile the practice has won over a few skeptics, acupuncture is still a controversial treatment in the medical community.

Critics say that there aren’t enough solid studies to make the technique scientifically credible and often point to a 2017 review that picked apart a slew of acupuncture trials for a wide variety of conditions. After the review was published, Edzard Ernst — a former professor of complementary medicine at the University of Exeter and critic of the procedure — posted a commentary on his website, writing that “It would be hard to dispute the conclusion that there is no convincing evidence that acupuncture is an effective therapy, I believe.” The website Science-Based Medicine has several posts criticizing the insufficient evidence for the technique, as does Coyne of the Realm.But if acupuncture does work for pain, the benefits likely come from a combination of things — including the specific needling technique used, the amount of pressure applied on the body and the natural analgesic effects produced, along with other factors. There is also a placebo effect, says Tang.As to whether it matters where needles are placed on the body, Tang says this aspect is "less important than what people think it is.” Langevin agrees and says this dogma of acupuncture bugs her. €œI have been one of the critics of the notion that there are specific points you are supposed to put the needle.” To help resolve the debate, Langevin is advocating for a reliable database that describes the exact anatomical location of the points, and the anatomical features that needles are interacting with. Such information could help researchers sort out whether there’s really something specific about applying needles to a particular location.“This thing about the points is dragging the field down,” says Langevin.

€œIt’s been heavily criticized, and if that can get cleaned up it would go a long way to rehabilitate the image of acupuncture as something that’s scientific and rational, as opposed to pre-scientific.”BCAA supplements are considered one of the most essential products for improving muscle growth, recovery, and exercise capacity. These types of supplements have such a positive impact, you'll find them in every serious athlete's supplement stash. They simply do wonders when it comes to helping you improve performance.But as you've probably already noticed, there are loads of supplements containing branched-chain amino acids (BCAAs) on the market. Due to the massive amount of products, finding the best and most effective product for you can be challenging.Our team of experts at M.D. Marketing have extensively tested most options on the market and evaluated which products are the best BCAA supplements.

You won't have to waste any time, money, or resources trying to evaluate dozens of products - we've done it for you. Top 5 Best BCAA Powder Supplements RevealedIt's time to introduce you to our top 5 best BCAA powder supplements. These products are the most stacked, most effective, and best bang for your buck. Our top 5 is ranked by the grams per scoop to show you how much active ingredients each contains.1. Huge BCAA – 19.4 grams per scoop. 2. Cellucor Alpha Amino – 12.8 grams per scoop.3. Xtend Sport – 11.5 grams per scoop.4. Evlution Nutrition BCAA5000 – 8.3 grams per scoop 5. Naked BCAAs - 5 grams per scoop.The reason we've included the grams per scoop is that this plays a massive role in effectiveness.

The more ingredients and dosages you get, the more impact it will have on your physique and performance. As you can see, our top-pick, Huge BCAA, contains 19.4 grams per scoop, which is double to triple the amount some products have.We will go over each supplement individually and highlight its strong points and what these products can offer you.#1. Huge BCAA by Huge Nutrition>. SHOP HUGE BCAAWhen we consider all criteria, there's no doubt that Huge BCAA by Huge Nutrition is the best BCAA supplement currently available. With a hefty 19.4 grams serving size per scoop, it beats all other alternatives on the market.

It has 8000mg of branched-chain amino acids powder in there at the scientifically backed 2:1:1 ratio. The massive dose in this supplement is what makes it so unique and effective. Since you will be getting considerably large amount of ingredients, you'll notice the effects and results more quickly.Why it's #1 on our list:● Most stacked option on the market.● Nearly 20 grams per scoop.● It keeps you hydrated during workouts.● 8000mg of BCAAs at 2:1:1 ratio.● It contains all the essential ingredients.● Combines BCAAs &. EAAs.● Best bang for your buck supplement.As you can see, there is a lot of reasons why this product has reached the top of our list, and you surely won't be disappointed by it. Huge BCAA can be bought directly from the official Huge Nutrition site.

Click here to visit the product page and check its availability.#2. Alpha Amino by CellucorThe second BCAA supplement we stand by is Cellucor's Alpha Amino. It's a well-known product that has been designed to help accelerate recovery and muscle growth. Each scoop of Alpha Amino provides a total of 12.8 grams, of which 5 grams are branched-chain amino acids. Not as much as our top pick, but still a substantial amount.Why it's #2 on our list:● Produced by a well-known supplement company.● It contains several types of Electrolytes.● It doesn't have any calories or sugar.● Designed to help optimize your performance.● Provides 5000mg of branched-chain amino acids.● Available in different tasty flavors.With this option, you'll stay hydrated during your workouts, and the product will make sure to maximize your recovery.

You can find Alpha Amino for sale on platforms such as Amazon and other sports supplement retailers.#3. Xtend Sport by ScivationAnother excellent BCAA supplement that has landed the third spot on our list is Xtend Sport by Scivation. Chances are that you've come across this product since it's well-known amongst athletes. It's often consumed during workouts by athletes to help them stay hydrated, pumped, and on top of their game. Why it's #3 on our list:● It holds 7 grams of BCAAs per serving.● Comes in several flavors.● Tested and trusted by third parties.● Calorie and sugar-free.● Focuses on improving muscle recovery.Xtend Sport contains a solid amount of amino acids that will positively impact your muscles' recovery process.

Each tub of this product holds 30 servings. Since Scivation's XTend is a relatively popular supplement, many online retailers carry it. #4. BCAA5000 by Evlution NutritionThe next product we have lined up is BCAA500 by Evlution Nutrition. It's often used by athletes looking to improve muscle mass, endurance, and recovery naturally.

Simply mix a scoop of this product with water and consume it before or during your workout, and you'll be reaping the benefits.Why it's #4 on our list:● Uses the science-backed 2:1:1 ratio of branched-chain amino acids.● It helps with preserving lean muscle mass.● This supplement contains 5 grams of premium BCAAs per scoop.● It helps fuel the muscles and prevents fatigue.● Free of gluten and tastes excellent. You've probably noticed that there are just 5 grams of amino acids in this product, which is less than the others mentioned on our list. However, that doesn't mean it's not a good product as it will still help with improving recovery and other aspects of your training.Let's move onto the final product that is featured in our article.#5. Naked BCAAs by Naked NutritionWe're finishing this top 5 with Naked BCAAs by Naked Nutrition. If you're a vegan, this is the perfect option for you.

Holding only pure, vegan branched-chain amino acids, this product doesn't contain any useless fillers whatsoever. You get what you buy.Why it's #5 on our list:● It holds vegan BCAA powder, making it one of a kind.● Five grams of amino acids per 1 serving.● Assists with repairing muscle recovery.● Dissolves fast and absorbed quickly.● It Doesn't contain artificial sweeteners or coloring.And with this product, we finish our top 5 best BCAA supplements on the market. But, we're not done yet and want to give you some additional information about these supplements Please continue reading for more information on the benefits of BCAA supplements.Why BCAA Supplements Are BeneficialIf you're unsure why you should consider adding a BCAA supplement to your routine, allow me to explain. First, it's essential to know that there's a lot of scientific data available to back up the benefits these amino acids may have. The primary reasons they are being used is because:● They help reduce muscle soreness (1).● They boost muscle recovery.● They stimulate muscle protein synthesis.● They increase time to exhaustion (2).Based on the above benefits, there's no denying that these types of supplements are an excellent addition for anyone.

Whether you are new to working out or an experienced athlete, this stuff is great. We are massive fans of these amino acids simply because they've got a lot of data to prove their effectiveness. It's one of the main reasons we've written this article, to show our audience that there are, in fact, natural supplements that can help.Our recommendation would be to go with Huge BCAA powder since it holds the most aminoacids. It also contains other ingredients that will help with recovery and muscle growth.The Final VerdictThere are many BCAA supplements on the market, but we've managed to bring it down to just a handful of products through extensive research and testing. Our top 5 best BCAA powders consist of only the best quality products to help take your training and physique to a new level.

Of course, there are other useful products out there besides the ones we have mentioned. It's essential to do your research on the ingredients in any supplement to be sure of their efficacy. But if you want our advice, choose a product from our list as you can't go wrong with them. Our top-rated product can be bought from HugeSupplements..

Online levitra

Blood transcriptomics have revealed major characteristics of the immune response in active TB, but the signature online levitra early after is unknown. In a unique clinically and temporally well-defined cohort of household contacts of active TB patients that progressed to TB, we define minimal changes in gene expression in incipient TB increasing in subclinical and clinical TB. While increasing with time, changes in gene expression were highest at 30 d before diagnosis, with heterogeneity in the response in household TB contacts and in a published cohort of TB progressors online levitra as they progressed to TB, at a bulk cohort level and in individual progressors. Blood signatures from patients before and during anti-TB treatment robustly monitored the treatment response distinguishing early and late responders.

Blood transcriptomics thus reveal the evolution and resolution of the immune response in TB, which may help in clinical management of the disease. Tuberculosis (TB) resulted in 1.5 online levitra million deaths in 2018. Although a quarter of the world’s population is estimated to have been infected by Mycobacterium tuberculosis (WHO, 2019), most infected individuals remain asymptomatic (latently infected [LTBI]. Richeldi, 2006) and are suggested to have a 5–15% lifetime risk of developing TB (Vynnycky and Fine, 2000).

However, recent epidemiological studies suggest that most cases occur within 2 yr online levitra after (Behr et al., 2018. Behr et al., 2019. Behr et al., 2021) with the median time to TB disease during occurring in the first year or earlier (Emery et al., 2021. Menzies et al., 2021), implicating early immune events as key determinants of outcome (Cadena et al., online levitra 2016).

Heterogeneity of LTBI in HIV-coinfected humans and nonhuman primates has been reported (Barry et al., 2009. Esmail et al., 2016. Lin et al., 2016), but current assays cannot characterize the underlying heterogeneity of immune responses to M online levitra. Tuberculosis determining TB risk or those that accompany disease progression.

Clinically, the progressor LTBI state has been categorized into two phenotypes. (1) incipient online levitra TB, no clinical symptoms, radiological abnormalities or microbiological evidence of active TB disease. And (2) subclinical TB, no clinical symptoms, but either radiological changes or microbiological evidence of active TB disease (Davies and Pai, 2008. Drain et al., 2018.

Kendall et online levitra al., 2021. Pfyffer et al., 1997. Richeldi, 2006. WHO, 2019) online levitra.

Clinical TB patients display radiological features and microbiological evidence of active TB disease (Davies and Pai, 2008. Drain et al., online levitra 2018. Kendall et al., 2021. Pfyffer et al., 1997.

Richeldi, 2006 online levitra. WHO, 2019). Thus, a proportion of patients presumed as LTBI may either be incipient or already have subclinical disease, contributing to onward transmission of (Dowdy et al., 2013. Drain et online levitra al., 2018.

Kendall et al., 2021). Reported reduced blood transcriptional signatures of TB risk were not related to subclinical TB or incipient disease or to the blood signature of active TB (Gupta et al., 2020. Penn-Nicholson et al., 2020 online levitra. Scriba et al., 2017.

Singhania et al., 2018a. Singhania et online levitra al., 2018b. Suliman et al., 2018. Zak et al., 2016).

Earlier detection could online levitra inform treatment and limit transmission. Diagnosis of active pulmonary TB requires microbiological samples for evidence of , which can be difficult to obtain (Davies and Pai, 2008. Richeldi, 2006). A blood transcriptional signature has been reported in patients with active TB (Berry et al., online levitra 2010.

Blankley et al., 2016. Bloom et al., 2013. Joosten et al., 2013 online levitra. Maertzdorf et al., 2011.

Ottenhoff et al., 2012. Roe et al., online levitra 2016. Scriba et al., 2017), which is dominated by type I IFN signaling, reflects the extent of radiographical lung disease (Berry et al., 2010. Moreira-Teixeira et al., 2020), online levitra and is diminished upon treatment (Berry et al., 2010.

Bloom et al., 2012. Cliff et al., 2013. Thompson et online levitra al., 2017). Biomarkers to monitor TB treatment success are needed to accelerate assessment of treatment responses and determine the required treatment duration to adapt drug treatment regimens.

The accepted biomarker is sputum conversion to negative culture after 2 mo, which has low sensitivity and modest specificity for prediction of treatment failure (Horne et al., 2010. Mitchison, 1993) online levitra. Chest x rays (CRXs) and inflammatory markers commonly used to assess the response to treatment are not universally available and difficult to standardize (Walzl et al., 2011). How the host response evolves after of humans with M.

Tuberculosis toward the peak response in active TB is as yet unclear online levitra. Sequential immune responses were reported during TB progression but not linked to the clinical disease status, with adolescents evaluated at enrollment and then only sampled every 6 mo with follow-up over 2 yr, or evaluated at baseline and at the end of 2 yr (Scriba et al., 2017). Since this study was not on household contacts, knowledge of when each individual was exposed to M. Tuberculosis could not be estimated, limiting online levitra the scope for detailed temporal evaluation of changes in the immune response during progressive .

Without detailed clinical characterization of patients upon serial sampling before TB diagnosis, differential gene expression during different phenotypic stages of disease ranging from incipient TB to subclinical TB to clinical TB cannot be assessed. Moreover, confounding interpretations due to re in high TB burden settings during the prospective period of observation and sampling cannot be ruled out (Charalambous et al., 2008. Van Helden online levitra et al., 2008. Van Rie et al., 2005.

Van Rie et al., 1999. Verver et online levitra al., 2005. Warren et al., 2004). Although blood transcriptional signatures have been shown to reflect the response to TB treatment (Berry et al., 2010.

Bloom et online levitra al., 2012. Cliff et al., 2013. Penn-Nicholson et al., 2020. Thompson et al., 2017), the patterns of resolution with treatment in different patient groups using online levitra detailed kinetic analysis at multiple time points has not been evaluated.

To address these questions, we undertook a prospective cohort study comprising participants with microbiologically confirmed pulmonary TB and household contacts of pulmonary TB at Leicester, UK, a high-income, moderate TB setting (TB incidence circa 40 per 100,000 population). An integrated clinical-research platform enabled recruitment, regular follow-up, and detailed characterization of participants at serial time points of online levitra prospective observation (Materials and methods), with a low probability of new community-acquired during prospective follow-up of TB contacts. In total, 356 household contacts of pulmonary TB and 74 participants with incident TB were recruited between 2015 and 2018 and prospectively followed for 24 mo. TB contacts were reviewed every 3–6 mo with RNA sequencing (RNA-Seq) samples collected, whole-genome sequencing of the M.

Tuberculosis strain to trace back contacts to the index case, and CRX performed to screen online levitra for subclinical TB at each visit, with detailed radiological characterization and clinical investigation, including invasive sampling (bronchoscopy), if x-ray abnormalities were suspected or symptoms reported. This allowed reliable characterization of participants with incipient, subclinical, or clinically active TB, and linking blood transcriptional signatures to the clinical phenotype as disease progressed. Active TB patients were sampled, and clinical characterization was undertaken before starting TB treatment, and prospectively at scheduled visits during treatment, with microbiological investigation, radiological surveillance with CRX, and computed tomography scan as clinically indicated. Changes in blood gene expression in different clinical subgroups of active TB patients were related to the time of diagnosis online levitra and to detailed time points during treatment.

Bioinformatics analysis of blood RNA-Seq data of contacts revealed minimal changes in gene expression in incipient TB, increasing as patients progressed to subclinical and clinical TB, with similar expression profiles in these clinical phenotypes for published reduced risk signatures of TB. Moreover, gene expression changes in the blood of Leicester TB progressors, and a published cohort of TB progressors from a high burden TB setting, were most pronounced at 30 d before diagnosis, although heterogeneity was observed over time before diagnosis. The signature of TB progression in the Leicester cohort online levitra was compared with active TB disease, before and during treatment, to understand the immune events underlying the evolution and resolution of TB disease (Fig. 1.

Study design). Our study provides information of the underlying host immune response at the different stages of disease and a roadmap to describe the temporality of gene expression changes that online levitra occur during progression and treatment of active TB, which may help in clinical management of TB patients. To determine how global changes in differential gene expression develop as individuals progress from incipient TB to subclinical TB and then to to clinical TB and whether these clinical phenotypes show a graded increase in the immune response, we performed detailed analysis of changes in gene expression over time in blood of clinically defined Leicester household TB contacts who then progressed to TB (Fig. 2).

Contacts who progressed to TB online levitra were subdivided according to their clinical phenotype at the time point of sampling (Table S1). In the 14 household contacts, incipient TB was concurrent with samples (n = 10) collected earlier than 40 d before diagnosis. Subclinical TB spread between earlier than day 40 (n = 1), 21–40 d (n = 3), and <20 d (n = 6) before diagnosis. Clinical TB spread online levitra between 21–40 d (n = 4) or <20 d (n = 14) before diagnosis (Table S1).

Numbers of up- and down-regulated genes were minimal in incipient TB (94 up-regulated and 48 down-regulated genes), increasing in subclinical TB (483 up-regulated genes and 81 down-regulated genes) and in clinical TB (572 up-regulated and 136 down-regulated genes. Fig. 2 A) online levitra. Fewer down-regulated genes were detected in each of the different clinical phenotypes of the TB contacts as they progressed to TB (Data S1).

Of the up-regulated genes, Metacore pathway analysis showed a dominance of the IFN-α/β signaling pathways in subclinical TB and clinical TB with an increase in the ratio in the number of genes per pathway, 14/62 and 16/62, respectively, with much lower representation in incipient TB, 5/62 (Fig. 2 B) online levitra. Incipient TB showed IFN-γ activation of macrophages and the classical complement as the top represented pathways. However, only 5/50 and 5/53 genes were represented in each pathway, although with a much lower online levitra ratio of genes per pathway overall (Fig.

2 B and Data S1). The P values for the different clinical subgroups showed a corresponding increasing statistical significance for the IFN-α/β signaling pathways, progressing from the incipient TB (8.65 × 10−5), subclinical TB (4.75 × 10−13), and clinical TB (2.44 × 10−14) respectively (Fig. 2 B) online levitra. Similarly, the type I IFN modules increased in subclinical TB and clinical TB, although clinical TB progressors showed additional changes resembling the signature of active TB, including an increase in the innate/hemopoeitic mediators module (Fig.

2 C). A decrease in the natural killer (NK) and T cell module in incipient, subclinical, and clinical TB was observed, with incipient TB showing no other detectable changes at this stringent level of statistical analysis (Fig online levitra. 2 C). The top 30 differentially expressed coding genes (false discovery rate [FDR] P <.

0.05, log2 fold change >1) ranked by fold online levitra change, selected from a total of 47 genes in incipient TB, 233 genes in subclinical TB, and 311 genes in clinical TB, showed that many genes were differentially expressed across all three clinical phenotypes, albeit to different levels (Table 1 and Data S1). These included the genes C1QC***, SERPING1***, ETV7***, and BATF2*** expressed in all three clinical phenotypes. C1QA**, C1QB**, C2**, and EXOC3L1** were expressed in two of the clinical phenotypes, and ANKRD22** and GBP6** were significantly expressed in subclinical TB and clinical TB and were barely elevated above controls in the incipient TB (Data S1, full incipient TB versus control tab). Although certain genes appeared to be uniquely expressed within each clinical phenotype, online levitra most could be detected across the three clinical phenotypes, albeit to differing levels.

Seemingly unique genes within the top 30 gene set of the incipient TB subgroup, such as CCL2, HESX1, PCGF2, LCN8, and SIGLEC1, were only elevated to a very low level against controls in the full set of differentially expressed genes, potentially suggesting that they may come up early in the immune response to M. Tuberculosis, although they were also expressed at a low level in clinical TB (Data S1. Full incipient TB, full clinical TB online levitra versus control tabs). The expression of the complement fixing genes C1QC and C1QB in the top 30 genes of the incipient TB versus control group is in keeping with the Metacore pathway analysis in Fig.

2 B. However, these genes were also differentially expressed online levitra within the top 30 genes of subclinical TB and clinical TB (Data S1. Full subclinical TB, full clinical TB versus control tabs). BATF2 expression increased significantly with increasing disease.

1.4 log2 fold change, P online levitra value 0.0011 in incipient TB. 2.9 log2 fold change, P value 7.6 × 10−12 in subclinical TB. And 3.48 log2 fold change, P value 6.07 × 10−24 in clinical TB. Expression of online levitra SERPING1 and ETV7 showed a similar increase in expression as individuals who progressed to TB (Table 1 and Data S1).

We next analyzed blood transcriptional changes that occurred over time in Leicester TB household contacts as they progressed to TB, together with patients sampled before they were diagnosed with TB (progressors) in view of our findings that high levels of differential gene expression are mainly seen in progressors with subclinical TB and clinical TB, rather than in incipient TB. RNA-Seq data online levitra were analyzed in blood from Leicester household contacts of active TB patients at different time points after recruitment as they progressed to clinical TB (Fig. 4 A. Table S2, top, n = 12 TB contacts.

Total of 21 online levitra samples) together with Leicester patients sampled before they were diagnosed with active TB by culture/microbiological/clinical positivity (Fig. 4 A. Table S2, bottom, n = 11 progressors, total of 12 samples), all before treatment. Active TB patients at the time of diagnosis (Fig online levitra.

4 A, far right. Table S2, top, n = 49 TB patients), all as compared with healthy controls (Table S2, top, n = 38 healthy controls). The biggest changes in gene expression (log2 fold, FDR P value of 0.05 cutoff) were observed at 0–20 d before TB diagnosis in the contacts (n = 11) online levitra and progressors (n = 9. 765 up-regulated and 125 down-regulated genes.

Fig. 4 A) online levitra. Although the change in the number of genes just before diagnosis appeared similar to that observed in active TB patients at the time of diagnosis (1,231 up-regulated and 511 down-regulated genes. Fig.

4 A, far right), the extent of differential expression in the blood of active TB patients at the time of diagnosis was online levitra higher (Fig. 4 A, far right, scale on y axis 0–100. ˆ’log10 P adjusted) as compared with the contacts and progressors sampled before diagnosis (Fig. 4 A online levitra.

0–20 d before diagnosis, scale on y axis 0–15. ˆ’log10 P adjusted). Changes in gene expression were substantially lower between 21–40 d before online levitra diagnosis with low level up-regulation of 185 and down-regulation of 80 genes (Fig. 4 A.

Representative of four TB contacts that progressed to TB and three TB progressors sampled before diagnosis). At 41–832 d before TB diagnosis, when six samples were all from TB contacts subsequently progressing to clinical TB, this change in online levitra gene expression was further reduced, with very low levels of 109 up-regulated and 34 down-regulated genes (Fig. 4 A, far left). We next performed more in-depth analyses on Leicester online levitra TB household contacts alone as they progressed to TB, recruited and sampled from 2015 to 2018 and followed up to date, by pooling our more recently recruited dataset with our previously published dataset (Singhania et al., 2018a.

Total 38 samples from 14 TB household contacts, sampled as they progressed to TB against matched controls. Fig. 4 B and online levitra Table S3). Lower levels of gene expression were now observed between 0 and 31 d before diagnosis with only 23 up-regulated and 1 down-regulated gene (Fig.

4 B. Log2 fold, FDR P value of 0.05 cutoff online levitra. Scale on y axis 0–4. ˆ’log10 P adjusted), including up-regulation of GBP5, SEPTIN4, ETV7, C1QC, BATF2, C1QB, FCGR1A, GBP6, and SERPING1.

Gene expression changes at earlier time points fluctuated over time, with 15 up-regulated and 8 down-regulated genes observed between days 86 and 150 but not at 32–58 before online levitra diagnosis (Fig. 4 B). Up-regulation of these genes was still detected, albeit to a much lower level, at all the earliest time points before diagnosis (Data S2). Some exceptions included C1QB, C1QC, and C1QA, which online levitra were in the top seven DEGs at the time points 86–150 before diagnosis, while in the 0–31 d before diagnosis, only C1QB and C1QC were in the top eight expressed genes at the level of fold change over controls, suggesting heterogeneity of gene expression over time.

We next analyzed a bigger dataset of individuals from South Africa reported to have subsequently progressed to TB, reported to have been sampled at 6 monthly intervals for blood RNA-Seq analysis before diagnosis, although without serial clinical follow-up (Scriba et al., 2017. Zak et al., 2016). We further subdivided the sampling into tighter time points and examined differential online levitra gene expression levels as compared with LTBI nonprogressors recruited in parallel. Again the highest levels of gene expression changes were observed between 0 and 31 d before diagnosis, with 1,155 up-regulated and 473 down-regulated genes (Fig.

4 C. Log2 fold, FDR P value online levitra of 0.05 cutoff. Scale on y axis 0–15. ˆ’log10 P adjusted), including up-regulation of SEPTIN4, SERPING1, BATF2, GBP6, ETV7, and FCGR1A, similar to those detected in our Leicester contacts 0–31 d before diagnosis (Fig.

4 B) online levitra. Expression of C1QA, C1QB, and C1QC was only detectable 0–31 d before diagnosis, perhaps reflecting the level of detectability over controls. Changes in differential gene expression at most other time points before diagnosis were very low (Fig. 4 C and Data S3), although fluctuations in differential gene expression were observed over time, for example with marked changes at 181–250 (88 up-regulated and 46 online levitra down-regulated genes) and 251–360 d (51 up-regulated and 3 down-regulated genes) before diagnosis, as compared with other time points showing minimal changes.

Among the top 30 genes found to be up-regulated between 181 and 250 d before diagnosis were SEPTIN4, GBP6, BATF2, ETV7, SERPING1, and FCGR1A, although these genes were also among the top up-regulated 30 genes at 0–31 d before diagnosis, albeit then at a more significant level (Data S3), suggesting a graded increase in the expression of these genes as progressors approached TB diagnosis, with some heterogeneity of gene expression over time. The blood modular signature of TB contacts and TB patients sampled prediagnosis as in Fig online levitra. 4 A showed a reduction in the NK and T cell module (dominated by IFNG and effector T and NK cell genes) at >40 d before diagnosis, followed by an increase in the innate/hemopoeitic mediator module from 40 d. Increased type I IFN–inducible and inflammasome/granulocyte modules together with a reduction in the NK and T cell, and T and B cell modules were detected at 0–20 d before diagnosis (Fig.

4 D), similar to the reported TB blood online levitra signature (Moreira-Teixeira et al., 2020. Singhania et al., 2018a). The decrease in the NK and T cell module fluctuated over time before diagnosis in progressors, which could reflect fluctuation in the response or heterogeneity in the progressors. Changes in the type I IFN/C’/myeloid and online levitra inflammasome/granulocyte modules together with a reduction in the NK and T cell module were observed in Leicester TB contacts alone, but to a lesser extent only detectable from 30 d before diagnosis using a nominal P value of 0.05 rather than FDR (Fig.

4 E). The initial change at 200–850 d before diagnosis again consisted of a reduction in the NK and T cell module, although this was not consistent, again reflecting heterogeneity over time. Changes in the Zak modular signature over time were also mainly detectable over time using a online levitra nominal P value of 0.05 rather than FDR (Fig. 4 F).

At 0–31 d before diagnosis, the modular signature for the Zak progressors (Fig. 4 F) was almost identical online levitra to that of active TB (Fig. 4 D, far right. Moreira-Teixeira et al., 2020.

Singhania et al., 2018a), although with less enrichment as at a nominal P value of 0.05 (Fig online levitra. 4 F), including enrichment of inflammasome/granulocytes, innate/hemopoetic mediators, innate immunity PRR/C’/granulocytes, IFN/PRR, and IFN/C’/myeloid modules and decreased enrichment of T cell, B cell, and NK and T cell modules. The modular signature was barely detectable at other time points before diagnosis, with the IFN/PRR and IFN/C’/myeloid modules missing at 32–78 and 79–104 d but then present at 181–250 and 251–360 d before diagnosis, again suggesting temporal heterogeneity of gene expression or potential re as reported in high-burden TB settings (Charalambous et al., 2008. Uys et online levitra al., 2015.

Van Helden et al., 2008. Van Rie et al., 2005. Van Rie online levitra et al., 1999. Verver et al., 2005).

To investigate the heterogeneity among Leicester TB household contacts and the Zak progressors, the average gene expression value of the 30-gene signatures (from Table 1) derived from incipient TB (blue), subclinical TB (orange), and clinical TB (red) was assessed at different time points before diagnosis in individual Leicester TB contacts (n = 9) and individual Zak progressors (SupTab1. SupTab6_RNA-Seq-Metadata from online levitra Zak et al., 2016. Training set n = 18. GEO accession online levitra no.

GSE79362) where two or more sampling time points were evident. The average 30-gene incipient TB, subclinical TB, and clinical TB signatures were shown to be marginally elevated over the baseline (dotted line for each) in four of the Leicester TB household contacts at all time points analyzed before TB diagnosis, 0–30 d, with the subclinical TB and clinical TB signatures showing slightly better performance (Fig. 5 A, online levitra shorter time points, n = 4). Three out of four of these contacts who progressed rapidly to TB disease had been infected with an outbreak strain of M.

Tuberculosis identified by whole genome sequencing. TB contacts who progressed over online levitra 100–200 d showed a greater elevation against baseline, similar for the incipient, subclinical, and clinical TB 30-gene signatures, increasing at times close to TB diagnosis (Fig. 5 A, longer time points). One TB contact (Patient ID 493) showed some fluctuation, although always above baseline for all three signatures (Fig.

5 A, longer time points, n online levitra = 5). The average 30-gene incipient TB, subclinical TB, and clinical TB signatures showed an increase over the baseline (dotted line, LTBI controls) in the Zak progressor patients between 4–600 d before diagnosis (Fig. 5 B), although a sharp increase in the signatures over time was only observed in around five of the progressors from just >200 d to a maximum before diagnosis. Other patients showed heterogeneity in expression of these 30-gene signatures over time, many showing elevated signatures maintained at the same online levitra level over time, with others actually decreasing (Fig.

5 B). The published Zak 16-gene signature showed almost superimposable curves with very similar increases above the baseline controls over time in the individual Leicester TB household contacts (Fig. S1 A, shorter time points and longer time points), and in the Zak progressors, with identical increases in the five individuals online levitra and the same heterogeneity as observed with the 30-gene signatures (Fig. S1 B).

There is currently a need for early biomarkers to monitor TB treatment success earlier and to evaluate robustly the duration of treatment required in TB patients to adapt drug treatment regimens. To establish treatment response signatures, RNA-Seq was performed on blood from 74 TB patients at diagnosis (treatment-naive), and longitudinally, at carefully online levitra planned time points during TB treatment. We first monitored the transcriptional response to treatment across the whole cohort, and second monitored the transcriptional response of individual patients to identify distinct profiles of their transcriptional response that might help to stratify clinical treatment phenotypes. Blood was collected and subjected to RNA-Seq from the 74 TB patients at diagnosis before treatment and thereafter, at 1 and 2 wk, at 1, 2, 4, 5, 6, 7/8, 9/10, and 11/12 mo, and at >1-yr after treatment (Fig.

S2, A and B) online levitra from clinically defined patients. Pulmonary TB, difficult TB cases, TB drug–resistant, outbreak TB strain, and other TB progressors (Table S4). TB patients received either standard anti-TB treatment (ATT. 200 d or less) or online levitra extended ATT (>200 d.

Table S4), according to their clinical assessment through treatment, with smear-positive patients mostly falling within the extended ATT patient group (Fig. S2 C) online levitra. The sample-to-sample correlation heatmap and principal component analysis (PCA) of all the active TB patients at diagnosis before treatment and at the different time points during the treatment course showed samples to mainly cluster according to time points, with some heterogeneity (Fig. S2, D and E).

The top 1,000 most online levitra variable gene expression heatmap distinguished patients according to time of treatment, and according to smear positivity and negativity at treatment initiation (T0. Fig. S2 D). The innate/hemopoietic, IFN/PRR, and IFN/C’/myeloid modules were found to be over-abundant online levitra as compared with controls before treatment and decrease in abundance to different degrees within all the subgroups after T0, except for in the TB drug resistant subgroup (Fig.

6 A). These modules decreased in abundance after 1 wk of treatment and were completely abrogated after 4 mo of treatment in the standard ATT subgroup. Although the extended ATT and difficult TB cases subgroups showed a similar pattern to the standard ATT subgroup, online levitra a stronger modular signature before treatment and an incomplete diminishment after 6 mo were observed, when a standard treatment course would be completed. The outbreak TB strains subgroup showed a similar but weaker global modular signature to the standard ATT subgroup, also resolving within 4 mo of treatment.

However, a small subgroup of four patients, the TB drug–resistant subgroup, showed a distinct modular signature that for the most part was not diminished, in accordance with these patients requiring altered drug treatment regimens for a longer period (Fig. 6 A) online levitra. The standard and extended ATT subgroups contained a large number of patients such that the modular signature was more robust than in the other three subgroups, which contained lower numbers of patients (Fig. 6 A).

We therefore examined the online levitra members of each of these three subgroups individually and show that at the level of the individual, these modular responses to treatment are heterogeneous and so should be validated in larger cohorts in future studies (Fig. S3). The number of DEGs compared with controls was also reduced upon treatment (Fig. 6 B) online levitra.

Smear-positive and smear-negative TB patients showed a similar modular and gene expression decrease during treatment with complete diminishment by 4–5 mo, although the smear-positive patients had a stronger modular signature before treatment (data not shown). We then identified a 212-gene signature (TREAT-TB212) that showed the response to treatment across the whole Leicester cohort, mainly showing decreased gene expression as compared with controls over the treatment course, which reverted to the expression profile of healthy controls by 4 mo of treatment in most but not all of the patients (Fig. 7 A) and in online levitra an independent treatment response cohort dataset (Fig. 7 B.

Thompson et al., 2017). The TREAT-TB212 online levitra signature in not-cured patients from the Thompson cohort was sustained at all time points up to 24 wk at similar levels to that of the pre- and very early treatment response signatures (Fig. 7 B), at a comparable level to the Leicester cohort of active TB patients, TB progressors recruited as TB household contacts after diagnosis, and most different clinical treatment response subgroups (Fig. 7 A) online levitra.

A higher TREAT-TB212 signature was observed in patients receiving treatment for >200 d but this was observed only early after T0, as compared with those receiving standard treatment of up to 200 d (Fig. 7 A). In keeping with the modular and differential gene expression analyses, the TREAT-TB212 signature was only different in the smear-positive and -negative patients at T0 but not between 1 online levitra wk to 1 yr after T0, indicating that the patients were responding similarly to treatment (Fig. 7 A).

The log2 fold-change of all TREAT-TB212 genes against controls verified changes in gene expression upon treatment (Fig. 7 C, Leicester cohort) with online levitra a similar profile in the Thompson cohort (Fig. 7 D). Again, most of the gene expression profiles reverted to that of healthy controls by 4 mo of treatment (Fig.

7 C, Leicester cohort), although this could not be evaluated online levitra in the Thompson cohort due to fewer sampling visits that did not include this time point (Fig. 7 D). The TREAT-TB212 signature was further reduced to a 27-gene signature (TREAT-TB27), which selected genes with the greatest changes in expression over the treatment course in the whole cohort (Fig. 7 E), and its validity was confirmed also in the Thompson cohort (Fig online levitra.

7 F). Although TB patients had been subgrouped according to their clinical phenotype in response to treatment, as standard ATT, extended ATT, difficult TB cases, TB drug–resistant, and outbreak TB strains, the TREAT-TB212 signature did not show a clear transcriptional response trend according to their clinical definition except for most of the drug-resistant group (Fig. 7 G, online levitra compared with T0). However, by monitoring the transcriptional response of individual patients according to their TREAT-TB212 signature profile, regardless of their clinical subgroups but where samples at all time points had been obtained, four distinct transcriptional profiles were revealed.

Expected, resembling standard ATT. Weaker, as compared online levitra with standard ATT. Or stronger initial or stronger delayed, as compared with standard ATT (Fig. 7 H, compared with T0).

Strikingly, stronger initial or stronger delayed transcriptional response patient groups showed online levitra differences in the transcriptional response already at 1 and 2 wk after T0, although at week 1 after treatment, C-reactive protein (CRP) levels in both groups were comparable (46.00 mg/l, stronger initial. 34.00 mg/l, stronger delayed). The stronger delayed patient group displayed elevated levels of CRP even after 1 mo of treatment as compared to stronger initial (8 mg/l, stronger initial. 38 mg/l, stronger delayed group), also correlating with minimal changes in radiographical signs of disease online levitra (data not shown), suggesting continued inflammation and potentially in the stronger delayed.

Thus the treatment response could not be predicted clinically by CRP levels early but could be predicted by the different kinetics of the transcriptional response observed as early as 1 wk after T0 in the stronger delayed as compared with the stronger initial group, supporting the role of transcriptional biomarkers as more sensitive measures of the treatment response than existing clinical markers. To develop a reduced transcriptional signature that may enable early identification of poorer treatment responders, based on the stronger initial and stronger delayed groups, the differential expression of TREAT-TB212 online levitra between two consecutive time points from T0 to 1 wk, 1 to 2 wk, and 2 wk to 1 mo was computed leading to a reduced signature (EarlyRESP-TB25. Fig. 7 H and Fig.

S4). EarlyRESP-TB25 showed differences in the stronger initial and stronger delayed patient groups by their different transcriptomic profiles at 1–2 wk after T0 (Fig. 7 J), with similar but not optimal results observed for TREAT-TB27 (Fig. 7 I.

Derived gene lists TREAT-TB27, EarlyRESP-TB25. Fig. S4, A and B). Reported reduced blood signatures of TB diagnosis or risk show little to no overlap with each other, and most have been tested for distinguishing active TB from LTBI but not active TB from other diseases (ODs.

Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016. Singhania et al., 2018a.

Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016. Reviewed in Singhania et al., 2018b).

We set out to develop an optimized reduced signature that would distinguish active TB from ODs as well as LTBI. Combined reduced blood signatures of TB diagnosis or risk comprising 101 distinct genes (Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016.

Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016.

Unpublished data) were analyzed in 10 published datasets from multiple clinical disease cohorts including active TB, LTBI, and ODs and healthy controls (Bloom et al., 2013. Parnell et al., 2012. Suarez et al., 2015. Zhai et al., 2015), and the pooled dataset was batch-corrected (Fig.

S5, A and B. And Materials and methods). 12 genes were identified in the reduced signature that were shared between the top 30 genes distinguishing active TB from LTBI, and active TB from ODs, ranked by decreasing importance (mean decrease accuracy. Fig.

8, A and B), further reduced to 10 genes (TB10) based on performance (area under the curve [AUC] and accuracy) on pooled cohort datasets with independent validation (Fig. S5 C and Table S5). This TB10 signature originating from the different reduced reported signatures (Fig. 8 C) comprised genes that were either up- or down-regulated in active TB as compared with controls, LTBI, and ODs (Fig.

8 D) and was shown to be significantly different in TB versus ODs and LTBI and controls using ANOVA (Data S4). Removal of the GBP5 gene (12th in rank) reported to discriminate active TB and LTBI did not improve the performances for discrimination of active TB from ODs. Individual gene expression was heterogeneous across patients with active TB and the ODs (Fig. 8 D, likely reflecting the different extents of morbidity.

Berry et al., 2010. Bloom et al., 2013). The performances of TB10 were tested and compared with the signatures that we describe in this study, including the 30-gene incipient TB, the 30-gene subclinical TB, the 30-gene clinical TB, the TREAT-TB27, and the EarlyRESP-TB25 (Fig. S5 D), and previously published reduced signatures (Fig.

8 E and Fig. S5 E. Kaforou et al., 2013. Maertzdorf et al., 2016.

Roe et al., 2016. Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016.

Zak et al., 2016. Described in Materials and methods). TB10 showed the best performance for TB versus ODs (Fig. 8 E and Fig.

S5 D. AUC, 0.999. Accuracy, 0.976. And 95% confidence interval [CI], 0.9959–1), and TB versus LTBI (Fig.

S5 E. AUC, 0.971. Accuracy, 0.943. 95% CI, 0.9343–1).

Although the Suliman reduced signature was comparable to TB10 for distinguishing TB from LTBI (Fig. S5 E), it showed poorer performance for distinguishing TB from ODs (Fig. 8 E). Blood transcriptomics have revealed major characteristics of the immune response in TB, show promise to support TB diagnosis, and would be of great use to identify individuals with asymptomatic incipient TB or subclinical TB before they progress to clinical TB to facilitate targeted early treatment and reduce onward transmission.

Moreover, new tools for effective TB treatment monitoring are needed to determine when and if patients are responding to treatment to provide a personalized approach to treatment and accelerate screening of new anti-TB drugs. To achieve this, a detailed knowledge of how the host immune response develops over time and relates to the state of M. Tuberculosis is needed. We now show, in a unique clinically and temporally well-defined cohort of household contacts of active TB patients, that minimal changes in blood gene expression are detectable in incipient TB, increasing as patients progress to subclinical TB, and maximal at the time of presentation with clinical TB, with similar results for published reduced risk signatures of TB.

Although the transcriptional signatures increased with time and were most highly expressed around 30 d before diagnosis, there was heterogeneity over time in the response in the TB contacts as they progressed to TB and a published cohort of TB progressors from a high-burden TB setting. Blood signatures at detailed time points during ATT additionally allowed us to define signatures that can distinguish early and late responders. Finally, we demonstrate comparable performance of immune signatures developed for TB diagnosis and detection of early stages of M. Tuberculosis and their reduction upon TB treatment monitoring, with subtle differences for different signatures at different stages of development and diminishment of TB disease.

The temporality of gene expression changes during progression, and resolution of active TB may provide mechanistic insights toward the development of host therapies and supports a framework for future development of biomarkers to improve the clinical management of LTBI and active TB. TB contacts that progress to TB showed a decrease in the NK and T cell effector modular signature, which was detectable from the earliest stages of progression, in keeping with findings that cytotoxic effector molecules and NK cells are important for protection against M. Tuberculosis in human TB (Roy Chowdhury et al., 2018). We observed an increase in the inflammatory and IFN modular signatures in subclinical TB and clinical TB at time points closest to TB diagnosis in both Leicester contacts and Zak TB progressors, but not in incipient TB.

That the IFN modular signature fluctuated with time in Zak progressors potentially explains differing reports that type I/II IFN signaling and the complement cascade were elevated 18 mo before TB disease diagnosis in this cohort (Scriba et al., 2017), with others suggesting increased expression of complement genes in subclinical TB closer to diagnosis (Esmail et al., 2018). Our analysis at more detailed time points of the Zak cohort suggests elevated type I IFN signaling, and complement genes at 18 mo before diagnosis may indicate individual patient heterogeneity, as we discuss below. A marked decrease in the B and T cell modular signatures and increase in other modules found in active TB including myeloid inflammation, lymphoid, and monocyte and neutrophil gene modules occurred in progressors more proximally to TB disease, in keeping with Scriba et al. (2017).

A reciprocal reduction in the inflammation and IFN modules was observed after a week of successful treatment and was restored to that of healthy controls by 4 mo, together with the B and T cell modular signatures. Our findings that the evolution of the immune response on progression to TB shows the reverse upon treatment is suggestive that reduced signatures optimized to support early diagnosis of TB may also reflect the changes that occur in response to treatment. Reduced blood signatures have been proposed to determine the risk of exposed individuals to their subsequently developing TB (Penn-Nicholson et al., 2020. Singhania et al., 2018a.

Singhania et al., 2018b. Suliman et al., 2018. Zak et al., 2016). However, it was unclear at the time whether any of these reported blood signatures of TB risk predicted progressors at stages of incipient or subclinical TB.

We now show that while these published gene risk signatures are expressed during subclinical TB and clinical TB, only SERPING1, ETV7, and BATF2 from the 16-gene Zak signature were up-regulated, albeit at a very low level, in incipient TB, similarly to the global transcriptional expression signature in these Leicester contact clinical phenotypes. 7 of the 16-gene signature reported by Zak et al. (2016) were among the 30 most highly expressed genes across the Leicester contact clinical phenotypes of TB progression, including FCRGR1A, SEPT4, GBP5, ANKRD22, SERPING1, ETV7, and BATF2. Although C1QC, SERPING1, ETV7, and BATF2 were among the top 30 DEGs in all the clinical phenotypes, increasing statistical significance of differential gene expression was observed with progression from incipient to subclinical to clinical TB, suggesting that they may be early indicators of M.

Tuberculosis and reflect the evolution of the immune response with time after . In support of this, IFN-α/β signaling pathways by modular and Metacore analysis were observed in subclinical TB and more so in clinical TB, but were not apparent in incipient TB. Collectively, our findings suggest that the levels of gene expression increase with progressive from incipient to subclinical to active TB, potentially explaining why distinct published signatures of TB risk show little overlap with each other (Penn-Nicholson et al., 2020. Singhania et al., 2018a.

Suliman et al., 2018. Zak et al., 2016. Reviewed in Singhania et al., 2018b) due to differential levels of detectable gene expression and/or uncertainty in the stage of at which sampling was performed. The unique gene set expressed in incipient TB is characterized by low-level gene expression changes across multiple pathways, which may limit their value for predicting which incipient TB patients will progress to clinical TB.

However, genes that were differentially expressed in subclinical TB and clinical TB at a high and significant level were detectable in incipient TB, supporting suggestions that serial testing among carefully selected clinical target groups might be required for optimal implementation of biomarkers for TB risk (Esmail et al., 2020. Gupta et al., 2020). Indeed, inclusion of clinical details of TB progressors over detailed sampling times after exposure/ with M. Tuberculosis allowed us to define progressors as incipient TB and subclinical TB (Drain et al., 2018) and to assign changes in blood gene expression at an early stage of to each clinical phenotype, providing a framework for improved biomarker selection to target early TB treatment and block onward transmission.

Our findings of temporal heterogeneity in the blood transcriptional response of Leicester TB progressors and more so in the Zak TB progressors (Zak et al., 2016), both at bulk cohort levels as well as in individual progressors, likely reflect the dynamic nature of the host–pathogen interaction over time, and are consistent with observations in positron emission tomography scan and computed tomography scan studies of progressive in humans with LTBI coinfected with HIV/TB and in nonhuman primates (Barry et al., 2009. Esmail et al., 2016. Lin et al., 2016). The majority of Leicester contacts progressed over 100–200 d, displaying a greater elevation in their blood signatures against the baseline as they progressed to TB.

However, a subset showed rapid progression with an increased signature close to diagnosis, mostly explained by with an outbreak strain of M. Tuberculosis, supporting previous reports of differing virulence of M. Tuberculosis strains (Coscolla and Gagneux, 2014), suggesting that the time at which protective immune responses were overwhelmed leading to TB progression could differ according to the infecting strain of M. Tuberculosis.

Detailed analysis of individuals in the Zak et al. (2016) cohort, sampled for >600 d, showed a sharp increase in the signatures over time in only five progressors from 200 d before diagnosis, progressing sharply to the highest signature before diagnosis, again suggesting that immune responses had been overwhelmed. However, in other Zak progressors, the fate of appeared to hang in the balance for a prolonged period until diagnosis, possibly reflecting subclinical disease, although in-depth clinical analysis was not performed at these earlier sampling time points in the study. Moreover, since it was not a study of recent TB contacts, there was no knowledge of time of exposure or the infecting M.

Tuberculosis strain. Our findings are consistent with a recent randomized controlled clinical trial for biomarker-guided tuberculosis preventive therapy (termed CORTIS. NCT02735590), which concluded that a reduced signature RISK11 derived from the Zak 16-gene signature (Zak et al., 2016) was better suited to screening of symptomatic individuals with possible early clinical TB than for mass community-based screening for incipient TB (Scriba et al., 2021). It is unclear whether this signature can distinguish subclinical or active TB from other s, particularly viral s, which may present with symptoms similar to clinical TB and are also dominated by type I IFN signaling (Singhania et al., 2018a.

Singhania et al., 2018b). Improved biomarkers to monitor TB treatment success are needed to reliably evaluate the duration of treatment required in individual TB patients and deliver optimal drug treatment regimens. We adopted both clinical and bioinformatics approaches to develop blood signatures of the treatment response. Using the clinical approach, individual patients of the Leicester TB cohort were stratified on the basis of treatment response (standard ATT, extended ATT, and difficult TB) and infecting strain characteristics (drug-resistant TB and outbreak TB strains).

The treatment response signature corresponded with their clinical treatment response, discriminating both the subgroup of drug resistant patients in Leicester responding more slowly to treatment, and the subgroup labeled as not cured in the African Thompson cohort (Thompson et al., 2017), supporting identification of patients not responding to treatment. Using the bioinformatics approach, we monitored the transcriptional response of individual patients, independent of their clinically defined treatment phenotype, and defined four types of TB patients. The expected group responding standardly to treatment. The weaker group defining a subgroup with a lower grade of (low CRP, and longer time to M.

Tuberculosis culture positivity). And the stronger initial and stronger delayed groups, showing differences in their transcriptional response at 1 wk after T0. Differences between these latter two subgroups were not discernible by clinical measures of their treatment response, such as CRP and x ray, at these early time points, supporting the utility of transcriptional biomarkers as more sensitive measures of the treatment response than existing clinical markers, to inform clinical management of TB patients and to support drug development platforms and future drug treatment trials. Existing TB diagnostic tools are limited in their scope and dependent on sputum availability for rapid identification of TB.

Diagnostic blood transcriptomic signatures of TB may provide a pathway to support early diagnosis for a broader spectrum of disease phenotypes, although there is little consensus between reported reduced signatures for TB risk and those that distinguish active TB from LTBI (Kaforou et al., 2013. Maertzdorf et al., 2016. Penn-Nicholson et al., 2020. Roe et al., 2016.

Scriba et al., 2021. Singhania et al., 2018a. Singhania et al., 2018b. Suliman et al., 2018.

Sweeney et al., 2016. Zak et al., 2016). Moreover, there is a need to distinguish TB from other confounding diseases (Kaforou et al., 2013. Singhania et al., 2018a.

Singhania et al., 2018b). Our blood signature, TB10, derived from published reduced signatures tested on multiple disease cohorts, optimally distinguished patients with TB from those with LTBI, and TB from those with ODs. Although some of the published signatures had similar performance in distinguishing TB from LTBI, they had poorer performance than TB10 in distinguishing TB from ODs. All reduced signatures derived in this study, including the 30-gene signatures of incipient TB, subclinical TB, and clinical TB progression and the new treatment-monitoring signatures TREAT-TB27 and EarlyRESP-TB25, showed poorer performances than TB10 in distinguishing TB from LTBI and in distinguishing TB from ODs, suggesting that the top gene expression changes that occur temporally upon progression to TB may not exactly match the top gene set that is temporally diminished after TB treatment.

All signatures increased in subclinical TB and maximally in clinical TB, also decreasing at week 1 after treatment, with a further decrease at month 2 and complete disappearance of gene expression changes by month 6. However, all signatures barely showed a significant increase in expression in incipient TB above controls, with the exception of the 30-gene incipient TB signature, which potentially could be further reduced to the most highly expressed genes and optimized to give the highest sensitivity of gene expression to detect what is likely to be early M. Tuberculosis in asymptomatic individuals with incipient TB and subclinical TB, at risk of progression to TB. The global aim of our study, however, was not to develop optimized signatures of risk and progression to TB, but to use signatures developed at different stages of disease progression and treatment to determine how and to what extent they are perturbed in the different clinical phenotypes of progression to TB, during active TB, and upon treatment.

In conclusion, using in-depth temporal analysis of gene expression changes over time in a cohort of clinically well-characterized household contacts of TB patients from a moderate-burden TB setting with minimal risk of re, together with reanalysis of gene expression at more detailed time points in a published cohort of TB progressors from a high TB burden setting, we demonstrate significant heterogeneity in changes of gene expression, at both the bulk cohort level and in individual patients, as they progress to TB. This has major implications for assessing TB risk in individuals with LTBI. Our characterization of the immune response underlying the evolution and resolution of TB provides a framework for biomarker development to improve clinical management of this disease. Between September 2015 and September 2018, longitudinal cohorts of active TB and TB contacts were recruited from the clinical TB service at Glenfield Hospital, University Hospitals of Leicester National Health Service Trust, Leicester, UK (Table S1, top).

All participants were prospectively enrolled and sampled before the initiation of any TB treatment. The Research Ethics Committee for East Midlands–Nottingham 1, Nottingham, UK (REC 15/EM/0109) approved the study. All participants gave written informed consent. All active TB patients had microbiologically confirmed disease with whole-genome sequencing of culture isolates performed for case linkage with contacts.

All participants were prospectively followed with visits at scheduled time points from the time of diagnosis (pretreatment) until 12 mo after completing treatment. Household TB contacts were identified through routine contact tracing and underwent systematic baseline investigation with routine CXR, IFN-γ release assay for M. Tuberculosis reactivity (IGRA) testing, and a symptom questionnaire. Sputum was collected if participants were spontaneously expectorating, and bronchoscopy performed in those not expectorating with clinical or radiological suspicion of TB.

On this basis, participants were classified with LTBI, subclinical TB, or active TB. Participants with LTBI were prospectively followed up for a minimum period of 2 yr with scheduled follow-up visits at 3–6 monthly intervals. At each visit, a symptom screen and CXR was performed and a blood RNA sample collected. In total, 356 TB contacts were recruited (150 IGRA-positive and 206 IGRA-negative [IGRA-ve]).

To date, 20 participants from this cohort have been diagnosed with TB and classified as TB progressors, although 6 were excluded from the gene expression study due to either cDNA library failure (n = 1) or failure to secure microbiological confirmation (n = 5) essential for case linkage with the index case. In these cases, the diagnosis of active TB was based on clinical symptoms, typical radiological features, and supporting histology from the site of . At each visit, a symptom screen and CXR were performed and a blood RNA sample collected. At the time of our publication in 2018 (Singhania et al., 2018a), we reported a modified disease risk score using a TB-specific 20-gene signature in 9 TB contacts who had developed TB during the study, with 99 contacts remaining healthy for 2 yr or more, but performed no detailed analysis on changes in gene expression and the immune response.

Since then, not considering the excluded progressors detailed above, an additional five TB contacts (n = 5) developed TB and were included for in-depth temporal analysis of the blood signature of TB progressors at different time points before diagnosis. In total, blood samples from 14 contacts who progressed to TB were subjected to RNA sequencing out of the 20 contacts who progressed to TB. Six were excluded from the RNA-Seq gene expression study due to either cDNA library failure (n = 1) or failure to secure microbiological confirmation (n = 5). This was important as essential for case linkage with the index case, although they were confirmed as progressing to TB by positive histology showing caseating lungs.

The contacts had the following characteristics. Gender, 35.7% male and 64.3% female. Ethnicity, 28.6% South Asian, 14.3% East African, 42.9% British Caucasian (5/6 outbreak strain), and 14.3% European. The controls had the following characteristics.

Gender, 47.1% male and 52.9% female. Ethnicity, 64.7% South Asian, 11.8% East African, 5.9% European, and 17.6% British Indian. Since these were small numbers and skewed somewhat by the British Caucasian and gender, we applied COMBAT batch correction as described later in the Materials and methods. We collected blood at detailed time points to examine in detail changes in gene expression in the different clinical phenotypes, incipient TB, subclinical TB, and clinical TB (Table S1), and also to examine changes in gene expression occurring over time in Leicester cohorts of contacts of active TB patients (Table S2, top.

N = 12 TB contacts. 25 samples. Singhania et al., 2018a) and from noncontact patients with symptoms before they were diagnosed with active TB by culture/microbiological/clinical positivity (Table S2, bottom. N = 10 TB progressors.

14 samples), all before treatment, and from active TB patients at the time of diagnosis (Table S2, top, n = 49 TB patients), all as compared with healthy controls (Table S2, top, n = 38 healthy controls). Blood from TB contacts who progressed to TB and from TB progressors was subjected to RNA-Seq and analysis at the time points indicated (Table S2), together with that from 49 newly recruited active TB patients at the time of diagnosis, before initiation of treatment. To investigate this further in Leicester TB contacts who progressed to TB only, datasets representing all time points, sampled before diagnosis throughout 2015 to 2018, were analyzed in TB contacts only that progressed to TB, by pooling our previously published TB contact dataset (Singhania et al., 2018a), which had not been investigated in depth with respect to kinetic changes in the immune responses, with our more recently recruited TB contact dataset (Table S3). This pooled dataset, now consisting of 38 samples from 14 TB contacts as they progressed to TB, was batch corrected and analyzed against matched controls, as described later in the Materials and methods.

The treatment response cohort had the following characteristics. The Leicester active TB cohort composed of 74 patients with pulmonary TB was simultaneously recruited between September 2015 and September 2018, at the Glenfield Hospital, University Hospitals of Leicester National Health Service Trust, Leicester, UK, at the time of diagnosis (treatment-naive. Fig. S2 A and Table S4).

A cohort of 38 healthy IGRA-ve controls was recruited in parallel. To follow the transcriptional response after treatment, whole blood samples were collected and were subjected to RNA-Seq at diagnosis before initiation of any ATT (T0), and thereafter, at 1 and 2 wk. 1, 2, 4, 5, 6, 7/8, 9/10, and 11/12 mo. And >1 yr after T0 with clinical assessment including CXR, CRP, and symptom assessment.

All RNA isolation and processing were performed on all blood samples simultaneously (Fig. S2 and Table S4). Patients who had previous TB, had previous treatment for LTBI, were pregnant, were under 16 yr age, or were immunosuppressed were excluded from this study. All participants had routine HIV testing, and patients with a positive result were excluded.

Patients with active TB were all confirmed by laboratory isolation of M. Tuberculosis on the culture of a respiratory specimen (sputum or bronchoalveolar wash/lavage) with sensitivity testing performed by the Public Health Laboratory Birmingham, Heart of England National Health Service Foundation Trust, Birmingham, UK. All participants were prospectively enrolled and sampled before the initiation of any TB treatment. The Research Ethics Committee for East Midlands–Nottingham 1, Nottingham, UK (REC 15/EM/0109), approved the study.

All participants gave written informed consent. We used 10 published blood RNA-Seq or microarray datasets (Berry et al., 2010. Bloom et al., 2013. Leong et al., 2018.

Parnell et al., 2012. Singhania et al., 2018a. Suarez et al., 2015. Thompson et al., 2017.

Zak et al., 2016. Zhai et al., 2015) from multiple clinical disease cohorts including active TB (225 patients) and LTBI (217 individuals) from Berry, London and South Africa (GEO accession nos. GSE107991 and GSE107992). Bloom (GEO accession no.

GSE42834). Singhania, Leicester (GEO accession no. GSE107993). Zak (GEO accession no.

GSE79362). Thompson (GEO accession no. GSE89403). Leong (GEO accession no.

GSE101705). And ODs influenza (Parnell [GEO accession no. GSE40012] and Zhai [GEO accession no. GSE68310]), Bloom lung cancer (GEO accession no.

GSE42834), Bloom pneumonia (GEO accession no. GSE42834), Bloom sarcoidosis (GEO accession no. GSE42834), and Suarez bacterial/viral s (GEO accession no. GSE60244.

Total 186 patients). And healthy controls from each respective dataset (223 individuals). We downloaded from GEO the filtered and normalized datasets, which have been normalized with different methods, according to type of data (RNA-Seq or Illumina microarray) or laboratory practices. We then pooled the 10 datasets together, matching the targets by gene names.

When a gene was absent in a least one dataset of origin, we completely removed a gene from the pooled dataset, so that we had a robust and stringent pooled dataset. A gene could be absent from any dataset for multiple reasons. One of the platforms did not target this gene, the gene annotation databases used were different for each dataset (different version of the genomes) and there was no correspondence of gene name, or the gene was filtered out due to low expression in the filtered dataset of origin. Using these stringent criteria, we had a pooled dataset containing 11,912 genes in total, regrouping 851 individual whole blood samples.

We then batch-corrected the pooled dataset, the batch being the origin of dataset, with the reference COMBAT algorithm (Johnson et al., 2007) from the sva library in R. We checked the impact of batch correction on a mix of RNA-Seq and microarray datasets by drawing PCA plots (Fig. S5 A). We also verified high correlations before/after batch correction per group of patients (Fig.

S5 B) and expression on gene of interest (data not shown). From the 11,912 batch-corrected genes, we then selected the 101 genes that were contained in at least one of the nine published reduced gene signatures (Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016.

Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016.

And reviewed in Singhania et al., 2018b. And unpublished data). Trang Tran’s 20-gene signature was independently derived from both Berry London (Berry et al., 2010. GEO accession no.

GSE107991) and Leicester RNA-Seq datasets (GEO accession no. GSE107993). Differential gene expression analyses were performed on active TB patients compared with controls, LTBI, or controls plus LTBI individuals, using Wald tests (DESeq2 library. Love et al., 2014) by fitting generalized linear models.

A gradient-boosting machine algorithm (gbm v2.1.7 library) was applied on the lists of DEGs to determine the high order ranking of genes predicting the active TB status. For signature reduction, we performed a random forest algorithm (randomForest library v4.6-14) based on cumulative sensitivity of genes in their importance order. Finally, the meta-data with cross-validation analysis combining two optimal signatures from microarray datasets from Berry et al. (2010) and six optimal signatures from the Berry London and Leicester RNA-Seq datasets (TB versus controls, TB versus LTBI, TB versus controls plus LTBI for each cohort) yielded 20 gene signatures (FCGR1A, GBP5, SEPT4, ANKRD22, BATF2, FCGR1B, GBP1, GBP6, LHFPL2, SERPING1, C1QB, CD274, GBP4, AIM2, FBXO6, PSTPIP2, ASPHD2, FCMR, RTP4,and APOL6).

Genes ranked by DESeq2 Wald statistic for TB progressor patients at different time points or with different clinical symptoms compared with controls were used to look for enrichment of either the hallmark gene set using Broad’s gene set enrichment analysis preranked analysis and default settings. The normalized enrichment score and the FDR were plotted using ggplot2. The different genes lists of DEG in the different clinical phenotypes, incipient TB, subclinical TB, and clinical TB (Fig. 2) were functionally annotated using Metacore (Thomson Reuters v 19.4).

R libraries used. All analyses have been made with R 3.5.1, using multiple libraries, and Bioconductor v3.8 (Anders et al., 2015). The libraries are. Arules v1.6-4, sva v3.30.1 (Leek et al., 2012), Boruta v6.0.0 (Kursa and Rudnicki, 2010), ranger v0.12.1, ImpulseDE2 v1.6.1 (Fischer, 2019), VennDiagram v1.6.20, caret v6.0-85, lattice v0.20-38, RColorBrewer v1.1-2, tibble v2.1.3, tidyr v0.8.3, dplyr v0.8.1, DESeq2 v1.22.2 (Love et al., 2014), ComplexHeatmap v2.3.1 (Gu et al., 2016), ROCR v1.0-7, randomForest v4.6-14, ggbiplot v0.55, ggplot2 v3.2.0, and qusage v2.16.1 (Yaari et al., 2013).

We acknowledge the Francis Crick Advanced Sequencing Facility, Bioinformatics and Biostatistics Science Technology Platforms, for their contribution to our sequencing processing, L. Moreira-Teixeira for reviewing the manuscript, and Marisol Alvarez Martinez for helpful advice on bioinformatic approaches used. A. O’Garra, O.

Tabone, C.M. Graham, and A. Singhania and part of the project were funded by the Francis Crick Institute (Crick 10126. Crick 10468), which receives its core funding from Cancer Research UK, the UK Medical Research Council, and the Wellcome Trust.

The project, part of O. Tabone's salary, and salaries for J. Lee and R. Verma were funded by the BIOASTER Microbiology Technology Institute, Lyon, France (with funding from the French Government Investissement d’Avenir program ANR-10-AIRT-03) and the Medical Diagnostic Discovery Department, bioMérieux SA, France.

P. Haldar and R. Verma were supported by the National Institute for Health Research Leicester Biomedical Research Centre and the University of Leicester. W.J.

Branchett is funded by a Wellcome Investigator Award to A. O’Garra (FC11028). Author contributions. A.

O’Garra and P. Haldar co-led the study. A. O’Garra, P.

Haldar, R. Verma, G. Woltmann, M. Rodrigue, and P.

Leissner designed the study and discussed the findings throughout the project with K. Kaiser, O. Tabone, P. Chakravarty, A.

Singhania, and W.J. Branchett. R. Verma and J.

Lee recruited TB, LTBI, and contacts for the Leicester cohort. C.M. Graham performed RNA-Seq sample processing and helped to organize receipt of samples at The Crick and identifiable storage. O.

Tabone and P. Chakravarty performed bioinformatics analysis with major input from A. Singhania, and some input from T. Trang and F.

Reynier, all overseen by A. O’Garra. A. O’Garra wrote the manuscript with input from P.

Haldar, W.J. Branchett, O. Tabone, and A. Singhania.

All co-authors read, reviewed, and approved the paper..

Blood transcriptomics have revealed major characteristics of the immune response in active TB, but the signature early after levitra price comparison is unknown. In a unique clinically and temporally well-defined cohort of household contacts of active TB patients that progressed to TB, we define minimal changes in gene expression in incipient TB increasing in subclinical and clinical TB. While increasing with time, changes in gene expression were highest at 30 d before diagnosis, with heterogeneity in the response in household levitra price comparison TB contacts and in a published cohort of TB progressors as they progressed to TB, at a bulk cohort level and in individual progressors. Blood signatures from patients before and during anti-TB treatment robustly monitored the treatment response distinguishing early and late responders.

Blood transcriptomics thus reveal the evolution and resolution of the immune response in TB, which may help in clinical management of the disease. Tuberculosis (TB) resulted in 1.5 levitra price comparison million deaths in 2018. Although a quarter of the world’s population is estimated to have been infected by Mycobacterium tuberculosis (WHO, 2019), most infected individuals remain asymptomatic (latently infected [LTBI]. Richeldi, 2006) and are suggested to have a 5–15% lifetime risk of developing TB (Vynnycky and Fine, 2000).

However, recent epidemiological studies suggest that most cases occur within levitra price comparison 2 yr after (Behr et al., 2018. Behr et al., 2019. Behr et al., 2021) with the median time to TB disease during occurring in the first year or earlier (Emery et al., 2021. Menzies et al., 2021), implicating early immune events as key determinants of outcome (Cadena et al., 2016) levitra price comparison.

Heterogeneity of LTBI in HIV-coinfected humans and nonhuman primates has been reported (Barry et al., 2009. Esmail et al., 2016. Lin et al., 2016), but current assays cannot characterize the underlying heterogeneity of immune responses to levitra price comparison M. Tuberculosis determining TB risk or those that accompany disease progression.

Clinically, the progressor LTBI state has been categorized into two phenotypes. (1) incipient TB, no clinical symptoms, radiological abnormalities or microbiological evidence of active TB levitra price comparison disease. And (2) subclinical TB, no clinical symptoms, but either radiological changes or microbiological evidence of active TB disease (Davies and Pai, 2008. Drain et al., 2018.

Kendall et al., 2021 levitra price comparison. Pfyffer et al., 1997. Richeldi, 2006. WHO, 2019) levitra price comparison.

Clinical TB patients display radiological features and microbiological evidence of active TB disease (Davies and Pai, 2008. Drain et levitra price comparison al., 2018. Kendall et al., 2021. Pfyffer et al., 1997.

Richeldi, 2006 levitra price comparison. WHO, 2019). Thus, a proportion of patients presumed as LTBI may either be incipient or already have subclinical disease, contributing to onward transmission of (Dowdy et al., 2013. Drain et al., 2018 levitra price comparison.

Kendall et al., 2021). Reported reduced blood transcriptional signatures of TB risk were not related to subclinical TB or incipient disease or to the blood signature of active TB (Gupta et al., 2020. Penn-Nicholson et al., levitra price comparison 2020. Scriba et al., 2017.

Singhania et al., 2018a. Singhania et levitra price comparison al., 2018b. Suliman et al., 2018. Zak et al., 2016).

Earlier detection could inform treatment and limit transmission levitra price comparison. Diagnosis of active pulmonary TB requires microbiological samples for evidence of , which can be difficult to obtain (Davies and Pai, 2008. Richeldi, 2006). A blood transcriptional signature has been reported in patients with active TB (Berry levitra price comparison et al., 2010.

Blankley et al., 2016. Bloom et al., 2013. Joosten et levitra price comparison al., 2013. Maertzdorf et al., 2011.

Ottenhoff et al., 2012. Roe et al., 2016 levitra price comparison. Scriba et al., 2017), which is dominated by type I IFN signaling, reflects the extent of radiographical lung disease (Berry et al., 2010. Moreira-Teixeira et al., 2020), and levitra price comparison is diminished upon treatment (Berry et al., 2010.

Bloom et al., 2012. Cliff et al., 2013. Thompson et al., levitra price comparison 2017). Biomarkers to monitor TB treatment success are needed to accelerate assessment of treatment responses and determine the required treatment duration to adapt drug treatment regimens.

The accepted biomarker is sputum conversion to negative culture after 2 mo, which has low sensitivity and modest specificity for prediction of treatment failure (Horne et al., 2010. Mitchison, 1993) levitra price comparison. Chest x rays (CRXs) and inflammatory markers commonly used to assess the response to treatment are not universally available and difficult to standardize (Walzl et al., 2011). How the host response evolves after of humans with M.

Tuberculosis toward the peak levitra price comparison response in active TB is as yet unclear. Sequential immune responses were reported during TB progression but not linked to the clinical disease status, with adolescents evaluated at enrollment and then only sampled every 6 mo with follow-up over 2 yr, or evaluated at baseline and at the end of 2 yr (Scriba et al., 2017). Since this study was not on household contacts, knowledge of when each individual was exposed to M. Tuberculosis could not be estimated, limiting the scope for detailed temporal evaluation of levitra price comparison changes in the immune response during progressive .

Without detailed clinical characterization of patients upon serial sampling before TB diagnosis, differential gene expression during different phenotypic stages of disease ranging from incipient TB to subclinical TB to clinical TB cannot be assessed. Moreover, confounding interpretations due to re in high TB burden settings during the prospective period of observation and sampling cannot be ruled out (Charalambous et al., 2008. Van Helden et al., 2008 levitra price comparison. Van Rie et al., 2005.

Van Rie et al., 1999. Verver et al., 2005 levitra price comparison. Warren et al., 2004). Although blood transcriptional signatures have been shown to reflect the response to TB treatment (Berry et al., 2010.

Bloom et levitra price comparison al., 2012. Cliff et al., 2013. Penn-Nicholson et al., 2020. Thompson et al., 2017), the patterns levitra price comparison of resolution with treatment in different patient groups using detailed kinetic analysis at multiple time points has not been evaluated.

To address these questions, we undertook a prospective cohort study comprising participants with microbiologically confirmed pulmonary TB and household contacts of pulmonary TB at Leicester, UK, a high-income, moderate TB setting (TB incidence circa 40 per 100,000 population). An integrated clinical-research platform enabled recruitment, regular levitra price comparison follow-up, and detailed characterization of participants at serial time points of prospective observation (Materials and methods), with a low probability of new community-acquired during prospective follow-up of TB contacts. In total, 356 household contacts of pulmonary TB and 74 participants with incident TB were recruited between 2015 and 2018 and prospectively followed for 24 mo. TB contacts were reviewed every 3–6 mo with RNA sequencing (RNA-Seq) samples collected, whole-genome sequencing of the M.

Tuberculosis strain to trace back contacts to the index case, and CRX performed to screen for subclinical TB at each visit, with detailed radiological characterization and clinical investigation, including invasive sampling (bronchoscopy), if x-ray abnormalities were suspected or symptoms reported levitra price comparison. This allowed reliable characterization of participants with incipient, subclinical, or clinically active TB, and linking blood transcriptional signatures to the clinical phenotype as disease progressed. Active TB patients were sampled, and clinical characterization was undertaken before starting TB treatment, and prospectively at scheduled visits during treatment, with microbiological investigation, radiological surveillance with CRX, and computed tomography scan as clinically indicated. Changes in blood gene expression in different levitra price comparison clinical subgroups of active TB patients were related to the time of diagnosis and to detailed time points during treatment.

Bioinformatics analysis of blood RNA-Seq data of contacts revealed minimal changes in gene expression in incipient TB, increasing as patients progressed to subclinical and clinical TB, with similar expression profiles in these clinical phenotypes for published reduced risk signatures of TB. Moreover, gene expression changes in the blood of Leicester TB progressors, and a published cohort of TB progressors from a high burden TB setting, were most pronounced at 30 d before diagnosis, although heterogeneity was observed over time before diagnosis. The signature levitra price comparison of TB progression in the Leicester cohort was compared with active TB disease, before and during treatment, to understand the immune events underlying the evolution and resolution of TB disease (Fig. 1.

Study design). Our study provides information of the underlying host immune response at the different stages of disease and a levitra price comparison roadmap to describe the temporality of gene expression changes that occur during progression and treatment of active TB, which may help in clinical management of TB patients. To determine how global changes in differential gene expression develop as individuals progress from incipient TB to subclinical TB and then to to clinical TB and whether these clinical phenotypes show a graded increase in the immune response, we performed detailed analysis of changes in gene expression over time in blood of clinically defined Leicester household TB contacts who then progressed to TB (Fig. 2).

Contacts who progressed to TB were subdivided according to their clinical phenotype at the time point of sampling (Table S1) levitra price comparison. In the 14 household contacts, incipient TB was concurrent with samples (n = 10) collected earlier than 40 d before diagnosis. Subclinical TB spread between earlier than day 40 (n = 1), 21–40 d (n = 3), and <20 d (n = 6) before diagnosis. Clinical TB spread between 21–40 d (n = 4) or <20 d (n levitra price comparison = 14) before diagnosis (Table S1).

Numbers of up- and down-regulated genes were minimal in incipient TB (94 up-regulated and 48 down-regulated genes), increasing in subclinical TB (483 up-regulated genes and 81 down-regulated genes) and in clinical TB (572 up-regulated and 136 down-regulated genes. Fig. 2 A) levitra price comparison. Fewer down-regulated genes were detected in each of the different clinical phenotypes of the TB contacts as they progressed to TB (Data S1).

Of the up-regulated genes, Metacore pathway analysis showed a dominance of the IFN-α/β signaling pathways in subclinical TB and clinical TB with an increase in the ratio in the number of genes per pathway, 14/62 and 16/62, respectively, with much lower representation in incipient TB, 5/62 (Fig. 2 B) levitra price comparison. Incipient TB showed IFN-γ activation of macrophages and the classical complement as the top represented pathways. However, only 5/50 and 5/53 genes were represented in levitra price comparison each pathway, although with a much lower ratio of genes per pathway overall (Fig.

2 B and Data S1). The P values for the different clinical subgroups showed a corresponding increasing statistical significance for the IFN-α/β signaling pathways, progressing from the incipient TB (8.65 × 10−5), subclinical TB (4.75 × 10−13), and clinical TB (2.44 × 10−14) respectively (Fig. 2 B) levitra price comparison. Similarly, the type I IFN modules increased in subclinical TB and clinical TB, although clinical TB progressors showed additional changes resembling the signature of active TB, including an increase in the innate/hemopoeitic mediators module (Fig.

2 C). A decrease in levitra price comparison the natural killer (NK) and T cell module in incipient, subclinical, and clinical TB was observed, with incipient TB showing no other detectable changes at this stringent level of statistical analysis (Fig. 2 C). The top 30 differentially expressed coding genes (false discovery rate [FDR] P <.

0.05, log2 fold change >1) ranked by fold change, selected from a levitra price comparison total of 47 genes in incipient TB, 233 genes in subclinical TB, and 311 genes in clinical TB, showed that many genes were differentially expressed across all three clinical phenotypes, albeit to different levels (Table 1 and Data S1). These included the genes C1QC***, SERPING1***, ETV7***, and BATF2*** expressed in all three clinical phenotypes. C1QA**, C1QB**, C2**, and EXOC3L1** were expressed in two of the clinical phenotypes, and ANKRD22** and GBP6** were significantly expressed in subclinical TB and clinical TB and were barely elevated above controls in the incipient TB (Data S1, full incipient TB versus control tab). Although certain genes levitra price comparison appeared to be uniquely expressed within each clinical phenotype, most could be detected across the three clinical phenotypes, albeit to differing levels.

Seemingly unique genes within the top 30 gene set of the incipient TB subgroup, such as CCL2, HESX1, PCGF2, LCN8, and SIGLEC1, were only elevated to a very low level against controls in the full set of differentially expressed genes, potentially suggesting that they may come up early in the immune response to M. Tuberculosis, although they were also expressed at a low level in clinical TB (Data S1. Full incipient levitra price comparison TB, full clinical TB versus control tabs). The expression of the complement fixing genes C1QC and C1QB in the top 30 genes of the incipient TB versus control group is in keeping with the Metacore pathway analysis in Fig.

2 B. However, these genes were also differentially expressed within the top 30 genes of subclinical TB and levitra price comparison clinical TB (Data S1. Full subclinical TB, full clinical TB versus control tabs). BATF2 expression increased significantly with increasing disease.

1.4 log2 fold levitra price comparison change, P value 0.0011 in incipient TB. 2.9 log2 fold change, P value 7.6 × 10−12 in subclinical TB. And 3.48 log2 fold change, P value 6.07 × 10−24 in clinical TB. Expression of SERPING1 and ETV7 showed a similar increase in expression as individuals who progressed to levitra price comparison TB (Table 1 and Data S1).

We next analyzed blood transcriptional changes that occurred over time in Leicester TB household contacts as they progressed to TB, together with patients sampled before they were diagnosed with TB (progressors) in view of our findings that high levels of differential gene expression are mainly seen in progressors with subclinical TB and clinical TB, rather than in incipient TB. RNA-Seq data were analyzed in blood from Leicester household contacts levitra price comparison of active TB patients at different time points after recruitment as they progressed to clinical TB (Fig. 4 A. Table S2, top, n = 12 TB contacts.

Total of 21 samples) together with Leicester patients sampled before they were diagnosed levitra price comparison with active TB by culture/microbiological/clinical positivity (Fig. 4 A. Table S2, bottom, n = 11 progressors, total of 12 samples), all before treatment. Active TB patients at the time of levitra price comparison diagnosis (Fig.

4 A, far right. Table S2, top, n = 49 TB patients), all as compared with healthy controls (Table S2, top, n = 38 healthy controls). The biggest changes in levitra price comparison gene expression (log2 fold, FDR P value of 0.05 cutoff) were observed at 0–20 d before TB diagnosis in the contacts (n = 11) and progressors (n = 9. 765 up-regulated and 125 down-regulated genes.

Fig. 4 A) levitra price comparison. Although the change in the number of genes just before diagnosis appeared similar to that observed in active TB patients at the time of diagnosis (1,231 up-regulated and 511 down-regulated genes. Fig.

4 A, far levitra price comparison right), the extent of differential expression in the blood of active TB patients at the time of diagnosis was higher (Fig. 4 A, far right, scale on y axis 0–100. ˆ’log10 P adjusted) as compared with the contacts and progressors sampled before diagnosis (Fig. 4 A levitra price comparison.

0–20 d before diagnosis, scale on y axis 0–15. ˆ’log10 P adjusted). Changes in gene expression were substantially lower between 21–40 d before diagnosis with low level levitra price comparison up-regulation of 185 and down-regulation of 80 genes (Fig. 4 A.

Representative of four TB contacts that progressed to TB and three TB progressors sampled before diagnosis). At 41–832 d before TB diagnosis, when six samples were all from TB contacts subsequently progressing to clinical levitra price comparison TB, this change in gene expression was further reduced, with very low levels of 109 up-regulated and 34 down-regulated genes (Fig. 4 A, far left). We next performed more in-depth analyses on Leicester TB household contacts alone as they progressed to TB, recruited and sampled from 2015 to 2018 and followed up to date, by pooling our more recently recruited dataset with our previously published dataset (Singhania et levitra price comparison al., 2018a.

Total 38 samples from 14 TB household contacts, sampled as they progressed to TB against matched controls. Fig. 4 B levitra price comparison and Table S3). Lower levels of gene expression were now observed between 0 and 31 d before diagnosis with only 23 up-regulated and 1 down-regulated gene (Fig.

4 B. Log2 fold, FDR P value of levitra price comparison 0.05 cutoff. Scale on y axis 0–4. ˆ’log10 P adjusted), including up-regulation of GBP5, SEPTIN4, ETV7, C1QC, BATF2, C1QB, FCGR1A, GBP6, and SERPING1.

Gene expression levitra price comparison changes at earlier time points fluctuated over time, with 15 up-regulated and 8 down-regulated genes observed between days 86 and 150 but not at 32–58 before diagnosis (Fig. 4 B). Up-regulation of these genes was still detected, albeit to a much lower level, at all the earliest time points before diagnosis (Data S2). Some exceptions included C1QB, C1QC, and C1QA, which were in the top seven DEGs at the levitra price comparison time points 86–150 before diagnosis, while in the 0–31 d before diagnosis, only C1QB and C1QC were in the top eight expressed genes at the level of fold change over controls, suggesting heterogeneity of gene expression over time.

We next analyzed a bigger dataset of individuals from South Africa reported to have subsequently progressed to TB, reported to have been sampled at 6 monthly intervals for blood RNA-Seq analysis before diagnosis, although without serial clinical follow-up (Scriba et al., 2017. Zak et al., 2016). We further subdivided the sampling into levitra price comparison tighter time points and examined differential gene expression levels as compared with LTBI nonprogressors recruited in parallel. Again the highest levels of gene expression changes were observed between 0 and 31 d before diagnosis, with 1,155 up-regulated and 473 down-regulated genes (Fig.

4 C. Log2 fold, FDR levitra price comparison P value of 0.05 cutoff. Scale on y axis 0–15. ˆ’log10 P adjusted), including up-regulation of SEPTIN4, SERPING1, BATF2, GBP6, ETV7, and FCGR1A, similar to those detected in our Leicester contacts 0–31 d before diagnosis (Fig.

4 B) levitra price comparison. Expression of C1QA, C1QB, and C1QC was only detectable 0–31 d before diagnosis, perhaps reflecting the level of detectability over controls. Changes in differential gene expression at most other time points before diagnosis were very low (Fig. 4 C and Data S3), although fluctuations in differential gene expression were observed over time, for levitra price comparison example with marked changes at 181–250 (88 up-regulated and 46 down-regulated genes) and 251–360 d (51 up-regulated and 3 down-regulated genes) before diagnosis, as compared with other time points showing minimal changes.

Among the top 30 genes found to be up-regulated between 181 and 250 d before diagnosis were SEPTIN4, GBP6, BATF2, ETV7, SERPING1, and FCGR1A, although these genes were also among the top up-regulated 30 genes at 0–31 d before diagnosis, albeit then at a more significant level (Data S3), suggesting a graded increase in the expression of these genes as progressors approached TB diagnosis, with some heterogeneity of gene expression over time. The blood modular signature of TB contacts and TB patients sampled prediagnosis as levitra price comparison in Fig. 4 A showed a reduction in the NK and T cell module (dominated by IFNG and effector T and NK cell genes) at >40 d before diagnosis, followed by an increase in the innate/hemopoeitic mediator module from 40 d. Increased type I IFN–inducible and inflammasome/granulocyte modules together with a reduction in the NK and T cell, and T and B cell modules were detected at 0–20 d before diagnosis (Fig.

4 D), similar to the reported levitra price comparison TB blood signature (Moreira-Teixeira et al., 2020. Singhania et al., 2018a). The decrease in the NK and T cell module fluctuated over time before diagnosis in progressors, which could reflect fluctuation in the response or heterogeneity in the progressors. Changes in the type I IFN/C’/myeloid levitra price comparison and inflammasome/granulocyte modules together with a reduction in the NK and T cell module were observed in Leicester TB contacts alone, but to a lesser extent only detectable from 30 d before diagnosis using a nominal P value of 0.05 rather than FDR (Fig.

4 E). The initial change at 200–850 d before diagnosis again consisted of a reduction in the NK and T cell module, although this was not consistent, again reflecting heterogeneity over time. Changes in the Zak modular signature over time were levitra price comparison also mainly detectable over time using a nominal P value of 0.05 rather than FDR (Fig. 4 F).

At 0–31 d before diagnosis, the modular signature for the Zak progressors (Fig. 4 F) was almost identical to that of levitra price comparison active TB (Fig. 4 D, far right. Moreira-Teixeira et al., 2020.

Singhania et al., 2018a), although with levitra price comparison less enrichment as at a nominal P value of 0.05 (Fig. 4 F), including enrichment of inflammasome/granulocytes, innate/hemopoetic mediators, innate immunity PRR/C’/granulocytes, IFN/PRR, and IFN/C’/myeloid modules and decreased enrichment of T cell, B cell, and NK and T cell modules. The modular signature was barely detectable at other time points before diagnosis, with the IFN/PRR and IFN/C’/myeloid modules missing at 32–78 and 79–104 d but then present at 181–250 and 251–360 d before diagnosis, again suggesting temporal heterogeneity of gene expression or potential re as reported in high-burden TB settings (Charalambous et al., 2008. Uys et levitra price comparison al., 2015.

Van Helden et al., 2008. Van Rie et al., 2005. Van Rie et al., 1999 levitra price comparison. Verver et al., 2005).

To investigate the heterogeneity among Leicester TB household contacts and the Zak progressors, the average gene expression value of the 30-gene signatures (from Table 1) derived from incipient TB (blue), subclinical TB (orange), and clinical TB (red) was assessed at different time points before diagnosis in individual Leicester TB contacts (n = 9) and individual Zak progressors (SupTab1. SupTab6_RNA-Seq-Metadata from Zak et al., levitra price comparison 2016. Training set n = 18. GEO accession levitra price comparison no.

GSE79362) where two or more sampling time points were evident. The average 30-gene incipient TB, subclinical TB, and clinical TB signatures were shown to be marginally elevated over the baseline (dotted line for each) in four of the Leicester TB household contacts at all time points analyzed before TB diagnosis, 0–30 d, with the subclinical TB and clinical TB signatures showing slightly better performance (Fig. 5 A, shorter time points, levitra price comparison n = 4). Three out of four of these contacts who progressed rapidly to TB disease had been infected with an outbreak strain of M.

Tuberculosis identified by whole genome sequencing. TB contacts who progressed over 100–200 levitra price comparison d showed a greater elevation against baseline, similar for the incipient, subclinical, and clinical TB 30-gene signatures, increasing at times close to TB diagnosis (Fig. 5 A, longer time points). One TB contact (Patient ID 493) showed some fluctuation, although always above baseline for all three signatures (Fig.

5 A, longer time points, n = 5) levitra price comparison. The average 30-gene incipient TB, subclinical TB, and clinical TB signatures showed an increase over the baseline (dotted line, LTBI controls) in the Zak progressor patients between 4–600 d before diagnosis (Fig. 5 B), although a sharp increase in the signatures over time was only observed in around five of the progressors from just >200 d to a maximum before diagnosis. Other patients showed heterogeneity in expression of these levitra price comparison 30-gene signatures over time, many showing elevated signatures maintained at the same level over time, with others actually decreasing (Fig.

5 B). The published Zak 16-gene signature showed almost superimposable curves with very similar increases above the baseline controls over time in the individual Leicester TB household contacts (Fig. S1 A, shorter time points and longer time points), and levitra price comparison in the Zak progressors, with identical increases in the five individuals and the same heterogeneity as observed with the 30-gene signatures (Fig. S1 B).

There is currently a need for early biomarkers to monitor TB treatment success earlier and to evaluate robustly the duration of treatment required in TB patients to adapt drug treatment regimens. To establish treatment response levitra price comparison signatures, RNA-Seq was performed on blood from 74 TB patients at diagnosis (treatment-naive), and longitudinally, at carefully planned time points during TB treatment. We first monitored the transcriptional response to treatment across the whole cohort, and second monitored the transcriptional response of individual patients to identify distinct profiles of their transcriptional response that might help to stratify clinical treatment phenotypes. Blood was collected and subjected to RNA-Seq from the 74 TB patients at diagnosis before treatment and thereafter, at 1 and 2 wk, at 1, 2, 4, 5, 6, 7/8, 9/10, and 11/12 mo, and at >1-yr after treatment (Fig.

S2, A and B) from clinically defined patients levitra price comparison. Pulmonary TB, difficult TB cases, TB drug–resistant, outbreak TB strain, and other TB progressors (Table S4). TB patients received either standard anti-TB treatment (ATT. 200 d or less) levitra price comparison or extended ATT (>200 d.

Table S4), according to their clinical assessment through treatment, with smear-positive patients mostly falling within the extended ATT patient group (Fig. S2 C) levitra price comparison. The sample-to-sample correlation heatmap and principal component analysis (PCA) of all the active TB patients at diagnosis before treatment and at the different time points during the treatment course showed samples to mainly cluster according to time points, with some heterogeneity (Fig. S2, D and E).

The top levitra price comparison 1,000 most variable gene expression heatmap distinguished patients according to time of treatment, and according to smear positivity and negativity at treatment initiation (T0. Fig. S2 D). The innate/hemopoietic, IFN/PRR, and IFN/C’/myeloid modules were found to be over-abundant as compared with controls before treatment and decrease in abundance to different degrees within all the subgroups after T0, except for in the TB drug resistant subgroup levitra price comparison (Fig.

6 A). These modules decreased in abundance after 1 wk of treatment and were completely abrogated after 4 mo of treatment in the standard ATT subgroup. Although the extended ATT and difficult TB cases subgroups showed a similar pattern to the standard ATT subgroup, a stronger modular signature before treatment and an incomplete diminishment levitra price comparison after 6 mo were observed, when a standard treatment course would be completed. The outbreak TB strains subgroup showed a similar but weaker global modular signature to the standard ATT subgroup, also resolving within 4 mo of treatment.

However, a small subgroup of four patients, the TB drug–resistant subgroup, showed a distinct modular signature that for the most part was not diminished, in accordance with these patients requiring altered drug treatment regimens for a longer period (Fig. 6 A) levitra price comparison. The standard and extended ATT subgroups contained a large number of patients such that the modular signature was more robust than in the other three subgroups, which contained lower numbers of patients (Fig. 6 A).

We therefore levitra price comparison examined the members of each of these three subgroups individually and show that at the level of the individual, these modular responses to treatment are heterogeneous and so should be validated in larger cohorts in future studies (Fig. S3). The number of DEGs compared with controls was also reduced upon treatment (Fig. 6 B) levitra price comparison.

Smear-positive and smear-negative TB patients showed a similar modular and gene expression decrease during treatment with complete diminishment by 4–5 mo, although the smear-positive patients had a stronger modular signature before treatment (data not shown). We then identified a 212-gene signature (TREAT-TB212) that showed the response to treatment across the whole Leicester cohort, mainly showing decreased gene expression as compared with controls over the treatment course, which reverted to the expression profile of healthy controls by 4 mo of treatment in most but not all of the patients (Fig. 7 A) levitra price comparison and in an independent treatment response cohort dataset (Fig. 7 B.

Thompson et al., 2017). The TREAT-TB212 signature in not-cured patients from the Thompson cohort was sustained levitra price comparison at all time points up to 24 wk at similar levels to that of the pre- and very early treatment response signatures (Fig. 7 B), at a comparable level to the Leicester cohort of active TB patients, TB progressors recruited as TB household contacts after diagnosis, and most different clinical treatment response subgroups (Fig. 7 A) levitra price comparison.

A higher TREAT-TB212 signature was observed in patients receiving treatment for >200 d but this was observed only early after T0, as compared with those receiving standard treatment of up to 200 d (Fig. 7 A). In keeping with the modular and differential gene expression analyses, the TREAT-TB212 signature was only different in levitra price comparison the smear-positive and -negative patients at T0 but not between 1 wk to 1 yr after T0, indicating that the patients were responding similarly to treatment (Fig. 7 A).

The log2 fold-change of all TREAT-TB212 genes against controls verified changes in gene expression upon treatment (Fig. 7 C, Leicester cohort) with a similar profile in the levitra price comparison Thompson cohort (Fig. 7 D). Again, most of the gene expression profiles reverted to that of healthy controls by 4 mo of treatment (Fig.

7 C, Leicester cohort), although this could not be evaluated in the Thompson cohort due to fewer sampling visits that did not include levitra price comparison this time point (Fig. 7 D). The TREAT-TB212 signature was further reduced to a 27-gene signature (TREAT-TB27), which selected genes with the greatest changes in expression over the treatment course in the whole cohort (Fig. 7 E), and levitra price comparison its validity was confirmed also in the Thompson cohort (Fig.

7 F). Although TB patients had been subgrouped according to their clinical phenotype in response to treatment, as standard ATT, extended ATT, difficult TB cases, TB drug–resistant, and outbreak TB strains, the TREAT-TB212 signature did not show a clear transcriptional response trend according to their clinical definition except for most of the drug-resistant group (Fig. 7 G, levitra price comparison compared with T0). However, by monitoring the transcriptional response of individual patients according to their TREAT-TB212 signature profile, regardless of their clinical subgroups but where samples at all time points had been obtained, four distinct transcriptional profiles were revealed.

Expected, resembling standard ATT. Weaker, as compared with standard ATT levitra price comparison. Or stronger initial or stronger delayed, as compared with standard ATT (Fig. 7 H, compared with T0).

Strikingly, stronger initial or stronger delayed transcriptional response patient groups showed differences in the transcriptional response already at 1 and 2 wk after T0, levitra price comparison although at week 1 after treatment, C-reactive protein (CRP) levels in both groups were comparable (46.00 mg/l, stronger initial. 34.00 mg/l, stronger delayed). The stronger delayed patient group displayed elevated levels of CRP even after 1 mo of treatment as compared to stronger initial (8 mg/l, stronger initial. 38 mg/l, stronger delayed group), also correlating with minimal changes in radiographical signs of disease (data not shown), suggesting continued inflammation and potentially in levitra price comparison the stronger delayed.

Thus the treatment response could not be predicted clinically by CRP levels early but could be predicted by the different kinetics of the transcriptional response observed as early as 1 wk after T0 in the stronger delayed as compared with the stronger initial group, supporting the role of transcriptional biomarkers as more sensitive measures of the treatment response than existing clinical markers. To develop a reduced transcriptional signature that may enable early identification of poorer treatment responders, based levitra price comparison on the stronger initial and stronger delayed groups, the differential expression of TREAT-TB212 between two consecutive time points from T0 to 1 wk, 1 to 2 wk, and 2 wk to 1 mo was computed leading to a reduced signature (EarlyRESP-TB25. Fig. 7 H and Fig.

S4). EarlyRESP-TB25 showed differences in the stronger initial and stronger delayed patient groups by their different transcriptomic profiles at 1–2 wk after T0 (Fig. 7 J), with similar but not optimal results observed for TREAT-TB27 (Fig. 7 I.

Derived gene lists TREAT-TB27, EarlyRESP-TB25. Fig. S4, A and B). Reported reduced blood signatures of TB diagnosis or risk show little to no overlap with each other, and most have been tested for distinguishing active TB from LTBI but not active TB from other diseases (ODs.

Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016. Singhania et al., 2018a.

Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016. Reviewed in Singhania et al., 2018b).

We set out to develop an optimized reduced signature that would distinguish active TB from ODs as well as LTBI. Combined reduced blood signatures of TB diagnosis or risk comprising 101 distinct genes (Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016.

Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016.

Unpublished data) were analyzed in 10 published datasets from multiple clinical disease cohorts including active TB, LTBI, and ODs and healthy controls (Bloom et al., 2013. Parnell et al., 2012. Suarez et al., 2015. Zhai et al., 2015), and the pooled dataset was batch-corrected (Fig.

S5, A and B. And Materials and methods). 12 genes were identified in the reduced signature that were shared between the top 30 genes distinguishing active TB from LTBI, and active TB from ODs, ranked by decreasing importance (mean decrease accuracy. Fig.

8, A and B), further reduced to 10 genes (TB10) based on performance (area under the curve [AUC] and accuracy) on pooled cohort datasets with independent validation (Fig. S5 C and Table S5). This TB10 signature originating from the different reduced reported signatures (Fig. 8 C) comprised genes that were either up- or down-regulated in active TB as compared with controls, LTBI, and ODs (Fig.

8 D) and was shown to be significantly different in TB versus ODs and LTBI and controls using ANOVA (Data S4). Removal of the GBP5 gene (12th in rank) reported to discriminate active TB and LTBI did not improve the performances for discrimination of active TB from ODs. Individual gene expression was heterogeneous across patients with active TB and the ODs (Fig. 8 D, likely reflecting the different extents of morbidity.

Berry et al., 2010. Bloom et al., 2013). The performances of TB10 were tested and compared with the signatures that we describe in this study, including the 30-gene incipient TB, the 30-gene subclinical TB, the 30-gene clinical TB, the TREAT-TB27, and the EarlyRESP-TB25 (Fig. S5 D), and previously published reduced signatures (Fig.

8 E and Fig. S5 E. Kaforou et al., 2013. Maertzdorf et al., 2016.

Roe et al., 2016. Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016.

Zak et al., 2016. Described in Materials and methods). TB10 showed the best performance for TB versus ODs (Fig. 8 E and Fig.

S5 D. AUC, 0.999. Accuracy, 0.976. And 95% confidence interval [CI], 0.9959–1), and TB versus LTBI (Fig.

S5 E. AUC, 0.971. Accuracy, 0.943. 95% CI, 0.9343–1).

Although the Suliman reduced signature was comparable to TB10 for distinguishing TB from LTBI (Fig. S5 E), it showed poorer performance for distinguishing TB from ODs (Fig. 8 E). Blood transcriptomics have revealed major characteristics of the immune response in TB, show promise to support TB diagnosis, and would be of great use to identify individuals with asymptomatic incipient TB or subclinical TB before they progress to clinical TB to facilitate targeted early treatment and reduce onward transmission.

Moreover, new tools for effective TB treatment monitoring are needed to determine when and if patients are responding to treatment to provide a personalized approach to treatment and accelerate screening of new anti-TB drugs. To achieve this, a detailed knowledge of how the host immune response develops over time and relates to the state of M. Tuberculosis is needed. We now show, in a unique clinically and temporally well-defined cohort of household contacts of active TB patients, that minimal changes in blood gene expression are detectable in incipient TB, increasing as patients progress to subclinical TB, and maximal at the time of presentation with clinical TB, with similar results for published reduced risk signatures of TB.

Although the transcriptional signatures increased with time and were most highly expressed around 30 d before diagnosis, there was heterogeneity over time in the response in the TB contacts as they progressed to TB and a published cohort of TB progressors from a high-burden TB setting. Blood signatures at detailed time points during ATT additionally allowed us to define signatures that can distinguish early and late responders. Finally, we demonstrate comparable performance of immune signatures developed for TB diagnosis and detection of early stages of M. Tuberculosis and their reduction upon TB treatment monitoring, with subtle differences for different signatures at different stages of development and diminishment of TB disease.

The temporality of gene expression changes during progression, and resolution of active TB may provide mechanistic insights toward the development of host therapies and supports a framework for future development of biomarkers to improve the clinical management of LTBI and active TB. TB contacts that progress to TB showed a decrease in the NK and T cell effector modular signature, which was detectable from the earliest stages of progression, in keeping with findings that cytotoxic effector molecules and NK cells are important for protection against M. Tuberculosis in human TB (Roy Chowdhury et al., 2018). We observed an increase in the inflammatory and IFN modular signatures in subclinical TB and clinical TB at time points closest to TB diagnosis in both Leicester contacts and Zak TB progressors, but not in incipient TB.

That the IFN modular signature fluctuated with time in Zak progressors potentially explains differing reports that type I/II IFN signaling and the complement cascade were elevated 18 mo before TB disease diagnosis in this cohort (Scriba et al., 2017), with others suggesting increased expression of complement genes in subclinical TB closer to diagnosis (Esmail et al., 2018). Our analysis at more detailed time points of the Zak cohort suggests elevated type I IFN signaling, and complement genes at 18 mo before diagnosis may indicate individual patient heterogeneity, as we discuss below. A marked decrease in the B and T cell modular signatures and increase in other modules found in active TB including myeloid inflammation, lymphoid, and monocyte and neutrophil gene modules occurred in progressors more proximally to TB disease, in keeping with Scriba et al. (2017).

A reciprocal reduction in the inflammation and IFN modules was observed after a week of successful treatment and was restored to that of healthy controls by 4 mo, together with the B and T cell modular signatures. Our findings that the evolution of the immune response on progression to TB shows the reverse upon treatment is suggestive that reduced signatures optimized to support early diagnosis of TB may also reflect the changes that occur in response to treatment. Reduced blood signatures have been proposed to determine the risk of exposed individuals to their subsequently developing TB (Penn-Nicholson et al., 2020. Singhania et al., 2018a.

Singhania et al., 2018b. Suliman et al., 2018. Zak et al., 2016). However, it was unclear at the time whether any of these reported blood signatures of TB risk predicted progressors at stages of incipient or subclinical TB.

We now show that while these published gene risk signatures are expressed during subclinical TB and clinical TB, only SERPING1, ETV7, and BATF2 from the 16-gene Zak signature were up-regulated, albeit at a very low level, in incipient TB, similarly to the global transcriptional expression signature in these Leicester contact clinical phenotypes. 7 of the 16-gene signature reported by Zak et al. (2016) were among the 30 most highly expressed genes across the Leicester contact clinical phenotypes of TB progression, including FCRGR1A, SEPT4, GBP5, ANKRD22, SERPING1, ETV7, and BATF2. Although C1QC, SERPING1, ETV7, and BATF2 were among the top 30 DEGs in all the clinical phenotypes, increasing statistical significance of differential gene expression was observed with progression from incipient to subclinical to clinical TB, suggesting that they may be early indicators of M.

Tuberculosis and reflect the evolution of the immune response with time after . In support of this, IFN-α/β signaling pathways by modular and Metacore analysis were observed in subclinical TB and more so in clinical TB, but were not apparent in incipient TB. Collectively, our findings suggest that the levels of gene expression increase with progressive from incipient to subclinical to active TB, potentially explaining why distinct published signatures of TB risk show little overlap with each other (Penn-Nicholson et al., 2020. Singhania et al., 2018a.

Suliman et al., 2018. Zak et al., 2016. Reviewed in Singhania et al., 2018b) due to differential levels of detectable gene expression and/or uncertainty in the stage of at which sampling was performed. The unique gene set expressed in incipient TB is characterized by low-level gene expression changes across multiple pathways, which may limit their value for predicting which incipient TB patients will progress to clinical TB.

However, genes that were differentially expressed in subclinical TB and clinical TB at a high and significant level were detectable in incipient TB, supporting suggestions that serial testing among carefully selected clinical target groups might be required for optimal implementation of biomarkers for TB risk (Esmail et al., 2020. Gupta et al., 2020). Indeed, inclusion of clinical details of TB progressors over detailed sampling times after exposure/ with M. Tuberculosis allowed us to define progressors as incipient TB and subclinical TB (Drain et al., 2018) and to assign changes in blood gene expression at an early stage of to each clinical phenotype, providing a framework for improved biomarker selection to target early TB treatment and block onward transmission.

Our findings of temporal heterogeneity in the blood transcriptional response of Leicester TB progressors and more so in the Zak TB progressors (Zak et al., 2016), both at bulk cohort levels as well as in individual progressors, likely reflect the dynamic nature of the host–pathogen interaction over time, and are consistent with observations in positron emission tomography scan and computed tomography scan studies of progressive in humans with LTBI coinfected with HIV/TB and in nonhuman primates (Barry et al., 2009. Esmail et al., 2016. Lin et al., 2016). The majority of Leicester contacts progressed over 100–200 d, displaying a greater elevation in their blood signatures against the baseline as they progressed to TB.

However, a subset showed rapid progression with an increased signature close to diagnosis, mostly explained by with an outbreak strain of M. Tuberculosis, supporting previous reports of differing virulence of M. Tuberculosis strains (Coscolla and Gagneux, 2014), suggesting that the time at which protective immune responses were overwhelmed leading to TB progression could differ according to the infecting strain of M. Tuberculosis.

Detailed analysis of individuals in the Zak et al. (2016) cohort, sampled for >600 d, showed a sharp increase in the signatures over time in only five progressors from 200 d before diagnosis, progressing sharply to the highest signature before diagnosis, again suggesting that immune responses had been overwhelmed. However, in other Zak progressors, the fate of appeared to hang in the balance for a prolonged period until diagnosis, possibly reflecting subclinical disease, although in-depth clinical analysis was not performed at these earlier sampling time points in the study. Moreover, since it was not a study of recent TB contacts, there was no knowledge of time of exposure or the infecting M.

Tuberculosis strain. Our findings are consistent with a recent randomized controlled clinical trial for biomarker-guided tuberculosis preventive therapy (termed CORTIS. NCT02735590), which concluded that a reduced signature RISK11 derived from the Zak 16-gene signature (Zak et al., 2016) was better suited to screening of symptomatic individuals with possible early clinical TB than for mass community-based screening for incipient TB (Scriba et al., 2021). It is unclear whether this signature can distinguish subclinical or active TB from other s, particularly viral s, which may present with symptoms similar to clinical TB and are also dominated by type I IFN signaling (Singhania et al., 2018a.

Singhania et al., 2018b). Improved biomarkers to monitor TB treatment success are needed to reliably evaluate the duration of treatment required in individual TB patients and deliver optimal drug treatment regimens. We adopted both clinical and bioinformatics approaches to develop blood signatures of the treatment response. Using the clinical approach, individual patients of the Leicester TB cohort were stratified on the basis of treatment response (standard ATT, extended ATT, and difficult TB) and infecting strain characteristics (drug-resistant TB and outbreak TB strains).

The treatment response signature corresponded with their clinical treatment response, discriminating both the subgroup of drug resistant patients in Leicester responding more slowly to treatment, and the subgroup labeled as not cured in the African Thompson cohort (Thompson et al., 2017), supporting identification of patients not responding to treatment. Using the bioinformatics approach, we monitored the transcriptional response of individual patients, independent of their clinically defined treatment phenotype, and defined four types of TB patients. The expected group responding standardly to treatment. The weaker group defining a subgroup with a lower grade of (low CRP, and longer time to M.

Tuberculosis culture positivity). And the stronger initial and stronger delayed groups, showing differences in their transcriptional response at 1 wk after T0. Differences between these latter two subgroups were not discernible by clinical measures of their treatment response, such as CRP and x ray, at these early time points, supporting the utility of transcriptional biomarkers as more sensitive measures of the treatment response than existing clinical markers, to inform clinical management of TB patients and to support drug development platforms and future drug treatment trials. Existing TB diagnostic tools are limited in their scope and dependent on sputum availability for rapid identification of TB.

Diagnostic blood transcriptomic signatures of TB may provide a pathway to support early diagnosis for a broader spectrum of disease phenotypes, although there is little consensus between reported reduced signatures for TB risk and those that distinguish active TB from LTBI (Kaforou et al., 2013. Maertzdorf et al., 2016. Penn-Nicholson et al., 2020. Roe et al., 2016.

Scriba et al., 2021. Singhania et al., 2018a. Singhania et al., 2018b. Suliman et al., 2018.

Sweeney et al., 2016. Zak et al., 2016). Moreover, there is a need to distinguish TB from other confounding diseases (Kaforou et al., 2013. Singhania et al., 2018a.

Singhania et al., 2018b). Our blood signature, TB10, derived from published reduced signatures tested on multiple disease cohorts, optimally distinguished patients with TB from those with LTBI, and TB from those with ODs. Although some of the published signatures had similar performance in distinguishing TB from LTBI, they had poorer performance than TB10 in distinguishing TB from ODs. All reduced signatures derived in this study, including the 30-gene signatures of incipient TB, subclinical TB, and clinical TB progression and the new treatment-monitoring signatures TREAT-TB27 and EarlyRESP-TB25, showed poorer performances than TB10 in distinguishing TB from LTBI and in distinguishing TB from ODs, suggesting that the top gene expression changes that occur temporally upon progression to TB may not exactly match the top gene set that is temporally diminished after TB treatment.

All signatures increased in subclinical TB and maximally in clinical TB, also decreasing at week 1 after treatment, with a further decrease at month 2 and complete disappearance of gene expression changes by month 6. However, all signatures barely showed a significant increase in expression in incipient TB above controls, with the exception of the 30-gene incipient TB signature, which potentially could be further reduced to the most highly expressed genes and optimized to give the highest sensitivity of gene expression to detect what is likely to be early M. Tuberculosis in asymptomatic individuals with incipient TB and subclinical TB, at risk of progression to TB. The global aim of our study, however, was not to develop optimized signatures of risk and progression to TB, but to use signatures developed at different stages of disease progression and treatment to determine how and to what extent they are perturbed in the different clinical phenotypes of progression to TB, during active TB, and upon treatment.

In conclusion, using in-depth temporal analysis of gene expression changes over time in a cohort of clinically well-characterized household contacts of TB patients from a moderate-burden TB setting with minimal risk of re, together with reanalysis of gene expression at more detailed time points in a published cohort of TB progressors from a high TB burden setting, we demonstrate significant heterogeneity in changes of gene expression, at both the bulk cohort level and in individual patients, as they progress to TB. This has major implications for assessing TB risk in individuals with LTBI. Our characterization of the immune response underlying the evolution and resolution of TB provides a framework for biomarker development to improve clinical management of this disease. Between September 2015 and September 2018, longitudinal cohorts of active TB and TB contacts were recruited from the clinical TB service at Glenfield Hospital, University Hospitals of Leicester National Health Service Trust, Leicester, UK (Table S1, top).

All participants were prospectively enrolled and sampled before the initiation of any TB treatment. The Research Ethics Committee for East Midlands–Nottingham 1, Nottingham, UK (REC 15/EM/0109) approved the study. All participants gave written informed consent. All active TB patients had microbiologically confirmed disease with whole-genome sequencing of culture isolates performed for case linkage with contacts.

All participants were prospectively followed with visits at scheduled time points from the time of diagnosis (pretreatment) until 12 mo after completing treatment. Household TB contacts were identified through routine contact tracing and underwent systematic baseline investigation with routine CXR, IFN-γ release assay for M. Tuberculosis reactivity (IGRA) testing, and a symptom questionnaire. Sputum was collected if participants were spontaneously expectorating, and bronchoscopy performed in those not expectorating with clinical or radiological suspicion of TB.

On this basis, participants were classified with LTBI, subclinical TB, or active TB. Participants with LTBI were prospectively followed up for a minimum period of 2 yr with scheduled follow-up visits at 3–6 monthly intervals. At each visit, a symptom screen and CXR was performed and a blood RNA sample collected. In total, 356 TB contacts were recruited (150 IGRA-positive and 206 IGRA-negative [IGRA-ve]).

To date, 20 participants from this cohort have been diagnosed with TB and classified as TB progressors, although 6 were excluded from the gene expression study due to either cDNA library failure (n = 1) or failure to secure microbiological confirmation (n = 5) essential for case linkage with the index case. In these cases, the diagnosis of active TB was based on clinical symptoms, typical radiological features, and supporting histology from the site of . At each visit, a symptom screen and CXR were performed and a blood RNA sample collected. At the time of our publication in 2018 (Singhania et al., 2018a), we reported a modified disease risk score using a TB-specific 20-gene signature in 9 TB contacts who had developed TB during the study, with 99 contacts remaining healthy for 2 yr or more, but performed no detailed analysis on changes in gene expression and the immune response.

Since then, not considering the excluded progressors detailed above, an additional five TB contacts (n = 5) developed TB and were included for in-depth temporal analysis of the blood signature of TB progressors at different time points before diagnosis. In total, blood samples from 14 contacts who progressed to TB were subjected to RNA sequencing out of the 20 contacts who progressed to TB. Six were excluded from the RNA-Seq gene expression study due to either cDNA library failure (n = 1) or failure to secure microbiological confirmation (n = 5). This was important as essential for case linkage with the index case, although they were confirmed as progressing to TB by positive histology showing caseating lungs.

The contacts had the following characteristics. Gender, 35.7% male and 64.3% female. Ethnicity, 28.6% South Asian, 14.3% East African, 42.9% British Caucasian (5/6 outbreak strain), and 14.3% European. The controls had the following characteristics.

Gender, 47.1% male and 52.9% female. Ethnicity, 64.7% South Asian, 11.8% East African, 5.9% European, and 17.6% British Indian. Since these were small numbers and skewed somewhat by the British Caucasian and gender, we applied COMBAT batch correction as described later in the Materials and methods. We collected blood at detailed time points to examine in detail changes in gene expression in the different clinical phenotypes, incipient TB, subclinical TB, and clinical TB (Table S1), and also to examine changes in gene expression occurring over time in Leicester cohorts of contacts of active TB patients (Table S2, top.

N = 12 TB contacts. 25 samples. Singhania et al., 2018a) and from noncontact patients with symptoms before they were diagnosed with active TB by culture/microbiological/clinical positivity (Table S2, bottom. N = 10 TB progressors.

14 samples), all before treatment, and from active TB patients at the time of diagnosis (Table S2, top, n = 49 TB patients), all as compared with healthy controls (Table S2, top, n = 38 healthy controls). Blood from TB contacts who progressed to TB and from TB progressors was subjected to RNA-Seq and analysis at the time points indicated (Table S2), together with that from 49 newly recruited active TB patients at the time of diagnosis, before initiation of treatment. To investigate this further in Leicester TB contacts who progressed to TB only, datasets representing all time points, sampled before diagnosis throughout 2015 to 2018, were analyzed in TB contacts only that progressed to TB, by pooling our previously published TB contact dataset (Singhania et al., 2018a), which had not been investigated in depth with respect to kinetic changes in the immune responses, with our more recently recruited TB contact dataset (Table S3). This pooled dataset, now consisting of 38 samples from 14 TB contacts as they progressed to TB, was batch corrected and analyzed against matched controls, as described later in the Materials and methods.

The treatment response cohort had the following characteristics. The Leicester active TB cohort composed of 74 patients with pulmonary TB was simultaneously recruited between September 2015 and September 2018, at the Glenfield Hospital, University Hospitals of Leicester National Health Service Trust, Leicester, UK, at the time of diagnosis (treatment-naive. Fig. S2 A and Table S4).

A cohort of 38 healthy IGRA-ve controls was recruited in parallel. To follow the transcriptional response after treatment, whole blood samples were collected and were subjected to RNA-Seq at diagnosis before initiation of any ATT (T0), and thereafter, at 1 and 2 wk. 1, 2, 4, 5, 6, 7/8, 9/10, and 11/12 mo. And >1 yr after T0 with clinical assessment including CXR, CRP, and symptom assessment.

All RNA isolation and processing were performed on all blood samples simultaneously (Fig. S2 and Table S4). Patients who had previous TB, had previous treatment for LTBI, were pregnant, were under 16 yr age, or were immunosuppressed were excluded from this study. All participants had routine HIV testing, and patients with a positive result were excluded.

Patients with active TB were all confirmed by laboratory isolation of M. Tuberculosis on the culture of a respiratory specimen (sputum or bronchoalveolar wash/lavage) with sensitivity testing performed by the Public Health Laboratory Birmingham, Heart of England National Health Service Foundation Trust, Birmingham, UK. All participants were prospectively enrolled and sampled before the initiation of any TB treatment. The Research Ethics Committee for East Midlands–Nottingham 1, Nottingham, UK (REC 15/EM/0109), approved the study.

All participants gave written informed consent. We used 10 published blood RNA-Seq or microarray datasets (Berry et al., 2010. Bloom et al., 2013. Leong et al., 2018.

Parnell et al., 2012. Singhania et al., 2018a. Suarez et al., 2015. Thompson et al., 2017.

Zak et al., 2016. Zhai et al., 2015) from multiple clinical disease cohorts including active TB (225 patients) and LTBI (217 individuals) from Berry, London and South Africa (GEO accession nos. GSE107991 and GSE107992). Bloom (GEO accession no.

GSE42834). Singhania, Leicester (GEO accession no. GSE107993). Zak (GEO accession no.

GSE79362). Thompson (GEO accession no. GSE89403). Leong (GEO accession no.

GSE101705). And ODs influenza (Parnell [GEO accession no. GSE40012] and Zhai [GEO accession no. GSE68310]), Bloom lung cancer (GEO accession no.

GSE42834), Bloom pneumonia (GEO accession no. GSE42834), Bloom sarcoidosis (GEO accession no. GSE42834), and Suarez bacterial/viral s (GEO accession no. GSE60244.

Total 186 patients). And healthy controls from each respective dataset (223 individuals). We downloaded from GEO the filtered and normalized datasets, which have been normalized with different methods, according to type of data (RNA-Seq or Illumina microarray) or laboratory practices. We then pooled the 10 datasets together, matching the targets by gene names.

When a gene was absent in a least one dataset of origin, we completely removed a gene from the pooled dataset, so that we had a robust and stringent pooled dataset. A gene could be absent from any dataset for multiple reasons. One of the platforms did not target this gene, the gene annotation databases used were different for each dataset (different version of the genomes) and there was no correspondence of gene name, or the gene was filtered out due to low expression in the filtered dataset of origin. Using these stringent criteria, we had a pooled dataset containing 11,912 genes in total, regrouping 851 individual whole blood samples.

We then batch-corrected the pooled dataset, the batch being the origin of dataset, with the reference COMBAT algorithm (Johnson et al., 2007) from the sva library in R. We checked the impact of batch correction on a mix of RNA-Seq and microarray datasets by drawing PCA plots (Fig. S5 A). We also verified high correlations before/after batch correction per group of patients (Fig.

S5 B) and expression on gene of interest (data not shown). From the 11,912 batch-corrected genes, we then selected the 101 genes that were contained in at least one of the nine published reduced gene signatures (Kaforou et al., 2013. Maertzdorf et al., 2016. Roe et al., 2016.

Singhania et al., 2018a. Suliman et al., 2018. Sweeney et al., 2016. Zak et al., 2016.

And reviewed in Singhania et al., 2018b. And unpublished data). Trang Tran’s 20-gene signature was independently derived from both Berry London (Berry et al., 2010. GEO accession no.

GSE107991) and Leicester RNA-Seq datasets (GEO accession no. GSE107993). Differential gene expression analyses were performed on active TB patients compared with controls, LTBI, or controls plus LTBI individuals, using Wald tests (DESeq2 library. Love et al., 2014) by fitting generalized linear models.

A gradient-boosting machine algorithm (gbm v2.1.7 library) was applied on the lists of DEGs to determine the high order ranking of genes predicting the active TB status. For signature reduction, we performed a random forest algorithm (randomForest library v4.6-14) based on cumulative sensitivity of genes in their importance order. Finally, the meta-data with cross-validation analysis combining two optimal signatures from microarray datasets from Berry et al. (2010) and six optimal signatures from the Berry London and Leicester RNA-Seq datasets (TB versus controls, TB versus LTBI, TB versus controls plus LTBI for each cohort) yielded 20 gene signatures (FCGR1A, GBP5, SEPT4, ANKRD22, BATF2, FCGR1B, GBP1, GBP6, LHFPL2, SERPING1, C1QB, CD274, GBP4, AIM2, FBXO6, PSTPIP2, ASPHD2, FCMR, RTP4,and APOL6).

Genes ranked by DESeq2 Wald statistic for TB progressor patients at different time points or with different clinical symptoms compared with controls were used to look for enrichment of either the hallmark gene set using Broad’s gene set enrichment analysis preranked analysis and default settings. The normalized enrichment score and the FDR were plotted using ggplot2. The different genes lists of DEG in the different clinical phenotypes, incipient TB, subclinical TB, and clinical TB (Fig. 2) were functionally annotated using Metacore (Thomson Reuters v 19.4).

R libraries used. All analyses have been made with R 3.5.1, using multiple libraries, and Bioconductor v3.8 (Anders et al., 2015). The libraries are. Arules v1.6-4, sva v3.30.1 (Leek et al., 2012), Boruta v6.0.0 (Kursa and Rudnicki, 2010), ranger v0.12.1, ImpulseDE2 v1.6.1 (Fischer, 2019), VennDiagram v1.6.20, caret v6.0-85, lattice v0.20-38, RColorBrewer v1.1-2, tibble v2.1.3, tidyr v0.8.3, dplyr v0.8.1, DESeq2 v1.22.2 (Love et al., 2014), ComplexHeatmap v2.3.1 (Gu et al., 2016), ROCR v1.0-7, randomForest v4.6-14, ggbiplot v0.55, ggplot2 v3.2.0, and qusage v2.16.1 (Yaari et al., 2013).

We acknowledge the Francis Crick Advanced Sequencing Facility, Bioinformatics and Biostatistics Science Technology Platforms, for their contribution to our sequencing processing, L. Moreira-Teixeira for reviewing the manuscript, and Marisol Alvarez Martinez for helpful advice on bioinformatic approaches used. A. O’Garra, O.

Tabone, C.M. Graham, and A. Singhania and part of the project were funded by the Francis Crick Institute (Crick 10126. Crick 10468), which receives its core funding from Cancer Research UK, the UK Medical Research Council, and the Wellcome Trust.

The project, part of O. Tabone's salary, and salaries for J. Lee and R. Verma were funded by the BIOASTER Microbiology Technology Institute, Lyon, France (with funding from the French Government Investissement d’Avenir program ANR-10-AIRT-03) and the Medical Diagnostic Discovery Department, bioMérieux SA, France.

P. Haldar and R. Verma were supported by the National Institute for Health Research Leicester Biomedical Research Centre and the University of Leicester. W.J.

Branchett is funded by a Wellcome Investigator Award to A. O’Garra (FC11028). Author contributions. A.

O’Garra and P. Haldar co-led the study. A. O’Garra, P.

Haldar, R. Verma, G. Woltmann, M. Rodrigue, and P.

Leissner designed the study and discussed the findings throughout the project with K. Kaiser, O. Tabone, P. Chakravarty, A.

Singhania, and W.J. Branchett. R. Verma and J.

Lee recruited TB, LTBI, and contacts for the Leicester cohort. C.M. Graham performed RNA-Seq sample processing and helped to organize receipt of samples at The Crick and identifiable storage. O.

Tabone and P. Chakravarty performed bioinformatics analysis with major input from A. Singhania, and some input from T. Trang and F.

Reynier, all overseen by A. O’Garra. A. O’Garra wrote the manuscript with input from P.

Haldar, W.J. Branchett, O. Tabone, and A. Singhania.

All co-authors read, reviewed, and approved the paper..