Monthly Archives: March 2021

If theres one thing the last several decades of nutrition research have proven, its that theres no one-size-fits-all diet. While many factors are at play, one reason certain eating plans work for one person but not another may have to do with our genetics.

Nutrigenomics is a fascinating, up-and-coming field that uses genetic testing to determine the interplay between genes, nutrition, and health. This information is used to help pinpoint the ideal diet for each individual.

Heres a look at what nutrigenomics is, what you can expect if you try it, and how it might shape the future of personalized nutrition.

Nutrigenomics is the study of the relationship between genomics, nutrition, and health, says geneticist Jan Lim, MS, of CRI Genetics. The field includes both the study of how the whole body responds to micro- and macronutrients, as well as the relationship between single genes and single gene/food compound interactions.

You may sometimes hear this field referred to as nutrigenetics.

Technically, nutrigenomics refers to how nutrients influence your body to express genes, while nutrigenetics refers to how your body responds to nutrients because of your existing genetic makeup. However, many people use the terms interchangeably.

Though the science of nutrition genetics is still in its infancy, the idea that our genes can determine our best diet isnt as space-age as it might seem.

In fact, as far back as the early 20th century, British physician Archibald Garrod is credited with establishing a connection between nutrition, genetics, and phenotype.

The Human Genome Project of the 1990s, which mapped out human DNA, paved the way for the modern era of nutrigenomics. Since then, hundreds of studies have examined genes influence on the bodys response to diet, as well as the other way around.

Today, its not uncommon for practitioners like dietitians and doctors to use genetic testing to assess patients dietary needs and set customized health goals.

Genetic testing as part of nutrition counseling might sound rather extreme. A genetic workup just to see if you should eat low carb or get more vitamin C?

However, as part of an integrative nutrition approach, nutrigenomics can shed light on issues a simple health history cant. This includes everything from a predisposition to heart disease to why youre not losing weight when youve tried everything.

Genomic testing truly is useful for anyone wanting to be proactive about their health, says dietitian and certified genomic medical clinician Andrea Chernus, MS, RD, CGMC. Genomic testing can help to explain why situations exist for a patient, such as which style of eating might suit them best.

By looking at your genetic makeup, a practitioner may be able to advise you on certain eating patterns that will or wont work well for you. For example, gene variants might mean your body wouldnt benefit from a vegan diet or wouldnt adapt well to a keto diet due to genomic tendencies for fat metabolism.

A nutrigenomic test can even uncover your personal best sources of both macro- and micronutrients.

Perhaps your body is unable to optimally use omega-3 fatty acids from plant sources, or you have trouble converting sunshine into vitamin D. With this data, a trained practitioner can instruct you on which foods to eat or supplements to take to meet your needs.

Likewise, predispositions toward certain diseases may show up on a nutrigenomics test.

We may be able to see gene variants that increase ones risk for breast cancer due to the genes involved in estrogen metabolism, for example, Chernus notes. Heart disease, diabetes, obesity, and mental health have all been linked to genetic expressions, and all have dietary prevention strategies.

Empowered with this information, you can make preventative choices to mitigate risk through diet.

Interested in pursuing a genetic approach to nutrition, but not sure what to expect? Nutrition counseling using nutrigenomics is surprisingly painless.

The experience should start with a detailed health questionnaire so the practitioner has a complete understanding of the patients health status, history, family history, and current and past lifestyles, says Chernus. The actual test involves an at-home cheek swab. Its typical for a test to evaluate anywhere from 80 to 150 or more genes. Its quite simple to do.

In some cases, if your results raise additional questions, a blood test may follow.

Once your test results are back, your dietitian or other health professional will evaluate them and work with you to develop an action plan for eating.

Although extensive research has been conducted on the connection between genetics, diet, and health, the science of nutrigenomics is still emerging. Nutrigenomics is a relatively new field of research, so we still have a lot to learn, says Lim.

This isnt to say that genetics arent a helpful piece of the puzzle when it comes to nutrition counseling. Just recognize that nutrigenomics wont solve every diet conundrum, and that genes are just one of many factors that influence health and ideal dietary choices.

Genomic testing should not be the sole criteria used to make recommendations, says Chernus. We need to include lifestyle, health history, health status, personal preferences, cultural identity, willingness of the patient to change, and their own health goals in our work.

The availability of direct-to-consumer genetic testing for diet purposes, while it may seem exciting and convenient, is another potential drawback.

The main drawback [of these tests] is that theyre not interpreted by a skilled clinician, Chernus says. Skilled practitioners use a polygenic approach: how all of the genes are part of bigger systems in the body. They interpret how these systems work together in the totality of ones health.

To understand the relationship between your own genome and diet, its always best to consult with a health professional who specializes in nutrition genetics.

Every body is different isnt just true of our shape or physical type. Its true down to the genetic level. Although nutrigenomics is a relatively new field thats still gaining mainstream acceptance, many nutrition experts believe its the future of dietary counseling.

As part of a comprehensive nutrition philosophy that looks at the whole person, nutrigenomics could help people overcome health obstacles other approaches cant.

The more personalized our approach, the better the outcome, says Chernus.

Sarah Garone, NDTR, is a nutritionist, freelance health writer, and food blogger. She lives with her husband and three children in Mesa, Arizona. Find her sharing down-to-earth health and nutrition info and (mostly) healthy recipes at A Love Letter to Food.

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Nutrigenomics Might Be the Future of How You Eat - Healthline

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Investors hope long and costly timelines for discovering drugs often as long as 10 years can be slashed using new technology

Author of the article:

Financial Times

Hannah Kuchler

A machine learning start-up that is aiming to speed up drug discovery and shake up the pharmaceutical industry has raised US$400 milllion from investors including, the Canadian Plan Investment Board, SoftBank and Temasek.

Insitro, which is run by former Stanford artificial intelligence professor Daphne Koller, has partnerships with Gilead Sciences and Bristol-Myers Squibb to discover new biological targets for drug treatments, and the drugs themselves.

The pandemic has given investors hope that the long and costly timelines for discovering drugs often as long as 10 years can be slashed using new technology. The funding round, which is led by the CPPIB, also includes further investment from existing investors such as Andreessen Horowitz, T Rowe Price and BlackRock.

Koller said Big Pharmas views about using machine learning to help discover drugs had changed significantly from when the company was founded just three years ago.

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It demonstrates the ability to engineer a drug, as opposed to discovering a drug

Insitro CEO Daphne Koller

Everyone now realizes that this isgoing to be transformative. That it is going to really drastically change timelines on at least some parts of the process and hopefully change the probability of success, she said.

While artificial intelligence has not been used to find new COVID-19 treatments or vaccines, the speed to market of the messengerRNA vaccines from BioNTech/Pfizer and Moderna, and antibody treatments from Eli Lilly and AbCellera, has proved the value of a new approach, Koller said.

It demonstrates the ability to engineer a drug, as opposed to discovering a drug. Thats a complete shift in mindset, she said.

Insitro uses machine learning to analyze human genetics and work out where to target drugs. It can use these insights to identify under-appreciated drugs left on the shelf at other companies and buy them, which would likely be its first assets to market.

The start-up is also investing in proprietary human biology databases, such as analysis of biopsy samples, and it recently bought Haystack Sciences, for its ability to predict how drugs interact with the body.

Koller said its partnership with Gilead, examining biopsies of patients suffering from the increasingly prevalent fatty liver disease Nash, had shown it can crack open the genetic drivers of the condition from a surprisingly small sample of patients.

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Roger Perlmutter, a veteran drug discoverer who led research at Merck for decades, is on the board. He said Insitro stands out because of its focus on building data sets for machine learning.

It requires an enormous amount of work to create that underlying structure and, hence, a lot of funding to do that over a long period of time. But at the end of the day Im convinced that its going to have an impact, he said.

He said this data will help address the fundamental issue that we have very little understanding of how the human body works.

If you dont know whats wrong with it when its broken, its pretty hard to figure out how to fix it. And in that sense, its a bloody miracle anytime we make a drug, he said.

Paul McCracken, senior portfolio manager at the CPPIB, said the pandemic had turned some investors from skeptics into believers in the new technologies shaking up the pharmaceuticals industry.

The arrival of modern technology is more imminent than they otherwise might have thought, he said.

2021 The Financial Times Ltd

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CPPIB leads group investing $400-million in startup that can 'crack open' genetic drivers of diseases - Financial Post

Introduction

Antiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality globally. However, the emerging threat of HIV drug resistance may reduce ART efficacy resulting in pronounced negative public health impact, especially in sub-Saharan Africa which account for about 70% of the global HIV epidemic.1 Scale-up of ART availability has been implemented and, in 2019, a global median of 67% of those in need (90% in Botswana)2,3 have had access to ART.4 Among the antiretroviral drugs (ARVs) used as first-line ART regimen, first-generation Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs), such as Efavirenz (EFV) and Nevirapine (NVP), are still largely used for HIV treatment in sub-Saharan Africa,5 although since 2017 the World Health Organization (WHO) has been recommending an EFV/NVP-sparing ART regimen in countries in which resistance to NNRTIs exceeds 10%.6 Despite the introduction of Dolutegravir (DTG)-based first-line ART in Botswana in June 2016, a significant proportion of HIV patients are still on EFV- or NVP-containing ART regimen.7 A recent (2019) WHO HIV drug resistance report encompassing 18 countries (6 from sub-Saharan Africa) revealed that pre-treatment HIV drug resistance rate to EFV/NVP exceeds 10% amongst adults initiating first-line ART (nearly twice as high in women than men).5 The rate was even higher (up to 30%) in those previously exposed to ARVs, including women having taken ARVs for the prevention of mother-to-child transmission.5

Antiretroviral therapy efficacy largely depends on adequate drug exposure to suppress viral replication and allow the immune system to recover. However, occurrence of drug toxicity, suboptimal patients compliance, suboptimal virologic responses, incomplete immune reconstitution and/or emergence of drug resistance limit therapeutic outcomes.8 HIV drug resistance, beside known viral factors, more frequently occurs because of sub-therapeutic ARV drug exposure and/or acquisition of drug-resistant strains. In resource-limited settings such as Botswana, HIV-diagnosed individuals with virologic failure are more likely to stay on virologically failing regimens for prolonged periods, because of lack of adequate virological follow-up. This may result in an ineffective drug exposure potentially causing drug toxicity and a higher risk of selecting and transmitting drug-resistant viruses.9 Moreover, the presence of HIV drug resistance mutations (DRMs) in minor viral populations is associated with an increased risk of virologic failure, in particular for NNRTI-based ART regimens, regardless of adherence, ethnicity, and immuno-virological basal characteristics of patients.8

Efavirenz and NVP are primarily metabolized in the liver by the cytochrome P450 2B6 enzyme (CYP2B6) with a minor contribution from other cytochromes (i.e. CYP2A6, CYP3A4/5).1012 Studies on several populations have shown that Africans display the greatest level of genetic diversity in the CYP2B6 gene.13 Cytochrome P450 2B6 is one of the most polymorphic CYP450 genes in humans with over 100 described single nucleotide polymorphisms (SNPs), numerous complex haplotypes and distinct ethnic frequencies.14 Cytochrome P450 2B6 gene polymorphism has been associated with interindividual differences in drug pharmacokinetics and consequent plasma exposure, with possible consequences on drug efficacy and safety.14 There are different SNPs in the CYP2B6 gene that, according to their combination as haplotypes, may lead to different degrees of slow and/or fast EFV/NVP metabolizer phenotypes.14 Among those SNPs, 516G>T (rs3745274) and 983T>C (rs28399499) are associated with a significant loss of CYP2B6 function, leading to reduced clearance and prolonged half-life both for EFV1518 and NVP.19,20 The 983T>C SNP affects the metabolism of both EFV and NVP; the 516G>T SNP influences mainly the EFV metabolism, while data on its impact on NVP metabolism are less conclusive.21,22 Conversely, two other SNPs, namely 785A>G (rs2279343) and 82T>C (rs34223104) are associated with a gain of CYP2B6 function, leading to lower drug exposure.2325 Indeed, the 785A>G SNP increases EFV26 and NVP27 metabolism, whereas there are no studies assessing the clinical/pharmacological impact in vivo of the 82T>C SNP. Nonetheless, CYP2B6 82T>C has recently been included in the panel of CYP2B6 SNPs that should be considered for the evaluation of therapeutic impact by the Clinical Pharmacogenetics Implementation Consortium.28

Pharmacogenetic studies of EFV and NVP have mostly been based on the CYP2B6 516G>T and 983T>C SNPs, with little or no clear assessment of the impact of the CYP2B6 785A>G and 82T>C SNPs.14 Studies from Botswana on HIV patients taking EFV-based ART showed that the CYP2B6 516T allele was protective against 1-year,29 but not at 6-months,30 virologic breakthrough. However, no HIV DRMs have been assessed in either study. Similar results on the influence of the CYP2B6 516G>T substitution on virologic outcome were observed in studies from the US involving HIV patients of African ancestry31 and HIV-diagnosed women from a multi-ethnic cohort,32 whereas other studies did not find any evidence of protection.3335 Another work, on 66 HIV-diagnosed women from Kenya taking NVP-based ART, showed no associations of CYP2B6 516G>T and 983T>C with virologic response and toxicity at 12 months of follow-up.20

Study results concerning the association of CYP2B6 slow metabolizer profiles (defined by the presence of 516T and/or 983C alleles) and EFV and NVP-related adverse events and/or toxicity are also conflicting, with some showing an association15,3639 and others not.1820,30,31,40,41 Notably, due to the complexity of the CYP2B6 polymorphisms and the highest frequency of slow/intermediate genotypes among individuals of African ancestry, it is likely that haplotypes rather than a single polymorphism are better predictors of EFV/NVP plasma concentrations,14 as well as of toxicity, in which polymorphisms in genes other than the CYP450 system may also play a role.42,43

Efavirenz and NVP have a long half-life (estimated at 40115 and 25164hrs, respectively), a low genetic barrier for HIV drug resistance, and complex pharmacogenetics, which raises the possibility of sub-therapeutic drug concentration in plasma, especially among CYP2B6 fast metabolizers, an aspect that has not been fully studied. The CYP2B6 fast metabolizer profile may allow low EFV/NVP plasma exposure, possibly leading to the selection and spread of HIV mutations and consequent viral drug resistance. On the other hand, EFV/NVP CYP2B6 slow metabolizers are exposed to higher drug plasma concentration, leading to potential higher toxicity and consequently reduced patients adherence and/or loss to care with possibility of sub-therapeutic plasma exposure and higher risk of HIV drug resistance (at least in a long-term perspective). While studies produced conflicting results, a posology adjustment according to the CYP2B6 polymorphism background has been proposed to address these potential issues in the context of personalized medicine.44,45

In summary, different CYP2B6 genotypes may influence immuno-virological response and/or toxicity by affecting EFV and NVP plasma concentration.14 We explored the possible impact of CYP2B6 genetic (and haplotype) variation on the risk of selection, accumulation and spread of HIV DRMs, providing a particular attention to the CYP2B6 fast metabolizer profile. To date, to the best of our knowledge, this aspect has not yet been fully evaluated.

This study was performed in Botswana with the aim to: i) assess CYP2B6 genotypes (for SNPs 82T>C, 516G>T, 785A>G, 983 T>C) in HIV-diagnosed adults taking EFV or NVP containing ART, and to classify corresponding CYP2B6 phenotypes as very slow, slow, extensive, rapid and ultra-rapid metabolizers; ii) construct haplotypes and apply a metabolic score according to the DRMs profile; iii) determine if there is any association of the CYP2B6 genotypes and haplotypes with the presence of EFV/NVP-resistant infections.

This retrospective casecontrol study was part of a larger Tshepo study46 that was conducted at Botswana-Harvard AIDS Institute Partnership (BHP) between 2002 and 2007. The Tshepo study was a 5-years open-label, randomized study with a sample of 650 HIV-1 diagnosed ART nave Botswana citizens (451 females and 199 males) attending the Infectious Disease Care Clinic (IDCC) in Princess Marina Hospital in Gaborone. The aim of Tshepo study was to evaluate the efficacy, tolerability and occurrence of drug resistance of six (6) different first-line ART regimens, all including an NNRTI either EFV or NVP, during a follow-up period of 156 weeks. For the purpose of the present study, being patients adherence assessed and comparable results,46 cases were defined as HIV-diagnosed individuals taking EFV or NVP containing ART and having virological failure (evaluated after at least 4 months of ART) related to DRMs assessed by HIV genotyping, whereas controls were HIV-diagnosed individuals taking EFV or NVP-based ART without virological failure.46

Overall, 242 available residual samples were included in the present study. Of them, 40 were available cases that developed NNRTI resistance mutations, and 202 were controls.

Genomic DNA was extracted using Qiagen kits manual platform according to the manufacturers protocol (Qiagen, Hilden, Germany) from about 200L of peripheral blood mononuclear cells (PMBCs) stored at 80C.

CYP2B6 516G>T (rs3745274) detection was carried out using PCR-RFLP technique according to Lavandera et al47 protocol with minor modifications. CYP2B6 983T>C (rs28399499) detection was carried out using a touchdown PCR-RFLP assay published by Paganotti et al.48 CYP2B6 785A>G (rs2279343) detection was done using an in-house optimized RFLP-PCR protocol.49

For purposes of this study, we also adopted a new in-house assay for analysis of the CYP2B6 82T>C (rs34223104) polymorphism. We designed two (2) primers (forward primer: 5-CAAGCAGGAAGTCTGGGTTC-3; reverse primer: 3-AGTTCCATGGTCCTGGTCT-5). PCR reaction was conducted in a total volume of 20L containing 100ng genomic DNA. PCR protocol with the following conditions was used: 3min of denaturation at 94C, 30s at 94 oC, 30s at 64 oC and 60s at 72 oC for 35 cycles with a final step of 5min at 72 oC. The PCR product was then digested with PsiI restriction enzyme at 37 oC for 90 min. The enzyme cuts the wild-type allele (T) in two fragments of 92 bp and 88bp; while the mutant allele is not cut. The digested fragments were visualized on a 4% metaphor gel stained with ethidium bromide.

CYP2B6 genotype and haplotype information was translated into a measure of phenotype using the metabolic score (MS) system,49 already adopted as activity score for CYP2A6,50 CYP2C19,51 CYP2D6,52 and also consistent with Vujkovic et al,41 for CYP2B6. The MS translates composite genotype and/or haplotype information into a qualitative measure of phenotype. The scores are based on the algebraic sum of the individual allele values according to an additive model for CYP2B6.49 The MS was set conferring a 1 value for each slow metabolism alleles (516T, 983C) and +1 for rapid metabolism alleles (82C, 785G), while an extensive metabolism allele was scored 0 (82T, 516G, 785A, 983T) both for composite CYP2B6 genotypes and haplotypes.49

Several methods were applied. We used the Arlequin software (v3.5.2.2)53 to test for HardyWeinberg Equilibrium (HWE) and the genetic fixation index (FST) with default settings, while Linkage Disequilibrium (LD) was tested using the Expectation-Maximization (EM) algorithm with 20,000 permutations and three initial conditions. Binary Logistic Regression (BLR) analysis (run on IBM SPSS statistical package, version 20) was applied to find any association between the dependent variable drug resistance with the independent variables (age, BMI, baseline CD4+ T-cell count and viral load, CYP2B6 genotype). Fishers exact test, chi-square test and z-statistic were applied for statistical significance where needed.

Out of the 242 samples, 15 were excluded (5 due to the lack of complete genotypic information and 10 failed PCR). Thus, 227 samples were retained for analysis, with 40 (18.6%) belonging to the group that developed virological failure with EFV/NVP DRMs. The remaining 187 samples (81.4%) belonged to the group that did not develop virological failure during the follow-up period. Information about gender was available for 223 individuals, 146 (65.5%) being females. The study population characteristics at baseline were available for 225 individuals, being as follows: mean age 33.7 years (range: 20.450.9); mean BMI 21.3 (range: 14.534.6); median baseline CD4+ T-cell count 188 cells/L (IQR: 147221); median baseline viral load 5.30 Log10 copies/mL (IQR: 4.835.71). The ART regimen data were available for 225 individuals, with 117 (50.6%) receiving EFV, and 108 (46.8%) receiving NVP containing ART. Table 1 summarises the baseline characteristics of the study population according to the EFV/NVP resistance status.

Table 1 Baseline Characteristics of the Study Population

This study was based on a subsample of the Tshepo study46 conducted in Botswana on HIV-diagnosed individuals taking EFV/NVP-based ART regimen and followed up to 3-years (156 weeks) after the treatments start. All cases of virologic failure reported in the Tshepo study underwent HIV genotyping46 and EFV/NVP DRMs were detected in all the 40 cases used in the present study (Supplementary Table 1). Thus, in the present study, virologic failure and EFV/NVP DRMs coincide.46 The median interval between start of ART and appearance of virologic failure was 72 weeks (IQR: 45.5104).

The CYP2B6 genotype distribution and allele frequencies of the four SNPs (82T>C, 516G>T, 785A>G, 983T>C) are summarized in Table 2. The comparisons of the three genotypes distribution between the subjects with and those without HIV DRM for each single SNP were all not statistically different but for the CYP2B6 516G>T polymorphism (chi-square = 8.121; P = 0.017) (Figure 1), with the wild-type extensive metaboliser 516G allele at higher frequency among resistant than sensitive infections (70% vs 54%, calculated from Table 2).

Table 2 CYP2B6 Genotype and Allele Frequencies of the Four SNPs

Figure 1 Distribution of CYP2B6-516 genotypes according to NNRTI-resistance status. Chi-square associated P-value is 0.017.

HardyWeinberg Equilibrium analysis showed that CYP2B6 983 locus displayed significant deviations from HWE in EFV/NVP-resistant, EFV/NVP-susceptible and combined (overall) samples (P = 0.001; P < 0.001; P < 0.001, respectively). A reason for this deviation may be due to a defect in heterozygous samples. Furthermore, CYP2B6 82 and CYP2B6 516 did not show a statistically significant deviation from HWE in the EFV/NVP-resistant HIV infections (P = 0.449 and P = 0.230, respectively), whereas a statistically significant deviation from HWE was noted in the EFV/NVP-susceptible (P = 0.039 and P = 0.031, respectively) and all the samples combined (P = 0.030 and P = 0.010, respectively), with an excess of heterozygotes in both groups. CYP2B6 785 genotypes were in equilibrium in all the groups analysed (EFV/NVP-resistant, EFV/NVP-susceptible and both combined).

Linkage disequilibrium was observed between CYP2B6 82 and CYP2B6 516 in all groups (Table 3). Linkage disequilibrium was also observed between CYP2B6 82 and CYP2B6 983, as well as between CYP2B6 516 and CYP2B6 983, in the EFV/NVP-susceptible and overall samples, but not in the EFV/NVP-resistant group (Table 3). Finally, CYP2B6 785 did not show strong association with the other three loci, thus justifying the respect of HWE in all the three groups (EFV/NVP-resistant, EFV/NVP-susceptible, overall samples) (Table 3).

Table 3 Pairwise Linkage Disequilibrium (LD) Analysis for the Four Polymorphic Loci (CYP2B6 82, 516, 785, 983)

The fixation index (FST) between EFV/NVP-resistant and susceptible samples gave a distance of 0.023 (P = 0.008 0.003, 1023 permutations). While significant, this difference is consistent with panmixia between the two groups. Furthermore, an exact test of population differentiation revealed no statistical difference between the two groups (P = 0.060 0.008; 100,000 Markov chain steps).

Binary Logistic Regression analysis was set with the dependent dichotomic variable assuming two values according to the presence of EFV/NVP-susceptible or EFV/NVP-resistant HIV infections. Factors tested were age, BMI, baseline lymphocytes T-CD4+, viral load, genotypes for CYP2B6 82, CYP2B6 516, CYP2B6 785, CYP2B6 983 (see Supplementary Table 2). First step results showed only one significant factor (CYP2B6 516G). After removal of the non-significant factors, the BLR was repeated with only one (significant) factor in the model (CYP2B6 516G>T). This second step analysis revealed a significant statistical association between presence of CYP2B6 516G and DRMs status (OR: 2.26; 95% CI:1.274.01; P = 0.005).

Finally, the CYP2B6 516T allele presence (GT and TT genotypes) was tested for its possible protection against virologic failure (corresponding to EFV/NVP resistance in the present study as stated in the Methods section): no association was found at 6 months, nor beyond six months (up to 3 years) (Fishers exact test, P > 0.05).

Haplotype frequencies for CYP2B6 were estimated using the EM algorithm in Arlequin (Table 4). For all the samples, based on the four SNPs of CYP2B6 analysed (82T>C, 516G>T, 785A>G, 983T>C), the TGAT haplotype was the most common, whereas the CGAC, CTAT and CGGT haplotypes the rarest (Table 4). The haplotype TGAT was also the most abundant haplotype when EFV/NVP-resistant and susceptible samples were analysed separately, whereas no CGGT haplotypes were estimated in the EFV/NVP-susceptible group, and no TGGC, CGAC and CTAT haplotypes were estimated in the EFV/NVP-resistant group (Table 4).

Table 4 Estimated and Maximum-Likelihood (ML) Haplotype Frequencies by Phenotype and for All the Samples Combined. Maximum-Likelihood Haplotype Frequencies are Shown in Parenthesis with Their Standard Deviations (SD). The Order of Nucleotides in the Reconstructed Haplotypes is Made According to the SNP Position in the CYP2B6 Gene (82T>C, 516G>T, 785A>G, 983T>C)

The inferred MS was calculated for the haplotypes and they are shown in Table 4. In order to assess the possible risk of carrying EFV/NVP-resistant HIV infections according to the CYP2B6 fast metabolizer profile, the metabolic phenotypes were classified as follows: MS0, this including extensive, slow and very slow inferred metabolic phenotypes; MS1, this including rapid and ultra-rapid inferred metabolic phenotypes. The breakdown of metabolic phenotypes between groups according to the EFV/NVP resistance status are shown in Table 5. The comparison between EFV/NVP-resistant and susceptible HIV infections by MS was associated to a z-statistic of 1.812 (P = 0.035), therefore showing that the rate of EFV/NV resistance was significantly higher among fast metabolizers haplotypes compared to the other group (30.8% vs 16.8%, respectively).

Table 5 EFV/NVP Resistance by CYP2B6 Metabolic Phenotype

In line with the BLR results, the CYP2B6 516G allele was present in 100% (n=30/30) of rapid (CGAT and TGGT) and ultra-rapid (CGGT) haplotypes, whereas it was only present in 53.8% (n=228/424) of extensive/slow metabolizers haplotypes (Fishers exact test, P < 0.001) (see Table 4, overall column).

Understanding the factors that modulate the selection of DRMs associated with HIV-1 infection is essential to design efficient control strategies. Drug resistance usually emerges rapidly when ARV drugs are administered as monotherapy or in the presence of incomplete viral suppression, suggesting that resistance is caused by the selection of mutant viruses within the host.54 Besides known viral factors (HIV diversity, HIV replication, drug selection pressure and fitness of drug-resistant viral sub-populations) and patient ART adherence, human genetic background is a possible further, not yet fully understood, co-factor affecting HIV drug resistance selection. In this study, we addressed the hypothesis that human pharmacogenetics can drive the selection of HIV drug resistance. We therefore found a statistically significant association between EFV/NVP-resistant HIV infections and CYP2B6 516G allele presence (OR: 2.26; 95% CI:1.274.01; P = 0.005). In fact, EFV/NVP resistant infections had higher 516G allele frequency (Table 2 and Figure 1). Further information comes from the haplotype reconstruction where 100% of rapid (CGAT and TGGT) and ultra-rapid (CGGT) CYP2B6 haplotypes carried the 516G allele (Table 4), whereas only 53.8% of the extensive, slow (TGAC, TTAT and TTGC) and very slow (TTAC) haplotypes carried it. Moreover,

CYP2B6 rapid and ultra-rapid metabolizers showed a significantly higher frequency of EFV/NVP-resistant HIV infections than extensive and slow metabolizers (30.8% vs 16.8%; z-statistic = 1.812; P = 0.035), based on their haplotype reconstruction (Table 5). Nevertheless, it is important to note that the rate of rapid and ultra-rapid haplotypes is less than 6% (26/454) of the total haplotypes.

A possible interpretation of these results is that EFV/NVP resistance tends to accumulate based on the presence of the CYP2B6 516G allele (GG>GT>TT), and that in rapid and ultra-rapid metabolisers might happen at a higher rate. This could be an important model for drug resistance selection that may be verified on a larger longitudinal cohort and tested on different pathogens, different antimicrobial drugs and epidemiological contexts.

These results are partially in line with similar findings on malaria drug resistance where it has been demonstrated that the CYP2C8 slow metabolizer phenotype is associated with the risk of carrying chloroquine- and amodiaquine-resistant parasites.55,56

We also found that LD into the 1000 bp region analysed (spanning from CYP2B6 82 to 983 nucleotide positions) was strong (P < 0.001) and departures from the HWE were coherent with the physical and genetic linkage between SNPs. Genotype and allele frequencies for the four CYP2B6 SNPs analysed in this study were in line with literature, including most if not all studies from Botswana (Table 6). The only discrepancy was represented by a study41 conducted in the same area as the present research that indicated an allele frequency for CYP2B6 785G of 6%, well outside the accepted range of 35.252.8% for Southern Africa (Table 6). Finally, the two groups (with and without EFV/NVP-resistant HIV infections) into which we subdivided our study population appeared to be in panmixia, therefore offering an ideal situation for the comparison.

Table 6 CYP2B6 SNPs (82T>C, 516G>T, 785A>G, 983T>C) Frequency in Sub-Saharan Africa

In summary, subjects carrying the CYP2B6 516G allele were more likely to carry HIV drug-resistant infections. Coherently with our hypothesis, the 516G allele is always present in the rapid and ultra-rapid haplotypes, confirming the possibility that rug resistance selection is enhanced when drug metabolism is faster. However, the rate of fast metabolisers was not high in this cohort, therefore reducing its possible impact.

The present study has few limitations: i) the sample size was relatively small, therefore haplotypes reconstruction provided higher statistical power in the comparisons, ii) it would have been more powerful to use matched-case control, to avoid any confounding factors, and iii) the lack of data on EFV/NVP plasma exposure hampered the quantitative confirmation of CYP2B6 metabolic phenotypes. Nonetheless, our findings suggest a trend towards a role for the genetic background of patients affecting drug therapy outcomes, and warrants further studies.

In conclusion, this work indicates that the CYP2B6 516G allele, and its combination into rapid and ultra-rapid metabolizer profiles, as defined by the correspondent haplotypes, is directly associated with the risk of development of drug resistance in HIV-diagnosed individuals receiving EFV- or NVP-containing ART in Botswana. However, larger studies will be needed to confirm this association. In general, our findings support the hypothesis that pharmacogenetics may play a significant role in HIV therapy outcomes. Besides the known possible impact of slow EFV/NVP metabolism on ART toxicity and compliance, fast EFV/NVP metabolism may also affect ART outcomes. A deeper knowledge of the genetic background at an individual level could thus be highly beneficial in personalising ART therapies and improving their efficacy, especially in patients who show poor response following initiation of treatment.

This was a retrospective casecontrol study approved by Health Research Division Office (HRDC) of the Botswana Ministry of Health and Wellness. The approval was done in accordance with the amendments made to the initial permit of The host genetics of HIV-1 subtype C infection progression and treatment in Africa/Gwas on determinants of HIV-1 subtype C infection [Reference No: HPDME 13/18/1 X1 (163)]. For the purpose of this study signed informed consent was sought from the participants. In addition, Botswana-Harvard AIDS Institute Partnership, as the database owner authorized by HRDC, gave permission to use its data and samples for the current study. This study was conducted in accordance with the Declaration of Helsinki.

We would like to express our gratitude to SANTHE for funding this research in collaboration with Botswana Harvard AIDS Institute Partnership. We also express gratitude to the University of Botswana, Faculty of Health Sciences, School of Allied Health Professions and Botswana-University of Pennsylvania Partnership laboratory staff for their help, assistance and continuous support to this study. These authors have joint senior authorship: Gianluca Russo, Simani Gaseitsiwe, Giacomo M Paganotti.

This work was supported through the Sub-Saharan African Network for TB/HIV Research Excellence (SANTHE), a DELTAS Africa Initiative [grant # DEL-15-006]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africas Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [grant # 107752/Z/15/Z] and the UK government. The views expressed in this publication are those of the author(s) and not necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK government. The work was also supported by the Penn Center for AIDS Research [grant # P30 AI045008].

The authors report no conflicts of interest in this work.

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Dickson, Edelson, Musiek, Philips and Stitziel honored

Washington University School of Medicine in St. Louis physician-scientists (clockwise from top left), Jennifer Philips, MD, PhD, Patricia Dickson, MD, Brian T. Edelson, MD, PhD, Erik Musiek, MD, PhD, and Nathan O. Stitziel, MD, PhD, have been elected members of the American Society of Clinical Investigation in recognition of their accomplishments in medical research.

Five physician-scientists at Washington University School of Medicine in St. Louis have been elected members of the American Society for Clinical Investigation in recognition of original, creative and independent investigations in the clinical or allied sciences of medicine. The new members will be inducted April 8.

Patricia Dickson, MD, is the Centennial Professor of Pediatrics, and director of theDivision of Genetics and Genomic Medicine. Her research focuses ongenetic lysosomal storage diseases, which are rare metabolic disorders caused by the bodys inability to produce specific enzymes. The condition can affect various parts of the body such as the brain, heart, skeleton and central nervous system.

The work of immunologistBrian T. Edelson, MD, PhD,an associate professor of pathology & immunology, encompasses autoimmune and infectious diseases. He studies how cytokine production by T cells is regulated, with a focus on how autoreactive T cells mediate the autoimmune disease multiple sclerosis (MS).

Erik Musiek, MD, PhD, an associate professor of neurology, studies the role of circadian rhythm in neurodegenerative diseases such as Alzheimers disease. He has discovered that body clock disturbances are an early sign of Alzheimers and raise the levels of damaging Alzheimers proteins in the brain.

Tuberculosis (TB) expertJennifer Philips, MD, PhD, is an associate professor of medicine and co-director of theDivision of Infectious Diseases. Her work focuses on how TB bacteria evade the bodys immune defenses and cause disease, a key step in developing better drugs and vaccines to combat the lethal infection.

Cardiologist Nathan O.Stitziel, MD, PhD, an associate professor of medicine, uses human genetic studies to understand the inherited basis of cardiovascular diseases caused by either a single gene or by complex interactions of multiple genes. His work aims to leverage insights from these studies to identify and validate novel therapeutic targets for patients with heart disease.

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Researchers elected to American Society for Clinical Investigation - Washington University School of Medicine in St. Louis