Increasing intra- and inter-subtype HIV diversity despite declining HIV incidence in Uganda.

HIV incidence has been declining in Africa with scale-up of HIV interventions. However, there is limited data on HIV evolutionary trends in African populations with waning epidemics. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a twenty-five-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from persons living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994 to 2018) and four hyperendemic Lake Victoria fishing communities (2011 to 2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was estimated using the Shannon diversity index and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Evolutionary dynamics were assessed among demographic and behavioral sub-groups, including by migration status. 9,931 HIV sequences were available from 4,999 PLHIV, including 3,060 and 1,939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (p<0.001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (p<0.001). The proportion of viruses classified as recombinants significantly increased by more than four-fold. Inter-subtype HIV diversity has generally increased. While p24 intra-subtype genetic diversity and divergence leveled off after 2014, diversity and divergence of gp41 increased through 2017. Inter- and intra-subtype viral diversity increased across all population sub-groups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics in declining African HIV epidemics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.

Within-host evolutionary dynamics and tissue compartmentalization during acute SARS-CoV-2 infection.

The global evolution of SARS-CoV-2 depends in part upon the evolutionary dynamics within individual hosts with varying immune histories. To characterize the within-host evolution of acute SARS-CoV-2 infection, we sequenced saliva and nasal samples collected daily from vaccinated and unvaccinated individuals early during infection. We show that longitudinal sampling facilitates high-confidence genetic variant detection and reveals evolutionary dynamics missed by less-frequent sampling strategies. Within-host dynamics in both unvaccinated and vaccinated individuals appeared largely stochastic; however, in rare cases, minor genetic variants emerged to frequencies sufficient for forward transmission. Finally, we detected significant genetic compartmentalization of viral variants between saliva and nasal swab sample sites in many individuals. Altogether, these data provide a high-resolution profile of within-host SARS-CoV-2 evolutionary dynamics.IMPORTANCEWe detail the within-host evolutionary dynamics of SARS-CoV-2 during acute infection in 31 individuals using daily longitudinal sampling. We characterized patterns of mutational accumulation for unvaccinated and vaccinated individuals, and observed that temporal variant dynamics in both groups were largely stochastic. Comparison of paired nasal and saliva samples also revealed significant genetic compartmentalization between tissue environments in multiple individuals. Our results demonstrate how selection, genetic drift, and spatial compartmentalization all play important roles in shaping the within-host evolution of SARS-CoV-2 populations during acute infection.

Transmission Bottleneck Size Estimation from De Novo Viral Genetic Variation.

Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, these approaches have the potential to substantially underestimate true transmission bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arise de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these 2 respiratory viruses.

Transmission bottleneck size estimation from de novo viral genetic variation.

Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, allele frequencies can change dramatically over the course of an individual's infection, such that sites that are polymorphic in the donor at the time of transmission may not be polymorphic in the donor at the time of sampling and allele frequencies at donor-polymorphic sites may change dramatically over the course of a recipient's infection. Because of this, transmission bottleneck sizes estimated using allele frequencies observed at a donor's polymorphic sites may be considerable underestimates of true bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arose de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these two respiratory viruses, using an approach that does not tend to underestimate transmission bottleneck sizes.

Epidemiological inference for emerging viruses using segregating sites.

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Pre-epidemic evolution of the MRSA USA300 clade and a molecular key for classification.

Introduction: USA300 has remained the dominant community and healthcare associated methicillin-resistant Staphylococcus aureus (MRSA) clone in the United States and in northern South America for at least the past 20 years. In this time, it has experienced epidemic spread in both of these locations. However, its pre-epidemic evolutionary history and origins are incompletely understood. Large sequencing databases, such as NCBI, PATRIC, and Staphopia, contain clues to the early evolution of USA300 in the form of sequenced genomes of USA300 isolates that are representative of lineages that diverged prior to the establishment of the South American epidemic (SAE) clade and North American epidemic (NAE) clade. In addition, historical isolates collected prior to the emergence of epidemics can help reconstruct early events in the history of this lineage. Methods: Here, we take advantage of the accrued, publicly available data, as well as two newly sequenced pre-epidemic historical isolates from 1996, and a very early diverging ACME-negative NAE genome, to understand the pre-epidemic evolution of USA300. We use database mining techniques to emphasize genomes similar to pre-epidemic isolates, with the goal of reconstructing the early molecular evolution of the USA300 lineage. Results: Phylogenetic analysis with these genomes confirms that the NAE and SAE USA300 lineages diverged from a most recent common ancestor around 1970 with high confidence, and it also pinpoints the independent acquisition events of the of the ACME and COMER loci with greater precision than in previous studies. We provide evidence for a North American origin of the USA300 lineage and identify multiple introductions of USA300 into South and North America. Notably, we describe a third major USA300 clade (the pre-epidemic branching clade; PEB1) consisting of both MSSA and MRSA isolates circulating around the world that diverged from the USA300 lineage prior to the establishment of the South and North American epidemics. We present a detailed analysis of specific sequence characteristics of each of the major clades, and present diagnostic positions that can be used to classify new genomes.

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic.

In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state's first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.

The changing epidemiology of SARS-CoV-2.

We have come a long way since the start of the COVID-19 pandemic-from hoarding toilet paper and wiping down groceries to sending our children back to school and vaccinating billions. Over this period, the global community of epidemiologists and evolutionary biologists has also come a long way in understanding the complex and changing dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. In this Review, we retrace our steps through the questions that this community faced as the pandemic unfolded. We focus on the key roles that mathematical modeling and quantitative analyses of empirical data have played in allowing us to address these questions and ultimately to better understand and control the pandemic.

Comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2".

A reanalysis of SARS-CoV-2 deep sequencing data from donor-recipient pairs indicates that transmission bottlenecks are very narrow (one to three virions).

Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread.

Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties following the statewide "Safer at Home" order, which went into effect 25 March 2020. Our results suggest patterns of SARS-CoV-2 transmission may vary substantially even in nearby communities. Understanding these local patterns will enable better targeting of public health interventions.

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel.

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Distinct patterns of SARS-CoV-2 transmission in two nearby communities in Wisconsin, USA.

Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties, and limited viral transmission between counties, following the statewide Safer-at-Home public health order, which went into effect 25 March 2020. Our results suggest that early containment efforts suppressed the spread of SARS-CoV-2 within Wisconsin.

The impact of a history of poor mental health on health care costs in the perinatal period.

The perinatal period is a critical time for mental health and is also associated with high health care expenditure. Our previous work has identified a history of poor mental health as the strongest predictor of poor perinatal mental health. This study aims to examine the impact of a history of poor mental health on health care costs during the perinatal period. Data from the 1973-1978 cohort of the Australian Longitudinal Study on Women's Health (ALSWH) were linked with a number of administrative datasets including the NSW Admitted Patient Data Collection and Perinatal Data Collection, the Medicare Benefits Scheme and the Pharmaceuticals Benefits Scheme between 2002 and 2011. Even when taking birth type and private health insurance status into account, a history of poor mental health resulted in an average increase of over 11% per birth across the perinatal period. These findings indicate that an investment in prevention and early treatment of poor mental health prior to child bearing may result in a cost saving in the perinatal period and a reduction of the incidence of women experiencing poor perinatal mental health.

Exploratory Study of 6-Month Pain Trajectories in Individuals With Predominant Patellofemoral Osteoarthritis: A Cohort Study.

BACKGROUND: Knowledge of patellofemoral osteoarthritis (OA) pain trajectories is vital to helping clinicians and patients make shared disease-specific decisions regarding treatment options and coping strategies. OBJECTIVES: To describe the pain trajectories of people living with patellofemoral OA who present to a chronic care management program, and to explore baseline characteristics associated with different trajectories. METHODS: In this prospective longitudinal cohort study, 88 participants who presented to a chronic care management program reported their worst pain over the previous week at baseline and at 6, 12, 18, and 26 weeks using a 10-cm visual analog scale. Trajectories (classes) were identified using latent class growth analysis. Demographics, pain, physical performance, strength, quality of life, mental health, and lower limb/foot structural measures obtained at baseline were assessed for association with trajectory class membership. RESULTS: Individuals in class 1 (28%) exhibited high, persistent pain from baseline (7.8 ± 1.7 cm), which continued over time (P = .52). Class 2 (57%) displayed moderate baseline pain (4.8 ± 1.8 cm), which also remained persistent (P = .97). Individuals in class 3 (15%) showed low, improving pain (baseline pain, 2.6 ± 1.2 cm) over time (P = .017). At baseline, poor Knee injury and Osteoarthritis Outcome Score (KOOS) scores, local and proximal sensitivity to pressure, and lower knee extensor strength were associated with increased odds of following the high-pain trajectory (range [95% confidence interval], 1.03 [1.00, 1.07] to 16.24 [2.53, 104.34]). CONCLUSION: Distinct pain trajectories appear to exist in people with patellofemoral OA presenting to a chronic care management program. Baseline variables may be useful for identifying individuals at risk of poorer prognosis. Larger studies are needed to confirm the efficacy of this finding. LEVEL OF EVIDENCE: Prognosis, level 2b. J Orthop Sports Phys Ther 2019;49(1):5-16. Epub 12 Sep 2018. doi:10.2519/jospt.2019.8354.

Within-host Mycobacterium tuberculosis diversity and its utility for inferences of transmission.

Whole genome sequencing in conjunction with traditional epidemiology has been used to reconstruct transmission networks of Mycobacterium tuberculosis during outbreaks. Given its low mutation rate, genetic diversity within M. tuberculosis outbreaks can be extremely limited - making it difficult to determine precisely who transmitted to whom. In addition to consensus SNPs (cSNPs), examining heterogeneous alleles (hSNPs) has been proposed to improve resolution. However, few studies have examined the potential biases in detecting these hSNPs. Here, we analysed genome sequence data from 25 specimens from British Columbia, Canada. Specimens were sequenced to a depth of 112-296×. We observed biases in read depth, base quality, strand distribution and read placement where possible hSNPs were initially identified, so we applied conservative filters to reduce false positives. Overall, there was phylogenetic concordance between the observed 2542 cSNP and 63 hSNP loci. Furthermore, we identified hSNPs shared exclusively by epidemiologically linked patients, supporting their use in transmission inferences. We conclude that hSNPs may add resolution to transmission networks, particularly where the overall genetic diversity is low.

No barrier to emergence of bathyal king crabs on the Antarctic shelf.

Cold-water conditions have excluded durophagous (skeleton-breaking) predators from the Antarctic seafloor for millions of years. Rapidly warming seas off the western Antarctic Peninsula could now facilitate their return to the continental shelf, with profound consequences for the endemic fauna. Among the likely first arrivals are king crabs (Lithodidae), which were discovered recently on the adjacent continental slope. During the austral summer of 2010 ‒ 2011, we used underwater imagery to survey a slope-dwelling population of the lithodid Paralomis birsteini off Marguerite Bay, western Antarctic Peninsula for environmental or trophic impediments to shoreward expansion. The population density averaged ∼ 4.5 individuals × 1,000 m(-2) within a depth range of 1,100 ‒ 1,500 m (overall observed depth range 841-2,266 m). Images of juveniles, discarded molts, and precopulatory behavior, as well as gravid females in a trapping study, suggested a reproductively viable population on the slope. At the time of the survey, there was no thermal barrier to prevent the lithodids from expanding upward and emerging on the outer shelf (400- to 550-m depth); however, near-surface temperatures remained too cold for them to survive in inner-shelf and coastal environments (<200 m). Ambient salinity, composition of the substrate, and the depth distribution of potential predators likewise indicated no barriers to expansion of lithodids onto the outer shelf. Primary food resources for lithodids--echinoderms and mollusks--were abundant on the upper slope (550-800 m) and outer shelf. As sea temperatures continue to rise, lithodids will likely play an increasingly important role in the trophic structure of subtidal communities closer to shore.

Abacavir pharmacogenetics--from initial reports to standard of care.

Abacavir is a nucleoside analogue reverse transcriptase inhibitor indicated for the treatment of human immunodeficiency virus infection as part of a multidrug, highly active antiretroviral therapy regimen. Despite its efficacy, approximately 5% of individuals who receive abacavir develop an immune-mediated hypersensitivity reaction (HSR) that warrants immediate discontinuation of abacavir and switching to an alternative antiretroviral regimen. Abacavir HSR is associated with individuals who carry the *57:01 variant in the human leukocyte antigen B (HLA-B) gene. There is a large volume of evidence to show that those who carry HLA-B*57:01 are at significantly increased risk of developing HSR and should not receive abacavir. Pharmacogenetic screening to ensure individuals who carry HLA-B*57:01 do not receive abacavir can reduce the incidence of HSR and is now considered the standard of care before prescribing abacavir. Genetic testing to prevent abacavir HSR is currently one of the best examples of integrating pharmacogenetic testing into clinical practice.

Liver X receptor α gene polymorphisms and variable cardiovascular outcomes in patients treated with antihypertensive therapy: results from the INVEST-GENES study.

BACKGROUND/AIMS: Liver X receptor-α (LXRA) is a nuclear receptor that regulates genes important in cholesterol homeostasis and inflammation. Several single nucleotide polymorphisms (SNPs) in the LXRA gene (NR1H3) have been earlier associated with metabolic phenotypes (dyslipidemia and elevated body mass index). Metabolic dysregulation is a major contributor to coronary disease; therefore, we assessed LXRA in International Verapamil Sustained Release SR Trandolapril Study Genetic Substudy (INVEST-GENES), a genetic-substudy of a large clinical trial in patients with hypertension and coronary artery disease. METHODS: Seven tag SNPs in the LXRA gene region (NR1H3) were selected for study: rs11039149, rs12221497, rs2279238, rs7120118, rs326213, rs11039159, and rs10501321. One thousand fifty-nine patients were genotyped from the INVEST-GENES case-control set (verapamil-sustained release-based or atenolol-based treatment strategies) that comprised of 297 cases frequency matched (approximately 2.5:1) with that of event-free controls by sex and race. The primary outcome was defined as first occurrence of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. Adjusted odds ratios (ORs) were calculated using logistic regression. RESULTS: Three of the seven SNPs were associated with significant effects on the primary outcome in nonBlacks. The variant G allele of rs11039149 and the variant A allele of rs12221497 were associated with reduced risk of experiencing the primary outcome [OR: 0.62, confidence interval (CI): 0.45-0.85, P=0.003 and OR: 0.60, CI: 0.39-0.91, P=0.016, respectively]. The rs2279238 genotype was associated with a significant increase in risk for the primary outcome (OR: 1.42, CI: 1.03-1.95, P=0.03). Furthermore, there was a significant genotype-treatment strategy interaction for carriers of the variant T allele of rs2279238 (OR for verapamil-sustained release strategy compared with atenolol strategy: 2.86, CI: 1.50-5.46, P=0.0015). Diplotype analyses showed that the SNPs are rarely coinherited and support the directionally opposite effects of the SNPs on the primary outcome. CONCLUSION: LXRA genotypes were associated with variable risk for cardiovascular outcomes and pharmacogenetic effect in INVEST-GENES. These novel findings suggest that LXRA is a genetic/pharmacogenetic target that should be further explored.

Does ignoring model selection when assessing the effect of particulate matter air pollution on mortality make us too vigilant?

PURPOSE: To investigate the extent to which standard errors can be underestimated in time-series studies of the association between particulate matter air pollution (PM) and mortality if model selection variation is not accounted for. METHODS: Actual-time series data from Cook County, Illinois, and Salt Lake County, Utah, for the period 1987 to 2000 were used to generate mortality time series. These series were used to examine the overconfidence resulting from ignoring variability introduced by the model selection process. RESULTS: When variation associated with a model selection process is not accounted for, we found that the estimated standard errors for the effect of PM on mortality were substantially smaller than the true standard errors that necessarily incorporate model selection variability. Because of this, the true standard errors are approximately 70% larger than the reported standard errors. We also found that not accounting for model selection effects can result in the observed size of tests of no association between PM and mortality being up to about five times the nominal significance level. CONCLUSIONS: Failing to account properly for the effect of model selection can reduce the accepted burden of proof for concluding a statistically significant association between PM and mortality.