Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Local Delivery of SBRT and IL12 by mRNA Technology Overcomes Immunosuppressive Barriers to Eliminate Pancreatic Cancer

The immunosuppressive milieu in pancreatic cancer (PC) is a significant hurdle to treatments, resulting in survival statistics that have barely changed in 5 decades. Here we present a combination treatment consisting of stereotactic body radiation therapy (SBRT) and IL-12 mRNA lipid nanoparticles delivered directly to pancreatic murine tumors. This treatment was effective against primary and metastatic models, achieving cures in both settings. IL-12 protein concentrations were transient and localized primarily to the tumor. Depleting CD4 and CD8 T cells abrogated treatment efficacy, confirming they were essential to treatment response. Single cell RNA sequencing from SBRT/IL-12 mRNA treated tumors demonstrated not only a complete loss of T cell exhaustion, but also an abundance of highly proliferative and effector T cell subtypes. SBRT elicited T cell receptor clonal expansion, whereas IL-12 licensed these cells with effector function. This is the first report demonstrating the utility of SBRT and IL-12 mRNA in PC.

Statement of significance

This study demonstrates the use of a novel combination treatment consisting of radiation and immunotherapy in murine pancreatic tumors. This treatment could effectively treat local and metastatic disease, suggesting it may have the potential to treat a cancer that has not seen a meaningful increase in survival in 5 decades.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Precision detection of recent HIV infections using high-throughput genomic incidence assay

HIV incidence is a key measure for tracking disease spread and identifying populations and geographic regions where new infections are most concentrated. The HIV sequence population provides a robust signal for the stage of infection. Large-scale and high-precision HIV sequencing is crucial for effective genomic incidence surveillance. We produced 1,034 full-length envelope gene sequences from a seroconversion cohort by conducting HIV microdrop sequencing and measuring the genomic incidence assay's genome similarity index (GSI) dynamics. The measured dynamics of 9 of 12 individuals aligned with the GSI distribution estimated independently using 417 publicly available incident samples. We enhanced the capacity to identify individuals with recent infections, achieving predicted detection accuracies of 92% (89%-94%) for cases within 6 months and 81% (74%-87%) for cases within 9 months. These accuracy levels agreed with the observed detection accuracy intervals of an independent validation data set. Additionally, we produced 131 full-length envelope gene sequences from eight individuals with chronic HIV infection. This analysis confirmed a false recency rate (FRR) of 0%, which was consistent with 162 publicly available chronic samples. The mean duration of recent infection (MDRI) was 238 (209-267) days, indicating an 83% improvement in performance compared to current recent infection testing algorithms. The shifted Poisson mixture model was then used to estimate the time since infection, and the model estimates showed an 88% consistency with the days post infection derived from HIV RNA test dates and/or seroconversion dates. HIV microdrop sequencing provides unique prospects for large-scale incidence surveillance using high-throughput sequencing. IMPORTANCE Accurate identification of recently infected individuals is vital for prioritizing specific populations for interventions, reducing onward transmission risks, and optimizing public health services. However, current HIV-specific antibody-based methods have not been satisfactory in accurately identifying incident cases, hindering the use of HIV recency testing for prevention efforts and partner protection. Genomic incidence assays offer a promising alternative for identifying recent infections. In our study, we used microdroplet technologies to produce a large number of complete HIV envelope gene sequences, enabling the accurate detection of early infection signs. We assessed the dynamics of the incidence assay's metrics and compared them with statistical models. Our approach demonstrated high accuracy in identifying individuals with recent infections, achieving predicted detection rates exceeding 90% within 6 months and over 80% within 9 months of infection. This high-resolution method holds significant potential for enhancing the effectiveness of HIV incidence screening for case-based surveillance in public health initiatives.

New insights into the responder/nonresponder divide in rectal cancer: Damage-induced Type I IFNs dictate treatment efficacy and can be targeted to enhance radiotherapy

Rectal cancer ranks as the second leading cause of cancer-related deaths. Neoadjuvant therapy for rectal cancer patients often results in individuals that respond well to therapy and those that respond poorly, requiring life-altering excision surgery. It is inadequately understood what dictates this responder/nonresponder divide. Our major aim is to identify what factors in the tumor microenvironment drive a fraction of rectal cancer patients to respond to radiotherapy. We also sought to distinguish potential biomarkers that would indicate a positive response to therapy and design combinatorial therapeutics to enhance radiotherapy efficacy. To address this, we developed an orthotopic murine model of rectal cancer treated with short course radiotherapy that recapitulates the bimodal response observed in the clinic. We utilized a robust combination of transcriptomics and protein analysis to identify differences between responding and nonresponding tumors. Our mouse model recapitulates human disease in which a fraction of tumors respond to radiotherapy (responders) while the majority are nonresponsive. We determined that responding tumors had increased damage-induced cell death, and a unique immune-activation signature associated with tumor-associated macrophages, cancer-associated fibroblasts, and CD8⁺ T cells. This signature was dependent on radiation-induced increases of Type I Interferons (IFNs). We investigated a therapeutic approach targeting the cGAS/STING pathway and demonstrated improved response rate following radiotherapy. These results suggest that modulating the Type I IFN pathway has the potential to improve radiation therapy efficacy in RC.

Radiation Therapy Exacerbates Tumor-Promoting Innervation and Nerve Signaling in Rectal Cancer

PURPOSE

Many solid tumors present with perineural invasion (PNI), and innervation correlates with worsened prognosis. The effects that commonly administered therapies such as radiation therapy (RT) have on PNI status remain unknown. We investigated the contribution of RT on the nervous system and elucidated the implications that increased nerve signaling can have on tumor burden using our previously developed orthotopic murine model of rectal cancer (RC) and our targeted and clinically relevant short-course RT (SCRT) regimen.

METHODS

Medical charts for patients with RC treated at the Wilmot Cancer Institute were obtained and PNI status was analyzed. Human data were accompanied by an orthotopic murine model of RC. Briefly, luciferase-expressing murine colon-38 (MC38-luc) tumor cells were injected orthotopically into the rectal wall of C57BL6 mice. Targeted SCRT (5 gray (Gy) per fraction for 5 consecutive fractions) was administered to the tumor. Intratumoral innervation was determined by immunohistochemistry (IHC), local norepinephrine (NE) concentration was quantified by enzyme-linked immunosorbent assay (ELISA), and β2-adrenergic receptor (B2AR) expression was assessed by flow cytometry. Chronic NE signaling was mirrored by daily isoproterenol treatment, and the effect on tumor burden was determined by overall survival, presence of metastatic lesions, and tumor size. Isoproterenol signaling was inhibited by administration of propranolol.

RESULTS

Human RC patients with PNI have decreased overall survival compared with patients without PNI. In our mouse model, SCRT induced the expression of genes involved in neurogenesis, increased local NE secretion, and upregulated B2AR expression. Treating mice with isoproterenol resulted in decreased overall survival, increased rate of metastasis, and reduced SCRT efficacy. Interestingly, the isoproterenol-induced decrease in SCRT efficacy could be abrogated by blocking the BAR through the use of propranolol, suggesting a direct role of BAR stimulation on impairing SCRT responses.

CONCLUSIONS

Our results indicate that while SCRT is a valuable treatment, it is accompanied by adverse effects on the nervous system that may impede the efficacy of therapy and promote tumor burden. Therefore, we could speculate that therapies aimed at targeting this signaling cascade or impairing nerve growth in combination with SCRT may prove beneficial in future cancer treatment.

Gut microbiome in people living with HIV is associated with impaired thiamine and folate syntheses

People living with HIV have a high incidence of cardiovascular and neurological diseases as comorbid disorders that are commonly linked to inflammation. While microbial translocation can augment inflammation during HIV infection, functional microbiome shifts that may increase pro-inflammatory responses have not been fully characterized. In addition, defining HIV-induced microbiome changes has been complicated by high variability among individuals. Here we conducted functional annotation of previously-published 16S ribosomal RNA gene sequences of 305 HIV positive and 249 negative individuals, with adjustment for geographic region, sex, sexual behavior, and age. Metagenome profiles were inferred from these individuals' 16S data. HIV infection was associated with impaired microbial vitamin B synthesis; around half of the gene families in thiamine and folate biosynthesis pathways were significantly less abundant in the HIV positive group than the negative control. These results are consistent with the high prevalence of thiamine and folate deficiencies in HIV infections. These HIV-induced microbiota shifts have the potential to influence cardiovascular and neurocognitive diseases, given the documented associations between B-vitamin deficiencies, inflammation, and these diseases. We also observed that most essential amino acid biosynthesis pathways were downregulated in the microbiome of HIV-infected individuals. Microbial vitamin B and amino acid synthesis pathways were not significantly recovered by antiretroviral treatment when we compared 262 ART positive and 184 ART negative individuals. Our meta-analysis provides a new outlook for understanding vitamin B and amino acid deficiencies in HIV patients, suggesting that interventions for reversing HIV-induced microbiome shifts may aid in lessening the burdens of HIV comorbidities.

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall's Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.

Multiple imputation and direct estimation for qPCR data with non-detects

BACKGROUND

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference.

RESULTS

We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects.

CONCLUSIONS

The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers’ ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. “Full models” were built using all four features types, while “nested models” were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and “black-box” (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

Analysis of the capacity of Salmonella enterica Typhimurium to infect the human Placenta

INTRODUCTION

Salmonella species are gram-negative facultative intracellular bacteria that are common causes of foodborne illness in North America. Infections by Salmonella during pregnancy are a significant cause of fetal loss in domestic livestock, and fetal and maternal mortality in mice. Furthermore, Salmonella infection is associated with miscarriage, stillbirth and preterm birth in pregnant women. Despite these collective associations, the extent to which Salmonella can infect the human placenta has not been investigated.

METHODS

Human placental villous explants from several gestational ages were exposed to Salmonella enterica serovar Typhimurium (STm) ex vivo. Infection was assessed by colony forming unit assay and whole mount immunofluorescence (WMIF).

RESULTS

Viable bacteria were recovered from placental villous explants of all gestational ages tested, but the bacterial burden was highest in 1st trimester explants. Bacterial numbers did not change appreciably with time post-infection in explants from any gestational age examined, suggesting that STm does not proliferate in placental villi. Exposure of villous explants to STm strains defective for the type III secretion systems revealed that Salmonella pathogenicity island 1 is essential for optimal invasion. In contrast to placental explants, STm infected and proliferated within villous cytotrophoblast cells isolated from term placentas. WMIF demonstrated that STm was restricted primarily to the syncytiotrophoblast layer in infected placentas.

DISCUSSION

Our study demonstrates that STm can invade into the syncytiotrophoblast but does not subsequently proliferate. Thus, the syncytiotrophoblast may function as a barrier to STm infection of the fetus.

HIITE: HIV-1 incidence and infection time estimator

Motivation

Around 2.1 million new HIV-1 infections were reported in 2015, alerting that the HIV-1 epidemic remains a significant global health challenge. Precise incidence assessment strengthens epidemic monitoring efforts and guides strategy optimization for prevention programs. Estimating the onset time of HIV-1 infection can facilitate optimal clinical management and identify key populations largely responsible for epidemic spread and thereby infer HIV-1 transmission chains. Our goal is to develop a genomic assay estimating the incidence and infection time in a single cross-sectional survey setting.

Results

We created a web-based platform, HIV-1 incidence and infection time estimator (HIITE), which processes envelope gene sequences using hierarchical clustering algorithms and informs the stage of infection, along with time since infection for incident cases. HIITE's performance was evaluated using 585 incident and 305 chronic specimens' envelope gene sequences collected from global cohorts including HIV-1 vaccine trial participants. HIITE precisely identified chronically infected individuals as being chronic with an error less than 1% and correctly classified 94% of recently infected individuals as being incident. Using a mixed-effect model, an incident specimen's time since infection was estimated from its single lineage diversity, showing 14% prediction error for time since infection. HIITE is the first algorithm to inform two key metrics from a single time point sequence sample. HIITE has the capacity for assessing not only population-level epidemic spread but also individual-level transmission events from a single survey, advancing HIV prevention and intervention programs.

Availability and implementation

Web-based HIITE and source code of HIITE are available at http://www.hayounlee.org/software.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Daily HIV pre-exposure prophylaxis (PrEP) with tenofovir disoproxil fumarate-emtricitabine reduced Streptococcus and increased Erysipelotrichaceae in rectal microbiota

Daily PrEP is highly effective at preventing HIV-1 acquisition, but risks of long-term tenofovir disoproxil fumarate plus emtricitabine (TDF-FTC) include renal decline and bone mineral density decrease in addition to initial gastrointestinal side effects. We investigated the impact of TDF-FTC on the enteric microbiome using rectal swabs collected from healthy MSM before PrEP initiation and after 48 to 72 weeks of adherent PrEP use. The V4 region of the 16S ribosomal RNA gene sequencing showed that Streptococcus was significantly reduced from 12.0% to 1.2% (p = 0.036) and Erysipelotrichaceae family was significantly increased from 0.79% to 3.3% (p = 0.028) after 48–72 weeks of daily PrEP. Catenibacterium mitsuokai, Holdemanella biformis and Turicibacter sanguinis were increased within the Erysipelotrichaceae family and Streptococcus agalactiae, Streptococcus oralis, Streptococcus mitis were reduced. These changes were not associated with host factors including PrEP duration, age, race, tenofovir diphosphate blood level, any drug use and drug abuse, suggesting that the observed microbiome shifts were likely induced by daily PrEP use. Long-term PrEP resulted in increases of Catenibacterium mitsuokai and Holdemanella biformis, which have been associated with gut microbiome dysbiosis. Our observations can aid in characterizing PrEP’s side effects, which is likely to improve PrEP adherence, and thus HIV-1 prevention.

The HIV Genomic Incidence Assay Meets False Recency Rate and Mean Duration of Recency Infection Performance Standards

HIV incidence is a primary metric for epidemic surveillance and prevention efficacy assessment. HIV incidence assay performance is evaluated via false recency rate (FRR) and mean duration of recent infection (MDRI). We conducted a meta-analysis of 438 incident and 305 chronic specimens' HIV envelope genes from a diverse global cohort. The genome similarity index (GSI) accurately characterized infection stage across diverse host and viral factors. All except one chronic specimen had GSIs below 0.67, yielding a FRR of 0.33 [0-0.98] %. We modeled the incidence assay biomarker dynamics with a logistic link function assuming individual variabilities in a Beta distribution. The GSI probability density function peaked close to 1 in early infection and 0 around two years post infection, yielding MDRI of 420 [361, 467] days. We tested the assay by newly sequencing 744 envelope genes from 59 specimens of 21 subjects who followed from HIV negative status. Both standardized residuals and Anderson-Darling tests showed that the test dataset was statistically consistent with the model biomarker dynamics. This is the first reported incidence assay meeting the optimal FRR and MDRI performance standards. Signatures of HIV gene diversification can allow precise cross-sectional surveillance with a desirable temporal range of incidence detection.

Total-Body Irradiation Exacerbates Dissemination of Cutaneous Candida Albicans Infection

Exposure to radiation, particularly a large or total-body dose, weakens the immune system through loss of bone marrow precursor cells, as well as diminished populations of circulating and tissue-resident immune cells. One such population is the skin-resident immune cells. Changes in the skin environment can be of particular importance as the skin is also host to a number of commensal organisms, including Candida albicans , a species of fungus that causes opportunistic infections in immunocompromised patients. In a previous study, we found that a 6 Gy sublethal dose of radiation in mice caused a reduction of cutaneous dendritic cells, indicating that the skin may have a poorer response to infection after irradiation. In this study, the same 6 Gy sublethal radiation dose led to a weakened response to a C. ablicans cutaneous infection, which resulted in systemic dissemination from the ear skin to the kidneys. However, this impaired response was mitigated through the use of interleukin-12 (IL-12) administered to the skin after irradiation. Concomitantly with this loss of local control of infection, we also observed a reduction of CD4⁺ and CD8⁺ T cells in the skin, as well as the reduced expression of IFN-γ, CXCL9 and IL-9, which influence T-cell infiltration and function in infected skin. These changes suggest a mechanism by which an impaired immune environment in the skin after a sublethal dose of radiation increases susceptibility to an opportunistic fungal infection. Thus, in the event of radiation exposure, it is important to include antifungal agents, or possibly IL-12, in the treatment regimen, particularly if wounds are involved that result in loss of the skin's physical barrier function.

Molecular clock of HIV-1 envelope genes under early immune selection

Background

The molecular clock hypothesis that genes or proteins evolve at a constant rate is a key tool to reveal phylogenetic relationships among species. Using the molecular clock, we can trace an infection back to transmission using HIV-1 sequences from a single time point. Whether or not a strict molecular clock applies to HIV-1’s early evolution in the presence of immune selection has not yet been fully examined.

Results

We identified molecular clock signatures from 1587 previously published HIV-1 full envelope gene sequences obtained since acute infection in 15 subjects. Each subject’s sequence diversity linearly increased during the first 150 days post infection, with rates ranging from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.54 \times 10^{ - 5}$$\end{document}1.54×10-5 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.91 \times 10^{ - 5}$$\end{document}3.91×10-5 with a mean of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.69 \times 10^{ - 5}$$\end{document}2.69×10-5 per base per day. The rate of diversification for 12 out of the 15 subjects was comparable to the neutral evolution rate. While temporal diversification was consistent with evolution patterns in the absence of selection, mutations from the founder virus were highly clustered on statistically identified selection sites, which diversified more than 65 times faster than non-selection sites. By mathematically quantifying deviations from the molecular clock under various selection scenarios, we demonstrate that the deviation from a constant clock becomes negligible as multiple escape lineages emerge. The most recent common ancestor of a virus pair from distinct escape lineages is most likely the transmitted founder virus, indicating that HIV-1 molecular dating is feasible even after the founder viruses are no longer detectable.

Conclusions

The ability of HIV-1 to escape from immune surveillance in many different directions is the driving force of molecular clock persistence. This finding advances our understanding of the robustness of HIV-1’s molecular clock under immune selection, implying the potential for molecular dating.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-016-0269-6) contains supplementary material, which is available to authorized users.

SPMM: estimating infection duration of multivariant HIV-1 infections

MOTIVATION

Illustrating how HIV-1 is transmitted and how it evolves in the following weeks is an important step for developing effective vaccination and prevention strategies. It is currently possible through DNA sequencing to account for the diverse array of viral strains within an infected individual. This provides an unprecedented opportunity to pinpoint when each patient was infected and which viruses were transmitted.

RESULTS

Here we develop a mathematical tool for early HIV-1 evolution within a subject whose infection originates either from a single or multiple viral variants. The shifted Poisson mixture model (SPMM) provides a quantitative guideline for segregating viral lineages, which in turn enables us to assess when a subject was infected. The infection duration estimated by SPMM showed a statistically significant linear relationship with that by Fiebig laboratory staging (P = 0.00059) among 37 acutely infected subjects. Our tool provides a functional approach to understanding early genetic diversity, one of the most important parameters for deciphering HIV-1 transmission and predicting the rate of disease progression.

AVAILABILITY AND IMPLEMENTATION

SPMM, webserver, is available at http://www.hayounlee.org/web-tools.html.

CONTACT

hayoun@usc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bayesian Models for Multiple Outcomes in Domains with Application to the Seychelles Child Development Study

The Seychelles Child Development Study (SCDS) examines the effects of prenatal exposure to methylmercury on the functioning of the central nervous system. The SCDS data include 20 outcomes measured on 9-year old children that can be classified broadly in four outcome classes or "domains": cognition, memory, motor, and social behavior. Previous analyses and scientific theory suggest that these outcomes may belong to more than one of these domains, rather than only a single domain as is frequently assumed for modeling. We present a framework for examining the effects of exposure and other covariates when the outcomes may each belong to more than one domain and where we also want to learn about the assignment of outcomes to domains. Each domain is defined by a sentinel outcome which is preassigned to that domain only. All other outcomes can belong to multiple domains and are not preassigned. Our model allows exposure and covariate effects to differ across domains and across outcomes within domains, and includes random subject-specific effects which model correlations between outcomes within and across domains. We take a Bayesian MCMC approach. Results from the Seychelles study and from extensive simulations show that our model can effectively determine sparse domain assignment, and at the same time give increased power to detect overall, domain-specific and outcome-specific exposure and covariate effects relative to separate models for each endpoint. When fit to the Seychelles data, several outcomes were classified as partly belonging to domains other than their originally assigned domains. In retrospect, the new partial domain assignments are reasonable and, as we discuss, suggest important scientific insights about the nature of the outcomes. Checks of model misspecification were improved relative to a model that assumes each outcome is in a single domain.

Neurodevelopmental outcomes at 5 years in children exposed prenatally to maternal dental amalgam: the Seychelles Child Development Nutrition Study

Limited human data are available to assess the association between prenatal mercury vapor (Hg⁰)) exposure from maternal dental amalgam restorations and neurodevelopment of children. We evaluated the association between maternal dental amalgam status during gestation and children's neurodevelopmental outcomes at 5 years in the Seychelles Child Development Nutrition Study (SCDNS). Maternal amalgam status was determined prospectively in a longitudinal cohort study examining the associations of prenatal exposure to nutrients and methylmercury (MeHg) with neurodevelopment. A total of 236 mother-child pairs initially enrolled in the SCDNS in 2001 were eligible to participate. Maternal amalgam status was measured as number of amalgam surfaces (the primary metric) and number of occlusal points. The neurodevelopmental assessment battery was comprised of age-appropriate tests of cognitive, language, and perceptual functions, and scholastic achievement. Linear regression analysis controlled for MeHg exposure, maternal fatty acid status, and other covariates relevant to child development. Maternal amalgam status evaluation yielded an average of 7.0 surfaces (range 0-28) and 11.0 occlusal points (range 0-40) during pregnancy. Neither the number of maternal amalgam surfaces nor occlusal points were associated with any outcome. Our findings do not provide evidence to support a relationship between prenatal exposure to Hg⁰ from maternal dental amalgam and neurodevelopmental outcomes in children at 5 years of age.

Latent factor regression models for grouped outcomes

We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set of identifiable models along this spectrum that extend an existing random effect model for multiple outcomes nested in domains. We characterize the tradeoffs between parsimony and flexibility in this set of models, applying them to both simulated data and data relating sexually dimorphic traits in male infants to explanatory variables.