Revealing mechanisms of infectious disease spread through empirical contact networks.

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Regulation of interferon stimulated gene expression levels at homeostasis.

Interferon stimulated genes (ISGs), a collection of genes important in the early innate immune response, are upregulated in response to stimulation by extracellular type I interferons. The regulation of ISGs has been extensively studied in cells exposed to significant interferon stimulation, but less is known about ISG regulation in homeostatic regimes in which extracellular interferon levels are low. Using a collection of pre-existing, publicly available microarray datasets, we investigated ISG regulation at homeostasis in CD4, pulmonary epithelial, fibroblast and macrophage cells. We used a linear regression model to predict ISG expression levels from regulator expression levels. Our results suggest significant regulation of ISG expression at homeostasis, both through the ISGF3 molecule and through IRF7 and IRF8 associated pathways. We find that roughly 50% of ISGs have expression levels significantly correlated with ISGF3 expression levels at homeostasis, supporting previous results suggesting that homeostatic IFN levels have broad functional consequences. We find that ISG expression levels varied in their correlation with ISGF3, with epithelial and macrophage cells showing more correlation than CD4 and fibroblast cells. Our analysis provides a novel approach for decomposing and quantifying ISG regulation.

High-Resolution Sequencing of Viral Populations during Early Simian Immunodeficiency Virus Infection Reveals Evolutionary Strategies for Rapid Escape from Emerging Env-Specific Antibody Responses.

Primate lentiviruses, including the human and simian immunodeficiency viruses (HIV and SIV), produce infections marked by persistent, ongoing viral replication. This occurs despite the presence of virus-specific adaptive immune responses, including antibodies targeting the viral envelope glycoprotein (Env), and evolution of antibody-escape variants is a well-documented feature of lentiviral infection. Here, we examined the evolutionary dynamics of the SIV env gene during early infection (≤29 weeks postinfection) in a cohort of four SIVmac251-infected rhesus macaques. We tracked env evolution during acute and early infection using frequent sampling and ultradeep sequencing of viral populations, capturing a transmission bottleneck and the subsequent reestablishment of Env diversity. A majority of changes in the gp120 subunit mapped to two short clusters, one in the first variable region (V1) and one in V4, while most changes in the gp41 subunit appeared in the cytoplasmic domain. Variation in V1 was dominated by short duplications and deletions of repetitive sequence, while variation in V4 was marked by short in-frame deletions and closely overlapping substitutions. The most common substitutions in both patches did not alter viral replicative fitness when tested using a highly sensitive, deep-sequencing-based competition assay. Our results, together with the observation that very similar or identical patterns of sequence evolution also occur in different macaque species infected with related but divergent strains of SIV, suggest that resistance to early, strain-specific anti-Env antibodies is the result of temporally and mutationally predictable pathways of escape that occur during the early stages of infection.IMPORTANCE The envelope glycoprotein (Env) of primate lentiviruses mediates entry by binding to host cell receptors followed by fusion of the viral membrane with the cell membrane. The exposure of Env complexes on the surface of the virion results in targeting by antibodies, leading to selection for virus escape mutations. We used the SIV/rhesus macaque model to track in vivo evolution of variation in Env during acute/early infection in animals with and without antibody responses to Env, uncovering remarkable variation in animals with antibody responses within weeks of infection. Using a deep-sequencing-based fitness assay, we found substitutions associated with antibody escape had little to no effect on inherent replicative capacity. The ability to readily propagate advantageous changes that incur little to no replicative fitness costs may be a mechanism to maintain continuous replication under constant immune selection, allowing the virus to persist for months to years in the infected host.

Rare HIV-1 transmitted/founder lineages identified by deep viral sequencing contribute to rapid shifts in dominant quasispecies during acute and early infection.

In order to inform the rational design of HIV-1 preventive and cure interventions it is critical to understand the events occurring during acute HIV-1 infection (AHI). Using viral deep sequencing on six participants from the early capture acute infection RV217 cohort, we have studied HIV-1 evolution in plasma collected twice weekly during the first weeks following the advent of viremia. The analysis of infections established by multiple transmitted/founder (T/F) viruses revealed novel viral profiles that included: a) the low-level persistence of minor T/F variants, b) the rapid replacement of the major T/F by a minor T/F, and c) an initial expansion of the minor T/F followed by a quick collapse of the same minor T/F to low frequency. In most participants, cytotoxic T-lymphocyte (CTL) escape was first detected at the end of peak viremia downslope, proceeded at higher rates than previously measured in HIV-1 infection, and usually occurred through the exploration of multiple mutational pathways within an epitope. The rapid emergence of CTL escape variants suggests a strong and early CTL response. Minor T/F viral strains can contribute to rapid and varied profiles of HIV-1 quasispecies evolution during AHI. Overall, our results demonstrate that early, deep, and frequent sampling is needed to investigate viral/host interaction during AHI, which could help identify prerequisites for prevention and cure of HIV-1 infection.

Correction: Rare HIV-1 transmitted/founder lineages identified by deep viral sequencing contribute to rapid shifts in dominant quasispecies during acute and early infection.

[This corrects the article DOI: 10.1371/journal.ppat.1006510.].

A penalized regression approach to haplotype reconstruction of viral populations arising in early HIV/SIV infection.

MOTIVATION: Next generation sequencing (NGS) has been increasingly applied to characterize viral evolution during HIV and SIV infections. In particular, NGS datasets sampled during the initial months of infection are characterized by relatively low levels of diversity as well as convergent evolution at multiple loci dispersed across the viral genome. Consequently, fully characterizing viral evolution from NGS datasets requires haplotype reconstruction across large regions of the viral genome. Existing haplotype reconstruction algorithms have not been developed with the particular characteristics of early HIV/SIV infection in mind, raising the possibility that better performance could be achieved through a specifically designed algorithm. RESULTS: Here, we introduce a haplotype reconstruction algorithm, RegressHaplo, specifically designed for low diversity and convergent evolution regimes. The algorithm uses a penalized regression that balances a data fitting term with a penalty term that encourages solutions with few haplotypes. The regression covariates are a large set of potential haplotypes and fitting the regression is made computationally feasible by the low diversity setting. Using simulated and in vivo datasets, we compare RegressHaplo to PredictHaplo and QuRe, two existing haplotype reconstruction algorithms. RegressHaplo performs better than these algorithms on simulated datasets with relatively low diversity levels. We suggest RegressHaplo as a novel tool for the investigation of early infection HIV/SIV datasets and, more generally, low diversity viral NGS datasets. CONTACT: sr286@georgetown.edu. AVAILABILITY AND IMPLEMENTATION: https://github.com/SLeviyang/RegressHaplo.

Broad CTL Response in Early HIV Infection Drives Multiple Concurrent CTL Escapes.

Recent studies have highlighted the ability of HIV to escape from cytotoxic T lymphocyte (CTL) responses that concurrently target multiple viral epitopes. Yet, the viral dynamics involved in such escape are incompletely understood. Previous analyses have made several strong assumptions regarding HIV escape from CTL responses such as independent or non-concurrent escape from individual CTL responses. Using experimental data from evolution of HIV half genomes in four patients we observe concurrent viral escape from multiple CTL responses during early infection (first 100 days of infection), providing confirmation of a recent result found in a study of one HIV-infected patient. We show that current methods of estimating CTL escape rates, based on the assumption of independent escapes, are biased and perform poorly when CTL escape proceeds concurrently at multiple epitopes. We propose a new method for analyzing longitudinal sequence data to estimate the rate of CTL escape across multiple epitopes; this method involves few parameters and performs well in simulation studies. By applying our novel method to experimental data, we find that concurrent multiple escapes occur at rates between 0.03 and 0.4 day(-1), a relatively broad range that reflects uncertainty due to sparse sampling and wide ranges of parameter values. However, we show that concurrent escape at rates 0.1-0.2 day(-1) across multiple epitopes is consistent with our patient datasets.

Constructing lower-bounds for CTL escape rates in early SIV infection.

Intrahost human and simian immunodeficiency virus (HIV and SIV) evolution is marked by repeated viral escape from cytotoxic T-lymphocyte (CTL) response. Typically, the first such CTL escape starts around the time of peak viral load and completes within one or two weeks. Many authors have developed methods to quantify CTL escape rates, but existing methods depend on sampling at two or more timepoints. Since many datasets capture the dynamics of the first CTL escape at a single timepoint, we develop inference methods applicable to single timepoint datasets. To account for model uncertainty, we construct estimators which serve as lower bounds for the escape rate. These lower-bound estimators allow for statistically meaningful comparison of escape rates across different times and different compartments. We apply our methods to two SIV datasets, showing that escape rates are relatively high during the initial days of the first CTL escape and drop to lower levels as the escape proceeds.

The coalescence of intrahost HIV lineages under symmetric CTL attack.

Cytotoxic T lymphocytes (CTLs) are immune system cells that are thought to play an important role in controlling HIV infection. We develop a stochastic ODE model of HIV-CTL interaction that extends current deterministic ODE models. Based on this stochastic model, we consider the effect of CTL attack on intrahost HIV lineages assuming that CTLs attack several epitopes with equal strength. In this setting, we introduce a limiting version of our stochastic ODE under which we show that the coalescence of HIV lineages can be described through Poisson-Dirichlet distributions. Through numerical experiments, we show that our results under the limiting stochastic ODE accurately reflect HIV lineages under CTL attack when the HIV population size is on the low end of its hypothesized range. Current techniques of HIV lineage construction depend on the Kingman coalescent. Our results give an explicit connection between CTL attack and HIV lineages.

Sampling HIV intrahost genealogies based on a model of acute stage CTL response.

Cytotoxic T lymphocytes (CTLs) play an important role in the immune response to HIV during the acute stage of infection, but the effect of CTLs on HIV intrahost genetic diversity is poorly understood. We introduce a model of CTL attack during the acute stage. Assuming this model, we develop a method to sample HIV intrahost genealogies. Using our sampling approach, we characterize the evolutionary forces that shape HIV genealogies. In particular, we show that early mutation events can have significant impact on HIV genealogies and that certain types of CTL attack are best at controlling HIV genetic diversity. Our sampler represents a first step toward using HIV genetic data to infer properties of CTL attack.

Analysis of a stochastic predator-prey model with applications to intrahost HIV genetic diversity.

During an infection, HIV experiences strong selection by immune system T cells. Recent experimental work has shown that MHC escape mutations form an important pathway for HIV to avoid such selection. In this paper, we study a model of MHC escape mutation. The model is a predator-prey model with two prey, composed of two HIV variants, and one predator, the immune system CD8 cells. We assume that one HIV variant is visible to CD8 cells and one is not. The model takes the form of a system of stochastic differential equations. Motivated by well-known results concerning the short life-cycle of HIV intrahost, we assume that HIV population dynamics occur on a faster time scale then CD8 population dynamics. This separation of time scales allows us to analyze our model using an asymptotic approach. Using this model we study the impact of an MHC escape mutation on the population dynamics and genetic evolution of the intrahost HIV population. From the perspective of population dynamics, we show that the competition between the visible and invisible HIV variants can reach steady states in which either a single variant exists or in which coexistence occurs depending on the parameter regime. We show that in some parameter regimes the end state of the system is stochastic. From a genetics perspective, we study the impact of the population dynamics on the lineages of an HIV sample taken after an escape mutation occurs. We show that the lineages go through severe bottlenecks and that in certain parameter regimes the lineage distribution can be characterized by a Kingman coalescent. Our results depend on methods from diffusion theory and coalescent theory.

Whole-genome analysis of SARS-CoV-2 in a 2020 infection cluster in a nursing home of Southern Italy.

BACKGROUND: Nursing homes have represented important hotspots of viral spread during the initial wave of COVID-19 pandemics. The proximity of patients inside nursing homes allows investigate the dynamics of viral transmission, which may help understand SARS-Cov2 biology and spread. METHODS: SARS-CoV-2 viral genomes obtained from 46 patients infected in an outbreak inside a nursing home in Calabria region (South Italy) were analyzed by Next Generation Sequencing. We also investigated the evolution of viral genomes in 8 patients for which multiple swabs were available. Phylogenetic analysis and haplotype reconstruction were carried out with IQ-TREE software and RegressHaplo tool, respectively. RESULTS: All viral strains isolated from patients infected in the nursing home were classified as B.1 lineage, clade G. Overall, 14 major single nucleotide variations (SNVs) (frequency > 80%) and 12 minor SNVs (frequency comprised between 20% and 80%) were identified with reference to the Wuhan-H-1 sequence (NC_045512.2). All patients presented the same 6 major SNVs: D614G in the S gene; P4715L, ntC3037T (F924F) and S5398P in Orf1ab gene; ntC26681T (F53F) in the M gene; and ntC241T in the non-coding UTR region. However, haplotype reconstruction identified a founder haplotype (Hap A) in 36 patients carrying only the 6 common SNVs indicated above, and 10 other haplotypes (Hap BK) derived from Hap A in the remaining 10 patients. Notably, no significant association between a specific viral haplotype and clinical parameters was found. CONCLUSION: The predominant viral strain responsible for the infection in a nursing home in Calabria was the B.1 lineage (clade G). Viral genomes were classified into 11 haplotypes (Hap A in 36 patients, Hap BK in the remaining patients).

Properties of Weir and Cockerham's Fst estimators and associated bootstrap confidence intervals.

Weir and Cockerham introduced single locus and multiloci F(st) estimators for the parameter θ. These estimators are commonly used, but little beyond their bias and variance is known. In this work, we develop formulas that allow us to describe how the underlying value of θ and the genetic diversity of sampled loci affect the distributions of these estimators. We show that in certain settings, these estimators are close to normal, while in others they are far from normal. We use these results to analyze confidence interval construction for θ, showing that the percentile-t bootstrap works well while the BCa bootstrap works poorly. Our results are derived using a novel coalescent based method.

The distribution of Fst and other genetic statistics for a class of population structure models.

We examine genetic statistics used in the study of structured populations. In a 1999 paper, Wakeley observed that the coalescent process associated with the finite island model can be decomposed into a scattering phase and a collecting phase. This decomposition becomes exact in the large population limit with the coalescent at the end of the scattering phase converging to the Ewens sampling formula and the coalescent during the collecting phase converging to the Kingman coalescent. In this paper we introduce a class of limiting models, which we refer to as G/KC models, that generalize Wakeley's decomposition. G in G/KC represents a completely general limit for the scattering phase, while KC represents a Kingman coalescent limit for the collecting phase. We show that both the island and two-dimensional stepping stone models converge to G/KC models in the large population limit. We then derive the distribution of the statistic F(st) for all G/KC models under a large sample limit for the cases of strong or weak mutation, thereby deriving the large population, large sample limiting distribution of F(st) for the island and two-dimensional stepping stone models as a special case of a general formula. Our methods allow us to take the large population and large sample limits simultaneously. In the context of large population, large sample limits, we show that the variance of F(st) in the presence of weak mutation collapses as O(1/log d) where d is the number of demes sampled. Further, we show that this O(1/log d) is caused by a heavy tail in the distribution of F(st). Our analysis of F(st) can be extended to an entire class of genetic statistics, and we use our approach to examine homozygosity measures. Our analysis uses coalescent based methods.

Interferon stimulated binding of ISRE is cell type specific and is predicted by homeostatic chromatin state.

The type I interferon (IFN) signaling pathway involves binding of the transcription factor ISGF3 to IFN-stimulated response elements, ISREs. Gene expression under IFN stimulation is known to vary across cell types, but variation in ISGF3 binding to ISRE across cell types has not been characterized. We examined ISRE binding patterns under IFN stimulation across six cell types using existing ChIPseq datasets. We find that ISRE binding is largely cell specific for ISREs distal to transcription start sites (TSS) and largely conserved across cell types for ISREs proximal to TSS. We show that bound ISRE distal to TSS associate with differential expression of ISGs, although at weaker levels than bound ISRE proximal to TSS. Using existing ATACseq and ChIPseq datasets, we show that the chromatin state of ISRE at homeostasis is cell type specific and is predictive of cell specific, ISRE binding under IFN stimulation. Our results support a model in which the chromatin state of ISRE in enhancer elements is modulated in a cell type specific manner at homeostasis, leading to cell type specific differences in ISRE binding patterns under IFN stimulation.

Tree-Based Co-Clustering Identifies Chromatin Accessibility Patterns Associated With Hematopoietic Lineage Structure.

Chromatin accessibility, as measured by ATACseq, varies between hematopoietic cell types in different lineages of the hematopoietic differentiation tree, e.g. T cells vs. B cells, but methods that associate variation in chromatin accessibility to the lineage structure of the differentiation tree are lacking. Using an ATACseq dataset recently published by the ImmGen consortium, we construct associations between chromatin accessibility and hematopoietic cell types using a novel co-clustering approach that accounts for the structure of the hematopoietic, differentiation tree. Under a model in which all loci and cell types within a co-cluster have a shared accessibility state, we show that roughly 80% of cell type associated accessibility variation can be captured through 12 cell type clusters and 20 genomic locus clusters, with the cell type clusters reflecting coherent components of the differentiation tree. Using publicly available ChIPseq datasets, we show that our clustering reflects transcription factor binding patterns with implications for regulation across cell types. We show that traditional methods such as hierarchical and kmeans clusterings lead to cell type clusters that are more dispersed on the tree than our tree-based algorithm. We provide a python package, chromcocluster, that implements the algorithms presented.

SIV infection duration largely determines broadening of neutralizing antibody response in macaques.

The development of broadly neutralizing antibodies (BNAbs) in HIV infection is a result of long-term coevolutionary interaction between viruses and antibodies. Understanding how this interaction promotes the increase of neutralization breadth during infection will improve the way in which AIDS vaccine strategies are designed. In this paper, we used SIV-infected rhesus macaques as a model to study the development of neutralization breadth by infecting rhesus macaques with longitudinal NAb escape variants and evaluating the kinetics of NAb response and viral evolution. We found that the infected macaques developed a stepwise NAb response against escape variants and increased neutralization breadth during the course of infection. Furthermore, the increase of neutralization breadth correlated with the duration of infection but was independent of properties of the inoculum, viral loads, or viral diversity during infection. These results imply that the duration of infection was the main factor driving the development of BNAbs. These data suggest the importance of novel immunization strategies to induce effective NAb response against HIV infection by mimicking long-term infection.

Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection.

Secretion of type I interferons (IFN) by infected cells mediates protection against many viruses, but prolonged or excessive type I IFN secretion can lead to immune pathology. A proper type I IFN response must therefore maintain a balance between protection and excessive IFN secretion. It has been widely noted that the type I IFN response is driven by positive feedback and is heterogeneous, with only a fraction of infected cells upregulating IFN expression even in clonal cell lines, but the functional roles of feedback and heterogeneity in balancing protection and excessive IFN secretion are not clear. To investigate the functional roles for feedback and heterogeneity, we constructed a mathematical model coupling IFN and viral dynamics that extends existing mathematical models by accounting for feedback and heterogeneity. We fit our model to five existing datasets, reflecting different experimental systems. Fitting across datasets allowed us to compare the IFN response across the systems and suggested different signatures of feedback and heterogeneity in the different systems. Further, through numerical experiments, we generated hypotheses of functional roles for IFN feedback and heterogeneity consistent with our mathematical model. We hypothesize an inherent tradeoff in the IFN response: a positive feedback loop prevents excessive IFN secretion, but also makes the IFN response vulnerable to viral antagonism. We hypothesize that cellular heterogeneity of the IFN response functions to protect the feedback loop from viral antagonism. Verification of our hypotheses will require further experimental studies. Our work provides a basis for analyzing the type I IFN response across systems.

Computational inference methods for selective sweeps arising in acute HIV infection.

During the first weeks of human immunodeficiency virus-1 (HIV-1) infection, cytotoxic T-lymphocytes (CTLs) select for multiple escape mutations in the infecting HIV population. In recent years, methods that use escape mutation data to estimate rates of HIV escape have been developed, thereby providing a quantitative framework for exploring HIV escape from CTL response. Current methods for escape-rate inference focus on a specific HIV mutant selected by a single CTL response. However, recent studies have shown that during the first weeks of infection, CTL responses occur at one to three epitopes and HIV escape occurs through complex mutation pathways. Consequently, HIV escape from CTL response forms a complex, selective sweep that is difficult to analyze. In this work, we develop a model of initial infection, based on the well-known standard model, that allows for a description of multi-epitope response and the complex mutation pathways of HIV escape. Under this model, we develop Bayesian and hypothesis-test inference methods that allow us to analyze and estimate HIV escape rates. The methods are applied to two HIV patient data sets, concretely demonstrating the utility of our approach.