Associations between phylogenetic clustering and HLA profile among HIV-infected individuals in San Diego, California

Molecular cluster, transmission characteristics, origin and dynamics analysis of HIV-1 CRF59_01B in China: A molecular epidemiology study

PURPOSE

This study investigated for the HIV-1 CRF59_01B epidemic's spatiotemporal dynamics and its transmission networks in China.

METHODS

Between 2007 and 2020, a total of 250 partial pol gene sequences of HIV-1 CRF59_01B were collected from four regions (10 Chinese provinces). Phylogenetic tree construction and cluster identification were then performed. The Bayesian skyline and birth-death susceptible-infected-removed models were employed for the phylodynamic analyses of subtypes and large clusters, respectively. Phylogenetic analyses and trait diffusion of these sequences were performed using Bayesian phylogenetic methods (beast-classic package). Distance-based molecular network analyses were performed to identify putative relationships.

RESULTS

Using a genetic distance threshold of 1.3 %, We identified 45 clusters that included 62.40 % (156/250) of the sequences. Three clusters (6.67 %, 3/45) had 10 or more sequences, and were considered "large clusters". Six clusters (13.33 %) included sequences from different regions (Southeast, Northeast, Southeast, and Central China). Thirteen clusters (28.89 %) included sequences of men who had sex with men only, three clusters (6.67 %) included sequences of heterosexuals only, and 12 clusters (26.67 %) included sequences of both groups. The substitution rate of CRF59_01B was 1.91 × 10-3 substitutions per site per year [95 % highest posterior density (HPD) interval: 1.39 × 10-3-2.49 × 10-3)], the time to the most recent common ancestor of CRF59_01B was to be 1992.83 (95 % HPD: 1977.97-2002.81). A Bayesian skyline plot revealed that the effective population size of CRF59_01B increased from 2000 to 2015 and remained stable after 2015. The large clusters showed continuous growth from 2013 to 2020. Phylogeographic analysis showed that CRF59_01B B most likely originated in Southeast China, with a posterior probability of 97.44 %, and then spread to other regions.

CONCLUSIONS

Our study revealed the temporal and geographical origins of HIV-1 CRF59_01B as well as the process of transmission among various regions and risk groups in China, which can help develop targeted HIV prevention strategies.

Factors influencing HIV-1 phylogenetic clustering

PURPOSE OF REVIEW

A major goal of public health in relation to HIV/AIDS is to prevent new transmissions in communities. Phylogenetic techniques have improved our understanding of the structure and dynamics of HIV transmissions. However, there is still no consensus about phylogenetic methodology, sampling coverage, gene target and/or minimum fragment size.

RECENT FINDINGS

Several studies use a combined methodology, which includes both a genetic or patristic distance cut-off and a branching support threshold to identify phylogenetic clusters. However, the choice about these thresholds remains an inherently subjective process, which affects the results of these studies. There is still a lack of consensus about the genomic region and the size of fragments that should be used, although there seems to be emerging a consensus that using longer segments, allied with the use of a realistic model of evolution and a codon alignment, increases the likelihood of inferring true transmission clusters. The pol gene is still the most used genomic region, but recent studies have suggested that whole genomes and/or sequences from nef and gp41 are also good targets for cluster reconstruction.

SUMMARY

The development and application of standard methodologies for phylogenetic clustering analysis will advance our understanding of factors associated with HIV transmission. This will lead to the design of more precise public health interventions.

TreeCluster: Clustering biological sequences using phylogenetic trees

Clustering homologous sequences based on their similarity is a problem that appears in many bioinformatics applications. The fact that sequences cluster is ultimately the result of their phylogenetic relationships. Despite this observation and the natural ways in which a tree can define clusters, most applications of sequence clustering do not use a phylogenetic tree and instead operate on pairwise sequence distances. Due to advances in large-scale phylogenetic inference, we argue that tree-based clustering is under-utilized. We define a family of optimization problems that, given an arbitrary tree, return the minimum number of clusters such that all clusters adhere to constraints on their heterogeneity. We study three specific constraints, limiting (1) the diameter of each cluster, (2) the sum of its branch lengths, or (3) chains of pairwise distances. These three problems can be solved in time that increases linearly with the size of the tree, and for two of the three criteria, the algorithms have been known in the theoretical computer scientist literature. We implement these algorithms in a tool called TreeCluster, which we test on three applications: OTU clustering for microbiome data, HIV transmission clustering, and divide-and-conquer multiple sequence alignment. We show that, by using tree-based distances, TreeCluster generates more internally consistent clusters than alternatives and improves the effectiveness of downstream applications. TreeCluster is available at https://github.com/niemasd/TreeCluster.

HIV-1 genetic transmission networks among men who have sex with men in Kunming, China

BACKGROUND

Yunnan has the greatest share of reported human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) cases in China. In recent years, HIV prevalence and incidence remained stubbornly high in men who have sex with men (MSM). To follow the dynamics of the HIV-1 epidemic among MSM, HIV-1 genetic characteristics and genetic transmission networks were investigated.

METHODS

Blood samples from 190 newly diagnosed HIV-1 cases among MSM were continuously collected at fixed sites from January 2013 to December 2015 in Kunming City, Yunnan Province. Partial gag, pol and env genes were sequenced and used for phylogenetic and genotypic drug resistance analyses. The genetic characteristics of the predominant HIV-1 strains were analyzed by the Bayesian Markov Chain Monte Carlo (MCMC) method. The genetic transmission networks were identified with a genetic distance of 0.03 substitutions/site and 90% bootstrap support.

RESULTS

Among the 190 HIV-1 positive MSM reported during 2013-2105, various genotypes were identified, including CRF01_AE (45.3%), CRF07_BC (35.8%), unique recombinant forms (URFs) (11.6%), CRF08_BC (3.2%), CRF55_01B (2.1%), subtype B (1.6%) and CRF59_01B (0.5%). The effective population sizes (EPS) for CRF01_AE and CRF07_BC increased exponentially from approximately 2001-2010 and 2005-2009, respectively. Genetic transmission networks were constructed with 308 pol sequences from MSM diagnosed during 2010-2015. Of the 308 MSM, 109 (35.4%) were identified in 38 distinct clusters. Having multiple male partners was associated with a high probability of identification in the genetic transmission networks. Of the 38 clusters, 27 (71.1%) contained individuals diagnosed in different years. Of the 109 individuals in the networks, 26 (23.9%) had ≥2 potential transmission partners (≥2 links). The proportion of MSM with ≥2 links was higher among those diagnosed from 2010-2012. The constituent ratios of their potential transmission partners by areas showed no significant difference among MSM from Kunming, other cities in Yunnan and other provinces. Additionally, surveillance drug resistance mutations (SDRMs) were identified in 5% of individuals.

CONCLUSION

This study revealed the various HIV-a genotypes circulating among MSM in Kunming. MSM with more partners were more easily detected in transmission networks, and early-diagnosed MSM remained active in transmission networks. These findings suggested that the routine interventions should be combined with HIV testing and linkage to care and early antiretroviral therapy among HIV-positive MSM.

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

Transmission clustering among newly diagnosed HIV patients in Chicago, 2008 to 2011: using phylogenetics to expand knowledge of regional HIV transmission patterns

INTRODUCTION

HIV transmission cluster analyses can inform HIV prevention efforts. We describe the first such assessment for transmission clustering among HIV patients in Chicago.

METHODS

We performed transmission cluster analyses using HIV pol sequences from newly diagnosed patients presenting to Chicago's largest HIV clinic between 2008 and 2011. We compared sequences through progressive pairwise alignment, using neighbor joining to construct an unrooted phylogenetic tree. We defined clusters as >2 sequences among which each sequence had at least 1 partner within a genetic distance of ≤1.5%. We used multivariable regression to examine factors associated with clustering and used geospatial analysis to assess geographic proximity of phylogenetically clustered patients.

RESULTS

We compared sequences from 920 patients, median age of 35 years, 75% male, 67% black, 23% Hispanic, and 8% had a rapid plasma reagin titer ≥1:16 concurrent with their HIV diagnosis. We had HIV transmission risk data for 54%; 43% identified as men who have sex with men (MSM). Phylogenetic analysis demonstrated 123 patients (13%) grouped into 26 clusters, the largest having 20 members. In multivariable regression, age <25, black race, MSM status, male gender, higher HIV viral load, and rapid plasma reagin ≥1:16 associated with clustering. We did not observe geographic grouping of genetically clustered patients.

DISCUSSION

Our results demonstrate high rates of HIV transmission clustering, without local geographic foci, among young black MSM in Chicago. Applied prospectively, phylogenetic analyses could guide prevention efforts and help break the cycle of transmission.

Hepatitis C virus infection is not an independent risk factor for obstructive lung disease

Several epidemiological studies have suggested that hepatitis C virus (HCV) infection is associated with the presence of obstructive lung disease (OLD). However, there is a strong link between HCV infection and tobacco abuse, a major risk factor for the development of OLD. In this study we analyzed clinical, laboratory and spirometric data from 1068 study participants to assess whether HCV infection, viremia, or HCV-associated end organ damage were associated with OLD. Demographics, risk behavior, serologic status for HCV and HIV, and spirometric measurements were collected from a cross-sectional analysis of the Acquired Immunodeficiency Syndrome (AIDS) Linked to the IntraVenous Experience (ALIVE) study, an observational cohort of IDUs followed in Baltimore, MD since 1988. Of 1,068 participants, 890 (83%) were HCV positive and 174 (16%) met spirometric criteria for OLD. Factors independently associated with OLD were age and BMI. HCV infection, viral load and HCV-associated end organ damage were similar in participants with and without OLD. In summary, there was no independent association between markers of HCV exposure, chronicity, viremia, or HCV-associated end-organ damage with OLD. Our findings support the strong correlation between HCV status, injection drug use, and smoking. These data suggest that HCV may not be a sole contributor to the increased prevalence of OLD described in previous studies of HCV-infected individuals.

Linkage of viral sequences among HIV-infected village residents in Botswana: estimation of linkage rates in the presence of missing data

Linkage analysis is useful in investigating disease transmission dynamics and the effect of interventions on them, but estimates of probabilities of linkage between infected people from observed data can be biased downward when missingness is informative. We investigate variation in the rates at which subjects' viral genotypes link across groups defined by viral load (low/high) and antiretroviral treatment (ART) status using blood samples from household surveys in the Northeast sector of Mochudi, Botswana. The probability of obtaining a sequence from a sample varies with viral load; samples with low viral load are harder to amplify. Pairwise genetic distances were estimated from aligned nucleotide sequences of HIV-1C env gp120. It is first shown that the probability that randomly selected sequences are linked can be estimated consistently from observed data. This is then used to develop estimates of the probability that a sequence from one group links to at least one sequence from another group under the assumption of independence across pairs. Furthermore, a resampling approach is developed that accounts for the presence of correlation across pairs, with diagnostics for assessing the reliability of the method. Sequences were obtained for 65% of subjects with high viral load (HVL, n = 117), 54% of subjects with low viral load but not on ART (LVL, n = 180), and 45% of subjects on ART (ART, n = 126). The probability of linkage between two individuals is highest if both have HVL, and lowest if one has LVL and the other has LVL or is on ART. Linkage across groups is high for HVL and lower for LVL and ART. Adjustment for missing data increases the group-wise linkage rates by 40–100%, and changes the relative rates between groups. Bias in inferences regarding HIV viral linkage that arise from differential ability to genotype samples can be reduced by appropriate methods for accommodating missing data.

The analysis of viral genomes has great potential for investigating transmission of disease, including the identification of risk factors and transmission clusters, and can thereby aid in targeting interventions. To make use of genetic data in this way, it is necessary to make inferences about population-level patterns of viral linkage. As with any rigorous statistical inference from sampled data to a population, it is important to consider the effect of the sampling strategy and the occurrence of missing data on the final inferences made. In this paper we highlight the effects of missing data on the resulting estimates of population level linkage rates and develop methods for adjusting for the presence of missing data. As an example, we consider comparing the rates of linkage of HIV sequences from subjects with high viral load, low viral load, or on antiretroviral treatment, and show that comparative inferences are compromised when adjustment is not made for missing sequences and bias in inferences can be reduced with proper adjustment.

Automated analysis of phylogenetic clusters

BACKGROUND

As sequence data sets used for the investigation of pathogen transmission patterns increase in size, automated tools and standardized methods for cluster analysis have become necessary. We have developed an automated Cluster Picker which identifies monophyletic clades meeting user-input criteria for bootstrap support and maximum genetic distance within large phylogenetic trees. A second tool, the Cluster Matcher, automates the process of linking genetic data to epidemiological or clinical data, and matches clusters between runs of the Cluster Picker.

RESULTS

We explore the effect of different bootstrap and genetic distance thresholds on clusters identified in a data set of publicly available HIV sequences, and compare these results to those of a previously published tool for cluster identification. To demonstrate their utility, we then use the Cluster Picker and Cluster Matcher together to investigate how clusters in the data set changed over time. We find that clusters containing sequences from more than one UK location at the first time point (multiple origin) were significantly more likely to grow than those representing only a single location.

CONCLUSIONS

The Cluster Picker and Cluster Matcher can rapidly process phylogenetic trees containing tens of thousands of sequences. Together these tools will facilitate comparisons of pathogen transmission dynamics between studies and countries.

An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles

Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications of bioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in a genome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. These methods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identify the influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Here we propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derive the basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to target genomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogenetic relationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in the current analysis. We compared our approach with two existing methods and our initial results show that the predictions have outperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-protein interaction network derived from protein-protein interaction database STRING. Our preliminary results indicates that improvement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the same scale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotating hypothetical proteins from prokaryotic genomes.