An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1

Deep learning guided prediction modeling of dengue virus evolving serotype

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63-65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.

The Role of Late Presenters in HIV-1 Transmission Clusters in Europe

BACKGROUND

Investigating the role of late presenters (LPs) in HIV-1 transmission is important, as they can contribute to the onward spread of HIV-1 virus before diagnosis, when they are not aware of their HIV status.

OBJECTIVE

To characterize individuals living with HIV-1 followed up in Europe infected with subtypes A, B, and G and to compare transmission clusters (TC) in LP vs. non-late presenter (NLP) populations.

METHODS

Information from a convenience sample of 2679 individuals living with HIV-1 was collected from the EuResist Integrated Database between 2008 and 2019. Maximum likelihood (ML) phylogenies were constructed using FastTree. Transmission clusters were identified using Cluster Picker. Statistical analyses were performed using R.

RESULTS

2437 (91.0%) sequences were from subtype B, 168 (6.3%) from subtype A, and 74 (2.8%) from subtype G. The median age was 39 y/o (IQR: 31.0-47.0) and 85.2% of individuals were males. The main transmission route was via homosexual (MSM) contact (60.1%) and 85.0% originated from Western Europe. In total, 54.7% of individuals were classified as LPs and 41.7% of individuals were inside TCs. In subtype A, individuals in TCs were more frequently males and natives with a recent infection. For subtype B, individuals in TCs were more frequently individuals with MSM transmission route and with a recent infection. For subtype G, individuals in TCs were those with a recent infection. When analyzing cluster size, we found that LPs more frequently belonged to small clusters (<8 individuals), particularly dual clusters (2 individuals).

CONCLUSION

LP individuals are more present either outside or in small clusters, indicating a limited role of late presentation to HIV-1 transmission.

High-Level Drug-Resistant Mutations among HIV-1 Subtype A6 and CRF02_AG in Kazakhstan

HIV incidence in Kazakhstan increased by 73% between 2010 and 2020, with an estimated 35,000 people living with HIV (PLHIV) in 2020. The development of antiretroviral drug resistance is a major threat to effective antiretroviral therapy (ART), yet studies on the prevalence of drug resistance in Kazakhstan are sparse. In this study on the molecular epidemiology of HIV in Kazakhstan, we analyzed 968 partial HIV-1 pol sequences that were collected between 2017 and 2020 from PLHIV across all regions of Kazakhstan, covering almost 3% of PLHIV in 2020. Sequences predominantly represented subtypes A6 (57%) and CRF02_AG (41%), with 32% of sequences exhibiting high-level drug resistance. We further identified distinct drug-resistant mutations (DRMs) in the two subtypes: subtype A6 showed a propensity for DRMs A62V, G190S, K101E, and D67N, while CRF02_AG showed a propensity for K103N and V179E. Codon usage analysis revealed that different mutational pathways for the two subtypes may explain the difference in G190S and V179E frequencies. Phylogenetic analysis highlighted differences in the timing and geographic spread of both subtypes within the country, with A62V-harboring subtype A6 sequences clustering on the phylogeny, indicative of sustained transmission of the mutation. Our findings suggest an HIV epidemic characterized by high levels of drug resistance and differential DRM frequencies between subtypes. This emphasizes the importance of drug resistance monitoring within Kazakhstan, together with DRM and subtype screening at diagnosis, to tailor drug regimens and provide effective, virally suppressive ART.

A large population sample of African HIV genomes from the 1980s reveals a reduction in subtype D over time associated with propensity for CXCR4 tropism

We present 109 near full-length HIV genomes amplified from blood serum samples obtained during early 1986 from across Uganda, which to our knowledge is the earliest and largest population sample from the initial phase of the HIV epidemic in Africa. Consensus sequences were made from paired-end Illumina reads with a target-capture approach to amplify HIV material following poor success with standard approaches. In comparisons with a smaller 'intermediate' genome dataset from 1998 to 1999 and a 'modern' genome dataset from 2007 to 2016, the proportion of subtype D was significantly higher initially, dropping from 67% (73/109), to 57% (26/46) to 17% (82/465) respectively (p < 0.0001). Subtype D has previously been shown to have a faster rate of disease progression than other subtypes in East African population studies, and to have a higher propensity to use the CXCR4 co-receptor ("X4 tropism"); associated with a decrease in time to AIDS. Here we find significant differences in predicted tropism between A1 and D subtypes in all three sample periods considered, which is particularly striking the 1986 sample: 66% (53/80) of subtype D env sequences were predicted to be X4 tropic compared with none of the 24 subtype A1. We also analysed the frequency of subtype in the envelope region of inter-subtype recombinants, and found that subtype A1 is over-represented in env, suggesting recombination and selection have acted to remove subtype D env from circulation. The reduction of subtype D frequency over three decades therefore appears to be a result of selective pressure against X4 tropism and its higher virulence. Lastly, we find a subtype D specific codon deletion at position 24 of the V3 loop, which may explain the higher propensity for subtype D to utilise X4 tropism.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

Detection of HIV-1 Transmission Clusters from Dried Blood Spots within a Universal Test-and-Treat Trial in East Africa

The Sustainable East Africa Research in Community Health (SEARCH) trial was a universal test-and-treat (UTT) trial in rural Uganda and Kenya, aiming to lower regional HIV-1 incidence. Here, we quantify breakthrough HIV-1 transmissions occurring during the trial from population-based, dried blood spot samples. Between 2013 and 2017, we obtained 549 gag and 488 pol HIV-1 consensus sequences from 745 participants: 469 participants infected prior to trial commencement and 276 SEARCH-incident infections. Putative transmission clusters, with a 1.5% pairwise genetic distance threshold, were inferred from maximum likelihood phylogenies; clusters arising after the start of SEARCH were identified with Bayesian time-calibrated phylogenies. Our phylodynamic approach identified nine clusters arising after the SEARCH start date: eight pairs and one triplet, representing mostly opposite-gender linked (6/9), within-community transmissions (7/9). Two clusters contained individuals with non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, both linked to intervention communities. The identification of SEARCH-incident, within-community transmissions reveals the role of unsuppressed individuals in sustaining the epidemic in both arms of a UTT trial setting. The presence of transmitted NNRTI resistance, implying treatment failure to the efavirenz-based antiretroviral therapy (ART) used during SEARCH, highlights the need to improve delivery and adherence to up-to-date ART recommendations, to halt HIV-1 transmission.

HIV-1 Subtype Shift in the Philippines is Associated with High Transmitted Drug Resistance, High Viral Loads and Fast Immunologic Decline

BACKGROUND

The Philippines has one of the fastest growing HIV epidemics. A subtype shift from B to CRF01_AE may have contributed to the rise in cases. We undertook a genotyping and transmitted drug resistance (TDR) study to determine if the dominant subtype has any advantages in resistance and transmission.

METHODS

Treatment-naive Filipinos living with HIV were recruited from two large government treatment hubs March 2016 to August 2018. HIV-1 viral load, CD4 count, genotyping and TDR testing were performed. Demographic and clinical data was collected and compared across subtypes.

RESULTS

Two hundred ninety-eight Filipino PLHIV were recruited. Median CD4 count was 143 cells/µL and HIV viral load was 2,345,431 copies/mL. Sanger-based sequencing showed 230/298 (77.2%) had subtype CRF01_AE, 41 (13.8%) subtype B, and the rest other subtypes or recombinants. Overall TDR was 11.7%. TDR was associated with lower viral loads and no previous HIV testing. CRF01_AE had a higher likelihood of a viral load >100,000 copies/mL and having a baseline CD4 count <50 cells/mm3.

CONCLUSIONS

TDR in the Philippines is high at 11.7%. CRF01_AE was observed to have a higher baseline viral load and lower CD4 counts compared to other co-circulating subtypes. Further research needs to confirm this observation since it suggests that CRF01_AE may have a survival advantage that led to replacement of subtype B as the dominant subtype. Drug-resistance testing is recommended in the Philippines when initiating NNRTI-based anti-retroviral therapy but may not be necessary for INSTI-based regimens.

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection

Recombination is a major mechanism through which HIV type 1 (HIV-1) maintains genetic diversity and interferes with viral eradication efforts. There is growing evidence demonstrating a recombinant origin of primate lentiviruses including HIV-1 group M (HIV-1/M). Inferring the extent of recombination across the entire HIV-1/M genome is of great importance as it provides deeper insights into the origin, dynamics, and evolution of the global pandemic. Here we propose an alternative method that can reconstruct the extent of genome-wide recombination in HIV-1, uncover reticulate patterns, and serve as a framework for HIV-1 classification. Our method provides an alternative approach for understanding the roles of virus recombination in the early evolutionary history of zoonosis for other emerging viruses.

The prevailing abundance of full-length HIV type 1 (HIV-1) genome sequences provides an opportunity to revisit the standard model of HIV-1 group M (HIV-1/M) diversity that clusters genomes into largely nonrecombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for simian immunodeficiency virus (SIV) and other HIV-1 groups. Here we develop an unsupervised nonparametric clustering approach, which does not rely on predefined nonrecombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (dynamic stochastic block model [DSBM]) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial generalized linear model (GLM), P<8×10−8), compared to other reference-free recombination detection programs (genetic algorithm for recombination detection [GARD], recombination detection program 4 [RDP4], and RDP5). When this method was applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 29 as the optimal number of DSBM clusters and used change-point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and undocumented recombination hotspots in the HIV-1 genome and evidence of intersubtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative framework for HIV-1 classification.

Network science inspires novel tree shape statistics

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree’s shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.

Identification, evolutionary analysis and functional diversification of RAV gene family in cotton (G. hirsutum L.)

Genome wide analysis, expression pattern analysis, and functional characterization of RAV genes highlight their roles in roots, stem development and hormonal response. RAV (Related to ABI3 and VP1) gene family members have been involved in tissues/organs growth and hormone signaling in various plant species. Here, we identified 247 RAVs from 12 different species with 33 RAV genes from G. hirsutum. Phylogenetic analysis classified RAV genes into four distinct groups. Analysis of gene structure showed that most GhRAVs lack introns. Motif distribution pattern and protein sequence logos indicated that GhRAV genes were highly conserved during the process of evolution. Promotor cis-acting elements revealed that promotor regions of GhRAV genes encode numerous elements related to plant growth, abiotic stresses and phytohormones. Chromosomal location information showed uneven distribution of 33 GhRAV genes on different chromosomes. Collinearity analysis identified 628 and 52 orthologous/ paralogous gene pairs in G. hirsutum and G. barbadense, respectively. Ka/Ks values indicated that GhRAV and GbRAV genes underwent strong purifying selection pressure. Selecton model and codon model selection revealed that GhRAV amino acids were under purifying selection and adaptive evolution exists among GhRAV proteins. Three dimensional structure of GhRAVs indicated the presence of numerous alpha helix and beta-barrels. Expression level revealed that some GhRAV genes exhibited high expression in roots (GhRAV3, GhRAV4, GhRAV11, GhRAV18, GhRAV20 and GhRAV30) and stem (GhRAV3 and GhRAV18), indicating their potential role in roots and stem development. GhRAV genes can be regulated by phytohormonal stresses (BL, JA and IAA). Our study provides a reference for future studies related to the functional analysis of GhRAVs in cotton.

Evolutionary dynamics of HIV-1 subtype C in Brazil

The extensive genetic diversity of HIV-1 is a major challenge for the prevention and treatment of HIV-1 infections. Subtype C accounts for most of the HIV-1 infections in the world but has been mainly localized in Southern Africa, Ethiopia and India. For elusive reasons, South Brazil harbors the largest HIV-1 subtype C epidemic in the American continent that is elsewhere dominated by subtype B. To investigate this topic, we collected clinical data and viral sequences from 2611 treatment-naïve patients diagnosed with HIV-1 in Brazil. Molecular epidemiology analysis supported 35 well-delimited transmission clusters of subtype C highlighting transmission within South Brazil but also from the South to all other Brazilian regions and internationally. Individuals infected with subtype C had lower probability to be deficient in CD4⁺ T cells when compared to subtype B. The HIV-1 epidemics in the South was characterized by high female-to-male infection ratios and women-to-child transmission. Our results suggest that HIV-1 subtype C probably takes advantage of longer asymptomatic periods to maximize transmission and is unlikely to outcompete subtype B in settings where the infection of women is relatively less relevant. This study contributes to elucidate factors possibly underlying the geographical distribution and expansion patterns of the most spread HIV-1 subtypes.

Molecular epidemiology and HIV-1 variant evolution in Poland between 2015 and 2019

The occurrence of HIV-1 subtypes differs worldwide and within Europe, with non-B variants mainly found across different exposure groups. In this study, we investigated the distribution and temporal trends in HIV-1 subtype variability across Poland between 2015 and 2019. Sequences of the pol gene fragment from 2518 individuals were used for the analysis of subtype prevalence. Subtype B was dominant (n = 2163, 85.90%). The proportion of subtype B-infected individuals decreased significantly, from 89.3% in 2015 to 80.3% in 2019. This was related to the increasing number of subtype A infections. In 355 (14.10%) sequences, non-B variants were identified. In 65 (2.58%) samples, recombinant forms (RFs) were noted. Unique recombinant forms (URFs) were found in 30 (1.19%) sequences. Three A/B recombinant clusters were identified of which two were A6/B mosaic viruses not previously described. Non-B clades were significantly more common among females (n = 81, 22.8%, p = 0.001) and heterosexually infected individuals (n = 45, 32.4%, p = 0.0031). The predominance of subtype B is evident, but the variability of HIV-1 in Poland is notable. Almost half of RFs (n = 65, 2.58%) was comprised of URFs (n = 30, 1.19%); thus those forms were common in the analyzed population. Hence, molecular surveillance of identified variants ensures recognition of HIV-1 evolution in Poland.

Taxonomy classification using genomic footprint of mitochondrial sequences

BACKGROUND

Advancement in the sequencing technology yields a huge number of genomes of a multitude of organisms in our planet. One of the fundamental tasks for processing and analyzing these sequences is to organize them in the existing taxonomic orders. <P> Method: Recently we proposed a novel approach, GenFooT, of taxonomy classification using the concept of genomic footprint (GFP). The technique is further refined and enhanced in this work leading to improved accuracies in the task of taxonomic classification on various benchmark datasets. GenFooT maps a genome sequence in a 2D coordinate space and extracts features from that representation. It uses two hyper-parameters, namely block size and number of fragments of genomic sequence while computing the feature. In this work, we propose an analysis for choosing values of those parameters adaptively from the sequences. The enhanced version of GenFooT is named GenFooT2. <P> Results and Conclusion: We have experimented GenFooT2 on ten different biological datasets of genomic sequences of various organisms belonging to different taxonomy ranks. Our experimental results indicate more than 3% improved classification performance of the proposed features with Logistic regression classifier than the GenFooT. We also performed the statistical test to compare the performance of GenFooT2 with the state-of-the-art methods including our previous method GenFooT. The experimental results as well as the statistical test exhibit that the performance of the proposed GenFooT2 is significantly better.

Explainable deep neural networks for novel viral genome prediction

Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as ‘unknown’ by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.

Accumulation of integrase strand transfer inhibitor resistance mutations confers high-level resistance to dolutegravir in non-B subtype HIV-1 strains from patients failing raltegravir in Uganda

BACKGROUND

Increasing first-line treatment failures in low- and middle-income countries (LMICs) have led to increased use of integrase strand transfer inhibitors (INSTIs) such as dolutegravir. However, HIV-1 susceptibility to INSTIs in LMICs, especially with previous raltegravir exposure, is poorly understood due to infrequent reporting of INSTI failures and testing for INSTI drug resistance mutations (DRMs).

METHODS

A total of 51 non-subtype B HIV-1 infected patients failing third-line (raltegravir-based) therapy in Uganda were initially selected for the study. DRMs were detected using Sanger and deep sequencing. HIV integrase genes of 13 patients were cloned and replication capacities (RCs) and phenotypic susceptibilities to dolutegravir, raltegravir and elvitegravir were determined with TZM-bl cells. Spearman's correlation coefficient was used to determine cross-resistance between INSTIs.

RESULTS

INSTI DRMs were detected in 47% of patients. HIV integrase-recombinant virus carrying one primary INSTI DRM (N155H or Y143R/S) was susceptible to dolutegravir but highly resistant to raltegravir and elvitegravir (>50-fold change). Two patients, one with E138A/G140A/Q148R/G163R and one with E138K/G140A/S147G/Q148K, displayed the highest reported resistance to raltegravir, elvitegravir and even dolutegravir. The former multi-DRM virus had WT RC whereas the latter had lower RCs than WT.

CONCLUSIONS

In HIV-1 subtype A- and D-infected patients failing raltegravir and harbouring INSTI DRMs, there is high-level resistance to elvitegravir and raltegravir. More routine monitoring of INSTI treatment may be advised in LMICs, considering that multiple INSTI DRMs may have accumulated during prolonged exposure to raltegravir during virological failure, leading to high-level INSTI resistance, including dolutegravir resistance.

Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning

MOTIVATION

Infection with strains of different subtypes and the subsequent crossover reading between the two strands of genomic RNAs by host cells' reverse transcriptase are the main causes of the vast HIV-1 sequence diversity. Such inter-subtype genomic recombinants can become circulating recombinant forms (CRFs) after widespread transmissions in a population. Complete prediction of all the subtype sources of a CRF strain is a complicated machine learning problem. It is also difficult to understand whether a strain is an emerging new subtype and if so, how to accurately identify the new components of the genetic source.

RESULTS

We introduce a multi-label learning algorithm for the complete prediction of multiple sources of a CRF sequence as well as the prediction of its chronological number. The prediction is strengthened by a voting of various multi-label learning methods to avoid biased decisions. In our steps, frequency and position features of the sequences are both extracted to capture signature patterns of pure subtypes and CRFs. The method was applied to 7185 HIV-1 sequences, comprising 5530 pure subtype sequences and 1655 CRF sequences. Results have demonstrated that the method can achieve very high accuracy (reaching 99%) in the prediction of the complete set of labels of HIV-1 recombinant forms. A few wrong predictions are actually incomplete predictions, very close to the complete set of genuine labels.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Runbin-tang/The-source-of-HIV-CRFs-prediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.

Topological Analysis for Sequence Variability: Case Study on more than 2K SARS-CoV-2 sequences of COVID-19 infected 54 countries in comparison with SARS-CoV-1 and MERS-CoV

The pandemic due to novel coronavirus, SARS-CoV-2 is a serious global concern now. More than thousand new COVID-19 infections are getting reported daily for this virus across the globe. Thus, the medical research communities are trying to find the remedy to restrict the spreading of this virus, while the vaccine development work is still under research in parallel. In such critical situation, not only the medical research community, but also the scientists in different fields like microbiology, pharmacy, bioinformatics and data science are also sharing effort to accelerate the process of vaccine development, virus prediction, forecasting the transmissible probability and reproduction cases of virus for social awareness. With the similar context, in this article, we have studied sequence variability of the virus primarily focusing on three aspects: (a) sequence variability among SARS-CoV-1, MERS-CoV and SARS-CoV-2 in human host, which are in the same coronavirus family, (b) sequence variability of SARS-CoV-2 in human host for 54 different countries and (c) sequence variability between coronavirus family and country specific SARS-CoV-2 sequences in human host. For this purpose, as a case study, we have performed topological analysis of 2391 global genomic sequences of SARS-CoV-2 in association with SARS-CoV-1 and MERS-CoV using an integrated semi-alignment based computational technique. The results of the semi-alignment based technique are experimentally and statistically found similar to alignment based technique and computationally faster. Moreover, the outcome of this analysis can help to identify the nations with homogeneous SARS-CoV-2 sequences, so that same vaccine can be applied to their heterogeneous human population.

Rapid screening and detection of inter-type viral recombinants using Phylo-K-Mers

Motivation

Novel recombinant viruses may have important medical and evolutionary significance, as they sometimes display new traits not present in the parental strains. This is particularly concerning when the new viruses combine fragments coming from phylogenetically distinct viral types. Here, we consider the task of screening large collections of sequences for such novel recombinants. A number of methods already exist for this task. However, these methods rely on complex models and heavy computations that are not always practical for a quick scan of a large number of sequences.

Results

We have developed SHERPAS, a new program to detect novel recombinants and provide a first estimate of their parental composition. Our approach is based on the precomputation of a large database of ‘phylogenetically-informed k-mers’, an idea recently introduced in the context of phylogenetic placement in metagenomics. Our experiments show that SHERPAS is hundreds to thousands of times faster than existing software, and enables the analysis of thousands of whole genomes, or long-sequencing reads, within minutes or seconds, and with limited loss of accuracy.

Availability and implementation

The source code is freely available for download at https://github.com/phylo42/sherpas.

Supplementary information

Supplementary data are available at Bioinformatics online.

You Will Never Walk Alone: Codispersal of JC Polyomavirus with Human Populations

JC polyomavirus (JCPyV) is one of the most prevalent human viruses. Findings based on the geographic distribution of viral subtypes suggested that JCPyV codiverged with human populations. This view was however challenged by data reporting a much more recent origin and expansion of JCPyV. We collected information on ∼1,100 worldwide strains and we show that their geographic distribution roughly corresponds to major human migratory routes. Bayesian phylogeographic analysis inferred a Subsaharan origin for JCPyV, although with low posterior probability. High confidence inference at internal nodes provided strong support for a long-standing association between the virus and human populations. In line with these data, pairwise FST values for JCPyV and human mtDNA sampled from the same areas showed a positive and significant correlation. Likewise, very strong relationships were found when node ages in the JCPyV phylogeny were correlated with human population genetic distances (nuclear-marker based FST). Reconciliation analysis detected a significant cophylogenetic signal for the human population and JCPyV trees. Notably, JCPyV also traced some relatively recent migration events such as the expansion of people from the Philippines/Taiwan area into Remote Oceania, the gene flow between North-Eastern Siberian and Ainus, and the Koryak contribution to Circum-Arctic Americans. Finally, different molecular dating approaches dated the origin of JCPyV in a time frame that precedes human out-of-Africa migration. Thus, JCPyV infected early human populations and accompanied our species during worldwide dispersal. JCPyV typing can provide reliable geographic information and the virus most likely adapted to the genetic background of human populations.

HIV-1 phylodynamic analysis among people who inject drugs in Pakistan correlates with trends in illicit opioid trade

Pakistan is considered by the World Health Organization to currently have a "concentrated" HIV-1 epidemic due to a rapid rise in infections among people who inject drugs (PWID). Prevalence among the country's nearly 105,000 PWID is estimated to be 37.8% but has been shown to be higher in several large urban centers. A lack of public health resources, the common use of professional injectors and unsafe injection practices are believed to have fueled the outbreak. Here we evaluate the molecular characteristics of HIV-1 sequences (n = 290) from PWID in several Pakistani cities to examine transmission dynamics and the association between rates of HIV-1 transmission with regards to regional trends in opioid trafficking. Tip-to-tip (patristic) distance based phylogenetic cluster inferences and BEAST2 Bayesian phylodynamic analyses of time-stamped data were performed on HIV-1 pol sequences generated from dried blood spots collected from 1,453 PWID as part of a cross-sectional survey conducted in Pakistan during 2014/2015. Overall, subtype A1 strains were dominant (75.2%) followed by CRF02_AG (14.1%), recombinants/unassigned (7.2%), CRF35_AD (2.1%), G (1.0%) and C (0.3%). Nearly three quarters of the PWID HIV-1 sequences belonged to one of five distinct phylogenetic clusters. Just below half (44.4%) of individuals in the largest cluster (n = 118) did seek help injecting from professional injectors which was previously identified as a strong correlate of HIV-1 infection. Spikes in estimated HIV-1 effective population sizes coincided with increases in opium poppy cultivation in Afghanistan, Pakistan's western neighbor. Structured coalescent analysis was undertaken in order to investigate the spatial relationship of HIV-1 transmission among the various cities under study. In general terms, our analysis placed the city of Larkana at the center of the PWID HIV-1 epidemic in Pakistan which is consistent with previous epidemiological data.

Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort

Keywords: HIV; Canada; molecular phylogenetics; viral evolution; person-to-person transmission inference; transmission network; summary statistics.

HIV-1 Unique Recombinant Forms Identified in Slovenia and Their Characterization by Near Full-Length Genome Sequencing

Surveillance of HIV circulating recombinant forms (CRFs) is important because HIV diversity can affect various aspects of HIV infection from prevention to diagnosis and patient management. A comprehensive collection of pol sequences obtained from individuals diagnosed with HIV-1 from 2000 to 2016 in Slovenia was subtyped to identify possible unique recombinant forms (URFs). Selected samples were subjected to near full-length genome (NFLG) sequencing and detailed recombination analyses. Discordant subtyping results were observed for 68/387 (17.6%) sequences and 20 sequences were identified as the most probable URFs and selected for NFLG characterization. Further, 11 NFLGs and two sequences of >7000 base pairs were obtained. Seven sequences were identified as “pure” subtypes or already characterized CRFs: subtype B (n = 5), sub-subtype A6 (n = 1), and CRF01_AE (n = 1). The remaining six sequences were determined to be URFs; four displayed a single recombination event and two exhibited a complex recombination pattern involving several subtypes or CRFs. Finally, three HIV strains were recognized as having epidemic potential and could be further characterized as new CRFs. Our study shows that the identification of new CRFs is possible, even in countries where HIV diversity is considered limited, emphasizing the importance of the surveillance of HIV recombinant forms.

Pervasive and non-random recombination in near full-length HIV genomes from Uganda

Recombination is an important feature of HIV evolution, occurring both within and between the major branches of diversity (subtypes). The Ugandan epidemic is primarily composed of two subtypes, A1 and D, that have been co-circulating for 50 years, frequently recombining in dually infected patients. Here, we investigate the frequency of recombinants in this population and the location of breakpoints along the genome. As part of the PANGEA-HIV consortium, 1,472 consensus genome sequences over 5 kb have been obtained from 1,857 samples collected by the MRC/UVRI & LSHTM Research unit in Uganda, 465 (31.6 per cent) of which were near full-length sequences (>8 kb). Using the subtyping tool SCUEAL, we find that of the near full-length dataset, 233 (50.1 per cent) genomes contained only one subtype, 30.8 per cent A1 (n = 143), 17.6 per cent D (n = 82), and 1.7 per cent C (n = 8), while 49.9 per cent (n = 232) contained more than one subtype (including A1/D (n = 164), A1/C (n = 13), C/D (n = 9); A1/C/D (n = 13), and 33 complex types). K-means clustering of the recombinant A1/D genomes revealed a section of envelope (C2gp120-TMgp41) is often inherited intact, whilst a generalized linear model was used to demonstrate significantly fewer breakpoints in the gag-pol and envelope C2-TM regions compared with accessory gene regions. Despite similar recombination patterns in many recombinants, no clearly supported circulating recombinant form (CRF) was found, there was limited evidence of the transmission of breakpoints, and the vast majority (153/164; 93 per cent) of the A1/D recombinants appear to be unique recombinant forms. Thus, recombination is pervasive with clear biases in breakpoint location, but CRFs are not a significant feature, characteristic of a complex, and diverse epidemic.

Prevalence of Non-B HIV-1 Subtypes in North Italy and Analysis of Transmission Clusters Based on Sequence Data Analysis

HIV-1 diversity is increasing in European countries due to immigration flows, as well as travels and human mobility, leading to the circulation of both new viral subtypes and new recombinant forms, with important implications for public health. We analyzed 710 HIV-1 sequences comprising protease and reverse-transcriptase (PR/RT) coding regions, sampled from 2011 to 2017, from naive patients in Spedali Civili Hospital, Brescia, Italy. Subtyping was performed by using a combination of different tools; the phylogenetic analysis with a structured coalescence model and Makarov Chain Monte Carlo was used on the datasets, to determine clusters and evolution. We detected 304 (43%) patients infected with HIV-1 non-B variants, of which only 293 sequences were available, with four pure subtypes and five recombinant forms; subtype F1 (17%) and CRF02_AG (51.1%) were most common. Twenty-five transmission clusters were identified, three of which included >10 patients, belonging to subtype CRF02_AG and subtype F. Most cases of alleged transmission were between heterosexual couples. Probably due to strong migratory flows, we have identified different subtypes with particular patterns of recombination or, as in the case of the subtype G (18/293, 6.1%), to a complete lack of relationship between the sequenced strains, revealing that they are all singletons. Continued HIV molecular surveillance is most important to analyze the dynamics of the boost of transmission clusters in order to implement public health interventions aimed at controlling the HIV epidemic.

SNAPPy: A snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing

Human immunodeficiency virus 1 (HIV-1) genome sequencing is routinely done for drug resistance monitoring in hospitals worldwide. Subtyping these extensive datasets of HIV-1 sequences is a critical first step in molecular epidemiology and evolution studies. The clinical relevance of HIV-1 subtypes is increasingly recognized. Several studies suggest subtype-related differences in disease progression, transmission route efficiency, immune evasion, and even therapeutic outcomes. HIV-1 subtyping is mainly done using web-servers. These tools have limitations in scalability and potential noncompliance with data protection legislation. Thus, the aim of this work was to develop an efficient method for large-scale local HIV-1 subtyping. We designed SNAPPy: a snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing. It contains several tasks of phylogenetic inference and BLAST queries, which can be executed sequentially or in parallel, taking advantage of multiple-core processing units. Although it was built for subtyping, SNAPPy is also useful to perform extensive HIV-1 alignments. This tool facilitates large-scale sequence-based HIV-1 research by providing a local, resource efficient and scalable alternative for HIV-1 subtyping. It is capable of analyzing full-length genomes or partial HIV-1 genomic regions (GAG, POL, and ENV) and recognizes more than ninety circulating recombinant forms. SNAPPy is freely available at: https://github.com/PMMAraujo/snappy/releases.

A novel alignment-free method for HIV-1 subtype classification

HIV-1 is the most common and pathogenic strain of human immunodeficiency virus consisting of many subtypes. To study the difference among HIV-1 subtypes in infection, diagnosis and drug design, it is important to identify HIV-1 subtypes from clinical HIV-1 samples. In this work, we propose an effective numeric representation called Subsequence Natural Vector (SNV) to encode HIV-1 sequences. Using the representation, we introduce an improved linear discriminant analysis method to classify HIV-1 viruses correctly. SNV is based on distribution of nucleotides in HIV-1 viral sequences. It not only computes the number of nucleotides, but also describes the position and variance of nucleotides in viruses. To validate our alignment-free method, 6902 complete genomes and 11,668 pol gene sequences of HIV-1 subtypes were collected from the up-to-date Los Alamos HIV database. SNV outperforms the three popular methods, Kameris, Comet and REGA, with almost 100% Sensitivity and Specificity, also with much less time. Our subtyping algorithm especially works better for circulating recombinant forms (CRFs) consisting of a few sequences. Our approach is also powerful to separate unique recombinant forms (URFs) from other subtypes with 100% Sensitivity and Specificity. Moreover, phylogenetic trees based on SNV representation are constructed using full-length HIV-1 genomes and pol genes respectively, where viruses from the same subtype are clustered together correctly.

Detection of HIV transmission hotspots in British Columbia, Canada: A novel framework for the prioritization and allocation of treatment and prevention resources

Background

Identifying populations at high risk of HIV transmission is critical for prioritizing treatment and prevention resources and achieving the UNAIDS 90-90-90 Targets.

Methods

HIV transmission rates can be estimated from phylogenetic trees as viral lineage-level diversification rates. To identify HIV-1 transmission foci in British Columbia, Canada, we inferred diversification rates from phylogenetic trees of 36 271 HIV-1 sequences from 9630 anonymized individuals. Diversification rates were combined with sociodemographic and clinical data, then aggregated by patients’ area of residence to predict the distribution of new HIV cases between 2008 and 2018. The predictive power of the model was compared with a phylogenetically uninformed model.

Findings

Aggregated diversification rate measures were predictive of new HIV cases in the subsequent year after adjusting for prevalent and incident cases in the previous year. For every one-unit increase in the mean of the top five diversification rates, the number of new HIV cases increased by on average 1·38-fold (95% CI, 1·28–1·49). In a blind prediction of 2018 cases, diversification rate improved the model's specificity by 12%, accuracy by 9%, top 20 agreement by 100%, and correlation of predicted and observed values by 162% relative to a model that incorporated epidemiological data alone.

Interpretation

By predicting the distribution of future HIV cases, a combined phylogenetic and epidemiological approach identifies hotspots where public health resources are needed most.

Funding

Canadian Institutes of Health Research, University of British Columbia, Public Health Agency of Canada, Genome Canada, Genome BC, Michael Smith Foundation for Health Research, and BC Centre for Excellence in HIV/AIDS.

Non-disclosed men who have sex with men in UK HIV transmission networks: phylogenetic analysis of surveillance data

BACKGROUND

Patients who do not disclose their sexuality, including men who do not disclose same-sex behaviour, are difficult to characterise through traditional epidemiological approaches such as interviews. Using a recently developed method to detect large networks of viral sequences from time-resolved trees, we localised non-disclosed men who have sex with men (MSM) in UK transmission networks, gaining crucial insight into the behaviour of this group.

METHODS

For this phylogenetic analysis, we obtained HIV pol sequences from the UK HIV Drug Resistance Database (UKRDB), a central repository for resistance tests done as part of routine clinical care throughout the UK. Sequence data are linked to demographic and clinical data held by the UK Collaborative HIV Cohort study and the national HIV/AIDS reporting system database. Initially, we reconstructed maximum likelihood phylogenies from these sequences, then sequences were selected for time-resolved analysis in BEAST if they were clustered with at least one other sequence at a genetic distance of 4·5% or less with support of at least 90%. We used time-resolved phylogenies to create networks by linking together nodes if sequences shared a common ancestor within the previous 5 years. We identified potential non-disclosed MSM (pnMSM), defined as self-reported heterosexual men who clustered only with men. We measured the network position of pnMSM, including betweenness (a measure of connectedness and importance) and assortativity (the propensity for nodes sharing attributes to link).

FINDINGS

14 405 individuals were in the network, including 8452 MSM, 1743 heterosexual women and 1341 heterosexual men. 249 pnMSM were identified (18·6% of all clustered heterosexual men) in the network. pnMSM were more likely to be black African (p<0·0001), less likely to be infected with subtype B (p=0·006), and were slightly older (p=0·002) than the MSM they clustered with. Mean betweenness centrality was lower for pnMSM than for MSM (1·31, 95% CI 0·48-2·15 in pnMSM vs 2·24, 0·98-3·51 in MSM; p=0·002), indicating that pnMSM were in peripheral positions in MSM clusters. Assortativity by risk group was higher than expected (0·037 vs -0·037, p=0·01) signifying that pnMSM were linked to each other. We found that self-reported heterosexual men were more likely to link MSM and heterosexual women than heterosexual women were to link MSM and heterosexual men (Fisher's exact test p=0·0004; OR 2·24) but the number of such transmission chains was small (only 54 in total vs 32 in women).

INTERPRETATION

pnMSM are a subgroup distinct from both MSM and from heterosexual men. They are more likely to choose sexual partners who are also pnMSM and might exhibit lower-risk sexual behaviour than MSM (eg, choosing low-risk partners or consistently using condoms). Heterosexual men are the group most likely to be diagnosed with late-stage disease (ie, low CD4 counts) and non-disclosed MSM might put female partners at higher risk than heterosexual men because non-disclosed MSM have male partners. Hence, pnMSM require specific consideration to ensure they are included in public health interventions.

FUNDING

National Institutes of Health.

Hotspots of Transmission Driving the Local Human Immunodeficiency Virus Epidemic in the Cologne-Bonn Region, Germany

BACKGROUND

Geographical allocation of interventions focusing on hotspots of human immunodeficiency virus (HIV) transmission has the potential to improve efficiency. We used phylogeographic analyses to identify hotspots of the HIV transmission in Cologne-Bonn, Germany.

METHODS

We included 714 HIV-1 infected individuals, followed up at the University Hospitals Cologne and Bonn. Distance-based molecular network analyses were performed to infer putative relationships. Characteristics of genetically linked individuals and assortativity (shared characteristics) were analyzed. Geospatial diffusion (ie, viral gene flow) was evaluated using a Slatkin-Maddison approach. Geospatial dispersal was determined by calculating the average distance between the residences of linked individuals (centroids of 3-digit zip code).

RESULTS

In sum, 217/714 (30.4%) sequences had a putative genetic linkage, forming 77 clusters (size range: 2-8). Linked individuals were more likely to live in areas surrounding the city center (P = .043), <30 years of age (P = .009). and infected with HIV-1 subtype B (P = .002). Clustering individuals were nonassortative by area of residency (-.0026, P = .046). Geospatial analyses revealed a median distance between genetically linked individuals of 23.4 kilometers (km), lower than expected (P < .001). Slatkin-Maddison analyses revealed increased gene flow from central Cologne toward the surrounding areas (P < .001).

CONCLUSION

Phylogeographic analysis suggests that central Cologne may be a significant driver of the regional epidemic. Although clustering individuals lived closer than unlinked individuals, they were less likely to be linked to others from their same zip code. These results could help public health entities better understand transmission dynamics, facilitating allocation of resources to areas of greatest need.

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Background

Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.

Results

We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%.

A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster.

We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy.

Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes.

Conclusions

Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

HIV-1 drug resistance testing is essential for heavily-treated patients switching from first- to second-line regimens in resource-limited settings: evidence from routine clinical practice in Cameroon

Background

With the phase-out of stavudine (d4T), change to first-line regimens with zidovudine (AZT) or tenofovir (TDF) in resource-limited settings (RLS) might increase risks of cross-resistance to nucleos(t) ide reverse transcriptase inhibitors (NRTI). This would restrict the scope of switching to the World Health Organisation (WHO)-recommended standard second-line combinations (SLC) without HIV drug resistance (HIVDR)-testing in routine clinical practice.

Methods

An observational study was conducted among 101 Cameroonian patients (55.4% male, median [IQR] age 34 [10–41] years) failing first-line antiretroviral therapy (ART) in 2016, and stratified into three groups according to NRTIs exposure: exposure to both thymidine analogues AZT “and” D4T (group-A, n = 55); exposure to both TDF and AZT “or” D4T (group-B, n = 22); exposure solely to D4T (group-C, n = 24). Protease-reverse transcriptase HIVDR was interpreted using the HIVdb penalty scores (≥60: high-resistance; 20–59: intermediate-resistance; < 20: susceptible). The acceptable threshold for potential-efficacy was set at 80%.

Results

The median [IQR] CD4, viral RNA, and time on ART, were respectively 129 [29–466] cells/μl, 71,630 [19,041-368,000] copies/ml, and 4 [2–5] years. Overall HIVDR-level was 89.11% (90/101), with 83.2% harbouring M184 V (high-level 3TC/FTC-resistance) and only 1.98% (2/101) major HIVDR-mutations to ritonavir-boosted protease-inhibitors (PI/r). Thymidine-analogue mutations (TAMs)-1 [T215FY (46.53%), M41 L (22.77%), L210 W (8.91%)], with cross-resistance to AZT and TDF, were higher compared to TAMs-2 [D67N (21.78%), K70R (19.80%), K219QE (18.81%)]. As expected, K65R was related with TDF-exposure: 0% (0/55) in group-A, 22.72% (5/22) group-B, 4.17% (1/24) group-C (p = 0.0013). The potential-efficacy of AZT vs. TDF was respectively 43.64% (24/55) vs. 70.91% (39/55) in group-A (p = 0.0038); 63.64% (14/22) vs. 68.28% (15/22) in group-B (p = 1.0000); and 37.50% (9/24) vs. 83.33% (20/24) in group-C (p = 0.0032). CRF02_AG was the prevailing subtype (63.40%), followed by CRF11.cpx (8.91%), A₁ (7.92%), G (5.94%); without any significant effect of the subtype-distribution on HIVDR (92.2% in CRF02_AG vs. 83.8% in non-AG; p = 0.204).

Conclusion

First-line ART-failure exhibits high-level NRTI-resistance, with potential lower-efficacy of AZT compared to TDF. Significantly, using our 80% efficacy-threshold, only patients without NRTI-substitution on first-line could effectively switch to SLC following the WHO-approach. Patients with multiple NRTI-substitutions (exposed to both thymidine-analogues and TDF) on first-line ART would require HIVDR-testing to select active NRTIs for SLC.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3871-0) contains supplementary material, which is available to authorized users.

Pretreatment human immunodeficiency virus type 1 (HIV-1) drug resistance in transmission clusters of the Cologne-Bonn region, Germany

OBJECTIVES

In Germany, previous reports have demonstrated transmitted human immunodeficiency virus type 1 (HIV-1) drug-resistance mutations (DRM) in 11% of newly diagnosed individuals, highlighting the importance of drug-resistance screening before the initiation of antiretroviral therapy (ART). Here, we sought to understand the molecular epidemiology of HIV DRM transmission in the Cologne-Bonn region of Germany, given one of the highest rates of new HIV diagnoses in western Europe (13.7 per 100 000 habitants).

METHODS

We analysed 714 HIV-1 ART-naive infected individuals diagnosed at the University Hospitals Cologne and Bonn between 2001 and 2016. Screening for DRM was performed according to the Stanford University Genotypic Resistance Interpretation. Shared DRM were defined as any DRM present in genetically linked individuals (<1.5% genetic distance). Phylogenetic and network analyses were performed to infer putative relationships and shared DRM.

RESULTS

The prevalence of any DRM at time of diagnosis was 17.2% (123/714 participants). Genetic transmission network analyses showed comparable frequencies of DRM in clustering versus non-clustering individuals (17.1% (85/497) versus 17.5% (38/217)). The observed rate of DRM in the region was higher than previous reports 10.8% (87/809) (p < 0.001), revealing the need to reduce onward transmission in this area. Genetically linked individuals harbouring shared DRM were more likely to live in suburban areas (24/38) than in central Cologne (1/38) (p < 0.001).

CONCLUSION

The rate of DRM was exceptionally high. Network analysis elucidated frequent cases of shared DRM among genetically linked individuals, revealing the potential spread of DRM and the need to prevent onward transmission of DRM in the Cologne-Bonn area.

Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations

Although fishing communities (FCs) in Uganda are disproportionately affected by HIV-1 relative to the general population (GP), the transmission dynamics are not completely understood. We earlier found most HIV-1 transmissions to occur within FCs of Lake Victoria. Here, we test the hypothesis that HIV-1 transmission in FCs is isolated from networks in the GP. We used phylogeography to reconstruct the geospatial viral migration patterns in 8 FCs and 2 GP cohorts and a Bayesian phylogenetic inference in BEAST v1.8.4 to analyse the temporal dynamics of HIV-1 transmission. Subtype A1 (pol region) was most prevalent in the FCs (115, 45.1%) and GP (177, 50.4%). More recent HIV transmission pairs from FCs were found at a genetic distance (GD) <1.5% than in the GP (Fisher’s exact test, p = 0.001). The mean time depth for pairs was shorter in FCs (5 months) than in the GP (4 years). Phylogeographic analysis showed strong support for viral migration from the GP to FCs without evidence of substantial viral dissemination to the GP. This suggests that FCs are a sink for, not a source of, virus strains from the GP. Targeted interventions in FCs should be extended to include the neighbouring GP for effective epidemic control.

Evidence for a recombinant origin of HIV-1 Group M from genomic variation

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the 'bootscanning' method for detecting breakpoints in inter-subtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

First-line HIV treatment failures in non-B subtypes and recombinants: a cross-sectional analysis of multiple populations in Uganda

Background

Our understanding of HIV-1 and antiretroviral treatment (ART) is strongly biased towards subtype B, the predominant subtype in North America and western Europe. Efforts to characterize the response to first-line treatments in other HIV-1 subtypes have been hindered by the availability of large study cohorts in resource-limited settings. To maximize our statistical power, we combined HIV-1 sequence and clinical data from every available study population associated with the Joint Clinical Research Centre (JCRC) in Uganda. These records were combined with contemporaneous ART-naive records from Uganda in the Stanford HIVdb database.

Methods

Treatment failures were defined by the presence of HIV genotype records with sample collection dates after the ART start dates in the JCRC database. Drug resistances were predicted by the Stanford HIVdb algorithm, and HIV subtype classification and recombination detection was performed with SCUEAL. We used Bayesian network analysis to evaluate associations between drug exposures and subtypes, and binomial regression for associations with recombination.

Results

This is the largest database of first-line treatment failures (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=1724$$\end{document}n=1724) in Uganda to date, with a predicted statistical power of 80% to detect subtype associations at an odds ratio of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 1.2$$\end{document}≥1.2. In the subset where drug regimen data were available, we observed that use of 3TC was associated with a higher rate of first line treatment failure, whereas regimens containing AZT and TDF were associated with reduced rates of failure. In the complete database, we found limited evidence of associations between HIV-1 subtypes and treatment failure, with the exception of a significantly lower frequency of failures among A/D recombinants that comprised about 7% of the population. First-line treatment failure was significantly associated with reduced numbers of recombination breakpoints across subtypes.

Conclusions

Expanding access to first-line ART should confer the anticipated public health benefits in Uganda, despite known differences in the pathogenesis of HIV-1 subtypes. Furthermore, the impact of ART may actually be enhanced by frequent inter-subtype recombination in this region.

Electronic supplementary material

The online version of this article (10.1186/s12981-019-0218-2) contains supplementary material, which is available to authorized users.

An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (Kameris) that operates on k-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n = 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.

Geographically-stratified HIV-1 group M pol subtype and circulating recombinant form sequences

Accurate classification of HIV-1 group M lineages, henceforth referred to as subtyping, is essential for understanding global HIV-1 molecular epidemiology. Because most HIV-1 sequencing is done for genotypic resistance testing pol gene, we sought to develop a set of geographically-stratified pol sequences that represent HIV-1 group M sequence diversity. Representative pol sequences differ from representative complete genome sequences because not all CRFs have pol recombination points and because complete genome sequences may not faithfully reflect HIV-1 pol diversity. We developed a software pipeline that compiled 6,034 one-per-person complete HIV-1 pol sequences annotated by country and year belonging to 11 pure subtypes and 70 CRFs and selected a set of sequences whose average distance to the remaining sequences is minimized for each subtype/CRF and country to generate a Geographically-Stratified set of 716 Pol Subtype/CRF (GSPS) reference sequences. We provide extensive data on pol diversity within each subtype/CRF and country combination. The GSPS reference set will also be useful for HIV-1 pol subtyping.

Tracking HIV-1 recombination to resolve its contribution to HIV-1 evolution in natural infection

Recombination in HIV-1 is well documented, but its importance in the low-diversity setting of within-host diversification is less understood. Here we develop a novel computational tool (RAPR (Recombination Analysis PRogram)) to enable a detailed view of in vivo viral recombination during early infection, and we apply it to near-full-length HIV-1 genome sequences from longitudinal samples. Recombinant genomes rapidly replace transmitted/founder (T/F) lineages, with a median half-time of 27 days, increasing the genetic complexity of the viral population. We identify recombination hot and cold spots that differ from those observed in inter-subtype recombinants. Furthermore, RAPR analysis of longitudinal samples from an individual with well-characterized neutralizing antibody responses shows that recombination helps carry forward resistance-conferring mutations in the diversifying quasispecies. These findings provide insight into molecular mechanisms by which viral recombination contributes to HIV-1 persistence and immunopathogenesis and have implications for studies of HIV transmission and evolution in vivo.

Absence of HIV-1 Drug Resistance Mutations Supports the Use of Dolutegravir in Uganda

To screen for drug resistance and possible treatment with Dolutegravir (DTG) in treatment-naive patients and those experiencing virologic failure during first-, second-, and third-line combined antiretroviral therapy (cART) in Uganda. Samples from 417 patients in Uganda were analyzed for predicted drug resistance upon failing a first- (N = 158), second- (N = 121), or third-line [all 51 involving Raltegravir (RAL)] treatment regimen. HIV-1 pol gene was amplified and sequenced from plasma samples. Drug susceptibility was interpreted using the Stanford HIV database algorithm and SCUEAL was used for HIV-1 subtyping. Frequency of resistance to nucleoside reverse transcriptase inhibitors (NRTIs) (95%) and non-NRTI (NNRTI, 96%) was high in first-line treatment failures. Despite lack of NNRTI-based treatment for years, NNRTI resistance remained stable in 55% of patients failing second-line or third-line treatment, and was also at 10% in treatment-naive Ugandans. DTG resistance (n = 366) was not observed in treatment-naive individuals or individuals failing first- and second-line cART, and only found in two patients failing third-line cART, while 47% of the latter had RAL- and Elvitegravir-resistant HIV-1. Secondary mutations associated with DTG resistance were found in 2%-10% of patients failing third-line cART. Of 14 drugs currently available for cART in Uganda, resistance was readily observed to all antiretroviral drugs (except for DTG) in Ugandan patients failing first-, second-, or even third-line treatment regimens. The high NNRTI resistance in first-line treatment in Uganda even among treatment-naive patients calls for the use of DTG to reach the UNAIDS 90:90:90 goals.

Diversity of HIV-1 in Aquitaine, Southwestern France, 2012-2016

We have estimated the prevalence of the different viral subtypes between January 2012 and December 2016 in HIV-1-infected patients of the Aquitaine region (southwest part of France) who had a routine HIV-1 genotype resistance testing (GRT) centralized at the Bordeaux University Hospital. GRT was performed on viral RNA (1,784 samples) before treatment initiation or at failure, whereas proviral DNA was used as template (1,420 samples) in the event of a treatment switch in patients with viral load below 50 copies/mL. Pol and integrase sequences were obtained; subtypes, circulating recombinant forms (CRFs), and unique recombinant forms (URFs) were assigned by combining the results of SCUEAL, REGA, COMET, and HIV BLAST. Globally, subtype B was predominant with 71.7%, whereas non-B subtypes accounted for 28.3%. Within the non-B viruses, CRF02_AG was the most prominent (11.6%) followed by non-B non-URF (13.5%), A, CRF01_AE, G, CRF06_cpx, F, C, D, H, J, and finally URF (3.2%). The analysis of the two compartments separately showed that RNA exhibits higher percentages of non-B viruses than DNA. This study reveals a high degree of diversity of HIV-1 non-B subtype strains in Aquitaine, with an increasing prevalence of CRF02_AG and URF in the population investigated for viral RNA, that is, including more recently detected HIV-1-infected patients. Future studies should attempt to identify the transmission clusters while paying special attention to URF, since they seem to be increasing in the population and could potentially host CRF.

Molecular Epidemiology of the HIV Epidemic in Three German Metropolitan Regions - Cologne/Bonn, Munich and Hannover, 1999-2016

Using HIV sequence data to characterize clusters of HIV transmission may provide insight into the epidemic. Phylogenetic and network analyses were performed to infer putative relationships between HIV-1 partial pol sequences from 2,774 individuals receiving care in three German regions between 1999-2016. The regions have in common that they host some of the largest annual festivals in Europe (Carnival and Oktoberfest). Putative links with sequences (n = 150,396) from the Los Alamos HIV Sequence database were evaluated. A total of 595/2,774 (21.4%) sequences linked with at least one other sequence, forming 184 transmission clusters. Clustering individuals were significantly more likely to be younger, male, and report sex with men as their main risk factor (p < 0.001 each). Most clusters (77.2%) consisted exclusively of men; 41 (28.9%) of these included men reporting sex with women. Thirty-two clusters (17.4%) contained sequences from more than one region; clustering men were significantly more likely to be in a position bridging regional HIV epidemics than clustering women (p = 0.027). We found 236 clusters linking 547 sequences from our sample with sequences from the Los Alamos database (n = 1407; 31% from other German centres). These results highlight the pitfalls of focusing HIV prevention efforts on specific risk groups or specific locales.

HIV-1 transmitted drug resistance in Slovenia and its impact on predicted treatment effectiveness: 2011-2016 update

HIV-positive individuals that have a detected transmitted drug resistance (TDR) at baseline have a higher risk of virological failure with antiretroviral therapy (ART). This study offers an update on the prevalence of TDR in Slovenia, looks for onward transmission of TDR, and reassesses the need for baseline drug resistance testing. Blinded questionnaires and partial pol sequences were obtained from 54.5% (168/308) of all of the patients diagnosed with HIV-1 from 2011 to 2016. Subtype B was detected in 82.7% (139/168) of patients, followed by subtype A (8.3%), subtype C (2.4%), and CRF01_AE (1.8%). Surveillance drug resistance mutations (SDRMs) were found in four individuals (2.4%), all of them men who have sex with men (MSM) and infected with subtype B. K103N was detected in two patients and T68D and T215D in one person each, corresponding to a prevalence of 0%, 1.2%, and 1.2% of TDR to protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-NRTIs (NNRTIs), respectively. The impact of mutations on drug susceptibility was found to be most pronounced for NNRTIs. No forward spread of TDR within the country was observed; however, phylogenetic analysis revealed several new introductions of HIV into Slovenia in recent years, possibly due to increased risky behavior by MSM. This was indirectly confirmed by a substantial increase in syphilis cases and HIV-1 non-B subtypes during the study period. A drug-resistant HIV variant with good transmission fitness is thus more likely to be imported into Slovenia in the near future, and so TDR should be closely monitored.

Impact of Public Safety Policies on Human Immunodeficiency Virus Transmission Dynamics in Tijuana, Mexico

Background

North Tijuana, Mexico is home to many individuals at high risk for transmitting and acquiring human immunodeficiency virus (HIV). Recently, policy shifts by local government impacted how these individuals were handled by authorities. Here we examined how this affected regional HIV transmission dynamics.

Methods

HIV pol sequences and associated demographic information were collected from 8 research studies enrolling persons in Tijuana and were used to infer viral transmission patterns. To evaluate the impact of recent policy changes on HIV transmission dynamics, qualitative interviews were performed on a subset of recently infected individuals.

Results

Between 2004 and 2016, 288 unique HIV pol sequences were obtained from individuals in Tijuana, including 46.4% from men who have sex with men, 42.1% from individuals reporting transactional sex, and 27.8% from persons who inject drugs (some individuals had >1 risk factor). Forty-two percent of sequences linked to at least 1 other sequence, forming 37 transmission clusters. Thirty-two individuals seroconverted during the observation period, including 8 between April and July 2016. Three of these individuals were putatively linked together. Qualitative interviews suggested changes in policing led individuals to shift locations of residence and injection drug use, leading to increased risk taking (eg, sharing needles).

Conclusions

Near real-time molecular epidemiologic analyses identified a cluster of linked transmissions temporally associated with policy shifts. Interviews suggested these shifts may have led to increased risk taking among individuals at high risk for HIV acquisition. With all public policy shifts, downstream impacts need to be carefully considered, as even well-intentioned policies can have major public health consequences.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis—where individuals are sampled sooner post-infection—rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Many pathogens evolve so rapidly that they accumulate genetic differences within a host before becoming transmitted to the next host. Consequently, clusters of sampled infections with nearly identical genomes may reveal outbreaks of recent or ongoing transmissions. There is rapidly growing interest in using model-free genetic clustering methods to guide public health responses to epidemics in near real-time, including HIV, Ebola virus and tuberculosis. However, we show that current methods are relatively ineffective at detecting transmission outbreaks; instead, they are predominantly influenced by how infections are sampled from the population. We describe a fundamentally new approach to genetic clustering that is based on modelling changes in transmission rates during the spread of the epidemic. We use simulated and real pathogen sequence data sets to demonstrate that this model-based approach is substantially more effective for detecting transmission outbreaks, and remains fast enough for real-time applications to large sequence databases.

Gag P2/NC and pol genetic diversity, polymorphism, and drug resistance mutations in HIV-1 CRF02_AG- and non-CRF02_AG-infected patients in Yaoundé, Cameroon

In HIV-1 subtype-B, specific mutations in Gag cleavage sites (CS) are associated with treatment failure, with limited knowledge among non-B subtypes. We analyzed non-B HIV-1 gag and pol (protease/reverse-transcriptase) sequences from Cameroonians for drug resistance mutations (DRMs) in the gag P2/NC CS, and pol major DRMs. Phylogeny of the 141 sequences revealed a high genetic diversity (12 subtypes): 67.37% CRF02_AG versus 32.6% non-CRF02_AG. Overall, 7.3% transmitted and 34.3% acquired DRMs were found, including M184V, thymidine analogue mutations (T215F, D67N, K70R, K219Q), NNRTIs (L100I, Y181C, K103N, V108I, Y188L), and PIs (V82L). Twelve subjects [10 with HIV-1 CRF02_AG, 8 treatment-naïve and 4 on 3TC-AZT-NVP] showed 3 to 4 mutations in the Gag P2/NC CS: S373Q/T/A, A374T/S/G/N, T375S/A/N/G, I376V, G381S, and R380K. Subjects with or without Gag P2/NC CS mutations showed no significant difference in viral loads. Treatment-naïve subjects harboring NRTI-DRMs had significantly lower CD4 cells than those with NRTI-DRMs on ART (p = 0.042). Interestingly, two subjects had major DRMs to NRTIs, NNRTIs, and 4 mutations in the Gag P2/NC CS. In this prevailing CRF02_AG population with little exposure to PIs (~3%), mutations in the Gag P2/NC CS could increase the risk of treatment failure if there is increased use of PIs-based therapy.

Intrasubtype B HIV-1 Superinfection Correlates with Delayed Neutralizing Antibody Response

Understanding whether the neutralizing antibody (NAb) response impacts HIV-1 superinfection and how superinfection subsequently modulates the NAb response can help clarify correlates of protection from HIV exposures and better delineate pathways of NAb development. We examined associations between the development of NAb and the occurrence of superinfection in a well-characterized, antiretroviral therapy (ART)-naive, primary infection cohort of men who have sex with men. Deep sequencing was applied to blood plasma samples from the cohort to detect cases of superinfection. We compared the NAb activity against autologous and heterologous viruses between 10 participants with intrasubtype B superinfection and 19 monoinfected controls, matched to duration of infection and risk behavior. Three to 6 months after primary infection, individuals who would later become superinfected had significantly weaker NAb activity against tier 1 subtype B viruses (P = 0.003 for SF-162 and P = 0.017 for NL4-3) and marginally against autologous virus (P = 0.054). Lower presuperinfection NAb responses correlated with weaker gp120 binding and lower plasma total IgG titers. Soon after superinfection, the NAb response remained lower, but between 2 and 3 years after primary infection, NAb levels strengthened and reached those of controls. Superinfecting viruses were typically not susceptible to neutralization by presuperinfection plasma. These observations suggest that recently infected individuals with a delayed NAb response against primary infecting and tier 1 subtype B viruses are more susceptible to superinfection.IMPORTANCE Our findings suggest that within the first year after HIV infection, a relatively weak neutralizing antibody response against primary and subtype-specific neutralization-sensitive viruses increases susceptibility to superinfection in the face of repeated exposures. As natural infection progresses, the immune response strengthens significantly in some superinfected individuals. These findings will inform HIV vaccine design by providing testable correlates of protection from initial HIV infection.

The changing molecular epidemiology of HIV in the Philippines

BACKGROUND

The Philippines has one of the fastest-growing HIV epidemics in the world. Possible reasons for this include increased testing, increased local transmission, and possibly more aggressive strains of HIV. This study sought to determine whether local molecular subtypes of HIV have changed.

METHODS

Viruses from 81 newly diagnosed, treatment-naive HIV patients were genotyped using protease and reverse transcriptase genes. Demographic characteristics and CD4 count data were collected.

RESULTS

The cohort had an average age of 29 years (range 19-51 years), CD4+ count of 255 cells/mm³ (range 2-744 cells/mm³), and self-reported acquisition time of 2.42 years (range 0.17-8.17 years). All were male, including 79 men who have sex with men (MSM). The genotype distribution was 77% CRF01_AE, 22% B, and 1% C. Previous data from 1985-2000 showed that most Philippine HIV infections were caused by subtype B (71%, n=100), followed by subtype CRF01_AE (20%). Comparison with the present cohort showed a significant shift in subtype (p<0.0001). Comparison between CRF01_AE and B showed a lower CD4+ count (230 vs. 350 cells/mm³, p=0.03). Survival data showed highly significant survival associated with antiretroviral (ARV) treatment (p<0.0001), but no significant difference in mortality or CD4 count increase on ARVs between subtypes.

CONCLUSIONS

The molecular epidemiology of HIV in the Philippines has changed, with the more aggressive CRF01_AE now being the predominant subtype.

Evolutionary and network analysis of virus sequences from infants infected with an Australian recombinant strain of human parechovirus type 3

We present the near complete virus genome sequences with phylogenetic and network analyses of potential transmission networks of a total of 18 Australian cases of human parechovirus type 3 (HPeV3) infection in infants in the period from 2012–2015. Overall the results support our previous finding that the Australian outbreak strain/lineage is a result of a major recombination event that took place between March 2012 and November 2013 followed by further virus evolution and possibly recombination. While the nonstructural coding region of unknown provenance appears to evolve significantly both at the nucleotide and amino acid level, the capsid encoding region derived from the Yamagata 2011 lineage of HPeV3 appears to be very stable, particularly at the amino acid level. The phylogenetic and network analyses performed support a temporal evolution from the first Australian recombinant virus sequence from November 2013 to March/April 2014, onto the 2015 outbreak. The 2015 outbreak samples fall into two separate clusters with a possible common ancestor between March/April 2014 and September 2015, with each cluster further evolving in the period from September to November/December 2015.