Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Addressing Ethical Challenges in US-Based HIV Phylogenetic Research.

In recent years, phylogenetic analysis of HIV sequence data has been used in research studies to investigate transmission patterns between individuals and groups, including analysis of data from HIV prevention clinical trials, in molecular epidemiology, and in public health surveillance programs. Phylogenetic analysis can provide valuable information to inform HIV prevention efforts, but it also has risks, including stigma and marginalization of groups, or potential identification of HIV transmission between individuals. In response to these concerns, an interdisciplinary working group was assembled to address ethical challenges in US-based HIV phylogenetic research. The working group developed recommendations regarding (1) study design; (2) data security, access, and sharing; (3) legal issues; (4) community engagement; and (5) communication and dissemination. The working group also identified areas for future research and scholarship to promote ethical conduct of HIV phylogenetic research.

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.

Promises and pitfalls of Illumina sequencing for HIV resistance genotyping.

Genetic sequencing ("genotyping") plays a critical role in the modern clinical management of HIV infection. This virus evolves rapidly within patients because of its error-prone reverse transcriptase and short generation time. Consequently, HIV variants with mutations that confer resistance to one or more antiretroviral drugs can emerge during sub-optimal treatment. There are now multiple HIV drug resistance interpretation algorithms that take the region of the HIV genome encoding the major drug targets as inputs; expert use of these algorithms can significantly improve to clinical outcomes in HIV treatment. Next-generation sequencing has the potential to revolutionize HIV resistance genotyping by lowering the threshold that rare but clinically significant HIV variants can be detected reproducibly, and by conferring improved cost-effectiveness in high-throughput scenarios. In this review, we discuss the relative merits and challenges of deploying the Illumina MiSeq instrument for clinical HIV genotyping.

Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study.

BACKGROUND: HIV evolves rapidly and therefore infections with similar genetic sequences are likely linked by recent transmission events. Clusters of related infections can represent subpopulations with high rates of transmission. We describe the implementation of an automated near real-time system to monitor and characterise HIV transmission hotspots in British Columbia, Canada. METHODS: In this implementation case study, we applied a monitoring system to the British Columbia drug treatment database, which holds more than 32 000 anonymised HIV genotypes for nearly 9000 residents of British Columbia living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated reanalysis of the entire database. We extracted clusters of five or more individuals with short phylogenetic distances between their respective HIV sequences. The system generated monthly reports of the growth and characteristics of clusters that were distributed to public health officers. FINDINGS: In June, 2014, the monitoring system detected the expansion of a cluster by 11 new cases during 3 months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and most cases achieved suppressed viral loads. During the next 12 months, we detected 12 new cases in this cluster with reduction in the onward transmission of drug resistance. INTERPRETATION: Our findings show the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near real-time monitoring of new cases, and can be implemented in all settings in which HIV genotyping is the standard of care. FUNDING: BC Centre for Excellence in HIV/AIDS, the Canadian Institutes for Health Research, the Genome Canada-CIHR Partnership in Genomics and Personalized Health, and the US National Institute on Drug Abuse.

Global Database-Driven Assessment of HIV-1 Adaptation to the Immune Repertoires of Human Populations.

Associations between HIV-1 cytotoxic T lymphocyte (CTL) escape mutations and their restricting human leukocyte antigen (HLA) alleles imply that HIV could adapt to divergent HLA repertoires of human populations globally. Using publicly available databases, we examine the relationship between the frequencies of 19 experimentally validated CTL escape mutations in HIV-1 reverse transcriptase and their restricting HLA alleles in 59 countries. From these extensive data, we find evidence of differential HIV adaptations to human populations at only a limited number of the studied epitope sites.

Theoretical and experimental assessment of degenerate primer tagging in ultra-deep applications of next-generation sequencing.

Primer IDs (pIDs) are random oligonucleotide tags used in next-generation sequencing to identify sequences that originate from the same template. These tags are produced by degenerate primers during the reverse transcription of RNA molecules into cDNA. The use of pIDs helps to track the number of RNA molecules carried through amplification and sequencing, and allows resolution of inconsistencies between reads sharing a pID. Three potential issues complicate the above applications. First, multiple cDNAs may share a pID by chance; we found that while preventing any cDNAs from sharing a pID may be unfeasible, it is still practical to limit the number of these collisions. Secondly, a pID must be observed in at least three sequences to allow error correction; as such, pIDs observed only one or two times must be rejected. If the sequencing product contains copies from a high number of RT templates but produces few reads, our findings indicate that rejecting such pIDs will discard a great deal of data. Thirdly, the use of pIDs could influence amplification and sequencing. We examined the effects of several intrinsic and extrinsic factors on sequencing reads at both the individual and ensemble level.

Parsing social network survey data from hidden populations using stochastic context-free grammars.

BACKGROUND: Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. METHODOLOGY/PRINCIPAL FINDINGS: Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. CONCLUSIONS: SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.

Mapping protease inhibitor resistance to human immunodeficiency virus type 1 sequence polymorphisms within patients.

Resistance genotyping provides an important resource for the clinical management of patients infected with human immunodeficiency virus type 1 (HIV-1). However, resistance to protease (PR) inhibitors (PIs) is a complex phenotype shaped by interactions among nearly half of the residues in HIV-1 PR. Previous studies of the genetic basis of PI resistance focused on fixed substitutions among populations of HIV-1, i.e., host-specific adaptations. Consequently, they are susceptible to a high false discovery rate due to founder effects. Here, we employ sequencing "mixtures" (i.e., ambiguous base calls) as a site-specific marker of genetic variation within patients that is independent of the phylogeny. We demonstrate that the transient response to selection by PIs is manifested as an excess of nonsynonymous mixtures. Using a sample of 5,651 PR sequences isolated from both PI-naive and -treated patients, we analyze the joint distribution of mixtures and eight PIs as a Bayesian network, which distinguishes residue-residue interactions from direct associations with PIs. We find that selection for resistance is associated with the emergence of nonsynonymous mixtures in two distinct groups of codon sites clustered along the substrate cleft and distal regions of PR, respectively. Within-patient evolution at several positions is independent of PIs, including those formerly postulated to be involved in resistance. These positions are under strong positive selection in the PI-naive patient population, implying that other factors can produce spurious associations with resistance, e.g., mutational escape from the immune response.