Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Supplementary data are available at Bioinformatics online.

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Peripheral neurons comprise a critical component of the tumor microenvironment (TME). The role of the autonomic innervation in cancer has been firmly established. However, the effect of the afferent (sensory) neurons on tumor progression remains unclear. Utilizing surgical and chemical skin sensory denervation methods, we showed that afferent neurons supported the growth of melanoma tumors in vivo and demonstrated that sensory innervation limited the activation of effective antitumor immune responses. Specifically, sensory ablation led to improved leukocyte recruitment into tumors, with decreased presence of lymphoid and myeloid immunosuppressive cells and increased activation of T-effector cells within the TME. Cutaneous sensory nerves hindered the maturation of intratumoral high endothelial venules and limited the formation of mature tertiary lymphoid-like structures containing organized clusters of CD4+ T cells and B cells. Denervation further increased T-cell clonality and expanded the B-cell repertoire in the TME. Importantly, CD8a depletion prevented denervation-dependent antitumor effects. Finally, we observed that gene signatures of inflammation and the content of neuron-associated transcripts inversely correlated in human primary cutaneous melanomas, with the latter representing a negative prognostic marker of patient overall survival. Our results suggest that tumor-associated sensory neurons negatively regulate the development of protective antitumor immune responses within the TME, thereby defining a novel target for therapeutic intervention in the melanoma setting.

Hepatitis C virus (HCV) causes chronic infection in up to 50% to 80% of infected individuals. Hypervariable region 1 (HVR1) variability is frequently studied to gain an insight into the mechanisms of HCV adaptation during chronic infection, but the changes to and persistence of HCV subpopulations during intrahost evolution are poorly understood. In this study, we used ultradeep pyrosequencing (UDPS) to map the viral heterogeneity of a single patient over 9.6 years of chronic HCV genotype 4a infection. Informed error correction of the raw UDPS data was performed using a temporally matched clonal data set. The resultant data set reported the detection of low-frequency recombinants throughout the study period, implying that recombination is an active mechanism through which HCV can explore novel sequence space. The data indicate that polyvirus infection of hepatocytes has occurred but that the fitness quotients of recombinant daughter virions are too low for the daughter virions to compete against the parental genomes. The subpopulations of parental genomes contributing to the recombination events highlighted a dynamic virome where subpopulations of variants are in competition. In addition, we provide direct evidence that demonstrates the growth of subdominant populations to dominance in the absence of a detectable humoral response. IMPORTANCE Analysis of ultradeep pyrosequencing data sets derived from virus amplicons frequently relies on software tools that are not optimized for amplicon analysis, assume random incorporation of sequencing errors, and are focused on achieving higher specificity at the expense of sensitivity. Such analysis is further complicated by the presence of hypervariable regions. In this study, we made use of a temporally matched reference sequence data set to inform error correction algorithms. Using this methodology, we were able to (i) detect multiple instances of hepatitis C virus intrasubtype recombination at the E1/E2 junction (a phenomenon rarely reported in the literature) and (ii) interrogate the longitudinal quasispecies complexity of the virome. Parallel to the UDPS, isolation of IgG-bound virions was found to coincide with the collapse of specific viral subpopulations.

RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations. We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters’ structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks. All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.