Phylogenetic Analysis of Spread of Hepatitis C Virus Identified during HIV Outbreak Investigation, Unnao, India.

An HIV outbreak investigation during 2017-2018 in Unnao District, Uttar Pradesh, India, unearthed high prevalence of hepatitis C virus (HCV) antibodies among the study participants. We investigated these HCV infections by analyzing NS5B and core regions. We observed no correlation between HIV-HCV viral loads and clustering of HCV sequences, regardless of HIV serostatus. All HCV isolates belonged to genotype 3a. Monophyletic clustering of isolates in NS5B phylogeny indicates emergence of the outbreak from a single isolate or its closely related descendants. The nucleotide substitution rate for NS5B was 6 × 10-3 and for core was 2 × 10-3 substitutions/site/year. Estimated time to most recent common ancestor of these isolates was 2012, aligning with the timeline of this outbreak, which might be attributable to unsafe injection practices while seeking healthcare. HIV-HCV co-infection underlines the need for integrated testing, surveillance, strengthening of healthcare systems, community empowerment, and molecular analyses as pragmatic public health tools.

MC1R diversity and its role in skin pigmentation variation in West Maharashtra, India.

OBJECTIVES: MC1R polymorphisms have been reported to be under a selective constraint in populations inhabiting high UVR regions such as Africans; however, these patterns are not consistent. Here we analyze the MC1R gene in West Maharashtra, India to see if sequence diversity corresponds to their diverse pigmentary profiles and if MC1R is constrained in dark skinned tribal as compared to lighter skinned caste populations. METHODS: A 2648 bp region of this gene was sequenced in 102 individuals and the data was compared for π, Ï´ diversity indices. Tajima's D was assessed for signatures of purifying selection and MC1R variants were associated with MI measures using the additive, dominant, and recessive models. Pairwise FST was tested among study populations and between study populations and 1000 Genomes regional samples. RESULTS: MC1R diversity was not uniquely patterned among castes and tribes. Non-synonymous variants rs2228479A, rs1805007_T, and rs885479_A showed low variability in these populations. Selection tests did not indicate any constraint on MC1R and pairwise FST were also low among the study populations (-0.0163 to 0.06112). The SNP rs3212359 was significantly associated with MI measures when tested using different association models. CONCLUSIONS: We do not find evidence of a selective constraint on MC1R. The presence of a large number of unique haplotypes and low FST values at this locus suggests that MC1R polymorphisms may not be influencing pigmentation variation among castes and tribes in this region. Observed associations between rs3212359 and MI measures need to be validated through studies on larger samples and in-vitro functional studies.

Population genomics of dengue virus serotype 4: insights into genetic structure and evolution.

The spread of dengue disease has become a global public health concern. Dengue is caused by dengue virus, which is a mosquito-borne arbovirus of the genus Flavivirus, family Flaviviridae. There are four dengue virus serotypes (1-4), each of which is known to trigger mild to severe disease. Dengue virus serotype 4 (DENV-4) has four genotypes and is increasingly being reported to be re-emerging in various parts of the world. Therefore, the population structure and factors shaping the evolution of DENV-4 strains across the world were studied using genome-based population genetic, phylogenetic and selection pressure analysis methods. The population genomics study helped to reveal the spatiotemporal structure of the DENV-4 population and its primary division into two spatially distinct clusters: American and Asian. These spatial clusters show further time-dependent subdivisions within genotypes I and II. Thus, the DENV-4 population is observed to be stratified into eight genetically distinct lineages, two of which are formed by American strains and six of which are formed by Asian strains. Episodic positive selection was observed in the structural (E) and non-structural (NS2A and NS3) genes, which appears to be responsible for diversification of Asian lineages in general and that of modern lineages of genotype I and II in particular. In summary, the global DENV-4 population is stratified into eight genetically distinct lineages, in a spatiotemporal manner with limited recombination. The significant role of adaptive evolution in causing diversification of DENV-4 lineages is discussed. The evolution of DENV-4 appears to be governed by interplay between spatiotemporal distribution, episodic positive selection and intra/inter-genotype recombination.

Understanding the Genetic Diversity of Mycobacterium africanum Using Phylogenetics and Population Genomics Approaches.

A total of two lineages of Mycobacterium tuberculosis var. africanum (Maf), L5 and L6, which are members of the Mycobacterium tuberculosis complex (MTBC), are responsible for causing tuberculosis in West Africa. Regions of difference (RDs) are usually used for delineation of MTBC. With increased data availability, single nucleotide polymorphisms (SNPs) promise to provide better resolution. Publicly available 380 Maf samples were analyzed for identification of "core-cluster-specific-SNPs," while additional 270 samples were used for validation. RD-based methods were used for lineage-assignment, wherein 31 samples remained unidentified. The genetic diversity of Maf was estimated based on genome-wide SNPs using phylogeny and population genomics approaches. Lineage-based clustering (L5 and L6) was observed in the whole genome phylogeny with distinct sub-clusters. Population stratification using both model-based and de novo approaches supported the same observations. L6 was further delineated into three sub-lineages (L6.1-L6.3), whereas L5 was grouped as L5.1 and L5.2 based on the occurrence of RD711. L5.1 and L5.2 were further divided into two (L5.1.1 and L5.1.2) and four (L5.2.1-L5.2.4) sub-clusters, respectively. Unassigned samples could be assigned to definite lineages/sub-lineages based on clustering observed in phylogeny along with high-confidence posterior membership scores obtained during population stratification. Based on the (sub)-clusters delineated, "core-cluster-specific-SNPs" were derived. Synonymous SNPs (137 in L5 and 128 in L6) were identified as biomarkers and used for validation. Few of the cluster-specific missense variants in L5 and L6 belong to the central carbohydrate metabolism pathway which include His6Tyr (Rv0946c), Glu255Ala (Rv1131), Ala309Gly (Rv2454c), Val425Ala and Ser112Ala (Rv1127c), Gly198Ala (Rv3293) and Ile137Val (Rv0363c), Thr421Ala (Rv0896), Arg442His (Rv1248c), Thr218Ile (Rv1122), and Ser381Leu (Rv1449c), hinting at the differential growth attenuation. Genes harboring multiple (sub)-lineage-specific "core-cluster" SNPs such as Lys117Asn, Val447Met, and Ala455Val (Rv0066c; icd2) present across L6, L6.1, and L5, respectively, hinting at the association of these SNPs with selective advantage or host-adaptation. Cluster-specific SNPs serve as additional markers along with RD-regions for Maf delineation. The identified SNPs have the potential to provide insights into the genotype-phenotype correlation and clues for endemicity of Maf in the African population.

Analysis of H3K4me3-ChIP-Seq and RNA-Seq data to understand the putative role of miRNAs and their target genes in breast cancer cell lines.

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

Genetic diversity of 'Very Important Pharmacogenes' in two South-Asian populations.

OBJECTIVES: Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response. METHODS: Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni's multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score. RESULTS: Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be 'deleterious'. CONCLUSIONS: Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians.

GAMUT: A genomics big data management tool.

Efficient analysis of Single Nucleotide Polymorphisms (SNPs) across genomic samples enable in deciphering the relationship between genotype and phenotype. The core principle behind SNP comparison is to arrive at a probable list of variants that can differentiate two sets of data (populations). Such SNPs have direct applications in array design, genotype imputation and in cataloging of variants in regions of interest. We have developed GAMUT (Genomics bigdAta Management Tool), a big data-based solution for efficient run-time comparison of SNPs across large datasets based on partition of samples belonging to different populations taking into account user-defined splits. The tool is based on client-server architecture with MongoDB at the back-end and JSF with PrimeFaces as the front-end. It is readily deployable on wild-fly server as well as a docker container. Spark-based parallel data uploader enables optimal loading times. GAMUT enables dynamic querying of the large datasets consisting of multiple samples using text-based, chromosome position-based as well as gene-name based options. Various charting options like bar and pie charts along with tabular formats are available to ease the analysis of the queried data. The resultant data pertaining to comparison of genomewide SNPs can also be downloaded in different formats like text, html, json for further stand-alone analysis. GAMUT is available for download at: https://github.com/bioinformatics-cdac/gamut.

GenoVault: a cloud based genomics repository.

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user's permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

Erratum for Byukusenge et al., "Complete Genome Sequences of Four Bovine Coronavirus Isolates from Pennsylvania".

[This corrects the article DOI: 10.1128/genomeA.00467-18.].

Complete Genome Sequences of Four Bovine Coronavirus Isolates from Pennsylvania.

We report four full-genome sequences of bovine coronavirus (BCoV) isolates from dairy calves in Pennsylvania obtained in 2016 and 2017. BCoV is a pathogen of great importance to cattle health, and this is the first report of full-genome sequences of BCoV from PA cattle.

Comparative genomics of cell envelope components in mycobacteria.

Mycobacterial cell envelope components have been a major focus of research due to their unique features that confer intrinsic resistance to antibiotics and chemicals apart from serving as a low-permeability barrier. The complex lipids secreted by Mycobacteria are known to evoke/repress host-immune response and thus contribute to its pathogenicity. This study focuses on the comparative genomics of the biosynthetic machinery of cell wall components across 21-mycobacterial genomes available in GenBank release 179.0. An insight into survival in varied environments could be attributed to its variation in the biosynthetic machinery. Gene-specific motifs like 'DLLAQPTPAW' of ufaA1 gene, novel functional linkages such as involvement of Rv0227c in mycolate biosynthesis; Rv2613c in LAM biosynthesis and Rv1209 in arabinogalactan peptidoglycan biosynthesis were detected in this study. These predictions correlate well with the available mutant and coexpression data from TBDB. It also helped to arrive at a minimal functional gene set for these biosynthetic pathways that complements findings using TraSH.