Biology subjects

Crandall, K. A.

Publications and source records attributed to Crandall, K. A..

23 records · Page 2Linked to original sources

bioRxiv2019

Schizophrenia risk from locus-specific human endogenous retroviruses

Schizophrenia genome-wide association studies highlight the substantial contribution of risk attributed to the non-coding genome where human endogenous retroviruses (HERVs) are encoded. These ancient viral elements have previously been overlooked in genetic and transcriptomic studies due to their poor annotation and repetitive nature. Using a new, comprehensive HERV annotation, we found that the fraction of the genome where HERVs are located (the retrogenome) is enriched for schizophrenia risk variants, and that there are 148 disparate HERVs involved in susceptibility. Analysis of RNA-sequencing data from the dorsolateral prefrontal cortex of 259 schizophrenia cases and 279 controls from the CommonMind Consortium showed that HERVs are actively expressed in the brain (n = 3,979), regulated in cis by common genetic variants (n = 1,759), and differentially expressed in patients (n = 81). Convergent analyses implicate LTR25_6q21 and ERVLE_8q24.3h as HERVs of etiological relevance to schizophrenia, which are co-regulated with genes involved in neuronal and mitochondrial function, respectively. Our findings provide a strong rationale for exploring the retrogenome and the expression of these locus-specific HERVs as novel risk factors for schizophrenia and potential diagnostic biomarkers and treatment targets.

neuroscience↗

bioRxiv2019

Association of milk microbiome in bovine clinical mastitis and their functional implications in cows in Bangladesh

Milk microbiomes impose a significant influence on the pathophysiology of bovine mastitis. To assess the association, we compared the microbiome of clinical mastitis (CM) and healthy (H) milk samples through whole metagenomic deep sequencing. A total of 483.38 million reads generated from both metagenomes were analyzed through PathoScope and MG-RAST, and mapped to 380 bacterial, 56 archaeal, and 39 viral genomes. We observed distinct shifts and differences in abundance between the microbiome of CM and H milk in phyla Proteobacteria, Bacteroidetes, Firmicutes and Actinobacteria with an inclusion of 68.04% unreported and/or opportunistic species in CM milk. Additionally, 14 archaeal and 14 viral genera were found to be solely associated with CM. The functional metagenomics identified several pathways related to bacterial proliferation and colonization such as metabolism, chemotaxis and invasion, immune-diseases, oxidative stress, regulation and cell signaling, phage and prophases, antibiotic and heavy metal resistance to be associated with CM. Therefore, the present study provides conclusive data on milk microbiome diversity associated with bovine CM and its role in udder health.

molecular biology↗

bioRxiv2018

A phylogenomic framework, evolutionary timeline, and genomic resources for comparative studies of decapod crustaceans

Comprising over 15,000 living species, decapods (crabs, shrimp, and lobsters) are the most instantly recognizable crustaceans, representing a considerable global food source. Although decapod systematics have received much study, limitations of morphological and Sanger sequence data have yet to produce a consensus for higher-level relationships. Here we introduce a new anchored hybrid enrichment kit for decapod phylogenetics designed from genomic and transcriptomic sequences that we used to capture new high-throughput sequence data from 94 species, including 58 of 179 extant decapod families, and 11 of 12 major lineages. The enrichment kit yields 410 loci (>86,000 bp) conserved across all lineages of Decapoda, eight times more molecular data than any prior study. Phylogenomic analyses recover a robust decapod tree of life strongly supporting the monophyly of all infraorders, and monophyly of each of the reptant, lobster, and crab groups, with some results supporting pleocyemate monophyly. We show that crown decapods diverged in the Late Ordovician and most crown lineages diverged in the Triassic-Jurassic, highlighting a cryptic Paleozoic history, and post-extinction diversification. New insights into decapod relationships provide a phylogenomic window into morphology and behavior, and a basis to rapidly and cheaply expand sampling in this economically and ecologically significant invertebrate clade.

evolutionary biology↗

bioRxiv2018

ReQTL - an allele-level measure of variation-expression genomic relationships

MotivationBy testing for association of DNA genotypes with gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation can be measured at expressed genome regions, and differs from the DNA genotype in sites subjected to regulatory forces. Therefore, assessment of correlation between RNA genetic variation and gene expression can reveal regulatory genomic relationships in addition to eQTLs.\n\nResultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele frequency (VAF) at expressed SNV loci in the transcriptome. We exemplify the method on sets of RNA-sequencing data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx) and demonstrate that ReQTL analyses show consistently high performance and sufficient power to identify both previously known and novel molecular associations. The majority of the SNVs implicated in significant cis-ReQTLs identified by our analysis were previously reported as significant cis-eQTL loci. Notably, trans ReQTL loci in our data were substantially enriched in RNA-editing sites. In summary, ReQTL analyses are computationally feasible and do not require matched DNA data, hence they have a high potential to facilitate the discovery of novel molecular interactions through exploration of the increasingly accessible RNA-sequencing datasets.\n\nAvailability and implementationSample scripts used in our ReQTL analyses are available with the Supplementary Material (ReQTL_sample_code).\n\nContacthorvatha@gwu.edu or lfspurr@gwmail.gwu.edu\n\nSupplementary InformationRe_QTL_Supplementary_Data.zip

genomics↗

bioRxiv2018

Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression

Characterization of Human Endogenous Retrovirus (HERV) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity. We present Telescope, a computational software tool that provides accurate estimation of transposable element expression (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types. Furthermore, our approach is robust to differences in sequencing technology, and demonstrates that the retrotranscriptome has potential to be used for cell type identification. Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments, revealing a differential complexity in the transposable element biology of complex systems not previously observed. Telescope is available at github.com/mlbendall/telescope.\n\nAuthor SummaryAlmost half of the human genome is composed of Transposable elements (TEs), but their contribution to the transcriptome, their cell-type specific expression patterns, and their role in disease remains poorly understood. Recent studies have found many elements to be actively expressed and involved in key cellular processes. For example, human endogenous retroviruses (HERVs) are reported to be involved in human embryonic stem cell differentiation. Discovering which exact HERVs are differentially expressed in RNA-seq data would be a major advance in understanding such processes. However, because HERVs have a high level of sequence similarity it is hard to identify which exact HERV is differentially expressed. To solve this problem, we developed a computer program which addressed uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We call this program, \"Telescope\". We then used Telescope to identify HERV expression in 13 well-studied cell types from the ENCODE consortium and found that different cell types could be characterized by enrichment for different HERV families, and for locus specific expression. We also showed that Telescope performed better than other methods currently used to determine TE expression. The use of this computational tool to examine new and existing RNA-seq data sets may lead to new understanding of the roles of TEs in health and disease.

genomics↗