Biology subjects

Kim, A.

Publications and source records attributed to Kim, A..

8 recordsLinked to original sources

bioRxiv2018

Epigenomic landscape of the human pathogen Clostridium difficile

Clostridioides difficile is a leading cause of health care-associated infections. Although significant progress has been made in the understanding of its genome, the epigenome of C. difficile and its functional impact has not been systematically explored. Here, we performed the first comprehensive DNA methylome analysis of C. difficile using 36 human isolates and observed great epigenomic diversity. We discovered an orphan DNA methyltransferase with a well-defined specificity whose corresponding gene is highly conserved across our dataset and in all ~300 global C. difficile genomes examined. Inactivation of the methyltransferase gene negatively impacted sporulation, a key step in C. difficile disease transmission, consistently supported by multi-omics data, genetic experiments, and a mouse colonization model. Further experimental and transcriptomic analysis also suggested that epigenetic regulation is associated with cell length, biofilm formation, and host colonization. These findings open up a new epigenetic dimension to characterize medically relevant biological processes in this critical pathogen. This work also provides a set of methods for comparative epigenomics and integrative analysis, which we expect to be broadly applicable to bacterial epigenomics studies.

genomics↗

bioRxiv2018

In vivo gene expression analyses provide unique insights on P. vivax gametocytogenesis and chloroquine response

Studies of gene expression have provided insights on the regulation of Plasmodium parasites. However, few studies have targeted P. vivax, the cause of one third of all human malaria cases outside Africa, due to the lack of in vitro culture system and the difficulties associated with studying clinical samples. Here, we describe robust RNA-seq profiles of P. vivax parasites generated directly from infected patient blood. Gene expression deconvolution analysis reveals that most parasite mRNAs derive from trophozoites and that the asynchronicity of P. vivax infections is unlikely to confound gene expression studies. We also show that gametocyte genes form two clusters of co-regulated genes, possibly indicating the independent regulation of male and female gametocytogeneses. Finally, despite a large effect on parasitemia, we find that chloroquine does not alter trophozoite gene expression. Overall, our study highlights the biological knowledge that can be gathered by directly studying P. vivax patient infections.\n\nImportancePlasmodium vivax is the second most common cause of human malaria worldwide but, since it cannot be cultured in the laboratory, its biology remains poorly understood. In this study, we describe the analysis of the parasite gene expression profiles generated from 26 patient infections. We show that the proportion of male and female parasites varies greatly among infections, suggesting that they are independently regulated. We also compare the gene expression profiles of the same infections before and after treatment with chloroquine, a common antimalarial, and show that the drug efficiently kills most P. vivax parasites but appears to have little effect on one specific parasite stage, the trophozoites, in contrast with the effect of the drug on P. falciparum. Overall, our study exemplifies the biological insights that can be gained from applying modern genomic tools to study this difficult human pathogen.

microbiology↗

bioRxiv2018

Integrated Clinical and Omics Approach to Rare Diseases: Novel Genes and Oligogenic Inheritance in Holoprosencephaly

PurposeHoloprosencephaly (HPE) is a pathology of forebrain development characterized by high phenotypic and locus heterogeneity. Seventeen genes are known so far in HPE but the understanding of its genetic architecture remains to be refined. Here, we investigated the oligogenic nature of HPE resulting from accumulation of variants in different relevant genes.\n\nMethodsExome data from 29 patients diagnosed with HPE and 51 relatives from 26 unrelated families were analyzed. Standard variant classification approach was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis.\n\nResultsWe identified 232 rare deleterious variants in HPE patients representing 180 genes significantly associated with key pathways of forebrain development including Sonic Hedgehog (SHH) and Primary Cilia. Oligogenic events were observed in 10 families and involved novel HPE genes including recurrently mutated genes (FAT1, NDST1, COL2A1 and SCUBE2) and genes implicated in cilia function.\n\nConclusionsThis study reports novel HPE-relevant genes and reveals the existence of oligogenic cases resulting from several mutations in SHH-related genes. It also underlines that integrating clinical phenotyping in genetic studies will improve the identification of causal variants in rare disorders.

genetics↗

bioRxiv2017

Pharmacological manipulation of olfactory bulb granule cell excitability modulates beta oscillations: Testing a model

The mammalian olfactory bulb (OB) generates gamma (40 - 100 Hz) and beta (15 - 30 Hz) oscillations of the local field potential (LFP). Gamma oscillations arise at the peak of inhalation supported by dendrodendritic interactions between glutamatergic mitral cells (MCs) and GABAergic granule cells (GCs). Beta oscillations occur in response to odorants in learning or odor sensitization paradigms, but their generation mechanism and function are still poorly understood. When centrifugal inputs to the OB are blocked, beta oscillations disappear, but gamma oscillations persist. Centrifugal input targets primarily GABAergic interneurons in the GC layer (GCL) and regulates GC excitability, which suggests a causal link between beta oscillations and GC excitability. Previous modeling work from our laboratory predicted that convergence of excitatory/inhibitory inputs onto MCs and centrifugal inputs onto GCs can increase GC excitability sufficiently to drive beta oscillations primarily through voltage dependent calcium channel (VDCC) mediated GABA release, independently of NMDA channels. We test this model by examining the influence of NMDA and muscarinic acetylcholine receptors on GC excitability and beta oscillations. Intrabulbar scopolamine (muscarinic antagonist) infusion decreased or completely suppressed odor-evoked beta in response to a strong stimulus, but increased beta power in response to a weak stimulus, as predicted by our model. Piriform cortex (PC) beta power was unchanged. Oxotremorine (muscarinic agonist) tended to suppress all oscillations, probably from over-inhibition. APV, an NMDA receptor antagonist, suppressed gamma oscillations selectively (in OB and PC), lending support to the models prediction that beta oscillations can be supported by VDCC mediated currents.\n\nNew and NoteworthyO_LIOlfactory bulb beta oscillations rely on granule cell excitability.\nC_LIO_LIReducing granule cell excitability with scopolamine reduces high volatilityinduced beta power but increases low volatility-induced beta power.\nC_LIO_LIPiriform cortex beta oscillations maintain power when olfactory bulb beta power is low, and the system maintains beta band coherence.\nC_LI

neuroscience↗

bioRxiv2017

Detecting cancer vulnerabilities through gene networks under purifying selection in 4,700 cancer genomes

Large-scale cancer sequencing studies have uncovered dozens of mutations critical to cancer initiation and progression. However, a significant proportion of genes linked to tumor propagation remain hidden, often due to noise in sequencing data confounding low frequency alterations. Further, genes in networks under purifying selection (NPS), or those that are mutated in cancers less frequently than would be expected by chance, may play crucial roles in sustaining cancers but have largely been overlooked. We describe here a statistical framework that identifies genes that have a first order protein interaction network significantly depleted for mutations, to elucidate key genetic contributors to cancers. Not reliant on and thus, unbiased by, the gene of interests mutation rate, our approach has identified 685 putative genes linked to cancer development. Comparative analysis indicates statistically significant enrichment of NPS genes in previously validated cancer vulnerability gene sets, while further identifying novel cancer-specific candidate gene targets. As more tumor genomes are sequenced, integrating systems level mutation data through this network approach should become increasingly useful in pinpointing gene targets for cancer diagnosis and treatment.

bioinformatics↗

bioRxiv2017

A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

genomics↗

bioRxiv2017

Fine-mapping of genetic loci driving spontaneous clearance of hepatitis C virus infection

Approximately three quarters of acute HCV infections evolve to a chronic state, while one quarter are spontaneously cleared. Genetic predispositions strongly contribute to the development of chronicity. We have conducted a genome-wide association study to identify genomic variants underlying HCV spontaneous clearance using Immunochip in European and African ancestries. We confirmed two previously reported significant associations, in the IL28B/IFNL41,2 and MHC regions, with spontaneous clearance in the European population. We further fine-mapped the MHC association to a region of about 50 kilo base pairs, down from 1 mega base pairs in the previous study. Additional analyses suggested that the association in the MHC locus might be significantly stronger for virus subtype 1a than 1b, suggesting that viral subtype may have influenced the genetic mechanism underlying the clearance of HCV.

immunology↗

bioRxiv2017

Genoppi: A web application for interactive integration of experimental proteomics results with genetic datasets

SummaryIntegrating protein-protein interaction experiments and genetic datasets can lead to new insight into the cellular processes implicated in diseases, but this integration is technically challenging. Here, we present Genoppi, a web application that integrates quantitative interaction proteomics data and results from genome-wide association studies or exome sequencing projects, to highlight biological relationships that might otherwise be difficult to discern. Written in R, Python and Bash script, Genoppi is a user-friendly framework easily deployed across Mac OS and Linux distributions.\n\nAvailabilityGenoppi is open source and available at https://github.com/lagelab/Genoppi\n\nContactaprilkim@broadinstitute.org and lage.kasper@mgh.harvard.edu

bioinformatics↗