Biology subjects

Poirion, O. B.

Publications and source records attributed to Poirion, O. B..

4 recordsLinked to original sources

bioRxiv2018

Neo-formation of chromosomes in bacteria

Although the bacterial secondary chromosomes/megaplasmids/chromids, first noticed about forty years ago, are commonly held to originate from stabilized plasmids, their true nature and definition are yet to be resolved. On the premise that the integration of a replicon within the cell cycle is key to deciphering its essential nature, we show that the content in genes involved in the replication, partition and segregation of the replicons and in the cell cycle discriminates the bacterial replicons into chromosomes, plasmids, and another class of essential genomic elements that function as chromosomes. These latter do not derive directly from plasmids. Rather, they arise from the fission of a multi-replicon molecule corresponding to the co-integrated and rearranged ancestral chromosome and plasmid. All essential replicons in a distributed genome are thus neochromosomes. Having a distributed genome appears to extend and accelerate the exploration of the bacterial genome evolutionary landscape, producing complex regulation and leading to novel eco-phenotypes and species diversification.

genomics↗

bioRxiv2017

Phenotypic associations of consensus driver genes in hepatocellular carcinoma

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used TCGA mutations and copy number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy number variations and transcriptomic data reveals that these consensus driver mutations and their copy number variations are associated with majority (62.5%) of the mRNA transcriptome, but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53 and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

bioinformatics↗

bioRxiv2017

Deep Learning based multi-omics integration robustly predicts survival in liver cancer

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We build the DL based, survival-sensitive model on 360 HCC patients data using RNA-seq, miRNA-seq and methylation data from TCGA, which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL based model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, C-index=0.75), NCI cohort (n=221, C-index=0.67), Chinese cohort (n=166, C-index=0.69), E-TABM-36 cohort (n=40, C-index=0.77), and Hawaiian cohort (n=27, C-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction.

bioinformatics↗

bioRxiv2016

Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis

Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

bioinformatics↗