Major changes of cell function and toxicant sensitivity in cultured cells undergoing mild, quasi-natural genetic drift.

Genomic drift affects the functional properties of cell lines, and the reproducibility of data from in vitro studies. While chromosomal aberrations and mutations in single pivotal genes are well explored, little is known about effects of minor, possibly pleiotropic, genome changes. We addressed this question for the human dopaminergic neuronal precursor cell line LUHMES by comparing two subpopulations (SP) maintained either at the American-Type-Culture-Collection (ATCC) or by the original provider (UKN).

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.

The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease.

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson's disease, the relevance of this cellular model in the context of Parkinson's disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated.

Results: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations.

Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states.

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network.