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). Drastic differences in susceptibility towards the specific dopaminergic toxicant 1-methyl-4-phenylpyridinium (MPP+) were observed. Whole-genome sequencing was performed to identify underlying genetic differences. While both SP had normal chromosome structures, they displayed about 70 differences on the level of amino acid changing events. Some of these differences were confirmed biochemically, but none offered a direct explanation for the altered toxicant sensitivity pattern. As second approach, markers known to be relevant for the intended use of the cells were specifically tested. The "ATCC" cells rapidly down-regulated the dopamine-transporter and tyrosine-hydroxylase after differentiation, while "UKN" cells maintained functional levels. As the respective genes were not altered themselves, we conclude that polygenic complex upstream changes can have drastic effects on biochemical features and toxicological responses of relatively similar SP of cells.

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. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra- and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

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. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. CONCLUSIONS: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson's disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer's disease but lower than for Huntington's disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

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. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.