Biology subjects

Shutta, K. H.

Publications and source records attributed to Shutta, K. H..

3 recordsLinked to original sources

bioRxiv2022

SpaCeNet: Spatial Cellular Networks from omics data

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells together with the cells spatial distribution provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells. A python implementation of SpaCeNet is publicly available at https://github.com/sschrod/SpaCeNet.

bioinformatics↗

bioRxiv2022

Plasma and ovarian metabolomics responses to chronic stress in female mice

BackgroundChronic stress may affect metabolism of amino acids, lipids, and other small molecule metabolites, but these alterations may differ depending on tissue evaluated. We examined metabolomic changes in plasma and ovarian tissue samples from female mice due to chronic stress exposure. MethodsAt 12 weeks old, healthy, female, C57 black mice were randomly assigned to three weeks of chronic stress using daily restraint (2 hours/day; n=9) or normal care (n=10). Metabolomic profiling was conducted on plasma and ovarian tissues. Using the Wilcoxon Rank Test, Metabolite Set Enrichment Analysis, and Differential Network Analysis we identified metabolomic alterations occurring in response to restraint stress. All p-values were corrected for multiple testing using the false discovery rate approach. ResultsIn plasma, individual lysophosphatidylcholines (positively) and the metabolite classes carnitines (positively), diacylglycerols and triacylglycerols (inversely) were associated with restraint stress (adjusted-ps<0.2). In contrast, diacylglycerols and triacylglycerols were increased while carnitines were decreased in ovarian tissue from stressed mice (adjusted-ps<0.2). However, several metabolites (cholesteryl esters, phosphatidylcholines/ phosphatidylethanolamines plasmalogens and multiple amino acids) were consistently inversely associated with restraint stress in plasma and ovarian tissue (adjusted-ps<0.2). ConclusionWe identified differences in multiple lipid and amino acid metabolites in plasma and ovarian tissue of female mice after exposure to chronic stress. Some affected metabolites (primarily triacylglycerols and diacylglycerols) exhibited opposite associations with chronic stress in plasma (a marker of systemic influences) versus in ovarian tissue (representing local changes), suggesting research to understand the biological impact of chronic stress needs to consider both systemic and tissue-specific alterations.

animal behavior and cognition↗

bioRxiv2021

SpiderLearner: An ensemble approach to Gaussian graphical model estimation

Multivariate biological data are often modeled using networks in which nodes represent a biological variable (e.g., genes) and edges represent associations (e.g., coexpression). A Gaussian graphical model (GGM), or partial correlation network, is an undirected graphical model in which a weighted edge between two nodes represents the magnitude of their partial correlation, and the absence of an edge indicates zero partial correlation. A GGM provides a roadmap of direct dependencies between variables, providing a valuable systems-level perspective. Many methods exist for estimating GGMs; estimated GGMs are typically highly sensitive to choice of method, posing an outstanding statistical challenge. We address this challenge by developing SpiderLearner, a tool that combines a range of candidate GGM estimation methods to construct an ensemble estimate as a weighted average of results from each candidate. In simulation studies, SpiderLearner performs better than or comparably to the best of the candidate methods. We apply SpiderLearner to estimate a GGM for gene expression in a publicly available dataset of 260 ovarian cancer patients. Using the community structure of the GGM, we develop a network-based risk score which we validate in six independent datasets. The risk score requires only seven genes, each of which has important biological function. Our method is flexible, extensible, and has demonstrated potential to identify de novo biomarkers for complex diseases. An open-source implementation of our method is available at https://github.com/katehoffshutta/SpiderLearner.

bioinformatics↗