Biology subjects

Newman, N. K.

Publications and source records attributed to Newman, N. K..

4 recordsLinked to original sources

bioRxiv2025

SiSaNA: A Python-based command line interface for Single-Sample Network Analysis

MotivationReconstructing and analyzing single-sample regulatory networks has proven useful for understanding mechanisms of regulatory biology and its disruption in disease, but traditionally required a fair amount of programming experience and work from the user. ResultsHere, we present SiSaNA (Single-Sample Network Analysis), a workflow that removes the need of prior programming experience and allows for single-sample regulatory network reconstruction and analysis, all via a command line interface. Using example input files, we walk the user through a tutorial on how to run the SiSaNA pipeline, interpret the results, and obtain high-quality figures summarized in a single HTML file as output. AvailabilitySiSaNA is freely available to install with pip via the Python Package Index. It is additionally available on GitHub at https://github.com/kuijjerlab/sisana. Example input files can be found at https://doi.org/10.5281/zenodo.17190642.

bioinformatics↗

bioRxiv2023

Transkingdom Network Analysis (TkNA): a systems approach inferring causal factors underlying host-microbiota interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the networks local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

systems biology↗

bioRxiv2022

Reversing gut microbiome-driven adipose tissue inflammation alleviates metabolic syndrome

The gut microbiota contributes to macrophage-mediated inflammation in adipose tissue with consumption of an obesogenic diet, thus driving the development of metabolic syndrome. There is a need to identify and develop interventions that abrogate this condition. The hops-derived prenylated flavonoid xanthohumol (XN) and its semi-synthetic derivative tetrahydroxanthohumol (TXN) attenuate high-fat diet-induced obesity, hepatosteatosis and metabolic syndrome in C57Bl/6J mice. This coincides with a decrease in pro-inflammatory gene expression in the gut and adipose tissue, together with alterations in the gut microbiota and bile acid composition. In this study, we integrated and interrogated multi-omics data from different organs with fecal 16S sequences and systemic metabolic phenotypic data using a transkingdom network analysis. By incorporating cell type information from single cell RNA-seq data, we discovered TXN attenuates macrophage inflammatory processes in adipose tissue. TXN treatment also reversed levels of inflammation-inducing microbes, such as Oscillibacter valericigenes, that lead to adverse metabolic phenotypes. Furthermore, in vitro validation in macrophage cell lines and in vivo mouse supplementation showed addition of O. valericigenes supernatant induced the expression of metabolic macrophage signature genes that are downregulated by TXN in vivo. Our findings establish an important mechanism by which TXN mitigates adverse phenotypic outcomes from diet-induced obesity and metabolic syndrome. It primarily reduces the abundance of pro-inflammatory gut microbes that can otherwise promote macrophage-associated inflammation in adipose tissue.

systems biology↗

bioRxiv2020

Host response to cholestyramine can be mediated by the gut microbiota

The gut microbiome has been implicated as a major factor contributing to metabolic diseases as well as being contributors to the response to drugs used for the treatment of such diseases. In this study, using a diet-induced obesity mouse model, we tested the effect of cholestyramine, a bile acid sequestrant, on the murine gut microbiome and mammalian metabolism. We also explored the hypothesis that some beneficial effects of this drug on systemic metabolism can be attributed to alterations in gut microbiota. First, we demonstrated that cholestyramine can decrease glucose and epidydimal fat levels. Next, while investigating gut microbiota we found increased alpha diversity of the gut microbiome of cholestyramine-treated mice, with fourteen taxa showing restoration of abundance to levels resembling those in mice fed with a control diet. Analyzing expression of genes known to be regulated by cholestyramine (including Cyp7a1), we confirmed the expected effect of this drug in the liver and ileum. Finally, using a transkingdom network analysis we inferred Acetatifactor muris and Muribaculum intestinale as potential mediators/modifiers of cholestyramine effects on the mammalian host. In addition, A. muris correlated positively with glucagon (Gcg) expression in the ileum and negatively correlated with small heterodimer partner (Shp) expression in the liver. Interestingly, A. muris also correlated negatively with glucose levels, further indicating the potential probiotic role for A. muris. In conclusion, our results indicate the gut microbiome has a role in the beneficial effects of cholestyramine and suggest specific microbes as targets of future investigations.

pharmacology and toxicology↗