Biology subjects

Herrington, D.

Publications and source records attributed to Herrington, D..

6 recordsLinked to original sources

bioRxiv2021

Determining molecular archetype composition and expression from bulk tissues with unsupervised deconvolution

Complex tissues are composite ecological systems whose components interact with each other to create a unique physiological or pathophysiological state distinct from that found in other tissue microenvironments. To explore this ground yet dynamic state, molecular profiling of bulk tissues and mathematical deconvolution can be jointly used to characterize heterogeneity as an aggregate of molecularly distinct tissue or cell subtypes. We first introduce an efficient and fully unsupervised deconvolution method, namely the Convex Analysis of Mixtures - CAM3.0, that may aid biologists to confirm existing or generate novel scientific hypotheses about complex tissues in many biomedical contexts. We then evaluate the CAM3.0 functional pipelines using both simulations and benchmark data. We also report diverse case studies on bulk tissues with unknown number, proportion and expression patterns of the molecular archetypes. Importantly, these preliminary results support the concept that expression patterns of molecular archetypes often reflect the interactive not individual contributions of many known or novel cell types, and unsupervised deconvolution would be more powerful in uncovering novel multicellular or subcellular archetypes.

bioinformatics↗

bioRxiv2020

Divergent effects of Western and Mediterranean diets on behavior and monocyte polarization.

Dietary changes associated with industrialization substantially increase the prevalence of chronic diseases, such as obesity, type II diabetes, and cardiovascular disease, which are major contributors to the public health burden. The high prevalence of these chronic diseases is often attributed to an "evolutionary mismatch," between human physiology and modern nutritional environments. In support of this idea, Western diets enriched with foods that were scarce throughout human evolutionary history (e.g., simple sugars and saturated fats) promote inflammation and disease relative to diets more akin to hunter-gatherer diets, such as a Mediterranean diet; however, the mechanisms linking dietary mismatch to inflammation and chronic disease are poorly understood. We used a macaque model and whole diet manipulations to evaluate one possible mechanism - inflammatory polarization of monocytes - that potentially leads to this evolutionary mismatch. After consuming a Western- or Mediterranean-like diet for 15 months, monocytes from Western diet consumers exhibited a more proinflammatory phenotype, with 40% of their genes differentially expressed (FDR<0.05). Compared to the Mediterranean diet, the Western diet shifted the co-expression of 445 gene pairs, including small RNAs and transcription factors associated with metabolism and adiposity in humans, and dramatically altered animal behavior. For example, Western-fed individuals were more anxious and less socially integrated compared to the Mediterranean-fed subjects. These behavioral changes were also associated with some of the effects of diet on gene expression, suggesting an interaction between diet, central nervous system activity, and monocyte gene expression. The results of this study provide new insights into evolutionary mismatch at the molecular level and uncover new pathways through which Western diets generate inflammation and disease.

genomics↗

bioRxiv2019

Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection

MotivationLiquid chromatography - mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot completely correct misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning.\n\nResultsHere we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to the XCMS package.\n\nAvailability and ImplementationAn R package of ncGTW is freely available at https://github.com/ChiungTingWu/ncGTW. A detailed users manual and a vignette are provided within the package.\n\nContactyuewang@vt.edu, yug@vt.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Data-driven robust detection of tissue/cell-specific markers

Tissue or cell subtype-specific and differentially-expressed genes (SDEGs) are defined as being differentially expressed in a particular tissue or cell subtype among multiple subtypes. Detecting SDEGs plays a critical rolse in molecularly characterizing and identifying tissue or cell subtypes, and facilitating supervised deconvolution of complex tissues. Unfortunately, classic differential analysis assumes a convenient null hypothesis and associated test statistic that is subtype-non-specific and thus, resulting in a high false positive rate and/or lower detection power with respect to particular subtypes. Here we introduce One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. To assess the statistical significance of such test, we also propose the scaled test statistic OVE-sFC together with a mixture null distribution model and a tailored permutation scheme. Validated with realistic synthetic data sets on both type 1 error and detection power, OVE-FC/sFC test applied to two benchmark gene expression data sets detects many known and de novo SDEGs. Subsequent supervised deconvolution results, obtained using the SDEGs detected by OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.

bioinformatics↗

bioRxiv2018

Unsupervised deconvolution of molecular heterogeneity uncovers novel signatures and glia-neuron ratio

Introduction Introduction Results Discussion Methods AUTHOR CONTRIBUTIONS Competing interests Appendix References While the two major types of cells in the brain are known to be glia and neuron, the true ratio of glia to neurons in the brain remains a mystery. One of recent studies using efficient cell counting method provides compelling evidence for 1:1 ratio on four whole human brains 1. The same study also reveals that the ratio of glia to neurons in the brain varies from one region to another, sometimes dramatically, e.g., 3.76:1 in the cerebral cortex versus 1:4.3 in the cerebellum 1,2. However, other scientists have argued that more rigorous studies are needed in which just about every known ...

bioinformatics↗

bioRxiv2017

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. Here we describe the human arterial proteome and the proteomic features strongly associated with early atherosclerosis based on mass-spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling and bioinformatic analyses defined the composition, network re-wiring and likely regulatory features of the protein networks associated with early atherosclerosis. Among other things the results reveal major differences in mitochondrial protein mass between the coronary artery and distal aorta in both normal and atherosclerotic samples - highlighting the importance of anatomic specificity and dynamic network structures in in the study of arterial proteomics. The publicly available data resource and the description of the analysis pipeline establish a new foundation for understanding the proteomic architecture of atherosclerosis and provide a template for similar investigations of other chronic diseases characterized by multi-cellular tissue phenotypes.\n\nHighlightsO_LILC MS/MS analysis performed on 200 human aortic or coronary artery samples\nC_LIO_LINumerous proteins, networks, and regulatory pathways associated with early atherosclerosis\nC_LIO_LIMitochondrial proteins mass and selected metabolic regulatory pathways vary dramatically by disease status and anatomic location\nC_LIO_LIPublically available data resource and analytic pipeline are provided or described in detail\nC_LI

molecular biology↗