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Full-Text Articles in Medicine and Health Sciences
Microbiomes Of Blood-Feeding Arthropods: Genes Coding For Essential Nutrients And Relation To Vector Fitness And Pathogenic Infections. A Review, Daniel E. Sonenshine, Philip E. Stewart
Microbiomes Of Blood-Feeding Arthropods: Genes Coding For Essential Nutrients And Relation To Vector Fitness And Pathogenic Infections. A Review, Daniel E. Sonenshine, Philip E. Stewart
Biological Sciences Faculty Publications
Blood-feeding arthropods support a diverse array of symbiotic microbes, some of which facilitate host growth and development whereas others are detrimental to vector-borne pathogens. We found a common core constituency among the microbiota of 16 different arthropod blood-sucking disease vectors, including Bacillaceae, Rickettsiaceae, Anaplasmataceae, Sphingomonadaceae, Enterobacteriaceae, Pseudomonadaceae, Moraxellaceae and Staphylococcaceae. By comparing 21 genomes of common bacterial symbionts in blood-feeding vectors versus non-blooding insects, we found that certain enteric bacteria benefit their hosts by upregulating numerous genes coding for essential nutrients. Bacteria of blood-sucking vectors expressed significantly more genes (p < 0.001) coding for these essential nutrients than those of non-blooding insects. Moreover, compared to endosymbionts, the genomes of enteric bacteria also contained significantly more genes (p < 0.001) that code for the synthesis of essential amino acids and proteins that detoxify reactive oxygen species. In contrast, microbes in non-blood-feeding insects expressed few gene families coding for these nutrient categories. We also discuss specific midgut bacteria essential for the normal development of pathogens (e.g., Leishmania) versus …
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …