Detecting laterally transferred genes: use of entropic clustering methods and genome position

Most parametric methods for detecting foreign genes in bacterial genomes use a scoring function that measures the atypicality of a gene with respect to the bulk of the genome. Genes whose features are sufficiently atypical—lying beyond a threshold value—are deemed foreign. Yet these methods fail when the range of features of donor genomes overlaps with that of the recipient genome, leading to misclassification of foreign and native genes; existing parametric methods choose threshold parameters to balance these error rates. To circumvent this problem, we have developed a two-pronged approach to minimize the misclassification of genes. First, beyond classifying genes as merely atypical, a gene clustering method based on Jensen–Shannon entropic divergence identifies classes of foreign genes that are also similar to each other. Second, genome position is used to reassign genes among classes whose composition features overlap. This process minimizes the misclassification of either native or foreign genes that are weakly atypical. The performance of this approach was assessed using artificial chimeric genomes and then applied to the well-characterized Escherichia coli K12 genome. Not only were foreign genes identified with a high degree of accuracy, but genes originating from the same donor organism were effectively grouped

Identification of Novel Genomic Islands in Liverpool Epidemic Strain of Pseudomonas aeruginosa Using Segmentation and Clustering

This article utilizes a recursive segmentation and cluster procedure presented as a genome-mining tool, GEMINI, to decipher genomic islands and understand their contributions to the evolution of virulence and antibiotic resistance in Pseudomonas aeruginosa

CancerNet: a unified deep learning network for pan‑cancer diagnostics

Article states that despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. The author's proposed framework for cancer diagnostics detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas. Their model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, holding a great promise in early cancer detection

Molecular signatures in the progression of COVID-19 severity

Authors of the article discuss that to uncover genes and pathways involved in the differential clinical manifestations of COVID-19 they developed a novel gene co-expression network-based pipeline that uses gene expression obtained from different SARS-CoV-2 infected human tissues. Their study shines a new light on genes and their networks (modules) that drive the progression of COVID-19 from moderate to extremely severe condition, which could aid development of new therapeutics to combat COVID-19

Classifying Drug Resistance in the NCI60 Cancer Cell Lines Based on the mRNA Expression Levels of the 48 ABC Transporters

Poster for the 2014 MCBIOS Conference. This poster discusses classifying drug resistance in the NCI60 cancer cell lines based on the mRNA expression levels of the 48 ABC transporters

POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling

Article presents POSMM, Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. The authors claim that by combining POSMM with ultrafast classifiers such as Kraken2, their complementary strengths can be leveraged to produce higher overall accuracy in metagenomic sequence classification than by either as a standalone classifier. POSMM is a user-friendly and highly adaptable tool designed for broad use by the metagenome scientific community

Local and Systemic Metabolic Responses during Light-Induced Rapid Systemic Signaling

This article reports that local application of light stress to one rosette leaf of bolting Arabidopsis (Arabidopsis thaliana) plants resulted in a metabolic response that encompassed local, systemic and transport tissues, demonstrating a high degree of physical and metabolic continuity between different tissues throughout the plant

Phylogenetic analysis of eukaryotic NEET proteins uncovers a link between a key gene duplication event and the evolution of vertebrates

NEET proteins belong to a unique family of iron-sulfur proteins in which the 2Fe-2S cluster is coordinated by a CDGSH domain that is followed by the “NEET” motif. They are involved in the regulation of iron and reactive oxygen metabolism, and have been associated with the progression of diabetes, cancer, aging and neurodegenerative diseases. Despite their important biological functions, the evolution and diversification of eukaryotic NEET proteins are largely unknown. Here we used the three members of the human NEET protein family (CISD1, mitoNEET; CISD2, NAF-1 or Miner 1; and CISD3, Miner2) as our guides to conduct a phylogenetic analysis of eukaryotic NEET proteins and their evolution. Our findings identified the slime mold Dictyostelium discoideum’s CISD proteins as the closest to the ancient archetype of eukaryotic NEET proteins. We further identified CISD3 homologs in fungi that were previously reported not to contain any NEET proteins, and revealed that plants lack homolog(s) of CISD3. Furthermore, our study suggests that the mammalian NEET proteins, mitoNEET (CISD1) and NAF-1 (CISD2), emerged via gene duplication around the origin of vertebrates. Our findings provide new insights into the classification and expansion of the NEET protein family, as well as offer clues to the diverged functions of the human mitoNEET and NAF-1 proteins

C.elegans as a Diabetes & Ischemia Model: Identification of Genetic and Cellular Changes that Modulate the Survival of Hyperglycemia and Oxygen-Deprivation

Diet represents an exogenous influence that often yields colossal effects on an individual’s phenotype, physiology, long-term health and disease risk. The overconsumption of dietary sugars for example, has contributed to significant increases in obesity and type 2 diabetes, health issues that are costly both in terms of dollars and human life. Additionally, individuals with these conditions have compromised oxygen delivery and thus, an increased vulnerability to other oxygen-deprivation related disease states, including cardiovascular disease, ischemic strokes, vascular and coronary diseases and myocardial infarction. While human and other mammalian studies have shown that individuals with type 2 diabetes have a worse prognosis and recovery after being challenged with an oxygen-deprivation related injury, mechanistic understanding regarding why this is the case is lacking. We are using C. elegans to identify genetic and cellular changes that modulate responses to the combinatory stress of hyperglycemia and oxygen-deprivation. We have determined that C. elegans fed a high glucose diet have increased cellular glucose (hyperglycemia), increased lipid content and increased sensitivity to oxygen-deprivation (anoxia) and ROS induction. We have determined that the insulin-like signaling pathway, via fatty acid and ceramide synthesis, modulates the increased sensitivity to anoxia. In mammalian systems, specific ceramide species increase after an ischemic event and are also linked to detrimental effects observed in diabetic patients, underscoring the potential role these molecules have in modulating oxygen-deprivation and hyperglycemia responses in individuals. Specific fatty acids also have known roles as both signaling molecules and as integral membrane components, thus, we hypothesize that a high-glucose diet disrupts fatty acid and ceramide homeostasis resulting in aberrations in metabolic processes and stress response pathways that are essential for the survival of oxygen-deprivation. Additionally, gene expression analysis (via RNAseq) on C. elegans fed either a standard or glucose-supplemented diet revealed that glucose impacts the expression of genes involved with multiple cellular processes, including lipid and carbohydrate metabolism, stress responses, cell division and extracellular functions. Several of the genes we identified are also differentially regulated in obese and type-2 diabetic human individuals, indicating a high degree of conserved gene expression changes between C. elegans fed a glucose-supplemented diet and in diabetic and/or obese human individuals. Together this work underscores how both diet and genotype impact stress responses and supports the use of C. elegans as a model for further elucidating the molecular mechanisms regulating dietary-induced metabolic diseases

Simplifying the mosaic description of DNA sequences

By using the Jensen-Shannon divergence, genomic DNA can be divided into compositionally distinct domains through a standard recursive segmentation procedure. Each domain, while significantly different from its neighbours, may however share compositional similarity with one or more distant (non--neighbouring) domains. We thus obtain a coarse--grained description of the given DNA string in terms of a smaller set of distinct domain labels. This yields a minimal domain description of a given DNA sequence, significantly reducing its organizational complexity. This procedure gives a new means of evaluating genomic complexity as one examines organisms ranging from bacteria to human. The mosaic organization of DNA sequences could have originated from the insertion of fragments of one genome (the parasite) inside another (the host), and we present numerical experiments that are suggestive of this scenario.Comment: 16 pages, 1 figure, Accepted for publication in Phys. Rev.