Convergent evolution of modularity in metabolic networks through different community structures

Background: It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of themodularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results: In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions: We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organismﾒs metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations

The response to influenza vaccination is associated with DNA methylation-driven regulation of T cell innate antiviral pathways.

BACKGROUND: The effect of vaccination on the epigenome remains poorly characterized. In previous research, we identified an association between seroprotection against influenza and DNA methylation at sites associated with the RIG-1 signaling pathway, which recognizes viral double-stranded RNA and leads to a type I interferon response. However, these studies did not fully account for confounding factors including age, gender, and BMI, along with changes in cell-type composition. RESULTS: Here, we studied the influenza vaccine response in a longitudinal cohort vaccinated over two consecutive years (2019-2020 and 2020-2021), using peripheral blood mononuclear cells and a targeted DNA methylation approach. To address the effects of multiple factors on the epigenome, we designed a multivariate multiple regression model that included seroprotection levels as quantified by the hemagglutination-inhibition (HAI) assay test. CONCLUSIONS: Our findings indicate that 179 methylation sites can be combined as potential signatures to predict seroprotection. These sites were not only enriched for genes involved in the regulation of the RIG-I signaling pathway, as found previously, but also enriched for other genes associated with innate immunity to viruses and the transcription factor binding sites of BRD4, which is known to impact T cell memory. We propose a model to suggest that the RIG-I pathway and BRD4 could potentially be modulated to improve immunization strategies

novoBreak: local assembly for breakpoint detection in cancer genomes.

We present novoBreak, a genome-wide local assembly algorithm that discovers somatic and germline structural variation breakpoints in whole-genome sequencing data. novoBreak consistently outperformed existing algorithms on real cancer genome data and on synthetic tumors in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge primarily because it more effectively utilized reads spanning breakpoints. novoBreak also demonstrated great sensitivity in identifying short insertions and deletions

Comprehensive evaluation of extracellular small RNA isolation methods from serum in high throughput sequencing

Supplementary Tables S1-S9. (XLSX 576 kb

Functional consequence of the MET-T1010I polymorphism in breast cancer.

Major breast cancer predisposition genes, only account for approximately 30% of high-risk breast cancer families and only explain 15% of breast cancer familial relative risk. The HGF growth factor receptor MET is potentially functionally altered due to an uncommon germline single nucleotide polymorphism (SNP), MET-T1010I, in many cancer lineages including breast cancer where the MET-T1010I SNP is present in 2% of patients with metastatic breast cancer. Expression of MET-T1010I in the context of mammary epithelium increases colony formation, cell migration and invasion in-vitro and tumor growth and invasion in-vivo. A selective effect of MET-T1010I as compared to wild type MET on cell invasion both in-vitro and in-vivo suggests that the MET-T1010I SNP may alter tumor pathophysiology and should be considered as a potential biomarker when implementing MET targeted clinical trials

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others

Properties of metabolic graphs: biological organization or representation artifacts?

<p>Abstract</p> <p>Background</p> <p>Standard graphs, where each edge links two nodes, have been extensively used to represent the connectivity of metabolic networks. It is based on this representation that properties of metabolic networks, such as hierarchical and small-world structures, have been elucidated and null models have been proposed to derive biological organization hypotheses. However, these graphs provide a simplistic model of a metabolic network's connectivity map, since metabolic reactions often involve more than two reactants. In other words, this map is better represented as a hypergraph. Consequently, a question that naturally arises in this context is whether these properties truly reflect biological organization or are merely an artifact of the representation.</p> <p>Results</p> <p>In this paper, we address this question by reanalyzing topological properties of the metabolic network of <it>Escherichia coli </it>under a hypergraph representation, as well as standard graph abstractions. We find that when clustering is properly defined for hypergraphs and subsequently used to analyze metabolic networks, the scaling of clustering, and thus the hierarchical structure hypothesis in metabolic networks, become unsupported. Moreover, we find that incorporating the distribution of reaction sizes into the null model further weakens the support for the scaling patterns.</p> <p>Conclusions</p> <p>These results combined suggest that the reported scaling of the clustering coefficients in the metabolic graphs and its specific power coefficient may be an artifact of the graph representation, and may not be supported when biochemical reactions are atomically treated as hyperedges. This study highlights the implications of the way a biological system is represented and the null model employed on the elucidated properties, along with their support, of the system.</p

Analytical protocol to identify local ancestry-associated molecular features in cancer

People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the "local" ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et&nbsp;al. (2020)