Tatemayo4119

Haemophilus influenzae has contributed to key bacterial genome sequencing hallmarks, as being not only the first bacterium to be genome-sequenced, but also starring the first genome-wide analysis of chromosomes directly transformed with DNA from a divergent genotype, and pioneering Tn-seq methodologies. Over the years, the phenomenal and constantly evolving development of -omic technologies applied to a whole range of biological questions of clinical relevance in the H. influenzae-host interplay, has greatly moved forward our understanding of this human-adapted pathogen, responsible for multiple acute and chronic infections of the respiratory tract. In this way, essential genes, virulence factors, pathoadaptive traits, and multi-layer gene expression regulatory networks with both genomic and epigenomic complexity levels are being elucidated. Likewise, the unstoppable increasing whole genome sequencing information underpinning H. influenzae great genomic plasticity, mainly when referring to non-capsulated strains, poses major challenges to understand the genomic basis of clinically relevant phenotypes and even more, to clearly highlight potential targets of clinical interest for diagnostic, therapeutic or vaccine development. We review here how genomic, transcriptomic, proteomic and metabolomic-based approaches are great contributors to our current understanding of the interactions between H. influenzae and the human airways, and point possible strategies to maximize their usefulness in the context of biomedical research and clinical needs on this human-adapted bacterial pathogen.Human serine hydroxymethyltransferase (SHMT) regulates the serine-glycine one carbon metabolism and plays a role in cancer metabolic reprogramming. Two SHMT isozymes are acting in the cell SHMT1 encoding the cytoplasmic isozyme, and SHMT2 encoding the mitochondrial one. Here we present a molecular model built on experimental data reporting the interaction between SHMT1 protein and SHMT2 mRNA, recently discovered in lung cancer cells. Using a stochastic dynamic model, we show that RNA moieties dynamically regulate serine and glycine concentration, shaping the system behaviour. For the first time we observe an active functional role of the RNA in the regulation of the serine-glycine metabolism and availability, which unravels a complex layer of regulation that cancer cells exploit to fine tune amino acids availability according to their metabolic needs. The quantitative model, complemented by an experimental validation in the lung adenocarcinoma cell line H1299, exploits RNA molecules as metabolic switches of the SHMT1 activity. Our results pave the way for the development of RNA-based molecules able to unbalance serine metabolism in cancer cells.In recent years, the amount of available literature, data and computational tools has increased exponentially, providing opportunities and challenges to make use of this vast amount of material. Here, we describe how we utilized publicly available information to identify the previously hardly characterized protein SAMD1 (SAM domain-containing protein 1) as a novel unmethylated CpG island-binding protein. This discovery is an example, how the richness of material and tools on the internet can be used to make scientific breakthroughs, but also the hurdles that may occur. Specifically, we discuss how the misrepresentation of SAMD1 in literature and databases may have prevented an earlier characterization of this protein and we address what can be learned from this example.RNA modifications, in particular N 6-methyladenosine (m6A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.Intracellular protein trafficking processes consisting of three intracellular states are described by three differential equations. To solve the equations, a quadratic equation is required, and its roots are generally real or complex. The purpose of the present study is to clarify the meanings of roots of real and complex numbers. To clarify the point, we define that 1) ' k I ' is the insertion rate from an insertion state trafficking to the plasma membrane state; 2) ' k E ', the endocytotic rate from the plasma membrane state trafficking to a recycling state; 3) ' k R ', the recycling rate from the recycling state trafficking to the insertion state. Amounts of proteins in three states are expressed as α e lt + β e mt + γ with α , β , γ = constant and l and m are roots of a quadratic equation, r 2 + k I + k E + k R r + k I k E + k I k R + k E k R = 0 . When l and m are real k I 2 + k E 2 + k R 2 > 2 k I k E + k E k R + k R k I , amounts of proteins in three states shows no oscillatory change but a monotonic change after a transient increase (or decrease); when l and m are complex k I 2 + k E 2 + k R 2 less then 2 k I k E + k E k R + k R k I , amounts of proteins in three states are expressed as α e lt + β e mt + γ = 2 g 2 + h 2 sin b t + σ e at + γ ( α , β , l , m = complex and γ , a , b , g , h , σ = real α , β = conjugate each other; l , m = conjugate each other), showing an oscillatory change with time. The frequency of oscillatory change appearance is evaluated to be 60% at random combinations of three trafficking rates, k I , k E and k R . The present study indicates that complex numbers have an essentially important meaning in appearance of oscillatory phenomena in bodily and cellular function.BV (bacterial vaginosis) influences 20%-40% of women but its etiology is still poorly understood. An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the major force driving the vaginal microbiota dysbiosis. Here, we recast the question of microbial causality of BV by asking if there are any prevalent 'signatures' (network motifs) in the vaginal microbiome networks associated with it? We apply a new framework [species dominance network analysis by Ma & Ellison (2019) Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the 16 s-rRNA gene sequencing datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio-motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel's community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected 'cults' associated with the occurrence of BV. The presence of the trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more mechanistic interpretation of its risks and etiology. We caution that scarcity of large longitudinal datasets of HVM also limited further verifications of our findings, and these findings require further clinical tests to launch their applications.Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.For the whole GFP family, a few cases, when a single mutation in the chromophore environment strongly inhibits maturation, were described. Here we study EYFP-F165G - a variant of the enhanced yellow fluorescent protein - obtained by a single F165G replacement, and demonstrated multiple fluorescent states represented by the minor emission peaks in blue and yellow ranges (~470 and ~530 nm), and the major peak at ~330 nm. The latter has been assigned to tryptophan fluorescence, quenched due to excitation energy transfer to the mature chromophore in the parental EYFP protein. EYFP-F165G crystal structure revealed two general independent routes of post-translational chemistry, resulting in two main states of the polypeptide chain with the intact chromophore forming triad (~85%) and mature chromophore (~15%). Our experiments thus highlighted important stereochemical role of the 165th position strongly affecting spectral characteristics of the protein. On the basis of the determined EYFP-F165G three-dimensional structure, new variants with ~ 2-fold improved brightness were engineered.