3,048 research outputs found
Use of systems biology to decipher host–pathogen interaction networks and predict biomarkers
AbstractIn systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host–pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host–pathogen interactions. Pioneering work was done to predict molecular host–pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host–pathogen interaction models
A network inference method for large-scale unsupervised identification of novel drug-drug interactions
Characterizing interactions between drugs is important to avoid potentially
harmful combinations, to reduce off-target effects of treatments and to fight
antibiotic resistant pathogens, among others. Here we present a network
inference algorithm to predict uncharacterized drug-drug interactions. Our
algorithm takes, as its only input, sets of previously reported interactions,
and does not require any pharmacological or biochemical information about the
drugs, their targets or their mechanisms of action. Because the models we use
are abstract, our approach can deal with adverse interactions,
synergistic/antagonistic/suppressing interactions, or any other type of drug
interaction. We show that our method is able to accurately predict
interactions, both in exhaustive pairwise interaction data between small sets
of drugs, and in large-scale databases. We also demonstrate that our algorithm
can be used efficiently to discover interactions of new drugs as part of the
drug discovery process
Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins
One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans’ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins
Molecular signatures of the aging brain: finding the links between genes and phenotypes
Aging is associated with cognitive decline and increased vulnerability to neurodegenerative diseases. The progressive extension of the average human lifespan is bound to lead to a corresponding increase in the fraction of cognitively impaired elderly individuals among the human population, with an enormous societal and economic burden. At the cellular and tissue levels, cognitive decline is linked to a reduction in specific neuronal subpopulations, a widespread decrease in synaptic plasticity and an increase in neuroinflammation due to an enhanced activation of astrocytes and microglia, but the molecular mechanisms underlying these functional changes during normal aging and in neuropathological conditions remain poorly understood. In this review, we summarize very recent and outstanding progress in elucidating the molecular changes associated with cognitive decline through the genome-wide profiling of aging brain cells at different molecular levels (genomic, epigenomic, transcriptomic, proteomic). We discuss how the correlation of different molecular and phenotypic traits driven by mathematical and computational analyses of large datasets has led to the prediction of key molecular nodes of neurodegenerative pathways, and provide a few examples of candidate regulators of cognitive decline identified with these approaches. Furthermore, we highlight the dysregulation of the synaptic transcriptome in neuronal cells and of the inflammatory transcriptome in glial cells as some of the key events during normal and neuropathological human brain aging
Circ Res
Rationale and ObjectiveIn this Emerging Science Review, we discuss a systems genetics strategy, which we call Gene Module Association Study (GMAS), as a novel approach complementing Genome Wide Association Studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly.MethodsThe first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high throughput) data from the population to construct gene co-expression networks. Co-expression analysis typically groups 20,000 genes into 20\u201330 modules containing 10\u2019s to 100\u2019s of genes, whose aggregate behavior can be represented by the module\u2019s \u201ceigengene.\u201d The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of SNPs.Results and ConclusionsThe goal of the GMAS approach is to identify groups of co-regulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the \u201cgood enough solution\u201d concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, i.e. a \u201cgood enough solution\u201d to accomplish normal biological functions. However, when faced with environmental stresses, some \u201cgood enough solutions\u201d adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations.R01 HL094322/HL/NHLBI NIH HHS/United StatesR21 HL110667/HL/NHLBI NIH HHS/United StatesP01 HL28481/HL/NHLBI NIH HHS/United StatesP01 HL080111/HL/NHLBI NIH HHS/United StatesHHSN268201000035C/HL/NHLBI NIH HHS/United StatesK25 HL080079/HL/NHLBI NIH HHS/United StatesP01 HL078931/HL/NHLBI NIH HHS/United StatesP01 HL028481/HL/NHLBI NIH HHS/United StatesUL1 TR000124/TR/NCATS NIH HHS/United StatesT32HL69766/HL/NHLBI NIH HHS/United StatesR01 HL070748/HL/NHLBI NIH HHS/United States1DP3 D094311/DP/NCCDPHP CDC HHS/United StatesR01 HL101228/HL/NHLBI NIH HHS/United StatesP01 HL30568/HL/NHLBI NIH HHS/United StatesR21 HL110667-01/HL/NHLBI NIH HHS/United StatesP01 HL030568/HL/NHLBI NIH HHS/United StatesT32 HL069766/HL/NHLBI NIH HHS/United StatesHHSN268201000035C/PHS HHS/United StatesR01 GM095656/GM/NIGMS NIH HHS/United States2013-08-03T00:00:00Z22859671PMC3428228vault:743
Indentifying sub-network functional modules in protein undirected networks
Protein networks are usually used to describe the interacting behaviours of complex biosystems.
Bioinformatics must be able to provide methods to mine protein undirected networks and to infer subnetworks
of interacting proteins for identifying relevant biological pathways.
Here we present FunMod an innovative Cytoscape version 2.8 plugin able to identify biologically
significant sub-networks within informative protein networks, enabling new opportunities for elucidating
pathways involved in diseases. Moreover FunMod calculates three topological coefficients for each subnetwork,
for a better understanding of the cooperative interactions between proteins and discriminating the
role played by each protein within a functional module.
FunMod is the first Cytoscape plugin with the ability of combining pathways and topological analysis
allowing the identification of the key proteins within sub-network functional modules
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19
The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of
effective drugs and vaccines gave rise to a wide variety of strategies employed
to fight this worldwide pandemic. Many of these strategies rely on the
repositioning of existing drugs that could shorten the time and reduce the cost
compared to de novo drug discovery. In this study, we presented a new
network-based algorithm for drug repositioning, called SAveRUNNER (Searching
off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by
quantifying the interplay between the drug targets and the disease-specific
proteins in the human interactome via a novel network-based similarity measure
that prioritizes associations between drugs and diseases locating in the same
network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14
selected diseases with a consolidated knowledge about their disease-causing
genes and that have been found to be related to COVID-19 for genetic
similarity, comorbidity, or for their association to drugs tentatively
repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we
identified 282 repurposable drugs, including some the most rumored off-label
drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs,
actually used in clinical practice. Furthermore, to maximize the efficiency of
putative downstream validation experiments, we prioritized 24 potential
anti-SARS-CoV repurposable drugs based on their network-based similarity
values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies,
and thrombin inhibitors. Finally, our findings were in-silico validated by
performing a gene set enrichment analysis, which confirmed that most of the
network-predicted repurposable drugs may have a potential treatment effect
against human coronavirus infections.Comment: 42 pages, 9 figure
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