Network portal: a database for storage, analysis and visualization of biological networks

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics

Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient.

System-wide coordinates of higher order functions in host-pathogen environment upon Mycobacterium tuberculosis infection

Molecular signatures and their interactions behind the successful establishment of infection of Mycobacterium tuberculosis (Mtb) inside macrophage are largely unknown. In this work, we present an inter-system scale atlas of the gene expression signatures, their interactions and higher order gene functions of macrophage-Mtb environment at the time of infection. We have carried out large-scale meta-analysis of previously published gene expression microarray studies andhave identified a ranked list of differentially expressed genes and their higher order functions in intracellular Mtb as well as the infected macrophage. Comparative analysis of gene expression signatures of intracellular Mtb with the in vitro dormant Mtb at different hypoxic and oxidative stress conditions led to the identification of the large number of Mtb functional groups, namely operons, regulons and pathways that were common and unique to the intracellular environment and dormancy state. Some of the functions that are specific to intracellular Mtb are cholesterol degradation and biosynthesis of immunomodulatory phenolic compounds. The molecular signatures we have identified to be involved in adaptation to different stress conditions in macrophage environment may be critical for designing therapeutic interventions against tuberculosis. And, our approach may be broadly applicable for investigating other host-pathogen interactions.

Exploring and visualizing multidimensional data in translational research platforms

The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researcher's abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.

On the Variable Ordering in Subgraph Isomorphism Algorithms

Graphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases.

Constrained inference of protein interaction networks for invadopodium formation in cancer

Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns. CGLN finds constraint-satisfying trajectories by solving a k-stops problem in the state space of dynamic networks and then reconstructs candidate networks. They benchmarked CGLN on randomly generated networks, and CGLN outperformed its alternatives when 50% or more interactions in a network are given as local constraints. CGLN is then applied to infer dynamic protein interaction networks regulating invadopodium formation in motile cancer cells. CGLN predicted 134 novel protein interactions for their involvement in invadopodium formation. The most frequently predicted interactions centre around focal adhesion kinase and tyrosine kinase substrate TKS4, and 14 interactions are supported by the literature in molecular contexts related to invadopodium formation. As an alternative to the Bayesian paradigm, the CGLN method offers constrained network inference without requiring prior probabilities and thus can promote novel interactions, consistent with the discovery process of scientific facts that are not yet in common beliefs.

Metabolic modeling of clostridia: current developments and applications

Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.

cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism

The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens, that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2.

A Post-Genomic View of the Ecophysiology, Catabolism and Biotechnological Relevance of Sulphate-Reducing Prokaryotes

Dissimilatory sulphate reduction is the unifying and defining trait of sulphate-reducing prokaryotes (SRP). In their predominant habitats, sulphate-rich marine sediments, SRP have long been recognized to be major players in the carbon and sulphur cycles. Other, more recently appreciated, ecophysiological roles include activity in the deep biosphere, symbiotic relations, syntrophic associations, human microbiome/health and long-distance electron transfer. SRP include a high diversity of organisms, with large nutritional versatility and broad metabolic capacities, including anaerobic degradation of aromatic compounds and hydrocarbons. Elucidation of novel catabolic capacities as well as progress in the understanding of metabolic and regulatory networks, energy metabolism, evolutionary processes and adaptation to changing environmental conditions has greatly benefited from genomics, functional OMICS approaches and advances in genetic accessibility and biochemical studies. Important biotechnological roles of SRP range from (i) wastewater and off gas treatment, (ii) bioremediation of metals and hydrocarbons and (iii) bioelectrochemistry, to undesired impacts such as (iv) souring in oil reservoirs and other environments, and (v) corrosion of iron and concrete. Here we review recent advances in our understanding of SRPs focusing mainly on works published after 2000. The wealth of publications in this period, covering many diverse areas, is a testimony to the large environmental, biogeochemical and technological relevance of these organisms and how much the field has progressed in these years, although many important questions and applications remain to be explored.

Databases for Microbiologists

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. The purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.

Systems biology approaches to defining transcription regulatory networks in halophilic archaea

To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

A comprehensive map of genome-wide gene regulation in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets.

Rex (encoded by DVU_0916) in Desulfovibrio vulgaris Hildenborough is a repressor of sulfate adenylyl transferase and is regulated by NADH

Although the enzymes for dissimilatory sulfate reduction by microbes have been studied, the mechanisms for transcriptional regulation of the encoding genes remain unknown. In a number of bacteria the transcriptional regulator Rex has been shown to play a key role as a repressor of genes producing proteins involved in energy conversion. In the model sulfate-reducing microbe Desulfovibrio vulgaris Hildenborough, the gene DVU_0916 was observed to resemble other known Rex proteins. Therefore, the DVU_0916 protein has been predicted to be a transcriptional repressor of genes encoding proteins that function in the process of sulfate reduction in D. vulgaris Hildenborough. Examination of the deduced DVU_0916 protein identified two domains, one a winged helix DNA-binding domain common for transcription factors, and the other a Rossman fold that could potentially interact with pyridine nucleotides. A deletion of the putative rex gene was made in D. vulgaris Hildenborough, and transcript expression studies of sat, encoding sulfate adenylyl transferase, showed increased levels in the D. vulgaris Hildenborough Rex (RexDvH) mutant relative to the parental strain. The RexDvH-binding site upstream of sat was identified, confirming RexDvH to be a repressor of sat. We established in vitro that the presence of elevated NADH disrupted the interaction between RexDvH and DNA. Examination of the 5' transcriptional start site for the sat mRNA revealed two unique start sites, one for respiring cells that correlated with the RexDvH-binding site and a second for fermenting cells. Collectively, these data support the role of RexDvH as a transcription repressor for sat that senses the redox status of the cell.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.