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Abstract Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea . It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Summary: Analyzing host-microbe interactions is essential for understanding how microbiota changes disrupt host homeostasis. Here, we present a protocol for predicting host-microbe protein-protein interactions and their downstream effects using MicrobioLink. We describe steps for setting up the environment, installing software, and preparing human transcriptomic and bacterial proteomic data. The protocol outlines procedures for predicting protein-protein interactions through domain-motif interactions, integrating multi-omic datasets to map downstream effects, performing network analyses to identify key regulatory pathways, and visualizing multi-layered networks for systems-level data interpretation.For complete details on the use and execution of this protocol, please refer to Gul et al.1 and Poletti et al.2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Immune checkpoint inhibitors (CPIs) are a relatively newly licenced cancer treatment, which make a once previously untreatable disease now amenable to a potential cure. Combination regimens of anti-CTLA4 and anti-PD-1 show enhanced efficacy but are prone to off-target immune-mediated tissue injury, particularly at the barrier surfaces. To probe the impact of immune checkpoints on intestinal homoeostasis, mice are challenged with anti-CTLA4 and anti-PD-1 immunotherapy and manipulation of the intestinal microbiota. The immune profile of the colon of these mice with CPI-colitis is analysed using bulk RNA sequencing, single-cell RNA sequencing and flow cytometry. CPI-colitis in mice is dependent on the composition of the intestinal microbiota and by the induction of lymphocytes expressing interferon-γ (IFNγ), cytotoxicity molecules and other pro-inflammatory cytokines/chemokines. This pre-clinical model of CPI-colitis could be attenuated following blockade of the IL23/IFNγ axis. Therapeutic targeting of IFNγ-producing lymphocytes or regulatory networks, may hold the key to reversing CPI-colitis.

Abstract Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial–immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial–immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial–immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Abstract We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Abstract Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath’s web service ( https://omnipathdb.org/ ), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

ABSTRACT Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens could identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range, and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here, we present SalmoNet2, a major update to SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high-quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyze their data using SalmoNet2.

Abstract Background Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. Results To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait—the visual system—for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Conclusions Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.

Graphical Abstract

Hyper-induction of pro-inflammatory cytokines, also known as a cytokine storm or cytokine release syndrome (CRS), is one of the key aspects of the currently ongoing SARS-CoV-2 pandemic. This process occurs when a large number of innate and adaptive immune cells activate and start producing pro-inflammatory cytokines, establishing an exacerbated feedback loop of inflammation. It is one of the factors contributing to the mortality observed with coronavirus 2019 (COVID-19) for a subgroup of patients. CRS is not unique to the SARS-CoV-2 infection; it was prevalent in most of the major human coronavirus and influenza A subtype outbreaks of the past two decades (H5N1, SARS-CoV, MERS-CoV, and H7N9). With a comprehensive literature search, we collected changing the cytokine levels from patients upon infection with the viral pathogens mentioned above. We analyzed published patient data to highlight the conserved and unique cytokine responses caused by these viruses. Our curation indicates that the cytokine response induced by SARS-CoV-2 is different compared to other CRS-causing respiratory viruses, as SARS-CoV-2 does not always induce specific cytokines like other coronaviruses or influenza do, such as IL-2, IL-10, IL-4, or IL-5. Comparing the collated cytokine responses caused by the analyzed viruses highlights a SARS-CoV-2-specific dysregulation of the type-I interferon (IFN) response and its downstream cytokine signatures. The map of responses gathered in this study could help specialists identify interventions that alleviate CRS in different diseases and evaluate whether they could be used in the COVID-19 cases.

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

Abstract Background Central nervous system (CNS) infections caused by Actinomyces spp. including brain abscess, actinomycoma, subdural empyema and epidural abscess are well described, however reports of Actinomyces-associated meningitis are scarcely reported. Case report We present the case of a 43-year-old Hungarian male patient with poor socioeconomic status who developed acute bacterial meningitis caused by Actinomyces turicensis originating from the left side mastoiditis. The bacterial cultures of both cerebrospinal fluid (CSF) and purulent discharge collected during the mastoid surgery showed slow growing Gram-positive rods that were identified by automated systems (API, VITEK) as A. turicensis The bacterial identification was confirmed by 16S rRNA PCR and subsequent nucleic acid sequencing. No bacterial growth was detected in blood culture bottles after 5 days of incubation. Hence, multiple antibacterial treatments and surgical intervention the patient passed away. Conclusions Anaerobes are rarely involved in CNS infections therefore anaerobic culture of CSF samples is routinely not performed. However, anaerobic bacteria should be considered as potential pathogens when certain risk factors are present, such as paranasal sinusitis, mastoiditis in patients with poor socioeconomic condition. To the best of our knowledge, our case report is the first description of A. turicensis meningitis that has been diagnosed as consequence of purulent mastoiditis.

Intercellular communication mediated by cytokines is critical to the development of immune responses, particularly in the context of infectious and inflammatory diseases. By releasing these small molecular weight peptides, the source cells can influence numerous intracellular processes in the target cells, including the secretion of other cytokines downstream. However, there are no readily available bioinformatic resources that can model cytokine–cytokine interactions. In this effort, we built a communication map between major tissues and blood cells that reveals how cytokine-mediated intercellular networks form during homeostatic conditions. We collated the most prevalent cytokines from the literature and assigned the proteins and their corresponding receptors to source tissue and blood cell types based on enriched consensus RNA-Seq data from the Human Protein Atlas database. To assign more confidence to the interactions, we integrated the literature information on cell–cytokine interactions from two systems of immunology databases, immuneXpresso and ImmunoGlobe. From the collated information, we defined two metanetworks: a cell–cell communication network connected by cytokines; and a cytokine–cytokine interaction network depicting the potential ways in which cytokines can affect the activity of each other. Using expression data from disease states, we then applied this resource to reveal perturbations in cytokine-mediated intercellular signalling in inflammatory and infectious diseases (ulcerative colitis and COVID-19, respectively). For ulcerative colitis, with CytokineLink, we demonstrated a significant rewiring of cytokine-mediated intercellular communication between non-inflamed and inflamed colonic tissues. For COVID-19, we were able to identify cell types and cytokine interactions following SARS-CoV-2 infection, highlighting important cytokine interactions that might contribute to severe illness in a subgroup of patients. Such findings have the potential to inform the development of novel, cytokine-targeted therapeutic strategies. CytokineLink is freely available for the scientific community through the NDEx platform and the project github repository.

There is an increasing volume of data connecting capacity to respond to exercise training with quality of life and aging. In this study, we used a rat model in which animals were selectively bred for low and high gain in running distance to test t whether genetic segregation for trainability is associated with brain function and signaling processes in the hippocampus. Rats selected for low response (LRT) and high response training (HRT) were randomly divided into control or exercise group that trained five times a week for 30 min per day for three months at 70% VO2max. All four groups had similar running distance before training. With training, HRT rats showed significantly greater increases in VO2max and running distance than LRT rats (p < 0.05). On the reverse Morris Maze test HRT-trained rats outperformed HRT control ones. Significant difference was noted between LRT and HRT groups in redox milieu as assessed by levels of reactive oxygen species (ROS), carbonylation of proteins, nNOS and S-nitroso-cysteine. Moreover the silent information regulator 1 (SIRT1), brain-derived neurotrophic factor (BDNF), ratio of phospho and total cAMP-response element binding protein (CREB), and apoptotic index, also showed significant differences between LRT and HRT groups. These findings suggest that aerobic training responses are not localized to skeletal muscle, but differently involve signaling processes in the brain of LRT and HRT rats., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Purpose: This study examined agreement between all 3 standards (as well as relative diagnostic associations with metabolic syndrome) using a representative sample of youth from the Hungarian National Youth Fitness Study., Method: Body mass index (BMI) was assessed in a field sample of 2,352 adolescents (ages 10-18.5 years) and metabolic syndrome status was assessed in a laboratory subsample of 373 youth. All youth were categorized into weight status groups based on the FITNESSGRAM®, International Obesity Task Force (IOTF), and Hungarian growth standards. Classification agreement was compared between all pairs of standards via cross-tabulation. Logistic regression was used to estimate the odds of metabolic syndrome by weight status., Results: The 3 BMI standards agreed on ≥  88% of cases, with better agreement on girls' standards than boys' standards. Kappa values ranged from .65 to .89. Using the Hungarian standards over the Fitnessgram or IOTF standards resulted in 5% to 10% more youth being classified as normal weight. The overweight/obesity groups were 4 times to 6 times more likely to have metabolic syndrome than those classified as normal weight regardless of the classification standards. These odds ratios increased to 8 times to 17 times when comparing the normal-weight/overweight groups to the obesity category. Odds ratios for boys tended to be slightly larger than those for girls., Conclusions: All 3 standards provide similar information about weight status and metabolic syndrome classification. To more easily facilitate international comparisons, it may be of greater benefit to use the IOTF standards, which also had better agreement with the U.S. Fitnessgram thresholds.

Trainability is important in elite sport and in recreational physical activity, and the wide range for response to training is largely dependent on genotype. In this study, we compare a newly developed rat model system selectively bred for low and high gain in running distance from aerobic training to test whether genetic segregation for trainability associates with differences in factors associated with mitochondrial biogenesis. Low response trainer (LRT) and high response trainer (HRT) rats from generation 11 of artificial selection were trained five times a week, 30 min per day for 3 months at 70 % VO2max to study the mitochondrial molecular background of trainability. As expected, we found significant differential for the gain in running distance between LRT and HRT groups as a result of training. However, the changes in VO2max, COX-4, redox homeostasis associated markers (reactive oxygen species (ROS)), silent mating-type information regulation 2 homolog (SIRT1), NAD(+)/NADH ratio, proteasome (R2 subunit), and mitochondrial network related proteins such as mitochondrial fission protein 1 (Fis1) and mitochondrial fusion protein (Mfn1) suggest that these markers are not strongly involved in the differences in trainability between LRT and HRT. On the other hand, according to our results, we discovered that differences in basal activity of AMP-activated protein kinase alpha (AMPKα) and differential changes in aerobic exercise-induced responses of citrate synthase, carbonylated protein, peroxisome proliferator-activated receptor gamma coactivator-1α (PGC1-α), nuclear respiratory factor 1 (NRF1), mitochondrial transcription factor A (TFAM), and Lon protease limit trainability between these selected lines. From this, we conclude that mitochondrial biogenesis-associated factors adapt differently to aerobic exercise training in training sensitive and training resistant rats.

The effects of treadmill running (8 weeks, 5 times/week, 1h/day at 27 m/min), caloric restriction, and cocoa supplementation on brain function and oxidative stress markers were tested. The Morris maze test was used to appraise rat memory. Regular exercise significantly improved spatial learning performance. The level of oxidative stress was measured by the concentration of carbonylated proteins. The free radical concentration increased in brain of the training groups but not the controls. The content of reactive carbonyl derivates did not change with exercise, suggesting that the increased production of reactive oxygen species (ROS) were well tolerated in this experimental model. Caloric restriction (CR) decreased the accumulation of free radicals in the frontal lobe. The protein content of brain-derived neutrophic factors (BDNFs) was evaluated and changes did not occur either with exercise or cocoa supplementation treatments. These data did not show significant effects of the administration of cocoa (2% w/w) on the concentration of ROS, BDNF or on spatial memory. Conversely, exercise and CR can play a role in ROS generation and brain function., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Aging is associated with a gradual decline in cognitive and motor functions, the result of complex biochemical processes including pre- and posttranslational modifications of proteins. Sirtuins are NAD(+) dependent protein deacetylases. These enzymes modulate the aging process by lysine deacetylation, which alters the activity and stability of proteins. Exercise can increase mean life-span and improve quality of life. Data from our laboratories revealed that 4 weeks of treadmill running improves performance in the Morris Maze test for young (4 months, old) but not old (30 months, old) male rats, and the exercise could not prevent the age-associated loss in muscle strength assessed by a gripping test. The positive correlation between protein acetylation and the gripping test suggests that the age-dependent decrease in relative activity of SIRT1 in the cerebellum impairs motor function. Similarly to the acetylation level of total proteins, the acetylation of ά -tubulin is also increased with aging, while the effect of exercise training was not found to be significant. Moreover, the protein content of nicotinamide phosphoribosyltransferase, one of the key enzymes of NAD biosynthesis, decreased in the young exercise group. These data suggest that aging results in decreased specific activity of SIRT1 in cerebellum, which could lead to increased acetylation of protein residues, including ά-tubulin, that interfere with motor function.