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Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets. [ABSTRACT FROM AUTHOR]

The gastrointestinal (GI) tract harbours a complex microbial community, which contributes to its homeostasis. A disrupted microbiome can cause GI‐related diseases, including inflammatory bowel disease (IBD), therefore identifying host‐microbe interactions is crucial for better understanding gut health. Bacterial extracellular vesicles (BEVs), released into the gut lumen, can cross the mucus layer and access underlying immune cells. To study BEV‐host interactions, we examined the influence of BEVs generated by the gut commensal bacterium, Bacteroides thetaiotaomicron, on host immune cells. Single‐cell RNA sequencing data and host‐microbe protein‐protein interaction networks were used to predict the effect of BEVs on dendritic cells, macrophages and monocytes focusing on the Toll‐like receptor (TLR) pathway. We identified biological processes affected in each immune cell type and cell‐type specific processes including myeloid cell differentiation. TLR pathway analysis highlighted that BEV targets differ among cells and between the same cells in healthy versus disease (ulcerative colitis) conditions. The in silico findings were validated in BEV‐monocyte co‐cultures demonstrating the requirement for TLR4 and Toll‐interleukin‐1 receptor domain‐containing adaptor protein (TIRAP) in BEV‐elicited NF‐kB activation. This study demonstrates that both cell‐type and health status influence BEV‐host communication. The results and the pipeline could facilitate BEV‐based therapies for the treatment of IBD. [ABSTRACT FROM AUTHOR]

Abstract Background Mutans streptococci are a group of gram-positive bacteria including the primary cariogenic dental pathogen Streptococcus mutans and closely related species. Two component systems (TCSs) composed of a signal sensing histidine kinase (HK) and a response regulator (RR) play key roles in pathogenicity, but have not been comparatively studied for these oral bacterial pathogens. Results HKs and RRs of 8 newly sequenced mutans streptococci strains, including S. sobrinus DSM20742, S. ratti DSM20564 and six S. mutans strains, were identified and compared to the TCSs of S. mutans UA159 and NN2025, two previously genome sequenced S. mutans strains. Ortholog analysis revealed 18 TCS clusters (HK-RR pairs), 2 orphan HKs and 2 orphan RRs, of which 8 TCS clusters were common to all 10 strains, 6 were absent in one or more strains, and the other 4 were exclusive to individual strains. Further classification of the predicted HKs and RRs revealed interesting aspects of their putative functions. While TCS complements were comparable within the six S. mutans strains, S. sobrinus DSM20742 lacked TCSs possibly involved in acid tolerance and fructan catabolism, and S. ratti DSM20564 possessed 3 unique TCSs but lacked the quorum-sensing related TCS (ComDE). Selected computational predictions were verified by PCR experiments. Conclusions Differences in the TCS repertoires of mutans streptococci strains, especially those of S. sobrinus and S. ratti in comparison to S. mutans, imply differences in their response mechanisms for survival in the dynamic oral environment. This genomic level study of TCSs should help in understanding the pathogenicity of these mutans streptococci strains.

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Background Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating timeresolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out. Results The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene-pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network. Conclusion The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci. [ABSTRACT FROM AUTHOR]

Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub. [ABSTRACT FROM AUTHOR]

The impact of beef consumption on sustainability is a complex and evolving area, as sustainability covers many areas from human nutrient adequacy to ecosystem stability. Three sustainability assessment frameworks have been created to help policy makers unpack the complexities of sustainable food systems and healthy sustainable dietary change. However, none of these frameworks have yet to be applied to a case study or individual policy issue. This paper uses a hybrid version of the sustainability assessment frameworks to investigate the impact of reducing beef consumption (with a concurrent increase in consumption of plant-based foods, with a focus on legumes) on sustainability at a UK level. The aim of this paper is to understand the applicability of these overarching frameworks at the scale of an individual policy. Such an assessment is important, as this application of previously high-level frameworks to individual policies makes it possible to summarise, at a glance, the various co-benefits and trade-offs associated with a given policy, which may be of particular value in terms of stakeholder decision-making. We find that many of the proposed metrics found within the sustainability assessment frameworks are difficult to implement at an individual issue level; however, overall they show that a reduction in beef consumption and an increase in consumption of general plant-based foods, with a focus around legumes production, would be expected to be strongly beneficial in five of the eight overarching measures which were assessed. [ABSTRACT FROM AUTHOR]

Bacterial extracellular vesicles (BEVs) released from both Gram-negative and Gram-positive bacteria provide an effective means of communication and trafficking of cell signaling molecules. In the gastrointestinal tract (GIT) BEVs produced by members of the intestinal microbiota can impact host health by mediating microbe-host cell interactions. A major unresolved question, however, is what factors influence the composition of BEV proteins and whether the host influences protein packaging into BEVs and secretion into the GIT. To address this, we have analyzed the proteome of BEVs produced by the major human gut symbiont Bacteroides thetaiotaomicron both in vitro and in vivo in the murine GIT in order to identify proteins specifically enriched in BEVs produced in vivo. We identified 113 proteins enriched in BEVs produced in vivo, the majority (62/113) of which accumulated in BEVs in the absence of any changes in their expression by the parental cells. Among these selectively enriched proteins, we identified dipeptidyl peptidases and an asparaginase and confirmed their increased activity in BEVs produced in vivo. We also showed that intact BEVs are capable of degrading bile acids via a bile salt hydrolase. Collectively these findings provide additional evidence for the dynamic interplay of host-microbe interactions in the GIT and the existence of an active mechanism to drive and enrich a selected group of proteins for secretion into BEVs in the GIT. [ABSTRACT FROM AUTHOR]

Background: Recent genetic and transcriptomic data highlight the need for improved molecular characterisation of inflammatory bowel disease (IBD). Proteomics may advance the delineation of IBD phenotypes since it accounts for post-transcriptional modifications., Aim: We aimed to assess the IBD spectrum based on inflammatory serum proteins and identify discriminative patterns of underlying biological subtypes across multiple European cohorts., Methods: Using proximity extension methodology, we measured 86 inflammation-related serum proteins in 1551 IBD patients and 312 healthy controls (HC). We screened for proteins exhibiting significantly different levels among IBD subtypes and between IBD and HC. Classification models for differentiating between Crohn's disease (CD) and ulcerative colitis (UC) were employed to explore the IBD spectrum based on estimated probability scores., Results: Levels of multiple proteins, such as IL-17A, MMP-10, and FGF-19, differed (fold-change>1.2; FDR<0.05) between ileal vs colonic IBD. Using multivariable models, a protein signature reflecting the IBD spectrum was identified, positioning colonic CD between UC and ileal CD, which were at opposite ends of the spectrum. Based on area under the curve (AUC) estimates, classification models more accurately differentiated UC from ileal CD (median AUCs>0.73) than colonic CD (median AUCs<0.62). Models differentiating colonic CD from ileal CD demonstrated intermediate performance (median AUCs 0.67-0.69)., Conclusion: Our findings in serum proteins support the presence of a continuous IBD spectrum rather than a clear separation of CD and UC. Within the spectrum, disease location may reflect a more similar disease than CD vs UC, as colonic CD resembled UC more closely than ileal CD., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.)

Inflammatory responses of the intestinal epithelial barrier in patients with Crohn's disease (CD), a chronic inflammatory bowel disease (IBD), are associated with gut microbial alterations. At a community level, there is scarce mechanistic evidence on the effects of gut microbial alterations on host mucosal barrier responses. We used a computational microbe-host interaction prediction framework based on network diffusion and systems biology to integrate publicly available paired gut microbial and intestinal gene expression datasets. The ileal signaling network potentially modulated by the microbiota was enriched with immune-related pathways such as those associated with IL-4, IL-2, IL-13, NFkB, and toll-like receptors. We identified bacterial proteins eliciting post-translational modifications on host receptors, resulting in the de-repression of pro-inflammatory cytokines via critical hub proteins such as NFkB. The signaling networks were over-represented with CD associated genes and CD drug targets. Using datasets generated from our validation cohorts, we confirmed some of the results., Competing Interests: BV reports financial support for research from 10.13039/100004319Pfizer; lecture fees from 10.13039/100006483Abbvie, 10.13039/501100003122Ferring, 10.13039/100005570Takeda Pharmaceuticals, 10.13039/100005205Janssen, and R Biopharm; consultancy fees from Janssen and Sandoz. JS reports lecture fees from Abbvie, Takeda, Janssen, and 10.13039/100010886Nestle Health Sciences. MF reports financial support for: research from AbbVie, 10.13039/100000042Amgen, Biogen, Janssen, Pfizer, Takeda; consultancy from Abbvie, Boehringer-Ingelheim, Lilly, MSD, Pfizer,Sandoz,Takeda, and Thermo Fisher; speaking from Abbvie, Amgen, Biogen, Boehringer-Ingelheim, Falk, Ferring, Janssen, Lamepro, MSD, Mylan, Pfizer, Sandoz, Takeda, and Truvion Healthcare. GM received financial support for research from DSM Nutritional Products, Karyopharm Therapeutics, and Janssen. SV reports financial support for: research from MSD, AbbVie, Takeda, Pfizer, J&J; lectures from MSD, AbbVie, Takeda, Ferring, Centocor, Hospira,Pfizer, J&J, Genentech/Roche; consultancy fromMSD,AbbVie,Takeda,Ferring, Centocor, Hospira,Pfizer, J&J,Genentech/Roche,Celgene, Mundipharma, Celltrion, Second Genome, Prometheus, Shire, Prodigest, Gilead, Galapagos., (© 2022 The Authors.)

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research., Competing Interests: Competing interests: BV received lecture fees from Abbvie, Ferring Pharmaceuticals, Janssen, R-Biopharm and Takeda; consultancy fees from Janssen and Sandoz. SV: research grant: MSD, AbbVie, Takeda, Pfizer, J&J; lecture fee: MSD, AbbVie, Takeda, Ferring, Centocor, Hospira, Pfizer, J&J, Genentech/Roche; consultancy: MSD, AbbVie, Takeda, Ferring, Centocor, Hospira, Pfizer, J&J, Genentech/Roche, Celgene, Mundipharma, Celltrion, SecondGenome, Prometheus, Shire, Prodigest, Gilead, Galapagos. SV is a senior clinical investigator of the Research Foundation–Flanders (FWO). The work of MM and TK is supported by BenevolentAI, and TK’s work is also supported by Unilever., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Pancreatic ductal adenocarcinoma (PDAC) is driven by metabolic changes in pancreatic cells caused by oncogenic mutations and dysregulation of p53. PDAC cell lines and PDAC-derived xenografts grow as a result of altered metabolic pathways, changes in stroma, and autophagy. Selective targeting and inhibition of one of these may open avenues for the development of new therapeutic strategies. In this study, we performed a genome-wide siRNA screen in a PDAC cell line using endogenous autophagy as a readout and identified several regulators of autophagy that were required for autophagy-dependent PDAC cell survival. Validation of two promising candidates, MPP7 (MAGUK p55 subfamily member 7, a scaffolding protein involved in cell-cell contacts) and MDH1 (cytosolic Malate dehydrogenase 1), revealed their role in early stages of autophagy during autophagosome formation. MPP7 was involved in the activation of YAP1 (a transcriptional coactivator in the Hippo pathway), which in turn promoted autophagy, whereas MDH1 was required for maintenance of the levels of the essential autophagy initiator serine-threonine kinase ULK1, and increased in the activity upon induction of autophagy. Our results provide a possible explanation for how autophagy is regulated by MPP7 and MDH1, which adds to our understanding of autophagy regulation in PDAC. SIGNIFICANCE: This study identifies and characterizes MPP7 and MDH1 as novel regulators of autophagy, which is thought to be responsible for pancreatic cancer cell survival., (©2019 American Association for Cancer Research.)

Paneth cells are key epithelial cells that provide an antimicrobial barrier and maintain integrity of the small-intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and are often associated with digestive pathologies such as Crohn's disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process that recycles cellular components. The direct effect of autophagy impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells, making these cells impaired in autophagy. Using three-dimensional intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy-targeted proteins and functional information to identify the mechanistic link between autophagy impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%) that are predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms that degrade key proteins in Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration have effects at the protein level, not on gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred downregulation of exocytosis. Our observations could explain how protein-level alterations affect Paneth cell homeostatic functions upon autophagy impairment.This article has an associated First Person interview with the joint first authors of the paper., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2019. Published by The Company of Biologists Ltd.)

The field of systems biology endeavors to map, study, and simulate cellular systems and their underlying mechanisms. The internal mechanisms of biological systems can be represented with networks comprising nodes and edges. Nodes denote the constituents of the biological system whereas edges indicate the relationships among them. Likewise, every layer of cellular organization can be represented by networks. Multilayered networks capture interactions between various network types, such as transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks from the same biological system. This property makes multilayered networks representative of the system while its internal mechanisms are investigated. However, there are not many multilayered networks containing integrated data for nonmodel organisms including the bacterial pathogens Salmonella. Here, we outline the steps to create such an integrated network database, through the example of SalmoNet, the first integrated multilayered data resource for multiple strains belonging to distinct Salmonella serovars.

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.

Identification of the gene network through which Pseudomonas aeruginosa PAO1 (PA) adapts to altered oxygen-availability environments is essential for a better understanding of stress responses and pathogenicity of PA. We performed high-time-resolution (HTR) transcriptome analyses of PA in a continuous cultivation system during the transition from high oxygen tension to low oxygen tension (HLOT) and the reversed transition from low to high oxygen tension (LHOT). From those genes responsive to both transient conditions, we identified 85 essential oxygen-availability responsive genes (EORGs), including the expected ones (arcDABC) encoding enzymes for arginine fermentation. We then constructed the regulatory network for the EORGs of PA by integrating information from binding motif searching, literature and HTR data. Notably, our results show that only the sub-networks controlled by the well-known oxygen-responsive transcription factors show a very high consistency between the inferred network and literature knowledge, e.g. 87.5% and 83.3% of the obtained sub-network controlled by the anaerobic regulator (ANR) and a quorum sensing regulator RhIR, respectively. These results not only reveal stringent EORGs of PA and their transcription regulatory network, but also highlight that achieving a high accuracy of the inferred regulatory network might be feasible only for the apparently affected regulators under the given conditions but not for all the expressed regulators on a genome scale.