Distinct transcriptional signatures in purified circulating immune cells drive heterogeneity in disease location in IBD.

OBJECTIVE: To infer potential mechanisms driving disease subtypes among patients with inflammatory bowel disease (IBD), we profiled the transcriptome of purified circulating monocytes and CD4 T-cells. DESIGN: RNA extracted from purified monocytes and CD4 T-cells derived from the peripheral blood of 125 endoscopically active patients with IBD was sequenced using Illumina HiSeq 4000NGS. We used complementary supervised and unsupervised analytical methods to infer gene expression signatures associated with demographic/clinical features. Expression differences and specificity were validated by comparison with publicly available single cell datasets, tissue-specific expression and meta-analyses. Drug target information, druggability and adverse reaction records were used to prioritise disease subtype-specific therapeutic targets. RESULTS: Unsupervised/supervised methods identified significant differences in the expression profiles of CD4 T-cells between patients with ileal Crohn's disease (CD) and ulcerative colitis (UC). Following a pathway-based classification (Area Under Receiver Operating Characteristic - AUROC=86%) between ileal-CD and UC patients, we identified MAPK and FOXO pathways to be downregulated in UC. Coexpression module/regulatory network analysis using systems-biology approaches revealed mediatory core transcription factors. We independently confirmed that a subset of the disease location-associated signature is characterised by T-cell-specific and location-specific expression. Integration of drug-target information resulted in the discovery of several new (BCL6, GPR183, TNFAIP3) and repurposable drug targets (TUBB2A, PRKCQ) for ileal CD as well as novel targets (NAPEPLD, SLC35A1) for UC. CONCLUSIONS: Transcriptomic profiling of circulating CD4 T-cells in patients with IBD demonstrated marked molecular differences between the IBD-spectrum extremities (UC and predominantly ileal CD, sandwiching colonic CD), which could help in prioritising particular drug targets for IBD subtypes.

Holistic healthcare in inflammatory bowel disease: time for patient-centric approaches?

Inflammatory bowel disease (IBD) is an emerging global disease characterised by chronic inflammation of the gastrointestinal tract. However, IBD is also manifested by several extraintestinal symptoms which, along with the intestinal symptoms, impact on the mental and emotional well-being of patients. Despite therapeutic advancements, only one-third of the diagnosed patients receiving approved medical treatments achieve short-term to medium-term remission. Consequently, patients who do not get successfully treated might resort to using complementary and alternative approaches to manage their symptoms, with or without consulting their treating clinician. Despite their possible potential, such approaches have various risks stemming from unknown adverse reactions and possible interference with medically approved therapies. In this study, we present the results of a well-performed literature review where we included randomised clinical trials which have assessed the efficacy of complementary approaches and dietary therapy on at least one of the following four outcomes: clinical remission, endoscopic remission, modulation of molecular biomarkers or quality of life metrics. By pointing out intraoutcome and interoutcome concordance, we identified possible candidates for clinical adoption and further study in larger randomised clinical trials covering the broad spectrum of IBD heterogeneity. We finally proposed a patient-centric clinical care model and a series of recommendations for stakeholders, with special attention to complementary approaches and dietary strategies, aimed at achieving holistic remission.

Multilayered Networks of SalmoNet2 Enable Strain Comparisons of the Salmonella Genus on a Molecular Level.

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.

The Proteome of Extracellular Vesicles Produced by the Human Gut Bacteria Bacteroides thetaiotaomicron In Vivo Is Influenced by Environmental and Host-Derived Factors.

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. IMPORTANCE The gastrointestinal tract (GIT) harbors a complex community of microbes termed the microbiota that plays a role in maintaining the host's health and wellbeing. How this comes about and the nature of microbe-host cell interactions in the GIT is still unclear. Recently, nanosized vesicles naturally produced by bacterial constituents of the microbiota have been shown to influence responses of different host cells although the molecular basis and identity of vesicle-born bacterial proteins that mediate these interactions is unclear. We show here that bacterial extracellular vesicles (BEVs) produced by the human symbiont Bacteroides thetaiotaomicron in the GIT are enriched in a set of proteins and enzymes, including dipeptidyl peptidases, an asparaginase and a bile salt hydrolase that can influence host cell biosynthetic pathways. Our results provide new insights into the molecular basis of microbiota-host interactions that are central to maintaining GIT homeostasis and health.

Microbiota, not host origin drives ex vivo intestinal epithelial responses.

Microbial dysbiosis is an established finding in patients with inflammatory bowel disease (IBD), but host-microbial interactions are poorly understood. We aimed to unravel the effect of microbiota exposure on intestinal epithelial cells. Confluent Transwell® organoid monolayers of eight UC patients and eight non-IBD controls were co-cultured for six hours with microbiota (3x108 cells) of UC patients or a healthy volunteer (HV), in the presence or absence of an inflammatory cytokine mix. Transepithelial electrical resistance (TEER), fluorescein isothiocyanate (FITC) dextran measurements, and RNA sequencing were performed on epithelial cells, and 16S rRNA sequencing on microbiota samples before and after co-culture. Transcriptomic response following microbiota exposure was not different between epithelial cells from UC patients or non-IBD controls. Following UC microbiota exposure, but not HV microbiota, a strong decrease in epithelial barrier integrity was observed in both UC and HV epithelial cells by TEER and FITC dextran measurements. Exposure of inflamed epithelium to UC microbiota induced transcriptomic stress pathways including activation of EGR1, MAPK and JAK/STAT signaling, as well as AP-1 family and FOSL transcripts. Stress responses after HV microbiota stimulation were milder. We conclude that not the epithelial cell origin (UC versus non-IBD) but the microbial donor drives transcriptomic responses, as exposure to UC microbiota was sufficient to induce stress responses in all epithelial cells. Further research on therapies to restore the microbial balance, to remove the constant trigger of dysbiosis, is required.

Integrated analysis of microbe-host interactions in Crohn's disease reveals potential mechanisms of microbial proteins on host gene expression.

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.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis.

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All.

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.

Extracellular vesicles produced by the human commensal gut bacterium Bacteroides thetaiotaomicron affect host immune pathways in a cell-type specific manner that are altered in inflammatory bowel disease.

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.

SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.

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.

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways.

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.

Evolution of regulatory networks associated with traits under selection in cichlids.

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.

Understanding the Molecular Drivers of Disease Heterogeneity in Crohn's Disease Using Multi-omic Data Integration and Network Analysis.

Crohn's disease (CD), a form of inflammatory bowel disease (IBD), is characterized by heterogeneity along multiple clinical axes, which in turn impacts disease progression and treatment modalities. Using advanced data integration approaches and systems biology tools, we studied the contribution of CD susceptibility variants and gene expression in distinct peripheral immune cell subsets (CD14+ monocytes and CD4+ T cells) to relevant clinical traits. Our analyses revealed that most clinical traits capturing CD heterogeneity could be associated with CD14+ and CD4+ gene expression rather than disease susceptibility variants. By disentangling the sources of variation, we identified molecular features that could potentially be driving the heterogeneity of various clinical traits of CD patients. Further downstream analyses identified contextual hub proteins such as genes encoding barrier functions, antimicrobial peptides, chemokines, and their receptors, which are either targeted by drugs used in CD or other inflammatory diseases or are relevant to the biological functions implicated in disease pathology. These hubs could be used as cell type-specific targets to treat specific subtypes of CD patients in a more individualized approach based on the underlying biology driving their disease subtypes. Our study highlights the importance of data integration and systems approaches to investigate complex and heterogeneous diseases such as IBD.

ULK1 and ULK2 are less redundant than previously thought: computational analysis uncovers distinct regulation and functions of these autophagy induction proteins.

Macroautophagy, the degradation of cytoplasmic content by lysosomal fusion, is an evolutionary conserved process promoting homeostasis and intracellular defence. Macroautophagy is initiated primarily by a complex containing ULK1 or ULK2 (two paralogs of the yeast Atg1 protein). To understand the differences between ULK1 and ULK2, we compared the human ULK1 and ULK2 proteins and their regulation. Despite the similarity in their enzymatic domain, we found that ULK1 and ULK2 have major differences in their autophagy-related interactors and their post-translational and transcriptional regulators. We identified 18 ULK1-specific and 7 ULK2-specific protein motifs serving as different interaction interfaces. We found that interactors of ULK1 and ULK2 all have different tissue-specific expressions partially contributing to diverse and ULK-specific interaction networks in various tissues. We identified three ULK1-specific and one ULK2-specific transcription factor binding sites, and eight sites shared by the regulatory region of both genes. Importantly, we found that both their post-translational and transcriptional regulators are involved in distinct biological processes-suggesting separate functions for ULK1 and ULK2. Unravelling differences between ULK1 and ULK2 could lead to a better understanding of how ULK-type specific dysregulation affects autophagy and other cellular processes that have been implicated in diseases such as inflammatory bowel disease and cancer.

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions.

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.

Big data in IBD: big progress for clinical practice.

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.

Targeted interplay between bacterial pathogens and host autophagy.

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert it. Despite previous key findings of bacteria-autophagy interplay, asystems-level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors SQSTM1/p62, CALCOCO2/NDP52 and MAP1LC3/LC3. Using structure-based interaction prediction, we identified bacterial proteins capable to modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus-specific proteins, and not those present in multiple genera. The complementarity between the predicted SQSTM1/p62 and CALCOCO2/NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. This complementarity potentially leaves the host more susceptible to chronic infections upon the mutation of autophagy receptors. Proteins derived from enterotoxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed apathogen-specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins that could modulate autophagy, and in turn being targeted by autophagy as ahost defense mechanism. We confirmed experimentally an interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognizes these pathogenic effectors. Abbreviations: ATG5: autophagy related 5; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; GST: glutathione S-transferase; LIR: MAP1LC3/LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3 alpha; OMV: outer membrane vesicles; SQSTM1/p62: sequestosome 1; SCV: Salmonella containing vesicle; TECPR1: tectonin beta-propeller repeat containing 1; YhjJ: hypothetical zinc-protease.

Integrative analysis of Paneth cell proteomic and transcriptomic data from intestinal organoids reveals functional processes dependent on autophagy.

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.

MDH1 and MPP7 Regulate Autophagy in Pancreatic Ductal Adenocarcinoma.

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.