The clinical course of comorbid substance use disorder and attention deficit/hyperactivity disorder: protocol and clinical characteristics of the INCAS study.

BACKGROUND: Substance use disorders (SUD) often co-occur with attention deficit hyperactivity disorder (ADHD). Although the short-term effects of some specific interventions have been investigated in randomized clinical trials, little is known about the long-term clinical course of treatment-seeking SUD patients with comorbid ADHD. AIMS: This paper presents the protocol and baseline clinical characteristics of the International Naturalistic Cohort Study of ADHD and SUD (INCAS) designed and conducted by the International Collaboration on ADHD and Substance Abuse (ICASA) foundation. The overall aim of INCAS is to investigate the treatment modalities provided to treatment-seeking SUD patients with comorbid ADHD, and to describe the clinical course and identify predictors for treatment outcomes. This ongoing study employs a multicentre observational prospective cohort design. Treatment-seeking adult SUD patients with comorbid ADHD are recruited, at 12 study sites in nine different countries. During the follow-up period of nine months, data is collected through patient files, interviews, and self-rating scales, targeting a broad range of cognitive and clinical symptom domains, at baseline, four weeks, three months and nine months. RESULTS: A clinically representative sample of 578 patients (137 females, 441 males) was enrolled during the recruitment period (June 2017-May 2021). At baseline, the sample had a mean age (SD) of 36.7 years (11.0); 47.5% were inpatients and 52.5% outpatients; The most prevalent SUDs were with alcohol 54.2%, stimulants 43.6%, cannabis 33.1%, and opioids 14.5%. Patients reported previous treatments for SUD in 71.1% and for ADHD in 56.9%. Other comorbid mental disorders were present in 61.4% of the sample: major depression 31.5%, post-traumatic stress disorder 12.1%, borderline personality disorder 10.2%. CONCLUSIONS: The first baseline results of this international cohort study speak to its feasibility. Data show that many SUD patients with comorbid ADHD had never received treatment for their ADHD prior to enrolment in the study. Future reports on this study will identify the course and potential predictors for successful pharmaceutical and psychological treatment outcomes. TRIAL REGISTRATION: ISRCTN15998989 20/12/2019.

Effects of substance misuse on inhibitory control in patients with attention-deficit/hyperactivity disorder.

Patients with attention-deficit/hyperactivity disorder (ADHD) are often diagnosed with comorbid substance misuse (SM), which is associated with poor treatment efficacy. Although literature indicates similar inhibitory control deficits in both conditions, it is unclear whether SM in ADHD exaggerates pre-existing deficits, with additive or distinct impairments in patients. Our aim was to examine SM effects on inhibitory control in ADHD. Behavioural and functional magnetic resonance imaging (fMRI) data from a stop-signal task were compared across ADHD patients with and without SM (ADHD + SM and ADHD-only, respectively) and controls (n = 33/group; 79 males, mean age 18.02 ± 2.45). To limit substance use disorder (SUD) trait effects, groups were matched for parental SUD. Overall, we found worse performance for ADHD-only and/or ADHD + SM compared with controls but no difference between the ADHD groups. Moreover, the ADHD groups showed decreased frontostriatal and frontoparietal activity during successful and failed stop trials. There were no differences between the ADHD groups in superior frontal nodes, but there was more decreased activation in temporal/parietal nodes in ADHD-only compared with ADHD + SM. During go-trials, ADHD + SM showed decreased activation in inferior frontal nodes compared with ADHD-only and controls. Findings during response inhibition showed deficits in inhibition and attentional processes for ADHD patients with and without SM. Despite no evidence for SM effects during response inhibition, results during go-trials suggest distinct effects on nodes that are associated with several executive functions. Future studies should investigate whether distinct deficits in ADHD + SM relate to poor treatment results and can direct development of distinct ADHD treatment strategies for these patients.

Effects of family history of substance use disorder on reward processing in adolescents with and without attention-deficit/hyperactivity disorder.

Patients with attention-deficit/hyperactivity disorder (ADHD) often develop early onset substance use disorder (SUD) and show poor treatment outcomes. Both disorders show similar reward-processing alterations, but it is unclear whether these are associated with familial vulnerability to SUD. Our aim was to investigate effects of family history of SUD (FH) on reward processing in individuals with and without ADHD, without substance misuse. Behavioural and functional magnetic resonance imaging (fMRI) data from a modified monetary incentive delay task were compared between participants with and without FH (FH positive [FH+]: n = 76 and FH negative [FH-]: n = 69; 76 with ADHD, aged 16.74 ± 3.14, 82 males), while accounting for continuous ADHD scores. The main analysis showed distinct positive association between ADHD scores and reaction times during neutral versus reward condition. ADHD scores were also positively associated with anticipatory responses of dorsolateral prefrontal cortex, independent of FH. There were no main FH effects on brain activation. Yet, FH+ participants showed distinct neural alterations in ventrolateral prefrontal cortex (VLPFC), dependent on ADHD. This was driven by positive association between ADHD scores and VLPFC activation during reward outcome, only in FH+. Sensitivity analysis with stricter SUD index showed hyperactivation of anterior cingulate cortex for FH+, independent of ADHD, during reward anticipation. There were no FH or ADHD effects on activation of ventral striatum in any analysis. Findings suggest both FH and ADHD effects in circuits of reward and attention/memory during reward processing. Future studies should examine whether these relate to early substance use initiation in ADHD and explore the need for adjusted SUD prevention strategies.

DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts.

DIANA-LncBase v3.0 (www.microrna.gr/LncBase) is a reference repository with experimentally supported miRNA targets on non-coding transcripts. Its third version provides approximately half a million entries, corresponding to ∼240 000 unique tissue and cell type specific miRNA-lncRNA pairs. This compilation of interactions is derived from the manual curation of publications and the analysis of >300 high-throughput datasets. miRNA targets are supported by 14 experimental methodologies, applied to 243 distinct cell types and tissues in human and mouse. The largest part of the database is highly confident, AGO-CLIP-derived miRNA-binding events. LncBase v3.0 is the first relevant database to employ a robust CLIP-Seq-guided algorithm, microCLIP framework, to analyze 236 AGO-CLIP-Seq libraries and catalogue ∼370 000 miRNA binding events. The database was redesigned from the ground up, providing new functionalities. Known short variant information, on >67,000 experimentally supported target sites and lncRNA expression profiles in different cellular compartments are catered to users. Interactive visualization plots, portraying correlations of miRNA-lncRNA pairs, as well as lncRNA expression profiles in a wide range of cell types and tissues, are presented for the first time through a dedicated page. LncBase v3.0 constitutes a valuable asset for ncRNA research, providing new insights to the understanding of the still widely unexplored lncRNA functions.

The combination of TPL2 knockdown and TNFα causes synthetic lethality via caspase-8 activation in human carcinoma cell lines.

Most normal and tumor cells are protected from tumor necrosis factor α (TNFα)-induced apoptosis. Here, we identify the MAP3 kinase tumor progression locus-2 (TPL2) as a player contributing to the protection of a subset of tumor cell lines. The combination of TPL2 knockdown and TNFα gives rise to a synthetic lethality phenotype via receptor-interacting serine/threonine-protein kinase 1 (RIPK1)-dependent and -independent mechanisms. Whereas wild-type TPL2 rescues the phenotype, its kinase-dead mutant does not. Comparison of the molecular events initiated by small interfering RNA for TPL2 (siTPL2) ± TNFα in treatment-sensitive and -resistant lines revealed that the activation of caspase-8, downstream of miR-21-5p and cFLIP, is the dominant TPL2-dependent event. More important, comparison of the gene expression profiles of all of the tested cell lines results in the clustering of sensitive and resistant lines into distinct groups, providing proof of principle for the feasibility of generating a predictive tool for treatment sensitivity.

DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions.

DIANA-TarBase v8 (http://www.microrna.gr/tarbase) is a reference database devoted to the indexing of experimentally supported microRNA (miRNA) targets. Its eighth version is the first database indexing >1 million entries, corresponding to ∼670 000 unique miRNA-target pairs. The interactions are supported by >33 experimental methodologies, applied to ∼600 cell types/tissues under ∼451 experimental conditions. It integrates information on cell-type specific miRNA-gene regulation, while hundreds of thousands of miRNA-binding locations are reported. TarBase is coming of age, with more than a decade of continuous support in the non-coding RNA field. A new module has been implemented that enables the browsing of interactions through different filtering combinations. It permits easy retrieval of positive and negative miRNA targets per species, methodology, cell type and tissue. An incorporated ranking system is utilized for the display of interactions based on the robustness of their supporting methodologies. Statistics, pie-charts and interactive bar-plots depicting the database content are available through a dedicated result page. An intuitive interface is introduced, providing a user-friendly application with flexible options to different queries.

Effects of Substance Misuse and Family History of Substance Use Disorder on Delay Discounting in Adolescents and Young Adults with Attention-Deficit/Hyperactivity Disorder.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) and substance use disorder (SUD) often co-occur. Both disorders are characterized by impulsive choice. However, little is known about the effects of substance misuse (SM) and family history of SUD (FH) on impulsive choice in ADHD-SUD comorbidity. Impulsive choice is also linked to callous-unemotional (CU) traits, which are suggested to play a role in ADHD-SUD comorbidity. Our aim was to examine the effects of (1) FH and (2) SM on impulsive choice, while exploring the role of CU traits. METHODS: Impulsive choice was assessed with the delay discounting (DD) task. We compared task performance across (1) ADHD patients and controls with or without FH of SUD (ADHD FH+: n = 86; ADHD FH-: n = 63; control FH+: n = 49; control FH-: n = 72; mean age of the whole sample [n = 270]: 16.39, SD: 3.43) and (2) family history-matched ADHD groups with and without SM and controls (ADHD + SM: n = 62; ADHD-only: n = 62; controls: n = 62; mean age of the whole sample [n = 186]: 18.01, SD: 2.71). Effects of CU traits were explored by adding this as a covariate in all analyses. RESULTS: (1) There was no main effect of FH on DD. (2) We found increased DD in ADHD + SM compared to ADHD-only and no difference between ADHD-only and controls. Finally, increased DD was associated with increased callous traits only in ADHD FH+ and control FH+. CONCLUSIONS: In adolescents and young adults with ADHD, high impulsive choice might only be present in those with comorbid SM and in an FH+ subgroup with high callous traits. This suggests that impulsive choice in ADHD might result from (1) effects of SM and (2) a combined effect of SUD vulnerability and high callousness. Future studies should investigate efficacy of early interventions, targeting CU traits.

RNAcentral: a comprehensive database of non-coding RNA sequences.

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.

DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators.

microRNAs (miRNAs) are small non-coding RNAs that actively fine-tune gene expression. The accurate characterization of the mechanisms underlying miRNA transcription regulation will further expand our knowledge regarding their implication in homeostatic and pathobiological networks. Aim of DIANA-miRGen v3.0 (http://www.microrna.gr/mirgen) is to provide for the first time accurate cell-line-specific miRNA gene transcription start sites (TSSs), coupled with genome-wide maps of transcription factor (TF) binding sites in order to unveil the mechanisms of miRNA transcription regulation. To this end, more than 7.3 billion RNA-, ChIP- and DNase-Seq next generation sequencing reads were analyzed/assembled and combined with state-of-the-art miRNA TSS prediction and TF binding site identification algorithms. The new database schema and web interface facilitates user interaction, provides advanced queries and innate connection with other DIANA resources for miRNA target identification and pathway analysis. The database currently supports 276 miRNA TSSs that correspond to 428 precursors and >19M binding sites of 202 TFs on a genome-wide scale in nine cell-lines and six tissues of Homo sapiens and Mus musculus.

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts.

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads.

DIANA-mirExTra v2.0: Uncovering microRNAs and transcription factors with crucial roles in NGS expression data.

Differential expression analysis (DEA) is one of the main instruments utilized for revealing molecular mechanisms in pathological and physiological conditions. DIANA-mirExTra v2.0 (http://www.microrna.gr/mirextrav2) performs a combined DEA of mRNAs and microRNAs (miRNAs) to uncover miRNAs and transcription factors (TFs) playing important regulatory roles between two investigated states. The web server uses as input miRNA/RNA-Seq read count data sets that can be uploaded for analysis. Users can combine their data with 350 small-RNA-Seq and 65 RNA-Seq in-house analyzed libraries which are provided by DIANA-mirExTra v2.0.The web server utilizes miRNA:mRNA, TF:mRNA and TF:miRNA interactions derived from extensive experimental data sets. More than 450 000 miRNA interactions and 2 000 000 TF binding sites from specific or high-throughput techniques have been incorporated, while accurate miRNA TSS annotation is obtained from microTSS experimental/in silico framework. These comprehensive data sets enable users to perform analyses based solely on experimentally supported information and to uncover central regulators within sequencing data: miRNAs controlling mRNAs and TFs regulating mRNA or miRNA expression. The server also supports predicted miRNA:gene interactions from DIANA-microT-CDS for 4 species (human, mouse, nematode and fruit fly). DIANA-mirExTra v2.0 has an intuitive user interface and is freely available to all users without any login requirement.

BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets.

BACKGROUND: A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives. RESULTS: We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques. CONCLUSIONS: BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster.

DIANA-miRPath v3.0: deciphering microRNA function with experimental support.

The functional characterization of miRNAs is still an open challenge. Here, we present DIANA-miRPath v3.0 (http://www.microrna.gr/miRPathv3) an online software suite dedicated to the assessment of miRNA regulatory roles and the identification of controlled pathways. The new miRPath web server renders possible the functional annotation of one or more miRNAs using standard (hypergeometric distributions), unbiased empirical distributions and/or meta-analysis statistics. DIANA-miRPath v3.0 database and functionality have been significantly extended to support all analyses for KEGG molecular pathways, as well as multiple slices of Gene Ontology (GO) in seven species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Gallus gallus and Danio rerio). Importantly, more than 600 000 experimentally supported miRNA targets from DIANA-TarBase v7.0 have been incorporated into the new schema. Users of DIANA-miRPath v3.0 can harness this wealth of information and substitute or combine the available in silico predicted targets from DIANA-microT-CDS and/or TargetScan v6.2 with high quality experimentally supported interactions. A unique feature of DIANA-miRPath v3.0 is its redesigned Reverse Search module, which enables users to identify and visualize miRNAs significantly controlling selected pathways or belonging to specific GO categories based on in silico or experimental data. DIANA-miRPath v3.0 is freely available to all users without any login requirement.

DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions.

microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available.

BiDaS: a web-based Monte Carlo BioData Simulator based on sequence/feature characteristics.

BiDaS is a web-application that can generate massive Monte Carlo simulated sequence or numerical feature data sets (e.g. dinucleotide content, composition, transition, distribution properties) based on small user-provided data sets. BiDaS server enables users to analyze their data and generate large amounts of: (i) Simulated DNA/RNA and aminoacid (AA) sequences following practically identical sequence and/or extracted feature distributions with the original data. (ii) Simulated numerical features, presenting identical distributions, while preserving the exact 2D or 3D between-feature correlations observed in the original data sets. The server can project the provided sequences to multidimensional feature spaces based on: (i) 38 DNA/RNA features describing conformational and physicochemical nucleotide sequence features from the B-DNA-VIDEO database, (ii) 122 DNA/RNA features based on conformational and thermodynamic dinucleotide properties from the DiProDB database and (iii) Pseudo-aminoacid composition of the initial sequences. To the best of our knowledge, this is the first available web-server that allows users to generate vast numbers of biological data sets with realistic characteristics, while keeping between-feature associations. These data sets can be used for a wide variety of current biological problems, such as the in-depth study of gene, transcript, peptide and protein groups/families; the creation of large data sets from just a few available members and the strengthening of machine learning classifiers. All simulations use advanced Monte Carlo sampling techniques. The BiDaS web-application is available at http://bioserver-3.bioacademy.gr/Bioserver/BiDaS/.

DIANA-LncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs.

Recently, the attention of the research community has been focused on long non-coding RNAs (lncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing lncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www.microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-lncRNA putative functional interactions. This study provides, for the first time, a comprehensive annotation of miRNA targets on lncRNAs. DIANA-LncBase hosts transcriptome-wide experimentally verified and computationally predicted miRNA recognition elements (MREs) on human and mouse lncRNAs. The analysis performed includes an integration of most of the available lncRNA resources, relevant high-throughput HITS-CLIP and PAR-CLIP experimental data as well as state-of-the-art in silico target predictions. The experimentally supported entries available in DIANA-LncBase correspond to >5000 interactions, while the computationally predicted interactions exceed 10 million. DIANA-LncBase hosts detailed information for each miRNA-lncRNA pair, such as external links, graphic plots of transcripts' genomic location, representation of the binding sites, lncRNA tissue expression as well as MREs conservation and prediction scores.

DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows.

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANA-microT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA-gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANA-microT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.

DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways.

MicroRNAs (miRNAs) are key regulators of diverse biological processes and their functional analysis has been deemed central in many research pipelines. The new version of DIANA-miRPath web server was redesigned from the ground-up. The user of DNA Intelligent Analysis (DIANA) DIANA-miRPath v2.0 can now utilize miRNA targets predicted with high accuracy based on DIANA-microT-CDS and/or experimentally verified targets from TarBase v6; combine results with merging and meta-analysis algorithms; perform hierarchical clustering of miRNAs and pathways based on their interaction levels; as well as elaborate sophisticated visualizations, such as dendrograms or miRNA versus pathway heat maps, from an intuitive and easy to use web interface. New modules enable DIANA-miRPath server to provide information regarding pathogenic single nucleotide polymorphisms (SNPs) in miRNA target sites (SNPs module) or to annotate all the predicted and experimentally validated miRNA targets in a selected molecular pathway (Reverse Search module). DIANA-miRPath v2.0 is an efficient and yet easy to use tool that can be incorporated successfully into miRNA-related analysis pipelines. It provides for the first time a series of highly specific tools for miRNA-targeted pathway analysis via a web interface and can be accessed at http://www.microrna.gr/miRPathv2.

GPR15 in colon cancer development and anti-tumor immune responses.

Introduction: The chemoattractant receptor, G protein-coupled receptor 15 (GPR15), promotes colon homing of T cells in health and colitis. GPR15 function in colon cancer is largely unexplored, motivating our current studies. Methods: In human study, immune cells were isolated from tumor tissues and healthy surgical tumor margins (STM), and their proportions as well as expression of GPR15 was analyzed by flow cytometry. In mouse studies, colon cancer was induced in GPR15-deficient (KO) and GPR15-suficient (Het) mice using azoxymethane (AOM) and dextran sulfate sodium (DSS) solution in drinking water. Serial endoscopy was performed in mice to monitor and visualize the distal region of colon. Mice were euthanized 10 weeks after the initial DSS administration, and the colon length and the number of polyps were recorded. Next, we identified the effects of GPR15L on established tumors in the MC38-colorectal cancer (CRC) mouse model. Immune cells were isolated from the mice colons or tumors and assessed by flow cytometry. Results: Our analysis of human CRC tissue revealed a significant reduction in GPR15+ immune cell frequencies in tumors compared to 'tumor-free' surgical margins. Similarly, our data analysis using The Cancer Genome Atlas (TCGA) indicated that lower GPR15 expression is associated with poor survival in human colon cancer. In the AOM/DSS colitis-associated colon cancer model, we observed increased colonic polyps and lower survival in Gpr15 +-KO compared to Gpr15-Het mice. Analysis of immune cell infiltrates in the colonic polyps showed significantly decreased CD8+ T cells and increased IL-17+ CD4+ and IL-17+ CD8+ T cells in Gpr15-KO than in Het mice. Consistent with a protective role of GPR15, administration of GPR15L to established tumors in the MC38-CRC model increased CD45+ cell infiltration, enhanced TNFa expression on CD4+ and CD8+ T cells at the tumor site and dramatically reduced tumor burden. Discussion: Our findings highlight an important, unidentified role of the GPR15-GPR15L axis in promoting a tumor-suppressive immune microenvironment and unveils a novel, colon-specific therapeutic target for CRC.

Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis.

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.