Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm, artificial intelligence (AI) is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations. Living human organoids (LOs) have a great potential for in vitro reshaping many aspects of in vivo true human organs, including organ development, disease occurrence, and drug responses. To date, AI has driven the revolutionary advances of human organoids in life science, precision medicine and pharmaceutical science in an unprecedented way. Herein, we provide a forward-looking review, the frontiers of LOs, covering the engineered construction strategies and multidisciplinary technologies for developing LOs, highlighting the cutting-edge achievements and the prospective applications of AI in LOs, particularly in biological study, disease occurrence, disease diagnosis and prediction and drug screening in preclinical assay. Moreover, we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies. The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.

Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan.

A living organoid biobank of patients with Crohn's disease reveals molecular subtypes for personalized therapeutics

Crohn's disease (CD) is a complex and heterogeneous condition with no perfect preclinical model or cure. To address this, we explore adult stem cell-derived organoids that retain their tissue identity and disease-driving traits. We prospectively create a biobank of CD patient-derived organoid cultures (PDOs) from colonic biopsies of 53 subjects across all clinical subtypes and healthy subjects. Gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and identified two major molecular subtypes: immune-deficient infectious CD (IDICD) and stress and senescence-induced fibrostenotic CD (S2FCD). Each subtype shows internal consistency in the transcriptome, genome, and phenome. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. Drug screens reverse subtype-specific phenotypes, suggesting phenotyped-genotyped CD PDOs can bridge basic biology and patient trials by enabling preclinical phase "0" human trials for personalized therapeutics.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Opening the doors of precision medicine: novel tools to assess intestinal barrier in inflammatory bowel disease and colitis-associated neoplasia

Mounting evidence underscores the pivotal role of the intestinal barrier and its convoluted network with diet and intestinal microbiome in the pathogenesis of inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CRC). Moreover, the bidirectional association of the intestinal barrier with the liver and brain, known as the gut-brain axis, plays a crucial role in developing complications, including extraintestinal manifestations of IBD and CRC metastasis. Consequently, barrier healing represents a crucial therapeutic target in these inflammatory-dependent disorders, with barrier assessment predicting disease outcomes, response to therapy and extraintestinal manifestations.New advanced technologies are revolutionising our understanding of the barrier paradigm, enabling the accurate assessment of the intestinal barrier and aiding in unravelling the complexity of the gut-brain axis. Cutting-edge endoscopic imaging techniques, such as ultra-high magnification endocytoscopy and probe-based confocal laser endomicroscopy, are new technologies allowing real-time exploration of the 'cellular' intestinal barrier. Additionally, novel advanced spatial imaging technology platforms, including multispectral imaging, upconversion nanoparticles, digital spatial profiling, optical spectroscopy and mass cytometry, enable a deep and comprehensive assessment of the 'molecular' and 'ultrastructural' barrier. In this promising landscape, artificial intelligence plays a pivotal role in standardising and integrating these novel tools, thereby contributing to barrier assessment and prediction of outcomes.Looking ahead, this integrated and comprehensive approach holds the promise of uncovering new therapeutic targets, breaking the therapeutic ceiling in IBD. Novel molecules, dietary interventions and microbiome modulation strategies aim to restore, reinforce, or modulate the gut-brain axis. These advancements have the potential for transformative and personalised approaches to managing IBD.

Restore Intestinal Barrier Integrity: An Approach for Inflammatory Bowel Disease Therapy

The intestinal barrier maintained by various types of columnar epithelial cells, plays a crucial role in regulating the interactions between the intestinal contents (such as the intestinal microbiota), the immune system, and other components. Dysfunction of the intestinal mucosa is a significant pathophysiological mechanism and clinical manifestation of inflammatory bowel disease (IBD). However, current therapies for IBD primarily focus on suppressing inflammation, and no disease-modifying treatments specifically target the epithelial barrier. Given the side effects associated with chronic immunotherapy, effective alternative therapies that promote mucosal healing are highly attractive. In this review, we examined the function of intestinal epithelial barrier function and the mechanisms of behind its disruption in IBD. We illustrated the complex process of intestinal mucosal healing and proposed therapeutic approaches to promote mucosal healing strategies in IBD. These included the application of stem cell transplantation and organ-like tissue engineering approaches to generate new intestinal tissue. Finally, we discussed potential strategies to restore the function of the intestinal barrier as a treatment for IBD.

Breast Cancers That Disseminate to Bone Marrow Acquire Aggressive Phenotypes through CX43-related Tumor-Stroma Tunnels

Estrogen receptor-positive (ER+) breast cancer commonly disseminates to bone marrow, where interactions with mesenchymal stromal cells (MSCs) shape disease trajectory. We modeled these interactions with tumor-MSC co-cultures and used an integrated transcriptome-proteome-network-analyses workflow to identify a comprehensive catalog of contact-induced changes. Conditioned media from MSCs failed to recapitulate genes and proteins, some borrowed and others tumor-intrinsic, induced in cancer cells by direct contact. Protein-protein interaction networks revealed the rich connectome between 'borrowed' and 'intrinsic' components. Bioinformatics prioritized one of the 'borrowed' components, CCDC88A /GIV, a multi-modular metastasis-related protein that has recently been implicated in driving a hallmark of cancer, growth signaling autonomy. MSCs transferred GIV protein to ER+ breast cancer cells (that lack GIV) through tunnelling nanotubes via connexin (Cx)43-facilitated intercellular transport. Reinstating GIV alone in GIV-negative breast cancer cells reproduced ∼20% of both the 'borrowed' and the 'intrinsic' gene induction patterns from contact co-cultures; conferred resistance to anti-estrogen drugs; and enhanced tumor dissemination. Findings provide a multiomic insight into MSC→tumor cell intercellular transport and validate how transport of one such candidate, GIV, from the haves (MSCs) to have-nots (ER+ breast cancer) orchestrates aggressive disease states.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Development of an Inflamed High Throughput Stem-cell-based Gut Epithelium Model to Assess the Impact of Annexin A1

OBJECTIVE AND DESIGN

Annexin A1 (ANXA1) plays a role in maintaining intestinal hemostasis, especially following mucosal inflammation. The published data about ANXA1 was derived from experimental animal models where there is an overlapping between epithelial and immune cells. There is no in vitro gut epithelial model that can assess the direct effect of ANXA1 on the gut epithelium.

METHODS

We developed high-throughput stem-cell-based murine epithelial cells and bacterial lipopolysaccharides (LPS) were used to induce inflammation. The impact of ANXA1 and its functional part (Ac2-26) was evaluated in the inflamed model. Intestinal integrity was assessed by the transepithelial electrical resistance (TEER), and FITC-Dextran permeability. Epithelial junction proteins were assessed using confocal microscopy and RT-qPCR. Inflammatory cytokines were evaluated by RT-qPCR and ELISA.

RESULTS

LPS challenge mediated a damage in the epithelial cells as shown by a drop in the TEER and an increase in FITC-dextran permeability; reduced the expression of epithelial junctional proteins (Occludin, ZO-1, and Cadherin) and increased the expression of the gut leaky protein, Claudin - 2. ANXA1 and Ac2-26 treatment reduced the previous damaging effects. In addition, ANXA1 and Ac2-26 inhibited the inflammatory responses mediated by the LPS and increased the transcription of the anti-inflammatory cytokine, IL-10.

CONCLUSION

ANXA1 and Ac2-26 directly protect the epithelial integrity by affecting the expression of epithelial junction and inflammatory markers. The inflamed gut model is a reliable tool to study intestinal inflammatory diseases, and to evaluate the efficacy of potential anti-inflammatory drugs and the screening of new drugs that could be candidates for inflammatory bowel disease.

Intestinal Colonized Silkworm Chrysalis-Like Probiotic Composites for Multi-Crossed Comprehensive Synergistic Therapy of Inflammatory Bowel Disease

Inspired by the timely emergence of silkworm pupae from their cocoons, silkworm chrysalis-like probiotic composites (SCPCs) are developed for the comprehensive therapy of inflammatory bowel disease (IBD), in which probiotics are enveloped as the "pupa" in a sequential layering of silk sericin (SS), tannic acid (TA), and polydopamine, akin to the protective "cocoon". Compared to unwrapped probiotics, these composites not only demonstrate exceptional resistance to the harsh gastrointestinal environment and exhibit over 200 times greater intestinal colonization but also safeguard probiotics from the damage of IBD environment while enabling probiotics sustained release. The probiotics, in synergy with SS and TA, provide a multi-crossed comprehensive therapy for IBD that simultaneously addresses various pathological features of IBD, including intestinal barrier disruption, elevated pro-inflammatory cytokines, heightened oxidative stress, and disturbances in the intestinal microbiota. SCPCs exhibit remarkable outcomes, including a 9.7-fold reduction in intestinal permeability, an 8.9-fold decrease in IL-6 levels, and a 2.9-fold reduction in TNF-α levels compared to uncoated probiotics. Furthermore, SCPCs demonstrate an impressive 92.25% reactive oxygen species clearance rate, significantly enhance the richness of beneficial intestinal probiotics, and effectively diminish the abundance of pathogenic bacteria, indicating a substantial improvement in the overall therapeutic effect of IBD.

Organoids in gastrointestinal diseases: from bench to clinic

The etiology of gastrointestinal (GI) diseases is intricate and multifactorial, encompassing complex interactions between genetic predisposition and gut microbiota. The cell fate change, immune function regulation, and microenvironment composition in diseased tissues are governed by microorganisms and mutated genes either independently or through synergistic interactions. A comprehensive understanding of GI disease etiology is imperative for developing precise prevention and treatment strategies. However, the existing models used for studying the microenvironment in GI diseases-whether cancer cell lines or mouse models-exhibit significant limitations, which leads to the prosperity of organoids models. This review first describes the development history of organoids models, followed by a detailed demonstration of organoids application from bench to clinic. As for bench utilization, we present a layer-by-layer elucidation of organoid simulation on host-microbial interactions, as well as the application in molecular mechanism analysis. As for clinical adhibition, we provide a generalized interpretation of organoid application in GI disease simulation from inflammatory disorders to malignancy diseases, as well as in GI disease treatment including drug screening, immunotherapy, and microbial-targeting and screening treatment. This review draws a comprehensive and systematical depiction of organoids models, providing a novel insight into the utilization of organoids models from bench to clinic.

Molecular Signatures for Microbe-Associated Colorectal Cancers

Background

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics.

Methods

A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS).

Results

The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs.

Conclusions

Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

Preclinical evidence using synthetic compounds and natural products indicates that AMPK represents a potential pharmacological target for the therapy of pulmonary diseases

Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is a highly conserved eukaryotic enzyme discovered as a key regulator of cellular energy homeostasis, with anti-inflammation, antioxidative stress, anticancer, and antifibrosis beneficial effects. AMPK is dysregulated in human pulmonary diseases such as acute lung injury, nonsmall cell lung cancer, pulmonary fibrosis, chronic obstructive pulmonary disease, and asthma. This review provides an overview of the beneficial role of natural, synthetic, and Chinese traditional medicines AMPK modulators in pulmonary diseases, and highlights the role of the AMPK signaling pathway in the lung, emphasizing the importance of finding lead compounds and drugs that can target and modulate AMPK to treat the lung diseases.

Xihuang pill ameliorates colitis in mice by improving mucosal barrier injury and inhibiting inflammatory cell filtration through network regulation

ETHNOPHARMACOLOGICAL RELEVANCE

The prevalence of colitis is on the rise, and effective treatment options are currently lacking. Xihuang pill (XHP) is a traditional Chinese medicine formula mentioned in the "Volume 4 of Surgical Evidence and Treatment of the Whole Life" authored by the renowned doctor Hong-Xu Wang during the Qing Dynasty. It is now part of the "Volume 9 of Chinese medicine formula preparation in Drug Standard." XHP and its primary ingredients have been demonstrated anti-inflammatory properties against colitis. However, the specific effects and underlying mechanisms of XHP in treating colitis remain unknown.

AIM OF THE STUDY

This study aimed to investigate the potential impact of XHP on colitis and uncover the underlying mechanisms involved.

MATERIALS AND METHODS

An acute colitis model was developed in C57BL/6N mice, and the effects on weight loss, colon length, the permeability of the colonic mucosa barrier, Claudin-5 and Occludin expression, number of both infiltrating MPO-positive cells and CD68-positive cells, and the content of pro-inflammatory cytokines (IL-6, IL-22, IL-1β, and TNF-α) in the colon tissue were investigated. Low-, medium-, and high-dose XHP (0.45, 0.9, and 1.8 g/kg/day) (batch number: z21021222) were administered to the mice by gavage over the course of two weeks. Additionally, the protein expression levels in colon tissue from the control group, colitis group, and XHP low-dose administration group mice were analyzed by quantitative proteomics techniques. The comprehensive profiling and characterization of absorbed components in mice blood following oral administration of XHP were identified by HPLC/Q-TOF-MS techniques, and the absorbed components in blood were combined with proteomics to reveal the mechanism of enteritis inhibition by XHP.

RESULTS

Our findings indicated that XHP enhanced weight loss and colonic shortening of colitis mice. Additionally, XHP reduced the increase in permeability of the colonic mucosa barrier and decreased expression of Claudin-5 and Occludin, while significantly reducing the number of infiltrating MPO-positive cells and CD68-positive cells in the colon tissue. We found that XHP reduced the production of pro-inflammatory cytokines, including IL-6, IL-22, IL-1β, and TNF-α in colon tissue. Pharmacokinetic analysis suggested that XHP contained 24 blood-entering prototype ingredients, which improved colitis through the regulation of various proteins (e.g., Ctsb, Sting1, and Abat) linked to mucosal barrier injury and inflammation.

CONCLUSION

XHP improved intestinal mucosal barrier injury and reduced MPO-positive cells and CD68-positive cell infiltration through multiple targets and pathways, providing support for XHP as a promising therapy for colitis.

C1QA is an invariant biomarker for tissue macrophages

Macrophages play a pivotal role in immune responses, particularly in the context of combating microbial threats within tissues. The identification of reliable biomarkers associated with macrophage function is essential for understanding their diverse roles in host defense. This study investigates the potential of C1QA as an invariant biomarker for tissue macrophages, focusing on its correlation with the anti-microbial pathway. C1QA, a component of the complement system, has been previously implicated in various immune functions. Our research delves into the specific association of C1QA with tissue-resident macrophages and its implications in the context of anti-microbial responses. Through comprehensive systems biology and Boolean analysis of gene expression, we aim to establish C1QA as a consistent and reliable marker for identifying tissue macrophages. Furthermore, we explore the functional significance of C1QA in the anti-microbial pathway. This research seeks to provide valuable insights into the molecular mechanisms underlying the anti-microbial functions of tissue macrophages, with C1QA emerging as a potential key player in this intricate regulatory network. Understanding the relationship between C1QA, tissue macrophages, and the anti-microbial pathway could pave the way for the development of targeted therapeutic strategies aimed at enhancing the host's ability to combat infections. Ultimately, our findings contribute to the expanding knowledge of macrophage biology and may have implications for the diagnosis and treatment of infectious diseases.

One Sentence Summary

C1QA is a fundamental biomarker of tissue macrophages.

Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors

BACKGROUND

Although differentiation therapy can cure some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).

METHODS

A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (~5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC lines in vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).

RESULTS

The drug shifts the network predictably, induces CDX2 and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards all CDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (~1700 CRCs), revealing the impact of CDX2-reinstatement therapy could translate into a ~50% reduction in the risk of mortality/recurrence.

CONCLUSIONS

Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine.

Macrophage states: there's a method in the madness

Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.

Colitis-associated carcinogenesis: crosstalk between tumors, immune cells and gut microbiota

Colorectal cancer (CRC) is the third most common cancer worldwide. One of the main causes of colorectal cancer is inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD). Intestinal epithelial cells (IECs), intestinal mesenchymal cells (IMCs), immune cells, and gut microbiota construct the main body of the colon and maintain colon homeostasis. In the development of colitis and colitis-associated carcinogenesis, the damage, disorder or excessive recruitment of different cells such as IECs, IMCs, immune cells and intestinal microbiota play different roles during these processes. This review aims to discuss the various roles of different cells and the crosstalk of these cells in transforming intestinal inflammation to cancer, which provides new therapeutic methods for chemotherapy, targeted therapy, immunotherapy and microbial therapy.

Inflammation, Autoinflammation and Autoimmunity in Inflammatory Bowel Diseases

In this review, the role of innate and adaptive immunity in the pathogenesis of inflammatory bowel diseases (IBD) is reported. In IBD, an altered innate immunity is often found, with increased Th17 and decreased Treg cells infiltrating the intestinal mucosa. An associated increase in inflammatory cytokines, such as IL-1 and TNF-α, and a decrease in anti-inflammatory cytokines, such as IL-10, concur in favoring the persistent inflammation of the gut mucosa. Autoinflammation is highlighted with insights in the role of inflammasomes, which activation by exogenous or endogenous triggers might be favored by mutations of NOD and NLRP proteins. Autoimmunity mechanisms also take place in IBD pathogenesis and in this context of a persistent immune stimulation by bacterial antigens and antigens derived from intestinal cells degradation, the adaptive immune response takes place and results in antibodies and autoantibodies production, a frequent finding in these diseases. Inflammation, autoinflammation and autoimmunity concur in altering the mucus layer and enhancing intestinal permeability, which sustains the vicious cycle of further mucosal inflammation.

Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network

Although induction of differentiation represents an effective strategy for neuroblastoma treatment, the mechanisms underlying neuroblastoma differentiation are poorly understood. We generated a computational model of neuroblastoma differentiation consisting of interconnected gene clusters identified based on symmetric and asymmetric gene expression relationships. We identified a differentiation signature consisting of series of gene clusters comprised of 1251 independent genes that predicted neuroblastoma differentiation in independent datasets and in neuroblastoma cell lines treated with agents known to induce differentiation. This differentiation signature was associated with patient outcomes in multiple independent patient cohorts and validated the role of MYCN expression as a marker of neuroblastoma differentiation. Our results further identified novel genes associated with MYCN via asymmetric Boolean implication relationships that would not have been identified using symmetric computational approaches and that were associated with both neuroblastoma differentiation and patient outcomes. Our differentiation signature included a cluster of genes involved in intracellular signaling and growth factor receptor trafficking pathways that is strongly associated with neuroblastoma differentiation, and we validated the associations of UBE4B, a gene within this cluster, with neuroblastoma cell and tumor differentiation. Our findings demonstrate that Boolean network analyses of symmetric and asymmetric gene expression relationships can identify novel genes and pathways relevant for neuroblastoma tumor differentiation that could represent potential therapeutic targets.

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study

BACKGROUND

Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD.

METHODS

OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS).

RESULTS

A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization.

CONCLUSIONS

This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

A Living Organoid Biobank of Crohn's Disease Patients Reveals Molecular Subtypes for Personalized Therapeutics

Crohn's disease (CD) is a complex, clinically heterogeneous disease of multifactorial origin; there is no perfect pre-clinical model, little insight into the basis for such heterogeneity, and still no cure. To address these unmet needs, we sought to explore the translational potential of adult stem cell-derived organoids that not only retain their tissue identity, but also their genetic and epigenetic disease-driving traits. We prospectively created a biobank of CD patient-derived organoid cultures (PDOs) using biopsied tissues from colons of 34 consecutive subjects representing all clinical subtypes (Montreal Classification B1-B3 and perianal disease). PDOs were generated also from healthy subjects. Comparative gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and revealed that despite the clinical heterogeneity there are two major molecular subtypes: immune-deficient infectious-CD [IDICD] and stress and senescence-induced fibrostenotic-CD [S2FCD]. The transcriptome, genome and phenome show a surprising degree of internal consistency within each molecular subtype. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. These insights enabled drug screens that reversed subtype-specific phenotypes, e.g., impaired microbial clearance in IDICD was reversed using agonists for nuclear receptors, and senescence in S2FCD was rectified using senotherapeutics, but not vice versa . Phenotyped-genotyped CD-PDOs may fill the gap between basic biology and patient trials by enabling pre-clinical Phase '0' human trials for personalized therapeutics.

GRAPHIC ABSTRACT

In Brief

This work creates a prospectively biobanked phenotyped-genotyped Crohn's disease patient-derived organoids (CD-PDOs) as platforms for molecular subtyping of disease and for ushering personalized therapeutics.

HIGHLIGHTS

Prospectively biobanked CD-organoids recapitulate the disease epithelium in patientsThe phenome-transcriptome-genome of CD-organoids converge on two molecular subtypesOne subtype shows impaired microbial clearance, another increased cellular senescencePhenotyped-genotyped PDOs are then used for integrative and personalized therapeutics.

Artificial intelligence-guided discovery of gastric cancer continuum

BACKGROUND

Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.

METHODS

We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.

RESULTS

Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.

CONCLUSIONS

A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

Understanding disruption of the gut barrier during inflammation: Should we abandon traditional epithelial cell lines and switch to intestinal organoids?

Disruption of the intestinal epithelial barrier is a hallmark of mucosal inflammation. It increases exposure of the immune system to luminal microbes, triggering a perpetuating inflammatory response. For several decades, the inflammatory stimuli-induced breakdown of the human gut barrier was studied in vitro by using colon cancer derived epithelial cell lines. While providing a wealth of important data, these cell lines do not completely mimic the morphology and function of normal human intestinal epithelial cells (IEC) due to cancer-related chromosomal abnormalities and oncogenic mutations. The development of human intestinal organoids provided a physiologically-relevant experimental platform to study homeostatic regulation and disease-dependent dysfunctions of the intestinal epithelial barrier. There is need to align and integrate the emerging data obtained with intestinal organoids and classical studies that utilized colon cancer cell lines. This review discusses the utilization of human intestinal organoids to dissect the roles and mechanisms of gut barrier disruption during mucosal inflammation. We summarize available data generated with two major types of organoids derived from either intestinal crypts or induced pluripotent stem cells and compare them to the results of earlier studies with conventional cell lines. We identify research areas where the complementary use of colon cancer-derived cell lines and organoids advance our understanding of epithelial barrier dysfunctions in the inflamed gut and identify unique questions that could be addressed only by using the intestinal organoid platforms.

Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic inflammatory condition caused by multiple genetic and environmental factors. Numerous genes are implicated in the etiology of IBD, but the diagnosis of IBD is challenging. Here, XGBoost, a machine learning prediction model, has been used to distinguish IBD from healthy cases following elaborative feature selection. Using combined unsupervised clustering analysis and the XGBoost feature selection method, we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy. The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system. The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status. Therefore, this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Advanced Development and New Horizons

Inflammatory bowel disease (IBD) is a complex chronic immune disease with two subtypes: Crohn's disease and ulcerative colitis. Considering the differences in pathogenesis, etiology, clinical presentation, and response to therapy among patients, gastroenterologists mainly rely on endoscopy to diagnose and treat IBD during clinical practice. However, as exemplified by the increasingly comprehensive ulcerative colitis endoscopic scoring system, the endoscopic diagnosis, evaluation, and treatment of IBD still rely on the subjective manipulation and judgment of endoscopists. In recent years, the use of artificial intelligence (AI) has grown substantially in various medical fields, and an increasing number of studies have investigated the use of this emerging technology in the field of gastroenterology. Clinical applications of AI have focused on IBD pathogenesis, etiology, diagnosis, and patient prognosis. Large-scale datasets offer tremendous utility in the development of novel tools to address the unmet clinical and practice needs for treating patients with IBD. However, significant differences among AI methodologies, datasets, and clinical findings limit the incorporation of AI technology into clinical practice. In this review, we discuss practical AI applications in the diagnosis of IBD via gastroenteroscopy and speculate regarding a future in which AI technology provides value for the diagnosis and treatment of IBD patients.

Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and AD models have focused on a small subset of genes identified in familial AD. Microarray studies have identified thousands of dysregulated genes in the brains of patients with AD yet identifying the best gene candidates to both model and treat AD remains a challenge. We performed a meta-analysis of microarray data from the frontal cortex (n = 697) and cerebellum (n = 230) of AD patients and healthy controls. A two-stage artificial intelligence approach, with both unsupervised and supervised machine learning, combined with a functional network analysis was used to identify functionally connected and biologically relevant novel gene candidates in AD. We found that in the frontal cortex, genes involved in mitochondrial energy, ATP, and oxidative phosphorylation, were the most significant dysregulated genes. In the cerebellum, dysregulated genes were involved in mitochondrial cellular biosynthesis (mitochondrial ribosomes). Although there was little overlap between dysregulated genes between the frontal cortex and cerebellum, machine learning models comprised of this overlap. A further functional network analysis of these genes identified that two downregulated genes, ATP5L and ATP5H, which both encode subunits of ATP synthase (mitochondrial complex V) may play a role in AD. Combined, our results suggest that mitochondrial dysfunction, particularly a deficit in energy homeostasis, may play an important role in AD.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

AI-assisted discovery of an ethnicity-influenced driver of cell transformation in esophageal and gastroesophageal junction adenocarcinomas

Although Barrett's metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of cellular transformation in BE remain incompletely understood. We use an artificial intelligence-guided network approach to study EAC initiation and progression. Key predictions are subsequently validated in a human organoid model, in patient-derived biopsy specimens of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. Our model classified healthy esophagus from BE and BE from EACs in several publicly available gene expression data sets (n = 932 samples). The model confirmed that all EACs must originate from BE and pinpointed a CXCL8/IL8↔neutrophil immune microenvironment as a driver of cellular transformation in EACs and gastroesophageal junction adenocarcinomas. This driver is prominent in White individuals but is notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8 expression, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with changes in ANC by ethnicity (e.g., benign ethnic neutropenia [BEN]) modify that risk. Findings define a racially influenced immunological basis for cell transformation and suggest that BEN in AAs may be a deterrent to BE→EAC progression.

An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease

Multisystem inflammatory syndrome in children (MIS-C) is an illness that emerged amidst the COVID-19 pandemic but shares many clinical features with the pre-pandemic syndrome of Kawasaki disease (KD). Here we compare the two syndromes using a computational toolbox of two gene signatures that were developed in the context of SARS-CoV-2 infection, i.e., the viral pandemic (ViP) and severe-ViP signatures and a 13-transcript signature previously demonstrated to be diagnostic for KD, and validated our findings in whole blood RNA sequences, serum cytokines, and formalin fixed heart tissues. Results show that KD and MIS-C are on the same continuum of the host immune response as COVID-19. Both the pediatric syndromes converge upon an IL15/IL15RA-centric cytokine storm, suggestive of shared proximal pathways of immunopathogenesis; however, they diverge in other laboratory parameters and cardiac phenotypes. The ViP signatures reveal unique targetable cytokine pathways in MIS-C, place MIS-C farther along in the spectrum in severity compared to KD and pinpoint key clinical (reduced cardiac function) and laboratory (thrombocytopenia and eosinopenia) parameters that can be useful to monitor severity.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Artificial intelligence-rationalized balanced PPARα/γ dual agonism resets dysregulated macrophage processes in inflammatory bowel disease

A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Here we used BoNE to query an Inflammatory Bowel Disease (IBD)-map and prioritize a therapeutic strategy that involves dual agonism of two nuclear receptors, PPARα/γ. Balanced agonism of PPARα/γ was predicted to modulate macrophage processes, ameliorate colitis, 'reset' the gene expression network from disease to health. Predictions were validated using a balanced and potent PPARα/γ-dual-agonist (PAR5359) in Citrobacter rodentium- and DSS-induced murine colitis models. Using inhibitors and agonists, we show that balanced-dual agonism promotes bacterial clearance efficiently than individual agonists, both in vivo and in vitro. PPARα is required and sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPARγ-agonism blunts these responses, delays microbial clearance; balanced dual agonism achieved controlled inflammation while protecting the gut barrier and 'reversal' of the transcriptomic network. Furthermore, dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients. These findings not only deliver a macrophage modulator for use as barrier-protective therapy in IBD, but also highlight the potential of BoNE to rationalize combination therapy.

Computational models in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic and relapsing disease with multiple underlying influences and notable heterogeneity among its clinical and response-to-treatment phenotypes. There is no cure for IBD, and none of the currently available therapies have demonstrated clinical efficacies beyond 40%-60%. Data collected about its omics, pathogenesis, and treatment strategies have grown exponentially with time making IBD a prime candidate for artificial intelligence (AI) mediated discovery support. AI can be leveraged to further understand or identify IBD features to improve clinical outcomes. Various treatment candidates are currently under evaluation in clinical trials, offering further approaches and opportunities for increasing the efficacies of treatments. However, currently, therapeutic plans are largely determined using clinical features due to the lack of specific biomarkers, and it has become necessary to step into precision medicine to predict therapeutic responses to guarantee optimal treatment efficacy. This is accompanied by the application of AI and the development of multiscale hybrid models combining mechanistic approaches and machine learning. These models ultimately lead to the creation of digital twins of given patients delivering on the promise of precision dosing and tailored treatment. Interleukin-6 (IL-6) is a prominent cytokine in cell-to-cell communication in the inflammatory responses' regulation. Dysregulated IL-6-induced signaling leads to severe immunological or proliferative pathologies, such as IBD and colon cancer. This mini-review explores multiscale models with the aim of predicting the response to therapy in IBD. Modeling IL-6 biology and generating digital twins enhance the credibility of their prediction.

Diarrheal pathogenesis in Salmonella infection may result from an imbalance in intestinal epithelial differentiation through reduced Notch signaling

KEY POINTS

Salmonella is a leading foodborne pathogen known to cause high chloride content diarrhea. Salmonella infection of murine enteroid-derived monolayers decreased DRA expression. Salmonella infection resulted in upregulation of the secretory epithelial marker ATOH1, the goblet cell marker Muc2, and the enteroendocrine cell marker ChgA. Downregulation of DRA may result from infection-induced Notch inhibition, as reflected by decreased expression of Notch intracellular domain and Hes1, as well as from decreased HNF1α signaling. The imbalance in intestinal epithelial differentiation favoring secretory over absorptive cell types is a possible mechanism by which Salmonella elicits diarrhea and may be relevant therapeutically.

ABSTRACT

Infections with non-typhoidal Salmonella spp. represent the most burdensome foodborne illnesses worldwide, yet despite their prevalence, the mechanism through which Salmonella elicits diarrhea is not entirely known. Intestinal ion transporters play important roles in fluid and electrolyte homeostasis in the intestine. We have previously shown that infection with Salmonella caused decreased colonic expression of the chloride/bicarbonate exchanger SLC26A3 (Down-Regulated in Adenoma; DRA) in a mouse model. In this study, we focused on the mechanism of DRA downregulation during Salmonella infection, by using murine epithelial enteroid-derived monolayers (EDM). The decrease in DRA expression caused by infection was recapitulated in EDM and accompanied by increased expression of ATOH1, the goblet cell marker Muc2, and the enteroendocrine cell marker ChgA. This suggested biased epithelial differentiation towards the secretory, rather than absorptive phenotype. In addition, the downstream Notch effector, Notch Intracellular Domain (NICD) and Hes1 were decreased following Salmonella infection. The relevance of Notch signaling was further investigated using a γ-secretase inhibitor, which recapitulated the downregulation in Hes1 and DRA as well as upregulation in ATOH1 and Muc2 seen following infection. Our findings suggest that Salmonella infection may result in a shift from absorptive to secretory cell types through Notch inhibition, which explains why there is a decreased capacity for absorption and ultimately the accumulation of diarrheal fluid. Our work also shows the value of EDM as a model to investigate mechanisms that might be targeted for therapy of diarrhea caused by Salmonella infection. Abstract figure legend Upon infection of the intestinal epithelium with Salmonella, diarrhea may be explained by an imbalance of intestinal epithelial differentiation. Downregulation of cell-fate commitment to the absorptive lineage, as reflected by decreased Hes1 and DRA, was observed. Conversely, upregulation of epithelial differentiation into secretory cell types was observed, as reflected by increased ATOH1, Muc2, and ChgA. This article is protected by copyright. All rights reserved.

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.

Gut Microbial Metabolite-Mediated Regulation of the Intestinal Barrier in the Pathogenesis of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. The disease has a multifactorial aetiology, involving genetic, microbial as well as environmental factors. The disease pathogenesis operates at the host-microbe interface in the gut. The intestinal epithelium plays a central role in IBD disease pathogenesis. Apart from being a physical barrier, the epithelium acts as a node that integrates environmental, dietary, and microbial cues to calibrate host immune response and maintain homeostasis in the gut. IBD patients display microbial dysbiosis in the gut, combined with an increased barrier permeability that contributes to disease pathogenesis. Metabolites produced by microbes in the gut are dynamic indicators of diet, host, and microbial interplay in the gut. Microbial metabolites are actively absorbed or diffused across the intestinal lining to affect the host response in the intestine as well as at systemic sites via the engagement of cognate receptors. In this review, we summarize insights from metabolomics studies, uncovering the dynamic changes in gut metabolite profiles in IBD and their importance as potential diagnostic and prognostic biomarkers of disease. We focus on gut microbial metabolites as key regulators of the intestinal barrier and their role in the pathogenesis of IBD.

SPT6 loss permits the transdifferentiation of keratinocytes into an intestinal fate that resembles Barrett's metaplasia

Transient depletion of the transcription elongation factor SPT6 in the keratinocyte has been recently shown to inhibit epidermal differentiation and stratification; instead, they transdifferentiate into a gut-like lineage. We show here that this phenomenon of transdifferentiation recapitulates Barrett's metaplasia, the only human pathophysiologic condition in which a stratified squamous epithelium that is injured due to chronic acid reflux is trans-committed into an intestinal fate. The evidence we present here not only lend support to the notion that the keratinocytes are potentially the cell of origin of Barrett's metaplasia but also provide mechanistic insights linking transient acid exposure, downregulation of SPT6, stalled transcription of the master regulator of epidermal fate TP63, loss of epidermal fate, and metaplastic progression. Because Barrett's metaplasia in the esophagus is a pre-neoplastic condition with no preclinical human models, these findings have a profound impact on the modeling Barrett's metaplasia-in-a-dish.

Strategies to apply 3Rs in preclinical testing

Animal experimentation has been fundamental in biological and biomedical research. To guarantee the maximum quality, efficacy and/or safety of products intended for the use in humans in vivo testing is necessary; however, for over 60 years, alternative methods have been developed in response to the necessity to reduce the number of animals used in experimentation, to guarantee their welfare; resorting to animal models only when strictly necessary. The three Rs (Replacement, Reduction, and Refinement), seek to ensure the rational and respectful use of laboratory animals and maintain an adequate projection in terms of bioethical considerations. This article describes different approaches to apply 3Rs in preclinical experimentation for either research or regulatory purposes.