An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease

Qingchang suppository ameliorates mucosal inflammation in ulcerative colitis by inhibiting the differentiation and effector functions of Th1 and Th17 cells

ETHNOPHARMACOLOGICAL RELEVANCE

Qing Chang Suppository (QCS), a traditional Chinese medicine formula, has been shown to effectively alleviate mucosal inflammation in patients with ulcerative colitis (UC). While the mechanism of QCS appears to be related to the regulation of CD4+T cell subset responses, direct evidence demonstrating that QCS inhibits Th1 and Th17 cell activation in UC (particularly based on human data) remains lacking. Additionally, the precise mechanisms through which QCS affects these cells have yet to be fully elucidated.

AIM OF STUDY

This study aimed to investigate the effects of QCS on Th1 and Th17 cell responses in UC and to explore the underlying mechanisms.

MATERIALS AND METHODS

Twenty-eight patients with mild-to-moderate UC were recruited and treated with QCS for 12 weeks. Symptoms were assessed every two weeks, with sigmoidoscopies performed at baseline and at week 12. Intestinal mucosal biopsies and peripheral blood (PB) were collected at these time points. At the end of the trial, patients were categorized into responder and non-responder groups based on a modified Mayo disease activity index score. Healthy controls (HCs) were defined as subjects without IBD or colorectal carcinoma but with colon polyps. The frequencies of IFN-γ+CD4+T cells and IL-17A+CD4+T cells in PB and colonic mucosa were measured using flow cytometry. The expression levels and localization of T-bet, RORγT, IFN-γ, TNF-α, and IL-17A were determined via immunofluorescence, and JNK signaling activation was assessed through immunoblotting and immunohistochemistry. All parameters were compared across the three groups.

RESULTS

At week 12, responders showed a significant reduction in colonic mucosal inflammation compared to baseline, accompanied by decreased frequencies of IFN-γ+CD4+T and IL-17A+CD4+ T cells in both PB and the colonic epithelial layer. Notably, Th1 and Th17 cell activity around intestinal epithelial cells (IECs) was nearly undetectable, as evidenced by the diminished expression of T-bet, RORγT, IFN-γ, TNF-α, and IL-17A. Additionally, JNK phosphorylation in these cells was significantly reduced. In contrast, non-responders exhibited no meaningful improvement; colonic pathology remained unchanged, and elevated levels of IFN-γ+CD4+T and IL-17A+CD 4+T cells persisted in both the PB and colonic epithelial layer. The presence of Th1 and Th17 cells and their associated cytokines around IECs remained substantial, and there was no significant change in JNK activation.

CONCLUSION

QCS attenuates mucosal inflammation in UC patients by inhibiting the differentiation and effector functions of Th1 and Th17 cells, primarily through the regulation of the JNK signaling pathway.

Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer's disease

Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. Finally, we assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.

Decoding the mosaic of inflammatory bowel disease: Illuminating insights with single-cell RNA technology

Inflammatory bowel diseases (IBD), comprising ulcerative colitis (UC) and Crohn's disease (CD), are complex chronic inflammatory intestinal conditions with a multifaceted pathology, influenced by immune dysregulation and genetic susceptibility. The challenges in understanding IBD mechanisms and implementing precision medicine include deciphering the contributions of individual immune and non-immune cell populations, pinpointing specific dysregulated genes and pathways, developing predictive models for treatment response, and advancing molecular technologies. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to address these challenges, offering comprehensive transcriptome profiles of various cell types at the individual cell level in IBD patients, overcoming limitations of bulk RNA sequencing. Additionally, single-cell proteomics analysis, T-cell receptor repertoire analysis, and epigenetic profiling provide a comprehensive view of IBD pathogenesis and personalized therapy. This review summarizes significant advancements in single-cell sequencing technologies for enhancing our understanding of IBD, covering pathogenesis, diagnosis, treatment, and prognosis. Furthermore, we discuss the challenges that persist in the context of IBD research, including the need for longitudinal studies, integration of multiple single-cell and spatial transcriptomics technologies, and the potential of microbial single-cell RNA-seq to shed light on the role of the gut microbiome in IBD.

Systematic analysis of human colorectal cancer scRNA-seq revealed limited pro-tumoral IL-17 production potential in gamma delta T cells

Gamma delta T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin-17 (IL-17) within the tumor microenvironment of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including poised effector-like T cells, tissue-resident memory T cells, progenitor exhausted-like T cells, and exhausted T cells, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. We proposed anti-tumor γδ T effector cells may arise from tissue-resident progenitor cells based on the trajectory analysis. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Systematic Analysis of Human Colorectal Cancer scRNA-seq Revealed Limited Pro-tumoral IL-17 Production Potential in Gamma Delta T Cells

Gamma delta (γδ) T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin- 17 (IL-17) within the tumor microenvironment (TME) of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing (scRNA-seq) datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including Teff, TRM, Tpex, and Tex, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.

AutoTransOP: translating omics signatures without orthologue requirements using deep learning

The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Key advances in vaccine development for tuberculosis-success and challenges

Breakthrough findings in the clinical and preclinical development of tuberculosis (TB) vaccines have galvanized the field and suggest, for the first time since the development of bacille Calmette-Guérin (BCG), that a novel and protective TB vaccine is on the horizon. Here we highlight the TB vaccines that are in the development pipeline and review the basis for optimism in both the clinical and preclinical space. We describe immune signatures that could act as immunological correlates of protection (CoP) to facilitate the development and comparison of vaccines. Finally, we discuss new animal models that are expected to more faithfully model the pathology and complex immune responses observed in human populations.

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.

Alzheimer's Disease Protein Relevance Analysis Using Human and Mouse Model Proteomics Data

The principles governing genotype-phenotype relationships are still emerging(1-3), and detailed translational as well as transcriptomic information is required to understand complex phenotypes, such as the pathogenesis of Alzheimer's disease. For this reason, the proteomics of Alzheimer disease (AD) continues to be studied extensively. Although comparisons between data obtained from humans and mouse models have been reported, approaches that specifically address the between-species statistical comparisons are understudied. Our study investigated the performance of two statistical methods for identification of proteins and biological pathways associated with Alzheimer's disease for cross-species comparisons, taking specific data analysis challenges into account, including collinearity, dimensionality reduction and cross-species protein matching. We used a human dataset from a well-characterized cohort followed for over 22 years with proteomic data available. For the mouse model, we generated proteomic data from whole brains of CVN-AD and matching control mouse models. We used these analyses to determine the reliability of a mouse model to forecast significant proteomic-based pathological changes in the brain that may mimic pathology in human Alzheimer's disease. Compared with LASSO regression, partial least squares discriminant analysis provided better statistical performance for the proteomics analysis. The major biological finding of the study was that extracellular matrix proteins and integrin-related pathways were dysregulated in both the human and mouse data. This approach may help inform the development of mouse models that are more relevant to the study of human late-onset Alzheimer's disease.

Intestinal cellular heterogeneity and disease development revealed by single-cell technology

The intestinal epithelium is responsible for food digestion and nutrient absorption and plays a critical role in hormone secretion, microorganism defense, and immune response. These functions depend on the integral single-layered intestinal epithelium, which shows diversified cell constitution and rapid self-renewal and presents powerful regeneration plasticity after injury. Derailment of homeostasis of the intestine epithelium leads to the development of diseases, most commonly including enteritis and colorectal cancer. Therefore, it is important to understand the cellular characterization of the intestinal epithelium at the molecular level and the mechanisms underlying its homeostatic maintenance. Single-cell technologies allow us to gain molecular insights at the single-cell level. In this review, we summarize the single-cell RNA sequencing applications to understand intestinal cell characteristics, spatiotemporal evolution, and intestinal disease development.

A transcriptome atlas and interactive analysis platform for autoimmune disease

With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell–cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available.

Database URL: http://galaxy.ustc.edu.cn/IAAA

GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources.

Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives

Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation

Inflammatory bowel diseases (IBDs) are genetically complex and exhibit significant inter-patient heterogeneity in disease presentation and therapeutic response. Here, we show that mouse models of IBD exhibit variable responses to inhibition of MK2, a pro-inflammatory serine/threonine kinase, and that MK2 inhibition suppresses inflammation by targeting inflammatory monocytes and neutrophils in murine models. Using a computational approach (TransComp-R) that allows for cross-species comparison of transcriptomic features, we identified an IBD patient subgroup that is predicted to respond to MK2 inhibition, and an independent preclinical model of chronic intestinal inflammation predicted to be non-responsive, which we validated experimentally. Thus, cross-species mouse-human translation approaches can help to identify patient subpopulations in which to deploy new therapies.

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MK2 kinase inhibition shows variable efficacy in different IBD mouse models

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TCT and TNFΔARE mice express distinct inflammatory and MK2-responsive genes

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“Response to MK2i” signature is enriched in monocytes and neutrophils

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Cross-species modeling identifies patient groups potentially responsive to MK2i

Recent advances in tissue stem cells

Stem cells are undifferentiated cells capable of self-renewal and differentiation, giving rise to specialized functional cells. Stem cells are of pivotal importance for organ and tissue development, homeostasis, and injury and disease repair. Tissue-specific stem cells are a rare population residing in specific tissues and present powerful potential for regeneration when required. They are usually named based on the resident tissue, such as hematopoietic stem cells and germline stem cells. This review discusses the recent advances in stem cells of various tissues, including neural stem cells, muscle stem cells, liver progenitors, pancreatic islet stem/progenitor cells, intestinal stem cells, and prostate stem cells, and the future perspectives for tissue stem cell research.

Computational Interspecies Translation Between Alzheimer's Disease Mouse Models and Human Subjects Identifies Innate Immune Complement, TYROBP, and TAM Receptor Agonist Signatures, Distinct From Influences of Aging

Mouse models are vital for preclinical research on Alzheimer’s disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.

Location is important: differentiation between ileal and colonic Crohn's disease

Crohn's disease can affect any part of the gastrointestinal tract; however, current European and national guidelines worldwide do not differentiate between small-intestinal and colonic Crohn's disease for medical treatment. Data from the past decade provide evidence that ileal Crohn's disease is distinct from colonic Crohn's disease in several intestinal layers. Remarkably, colonic Crohn's disease shows an overlap with regard to disease behaviour with ulcerative colitis, underlining the fact that there is more to inflammatory bowel disease than just Crohn's disease and ulcerative colitis, and that subtypes, possibly defined by location and shared pathophysiology, are also important. This Review provides a structured overview of the differentiation between ileal and colonic Crohn's disease using data in the context of epidemiology, genetics, macroscopic differences such as creeping fat and histological findings, as well as differences in regard to the intestinal barrier including gut microbiota, mucus layer, epithelial cells and infiltrating immune cell populations. We also discuss the translation of these basic findings to the clinic, emphasizing the important role of treatment decisions. Thus, this Review provides a conceptual outlook on a new mechanism-driven classification of Crohn's disease.

Facing the urgency of therapies for progressive MS - a Progressive MS Alliance proposal

Therapies for infiltrative inflammation in multiple sclerosis (MS) have advanced greatly, but neurodegeneration and compartmentalized inflammation remain virtually untargeted as in other diseases of the nervous system. Consequently, many therapies are available for the relapsing-remitting form of MS, but the progressive forms remain essentially untreated. The objective of the International Progressive MS Alliance is to expedite the development of effective therapies for progressive MS through new initiatives that foster innovative thinking and concrete advancements. Based on these principles, the Alliance is developing a new funding programme that will focus on experimental medicine trials. Here, we discuss the reasons behind the focus on experimental medicine trials, the strengths and weaknesses of these approaches and of the programme, and why we hope to advance therapies while improving the understanding of progression in MS. We are soliciting public and academic feedback, which will help shape the programme and future strategies of the Alliance.