Disease Mechanisms Research Articles

Time to split: biomarker trajectories in pediatric acute respiratory distress syndrome hint at underlying disease.

Pediatric acute respiratory distress syndrome (ARDS) is severe, noncardiac hypoxemic respiratory failure that carries a substantial risk of death. Given the complexity of this clinically defined syndrome and the repeated failure of therapeutic trials, there has been an effort to identify subphenotypes of ARDS that may share targetable mechanisms of disease. In this issue of the JCI, Yehya and colleagues measured 19 plasma biomarkers in 279 children over the first seven days of ARDS. Increases in select tissue injury makers and inflammatory cytokines in peripheral blood were associated with multiple organ dysfunction syndrome and death, but not persistent ARDS. These findings argue that splitting patients by clinical and molecular phenotype may be more informative than lumping them under the umbrella diagnosis of ARDS. However, future studies are needed to determine whether these plasma factors represent targetable pathways in lung injury or are a consequence of systemic organ dysfunction.

Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing.

Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.

Hot spots and trends in microbial disease research on cultural heritage: a bibliometric analysis.

This study is to understand and analyze the development history, research hotspots, and research trends in the study of microbial diseases of cultural heritage through bibliometric analyses in order to fill the current gap of no literature review in this research field and to make certain contributions to the research in this field and the protection of cultural heritage. Bibliometric and visual analyses of the literature on cultural heritage microbial diseases in the Web of Science (WoS) core collection were carried out using VOSviewer and R-bibliometrix, choosing the two main literature types of papers and reviews. The emphasis was placed on analyzing and summarizing core research strengths, hotspots, and trends. Six hundred sixty-seven documents (573 articles and 94 reviews) were retrieved. αIn the WoS core collection, the first literature on cultural heritage microbial disease research was published in January 2000, and the annual number of publications from 2000 to 2009 did not exceed one; the annual number of publications from 2010 onwards increased rapidly, and after 2018, the number of publications per year exceeded 60, reaching 94 in 2020, which indicates that cultural heritage microbial disease research is booming. Our research showed that Italy, the USA, and China were the leading research countries, and Univ Milan was the institution with the most publications. International Biodeterioration &Biodegradation was the most published and co-cited journal, and Gu JD was the most prolific author. The research hotspots in the study of microbial diseases of cultural heritage mainly include biological degradation of cultural heritage; identification of diseased microorganisms and disease mechanisms; cultural heritage microbial disease prevention and control methods; monitoring, prevention, and control of diseased microorganisms in indoor air; antibacterial agents, especially essential oils, nanoparticles, and other safe and efficient antibacterial products research and development; and exploration of the mechanisms of biofilm protection of cultural heritage on cultural heritage surfaces. Monitoring and identifying cultural heritage microbial communities, identifying disease mechanisms, and researching safe and efficient bacteriostatic products such as essential oils and nanoparticles will be the main research directions in the field of cultural heritage microbial disease prevention and control in the future.

Netrin-1 signaling pathway mechanisms in neurodegenerative diseases

Abstract Netrin-1 and its receptors play crucial roles in inducing axonal growth and neuronal migration during neuronal development. Their profound impacts then extend into adulthood to encompass the maintenance of neuronal survival and synaptic function. Increasing amounts of evidence highlight several key points: (1) Diminished Netrin- 1 levels exacerbate pathological progression in animal models of Alzheimer's disease and Parkinson's disease, and potentially, similar alterations occur in humans. (2) Genetic mutations of Netrin-1 receptors increase an individuals' susceptibility to neurodegenerative disorders. (3) Therapeutic approaches targeting Netrin-1 and its receptors offer the benefits of enhancing memory and motor function. (4) Netrin-1 and its receptors show genetic and epigenetic alterations in a variety of cancers. These findings provide compelling evidence that Netrin-1 and its receptors are crucial targets in neurodegenerative diseases. Through a comprehensive review of Netrin-1 signaling pathways, our objective is to uncover potential therapeutic avenues for neurodegenerative disorders.

A study protocol to characterise pathophysiological and molecular markers of rheumatic heart disease and degenerative aortic stenosis using multiparametric cardiovascular imaging and multiomics techniques.

Rheumatic heart disease (RHD), degenerative aortic stenosis (AS), and congenital valve diseases are prevalent in sub-Saharan Africa. Many knowledge gaps remain in understanding disease mechanisms, stratifying phenotypes, and prognostication. Therefore, we aimed to characterise patients through clinical profiling, imaging, histology, and molecular biomarkers to improve our understanding of the pathophysiology, diagnosis, and prognosis of RHD and AS. In this cross-sectional, case-controlled study, we plan to recruit RHD and AS patients and compare them to matched controls. Living participants will undergo clinical assessment, echocardiography, CMR and blood sampling for circulatory biomarker analyses. Tissue samples will be obtained from patients undergoing valve replacement, while healthy tissues will be obtained from cadavers. Immunohistology, proteomics, metabolomics, and transcriptome analyses will be used to analyse circulatory- and tissue-specific biomarkers. Univariate and multivariate statistical analyses will be used for hypothesis testing and identification of important biomarkers. In summary, this study aims to delineate the pathophysiology of RHD and degenerative AS using multiparametric CMR imaging. In addition to discover novel biomarkers and explore the pathomechanisms associated with RHD and AS through high-throughput profiling of the tissue and blood proteome and metabolome and provide a proof of concept of the suitability of using cadaveric tissues as controls for cardiovascular disease studies.

GPSFun: geometry-aware protein sequence function predictions with language models.

Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSiteand SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locationsand protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research.

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MotivationLong non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.ResultsWe propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.AvailabilitySource code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

TempEasy 3D Hydrogel Coculture System Provides Mechanistic Insights into Prostate Cancer Bone Metastasis.

Patients diagnosed with advanced prostate cancer (PCa) often experience incurable bone metastases; however, a lack of relevant experimental models has hampered the study of disease mechanisms and the development of therapeutic strategies. In this study, we employed the recently established Temperature-based Easy-separable (TempEasy) 3D cell coculture system to investigate PCa bone metastasis. Through coculturing PCa and bone cells for 7 days, our results showed a reduction in PCa cell proliferation, an increase in neovascularization, and an enhanced metastasis potential when cocultured with bone cells. Additionally, we observed increased cell proliferation, higher stemness, and decreased bone matrix protein expression in bone cells when cocultured with PCa cells. Furthermore, we demonstrated that the stiffness of the extracellular matrix had a negligible impact on molecular responses in both primary (PCa cells) and distant malignant (bone cells) sites. The TempEasy 3D hydrogel coculture system is an easy-to-use and versatile coculture system that provides valuable insights into the mechanisms of cell-cell communication and interaction in cancer metastasis.

Additional visual information on postural control mechanisms in Parkinson's disease: a pilot study

Additional visual information on postural control mechanisms in Parkinson's disease: a pilot study

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

Unveiling the Etiology of Urological Tumors: A Systematic Review of Mendelian Randomization Applications in Renal Cell Carcinoma, Bladder Cancer, and Prostate Cancer.

Our study aims to address two pivotal questions: "What are the recent advancements in understanding the etiology of urological tumors through Mendelian Randomization?" and "How can Mendelian Randomization be more effectively applied in clinical settings to enhance patient health outcomes in the future?" In our systematic review conducted in April 2023, we utilized databases like PubMed and Web of Science to explore the influence of Mendelian Randomization in urological oncological diseases. We focused on studies published from January 2018, employing keywords related to urological tumors and Mendelian Randomization, supplemented with MeSH terms and manual reference checks. Our inclusion criteria targeted original research studies, while we excluded reports and non-relevant articles. Data extraction followed a PICO-based approach, and bias risk was independently evaluated, with discrepancies resolved through discussion. This systematic approach adhered to PRISMA guidelines for accuracy and thoroughness in reporting. From the initial 457 publications, we narrowed down to 43 full-text articles after screening and quality assessments.A deeper understanding of Mendelian Randomization can help us explore risk factors with a clear causal relationship to urological tumors.This insight may pave the way for future research in early diagnosis, treatment, and management of associated diseases. Our review underscores the value of MR in urogenital tumor research, highlighting its efficacy in establishing causality and its potential to clarify disease mechanisms. Despite challenges like large sample sizes and variant identification, MR offers new perspectives for understanding and managing these tumors, suggesting a trend towards more inclusive and diverse research approaches.

Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids

Disease modeling with isogenic Induced Pluripotent Stem Cell (iPSC)-differentiated organoids serves as a powerful technique for studying disease mechanisms. Multiplexed coculture is crucial to mitigate batch effects when studying the genetic effects of disease-causing variants in differentiated iPSCs or organoids, and demultiplexing at the single-cell level can be conveniently achieved by assessing natural genetic barcodes. Here, to enable cost-efficient time-series experimental designs via multiplexed bulk and single-cell RNA-seq of hybrids, we introduce a computational method in our Vireo Suite, Vireo-bulk, to effectively deconvolve pooled bulk RNA-seq data by genotype reference, and thereby quantify donor abundance over the course of differentiation and identify differentially expressed genes among donors. Furthermore, with multiplexed scRNA-seq and bulk RNA-seq, we demonstrate the usefulness and necessity of a pooled design to reveal donor iPSC line heterogeneity during macrophage cell differentiation and to model rare WT1 mutation-driven kidney disease with chimeric organoids. Our work provides an experimental and analytic pipeline for dissecting disease mechanisms with chimeric organoids.

A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation

Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother–child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure–bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children’s serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure–activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.

Tiny Organs, Big Impact: How Microfluidic Organ-on-Chip Technology Is Revolutionizing Mucosal Tissues and Vasculature

Organ-on-chip (OOC) technology has gained importance for biomedical studies and drug development. This technology involves microfluidic devices that mimic the structure and function of specific human organs or tissues. OOCs are a promising alternative to traditional cell-based models and animals, as they provide a more representative experimental model of human physiology. By creating a microenvironment that closely resembles in vivo conditions, OOC platforms enable the study of intricate interactions between different cells as well as a better understanding of the underlying mechanisms pertaining to diseases. OOCs can be integrated with other technologies, such as sensors and imaging systems to monitor real-time responses and gather extensive data on tissue behavior. Despite these advances, OOCs for many organs are in their initial stages of development, with several challenges yet to be overcome. These include improving the complexity and maturity of these cellular models, enhancing their reproducibility, standardization, and scaling them up for high-throughput uses. Nonetheless, OOCs hold great promise in advancing biomedical research, drug discovery, and personalized medicine, benefiting human health and well-being. Here, we review several recent OOCs that attempt to overcome some of these challenges. These OOCs with unique applications can be engineered to model organ systems such as the stomach, cornea, blood vessels, and mouth, allowing for analyses and investigations under more realistic conditions. With this, these models can lead to the discovery of potential therapeutic interventions. In this review, we express the significance of the relationship between mucosal tissues and vasculature in organ-on-chip (OOC) systems. This interconnection mirrors the intricate physiological interactions observed in the human body, making it crucial for achieving accurate and meaningful representations of biological processes within OOC models. Vasculature delivers essential nutrients and oxygen to mucosal tissues, ensuring their proper function and survival. This exchange is critical for maintaining the health and integrity of mucosal barriers. This review will discuss the OOCs used to represent the mucosal architecture and vasculature, and it can encourage us to think of ways in which the integration of both can better mimic the complexities of biological systems and gain deeper insights into various physiological and pathological processes. This will help to facilitate the development of more accurate predictive models, which are invaluable for advancing our understanding of disease mechanisms and developing novel therapeutic interventions.

Combinatorial entropy determines the early stages of nucleation

AbstractBiomolecular phase separation, a complex phenomenon within living systems, has garnered significant interest due to its diverse roles in cellular organization and function. Despite its importance, studying phase separation dynamics experimentally, particularly in the early stages, poses challenges. Our study investigates the dynamics of biomolecular phase separation using a graph‐based simulation module, particularly emphasizing its early stages. Through a simplified model, we dissect the influences of various factors on collective behavior, highlighting the crucial role of combinatorial entropy in percolation dynamics. This study offers valuable insights into the fundamental principles governing biomolecular phase separation, with implications for understanding cellular processes and disease mechanisms.

The role and mechanism of 5-HTDRN-BNST neural circuit in anxiety and fear lesions

Central 5-hydroxytryptaminergic dorsal raphe nucleus (5-HTDRN)-bed nucleus of stria terminalis (BNST) neural circuit dysfunction is one of the important neurobiological basis of anxiety and fear disorders. Under stress, 5-hydroxytryptamine (5-HT) neurons act on BNST receptors to attenuate anxiety and fear responses or enhance anxiety and fear. In BNST, corticotropin releasing factor neurons play a role in regulating emotions by reversely regulating excitatory or inhibitory 5-HT neurons. The composition of 5-HTDRN-BNST neural circuit, the pathological changes of 5-HTDRN-BNST neural circuit function damage under stress, and the effects of 5-HTDRN-BNST neural circuit on anxiety disorder, panic disorder and post-traumatic stress disorder were analyzed and are summarized in this paper. The characteristics of functional changes of the neural circuit and its effects on brain functional activities provide a basis and ideas for the treatment of anxiety and fear disorders through the regulation of 5-HTDRN-BNST neural circuit, and they also provide a new perspective for understanding the pathological mechanism of such diseases.

Selective CAR-T cell mediated B cell depletion suppresses interferon signature in SLE.

Applying advanced molecular profiling together with highly specific targeted therapies offers the possibility to better dissect the mechanisms underlying immune mediated inflammatory diseases such as systemic lupus erythematosus (SLE) in humans. Here we apply a combination of single cell RNA sequencing and T/B cell repertoire analysis to perform an in-depth characterization of molecular changes in the immune-signature upon CD19 CAR T cell-mediated depletion of B cells in SLE patients. The resulting datasets do not only confirm a selective CAR T cell-mediated reset of the B cell response, but simultaneously reveal consequent changes in the transcriptional signature of monocyte and T cell subsets that respond with a profound reduction in type 1 interferon signaling. Our current data thus provide evidence for a causal relationship between the B cell response and the increased interferon signature observed in SLE and additionally demonstrate the usefulness of combining targeted therapies and novel analytic approaches to decipher molecular mechanisms of immune-mediated inflammatory diseases in humans.

Development of Noninvasive Method for the Automated Analysis of Nine Steroid Hormones in Human Saliva by Online Coupling of In-Tube Solid-Phase Microextraction with Liquid Chromatography–Tandem Mass Spectrometry

Accurate measurement of steroid hormones is crucial to elucidate new mechanisms of action and diagnose steroid metabolism-related diseases. This study presents a simple, sensitive, and automated analytical method for nine representative steroid hormones. The method involves on-line coupling of in-tube solid-phase microextraction (IT-SPME) with liquid chromatography–tandem mass spectrometry (LC–MS/MS). The steroid hormones were extracted and enriched on a Supel-Q PLOT capillary column using IT-SPME. Subsequently, they were separated and detected within 6 min using a Discovery HS F5-3 column and positive ion mode multiple reaction monitoring system via LC–MS/MS. Calibration curves of these compounds using each stable isotope-labeled internal standard (IS) showed linearity with correlation coefficients greater than 0.9990 in the range of 0.01–40 ng/mL, with limits of detection (S/N = 3) of 0.7–21 pg/mL. Moreover, intra- and inter-day variations were lower than 8.1 and 15% (n = 6), respectively. The recoveries of these compounds from saliva samples were in the range of 82–114%. The developed IT-SPME/LC–MS/MS method of steroid hormones is a highly sensitive, specific, and non-invasive analytical method that allows extraction and enrichment with no organic solvents, and enables direct automated online analysis by simply ultrafiltrating a small sample of saliva.

Animal models to study the pathogenesis and novel therapeutics of oral lichen planus

Oral lichen planus (OLP) is a prevalent oral mucosal disease characterized by an unknown etiology and a complex pathogenesis. Patients with OLP endure a chronic course marked by alternating non-erosive and erosive lesions, with no definitive cure currently available. Particularly challenging is the treatment of recalcitrant erosive OLP, highlighting an urgent need for therapies targeting specific pathogenic pathways. In diseases like OLP, where the etiopathogenesis is intricate and elusive, animal models are indispensable for hypothesis testing and elucidating disease mechanisms. To date, only three animal models for oral lichenoid lesions have been reported in the literature. This Perspective paper evaluates these existing models, along with a novel OLP mouse model introduced at the 3rd International Conference on Oral Mucosal Immunity and Microbiome. The validity of these models is critically assessed, and their potential future applications in advancing our understanding of OLP are discussed.