Mechanisms Of Complex Diseases Research Articles

Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding.

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

A model-based clustering via mixture of hierarchical models with covariate adjustment for detecting differentially expressed genes from paired design

The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no model-based gene clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method.

A Gene Set-Integrated Approach for Predicting Disease-Associated Genes.

It is important to identify disease-associated genes for studying the pathogenic mechanism of complex diseases. Recently, models for disease gene prediction are dominantly based on molecular expression data and networks, including gene expression, protein expression, co-expression networks, protein-protein interaction networks, etc. One limitation of these methods is that they do not consider the knowledge of annotated gene sets representing known pathways or functionally-related sets of genes. In this study, we propose a new approach to predict disease-associated genes by integrating annotated gene sets data from the Molecular Signature Database (MSigDB). It first represents and integrates the different types of annotated gene sets in the MSigDB database in the form of the signal matrix. It then uses the signal matrix as the gene feature to train the disease gene prediction model. We compare our method with existing methods in predicting genes for five complex diseases. The results show that our method is superior to other methods. Further, we perform a case study on autism spectrum disorder (ASD). We find that ASD predictions are associated with ASD based on the statistical analysis of biological networks and independent ASD studies. The source code, prediction results and datasets are publicly available on https://github.com/genemine/GSI.git.

Prediction of circRNA-MiRNA Association Using Singular Value Decomposition and Graph Neural Networks.

A large number of experimental studies have shown that circRNAs can act as molecular sponges of microRNAs, interacting with miRNAs to regulate gene expression levels, thereby affecting the development of human diseases. Exploring the potential associations between circRNAs and miRNAs can help understand complex disease mechanisms. Considering that biological experiments are time-consuming and labor-intensive, this study proposes a computational model using a graph neural network and singular value decomposition (CMASG) for circRNA-miRNA association prediction. Specifically, graph neural networks are used to learn nonlinear feature representations of nodes, followed by matrix factorization algorithms to learn linear feature representations of nodes, and then combined feature representations learned from different perspectives. Finally, the lightGBM algorithm was used for circRNA-miRNA association prediction. The proposed CMASG model achieved an AUC value of 0.8804. The experimental results demonstrate the superiority and effectiveness of the CMASG model in predicting circRNA-miRNA association tasks.

GMMAD: a comprehensive database of human gut microbial metabolite associations with diseases

BackgroundThe natural products, metabolites, of gut microbes are crucial effect factors on diseases. Comprehensive identification and annotation of relationships among disease, metabolites, and microbes can provide efficient and targeted solutions towards understanding the mechanism of complex disease and development of new markers and drugs.ResultsWe developed Gut Microbial Metabolite Association with Disease (GMMAD), a manually curated database of associations among human diseases, gut microbes, and metabolites of gut microbes. Here, this initial release (i) contains 3,836 disease-microbe associations and 879,263 microbe-metabolite associations, which were extracted from literatures and available resources and then experienced our manual curation; (ii) defines an association strength score and a confidence score. With these two scores, GMMAD predicted 220,690 disease-metabolite associations, where the metabolites all belong to the gut microbes. We think that the positive effective (with both scores higher than suggested thresholds) associations will help identify disease marker and understand the pathogenic mechanism from the sense of gut microbes. The negative effective associations would be taken as biomarkers and have the potential as drug candidates. Literature proofs supported our proposal with experimental consistence; (iii) provides a user-friendly web interface that allows users to browse, search, and download information on associations among diseases, metabolites, and microbes. The resource is freely available at http://guolab.whu.edu.cn/GMMAD.ConclusionsAs the online-available unique resource for gut microbial metabolite-disease associations, GMMAD is helpful for researchers to explore mechanisms of disease- metabolite-microbe and screen the drug and marker candidates for different diseases.

Study of Risk Factors and Foeto-maternal Outcome of Gestational Diabetes Mellitus

Introduction: Gestational diabetes mellitus (GDM) is a common pregnancy complication with complex disease mechanism. Several risk factors may contribute to its onset. Based on recent evidence up-to 15% of all pregnancies may be affected by GDM. Untreated GDM is a severe threat to maternal and neonatal health Objectives: To determine the risk factors and foeto- maternal outcome of GDM. Methods: This prospective observational study was conducted on patients who were admitted in Obstetrics & Gynaecology department of CMH, Bogura from June 2020 to May 2021 with the diagnosis of GDM. Data were collected from the patients as well as from the antenatal records in a preformed questionnaire and risk factors and outcomes measured. Results: A total of 679 patients were admitted during the period of 1 year from June 2020 to May 2021. Among them 50 patients were admitted with GDM. The prevalence of GDM was 7.36%. GDM risks were higher in women aged between 25-29 years and in those with maternal overweight and obesity (58%). Newborns of GDM mother had an increased risk of hypoglycemia (10%), macrosomia (20%), respiratory distress syndrome (8 %) and 42% required NICU admission. Most of the patients (92%) needed cesarean section. Conclusion: Pre-pregnancy BMI and family history of diabetes in first degree relatives and high parity were the predominant risk factor of GDM in this study. GDM patients are of increase risk of delivery by cesarean section, preterm birth, Newborns of GDM mother are at risk of RDS (Respiratory distress syndrome), macrosomia and neonatal hypoglycemia. JAFMC Bangladesh. Vol 18, No 2 (December) 2022: 46-50

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines.

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.

ILncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks.

Identification of disease-associated long non-coding RNAs (lncRNAs) is crucial for unveiling the underlying genetic mechanisms of complex diseases. Multiple types of similarity networks of lncRNAs (or diseases) can complementary and comprehensively characterize their similarities. Hence, in this study, we presented a computational model iLncDA-RSN based on reliable similarity networks for identifying potential lncRNA-disease associations (LDAs). Specifically, for constructing reliable similarity networks of lncRNAs and diseases, miRNA heuristic information with lncRNAs and diseases is firstly introduced to construct their respective Jaccard similarity networks; then Gaussian interaction profile (GIP) kernel similarity networks and Jaccard similarity networks of lncRNAs and diseases are provided based on the lncRNA-disease association network; a random walk with restart strategy is finally applied on Jaccard similarity networks, GIP kernel similarity networks, as well as lncRNA functional similarity network and disease semantic similarity network to construct reliable similarity networks. Depending on the lncRNA-disease association network and the reliable similarity networks, feature vectors of lncRNA-disease pairs are integrated from lncRNA and disease perspectives respectively, and then dimensionality reduced by the elastic net. Two random forests are at last used together on different lncRNA-disease association feature sets to identify potential LDAs. The iLncDA-RSN is evaluated by five-fold cross-validation to analyse its prediction performance, results of which show that the iLncDA-RSN outperforms the compared models. Furthermore, case studies of different complex diseases demonstrate the effectiveness of the iLncDA-RSN in identifying potential LDAs.

Proteomic analysis of the esophageal epithelium reveals key features of eosinophilic esophagitis pathophysiology.

Eosinophilic esophagitis (EoE) is a chronic non-IgE-mediated allergic disease of the esophagus. An unbiased proteomics approach was performed to investigate pathophysiological changes in esophageal epithelium. Additionally, an RNAseq-based transcriptomic analysis in paired samples was also carried out. Total proteins were purified from esophageal endoscopic biopsies in a cohort of adult EoE patients (n = 25) and healthy esophagus controls (n = 10). Differentially accumulated (DA) proteins in EoE patients compared to control tissues were characterized to identify altered biological processes and signaling pathways. Results were also compared with a quantitative proteome dataset of the human esophageal mucosa. Next, results were contrasted with those obtained after RNAseq analysis in paired samples. Finally, we matched up protein expression with two EoE-specific mRNA panels (EDP and Eso-EoE panel). A total of 1667 proteins were identified, of which 363 were DA in EoE. RNA sequencing in paired samples identified 1993 differentially expressed (DE) genes. Total RNA and protein levels positively correlated, especially in DE mRNA-proteins pairs. Pathway analysis of these proteins in EoE showed alterations in immune and inflammatory responses for the upregulated proteins, and in epithelial differentiation, cornification and keratinization in those downregulated. Interestingly, a set of DA proteins, including eosinophil-related and secreted proteins, were not detected at the mRNA level. Protein expression positively correlated with EDP and Eso-EoE, and corresponded with the most abundant proteins of the human esophageal proteome. We unraveled for the first time key proteomic features involved in EoE pathogenesis. An integrative analysis of transcriptomic and proteomic datasets provides a deeper insight than transcriptomic alone into understanding complex disease mechanisms.

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

Human amnion epithelial cells reduces hypertension induced vascular stiffening and cognitive impairment

Introduction: Vascular stiffening and inflammation are hallmarks of hypertension and contribute to the development of cardiovascular disease and cognitive impairment. Current treatments for hypertension may reduce blood pressure but do not target the associated pathological changes and end-organ damage. It is also inconclusive from clinical trials whether anti-hypertensive medication can improve cognitive impairment. Cell therapy has great therapeutic potential as unlike single pharmacological agents, cells can deliver multiple mediators which could more effectively target complex disease mechanisms. Human amnion epithelial cells (AECs) have many properties (eg. anti-inflammatory, anti-fibrotic, and immunologically inert) that make them attractive candidates for a cell-based therapy for vascular and brain pathology. Aim: To test the potential of AECs to treat vascular and brain pathology in angiotensin II-induced hypertension. Methods. Male C57Bl6 mice (8-12 weeks) were administered vehicle (saline; n=35) or angiotensin II (0.7 mg/kg/d, n=37) for 14 d via an osmotic minipump. After minipump implantation, a subset of mice were injected with 10 6 of AECs intravenously. Systolic blood pressure was measured using tail-cuff; pulse wave velocity was measured using ultrasound, inflammation was assessed using flow cytometry and RNA sequencing, markers of fibrosis using quantitative PCR and cognitive function using novel object recognition test. Results: Angiotensin II infusion increased blood pressure and aortic pulse wave velocity (n=6-7, P<0.05). In aorta, angiotensin II promoted accumulation of leukocytes, specifically macrophages and monocytes, which were elevated by ~3-fold compared to vehicle-infused mice (n=9-11, P<0.05). Angiotensin II also increased aortic mRNA expression of collagen type 1 alpha 1 ( Col1a1) by ~4-fold and collagen type 5 alpha 1 ( Col5a1) by ~7-fold compared to vehicle (n=6-8, P<0.05). Co-administration of AECs limited the development of hypertension and aortic stiffening by angiotensin II (185±5 mmHg vs 165±4 mmHg; n=9-11, P<0.05), as well as the aortic infiltration of macrophages and monocytes (n=9-11, P<0.05) and expression of Col1a1 and Col5a1 (n=6-8, P<0.05). Infusion with angiotensin II also impaired memory which was improved by AECs (n=7-9, P<0.05). RNA sequencing revealed that in the brain, angiotensin II increased expression of genes involved in inflammation ( Ptcra, Blk, Bpifb1), fibrosis ( Col6a5) and apoptosis ( Fas) and co-administration of AECs caused downregulation of these genes. Conclusions: Intravenous administration of AECs blunted angiotensin II-induced hypertension, aortic stiffening, inflammation and cognitive impairment in male mice. The beneficial effects of AECs on blood pressure and aortic stiffening could have contributed to the improvement in cognition. This study suggests that AECs or their cellular products could be explored as treatments for vascular and brain pathology during hypertension. La Trobe University Start Up grant This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Global neurology: It's time to take notice.

Global neurology: It's time to take notice.

Assessment of community efforts to advance network-based prediction of protein–protein interactions

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Modeling the enigma of complex disease etiology

BackgroundComplex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.MethodsHere we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.ResultsWe provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.ConclusionsThe Human Disease Ontology’s Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

ScGCL: an imputation method for scRNA-seq data based on graph contrastive learning.

Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due to high sparsity and complex gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as cell clustering and pseudo-time analysis. Restoring the expression levels of genes is essential for reducing technical noise and facilitating downstream analysis. However, existing scRNA-seq data imputation methods ignore the topological structure information of scRNA-seq data and cannot comprehensively utilize the relationships between cells. Here, we propose a single-cell Graph Contrastive Learning method for scRNA-seq data imputation, named scGCL, which integrates graph contrastive learning and Zero-inflated Negative Binomial (ZINB) distribution to estimate dropout values. scGCL summarizes global and local semantic information through contrastive learning and selects positive samples to enhance the representation of target nodes. To capture the global probability distribution, scGCL introduces an autoencoder based on the ZINB distribution, which reconstructs the scRNA-seq data based on the prior distribution. Through extensive experiments, we verify that scGCL outperforms existing state-of-the-art imputation methods in clustering performance and gene imputation on 14 scRNA-seq datasets. Further, we find that scGCL can enhance the expression patterns of specific genes in Alzheimer's disease datasets. The code and data of scGCL are available on Github: https://github.com/zehaoxiong123/scGCL. Supplementary data are available at Bioinformatics online.

An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease

Data-driven analysis and characterization of molecular phenotypes comprises an efficient way to decipher complex disease mechanisms. Using emerging next generation sequencing technologies, important disease-relevant outcomes are extracted, offering the potential for precision diagnosis and therapeutics in progressive disorders. Single-cell RNA sequencing (scRNA-seq) allows the inherent heterogeneity between individual cellular environments to be exploited and provides one of the most promising platforms for quantifying cell-to-cell gene expression variability. However, the high-dimensional nature of scRNA-seq data poses a significant challenge for downstream analysis, particularly in identifying genes that are dominant across cell populations. Feature selection is a crucial step in scRNA-seq data analysis, reducing the dimensionality of data and facilitating the identification of genes most relevant to the biological question. Herein, we present a need for an ensemble feature selection methodology for scRNA-seq data, specifically in the context of Alzheimer’s disease (AD). We combined various feature selection strategies to obtain the most dominant differentially expressed genes (DEGs) in an AD scRNA-seq dataset, providing a promising approach to identify potential transcriptome biomarkers through scRNA-seq data analysis, which can be applied to other diseases. We anticipate that feature selection techniques, such as our ensemble methodology, will dominate analysis options for transcriptome data, especially as datasets increase in volume and complexity, leading to more accurate classification and the generation of differentially significant features.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

Networks as Biomarkers: Uses and Purposes.

Networks-based approaches are often used to analyze gene expression data or protein-protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity.

The Power of Clinical Diagnosis for Deciphering Complex Genetic Mechanisms in Rare Diseases.

Complex genetic disease mechanisms, such as structural or non-coding variants, currently pose a substantial difficulty in frontline diagnostic tests. They thus may account for most unsolved rare disease patients regardless of the clinical phenotype. However, the clinical diagnosis can narrow the genetic focus to just a couple of genes for patients with well-established syndromes defined by prominent physical and/or unique biochemical phenotypes, allowing deeper analyses to consider complex genetic origin. Then, clinical-diagnosis-driven genome sequencing strategies may expedite the development of testing and analytical methods to account for complex disease mechanisms as well as to advance functional assays for the confirmation of complex variants, clinical management, and the development of new therapies.

Breakthrough of glycobiology in the 21st century.

As modern medicine began to emerge at the turn of the 20th century, glycan-based therapies advanced. DNA- and protein-centered therapies became widely available. The research and development of structurally defined carbohydrates have led to new tools and methods that have sparked interest in the therapeutic applications of glycans. One of the latest omics disciplines to emerge in the contemporary post-genomics age is glycomics. In addition, to providing hope for patients and people with different health conditions through a deeper understanding of the mechanisms of common complex diseases, this new specialty in system sciences has much to offer to communities involved in the development of diagnostics and therapeutics in medicine and life sciences.This review focuses on recent developments that have pushed glycan-based therapies into the spotlight in medicine and the technologies powering these initiatives, which we can take as the most significant success of the 21st century.