Mechanisms Of Complex Diseases Research Articles

Semi-CAM: A semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information

Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Existing deconvolution approaches can be divided into two major groups: supervised and unsupervised methods. Supervised deconvolution methods use cell type-specific prior information including cell proportions, reference cell type-specific gene signatures, or marker genes for each cell type, which may not be available in practice. Unsupervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM), in contrast, completely disregard prior information and thus are not efficient for data with partial cell type-specific information. In this paper, we propose a semi-supervised deconvolution method, semi-CAM, that extends CAM by utilizing marker information from partial cell types. Analysis of simulation and two benchmark data have demonstrated that semi-CAM outperforms CAM by yielding more accurate cell proportion estimations when markers from partial/all cell types are available. In addition, when markers from all cell types are available, semi-CAM achieves better or similar accuracy compared to the supervised method using signature genes, CIBERSORT, and the marker-based supervised methods semi-NMF and DSA. Furthermore, analysis of human chlamydia-infection data with bulk expression profiles from six cell types and prior marker information of only three cell types suggests that semi-CAM achieves more accurate cell proportion estimations than CAM.

LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization

Emerging evidence suggests that long non-coding RNAs (lncRNAs) play an important role in various biological processes and human diseases. Exploring the associations between lncRNAs and diseases can better understand the complex disease mechanisms. However, expensive and time-consuming for exploring by biological experiments, it is imperative to develop more accurate and efficient computational approaches to predicting lncRNA-disease associations. In this work, we develop a new computational approach to predict lncRNA-disease associations using graph regularized nonnegative matrix factorization (LDGRNMF), which considers disease-associated lncRNAs identification as recommendation system problem. More specifically, we calculate the similarity of disease based on Gaussian interaction profile kernel and disease semantic information, and calculate the similarity of lncRNA based on Gaussian interaction profile kernel. Secondly, the weighted K nearest known neighbor interaction profiles is applied to reconstruct lncRNA-disease association adjacency matrix. Finally, graph regularized nonnegative matrix factorization is exploited to predict the potential associations between lncRNAs and diseases. In the five-fold cross-validation experiments, LDGRNMF achieves AUC of 0.8985 which outperforms other compared methods. Moreover, in case studies for stomach cancer, breast cancer and lung cancer, 9, 8 and 6 of the top 10 candidate lncRNAs predicted by LDGRNMF are verified, respectively. Rigorous experimental results indicate that our method can be regarded as an effectively tool for predicting potential lncRNA-disease associations.

Applications of Network Pharmacology in Traditional Chinese Medicine Research.

Human diseases, especially infectious ones, have been evolving constantly. However, their treatment strategies are not developing quickly. Some diseases are caused by a variety of factors with very complex pathologies, and the use of a single drug cannot solve these problems. Traditional Chinese Medicine (TCM) medication is a unique treatment method in China. TCM formulae contain multiple herbs with multitarget, multichannel, and multilink characteristics. In recent years, with the flourishing development of network pharmacology, a new method for searching therapeutic drugs has emerged. The multitarget action in network pharmacology is consistent with the complex mechanisms of disease and drug action. Using network pharmacology to understand TCM is an emerging trend.

A practical view of fine-mapping and gene prioritization in the post-genome-wide association era.

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

From human genome epidemiology to systems epidemiology: current progress and future perspective.

The recent progress in human genome epidemiology (HuGE) is already having a profound impact on the practice of medicine and public health. First, the success of genome-wide association studies has greatly expanded the direction and content of epidemiological researches, including revealing new genetic mechanisms of complex diseases, identifying new targets for therapeutic interventions, and improving application in early screening of high-risk populations. At the same time, large-scale genomic studies make it possible to efficiently explore the gene-environment interactions, which will help better understand the biological pathways of complex diseases and identify individuals who may be more susceptible to diseases. Additionally, the emergence of systems epidemiology aims to integrate multi-omics together with epidemiological data to create a systems network that can comprehensively characterize the diverse range of factors contributing to disease development. These progress will help to apply HuGE findings into practice to improve the health of individuals and populations.

Label-Free Detection of Post-translational Modifications with a Nanopore.

Post-translational modifications (PTMs) of proteins play key roles in cellular processes. Hence, PTM identification is crucial for elucidating the mechanism of complex cellular processes and disease. Here we present a method for PTM detection at the single-molecule level using FraC biological nanopores. We focus on two major PTMs, phosphorylation and glycosylation, that mutually compete for protein modification sites, an important regulatory process that has been implicated in the pathogenic pathways of many diseases. We show that phosphorylated and glycosylated peptides can be clearly differentiated from nonmodified peptides by differences in the relative current blockade and dwell time in nanopore translocations. Furthermore, we show that these PTM modifications can be mutually differentiated, demonstrating the identification of phosphorylation and glycosylation in a label-free manner. The results represent an important step for the single-molecule, label-free identification of proteoforms, which have tremendous potential for disease diagnosis and cell biology.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms.

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This willrequire substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the currentenvironment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies willenable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissueor liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labelshas directly contributed to the paucity of etiologic and mechanistic understanding by directlyreducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animalmodels causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.

Weighted Fused Pathway Graphical Lasso for Joint Estimation of Multiple Gene Networks.

Gene regulatory networks (GRNs) are often inferred based on Gaussian graphical models that could identify the conditional dependence among genes by estimating the corresponding precision matrix. Classical Gaussian graphical models are usually designed for single network estimation and ignore existing knowledge such as pathway information. Therefore, they can neither make use of the common information shared by multiple networks, nor can they utilize useful prior information to guide the estimation. In this paper, we propose a new weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple networks by incorporating prior knowledge derived from known pathways and gene interactions. Based on the assumption that two genes are less likely to be connected if they do not participate together in any pathways, a pathway-based constraint is considered in our model. Moreover, we introduce a weighted fused lasso penalty in our model to take into account prior gene interaction data and common information shared by multiple networks. Our model is optimized based on the alternating direction method of multipliers (ADMM). Experiments on synthetic data demonstrate that our method outperforms other five state-of-the-art graphical models. We then apply our model to two real datasets. Hub genes in our identified state-specific networks show some shared and specific patterns, which indicates the efficiency of our model in revealing the underlying mechanisms of complex diseases.

Neuroinflammation in frontotemporal dementia.

Frontotemporal dementia (FTD) refers to a group of progressive neurodegenerative disorders with different pathological signatures, genetic variability and complex disease mechanisms, for which no effective treatments exist. Despite advances in understanding the underlying pathology of FTD, sensitive and specific fluid biomarkers for this disease are lacking. As in other types of dementia, mounting evidence suggests that neuroinflammation is involved in the progression of FTD, including cortical inflammation, microglial activation, astrogliosis and differential expression of inflammation-related proteins in the periphery. Furthermore, an overlap between FTD and autoimmune disease has been identified. The most substantial evidence, however, comes from genetic studies, and several FTD-related genes are also implicated in neuroinflammation. This Review discusses specific evidence of neuroinflammatory mechanisms in FTD and describes how advances in our understanding of these mechanisms, in FTD as well as in other neurodegenerative diseases, might facilitate the development and implementation of diagnostic tools and disease-modifying treatments for FTD.

ICircDA-MF: identification of circRNA-disease associations based on matrix factorization.

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Human α defensins promote the expression of the inflammatory cytokine interleukin-8 under high-glucose conditions: Novel insights into the poor healing of diabetic foot ulcers.

Sustained infection and chronic inflammation are the most common features and complex mechanisms of diabetic foot disease. In this study, we examined the expression and functional roles of human endogenous α defensins in diabetic foot ulcer. The expression levels of human α defensins HNP1, HNP3, and HNP4 were significantly higher in the wound center than the edge of diabetic foot ulcers. And the inflammatory cytokine interleukin IL-8 (IL-8) was also highly expressed in wound exudates. In human foreskin fibroblasts, these human α defensins were found only slightly to affect IL-8 expression directly. hemoglobin A1C (HbA1c) is the main clinical indicator of diabetic foot disease. Advanced glycation end products of bovine serum albumin (AGE-BSA), as HbA1c analogue, was found to promote IL-8 expression. Human α defensins, in the presence of AGE-BSA, further significantly promoted IL-8 expression. These findings showed that human α defensins aggravated the inflammatory response in diabetic foot ulcers patients, providing new insights in to the poor healing of diabetic foot ulcers.

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

BackgroundThe mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.ResultsIn this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.ConclusionsThe identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.

BackgroundAn integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data.ResultsWe developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network–based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data.ConclusionsOmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

Risk factors for gestational diabetes: An umbrella review of meta-analyses of observational studies.

Background/ObjectiveGestational diabetes mellitus (GDM) is a common pregnancy complication, with complex disease mechanisms, and several risk factors may contribute to its onset. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risk factors associated with GDM, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by convincing evidence.MethodsWe searched PubMed and ISI Web of Science from inception to December 2018 to identify meta-analyses examining associations between putative risk factors for GDM. For each meta-analysis we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias.ResultsThirty eligible meta-analyses were identified, providing data on 61 associations. Fifty (82%) associations had nominally statistically significant findings (P<0.05), while only 15 (25%) were significant at P<10−6 under the random-effects model. Only four risk factors presented convincing evidence:, low vs. normal BMI (cohort studies), BMI ~30–35 kg/m2 vs. normal BMI, BMI >35 kg/m2 vs. normal BMI, and hypothyroidism.ConclusionsThe compilation of results from synthesis of observational studies suggests that increased BMI and hypothyroidism show the strongest consistent evidence for an association with GDM. Diet and lifestyle modifications in pregnancy should be tested in large randomized trials. Our findings suggest that women with known thyroid disease may be offered screening for GDM earlier in pregnancy.

The genetic architecture of osteoporosis and fracture risk.

Osteoporosis and fracture risk are common complex diseases, caused by an interaction of numerous disease susceptibility genes and environmental factors. With the advances in genomic technologies, large-scale genome-wide association studies (GWAS) have been performed which have broadened our understanding of the genetic architecture and biological mechanisms of complex disease. Currently, more than ~90 loci have been found associated with DXA derived bone mineral density (BMD), over ~500 loci with heel estimated BMD and several others with other less widely available bone parameters such as bone geometry, shape, and microarchitecture. Notably, several of the pathways identified by the GWAS efforts correspond to pathways that are currently targeted for the treatment of osteoporosis. Overall, tremendous progress in the field of the genetics of osteoporosis has been achieved with the discovery of WNT16, EN1, DAAM2, and GPC6 among others. Assessment of the function and biological mechanisms of the remaining genes may further untangle the complex genetic landscape of osteoporosis and fracture risk. With this review we aimed to provide a general overview of the existing GWAS studies on osteoporosis traits and fracture risk.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

BackgroundObesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.ResultsIn this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study.ConclusionsMetabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.

Hybrid Rat Diversity Program (HRDP): A Rat Resource for Systems Genetics

Genetic susceptibility to disease, sensitivity to environmental elements, and pharmacogenomics are critical components of the concepts of precision medicine. Rats are a major model for studying complex disease mechanisms, behavioral phenotypes, environmental factors, and for drug development and discovery. Inbred rat strains control for genetic background and allow for repeated, reproducible molecular, cellular, and whole animal phenotyping. Through the development of the Hybrid Rat Diversity Program, a powerful panel of inbred rats with genomic, physiologic, and behavioral phenotype data will serve as a catalyst for research in many fields. The Hybrid Rat Diversity Panel (HRDP) will consist of 96 inbred rat strains to maximize power to detect specific genetic loci associated with complex traits while maximizing the genetic diversity among strains. The 96 strain panel consists of 33 genetically diverse inbred strains and two panels of recombinant inbred panels: FXLE/LEXF (33 strains, Japan) and HXB/BXH (30 strains, Czech Republic). Embryo resuscitation and breeding are underway at the Medical College of Wisconsin (MCW). Whole genome sequencing for all 96 strains will be performed by the Genomic Sciences and Precision Medicine Center (MCW) sequencing core to achieve 30X coverage (Illumina NovaSeq S4). Liver and brain transcriptome analysis will be done for the entire HRDP panel through PhenoGen (http://phenogen.ucdenver.edu) at the University of Colorado (Tabakoff and Saba). Genomic, phenotype, and strain information will be made available through the Hybrid Rat Diversity Panel portal at the Rat Genome Database (http://rgd.mcw.edu). MCW will partner with the Rat Resource &amp; Research Center (RRRC, http://www.rrrc.us) at the University of Missouri for strain distribution. The HRDP will provide a genetically stable population of rat strains with fully sequenced genomes, transcriptomes for brain and liver, and general phenotypic characterization to be used for systems genetic studies and fine mapping of complex traits.Support or Funding InformationNational Institutes of Health, Office of the Director, R24OD024617This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A simple approximation to bias in the genetic effect estimates when multiple disease states share a clinical diagnosis.

Case-control genome-wide association studies (CC-GWAS) might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease and cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished based on a biomarker evaluation that might not be feasible in the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates the frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or nongenetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a traditional logistic regression model with the clinical diagnosis as the outcome variable to the genetic effect estimates in the relationship to the true disease state of interest. We performed simulation studies to assess the accuracy of the approximation that we have derived. We next applied the derived approximation to the analysis of the genetic basis of the innate immune system of Alzheimer's disease.

Identification and Analysis of Human Microbe-Disease Associations by Matrix Decomposition and Label Propagation.

Studies have shown that microbes exist widely in the human body and are closely related to human complex diseases. Predicting potential associations between microbes and diseases is conducive to understanding the mechanisms of complex diseases and can also facilitate the diagnosis and prevention of human diseases. In this paper, we put forward the Matrix Decomposition and Label Propagation for Human Microbe-Disease Association prediction (MDLPHMDA) on the basis of the dataset of known microbe-disease associations collected from the database of HMDAD and the Gaussian interaction profile kernel similarity for diseases and microbes, disease symptom similarity. Moreover, the performance of our model was evaluated by means of leave-one-out cross validation and five-fold cross validation, and the corresponding AUCs of 0.9034 and 0.8954 ± 0.0030 were gained, respectively. In case studies, 10, 9, 9, and 8 out of the top 10 predicted microbes for asthma, colorectal carcinoma, liver cirrhosis, and type 1 diabetes were confirmed by literatures, respectively. Overall, evaluation results showed that MDLPHMDA has good performance in potential microbe-diseasepositive free parameter, which associations prediction.

Making Sense of the Epigenome Using Data Integration Approaches.

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.