Complex Human Diseases Research Articles

DREAM: an R package for druggability evaluation of human complex diseases.

De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.

Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features

MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

The interaction between enhancer variants and environmental factors as an overlooked aetiological paradigm in human complex disease.

The interactions between genetic and environmental risk factors contribute to the aetiology of complex human diseases. Genome-wide association studies (GWAS) have revealed that most of the genetic variants associated with complex diseases are located in the non-coding part of the genome, preferentially within enhancers. Enhancers are distal cis-regulatory elements composed of clusters of transcription factors binding sites that positively regulate the expression of their target genes. The generation of genome-wide maps for histone marks (e.g., H3K27ac), chromatin accessibility and transcription factor and coactivator (e.g., p300) binding profiles have enabled the identification of enhancers across many human cell types and tissues. Nonetheless, the functional and pathological consequences of the majority of disease-associated genetic variants located within enhancers seem to be rather minor under normal conditions, thus questioning their medical relevance. Here we propose that, due to the prevalence of enhancer redundancy, the pathological effects of many disease-associated non-coding genetic variants might be preferentially (or even only) manifested under environmental stress.

TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

Biomarker Identification via a Factorization Machine-Based Neural Network With Binary Pairwise Encoding.

Biomolecules, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play critical roles in diverse fundamental and vital biological processes. They can serve as disease biomarkers as their dysregulations could cause complex human diseases. Identifying those biomarkers is helpful with the diagnosis, treatment, prognosis, and prevention of diseases. In this study, we propose a factorization machine-based deep neural network with binary pairwise encoding, DFMbpe, to identify the disease-related biomarkers. First, to comprehensively consider the interdependence of features, a binary pairwise encoding method is designed to obtain the raw feature representations for each biomarker-disease pair. Second, the raw features are mapped into their corresponding embedding vectors. Then, the factorization machine is conducted to get the wide low-order feature interdependence, while the deep neural network is applied to obtain the deep high-order feature interdependence. Finally, two kinds of features are combined to get the final prediction results. Unlike other biomarker identification models, the binary pairwise encoding considers the interdependence of features even though they never appear in the same sample, and the DFMbpe architecture emphasizes both low-order and high-order feature interactions simultaneously. The experimental results show that DFMbpe greatly outperforms the state-of-the-art identification models on both cross-validation and independent dataset evaluation. Besides, three types of case studies further demonstrate the effectiveness of this model.

Bi-level structured functional analysis for genome-wide association studies.

Genome-wide association studies (GWAS) have led to great successes in identifying genotype-phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations, has emerged as a promising avenue for overcoming the high dimensionality challenges. However, the majority of the existing functional studies continue to be individual SNP based and are unable to sufficiently account for the intricate underpinning structures of SNP data. SNPs are often found in groups (e.g., genes or pathways) and have a natural group structure. Additionally, these SNP groups can be highly correlated with coordinated biological functions and interact in a network. Motivated by these unique characteristics of SNP data, we develop a novel bi-level structured functional analysis method and investigate disease-associated genetic variants at the SNP level and SNP group level simultaneously. The penalization technique is adopted for bi-level selection and also to accommodate the group-level network structure. Both the estimation and selection consistency properties are rigorously established. The superiority of the proposed method over alternatives is shown through extensive simulation studies. A type 2 diabetes SNP data application yields some biologically intriguingresults.

Identification of novel candidate genes in East Asian COPD patients by the functional summary-based imputation and the unified test for molecular signatures: a transcriptome-wide association study

Identification of novel candidate genes in East Asian COPD patients by the functional summary-based imputation and the unified test for molecular signatures: a transcriptome-wide association study

CRISPR-Cas-amplified urinary biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, bioengineered sensors that generate molecular reporters in diseased microenvironments, represent an emerging paradigm in precision diagnostics. Despite the utility of DNA barcodes as a multiplexing tool, their susceptibility to nucleases in vivo has limited their utility. Here we exploit chemically stabilized nucleic acids to multiplex synthetic biomarkers and produce diagnostic signals in biofluids that can be ‘read out’ via CRISPR nucleases. The strategy relies on microenvironmental endopeptidase to trigger the release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed urine. Our data suggest that DNA-encoded nanosensors can non-invasively detect and differentiate disease states in transplanted and autochthonous murine cancer models. We also demonstrate that CRISPR-Cas amplification can be harnessed to convert the readout to a point-of-care paper diagnostic tool. Finally, we employ a microfluidic platform for densely multiplexed, CRISPR-mediated DNA barcode readout that can potentially evaluate complex human diseases rapidly and guide therapeutic decisions.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

High-content synaptic phenotyping in human cellular models reveals a role for BET proteins in synapse assembly.

Resolving fundamental molecular and functional processes underlying human synaptic development is crucial for understanding normal brain function as well as dysfunction in disease. Based upon increasing evidence of species-divergent features of brain cell types, coupled with emerging studies of complex human disease genetics, we developed the first automated and quantitative high-content synaptic phenotyping platform using human neurons and astrocytes. To establish the robustness of our platform, we screened the effects of 376 small molecules on presynaptic density, neurite outgrowth, and cell viability, validating six small molecules that specifically enhanced human presynaptic density in vitro. Astrocytes were essential for mediating the effects of all six small molecules, underscoring the relevance of non-cell-autonomous factors in synapse assembly and their importance in synaptic screening applications. Bromodomain and extraterminal (BET) inhibitors emerged as the most prominent hit class and global transcriptional analyses using multiple BET inhibitors confirmed upregulation of synaptic gene expression. Through these analyses, we demonstrate the robustness of our automated screening platform for identifying potent synaptic modulators, which can be further leveraged for scaled analyses of human synaptic mechanisms and drug discovery efforts.

Effects of Repeated Freeze and Thaw Cycles on the Genome-Wide DNA Methylation Profile of Isolated Genomic DNA.

The characterization of DNA methylation patterns to identify epigenetic markers for complex human diseases is an important and rapidly evolving part in biomedical research. DNA samples collected and stored in clinical biobanks over the past years are an important source for future epigenetic studies. Isolated gDNA is considered stable when stored at low temperatures for several years. However, the effect of multiple use and the associated repeated thawing of long-term stored DNA samples on DNA methylation patterns has not yet been investigated. In this study, we examined the influence of up to 10 freeze and thaw cycles on global DNA methylation by comparing genome-wide methylation profiles. DNA samples from 19 healthy volunteers were either frozen at -80°C or subjected to up to 10 freeze and thaw cycles. Genome-wide DNA methylation was analyzed after 0, 1, 3, 5, or 10 thaw cycles using the Illumina Infinium MethylationEPIC BeadChip. Evaluation of the global DNA methylation profile by beta-value density plots and multidimensional scaling plots revealed an expected clear participant-dependent variability, but a very low variability depending on the freeze and thaw cycles. In accordance, no significant difference in any of the methylated cytosine/guanine sites studied could be detected in the performed statistical analyses. Our results suggest that long-term frozen DNA samples are still suitable for epigenetic studies after multiple thaw cycles.

Adaptive deep propagation graph neural network for predicting miRNA-disease associations.

A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations. In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs. Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.

Light-Deactivated Fluorescent Probes (FLASH-Off) for Multiplexed Imaging.

Highly multiplexed, cyclic fluorescence imaging has advanced our understanding of the biology, evolution, and complexity of human diseases. Currently available cyclic methods still have considerable limitations including the need for long quenching times and extensive wash steps. Here, we report a new series of fluorochromes that can be efficiently inactivated by a single light pulse (∼405 nm) by means of a photo-immolating triazene linker. Upon UV-light irradiation, the rhodamines are cleaved off from the antibody conjugates and undergo a fast intramolecular spirocyclization that inherently switches off their fluorescence emission without the need to wash or add exogenous chemicals. We show that these switch-off probes are fast, highly controllable, biocompatible, and allow spatiotemporal quenching control of live and fixed samples.

Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.

Traditional genome-wide association study focuses on testing one-to-one relationship between genetic variants and complex human diseases or traits. While its success in the past decade, this one-to-one paradigm lacks efficiency because it does not utilize the information of intrinsic genetic structure and pleiotropic effects. Due to privacy reasons, only summary statistics of current genome-wide association study data are publicly available. Existing summary statistics-based association tests do not consider covariates for regression model, while adjusting for covariates including population stratification factors is a routine issue. In this work, we first derive the correlation coefficients between summary Wald statistics obtained from linear regression model with covariates. Then, a new test is proposed by integrating three-level information including the intrinsic genetic structure, pleiotropy, and the potential information combinations. Extensive simulations demonstrate that the proposed test outperforms three other existing methods under most of the considered scenarios. Real data analysis of polyunsaturated fatty acids further shows that the proposed test can identify more genes than the compared existing methods. Code is available at https://github.com/bschilder/ThreeWayTest.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /− 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

Genetic variation in cis-regulatory domains suggests cell type-specific regulatory mechanisms in immunity

Studying the interplay between genetic variation, epigenetic changes, and regulation of gene expression is crucial to understand the modification of cellular states in various conditions, including immune diseases. In this study, we characterize the cell-specificity in three key cells of the human immune system by building cis maps of regulatory regions with coordinated activity (CRDs) from ChIP-seq peaks and methylation data. We find that only 33% of CRD-gene associations are shared between cell types, revealing how similarly located regulatory regions provide cell-specific modulation of gene activity. We emphasize important biological mechanisms, as most of our associations are enriched in cell-specific transcription factor binding sites, blood-traits, and immune disease-associated loci. Notably, we show that CRD-QTLs aid in interpreting GWAS findings and help prioritize variants for testing functional hypotheses within human complex diseases. Additionally, we map trans CRD regulatory associations, and among 207 trans-eQTLs discovered, 46 overlap with the QTLGen Consortium meta-analysis in whole blood, showing that mapping functional regulatory units using population genomics allows discovering important mechanisms in the regulation of gene expression in immune cells. Finally, we constitute a comprehensive resource describing multi-omics changes to gain a greater understanding of cell-type specific regulatory mechanisms of immunity.