Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

The trans-omics landscape of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer's Disease.

Alzheimer's disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data.

The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing.

Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer's brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.

Vascular amyloid accumulation alters the gabaergic synapse and induces hyperactivity in a model of cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is typified by the cerebrovascular deposition of amyloid. The mechanisms underlying the contribution of CAA to neurodegeneration are not currently understood. Although CAA is highly associated with the accumulation of ß-amyloid (Aß), other amyloids are known to associate with the vasculature. Alzheimer's disease (AD) is characterized by parenchymal Aß deposition and intracellular accumulation of tau as neurofibrillary tangles (NFTs), affecting synapses directly, leading to behavioral and physical impairment. CAA increases with age and is present in 70%-97% of individuals with AD. Studies have overwhelmingly focused on the connection between parenchymal amyloid accumulation and synaptotoxicity; thus, the contribution of vascular amyloid is mostly understudied. Here, synaptic alterations induced by vascular amyloid accumulation and their behavioral consequences were characterized using a mouse model of Familial Danish dementia (FDD), a neurodegenerative disease characterized by the accumulation of Danish amyloid (ADan) in the vasculature. The mouse model (Tg-FDD) displays a hyperactive phenotype that potentially arises from impairment in the GABAergic synapses, as determined by electrophysiological analysis. We demonstrated that the disruption of GABAergic synapse organization causes this impairment and provided evidence that GABAergic synapses are impaired in patients with CAA pathology. Understanding the mechanism that CAA contributes to synaptic dysfunction in AD-related dementias is of critical importance for developing future therapeutic interventions.

A1 reactive astrocytes and a loss of TREM2 are associated with an early stage of pathology in a mouse model of cerebral amyloid angiopathy.

BACKGROUND: Cerebral amyloid angiopathy (CAA) is typified by the cerebrovascular deposition of amyloid. The mechanisms underlying the contribution of CAA to neurodegeneration are not currently understood. Although CAA is highly associated with the accumulation of amyloid beta (Aß), other amyloids are known to associate with the vasculature. Alzheimer's disease (AD) is characterized by parenchymal Aß deposition, intracellular accumulation of tau, and significant neuroinflammation. CAA increases with age and is present in 85-95% of individuals with AD. A substantial amount of research has focused on understanding the connection between parenchymal amyloid and glial activation and neuroinflammation, while associations between vascular amyloid pathology and glial reactivity remain understudied. METHODS: Here, we dissect the glial and immune responses associated with early-stage CAA with histological, biochemical, and gene expression analyses in a mouse model of familial Danish dementia (FDD), a neurodegenerative disease characterized by the vascular accumulation of Danish amyloid (ADan). Findings observed in this CAA mouse model were complemented with primary culture assays. RESULTS: We demonstrate that early-stage CAA is associated with dysregulation in immune response networks and lipid processing, severe astrogliosis with an A1 astrocytic phenotype, and decreased levels of TREM2 with no reactive microgliosis. Our results also indicate how cholesterol accumulation and ApoE are associated with vascular amyloid deposits at the early stages of pathology. We also demonstrate A1 astrocytic mediation of TREM2 and microglia homeostasis. CONCLUSION: The initial glial response associated with early-stage CAA is characterized by the upregulation of A1 astrocytes without significant microglial reactivity. Gene expression analysis revealed that several AD risk factors involved in immune response and lipid processing may also play a preponderant role in CAA. This study contributes to the increasing evidence that brain cholesterol metabolism, ApoE, and TREM2 signaling are major players in the pathogenesis of AD-related dementias, including CAA. Understanding the basis for possible differential effects of glial response, ApoE, and TREM2 signaling on parenchymal plaques versus vascular amyloid deposits provides important insight for developing future therapeutic interventions.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. METHODS: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. RESULTS: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. CONCLUSIONS: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression.

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer's disease patients.

BACKGROUND: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer's disease (AD) patients and looked for their specific functions. METHODS: In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. RESULTS: We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology. CONCLUSIONS: Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.

txCoords: A Novel Web Application for Transcriptomic Peak Re-Mapping.

Since the development of new technologies such as RIP-Seq and m6A-seq, peak calling has become an important step in transcriptomic sequencing data analysis. However, many of the reported genomic coordinates of transcriptomic peaks are incorrect owing to negligence of the introns. There is currently a lack of a convenient tool to address this problem. Here, we present txCoords, a novel and easy-to-use web application for transcriptomic peak re-mapping. txCoords can be used to correct the incorrectly reported transcriptomic peaks and retrieve the true sequences. It also supports visualization of the re-mapped peaks in a schematic figure or from the UCSC Genome Browser. Our web server is freely available at http://www.bioinfo.tsinghua.edu.cn/txCoords.

RNAMethPre: A Web Server for the Prediction and Query of mRNA m6A Sites.

N6-Methyladenosine (m6A) is the most common mRNA modification; it occurs in a wide range of taxon and is associated with many key biological processes. High-throughput experiments have identified m6A-peaks and sites across the transcriptome, but studies of m6A sites at the transcriptome-wide scale are limited to a few species and tissue types. Therefore, the computational prediction of mRNA m6A sites has become an important strategy. In this study, we integrated multiple features of mRNA (flanking sequences, local secondary structure information, and relative position information) and trained a SVM classifier to predict m6A sites in mammalian mRNA sequences. Our method achieves ideal performance in both cross-validation tests and rigorous independent dataset tests. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from the literature for both human and mouse, and these can be queried and visualized in a genome browser. The RNAMethPre web server provides a user-friendly tool for the prediction and query of mRNA m6A sites, which is freely accessible for public use at http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html.

AthMethPre: a web server for the prediction and query of mRNA m6A sites in Arabidopsis thaliana.

N6-Methyladenosine (m6A) is the most prevalent and abundant modification in mRNA that has been linked to many key biological processes. High-throughput experiments have generated m6A-peaks across the transcriptome of A. thaliana, but the specific methylated sites were not assigned, which impedes the understanding of m6A functions in plants. Therefore, computational prediction of mRNA m6A sites becomes emergently important. Here, we present a method to predict the m6A sites for A. thaliana mRNA sequence(s). To predict the m6A sites of an mRNA sequence, we employed the support vector machine to build a classifier using the features of the positional flanking nucleotide sequence and position-independent k-mer nucleotide spectrum. Our method achieved good performance and was applied to a web server to provide service for the prediction of A. thaliana m6A sites. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from the literature for query and visualization. The AthMethPre web server is the first web server that provides a user-friendly tool for the prediction and query of A. thaliana mRNA m6A sites, which is freely accessible for public use at .