Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer's Disease.

Alzheimer's disease (AD), a multifactorial neurodegenerative disorder, is prevalent among the elderly population. It is a complex trait with mutations in multiple genes. Although the US Food and Drug Administration (FDA) has approved a few drugs for AD treatment, a definitive cure remains elusive. Research efforts persist in seeking improved treatment options for AD. Here, a hybrid pipeline is proposed to apply text mining to identify comorbid diseases for AD and an omics approach to identify the common genes between AD and five comorbid diseases-dementia, type 2 diabetes, hypertension, Parkinson's disease, and Down syndrome. We further identified the pathways and drugs for common genes. The rationale behind this approach is rooted in the fact that elderly individuals often receive multiple medications for various comorbid diseases, and an insight into the genes that are common to comorbid diseases may enhance treatment strategies. We identified seven common genes-PSEN1, PSEN2, MAPT, APP, APOE, NOTCH, and HFE-for AD and five comorbid diseases. We investigated the drugs interacting with these common genes using LINCS gene-drug perturbation. Our analysis unveiled several promising candidates, including MG-132 and Masitinib, which exhibit potential efficacy for both AD and its comorbid diseases. The pipeline can be extended to other diseases.

Modulating the Mesenchymal Stromal Cell Microenvironment Alters Exosome RNA Content and Ligament Healing Capacity.

Although mesenchymal stromal cell (MSC) based therapies hold promise in regenerative medicine, their clinical application remains challenging due to issues such as immunocompatibility. MSC-derived exosomes are a promising off-the-shelf therapy for promoting wound healing in a cell-free manner. However, the potential to customize the content of MSC-exosomes, and understanding how such modifications influence exosome effects on tissue regeneration remain underexplored. In this study, we used an in vitro system to compare the priming of human MSCs by two inflammatory inducers TNF-α and CRX-527 (a highly potent synthetic TLR4 agonist that can be used as a vaccine adjuvant or to induce anti-tumor immunity) on exosome molecular cargo, as well as on an in vivo rat ligament injury model to validate exosome potency. Different microenvironmental stimuli used to prime MSCs in vitro affected their exosomal microRNAs and mRNAs, influencing ligament healing. Exosomes derived from untreated MSCs significantly enhance the mechanical properties of healing ligaments, in contrast to those obtained from MSCs primed with inflammation-inducers, which not only fail to provide any improvement but also potentially deteriorate the mechanical properties. Additionally, a link was identified between altered exosomal microRNA levels and expression changes in microRNA targets in ligaments. These findings elucidate the nuanced interplay between MSCs, their exosomes, and tissue regeneration.

Unraveling the complex relationship between mRNA and protein abundances: a machine learning-based approach for imputing protein levels from RNA-seq data.

The correlation between messenger RNA (mRNA) and protein abundances has long been debated. RNA sequencing (RNA-seq), a high-throughput, commonly used method for analyzing transcriptional dynamics, leaves questions about whether we can translate RNA-seq-identified gene signatures directly to protein changes. In this study, we utilized a set of 17 widely assessed immune and wound healing mediators in the context of canine volumetric muscle loss to investigate the correlation of mRNA and protein abundances. Our data reveal an overall agreement between mRNA and protein levels on these 17 mediators when examining samples from the same experimental condition (e.g. the same biopsy). However, we observed a lack of correlation between mRNA and protein levels for individual genes under different conditions, underscoring the challenges in converting transcriptional changes into protein changes. To address this discrepancy, we developed a machine learning model to predict protein abundances from RNA-seq data, achieving high accuracy. Our approach also effectively corrected multiple extreme outliers measured by antibody-based protein assays. Additionally, this model has the potential to detect post-translational modification events, as shown by accurately estimating activated transforming growth factor ß1 levels. This study presents a promising approach for converting RNA-seq data into protein abundance and its biological significance.

Modulating Mesenchymal Stromal Cell Microenvironment Alters Exosome RNA Content and Ligament Healing Capacity.

Although mesenchymal stromal cell (MSC) based therapies hold promise in regenerative medicine, their applications in clinical settings remain challenging due to issues such as immunocompatibility and cell stability. MSC-derived exosomes, small vesicles carrying various bioactive molecules, are a promising cell-free therapy to promote tissue regeneration. However, it remains unknown mainly regarding the ability to customize the content of MSC-derived exosomes, how alterations in the MSC microenvironment influence exosome content, and the effects of such modifications on healing efficiency and mechanical properties in tissue regeneration. In this study, we used an in vitro system of human MSC-derived exosomes and an in vivo rat ligament injury model to address these questions. We found a context-dependent correlation between exosomal and parent cell RNA content. Under native conditions, the correlation was moderate but heightened with microenvironmental changes. In vivo rat ligament injury model showed that MSC-derived exosomes increased ligament max load and stiffness. We also found that changes in the MSCs' microenvironment significantly influence the mechanical properties driven by exosome treatment. Additionally, a link was identified between altered exosomal microRNA levels and expression changes in microRNA targets in ligaments. These findings elucidate the nuanced interplay between MSCs, their exosomes, and tissue regeneration.

Network Analysis of Anti-inflammatory Phytochemicals and Omics Data for Rheumatoid Arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that affects the synovial joints. Nearly 1.6 billion patients are affected by RA worldwide and the incidence of RA is about 0.5 to 1%. Recent studies reveal that immune cell responses and secretion of inflammatory factors are important for the control of RA. METHODS: In this study, a set of 402 phytochemicals with anti-inflammatory properties and 16 target proteins related to anti-inflammatory diseases were identified from the literature and they were subjected to network analysis. The protein-protein interaction (PPI) network was constructed using STRING (Search Tool for the Retrieval of Interacting Genes database) database. Visualization of the target gene-phytochemical network and its protein-protein interaction network was conducted using Cytoscape and further analyzed using MCODE (Molecular Complex Detection). The gene ontology and KEGG pathway analysis was performed using DAVID tool. RESULTS: Our results from the network approach indicate that the phytochemicals such as Withanolide, Diosgenin, and Butulin could act as potential substitute for anti-inflammatory drugs, including DMARDs. Genes such as Mitogen-activated protein kinase (MAPK) and Interleukin were found as hub genes and acted as best inhibitors for the target protein pathways. Curcumin, Catechin was also found to be involved in various signaling pathways such as NF-kappa B signaling pathway, ErbB signaling pathway and acted as the best inhibitor along with other candidate phytochemicals. CONCLUSION: In the current study, we were able to identify Withanolide, Diosgenin, and Butulin as potential anti-inflammatory phytochemicals and determine their association with key pathways involved in RA through network analysis. We hypothesized that natural compounds could significantly contribute to the reduction of dosage, improve the treatment and act as a therapeutic agent for more economical and safer treatment of RA.

Transcriptomic, Proteomic, and Morphologic Characterization of Healing in Volumetric Muscle Loss.

Skeletal muscle has a robust, inherent ability to regenerate in response to injury from acute to chronic. In severe trauma, however, complete regeneration is not possible, resulting in a permanent loss of skeletal muscle tissue referred to as volumetric muscle loss (VML). There are few consistently reliable therapeutic or surgical options to address VML. A major limitation in investigation of possible therapies is the absence of a well-characterized large animal model. In this study, we present results of a comprehensive transcriptomic, proteomic, and morphologic characterization of wound healing following VML in a novel canine model of VML which we compare to a nine-patient cohort of combat-associated VML. The canine model is translationally relevant as it provides both a regional (spatial) and temporal map of the wound healing processes that occur in human VML. Collectively, these data show the spatiotemporal transcriptomic, proteomic, and morphologic properties of canine VML healing as a framework and model system applicable to future studies investigating novel therapies for human VML. Impact Statement The spatiotemporal transcriptomic, proteomic, and morphologic properties of canine volumetric muscle loss (VML) healing is a translational framework and model system applicable to future studies investigating novel therapies for human VML.

Integrated Approaches to Identify miRNA Biomarkers Associated with Cognitive Dysfunction in Multiple Sclerosis Using Text Mining, Gene Expression, Pathways, and GWAS.

Multiple sclerosis (MS), a chronic autoimmune disorder, affects the central nervous system of many young adults. More than half of MS patients develop cognition problems. Although several genomic and transcriptomic studies are currently reported in MS cognitive impairment, a comprehensive repository dealing with all the experimental data is still underdeveloped. In this study, we combined text mining, gene regulation, pathway analysis, and genome-wide association studies (GWAS) to identify miRNA biomarkers to explore the cognitive dysfunction in MS, and to understand the genomic etiology of the disease. We first identified the dysregulated miRNAs associated with MS and cognitive dysfunction using PubTator (text mining), HMDD (experimental associations), miR2Disease, and PhenomiR database (differentially expressed miRNAs). Our results suggest that miRNAs such as hsa-mir-148b-3p, hsa-mir-7b-5p, and hsa-mir-7a-5p are commonly associated with MS and cognitive dysfunction. Next, we retrieved GWAS signals from GWAS Catalog, and analyzed the enrichment analysis of association signals in genes/miRNAs and their association networks. Then, we identified susceptible genetic loci, rs17119 (chromosome 6; p = 1 × 10-10), rs1843938 (chromosome 7; p = 1 × 10-10), and rs11637611 (chromosome 15; p = 1.00 × 10-15), associated with significant genetic risk. Lastly, we conducted a pathway analysis for the susceptible genetic variants and identified novel risk pathways. The ECM receptor signaling pathway (p = 3.98 × 10-8) and PI3K/Akt signaling pathway (p = 5.98 × 10-5) were found to be associated with differentially expressed miRNA biomarkers.

A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources.

Multiple sclerosis, a disease of central nervous system leads to potential disability. In the USA, one million cases are diagnosed with multiple sclerosis in 2019. Multiple sclerosis is identified as one of the diseases causing global burden. Cognitive disorder is highly prevalent among 43-70% of multiple sclerosis patients. However, treating cognitive disorder in multiple sclerosis patients is mostly ignored and this leads to several complications. We utilized various expert curated resources to identify potential drugs for multiple sclerosis and cognitive disorder, with specific focus on identifying drugs that are capable of treating both the conditions. We used simple text mining techniques to compile two databases, disease-drug association database and gene-drug interaction database from various existing standard resources. Our study suggests four drugs, Baclofen, Levodopa, Minocycline, and Vitamin B12, for treating both multiple sclerosis and cognitive disorder. In addition, our approach suggests six drugs for multiple sclerosis and 10 drugs for cognitive disorder. We obtained pharmacologist opinion on the drugs suggested for each condition and provided literature evidence for our claim. Here, we present our computational approach as a protocol such that it can be applied to other comorbid diseases that did not gain much attention so far.

A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around the globe. Despite of 141,975 published papers on COVID-19 and several hundreds of new studies carried out every day, this pandemic remains as a global challenge. Biomedical literature mining helps the researchers to understand the etiology of the disease and to gain an in-depth knowledge of the disease, potential drugs, vaccines developed and novel therapies. In addition to the available treatments, there is a huge need to address the comorbidity-based disease mortality in case of COVID-19 patients with type 2 diabetes mellitus (T2D), hypertension and cardiovascular disease (CVD). In this chapter, we provide a hybrid protocol based on biomedical literature mining, network analysis of omics data, and deep learning for the identification of most potential drugs for COVID-19.

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis.

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

BACKGROUND: MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. METHODS: miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. RESULTS: In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. CONCLUSION: To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/.

MicroRNA mediated network motifs in autoimmune diseases and its crosstalk between genes, functions and pathways.

Autoimmune diseases (AIDs) are incurable but suppressible diseases whose molecular mechanisms are yet to be elucidated. In this work, we selected five systemic autoimmune diseases such as Rheumatoid Arthritis (RA), Type 1 Diabetes (T1D), Inflammatory Bowel Disease (IBD), Autoimmune Thyroid Disease (ATD) and Systemic Lupus Erythematosus (SLE). Heterogeneous data such as miRNA, transcription factor (TF), target genes and protein-protein interactions involved in these AIDs were integrated to understand their roles at different functional levels of miRNA such as transcription initiation, gene regulatory network formation and post transcriptional regulation. To understand the functional characteristics of these complex biological networks, they can be simplified as network motifs (sub networks) and motif-motif interacting pairs (MMIs). The network motif patterns and motif-motif interacting pairs that occur for the selected five diseases were identified. To further understand the functional association between AIDs, functions and pathways were determined using gene set enrichment analysis and five selected immune signaling pathways (ISPs). The crosstalk within AIDs and between the immune signaling pathways (ISPs) could provide novel insights in deciphering disease mechanisms. This study represents the first investigation of miRNA-TF regulatory network for AIDs and its association with ISPs using sub-network motifs.

Prediction of microRNAs involved in immune system diseases through network based features.

MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.

Identification of Novel Inhibitors for Tobacco Mosaic Virus Infection in Solanaceae Plants.

Tobacco mosaic virus (TMV) infects several crops of economic importance (e.g., tomato) and remains as one of the major concerns to the farmers. TMV enters the host cell and produces the capping enzyme RNA polymerase. The viral genome replicates further to produce multiple mRNAs which encodes several proteins, including the coat protein and an RNA-dependent RNA polymerase (RdRp), as well as the movement protein. TMV replicase domain was chosen for the virtual screening studies against small molecules derived from ligand databases such as PubChem and ChemBank. Catalytic sites of the RdRp domain were identified and subjected to docking analysis with screened ligands derived from virtual screening LigandFit. Small molecules that interact with the target molecule at the catalytic domain region amino acids, GDD, were chosen as the best inhibitors for controlling the TMV replicase activity.

In Silico Analysis to Compare the Effectiveness of Assorted Drugs Prescribed for Swine flu in Diverse Medicine Systems.

The genome of the virus H1N1 2009 consists of eight segments but maximum number of mutations occurs at segments 1 and 4, coding for PB2 subunit of hemagglutinin. Comparatively less number of mutations occur at segment 6, coding for neuraminidase. Two antiviral drugs, oseltamivir and zanamivir are commonly prescribed for treating H1N1 infection. Alternate medical systems do compete equally; andrographolide in Siddha and gelsemine in Homeopathy. Recent studies confirm the efficacy of eugenol from Tulsi and vitamins C and E against H1N1. As the protein structures are unavailable, we modeled them using Modeller by identifying suitable templates, 1RUY and 3BEQ, for hemagglutinin and neuraminidase, respectively. Prior to docking simulations using AutoDock, the drug likeness properties of the ligands were screened using in silico techniques. Docking results showed interaction between the proteins individually into selected ligands, except for gelsemine and vitamin E no interactions were shown. The best docking simulation was reported by vitamin C interacting through six hydrogen bonds into proteins hemagglutinin and neuraminidase with binding energies -4.28 and -4.56 kcal/mol, respectively. Furthermore, vitamin C showed hydrophobic interactions with both proteins, two bonds with Arg119, Glu120 of HA, and one bond with Arg74 of NA. In silico docking studies thus recommend vitamin C to be more effective against H1N1.