Genetic Prioritization, Therapeutic Repositioning and Cross-Disease Comparisons Reveal Inflammatory Targets Tractable for Kidney Stone Disease

Machine learning and 4D-LFQ quantitative proteomic analysis explore the molecular mechanism of kidney stone formation

Background

Nephrolithiasis, a common and chronic urological condition, exerts significant pressure on both the general public and society as a whole. The precise mechanisms of nephrolith formation remain inadequately comprehended. Nevertheless, the utilization of proteomics methods has not been employed to examine the development of renal calculi in order to efficiently hinder and manage the creation and reappearance of nephrolith. Nowadays, with the rapid development of proteomics techniques, more efficient and more accurate proteomics technique is utilized to uncover the mechanisms underlying diseases. The objective of this study was to investigate the possible alterations of HK-2 cells when exposed to varying amounts of calcium oxalate (CaOx). The aim was to understand the precise development of stone formation and recurrence, in order to find effective preventive and treatment methods.

Methods

To provide a complete view of the proteins involved in the development of nephrolithiasis, we utilized an innovative proteomics method called 4D-LFQ proteomic quantitative techniques. HK-2 cells were selected as our experimental subjects. Three groups (n = 3) of HK-2 cells were treated with intervention solutions containing 0 (negative control, NC), 1 mM, and 2 mM CaOx, respectively. For the proteins that showed differential expression, various analyses were conducted including examination of Gene Ontology (GO), Clusters of Orthologous Groups of proteins (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, enrichment analysis of protein domains, and hierarchical clustering analysis. The STRING database was used to identify the interaction network of the chosen proteins. Candidate proteins were validated using parallel reaction monitoring (PRM) in the end.

Results

All three groups verified the repeatability of samples. According to the results of 4D-LFQ proteomic quantitative analysis, there were 120, 262, and 81 differentially expressed proteins (DEPs) in the 1 mM-VS-NC, 2 mM-VS-NC, and 2 mM-VS-1mM conditions, respectively. According to GO annotation, the functional enrichment analysis indicates that the differentially expressed proteins (DEPs) were notably enriched in promoting cell migration and the extracellular matrix, among other functions. Analysis of enrichment, based on the KEGG pathway, revealed significant enrichment of DEPs in complement and coagulation cascades, as well as in ECM-receptor (extracellular matrix-receptor) interaction and other related pathways. 14 DEPs of great interest were selected as candidate proteins, including FN1, TFRC, ITGA3, FBN1, HYOU1, SPP1, HSPA5, COL6A1, MANF, HIP1R, JUP, AXL, CTNNB1 and DSG2.The data from PRM demonstrated the variation trend of 14 DEPs was identical as 4D-LFQ proteomic quantitative analysis.

Conclusion

Proteomics studies of CaOx-induced HK-2 cells using 4D-LFQ proteomic quantitative analysis and PRM may help to provide crucial potential target proteins and signaling pathways for elucidating the mechanism of nephrolithiasis and better treating nephrolithiasis.

A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation

Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.

Priority index for critical Covid-19 identifies clinically actionable targets and drugs

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.

Inflammation-associated ectopic mineralization

Ectopic mineralization refers to the deposition of mineralized complexes in the extracellular matrix of soft tissues. Calcific aortic valve disease, vascular calcification, gallstones, kidney stones, and abnormal mineralization in arthritis are common examples of ectopic mineralization. They are debilitating diseases and exhibit excess mortality, disability, and morbidity, which impose on patients with limited social or financial resources. Recent recognition that inflammation plays an important role in ectopic mineralization has attracted the attention of scientists from different research fields. In the present review, we summarize the origin of inflammation in ectopic mineralization and different channels whereby inflammation drives the initiation and progression of ectopic mineralization. The current knowledge of inflammatory milieu in pathological mineralization is reviewed, including how immune cells, pro-inflammatory mediators, and osteogenic signaling pathways induce the osteogenic transition of connective tissue cells, providing nucleating sites and assembly of aberrant minerals. Advances in the understanding of the underlying mechanisms involved in inflammatory-mediated ectopic mineralization enable novel strategies to be developed that may lead to the resolution of these enervating conditions.

Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease

Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.

OpenXGR: a web-server update for genomic summary data interpretation

How to effectively convert genomic summary data into downstream knowledge discovery represents a major challenge in human genomics research. To address this challenge, we have developed efficient and effective approaches and tools. Extending our previously established software tools, we here introduce OpenXGR (http://www.openxgr.com), a newly designed web server that offers almost real-time enrichment and subnetwork analyses for a user-input list of genes, SNPs or genomic regions. It achieves so through leveraging ontologies, networks, and functional genomic datasets (such as promoter capture Hi-C, e/pQTL and enhancer-gene maps for linking SNPs or genomic regions to candidate genes). Six analysers are provided, each doing specific interpretations tailored to genomic summary data at various levels. Three enrichment analysers are designed to identify ontology terms enriched for input genes, as well as genes linked from input SNPs or genomic regions. Three subnetwork analysers allow users to identify gene subnetworks from input gene-, SNP- or genomic region-level summary data. With a step-by-step user manual, OpenXGR provides a user-friendly and all-in-one platform for interpreting summary data on the human genome, enabling more integrated and effective knowledge discovery.

PiER: web-based facilities tailored for genetic target prioritisation harnessing human disease genetics, functional genomics and protein interactions

Integrative prioritisation promotes translational use of disease genetic findings in target discovery. I report 'PiER' (http://www.genetictargets.com/PiER), web-based facilities that support ab initio and real-time genetic target prioritisation through integrative use of human disease genetics, functional genomics and protein interactions. By design, the PiER features two facilities: elementary and combinatory. The elementary facility is designed to perform specific tasks, including three online tools: eV2CG, utilising functional genomics to link disease-associated variants (particularly located at the non-coding genome) to core genes likely responsible for genetic associations in disease; eCG2PG, using knowledge of protein interactions to 'network' core genes and additional peripheral genes, producing a ranked list of core and peripheral genes; and eCrosstalk, exploiting the information of pathway-derived interactions to identify highly-ranked genes mediating crosstalk between molecular pathways. Each of elementary tasks giving results is sequentially piped to the next one. By chaining together elementary tasks, the combinatory facility automates genetics-led and network-based integrative prioritisation for genetic targets at the gene level (cTGene) and at the crosstalk level (cTCrosstalk). Together with a tutorial-like booklet describing instructions on how to use, the PiER facilities meet multi-tasking needs to accelerate computational translational medicine that leverages human disease genetics and genomics for early-stage target discovery and drug repurposing.

Genomic Evidence Supports the Recognition of Endometriosis as an Inflammatory Systemic Disease and Reveals Disease-Specific Therapeutic Potentials of Targeting Neutrophil Degranulation

Background

Endometriosis, classically viewed as a localized disease, is increasingly recognized as a systemic disease with multi-organ effects. This disease is highlighted by systemic inflammation in affected organs and by high comorbidity with immune-mediated diseases.

Results

We provide genomic evidence to support the recognition of endometriosis as an inflammatory systemic disease. This was achieved through our genomics-led target prioritization, called 'END', that leverages the value of multi-layered genomic datasets (including genome-wide associations in disease, regulatory genomics, and protein interactome). Our prioritization recovered existing proof-of-concept therapeutic targeting in endometriosis and outperformed competing prioritization approaches (Open Targets and Naïve prioritization). Target genes at the leading prioritization revealed molecular hallmarks (and possibly the cellular basis as well) that are consistent with systemic disease manifestations. Pathway crosstalk-based attack analysis identified the critical gene AKT1. In the context of this gene, we further identified genes that are already targeted by licensed medications in other diseases, such as ESR1. Such analysis was supported by current interests targeting the PI3K/AKT/mTOR pathway in endometriosis and by the fact that therapeutic agents targeting ESR1 are now under active clinical trials in disease. The construction of cross-disease prioritization map enabled the identification of shared and distinct targets between endometriosis and immune-mediated diseases. Shared target genes identified opportunities for repurposing existing immunomodulators, particularly disease-modifying anti-rheumatic drugs (such as TNF, IL6 and IL6R blockades, and JAK inhibitors). Genes highly prioritized only in endometriosis revealed disease-specific therapeutic potentials of targeting neutrophil degranulation - the exocytosis that can facilitate metastasis-like spread to distant organs causing inflammatory-like microenvironments.

Conclusion

Improved target prioritization, along with an atlas of in silico predicted targets and repurposed drugs (available at https://23verse.github.io/end), provides genomic insights into endometriosis, reveals disease-specific therapeutic potentials, and expands the existing theories on the origin of disease.