Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer

In this study, we explored the pyroptosis-related biomarkers and signatures of colorectal cancer (CRC). Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA)-COADREAD and were analyzed for differentially expressed genes (DEGs). DEGs in CRC‒pyroptosis-related genes (CRC‒PRGs) were obtained by intersecting DEGs associated with CRC and PRGs. The CRC‒PRGs were verified; functional enrichment analysis was performed with Gene Ontology (GO) followed by cluster analysis. Cox analyses and LASSO regression were used in TCGA dataset to construct a prognostic model for patients with CRC. A prognostic risk assessment model was constructed and efficacy was evaluated. Decision curve analysis was utilized to assess the role of the Lasso-Cox regression prognostic model for clinical utility at 1, 3, and 5 years. Twelve CRC‒PRGs were identified as prognostic pyroptosis-related DEGs. CXCL8, IL13RA2, MELK, and POP1 were selected as prognostic genes to construct features with a good prognostic performance in GEO and TCGA. Functional enrichment indicated that the 4-gene signature might be involved in CRC tumorigenesis and development through various pathways by playing a prognostic role in CRC. Furthermore, the results of the immune landscape analysis showed that the expression of CXCL8 and IL13RA2 in TCGA-COADREAD dataset was positively correlated with significant differential enrichment of most immune cells. A novel prognostic model consisting of four key genes, CXCL8, IL13RA2, MELK, and POP1, can accurately predict the survival of patients with CRC. This finding may provide a new perspective for the treatment of pyroptosis-related CRC.

konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network

MOTIVATION

The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network.

RESULTS

We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not.

AVAILABILITY AND IMPLEMENTATION

konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

Bioinformatics analysis of genes related to ferroptosis in hepatic ischemia-reperfusion injury

Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death worldwide in 2020, and it ranks fifth in global incidence. Liver resection or liver transplantation are the two most prominent surgical procedures for treating primary liver cancer. Both inevitably result in HIRI, causing severe complications for patients and affecting their prognosis and quality of survival. Ferroptosis, a newly discovered mode of cell death, is closely related to HIRI. We used bioinformatics analysis to explore the relationship between the two further. Methods: The GEO database dataset GSE112713 and the FerrDB database data were selected to use bioinformatic analysis methods (difference analysis, FRGs identification, GO analysis, KEGG analysis, PPI network construction and analysis, Hub gene screening with GO analysis and KEGG analysis, intergenic interaction prediction, drug-gene interaction prediction, miRNA prediction) for both for correlation analysis. The GEO database dataset GSE15480 was selected for preliminary validation of the screened Hub genes. Results: We analysed the dataset GSE112713 for differential gene expression before and after hepatic ischemia-reperfusion and identified by FRGs, yielding 11 genes. These 11 genes were subjected to GO, and KEGG analyses, and PPI networks were constructed and analysed. We also screened these 11 genes again to obtain 5 Hub genes and performed GO analysis, KEGG analysis, intergenic interaction prediction, drug-gene interaction prediction, and miRNA prediction on these 5 Hub genes. Finally, we obtained preliminary validation of all these 5 Hub genes by dataset GSE15480. Conclusion: There is a close relationship between HIRI and ferroptosis, and inhibition of ferroptosis can potentially be a new approach to mitigate HIRI treatment in the future.

Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach

A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery.

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The feasibility of utilizing genomic variants to facilitate drug repurposing for Tuberculosis.

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Genomic information can be effectively used for drug discovery and treatment through genomic-based therapies.

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Findings from our research support the possibility of drug repurposing for Tuberculosis based on genomic variations.

Exploring the zinc-related transcriptional landscape in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurological disorder, and increasing evidence suggests AD pathology is driven by metabolic dysfunction in the brain. Zinc is the second most abundant trace element found in the human body and is required by all living organisms. Zinc is used extensively in many biological processes, and alterations in zinc levels are implicated in the pathogenesis of numerous diseases, including AD. Since small fluctuations in brain zinc levels appear to effect AD progression, we investigated the zinc-related transcriptional responses in an AD versus non-AD state using microarray and RNA-sequencing (RNA-seq) datasets from cultured cells, mice, and humans. We identified 582 zinc-related differentially expressed genes (DEG) in human dorsolateral prefrontal cortex samples of late-onset AD (LOAD) versus non-AD controls, 146 zinc-related DEG in 5XFAD versus wild-type mice, and 95 zinc-related DEG in lipopolysaccharide (LPS)-stimulated N9 microglia versus unstimulated control cells, with 19 zinc-related DEG common to all three datasets. Of the 19 common DEG, functional enrichment and network analyses identified several biological processes and molecular functions, such as mRNA destabilization and nucleic acid binding, which may be important in neuroinflammation and AD development. Furthermore, therapeutic drugs targeting zinc-related DEG in the human dataset were identified. Taken together, these data provide insights into zinc utilization for gene transcription during AD progression which may further our understanding of AD pathogenesis and could identify new targets for therapeutic strategies targeted towards AD.

Transcriptomic profiling of iris tissue highlights LCK signaling and T cell-mediated immunity in Behcet's uveitis

Uveitis is the most common form of ocular lesions in Behcet's disease, severely affecting visual function. Molecular pathological changes of ocular lesions in patients with Behcet's uveitis (BU) are largely unknown. In this study, we performed the first comprehensive transcriptomic profiling of iris specimens from BU patients and healthy donors to provide an insight into intraocular immunopathogenesis. The mRNA sequencing identified 1633 differentially expressed genes (DEGs) between the BU group and healthy controls. GO functional enrichment analysis on DEGs showed that T cell activation was the most significantly enriched biological process. KEGG analysis of DEGs also revealed several prominently enriched T cell-related pathways, including the T cell receptor signaling pathway, Th17 cell differentiation, and Th1 and Th2 cell differentiation. The lymphocyte-specific protein tyrosine kinase (LCK) was identified as the key hub gene in the protein interaction network of DEGs. Western blot analysis further showed increased expression of active LCK in the BU group, suggesting activation of LCK signaling. Using publicly accessible single-cell RNA-sequencing data of the healthy iris, LCK was found to be expressed in clusters of activated T cells but not in other iris cell clusters, suggesting an overt association between LCK upregulation and T cell-mediated immune dysregulation. Additionally, 16 drugs were predicted to be potential inhibitors of LCK. Overall, these findings not only highlighted the central role of T cell-mediated immunity and previously unreported LCK signaling in intraocular immunopathogenesis but also revealed the potential value of LCK as a new therapeutic target for BU patients.

Integrating human brain proteomic data with genome-wide association study findings identifies novel brain proteins in substance use traits

Despite the identification of a growing number of genetic risk loci for substance use traits (SUTs), the impact of these loci on protein abundance and the potential utility of relevant proteins as therapeutic targets are unknown. We conducted a proteome-wide association study (PWAS) in which we integrated human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The 4 samples comprised GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and characterize significant genes through conditional, colocalization, and fine-mapping analyses. We identified 27 genes (Smk = 21, AUD = 3, CUD = 2, OUD = 1) that were significantly associated with cis-regulated brain protein abundance. Of these, 7 showed evidence for causality (Smk: NT5C2, GMPPB, NQO1, RHOT2, SRR and ACTR1B; and AUD: CTNND1). Cis-regulated transcript levels for 8 genes (Smk = 6, CUD = 1, OUD = 1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Functional studies of the high-confidence risk proteins identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Background

A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways.

Methods

The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification.

Results

We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible.

Conclusions

To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

Differentially Expressed Cell Cycle Genes and STAT1/3-Driven Multiple Cancer Entanglement in Psoriasis, Coupled with Other Comorbidities

Psoriasis is a persistent T-cell-supported inflammatory cutaneous disorder, which is defined by a significant expansion of basal cells in the epidermis. Cell cycle and STAT genes that control cell cycle progression and viral infection have been revealed to be comorbid with the development of certain cancers and other disorders, due to their abnormal or scanty expression. The purpose of this study is to evaluate the expression of certain cell cycle and STAT1/3 genes in psoriasis patients and to determine the types of comorbidities associated with these genes. To do so, we opted to adopt the in silico methodology, since it is a quick and easy way to discover any potential comorbidity risks that may exist in psoriasis patients. With the genes collected from early research groups, protein networks were created in this work using the NetworkAnalyst program. The crucial hub genes were identified by setting the degree parameter, and they were then used in gene ontology and pathway assessments. The transcription factors that control the hub genes were detected by exploring TRRUST, and DGIdb was probed for remedies that target transcription factors and hubs. Using the degree filter, the first protein subnetwork produced seven hub genes, including STAT3, CCNB1, STAT1, CCND1, CDC20, HSPA4, and MAD2L1. The hub genes were shown to be implicated in cell cycle pathways by the gene ontology and Reactome annotations. The former four hubs were found in signaling pathways, including prolactin, FoxO, JAK/STAT, and p53, according to the KEGG annotation. Furthermore, they enhanced several malignancies, including pancreatic cancer, Kaposi's sarcoma, non-small cell lung cancer, and acute myeloid leukemia. Viral infections, including measles, hepatitis C, Epstein-Barr virus, and HTLV-1 and viral carcinogenesis were among the other susceptible diseases. Diabetes and inflammatory bowel disease were conjointly annotated. In total, 129 medicines were discovered in DGIdb to be effective against the transcription factors BRCA1, RELA, TP53, and MYC, as opposed to 10 medications against the hubs, STAT3 and CCND1, in tandem with 8 common medicines. The study suggests that the annotated medications should be tested in suitable psoriatic cell lines and animal models to optimize the drugs used based on the kind, severity, and related comorbidities of psoriasis. Furthermore, a personalized medicine protocol must be designed for each psoriasis patient that displays different comorbidities.

Oral Lichen Planus and Oral Squamous Cell Carcinoma share key oncogenic signatures

To investigate similarities in the gene profile of Oral Lichen Planus and Oral Squamous Cell Carcinoma that may justify a carcinogenic potential, we analyzed the gene expression signatures of Oral Lichen Planus and Oral Squamous Cell Carcinoma in early and advanced stages. Based on gene expression data from public databases, we used a bioinformatics approach to compare expression profiles, estimate immune infiltrate composition, identify differentially and co-expressed genes, and propose putative therapeutic targets and associated drugs. Our results revealed gene expression patterns related to processes of keratinization, keratinocyte differentiation, cell proliferation and immune response in common between Oral Lichen Planus and early and advanced Oral Squamous Cell Carcinoma, with the cornified envelope formation and antigen processing cross-presentation pathways in common between Oral Lichen Planus and early Oral Squamous Cell Carcinoma. Together, these results reveal that key tumor suppressors and oncogenes such as PI3, SPRR1B and KRT17, as well as genes associated with different immune processes such as CXCL13, HIF1A and IL1B are dysregulated in OLP.

Targeting Class I Histone Deacetylases in Human Uterine Leiomyosarcoma

Uterine leiomyosarcoma (uLMS) is the most frequent subtype of uterine sarcoma that presents a poor prognosis, high rates of recurrence, and metastasis. Currently, the molecular mechanism of the origin and development of uLMS is unknown. Class I histone deacetylases (including HDAC1, 2, 3, and 8) are one of the major classes of the HDAC family and catalyze the removal of acetyl groups from lysine residues in histones and cellular proteins. Class I HDACs exhibit distinct cellular and subcellular expression patterns and are involved in many biological processes and diseases through diverse signaling pathways. However, the link between class I HDACs and uLMS is still being determined. In this study, we assessed the expression panel of Class I HDACs in uLMS and characterized the role and mechanism of class I HDACs in the pathogenesis of uLMS. Immunohistochemistry analysis revealed that HDAC1, 2, and 3 are aberrantly upregulated in uLMS tissues compared to adjacent myometrium. Immunoblot analysis demonstrated that the expression levels of HDAC 1, 2, and 3 exhibited a graded increase from normal and benign to malignant uterine tumor cells. Furthermore, inhibition of HDACs with Class I HDACs inhibitor (Tucidinostat) decreased the uLMS proliferation in a dose-dependent manner. Notably, gene set enrichment analysis of differentially expressed genes (DEGs) revealed that inhibition of HDACs with Tucidinostat altered several critical pathways. Moreover, multiple epigenetic analyses suggested that Tucidinostat may alter the transcriptome via reprogramming the oncogenic epigenome and inducing the changes in microRNA-target interaction in uLMS cells. In the parallel study, we also determined the effect of DL-sulforaphane on the uLMS. Our study demonstrated the relevance of class I HDACs proteins in the pathogenesis of malignant uLMS. Further understanding the role and mechanism of HDACs in uLMS may provide a promising and novel strategy for treating patients with this aggressive uterine cancer.

HCDT: an integrated highly confident drug-target resource

Drug-target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt.

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10-308) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10-5) and quantitative traits (p value = 1.6 × 10-7). We accompany our method with a web application ( http://drugnomeai.public.cgr.astrazeneca.com ) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

An ErbB Lineage Co-Regulon Harbors Potentially Co-Druggable Targets for Multimodal Precision Therapy in Head and Neck Squamous Cell Carcinoma

The ErbB lineage of oncogenic receptor tyrosine kinases is frequently overexpressed in head and neck squamous cell carcinomas. A common co-regulon triggered by the ErbB proteins; involving shared signaling circuitries; may harbor co-druggable targets or response biomarkers for potential future multimodal precision therapy in ErbB-driven head and neck squamous cell carcinoma. We here present a cohort-based; genome-wide analysis of 488 head and neck squamous cell carcinomas curated as part of The Cancer Genome Atlas Project to characterize genes that are significantly positively co-regulated with the four ErbB proteins and those that are shared among all ErbBs denoting a common ErbB co-regulon. Significant positive gene correlations involved hundreds of genes that were co-expressed with the four ErbB family members (q < 0.05). A common; overlapping co-regulon consisted of a core set of 268 genes that were uniformly co-regulated with all four ErbB genes and highly enriched for functions in chromatin organization and histone modifications. This high-priority set of genes contained ten putative antineoplastic drug-gene interactions. The nature and directionality of these ten drug-gene associations was an inhibiting interaction for seven (PIK3CB; PIK3C2B; HDAC4; FRK; PRKCE; EPHA4; and DYRK1A) of them in which the drug decreases the biological activity or expression of the gene target. For three (CHD4; ARID1A; and PBRM1) of the associations; the directionality of the interaction was such that the gene predicted sensitivit y to the drug suggesting utility as potential response biomarkers. Drug-gene interactions that predicted the gene product to be reduced by the drug included a variety of potential targeted molecular agent classes. This unbiased genome-wide analysis identified a target-rich environment for multimodal therapeutic approaches in tumors that are putatively ErbB-driven. The results of this study require preclinical validation before ultimately devising lines of combinatorial treatment strategies for ErbB-dependent head and neck squamous cell carcinomas that incorporate these findings.

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

Establishment and Characterization of Advanced Penile Cancer Patient-derived Tumor Xenografts: Paving the Way for Personalized Treatments

BACKGROUND

Systemic treatments for penile squamous cell carcinoma (pSCC) are toxic and inefficient. Patient-based preclinical models are essential to study novel treatments.

OBJECTIVE

To establish a library of patient-derived tumor xenograft (PDX) models of human papillomavirus-positive (HPV⁺) and -negative (HPV^-) pSCC and characterize these at the genomic and histological levels.

DESIGN, SETTING, AND PARTICIPANTS

Eighteen tumor samples from 14 patients with recurrent or metastatic pSCC were implanted in nude mice. A biobank of PDX tumors was established after passaging of patient samples (F0) for three generations (F1, F2, F3) and was characterized using histopathology and targeted next-generation sequencing (tNGS). Single-nucleotide polymorphism fingerprinting was used to confirm PDX genealogy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The engraftment rate, overall growth rate, and pSCC histomorphology were checked for each PDX generation. Staining for p40 (a pSCC marker) and p16 (a surrogate for HPV infection) was performed for F0 samples. The mutational profile according to a validated panel of 96 cancer genes was determined for F0 and F3 samples and compared to a larger tNGS database.

RESULTS AND LIMITATIONS

Including a previously established pilot model, 11 out of 18 tumor samples (61%) successfully engrafted in F1. The mean time from implantation in F1 to completion of F3 was 36 wk (standard deviation 18). Histological fidelity was demonstrated across generations. The patient mutational profiles were preserved in F3 and were representative of 277 pSCC samples in the Foundation Medicine database. The rapid progression of pSCC in patients from our selected high-risk cohort impeded the use of PDXs as avatars.

CONCLUSIONS

We successfully established the first library of 11 PDX models of HPV^- and HPV⁺ pSCC. Our PDX models showed high engraftment rates and histological and genomic fidelity to the tumor tissue of origin. These models may help in paving the way towards the development of novel treatments.

PATIENT SUMMARY

We established 11 animal models based on tumor tissue from patients with penile cancer. These models could play a vital role in selection of novel treatments according to genetic mutations. In the future, therapies with confirmed preclinical effects may have a profound impact on the development of personalized treatments in penile cancer.

Genome-Wide Integration of Genetic and Genomic Studies of Atopic Dermatitis: Insights into Genetic Architecture and Pathogenesis

Atopic dermatitis (AD) is a common heterogeneous, chronic, itching, and inflammatory skin disease. Genetic studies have identified multiple AD susceptibility genes. However, the genetic architecture of AD has not been elucidated. In this study, we conducted a large-scale meta-analysis of AD (35,647 cases and 1,013,885 controls) to characterize the genetic basis of AD. The heritability of AD in different datasets varied from 0.6 to 7.1%. We identified 31 previously unreported genes by integrating multiomics data. Among the 31 genes, MCL1 was identified as a potential treatment target for AD by mediating gene‒drug interactions. Tissue enrichment analyses and phenome-wide association study provided strong support for the role of the hemic and immune systems in AD. Across 1,207 complex traits and diseases, genetic correlations indicated that AD shared links with multiple respiratory phenotypes. The phenome-wide Mendelian randomization analysis (Mendelian randomization‒phenome-wide association study) revealed that the age of onset of diabetes exhibited a positive causal effect on AD (inverse-variance weighted β = 0.39, SEM = 0.09, P = 2.77 × 10^-5). Overall, these results provide important insights into the genetic architecture of AD and will lead to a more thorough and complete understanding of the molecular mechanisms underlying AD.

ASCancer Atlas: a comprehensive knowledgebase of alternative splicing in human cancers

Alternative splicing (AS) is a fundamental process that governs almost all aspects of cellular functions, and dysregulation in this process has been implicated in tumor initiation, progression and treatment resistance. With accumulating studies of carcinogenic mis-splicing in cancers, there is an urgent demand to integrate cancer-associated splicing changes to better understand their internal cross-talks and functional consequences from a global view. However, a resource of key functional AS events in human cancers is still lacking. To fill the gap, we developed ASCancer Atlas (https://ngdc.cncb.ac.cn/ascancer), a comprehensive knowledgebase of aberrant splicing in human cancers. Compared to extant databases, ASCancer Atlas features a high-confidence collection of 2006 cancer-associated splicing events experimentally proved to promote tumorigenesis, a systematic splicing regulatory network, and a suit of multi-scale online analysis tools. For each event, we manually curated the functional axis including upstream splicing regulators, splicing event annotations, downstream oncogenic effects, and possible therapeutic strategies. ASCancer Atlas also houses about 2 million computationally putative splicing events. Additionally, a user-friendly web interface was built to enable users to easily browse, search, visualize, analyze, and download all splicing events. Overall, ASCancer Atlas provides a unique resource to study the functional roles of splicing dysregulation in human cancers.

Characterization of Ferroptosis-Related Molecular Subtypes with Immune Infiltrations in Neuropathic Pain

Background

Neuropathic pain (NP) caused by a lesion or disease of the somatosensory nervous system is a common chronic pain condition that has a major impact on quality of life. However, NP pathogenesis remains unclear. The purpose of this study was to identify differentially expressed genes (DEGs) and specific and meaningful gene targets for the diagnosis and treatment of NP.

Methods

Data from rat spinal nerve ligations and the sham group were downloaded from the Gene Expression Omnibus (GEO) database. Based on the single-sample gene set enrichment analysis (ssGSEA) method, 29 immune gene sets were identified in each sample, and these samples were correlated with the immune infiltration phenotype. LASSO regression modeling was used to screen key genes to identify diagnostic gene markers. According to GSEA and GSVA, NP is concentrated in a large number of immune-related pathways and genes. Additionally, we used the DGIdb database and correlation test to construct gene-drug and transcription factor interaction networks for differentially expressed genes relevant to NP-related ferroptosis. We used WGCNA to identify gene co-expression modules of NP, and explored the relationship between gene networks and phenotypes. Finally, we crossed core genes with diagnostic markers and analyzed gene correlation with molecular subtypes and immune cells.

Results

We identified 224 DEGs, including 191 upregulated genes and 33 downregulated genes. APC co-stimulation, CCR, cytolytic activity, humid-promoting, neutrophils, NK cells, and RGS4, CXCL2, DRD4 and other 7 genes related to ferroptosis were involved in NP development. Key genes of RGS4 and HIF-1 signaling pathway were screened.

Conclusion

This study contributes to our understanding of the neuroimmune mechanism of neuropathic pain, provides a reference for NP biomarkers and drug targets. Ferroptosis may be the next research direction to explore NP mechanism.

Identifying Hub Genes Associated with Neoadjuvant Chemotherapy Resistance in Breast Cancer and Potential Drug Repurposing for the Development of Precision Medicine

Breast cancer is the second leading cause of morbidity and mortality in women worldwide. Despite advancements in the clinical application of neoadjuvant chemotherapy (NAC), drug resistance remains a major concern hindering treatment efficacy. Thus, identifying the key genes involved in driving NAC resistance and targeting them with known potential FDA-approved drugs could be applied to advance the precision medicine strategy. With this aim, we performed an integrative bioinformatics study to identify the key genes associated with NAC resistance in breast cancer and then performed the drug repurposing to identify the potential drugs which could use in combination with NAC to overcome drug resistance. In this study, we used publicly available RNA-seq datasets from the samples of breast cancer patients sensitive and resistant to chemotherapy and identified a total of 1446 differentially expressed genes in NAC-resistant breast cancer patients. Next, we performed gene co-expression network analysis to identify significantly co-expressed gene modules, followed by MCC (Multiple Correlation Clustering) clustering algorithms and identified 33 key hub genes associated with NAC resistance. mRNA–miRNA network analysis highlighted the potential impact of these hub genes in altering the regulatory network in NAC-resistance breast cancer cells. Further, several hub genes were found to be significantly involved in the poor overall survival of breast cancer patients. Finally, we identified FDA-approved drugs which could be useful for potential drug repurposing against those hub genes. Altogether, our findings provide new insight into the molecular mechanisms of NAC resistance and pave the way for drug repurposing techniques and personalized treatment to overcome NAC resistance in breast cancer.

PharmaKoVariome database for supporting genetic testing

Pharmacogenomics (PGx) provides information about routine precision medicine, based on the patient's genotype. However, many of the available information about human allele frequencies, and about clinical drug-gene interactions, is based on American and European populations. PharmaKoVariome database was constructed to support genetic testing for safe prescription and drug development. It consolidated and stored 2507 diseases, 11 459 drugs and 61 627 drug-target or druggable genes from public databases. PharmaKoVariome precomputed ethnic-specific abundant variants for approximately 120 M single-nucleotide variants of drug-target or druggable genes. A user can search by gene symbol, drug name, disease and reference SNP ID number (rsID) to statistically analyse the frequency of ethnical variations, such as odds ratio and P-values for related genes. In an example study, we observed five Korean-enriched variants in the CYP2B6 and CYP2D6 genes, one of which (rs1065852) is known to be incapable of metabolizing drug. It is also shown that 4-6% of North and East Asians have risk factors for drugs metabolized by the CYP2D6 gene. Therefore, PharmaKoVariome is a useful database for pharmaceutical or diagnostic companies for developing diagnostic technologies that can be applied in the Asian PGx industry. Database URL: http://www.pharmakovariome.com/.

Effect of Curcumin on Attenuation of Liver Cirrhosis via Genes/Proteins and Pathways: A System Pharmacology Study

Background: Liver cirrhosis is a life-threatening seqsuel of many chronic liver disorders of varying etiologies. In this study, we investigated protein targets of curcumin in liver cirrhosis based on a bioinformatics approach. Methods: Gene/protein associations with curcumin and liver cirrhosis were probed in drug−gene and gene−diseases databases including STITCH/DGIdb/DisGeNET/OMIM/DISEASES/CTD/Pharos and SwissTargetPrediction. Critical clustering groups (MCODE), hub candidates and critical hub genes in liver cirrhosis were identified, and connections between curcumin and liver cirrhosis-related genes were analyzed via Venn diagram. Interaction of hub genes with curcumin by molecular docking using PyRx-virtual screening tools was performed. Results: MCODE analysis indicated three MCODEs; the cluster (MCODE 1) comprised 79 nodes and 881 edges (score: 22.59). Curcumin database interactions recognized 318 protein targets. Liver cirrhosis genes and curcumin protein targets analysis demonstrated 96 shared proteins, suggesting that curcumin may influence 20 candidate and 13 hub genes, covering 81% of liver cirrhosis critical genes and proteins. Thirteen shared proteins affected oxidative stress regulation, RNA, telomerase activity, cell proliferation, and cell death. Molecular docking analysis showed the affinity of curcumin binding hub genes (Binding affinity: ΔG < −4.9 kcal/mol). Conclusions: Curcumin impacted on several critical liver cirrhosis genes mainly involved in extracellular matrix communication, focal adhesion, and the response to oxidative stress.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

The Pharmacological Mechanism of Curcumin against Drug Resistance in Non-Small Cell Lung Cancer: Findings of Network Pharmacology and Bioinformatics Analysis

The pharmacological mechanism of curcumin against drug resistance in non-small cell lung cancer (NSCLC) remains unclear. This study aims to summarize the genes and pathways associated with curcumin action as an adjuvant therapy in NSCLC using network pharmacology, drug-likeness, pharmacokinetics, functional enrichment, protein-protein interaction (PPI) analysis, and molecular docking. Prognostic genes were identified from the curcumin-NSCLC intersection gene set for the following drug sensitivity analysis. Immunotherapy, chemotherapy, and targeted therapy sensitivity analyses were performed using external cohorts (GSE126044 and IMvigor210) and the CellMiner database. 94 curcumin-lung adenocarcinoma (LUAD) hub targets and 41 curcumin-lung squamous cell carcinoma (LUSC) hub targets were identified as prognostic genes. The anticancer effect of curcumin was observed in KEGG pathways involved with lung cancer, cancer therapy, and other cancers. Among the prognostic curcumin-NSCLC intersection genes, 20 LUAD and 8 LUSC genes were correlated with immunotherapy sensitivity in the GSE126044 NSCLC cohort; 30 LUAD and 13 LUSC genes were associated with immunotherapy sensitivity in the IMvigor210 cohort; and 12 LUAD and 13 LUSC genes were related to chemosensitivity in the CellMiner database. Moreover, 3 LUAD and 5 LUSC genes were involved in the response to targeted therapy in the CellMiner database. Curcumin regulates drug sensitivity in NSCLC by interacting with cell cycle, NF-kappa B, MAPK, Th17 cell differentiation signaling pathways, etc. Curcumin in combination with immunotherapy, chemotherapy, or targeted drugs has the potential to be effective for drug-resistant NSCLC. The findings of our study reveal the relevant key signaling pathways and targets of curcumin as an adjuvant therapy in the treatment of NSCLC, thus providing pharmacological evidence for further experimental research.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

Bioinformatics Identification of Ferroptosis-Associated Biomarkers and Therapeutic Compounds in Psoriasis

Purpose. Psoriasis is closely linked to ferroptosis. This study aimed to identify potential ferroptosis-associated genes in psoriasis using bioinformatics. Methods. Data from the GSE30999 dataset was downloaded from the Gene Expression Omnibus (GEO), and the ferroptosis-associated genes were retrieved from FerrDb. The differentially expressed ferroptosis-associated genes were identified using Venn diagrams. Subsequently, a network of protein-protein interactions (PPIs) between psoriasis targets and ferroptosis-associated genes was constructed based on the STRING database and analyzed by Cytoscape software. The Metascape portal conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Moreover, the expression of ferroptosis-related genes was verified in the GSE13355 dataset. Finally, the verified genes were used to predict the therapeutic drugs for psoriasis using the DGIdb/CMap database. SwissDock was used to examine ligand docking, and UCSF Chimera displayed the results visually. Results. Among 85 pairs of psoriasis lesion (LS) and no-lesion (NL) samples from patients, 19 ferroptosis-associated genes were found to be differentially expressed (3 upregulated genes and 16 downregulated genes). Based on the PPI results, these ferroptosis-associated genes interact with each other. The GO and KEGG enrichment analysis of differentially expressed ferroptosis-related genes indicated several enriched terms related to the oxidative stress response. The GSE13355 dataset verified the results of the bioinformatics analysis obtained from the GSE30999 dataset regarding SLC7A5, SLC7A11, and CHAC1. Psoriasis-related compounds corresponding to SLC7A5 and SLC7A11 were also identified, including Melphalan, Quisqualate, Riluzole, and Sulfasalazine. Conclusion. We identified 3 differentially expressed ferroptosis-related genes through bioinformatics analysis. SLC7A5, SLC7A11, and CHAC1 may affect the development of psoriasis by regulating ferroptosis. These results open new avenues in understanding the treatment of psoriasis.

Identification of core genes and pathways between geriatric multimorbidity and renal insufficiency: potential therapeutic agents discovered using bioinformatics analysis

Background

Geriatric people are prone to suffer from multiple chronic diseases, which can directly or indirectly affect renal function. Through bioinformatics analysis, this study aimed to identify key genes and pathways associated with renal insufficiency in patients with geriatric multimorbidity and explore potential drugs against renal insufficiency.

Methods

The text mining tool Pubmed2Ensembl was used to detect genes associated with the keywords including "Geriatric", "Multimorbidity" and "Renal insufficiency". The GeneCodis program was used to specify Gene Ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape. Module analysis was performed using CytoHubba and Molecular Complex Detection (MCODE) plugins. GO and KEGG analysis of gene modules was performed using the Database for Annotation, Visualization and Integrated Discover (DAVID) platform database. Genes clustered in salient modules were selected as core genes. Then, the functions and pathways of core genes were visualized using ClueGO and CluePedia. Finally, the drug-gene interaction database was used to explore drug-gene interactions of the core genes to identify drug candidates for renal insufficiency in patients with geriatric multimorbidity.

Results

Through text mining, 351 genes associated with "Geriatric", "Multimorbidity" and "Renal insufficiency" were identified. A PPI network consisting of 216 nodes and 1087 edges was constructed and CytoHubba was used to sequence the genes. Five gene modules were obtained by MCODE analysis. The 26 genes clustered in module1 were selected as core candidate genes primarily associated with renal insufficiency in patients with geriatric multimorbidity. The HIF-1, PI3K-Akt, MAPK, Rap1, and FoxO signaling pathways were enriched. We found that 21 of the 26 selected genes could be targeted by 34 existing drugs.

Conclusion

This study indicated that CST3, SERPINA1, FN1, PF4, IGF1, KNG1, IL6, VEGFA, ALB, TIMP1, TGFB1, HGF, SERPINE1, APOA1, APOB, FGF23, EGF, APOE, VWF, TF, CP, GAS6, APP, IGFBP3, P4HB, and SPP1 were key genes potentially involved with renal insufficiency in patients with geriatric multimorbidity. In addition, 34 drugs were identified as potential agents for the treatment and management of renal insufficiency.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them

Background

Vascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown.

Materials and methods

We analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis.

Results

We identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment.

Conclusion

Collectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Genome-wide transcriptional profiling of pulmonary functional sequelae in ARDS- secondary to SARS-CoV-2 infection

BACKGROUND

Up to 80% of patients surviving acute respiratory distress syndrome (ARDS) secondary to SARS-CoV-2 infection present persistent anomalies in pulmonary function after hospital discharge. There is a limited understanding of the mechanistic pathways linked to post-acute pulmonary sequelae.

AIM

To identify the molecular underpinnings associated with severe lung diffusion involvement in survivors of SARS-CoV-2-induced ARDS.

METHODS

Survivors attended to a complete pulmonary evaluation 3 months after hospital discharge. RNA sequencing (RNA-seq) was performed using Illumina technology in whole-blood samples from 50 patients with moderate to severe diffusion impairment (D_LCO<60%) and age- and sex-matched individuals with mild-normal lung function (D_LCO≥60%). A transcriptomic signature for optimal classification was constructed using random forest. Transcriptomic data were analyzed for biological pathway enrichment, cellular deconvolution, cell/tissue-specific gene expression and candidate drugs.

RESULTS

RNA-seq identified 1357 differentially expressed transcripts. A model composed of 14 mRNAs allowed the optimal discrimination of survivors with severe diffusion impairment (AUC=0.979). Hallmarks of lung sequelae involved cell death signaling, cytoskeleton reorganization, cell growth and differentiation and the immune response. Resting natural killer (NK) cells were the most important immune cell subtype for the prediction of severe diffusion impairment. Components of the signature correlated with neutrophil, lymphocyte and monocyte counts. A variable expression profile of the transcripts was observed in lung cell subtypes and bodily tissues. One upregulated gene, TUBB4A, constitutes a target for FDA-approved drugs.

CONCLUSIONS

This work defines the transcriptional programme associated with post-acute pulmonary sequelae and provides novel insights for targeted interventions and biomarker development.

Identification of cross-talk pathways and ferroptosis-related genes in periodontitis and type 2 diabetes mellitus by bioinformatics analysis and experimental validation

Purpose

There is a bidirectional relationship between periodontitis and type 2 diabetes mellitus (T2DM). The aim of this study was to further explore the pathogenesis of this comorbidity, screen out ferroptosis-related genes involved in the pathological process, and predict potential drug targets to develop new therapeutic strategies.

Methods

Common cross-talk genes were identified from periodontitis datasets (GSE16134, GSE10334 and GSE106090) and T2DM databases (DisGeNET and GeneCard). Then, GO and KEGG enrichment analyses, PPI network analysis and hub gene identification were performed. The association between ferroptosis and periodontitis with T2DM was investigated by Pearson correlation analysis. Core ferroptosis-related cross-talk genes were identified and verified by qRT-PCR. Potential drugs targeting these core genes were predicted via DGIDB.

Results

In total, 67 cross-talk genes and two main signalling pathways (immuno-inflammatory pathway and AGE-RAGE signalling pathway) were identified. Pearson correlation analysis indicated that ferroptosis served as a crucial target in the pathological mechanism and treatment of periodontitis with T2DM. IL-1β, IL-6, NFE2L2 and ALOX5 were identified as core ferroptosis-related genes and the qRT-PCR detection results were statistically different. In total, 13 potential drugs were screened out, among which, Echinacea and Ibudilast should be developed first.

Conclusions

This study contributes to a deeper understanding of the common pathogenesis of periodontitis and T2DM and provides new insights into the role of ferroptosis in this comorbidity. In addition, two drugs with potential clinical application value were identified. The potential utility of these drugs requires further experimental investigation.

Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene

(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients’ health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets—FCGR2B, IGF1R, and KIT—substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.

Identification of hub genes associated with acute kidney injury induced by renal ischemia-reperfusion injury in mice

Background: Acute kidney injury (AKI) is a severe clinical syndrome, and ischemia-reperfusion injury is an important cause of acute kidney injury. The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia-reperfusion injury (IRI-AKI). Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)-hub gene and miRNA-hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb. Result: A total of 323 common differentially expressed genes were identified in the renal ischemia-reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein-Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes. Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia-reperfusion injury-acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia-reperfusion injury-acute kidney injury.

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

BACKGROUND

Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API).

RESULTS

To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink.

CONCLUSION

RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Hyperactivation of mTORC1 in a double hit mutant zebrafish model of tuberous sclerosis complex causes increased seizure susceptibility and neurodevelopmental abnormalities

Tuberous sclerosis complex (TSC) is a multisystem genetic disorder caused by pathogenic variants in TSC1 and TSC2 genes. TSC patients present with seizures and brain abnormalities such as tubers and subependymal giant cells astrocytoma (SEGA). Despite common molecular and clinical features, the severity of the disease varies greatly, even intrafamilially. The second hit hypothesis suggests that an additional, inactivating mutation in the remaining functional allele causes a more severe phenotype and therefore explains the phenotypic variability. Recently, second hit mutations have been detected frequently in mTORopathies. To investigate the pathophysiological effects of second hit mutations, several mouse models have been developed. Here, we opted for a double mutant zebrafish model that carries a LOF mutation both in the tsc2 and the depdc5 gene. To the best of our knowledge, this is the first time a second-hit model has been studied in zebrafish. Significantly, the DEP domain-containing protein 5 (DEPDC5) gene has an important role in the regulation of mTORC1, and the combination of a germline TSC2 and somatic DEPDC5 mutation has been described in a TSC patient with intractable epilepsy. Our depdc5^−/−x tsc2^−/− double mutant zebrafish line displayed greatly increased levels of mammalian target of rapamycin (mTORC1) activity, augmented seizure susceptibility, and early lethality which could be rescued by rapamycin. Histological analysis of the brain revealed ventricular dilatation in the tsc2 and double homozygotes. RNA-sequencing showed a linear relation between the number of differentially expressed genes (DEGs) and the degree of mTORC1 hyperactivity. Enrichment analysis of their transcriptomes revealed that many genes associated with neurological developmental processes were downregulated and mitochondrial genes were upregulated. In particular, the transcriptome of human SEGA lesions overlapped strongly with the double homozygous zebrafish larvae. The data highlight the clinical relevance of the depdc5^−/− x tsc2^−/− double mutant zebrafish larvae that showed a more severe phenotype compared to the single mutants. Finally, analysis of gene-drug interactions identified interesting pharmacological targets for SEGA, underscoring the value of our small zebrafish vertebrate model for future drug discovery efforts.

Effects of Coronary Artery Disease-Associated Variants on Vascular Smooth Muscle Cells

BACKGROUND

Genome-wide association studies have identified many genetic loci that are robustly associated with coronary artery disease (CAD). However, the underlying biological mechanisms are still unknown for most of these loci, hindering the progress to medical translation. Evidence suggests that the genetic influence on CAD susceptibility may act partly through vascular smooth muscle cells (VSMCs).

METHODS

We undertook genotyping, RNA sequencing, and cell behavior assays on a large bank of VSMCs (n>1499). Expression quantitative trait locus and splicing quantitative trait locus analyses were performed to identify genes with an expression that was influenced by CAD-associated variants. To identify candidate causal genes for CAD, we ascertained colocalizations of VSMC expression quantitative trait locus signals with CAD association signals by performing causal variants identification in associated regions analysis and the summary data-based mendelian randomization test. Druggability analysis was then performed on the candidate causal genes. CAD risk variants were tested for associations with VSMC proliferation, migration, and apoptosis. Collective effects of multiple CAD-associated variants on VSMC behavior were estimated by polygenic scores.

RESULTS

Approximately 60% of the known CAD-associated variants showed statistically significant expression quantitative trait locus or splicing quantitative trait locus effects in VSMCs. Colocalization analyses identified 84 genes with expression quantitative trait locus signals that significantly colocalized with CAD association signals, identifying them as candidate causal genes. Druggability analysis indicated that 38 of the candidate causal genes were druggable, and 13 had evidence of drug-gene interactions. Of the CAD-associated variants tested, 139 showed suggestive associations with VSMC proliferation, migration, or apoptosis. A polygenic score model explained up to 5.94% of variation in several VSMC behavior parameters, consistent with polygenic influences on VSMC behavior.

CONCLUSIONS

This comprehensive analysis shows that a large percentage of CAD loci can modulate gene expression in VSMCs and influence VSMC behavior. Several candidate causal genes identified are likely to be druggable and thus represent potential therapeutic targets.

Drug repurposing candidates to treat core symptoms in autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by high heritability and clinical heterogeneity. The main core symptoms are social communication deficits. There are no medications approved for the treatment of these symptoms, and medications used to treat non-specific symptoms have serious side effects. To identify potential drugs for repurposing to effectively treat ASD core symptoms, we studied ASD risk genes within networks of protein-protein interactions of gene products. We first defined an ASD network from network-based analyses, and identified approved drugs known to interact with proteins within this network. Thereafter, we evaluated if these drugs can change ASD-associated gene expression perturbations in genes in the ASD network. This was done by analyses of drug-induced versus ASD-associated gene expression, where opposite gene expression perturbations in drug versus ASD indicate that the drug could counteract ASD-associated perturbations. Four drugs showing significant (p < 0.05) opposite gene expression perturbations in drug versus ASD were identified: Loperamide, bromocriptine, drospirenone, and progesterone. These drugs act on ASD-related biological systems, indicating that these drugs could effectively treat ASD core symptoms. Based on our bioinformatics analyses of ASD genetics, we shortlist potential drug repurposing candidates that warrant clinical translation to treat core symptoms in ASD.

Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms

OBJECTIVES

This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA).

METHODS

GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics.

RESULTS

We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs.

CONCLUSION

Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.

Role of statins in regulating molecular pathways following traumatic brain injury: A system pharmacology study

Traumatic brain injury (TBI) is a serious disorder with debilitating physical and psychological complications. Previous studies have indicated that genetic factors have a critical role in modulating the secondary phase of injury in TBI. Statins have interesting pleiotropic properties such as antiapoptotic, antioxidative, and anti-inflammatory effects, which make them a suitable class of drugs for repurposing in TBI. In this study, we aimed to explore how statins modulate proteins and pathways involved in TBI using system pharmacology. We first explored the target associations with statins in two databases to discover critical clustering groups, candidate hub and critical hub genes in the network of TBI, and the possible connections of statins with TBI-related genes. Our results showed 1763 genes associated with TBI. Subsequently, the analysis of centralities in the PPI network displayed 55 candidate hub genes and 15 hub genes. Besides, MCODE analysis based on threshold score:10 determined four modular clusters. Intersection analysis of genes related to TBI and statins demonstrated 204 shared proteins, which suggested that statins influence 31 candidate hub and 9 hub genes. Moreover, statins had the highest interaction with MCODE1. The biological processes of the 31 shared proteins are related to gene expression, inflammation, antioxidant activity, and cell proliferation. Biological enriched pathways showed Programmed Cell Death proteins, AGE-RAGE signaling pathway, C-type lectin receptor signalling pathway, and MAPK signaling pathway as top clusters. In conclusion, statins could target several critical post-TBI genes mainly involved in inflammation and apoptosis, supporting the previous research results as a potential therapeutic agent.

The use of genomic variants to drive drug repurposing for chronic hepatitis B

Background

One of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB.

Method

The genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)).

Results

A total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores.

Conclusion

Through the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

Allelic expression imbalance in articular cartilage and subchondral bone refined genome-wide association signals in osteoarthritis

OBJECTIVES

To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses using RNA sequencing of articular cartilage and subchondral bone from OA patients.

METHODS

RNA sequencing from 65 articular cartilage and 24 subchondral bone from OA patients was used for AEI analysis. AEI was determined for all genes present in the 100 regions reported by the GWAS catalog that were also expressed in cartilage or bone. The count fraction of the alternative allele (φ) was calculated for each heterozygous individual with the risk-SNP or with the SNP in linkage disequilibrium (LD) with it (r2 > 0.6). Furthermore, a meta-analysis was performed to generate a meta-φ (null hypothesis median φ = 0.49) and P-value for each SNP.

RESULTS

We identified 30 transcript SNPs (28 in cartilage and 2 in subchondral bone) subject to AEI in 29 genes. Notably, 10 transcript SNPs were located in genes not previously reported in the GWAS catalog, including two long intergenic non-coding RNAs (lincRNAs), MALAT1 (meta-φ = 0.54, FDR = 1.7x10-4) and ILF3-DT (meta-φ = 0.6, FDR = 1.75x10-5). Moreover, 12 drugs were interacting with 7 genes displaying AEI, of which 7 drugs have been already approved.

CONCLUSIONS

By prioritizing proxy transcript SNPs that mark AEI in cartilage and/or subchondral bone at loci harboring GWAS signals, we present an unbiased approach to identify the most likely functional OA risk-SNP and gene. We identified 10 new potential OA risk genes ready for further, translation towards underlying biological mechanisms.

Glioblastoma CD105+ cells define a SOX2- cancer stem cell-like subpopulation in the pre-invasive niche

Glioblastoma (GBM) is the most common and most aggressive primary brain tumor in adults. Glioma stem like cells (GSC) represent the highest cellular hierarchy in GBM and have a determining role in tumor growth, recurrence and patient prognosis. However, a better definition of GSC subpopulations, especially at the surgical resection margin, is warranted for improved oncological treatment options. The present study interrogated cells expressing CD105 (CD105+) specifically within the tumor front and the pre-invasive niche as a potential GSC subpopulation. GBM primary cell lines were generated from patients (n = 18) and CD105+ cells were isolated and assessed for stem-like characteristics. In vitro, CD105+ cells proliferated and enriched in serum-containing medium but not in serum-free conditions. CD105+ cells were characterized by Nestin+, Vimentin+ and SOX2-, clearly distinguishing them from SOX2+ GCS. GBM CD105+ cells differentiated into osteocytes and adipocytes but not chondrocytes. Exome sequencing revealed that GBM CD105+ cells matched 83% of somatic mutations in the Cancer cell line encyclopedia, indicating a malignant phenotype and in vivo xenotransplantation assays verified their tumorigenic potential. Cytokine assays showed that immunosuppressive and protumorigenic cytokines such as IL6, IL8, CCL2, CXCL-1 were produced by CD105+ cells. Finally, screening for 88 clinical drugs revealed that GBM CD105+ cells are resistant to most chemotherapeutics except Doxorubicin, Idarubicin, Fludarabine and ABT-751. Our study provides a rationale for targeting tumoral CD105+ cells in order to reshape the tumor microenvironment and block GBM progression.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Multiplex protein profiling of bronchial aspirates reveals disease-, mortality- and respiratory sequelae-associated signatures in critically ill patients with ARDS secondary to SARS-CoV-2 infection

Introduction

Bronchial aspirates (BAS) obtained during invasive mechanical ventilation (IMV) constitutes a useful tool for molecular phenotyping and decision making.

Aim

To identify the proteomic determinants associated with disease pathogenesis, all-cause mortality and respiratory sequelae in BAS samples from critically ill patients with SARS-CoV-2-induced ARDS.

Methods

Multicenter study including 74 critically ill patients with COVID-19 and non-COVID-19 ARDS. BAS were obtained by bronchoaspiration after IMV initiation. Three hundred sixty-four proteins were quantified using proximity extension assay (PEA) technology. Random forest models were used to assess predictor importance.

Results

After adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-α were differentially detected in COVID-19 and non-COVID-19 patients. In random forest models for COVID-19, CST5, DPP7, NADK, KYAT1 and TYMP showed the highest variable importance. In COVID-19 patients, reduced levels of ENTPD2 and PTN were observed in nonsurvivors of ICU stay, even after adjustment. AGR2, NQO2, IL-1α, OSM and TRAIL showed the strongest associations with in-ICU mortality and were used to construct a protein-based prediction model. Kaplan-Meier curves revealed a clear separation in mortality risk between subgroups of PTN, ENTPD2 and the prediction model. Cox regression models supported these findings. In survivors, the levels of FCRL1, NTF4 and THOP1 in BAS samples obtained during the ICU stay correlated with lung function (i.e., D_LCO levels) 3 months after hospital discharge. Similarly, Flt3L and THOP1 levels were correlated with radiological features (i.e., TSS). These proteins are expressed in immune and nonimmune lung cells. Poor host response to viral infectivity and an inappropriate reparative mechanism seem to be linked with the pathogenesis of the disease and fatal outcomes, respectively.

Conclusion

BAS proteomics identified novel factors associated with the pathology of SARS-CoV-2-induced ARDS and its adverse outcomes. BAS-based protein testing emerges as a novel tool for risk assessment in the ICU.

Expression Profile of Inflammation Response Genes and Potential Regulatory Mechanisms in Dilated Cardiomyopathy

Background

The inflammatory response is important in dilated cardiomyopathy (DCM). However, the expression of inflammatory response genes (IRGs) and regulatory mechanisms in DCM has not been well characterized.

Methods

We analyzed 27,665 cells of single-cell RNA sequencing dataset of four DCM samples and two healthy controls (HC). IRGs among differentially expressed genes (DEGs) of active cell clusters were screened from the Molecular Signatures Database (MSigDB). The bulk sequencing dataset of 166 DCM patients and 166 HC was analyzed to explore the common IRGs. The biological functions of the IRGs were analyzed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. IRG-related transcription factors (TFs) were determined using the TRRUST database. The protein-protein interaction (PPI) network was constructed using the STRING database. Then, we established the noncoding RNA (ncRNA) regulatory network based on the StarBase database. Finally, the potential drugs that target IRGs were explored using the Drug Gene Interaction Database (DGIdb).

Results

The proportions of dendritic cells (DCs), B cells, NK cells, and T cells were increased in DCM patients, whereas monocytes were decreased. DCs expressed more IRGs in DCM. The GO and KEGG analyses indicated that the functional characteristics of active cells mainly focused on the immune response. Thirty-nine IRGs were commonly expressed among active cell cluster DEGs, bulk RNA DEGs, and inflammatory response-related genes. ETS1 plays an important role in regulation of IRG expression. The competing endogenous RNA regulatory network showed the relationship between ncRNA and IRGs. Sankey diagram showed that arachidonate 5-lipoxygenase (ALOX5) played a major role in regulation between TFs and potential drugs.

Conclusion

DCs infiltrate into the myocardium and contribute to the immune response in DCM. The transcription factor ETS1 plays an important role in regulation of IRGs. Moreover, ALOX5 may be a potential therapeutic target for DCM.

Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development

Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes. Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA. Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals. Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future. Systematic Review Registration: website, identifier registration number.