KEGG for representation and analysis of molecular networks involving diseases and drugs

The Gut Microbiome of an Indigenous Agropastoralist Population in a Remote Area of Colombia with High Rates of Gastrointestinal Infections and Dysbiosis

An Indigenous agropastoralist population called the Wiwa from the Sierra Nevada de Santa Marta, in North-East Colombia, shows high rates of gastrointestinal infections. Chronic gut inflammatory processes and dysbiosis could be a reason, suggesting an influence or predisposing potential of the gut microbiome composition. The latter was analyzed by 16S rRNA gene amplicon next generation sequencing from stool samples. Results of the Wiwa population microbiomes were associated with available epidemiological and morphometric data and compared to control samples from a local urban population. Indeed, locational-, age-, and gender-specific differences in the Firmicutes/Bacteriodetes ratio, core microbiome, and overall genera-level microbiome composition were shown. Alpha- and ß-diversity separated the urban site from the Indigenous locations. Urban microbiomes were dominated by Bacteriodetes, whereas Indigenous samples revealed a four times higher abundance of Proteobacteria. Even differences among the two Indigenous villages were noted. PICRUSt analysis identified several enriched location-specific bacterial pathways. Moreover, on a general comparative scale and with a high predictive accuracy, we found Sutterella associated with the abundance of enterohemorrhagic Escherichia coli (EHEC), Faecalibacteria associated with enteropathogenic Escherichia coli (EPEC) and helminth species Hymenolepsis nana and Enterobius vermicularis. Parabacteroides, Prevotella, and Butyrivibrio are enriched in cases of salmonellosis, EPEC, and helminth infections. Presence of Dialister was associated with gastrointestinal symptoms, whereas Clostridia were exclusively found in children under the age of 5 years. Odoribacter and Parabacteroides were exclusively identified in the microbiomes of the urban population of Valledupar. In summary, dysbiotic alterations in the gut microbiome in the Indigenous population with frequent episodes of self-reported gastrointestinal infections were confirmed with epidemiological and pathogen-specific associations. Our data provide strong hints of microbiome alterations associated with the clinical conditions of the Indigenous population.

Aberrant Methylation of the Imprinted C19MC and MIR371-3 Clusters in Patients with Non-Small Cell Lung Cancer

Epigenetic mechanisms have emerged as an important contributor to tumor development through the modulation of gene expression. Our objective was to identify the methylation profile of the imprinted C19MC and MIR371-3 clusters in patients with non-small cell lung cancer (NSCLC) and to find their potential target genes, as well as to study their prognostic role. DNA methylation status was analyzed in a NSCLC patient cohort (n = 47) and compared with a control cohort including COPD patients and non-COPD subjects (n = 23) using the Illumina Infinium Human Methylation 450 BeadChip. Hypomethylation of miRNAs located on chromosome 19q13.42 was found to be specific for tumor tissue. We then identified the target mRNA-miRNA regulatory network for the components of the C19MC and MIR371-3 clusters using the miRTargetLink 2.0 Human tool. The correlations of miRNA-target mRNA expression from primary lung tumors were analyzed using the CancerMIRNome tool. From those negative correlations identified, we found that a lower expression of 5 of the target genes (FOXF2, KLF13, MICA, TCEAL1 and TGFBR2) was significantly associated with poor overall survival. Taken together, this study demonstrates that the imprinted C19MC and MIR371-3 miRNA clusters undergo polycistronic epigenetic regulation leading to deregulation of important and common target genes with potential prognostic value in lung cancer.

Recognition of Differentially Expressed Molecular Signatures and Pathways Associated with COVID-19 Poor Prognosis in Glioblastoma Patients

Glioblastoma (GBM) is a type of brain cancer that is typically very aggressive and difficult to treat. Glioblastoma cases have been reported to have increased during COVID-19. The mechanisms underlying this comorbidity, including genomic interactions, tumor differentiation, immune responses, and host defense, are not completely explained. Therefore, we intended to investigate the differentially expressed shared genes and therapeutic agents which are significant for these conditions by using in silico approaches. Gene expression datasets of GSE68848, GSE169158, and GSE4290 studies were collected and analyzed to identify the DEGs between the diseased and the control samples. Then, the ontology of the genes and the metabolic pathway enrichment analysis were carried out for the classified samples based on expression values. Protein-protein interactions (PPI) map were performed by STRING and fine-tuned by Cytoscape to screen the enriched gene module. In addition, the connectivity map was used for the prediction of potential drugs. As a result, 154 overexpressed and 234 under-expressed genes were identified as common DEGs. These genes were found to be significantly enriched in the pathways involved in viral diseases, NOD-like receptor signaling pathway, the cGMP-PKG signaling pathway, growth hormone synthesis, secretion, and action, the immune system, interferon signaling, and the neuronal system. STAT1, CXCL10, and SAMDL were screened out as the top 03 out of the top 10 most critical genes among the DEGs from the PPI network. AZD-8055, methotrexate, and ruxolitinib were predicted to be the possible agents for the treatment. The current study identified significant key genes, common metabolic signaling networks, and therapeutic agents to improve our perception of the common mechanisms of GBM-COVID-19.

Recent development of machine learning models for the prediction of drug-drug interactions

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Integration of transcriptomic and metabolomic profiling of encystation in Cryptocaryon irritans regulated by rapamycin

Encystation in Cryptocaryon irritans is a fundamental process for environmental resistance and development. Autophagy participates in the encystation of ciliates, and rapamycin can induce autophagy in the cells. A set of genes and metabolites related to autophagy and encystation are highly elaborative. The existence of these genes and metabolites and their role are well characterized. However, little is known about their role in protozoans such as ciliates. The newly produced C. irritans protomonts were exposed to an optimal concentration of rapamycin (1400 nM), and the survival, encystation, microstructure/ultrastructure, transcriptomic and metabolomic profile in treated and control protomonts were investigated. The results showed that exposure of protomonts to rapamycin at 4 h significantly lowered the survival and encystation rates to 91.62 % and 98.44 % compared to the control group (100 %, p ≤ 0.05). Morphological alterations observed in light microscopy and transmission electron microscopy (TEM) demonstrated that the drug significantly changed cell symmetry by causing the formation of various autophagic vacuoles/vesicles. The transcriptome sequencing of rapamycin-treated protomont revealed that 2249 (1837 up-regulated and 977 down-regulated) differentially expressed genes (DEGs) were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that 226 DEGs were successfully annotated in 21 pathways (p˂0.05), including most enriched pathways apoptosis and phagosome with 25 and 24 DEGs, respectively. Most unigenes were assigned to autophagy-related pathways; 24 DEGs were classified into phagosomes, and 15 DEGs were assigned to lysosome pathways. Cytoskeleton and cell progression-associated genes were down-regulated. Besides, cell death-inducing proteins were up-regulated. The metabolomic analysis revealed exposure to rapamycin treatment enhanced protomont metabolites, including L-Cysteine, which is related to autophagy. Rapamycin had influenced the gene and metabolites of protomont; activating autophagy with inhibition of mechanistic target of rapamycin, (mTOR). The process negatively influences protomont morphology, encystation, and survival. Further autophagy-related gene silencing can be investigated via genome sequencing of C. irritans to study encystation.

Differential effects of proton pump inhibitors and vonoprazan on vascular endothelial growth factor expression in cancer cells

Proton pump inhibitors (PPIs) are potent inhibitors of gastric acid secretion, used as first-line agents in treating peptic ulcers. However, we have previously reported that PPIs may diminish the therapeutic effect of anti-vascular endothelial growth factor (VEGF) drugs in patients with cancer. In this study, we explored the effects of vonoprazan, a novel gastric acid secretion inhibitor used for the treatment of peptic ulcers, on the secretion of VEGF in cancer cells and attempted to propose it as an alternative PPI for cancer chemotherapy. The effects of PPI and vonoprazan on VEGF expression in cancer cells were compared by real-time reverse transcription-polymerase chain reaction and ELISA. The interaction of vonoprazan and PPIs with transcriptional regulators by docking simulation analysis. In various cancer cell lines, including the human colorectal cancer cell line (LS174T), PPI increased VEGF messenger RNA expression and VEGF protein secretion, while this effect was not observed with vonoprazan. Molecular docking simulation analysis showed that vonoprazan had a lower binding affinity for estrogen receptor alpha (ER-α), one of the transcriptional regulators of VEGF, compared to PPI. Although the PPI-induced increase in VEGF expression was counteracted by pharmacological ER-α inhibition, the effect of vonoprazan on VEGF expression was unchanged. Vonoprazan does not affect VEGF expression in cancer cells, which suggests that vonoprazan might be an alternative to PPIs, with no interference with the therapeutic effects of anti-VEGF cancer chemotherapy.

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.

The Oxidative Stress-Induced Hypothetical Protein PG_0686 in Porphyromonas gingivalis W83 Is a Novel Diguanylate Cyclase

The survival/adaptation of Porphyromonas gingivalis to the inflammatory environment of the periodontal pocket requires an ability to overcome oxidative stress. Several functional classes of genes, depending on the severity and duration of the exposure, were induced in P. gingivalis under H2O2-induced oxidative stress. The PG_0686 gene was highly upregulated under prolonged oxidative stress. PG_0686, annotated as a hypothetical protein of unknown function, is a 60 kDa protein that carries several domains including hemerythrin, PAS10, and domain of unknown function (DUF)-1858. Although PG_0686 showed some relatedness to several diguanylate cyclases (DGCs), it is missing the classical conserved, active site sequence motif (GGD[/E]EF), commonly observed in other bacteria. PG_0686-related proteins are observed in other anaerobic bacterial species. The isogenic mutant P. gingivalis FLL361 (ΔPG_0686::ermF) showed increased sensitivity to H2O2, and decreased gingipain activity compared to the parental strain. Transcriptome analysis of P. gingivalis FLL361 showed the dysregulation of several gene clusters/operons, known oxidative stress resistance genes, and transcriptional regulators, including PG_2212, CdhR and PG_1181 that were upregulated under normal anaerobic conditions. The intracellular level of c-di-GMP in P. gingivalis FLL361 was significantly decreased compared to the parental strain. The purified recombinant PG_0686 (rPG_0686) protein catalyzed the formation of c-di-GMP from GTP. Collectively, our data suggest a global regulatory property for PG_0686 that may be part of an unconventional second messenger signaling system in P. gingivalis. Moreover, it may coordinately regulate a pathway(s) vital for protection against environmental stress, and is significant in the pathogenicity of P. gingivalis and other anaerobes. IMPORTANCE Porphyromonas gingivalis is an important etiological agent in periodontitis and other systemic diseases. There is still a gap in our understanding of the mechanisms that P. gingivalis uses to survive the inflammatory microenvironment of the periodontal pocket. The hypothetical PG_0686 gene was highly upregulated under prolonged oxidative stress. Although the tertiary structure of PG_0686 showed little relatedness to previously characterized diguanylate cyclases (DGCs), and does not contain the conserved GGD(/E)EF catalytic domain motif sequence, an ability to catalyze the formation of c-di-GMP from GTP is demonstrated. The second messenger pathway for c-di-GMP was previously predicted to be absent in P. gingivalis. PG_0686 paralogs are identified in other anaerobic bacteria. Thus, PG_0686 may represent a novel class of DGCs, which is yet to be characterized. In conclusion, we have shown, for the first time, evidence for the presence of c-di-GMP signaling with environmental stress protective function in P. gingivalis.

Dusp1 regulates thermal tolerance limits in zebrafish by maintaining mitochondrial integrity

Temperature tolerance restricts the distribution of a species. However, the molecular and cellular mechanisms that set the thermal tolerance limits of an organism are poorly understood. Here, we report on the function of dual-specificity phosphatase 1 (DUSP1) in thermal tolerance regulation. Notably, we found that dusp1 ^-/- zebrafish grew normally but survived within a narrowed temperature range. The higher susceptibility of these mutant fish to both cold and heat challenges was attributed to accelerated cell death caused by aggravated mitochondrial dysfunction and over-production of reactive oxygen species in the gills. The DUSP1-MAPK-DRP1 axis was identified as a key pathway regulating these processes in both fish and human cells. These observations suggest that DUSP1 may play a role in maintaining mitochondrial integrity and redox homeostasis. We therefore propose that maintenance of cellular redox homeostasis may be a key mechanism for coping with cellular thermal stress and that the interplay between signaling pathways regulating redox homeostasis in the most thermosensitive tissue (i.e., gills) may play an important role in setting the thermal tolerance limit of zebrafish.

xcore: an R package for inference of gene expression regulators

BACKGROUND

Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types.

RESULTS

Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression.

CONCLUSIONS

xcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments.

Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma

BACKGROUND

Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy.

METHODS

We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05).

RESULTS

The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8+ cells between adjacent and tumour samples.

CONCLUSION

We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8+ cells and M1 macrophages in tumour samples.

Peptidomics analysis of plasma in patients with ankylosing spondylitis

Background

This study aimed to explore the differential expression of peptides associated with ankylosing spondylitis (AS) patients, enabling identification of potential functional peptides to provide the basis for the novel intervention targets for AS.

Material and Methods

3 AS patients and 3 healthy volunteers were enrolled in this study. The expression profiles for peptides present in the plasma of AS patients and the healthy individual were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The physicochemical properties and biological functions of identified peptides were further analyzed by bioinformatics. The results of peptide identification were verified by cell viability analysis, using CCK8 and Edu staining assay, and the differential peptides relevant to the disease were screened.

Results

52 differential peptides were successfully identified using mass spectrometry. 44 peptides were up-regulated, while eight were down-regulated. FGA-peptide (sequences: DSGEGDFLAEGGGVRGPR), C4A-peptide (sequences: NGFKSHAL), and TUBB-peptide (sequences: ISEQFTAMFR) were screened out that could significantly promote the proliferation of fibroblasts in AS patients. Bioinformatics analysis showed these differentially expressed peptides might be associated with "MHC class I protein binding" and "pathogenic Escherichia coli infection" pathways, which might further affect the progression of AS.

Conclusion

This pilot study shows 3 differentially expressed peptides may have the potential function for the occurrence and development of AS, may provide novel insights into the underlying molecular mechanisms of AS based on peptide omics.

Antimicrobial resistance in Klebsiella pneumoniae: identification of bacterial DNA adenine methyltransferase as a novel drug target from hypothetical proteins using subtractive genomics

Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection innosocomial settings. As reported by the World Health Organization, carbapenem-resistantEnterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgentthreat, and the greatest concern is that these bacterial pathogens may acquire genetictraits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expandantibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells;however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation proteindomain, suggesting a new potential site as a drug target. DNA methylation regulates theattenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could bea novel antibiotic.

Novel pyroptosis-associated genes signature for predicting the prognosis of sarcoma and validation

BACKGROUND

Sarcoma is a rare mesenchymal malignant tumor. Recently, pyroptosis has been reported to be a mode of programmed cell death. Nonetheless, levels of pyroptosis-associated genes in sarcoma and its relevance to prognostic outcomes are yet to be elucidated.

RESULTS

Sarcoma cases were classified into two subtypes with regards to differentially expressed genes. We established a profile composed of seven genes and classified the sarcoma patients into low- and high-risk groups through least absolute shrinkage and selection operator Cox regression. Survival rate of low-risk sarcoma patients was markedly higher, relative to high-risk group (P<0.001). In combination with clinical features, the risk score was established to be an independent predictive factor for OS of sarcoma patients. Chemotherapeutic drug sensitivity response analysis found 65 drugs with higher drug sensitivity in low-risk, than in high-risk group and 14 drugs with higher drug sensitivity in the high-risk patient group, compared with low-risk patient group. In addition, functional enrichment, pathway and gene mutation of the two modules were analyzed. Finally, we used qRT-PCR to detect the expression of seven pyroptosis-related genes in tumor cells, and human skeletal muscle cells, compared with human skeletal muscle cells, PODXL2, LRRC17, GABRA3, SCUBE3 and RFLNB genes show high expression levels in tumor cells, while IGHG2 and hepatic leukemia factor show low expression levels in tumor cells.

CONCLUSIONS

Our research suggest that pyroptosis is closely associated with sarcoma, and these findings confirm that pyroptosis-associated seven genes have a critical role in sarcoma and are potential prognostic factors for sarcoma.

New nitrogen-compounds, penicidones E and F, produced by the fungal strain Oidiodendron sp. FKI-7498

The nitrogen rule in mass spectrometry was used to search for new nitrogen-compounds from microbial metabolites. During this program, two new nitrogen-containing compounds, penicidones E and F, were discovered from the filamentous fungal strain FKI-7498, which was isolated from soil collected in Tokushima, Japan, and identified as Oidiodendron sp. by sequence analysis of the internal transcribed spacer region, including 5.8S ribosomal RNA. The structures of penicidones E and F were determined by mass spectrometry, nuclear magnetic resonance spectroscopy, and chemical modification analyses. These analyses revealed that penicidones E and F have a core structure of 3,5-dihydroxy-2-(4-pyridone-3-carbonyl)benzoic acid. Penicidone E exhibited hydroxyl radical scavenging activity.

Disentangling the lipid divide: Identification of key enzymes for the biosynthesis of membrane-spanning and ether lipids in Bacteria

Bacterial membranes are composed of fatty acids (FAs) ester-linked to glycerol-3-phosphate, while archaea have membranes made of isoprenoid chains ether-linked to glycerol-1-phosphate. Many archaeal species organize their membrane as a monolayer of membrane-spanning lipids (MSLs). Exceptions to this "lipid divide" are the production by some bacterial species of (ether-bound) MSLs, formed by tail-to-tail condensation of FAs resulting in the formation of (iso) diabolic acids (DAs), which are the likely precursors of paleoclimatological relevant branched glycerol dialkyl glycerol tetraether molecules. However, the enzymes responsible for their production are unknown. Here, we report the discovery of bacterial enzymes responsible for the condensation reaction of FAs and for ether bond formation and confirm that the building blocks of iso-DA are branched iso-FAs. Phylogenomic analyses of the key biosynthetic genes reveal a much wider diversity of potential MSL (ether)-producing bacteria than previously thought, with importantt implications for our understanding of the evolution of lipid membranes.

Upregulation of leucine-rich alpha-2 glycoprotein: A key regulator of inflammation and joint fibrosis in patients with severe knee osteoarthritis

Introduction

Osteoarthritis (OA) is a degenerative disease of the joints mainly affecting older individuals. Since the etiology behind the progression of OA is not well understood, several associated consequences, such as synovial joint stiffness and its progression due to joint fibrosis, are still poorly understood. Although a lot of developments have been achieved in the diagnosis and management of OA, synovial fibrosis remains one of the major challenging consequences. The present study was therefore focused on understanding the mechanism of synovial fibrosis, which may further contribute to improving symptomatic treatments, leading to overall improvements in the treatment outcomes of patients with OA.

Methods

We used advanced proteomic techniques including isobaric tag for relative and absolute quantitation and sequential window acquisition of all theoretical mass spectra for the identification of differentially expressed proteins in the plasma samples of patients with OA. An in silico study was carried out to evaluate the association of the identified proteins with their biological processes related to fibrosis and remodeling of the extracellular matrix (ECM). The most significantly upregulated protein was then validated by Western blot and enzyme-linked immunosorbent assay. The target protein was then further investigated for its role in inflammation and joint fibrosis using an in vitro study model.

Results

Leucine-rich alpha-2 glycoprotein (LRG1) was found to be the most highly differentially expressed upregulated (9.4-fold) protein in the plasma samples of patients with OA compared to healthy controls. The knockdown of LRG1 followed by in vitro studies revealed that this protein promotes the secretion of the ECM in synovial cells and actively plays a role in wound healing and cell migration. The knockdown of LRG1 further confirmed the reduction of the inflammatory- and fibrosis-related markers in primary cells.

Conclusion

LRG1 was identified as a highly significant upregulated protein in the plasma samples of patients with OA. It was found to be associated with increased fibrosis and cell migration, leading to enhanced inflammation and joint stiffness in OA pathogenesis.

Shoc2 controls ERK1/2-driven neural crest development by balancing components of the extracellular matrix

The extracellular signal-regulated kinase (ERK1/2) pathway is essential in embryonic development. The scaffold protein Shoc2 is a critical modulator of ERK1/2 signals, and mutations in the shoc2 gene lead to the human developmental disease known as Noonan-like syndrome with loose anagen hair (NSLH). The loss of Shoc2 and the shoc2 NSLH-causing mutations affect the tissues of neural crest (NC) origin. In this study, we utilized the zebrafish model to dissect the role of Shoc2-ERK1/2 signals in the development of NC. These studies established that the loss of Shoc2 significantly altered the expression of transcription factors regulating the specification and differentiation of NC cells. Using comparative transcriptome analysis of NC-derived cells from shoc2 CRISPR/Cas9 mutant larvae, we found that Shoc2-mediated signals regulate gene programs at several levels, including expression of genes coding for the proteins of extracellular matrix (ECM) and ECM regulators. Together, our results demonstrate that Shoc2 is an essential regulator of NC development. This study also indicates that disbalance in the turnover of the ECM may lead to the abnormalities found in NSLH patients.

COVID-19 Candidate Genes and Pathways Potentially Share the Association with Lung Cancer

COVID-19 is considered as the most challenging in the current situation but lung cancer is also the leading cause of death in the global population. These two malignancies are among the leading human diseases and are highly complex in terms of diagnostic and therapeutic approaches as well as the most frequent and highly complex and heterogeneous in nature. Based on the latest update, it is known that the patients suffering from lung cancer, are considered to be significantly at higher risk of COVID-19 infection in terms of survival and there are a number of evidences which support the hypothesis that these diseases may share the same functions and functional components. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major sources which promote a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutics. With so many challenges, it is critical to understand the relationships and the common shared aberrations between them which is difficult to unravel and understand. A simple approach has been applied for this study where differential gene expression analysis, pathway enrichment, and network level understanding are carried out. Since, gene expression changes and genomic alterations are related to the COVID-19 and lung cancer but their pattern varies significantly. Based on the recent studies, it appears that the patients suffering from lung cancer and and simultaneously infected with COVID-19, then survival chance is lessened. So, we have designed our goal to understand the genes commonly overexpressed and commonly enriched pathways in case of COVID-19 and lung cancer. For this purpose, we have presented the summarized review of the previous works where the pathogenesis of lung cancer and COVID-19 infection have been focused and we have also presented the new finding of our analysis. So, this work not only presents the review work but also the research work. This review and research study leads to the conclusion that growth promoting pathways (EGFR, Ras, and PI3K), growth inhibitory pathways (p53 and STK11), apoptotic pathways (Bcl- 2/Bax/Fas), and DDR pathways and genes are commonly and dominantly altered in both the cases COVID-19 and lung cancer.

Role of Potential COVID-19 Immune System Associated Genes and the Potential Pathways Linkage with Type-2 Diabetes

BACKGROUND

Coronavirus is an enclosed positive-sense RNA virus with club-like spikes extending from its surface. It is most typically associated with acute respiratory infections in humans, but its capacity to infect many host species and cause multiple illnesses makes it a complicated pathogen. The frequent encounters between wild animals and humans are a typical cause of infection. The zoonotic infections SARS-CoV and MERS-CoV are among the most common causes of serious respiratory illnesses in humans.

AIM

The main goal of this research was to look at gene expression profiles in human samples that were either infected with coronavirus or were not, and compare the varied expression patterns and their functional implications.

METHODS

The previously researched samples were acquired from a public database for this purpose, and the study was conducted, which included gene expression analysis, pathway analysis, and network-level comprehension. The results for differentially expressed genes, enriched pathways, and networks for prospective genes and gene sets are presented in the analysis. In terms of COVID-19 gene expression and its relationship to type 2 diabetes.

RESULTS

We see a lot of genes that have different gene expression patterns than normal for coronavirus infection, but in terms of pathways, it appears that there are only a few sets of functions that are affected by altered gene expression, and they are related to infection, inflammation, and the immune system.

CONCLUSION

Based on our study, we conclude that the potential genes which are affected due to infection are NFKBIA, MYC, FOXO3, BIRC3, ICAM1, IL8, CXCL1/2/5, GADD45A, RELB, SGK1, AREG, BBC3, DDIT3/4, EGR1, MTHFD2, and SESN2 and the functional changes are mainly associated with these pathways: TNF, cytokine, NF-kB, TLR, TCR, BCR, Foxo, and TGF signaling pathways are among them and there are additional pathways such as hippo signaling, apoptosis, estrogen signaling, regulating pluropotency of stem cells, ErbB, Wnt, p53, cAMP, MAPK, PI3K-AKT, oxidative phosphorylation, protein processing in endoplasmic reticulum, prolactin signaling, adipocytokine, neurotrophine signaling, and longevity regulating pathways. SMARCD3, PARL, GLIPR1, STAT2, PMAIP1, GP1BA, and TOX genes and PI3K-Akt, focal adhesion, Foxo, phagosome, adrenergic, osteoclast differentiation, platelet activation, insulin, cytokine- cytokine interaction, apoptosis, ECM, JAK-STAT, and oxytocin signaling appear as the linkage between COVID-19 and Type-2 diabetes.

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Regulome-based characterization of drug activity across the human diseasome

Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

WINNER: A network biology tool for biomolecular characterization and prioritization

BACKGROUND AND CONTRIBUTION

In network biology, molecular functions can be characterized by network-based inference, or "guilt-by-associations." PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

RESULTS

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding "non-seed" molecules to the original biomolecular interaction network consisting of "seed" molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree-preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

CONCLUSION

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Identification of molecular initiating events and key events leading to endocrine disrupting effects of PFOA: Integrated molecular dynamic, transcriptomic, and proteomic analyses

Perfluorooctanoic acid (PFOA) can rapidly activate signaling pathways independent of nuclear hormone receptors through membrane receptor regulation, which leads to endocrine disrupting effects. In the present work, the molecular initiating event (MIE) and the key events (KEs) which cause the endocrine disrupting effects of PFOA have been explored and determined based on molecular dynamics simulation (MD), fluorescence analysis, transcriptomics, and proteomics. MD modeling and fluorescence analysis proved that, on binding to the G protein-coupled estrogen receptor-1 (GPER), PFOA could induce a conformational change in the receptor, turning it into an active state. The results also indicated that the binding to GPER was the MIE that led to the adverse outcome (AO) of PFOA. In addition, the downstream signal transduction pathways of GPER, as regulated by PFOA, were further investigated through genomics and proteomics to identify the KEs leading to thr endocrine disrupting effects. Two pathways (Endocrine resistance, ERP and Estrogen signaling pathway, ESP) containing GPER were regulated by different concentration of PFOA and identified as the KEs. The knowledge of MIE, KEs, and AO of PFOA is necessary to understand the links between PFOA and the possible pathways that lead to its negative effects.

The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases

We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.

Immune cells transcriptome-based drug repositioning for multiple sclerosis

Objective

Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells.

Materials and Methods

Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

We obtained 50 hub target genes for CD4⁺ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-β (IFN-β) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19⁺ B cells and 15 target pathways for CD4⁺ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-β for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways.

Conclusion

We found that applying candidate drugs that target both the “PI3K-Akt signaling pathway” and “Chemokine signaling pathway” (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

Lipid and protein content profiling of isolated native autophagic vesicles

Autophagy is responsible for clearance of an extensive portfolio of cargoes, which are sequestered into vesicles, called autophagosomes, and are delivered to lysosomes for degradation. The pathway is highly dynamic and responsive to several stress conditions. However, the phospholipid composition and protein contents of human autophagosomes under changing autophagy rates are elusive so far. Here, we introduce an antibody-based FACS-mediated approach for the isolation of native autophagic vesicles and ensured the quality of the preparations. Employing quantitative lipidomics, we analyze phospholipids present within human autophagic vesicles purified upon basal autophagy, starvation, and proteasome inhibition. Importantly, besides phosphoglycerides, we identify sphingomyelin within autophagic vesicles and show that the phospholipid composition is unaffected by the different conditions. Employing quantitative proteomics, we obtain cargo profiles of autophagic vesicles isolated upon the different treatment paradigms. Interestingly, starvation shows only subtle effects, while proteasome inhibition results in the enhanced presence of ubiquitin-proteasome pathway factors within autophagic vesicles. Thus, here we present a powerful method for the isolation of native autophagic vesicles, which enabled profound phospholipid and cargo analyses.

Systematic analysis competing endogenous RNA coexpression network as a potentially prediction prognostic biomarker for colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common types of colon cancer, represents a major public health issue due to its high incidence and mortality. Competing endogenous RNAs (ceRNAs) hypothesis has generated a great interest in the study of molecular biological mechanisms of cancer progression. The aim of this study was to identify potential prediction prognostic biomarker associated with progression of COAD and illuminate regulatory mechanisms. Two RNA sequencing datasets downloaded from the Genotype-Tissue Expression and TCGA. The differentially expressed RNAs were analyzed. Weighted correlation network analysis was used to analyze the similarity of genes model with a trait in the network. Interactions between lncRNAs, miRNAs, and target mRNAs were predicted by MiRcode, starBase, miRTarBase, miRDB, and TargetScan, and the risk score of mRNAs was established. Based on the identified prognostic signature and independent clinical factors, then the nomogram survival model was built. Totally, we identified 3537 differentially expressed mRNAs, 2379 lncRNAs, and 449 microRNAs. Based on the 8 prognosis-associated mRNAs (CCNA2 + CEBPA + NEBL + SOX9 + DLG4 + RIMKLB + TCF7L1 + TUB), the risk score was proposed. After the independent clinical prognostic factors were identified, the nomogram survival model was built. LncRNA-miRNA-mRNA ceRNA network was built by 68 lncRNAs, 4 miRNAs, and 6 mRNAs, which might serve as prognostic biomarkers of COAD. These findings suggest several genes in ceRNA network might be novel important prognostic biomarkers and potential targets for COAD. CeRNA networks could provide further insight into the mRNA-related regulatory mechanism and COAD prognosis.

Small compound-based direct cell conversion with combinatorial optimization of pathway regulations

MOTIVATION

Direct cell conversion, direct reprogramming (DR), is an innovative technology that directly converts source cells to target cells without bypassing induced pluripotent stem cells. The use of small compounds (e.g. drugs) for DR can help avoid carcinogenic risk induced by gene transfection; however, experimentally identifying small compounds remains challenging because of combinatorial explosion.

RESULTS

In this article, we present a new computational method, COMPRENDRE (combinatorial optimization of pathway regulations for direct reprograming), to elucidate the mechanism of small compound-based DR and predict new combinations of small compounds for DR. We estimated the potential target proteins of DR-inducing small compounds and identified a set of target pathways involving DR. We identified multiple DR-related pathways that have not previously been reported to induce neurons or cardiomyocytes from fibroblasts. To overcome the problem of combinatorial explosion, we developed a variant of a simulated annealing algorithm to identify the best set of compounds that can regulate DR-related pathways. Consequently, the proposed method enabled to predict new DR-inducing candidate combinations with fewer compounds and to successfully reproduce experimentally verified compounds inducing the direct conversion from fibroblasts to neurons or cardiomyocytes. The proposed method is expected to be useful for practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The code supporting the current study is available at the http://labo.bio.kyutech.ac.jp/~yamani/comprendre.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A construction and comprehensive analysis of the immune-related core ceRNA network and infiltrating immune cells in peripheral arterial occlusive disease

Background: Peripheral arterial occlusive disease (PAOD) is a peripheral artery disorder that increases with age and often leads to an elevated risk of cardiovascular events. The purposes of this study were to explore the underlying competing endogenous RNA (ceRNA)-related mechanism of PAOD and identify the corresponding immune cell infiltration patterns.

Methods: An available gene expression profile (GSE57691 datasets) was downloaded from the GEO database. Differentially expressed (DE) mRNAs and lncRNAs were screened between 9 PAOD and 10 control samples. Then, the lncRNA-miRNA-mRNA ceRNA network was constructed on the basis of the interactions generated from the miRcode, TargetScan, miRDB, and miRTarBase databases. The functional enrichment and protein–protein interaction analyses of mRNAs in the ceRNA network were performed. Immune-related core mRNAs were screened out through the Venn method. The compositional patterns of the 22 types of immune cell fraction in PAOD were estimated through the CIBERSORT algorithm. The final ceRNA network and immune infiltration were validated using clinical tissue samples. Finally, the correlation between immune cells and mRNAs in the final ceRNA network was analyzed.

Results: Totally, 67 DE_lncRNAs and 1197 DE_mRNAs were identified, of which 130 DE_mRNAs (91 downregulated and 39 upregulated) were lncRNA-related. The gene ontology enrichment analysis showed that those down- and upregulated genes were involved in dephosphorylation and regulation of translation, respectively. The final immune-related core ceRNA network included one lncRNA (LINC00221), two miRNAs (miR-17-5p and miR-20b-5p), and one mRNA (CREB1). Meanwhile, we found that monocytes and M1 macrophages were the main immune cell subpopulations in PAOD. After verification, these predictions were consistent with experimental results. Moreover, CREB1 was positively correlated with naive B cells (R = 0.55, p = 0.035) and monocytes (R = 0.52, p = 0.049) and negatively correlated with M1 macrophages (R = −0.72, p = 0.004), resting mast cells (R = −0.66, p = 0.009), memory B cells (R = −0.55, p = 0.035), and plasma cells (R = −0.52, p = 0.047).

Conclusion: In general, we proposed that the immune-related core ceRNA network (LINC00221, miR-17-5p, miR-20b-5p, and CREB1) and infiltrating immune cells (monocytes and M1 macrophages) could help further explore the molecular mechanisms of PAOD.

Control of cell state transitions

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.

Breath response following a nutritional challenge monitored by secondary electrospray ionization high-resolution mass spectrometry

On-line breath analysis using secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a sensitive method for biomarker discovery. The strengths of this technology have already been demonstrated in the clinical environment. For the first time, this study demonstrates the application of SESI-HRMS in the field of nutritional science using a standardized nutritional intervention, consisting of a high-energy shake (950 kcal, 8% protein, 35% sugar and 57% fat). Eleven subjects underwent the intervention on three separate days and their exhaled breath was monitored up to six hours postprandially. In addition, sampling was performed during equivalent fasting conditions for selected subjects. To estimate the impact of inter- and intra-individual variability, analysis of variance simultaneous component analysis (ASCA) was conducted, revealing that the inter-individual variability accounted for 30 % of the data variation. To distinguish the effect of the intervention from fasting conditions, partial least squares discriminant analysis was performed. Candidate compound annotation was performed with pathway analysis and collision-induced dissociation (CID) experiments. Pathway analysis highlighted, among others, features associated with the metabolism of linoleate, butanoate and amino sugars. Tentative compounds annotated through CID measurements include fatty acids, amino acids, and amino acid derivatives, some of them likely derived from nutrients by the gut microbiome (e.g. propanoate, indoles), as well as organic acids from the Krebs cycle. Time-series clustering showed an overlap of observed kinetic trends with those reported previously in blood plasma.

Differences in intestinal microflora of birds among different ecological types

The intestinal microflora of animals plays a key role in metabolism, immunity, and development. Birds distributed across multiple ecological habitats. However, little is known about the differences in the intestinal microflora of birds among different ecological types. In this study, bird feces from different ecological types and orders were collected in Chongqing Zoo, China. In this study, high throughput sequencing of the 16S ribosomal RNA (rRNA) gene (amplicon sequencing) and metagenomics were used to analyze the composition and function differences of gut microbiota communities among different ecological types/orders. Firmicutes and Proteobacteria were the dominant bacteria phyla for all samples but there were significant differences in the α-diversity, community structure and microbial interactions between birds of different ecological types. The function differences involve most aspects of the body functions, especially for environmental information processing, organismal systems, human diseases, genetic information processing, and metabolism. These results suggest that diet and habitat are potential drivers of avian gut microbial aggregation. This preliminary study is of great significance for further research on the intestinal microflora of different ecological types of birds.

Mapping the single-cell landscape of acral melanoma and analysis of the molecular regulatory network of the tumor microenvironments

Acral melanoma (AM) exhibits a high incidence in Asian patients with melanoma, and it is not well treated with immunotherapy. However, little attention has been paid to the characteristics of the immune microenvironment in AM. Therefore, in this study, we collected clinical samples from Chinese patients with AM and conducted single-cell RNA sequencing to analyze the heterogeneity of its tumor microenvironments (TMEs) and the molecular regulatory network. Our analysis revealed that genes, such as TWIST1, EREG, TNFRSF9, and CTGF could drive the deregulation of various TME components. The molecular interaction relationships between TME cells, such as MIF-CD44 and TNFSF9-TNFRSF9, might be an attractive target for developing novel immunotherapeutic agents.

Acral melanoma is a type of cancer that affects the hands and feet. It tends to form on the palms, soles, and under the nails. It is rare in people of European descent, but in Asian populations it makes up more than half of all melanoma cases. Unlike other types of skin cancer, it does not respond well to immunotherapy, but scientists did not understand why.

Historically, cancer research has focused on the genetics of whole tumors. But cancer is complicated. Malignant cells recruit other cells to help them survive and grow, and to protect them from attacks by the immune system. Together, they create their own ecosystem, called the tumor microenvironment. The exact makeup of the tumor microenvironment differs depending on the type of cancer and on the genetics of the individual. Investigating the cells that ‘support’ the tumor could help to explain how acral melanoma develops and why it does not respond to treatment.

To address these questions, He et al. collected samples from six patients with acral melanoma and examined the genes used by more than 60,000 individual cells. This revealed nine different types of cells in the tumor microenvironment. Most were cancer cells, but there were also immune cells, blood vessel cells, skin cells, and a type of cell that makes connective tissue. He et al. also identified four genes that most likely shape the tumor microenvironment, and two gene pairs that may control some of the interactions between the cells.

Investigating these early findings in more detail could open new treatment avenues for acral melanoma. The number of samples in this study was small, but it provides a starting point for future investigation. With more data, researchers could start to develop treatments that target the unique tumor microenvironment of this type of cancer.

Comparative genomics of the waterfowl innate immune system

Animal species differ considerably in their ability to fight off infections. Finding the genetic basis of these differences is not easy, as the immune response is comprised of a complex network of proteins that interact with one another to defend the body against infection. Here, we used population- and comparative genomics to study the evolutionary forces acting on the innate immune system in natural hosts of the avian influenza virus (AIV). For this purpose, we used a combination of hybrid capture, next- generation sequencing and published genomes to examine genetic diversity, divergence, and signatures of selection in 127 innate immune genes at a micro- and macroevolutionary time scale in 26 species of waterfowl. We show across multiple immune pathways (AIV-, toll-like-, and RIG-I -like receptors signalling pathways) that genes involved genes in pathogen detection (i.e., toll-like receptors) and direct pathogen inhibition (i.e., antimicrobial peptides and interferon-stimulated genes), as well as host proteins targeted by viral antagonist proteins (i.e., mitochondrial antiviral-signaling protein, [MAVS]) are more likely to be polymorphic, genetically divergent, and under positive selection than other innate immune genes. Our results demonstrate that selective forces vary across innate immune signaling signalling pathways in waterfowl, and we present candidate genes that may contribute to differences in susceptibility and resistance to infectious diseases in wild birds, and that may be manipulated by viruses. Our findings improve our understanding of the interplay between host genetics and pathogens, and offer the opportunity for new insights into pathogenesis and potential drug targets.

Genome-Wide Association Analysis and Genetic Parameters for Feed Efficiency and Related Traits in Yorkshire and Duroc Pigs

Simple Summary

Genetic improvements in feed efficiency (FE) and related traits could considerably reduce pig production costs and energy consumption. Thus, we performed a genetic parameter estimation and genome-wide association study of four FE and FE-related traits, namely, average daily feed intake, average daily gain, the feed conversion ratio, and residual feed intake, of two pig breeds, Yorkshire and Duroc. The results demonstrate the genetic relationships of FE and FE-related traits with two growth traits, age and backfat thickness at 100 kg. We also identified many single-nucleotide polymorphisms (SNPs) and novel candidate genes related to these traits. In addition, we found many pathways significantly associated with FE and FE-related traits, and they are generally involved in digestive and metabolic processes. The results of this study are expected to provide a valuable reference for the genomic selection of FE and FE-related traits in pigs.

Abstract

Feed efficiency (FE) traits are key factors that can influence the economic benefits of pig production. However, little is known about the genetic architecture of FE and FE-related traits. This study aimed to identify SNPs and candidate genes associated with FE and FE-related traits, namely, average daily feed intake (ADFI), average daily gain (ADG), the feed conversion ratio (FCR), and residual feed intake (RFI). The phenotypes of 5823 boars with genotyped data (50 K BeadChip) from 1365 boars from a nucleus farm were used to perform a genome-wide association study (GWAS) of two breeds, Duroc and Yorkshire. Moreover, we performed a genetic parameter estimation for four FE and FE-related traits. The heritabilities of the FE and FE-related traits ranged from 0.13 to 0.36, and there were significant genetic correlations (−0.69 to 0.52) of the FE and FE-related traits with two growth traits (age at 100 kg and backfat thickness at 100 kg). A total of 61 significant SNPs located on eight different chromosomes associated with the four FE and FE-related traits were identified. We further identified four regions associated with FE and FE-related traits that have not been previously reported, and they may be potential novel QTLs for FE. Considering their biological functions, we finally identified 35 candidate genes relevant for FE and FE-related traits, such as the widely reported MC4R and INSR genes. A gene enrichment analysis showed that FE and FE-related traits were highly enriched in the biosynthesis, digestion, and metabolism of biomolecules. This study deepens our understanding of the genetic mechanisms of FE in pigs and provides valuable information for using marker-assisted selection in pigs to improve FE.

Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates

Meningitis creates a life-threatening clinical crisis. Moreover, the administered antibiotics result into multi-drug resistance, thereby necessitating development of alternative therapeutic strategies. This study aimed at identifying novel-drug targets in Neisseria meningitidis and therapeutic molecules which can be exploited for the treatment of meningitis. Novel targets were identified by applying a pathogenomic approach involving protein data-set mining, subtractive channel analysis and subsequent qualitative analysis comprising of in silico pharmacokinetics, molecular docking and pharmacophore generation. Pathogenomic studies revealed Neisserial Surface Protein A (NSP-A) and Iron-III-Substrate Binding Protein (Fe-IIISBP) as potential targets. Two pharmacophore models comprising of 2-(biaryl) carbapenems, efavirenz, praziquantel and pyrimethamine for NSP-A and 2-(biaryl) carbapenems, trimipramine and pyrimethamine for Fe-IIISBP, showed successful docking, followed drug-likeness criteria and generated pharmacophore model with a score of 8.08 and 8.818, respectively, which had further been docked to the target stably. Thus, our study identifies NSP-A and Fe-IIISBP as novel targets in Neisseria meningitidis for which 2-(biaryl) carbapenems, efavirenz, praziquantel, trimipramine and pyrimethamine may be employed for effective treatment of meningitis.

Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.

The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility

The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: “Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?”. Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility.

Genome-scale metabolic reconstructions represent all biochemical reactions that an organism can accomplish. These reconstructions are complex and often difficult to study in great detail. A way to overcome this limitation is to focus on specific pathways or subsystems. We present a novel method to identify metabolic modules based on the network topology. The method relies on reaction directions and ignores currency metabolites, which artificially connect distant metabolic reactions. In this way, topologically independent modules are built, where inputs and outputs are controlled by irreversible reactions. The method is automatic and unbiased, and, the result is a set of condition specific modules with defined metabolic functions. As a proof-of-concept we generated biologically relevant modules for the E.coli and Human genome-scale metabolic reconstructions supported by transcriptomic data. Finally, we applied the novel approach to study the network flexibility conferred by reversible reactions. In the case of the E. coli model, we found that the direction of 79% of structurally reversible reactions (those not directionally constrained by surrounding irreversible reactions) must be fixed to determine all the reaction directions in the network. Therefore, reversible reactions operate practically independent of each other.

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures

Motivation

A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results

In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.

Availability and implementation

Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].

Supplementary information

Supplementary data are available at Bioinformatics online.

Association of FXI activity with thrombo-inflammation, extracellular matrix, lipid metabolism and apoptosis in venous thrombosis

Animal experiments and early phase human trials suggest that inhibition of factor XIa (FXIa) safely prevents venous thromboembolism (VTE), and specific murine models of sepsis have shown potential efficacy in alleviating cytokine storm. These latter findings support the role of FXI beyond coagulation. Here, we combine targeted proteomics, machine learning and bioinformatics, to discover associations between FXI activity (FXI:C) and the plasma protein profile of patients with VTE. FXI:C was measured with a modified activated partial prothrombin time (APTT) clotting time assay. Proximity extension assay-based protein profiling was performed on plasma collected from subjects from the Genotyping and Molecular Phenotyping of Venous Thromboembolism (GMP-VTE) Project, collected during an acute VTE event (n = 549) and 12-months after (n = 187). Among 444 proteins investigated, N = 21 and N = 66 were associated with FXI:C during the acute VTE event and at 12 months follow-up, respectively. Seven proteins were identified as FXI:C-associated at both time points. These FXI-related proteins were enriched in immune pathways related to causes of thrombo-inflammation, extracellular matrix interaction, lipid metabolism, and apoptosis. The results of this study offer important new avenues for future research into the multiple properties of FXI, which are of high clinical interest given the current development of FXI inhibitors.

A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance

Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Differential expression analysis of genes and long non-coding RNAs associated with KRAS mutation in colorectal cancer cells

KRAS mutation is responsible for 40–50% of colorectal cancers (CRCs). RNA-seq data and bioinformatics methods were used to analyze the transcriptional profiles of KRAS mutant (mtKRAS) in comparison with the wild-type (wtKRAS) cell lines, followed by in-silico and quantitative real-time PCR (qPCR) validations. Gene set enrichment analysis showed overrepresentation of KRAS signaling as an oncogenic signature in mtKRAS. Gene ontology and pathway analyses on 600 differentially-expressed genes (DEGs) indicated their major involvement in the cancer-associated signal transduction pathways. Significant hub genes were identified through analyzing PPI network, with the highest node degree for PTPRC. The evaluation of the interaction between co-expressed DEGs and lncRNAs revealed 12 differentially-expressed lncRNAs which potentially regulate the genes majorly enriched in Rap1 and RAS signaling pathways. The results of the qPCR showed the overexpression of PPARG and PTGS2, and downregulation of PTPRC in mtKRAS cells compared to the wtKRAS one, which confirming the outputs of RNA-seq analysis. Further, significant upregualtion of miR-23b was observed in wtKRAS cells. The comparison between the expression level of hub genes and TFs with expression data of CRC tissue samples deposited in TCGA databank confirmed them as distinct biomarkers for the discrimination of normal and tumor patient samples. Survival analysis revealed the significant prognostic value for some of the hub genes, TFs, and lncRNAs. The results of the present study can extend the vision on the molecular mechanisms involved in KRAS-driven CRC pathogenesis.

MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information

The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug-target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.

Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer

Cancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. In order to understand complex diseases such as cancer, diabetes and kidney diseases, high-throughput data are generated at large scale and it has helped in the research and diagnostic advancement. However, to unravel the meaningful information from such large datasets for comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available dataset which are for two different stages (I and II) for renal cell carcinoma and furthermore, the TCGA and cBioPortal database have been utilized for clinical relevance understanding. In this work, we have applied computational approach to unravel the differentially expressed genes, their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are large number of differentially expressed genes which appears to be playing critical roles in survival.

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.