CD247, a novel T cell-derived diagnostic and prognostic biomarker for detecting disease progression and severity in patients with type 2 diabetes

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning

BACKGROUND

T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis.

METHODS

By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research.

RESULTS

We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered.

CONCLUSION

Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning

BACKGROUND

nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes.

METHODS

RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration.

RESULTS

By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways.

CONCLUSION

This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

Lactoferrin deficiency during lactation increases the risk of depressive-like behavior in adult mice

BACKGROUND

Lactoferrin is an active protein in breast milk that plays an important role in the growth and development of infants and is implicated as a neuroprotective agent. The incidence of depression is currently increasing, and it is unclear whether the lack of lactoferrin during lactation affects the incidence of depressive-like behavior in adulthood.

RESULTS

Lack of lactoferrin feeding during lactation affected the barrier and innate immune functions of the intestine, disrupted the intestinal microflora, and led to neuroimmune dysfunction and neurodevelopmental delay in the hippocampus. When exposed to external stimulation, adult lactoferrin feeding-deficient mice presented with worse depression-like symptoms; the mechanisms involved were activation of the LPS-TLR4 signalling pathway in the intestine and hippocampus, reduced BDNF-CREB signaling pathway in hippocampus, increased abundance of depression-related bacteria, and decreased abundance of beneficial bacteria.

CONCLUSIONS

Overall, our findings reveal that lactoferrin feeding deficient during lactation can increase the risk of depressive-like behavior in adults. The mechanism is related to the regulatory effect of lactoferrin on the development of the "microbial-intestinal-brain" axis.

Distinct immune and inflammatory response patterns contribute to the identification of poor prognosis and advanced clinical characters in bladder cancer patients

Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy's response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

Gene expression profiling reveals candidate biomarkers and probable molecular mechanisms in chronic stress

Chronic stress refers to nonspecific systemic reactions under the over-stimulation of different external and internal factors for a long time. Previous studies confirmed that chronic psychological stress had a negative effect on almost all tissues and organs. We intended to further identify potential gene targets related to the pathogenesis of chronic stress-induced consequences involved in different diseases. In our study, mice in the model group lived under the condition of chronic unpredictable mild stress (CUMS) until they expressed behaviors like depression which were supposed to undergo chronic stress. We applied high-throughput RNA sequencing to assess mRNA expression and obtained transcription profiles in lung tissue from CUMS mice and control mice for analysis. In view of the prediction of high-throughput RNA sequences and bioinformatics software, and mRNA regulatory network was constructed. First, we conducted differentially expressed genes (DEGs) and obtained 282 DEGs between CUMS (group A) and the control model (group B). Then, we conducted functional and pathway enrichment analyses. In general, the function of upregulated regulated DEGs is related to immune and inflammatory responses. PPI network identified several essential genes, of which ten hub genes were related to the T cell receptor signaling pathway. qRT-PCR results verified the regulatory network of mRNA. The expressions of CD28, CD3e, and CD247 increased in mice with CUMS compared with that in control. This illustrated immune pathways are related to the pathological molecular mechanism of chronic stress and may provide information for identifying potential biomarkers and early detection of chronic stress.

Whole-exome sequencing identified mutational profile of a case with T-cell chronic lymphocytic leukemia

We believe that our report and further case reports on T-cell chronic lymphocytic leukemia with genetic profile will contribute to the molecular classification of this rare but distinct disease.

Comparative gene expression profiling of milk somatic cells of Sahiwal cattle and Murrah buffaloes

India is the world's largest milk producing country because of massive contribution made by cattle and buffaloes. In the present investigation, comprehensive comparative profiling of transcriptomic landscape of milk somatic cells of Sahiwal cattle and Murrah buffaloes was carried out. Genes with highest transcript abundance in both species were enriched for biological processes such as lactation, immune response, cellular oxidant detoxification and response to hormones. Analysis of differential expression identified 377 significantly up-regulated and 847 significantly down-regulated genes with fold change >1.5 in Murrah buffaloes as compared to Sahiwal cattle (p_adj <0.05). Marked enrichment of innate and adaptive immune response related GO terms and higher expression of genes for various host defense peptides such as lysozyme, defensin β and granzymes were evident in buffaloes. Genes related to ECM-receptor interaction, complement and coagulation cascades, cytokine-cytokine receptor interaction and keratinization pathway showed more abundant expression in cattle. Network analysis of the up-regulated genes delineated highly connected genes representing immunity and haematopoietic cell lineage (CBL, CD28, CD247, PECAM1 and ITGA4). For the down-regulated dataset, genes with highest interactions were KRT18, FGFR1, GPR183, ITGB3 and DKK3. Our results lend support to more robust immune mechanisms in buffaloes, possibly explaining lower susceptibility to mammary infections as compared to cattle.

Identification of key miRNA‑mRNA pairs in septic mice by bioinformatics analysis

Sepsis is one of the most common causes of death among critically ill patients in intensive care units worldwide; however, the microRNAs (miRNAs/miRs) involved in the sepsis process (and their target genes) are largely unknown. The present study integrated miRNA and mRNA datasets to elucidate key sepsis-related miRNA-mRNA pairs. The datasets, GSE74952 and GSE55238 were downloaded from the Gene Expression Omnibus. By performing bioinformatics analysis such as GEO2R, miRNA target gene prediction, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and miRNA-mRNA network analysis, a total of four sepsis-related miRNA-mRNA pairs were successfully obtained. Mmu-miR-370-3p, cluster of differentiation (CD)8a, CD247, Zap70 and inhibitor of nuclear factor κ B kinase subunit β (Ikbkb) were identified as the components involved in these pairs, and these genes were enriched in the T-cell receptor signaling pathway. Finally, reverse transcription-quantitative PCR results validated that the expression levels of the four genes (CD8a, CD247, Zap70 and Ikbkb) in the sepsis model mice were consistent with the microarray analysis. In conclusion, the present study identified four sepsis-related miRNA-mRNA pairs using bioinformatics analysis. These results indicated that the candidate miRNA-mRNA pairs may be involved in the regulation of immunity in sepsis, which may in turn act as indicators or therapeutic targets for sepsis.

Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design

INTRODUCTION

The increasing prevalence of type 2 diabetes mellitus (T2DM) presents a significant burden on affected individuals and healthcare systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes.

METHODS AND ANALYSIS

Primary care data from the Clinical Practice Research Datalink, linked hospitalisation and mortality records between April 2007 and March 2017 for patients with T2DM in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion, are: diabetes duration, glycated haemoglobin, microvascular complications, comorbidities and coprescribed treatments. Severity scores will be developed by two approaches: (1) calculating a count score of severity domains; (2) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analyses for the association between weighted severity scores and future outcomes-cardiovascular events, hospitalisation (diabetes-related, cardiovascular) and mortality (diabetes-related, cardiovascular, all-cause mortality)-will be performed as statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service planning and policy-making.

ETHICS AND DISSEMINATION

The study protocol was approved by the Independent Scientific Advisory Committee. Some data were presented at the National Institute for Health Research School for Primary Care Research Showcase, September 2017, Oxford, UK and the Diabetes UK Professional Conference March 2018, London, UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.

Immune biomarkers for chronic inflammation related complications in non-cancerous and cancerous diseases

Chronic inflammation arising in a diverse range of non-cancerous and cancerous diseases, dysregulates immunity and exposes patients to a variety of complications. These include immunosuppression, tissue damage, cardiovascular diseases and more. In cancer, chronic inflammation and related immunosuppression can directly support tumor growth and dramatically reduce the efficacies of traditional treatments, as well as novel immune-based therapies, which require a functional immune system. Nowadays, none of the immune biomarkers, regularly used by clinicians can sense a developing chronic inflammation, thus complications can only be detected upon their appearance. This review focuses on the necessity for such immune status biomarkers, which could predict complications prior to their appearance. Herein we bring examples for the use of cellular and molecular biomarkers in diagnosis, prognosis and follow-up of patients suffering from various cancers, for prediction of response to immune-based anti-cancer therapy and for prediction of cardiovascular disease in type 2 diabetes patients. Monitoring such biomarkers is expected to have a major clinical impact in addition to unraveling of the entangled complexity underlying dysregulated immunity in chronic inflammation. Thus, newly discovered biomarkers and those that are under investigation are projected to open a new era towards combating the silent damage induced by chronic inflammation.

An epigenome-wide study of body mass index and DNA methylation in blood using participants from the Sister Study cohort

BACKGROUND/OBJECTIVES

The relationship between obesity and chronic disease risk is well-established; the underlying biological mechanisms driving this risk increase may include obesity-related epigenetic modifications. To explore this hypothesis, we conducted a genome-wide analysis of DNA methylation and body mass index (BMI) using data from a subset of women in the Sister Study.

SUBJECTS/METHODS

The Sister Study is a cohort of 50 884 US women who had a sister with breast cancer but were free of breast cancer themselves at enrollment. Study participants completed examinations which included measurements of height and weight, and provided blood samples. Blood DNA methylation data generated with the Illumina Infinium HumanMethylation27 BeadChip array covering 27,589 CpG sites was available for 871 women from a prior study of breast cancer and DNA methylation. To identify differentially methylated CpG sites associated with BMI, we analyzed this methylation data using robust linear regression with adjustment for age and case status. For those CpGs passing the false discovery rate significance level, we examined the association in a replication set comprised of a non-overlapping group of 187 women from the Sister Study who had DNA methylation data generated using the Infinium HumanMethylation450 BeadChip array. Analysis of this expanded 450 K array identified additional BMI-associated sites which were investigated with targeted pyrosequencing.

RESULTS

Four CpG sites reached genome-wide significance (false discovery rate (FDR) q<0.05) in the discovery set and associations for all four were significant at strict Bonferroni correction in the replication set. An additional 23 sites passed FDR in the replication set and five were replicated by pyrosequencing in the discovery set. Several of the genes identified including ANGPT4, RORC, SOCS3, FSD2, XYLT1, ABCG1, STK39, ASB2 and CRHR2 have been linked to obesity and obesity-related chronic diseases.

CONCLUSIONS

Our findings support the hypothesis that obesity-related epigenetic differences are detectable in blood and may be related to risk of chronic disease.

Paving the Road to Tumor Development and Spreading: Myeloid-Derived Suppressor Cells are Ruling the Fate

Cancer development is dependent on intrinsic cellular changes as well as inflammatory factors in the tumor macro and microenvironment. The inflammatory milieu nourishes the tumor and contributes to cancer progression. Numerous studies, including ours, have demonstrated that the tumor microenvironment is immunosuppressive, impairing the anticancer immune responses. Chronic inflammation was identified as the key process responsible for this immunosuppression via induction of immature myeloid-derived suppressor cells (MDSCs). Upon a prolonged immune response, MDSCs are polarized toward immunosuppressive cells meant to control the exacerbated immune response. In cancer, the chronic inflammatory response renders the MDSCs harmful. Polarized MDSCs suppress T-cells and natural killer cells, as well as antigen-presenting cells, abrogating the beneficial immune response. These changes in the immunological milieu could also lead to high frequency of mutations, enhanced cancer cell stemness, and angiogenesis, directly supporting tumor initiation, growth, and spreading. The presence of MDSCs in cancer poses a serious obstacle in a variety of immune-based therapies, which rely on the stimulation of antitumor immune responses. Cumulative data, including our own, suggest that the selection of an appropriate and effective anticancer therapy must take into consideration the host’s immune status as well as tumor-related parameters. Merging biomarkers for immune monitoring into the traditional patient’s categorization and follow-up can provide new predictive and diagnostic tools to the clinical practice. Chronic inflammation and MDSCs could serve as novel targets for therapeutic interventions, which can be combined with conventional cancer treatments such as chemotherapy, radiotherapy, and cancer cell-targeted and immune-based therapies. Intervention in environmental and tumor-specific inflammatory mechanisms will allow better clinical management of cancer toward more efficient treatment.