STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection

Tuberculosis (TB) is a severe infectious disease that seriously endangers human health. The immune defence mechanism of the body against TB is still unclear. The purpose of this study was to find the key molecules involved in the immune defence response during TB infection, and provide reference for the treatment of TB and further understanding of the immune defence mechanism of the body. Data from GSE83456 were downloaded from GEO data sets for analysis, and a total of 192 differentially expressed genes were screened out. Most of these genes are enriched in the interferon signalling pathway and are defence response–related. We also found that STAT1 plays an important role in the immune defence of TB infection and it is one of the key genes related to interferon signalling pathway. STAT1-related molecules including hsa-miR-448, hsa-miR-223-3p, SAMD8_hsa_circRNA 994 and TWF1_hsa_circRNA 9897 were therefore screened out. Furthermore, expression levels of hsa-miR-448 and hsa-miR-223-3p were then verified by qRT-PCR. Results showed that both hsa-miR-448 and hsa-miR-223-3p were down-regulated in plasma from patients with pulmonary TB. Taken together, our data indicate that an mRNA-miRNA-circRNA interaction chain may play an important role in the infection of MTB, and STAT1 and related molecules including hsa-miR-223-3p, has-miR-448, SAMD8_hsa_circRNA994 and TWF1_hsa_circRNA9897 were identified as potential biomarkers in the development of active TB.     

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a common disease with serious consequences in mortality and cost. Here we aim to screen the differentially expressed genes (DEGs) as biomarkers for early diagnosis of AMI. The microarray data of AMI was downloaded from Gene Expression Omnibus (GEO), including two independent types of research GSE66360 and GSE62646. The DEGs between control and processed samples were screened out by using limma package. Meanwhile, we performed functional analysis of GO terms and/or KEGG pathways to observe the enriched pathways of the DEGs. Finally, regression analysis of raw data was performed by using packet affyPLM in R language. Dataset GSE62646 contained 53 DEGs (FC log2>1 and P value <0.05) between first‐day samples from 28 STEMI patients and control samples from 14 patients without myocardial infarction history. There were 12 up‐regulated and 41 down‐regulated genes, 35 DEGs annotated. In GSE66360, a total of 3034 DEGs between 32 AMI patients and 38 healthy persons were obtained, including 1861 up‐regulated and 1173 down‐regulated DEGs. The comparison of the DEGs in two datasets revealed four common up‐regulated genes (EGR1, STAB1, SOCS3, TMEM176A). In enrichment analysis, STAB1, SOCS3, EGR1 involved in metabolic regulation and signaling pathways related to coronary artery disease with a characteristic change (P < 0.05). The DEGs, especially the four up‐regulated common genes, could serve as biomarkers for early diagnosis of AMI. Additionally, the relative biological pathways these DEGs enriched in might provide a good reference to explore the molecular expression mechanism of AMI. J. Cell. Biochem. 119: 650–658, 2018. © 2017 Wiley Periodicals, Inc.     

Disease state differentiation and identification of tuberculosis biomarkers via native antigen array profiling

A critical element of tuberculosis control is early and sensitive diagnosis of infection and disease. Our laboratories recently showed that different stages of disease were distinguishable via two-dimensional Western blot analyses of Mycobacterium tuberculosis culture filtrate proteins. However, this methodology is not suitable for high throughput testing. Advances in protein microarray technology provide a realistic mechanism to screen a large number of serum samples against thousands of proteins to identify biomarkers of disease states. Techniques were established for separation of native M. tuberculosis cytosol and culture filtrate proteins, resulting in 960 unique protein fractions that were used to generate protein microarrays. Evaluation of serological reactivity from 42 patients in three tuberculosis disease states and healthy purified protein derivative-positive individuals demonstrated that human immunodeficiency virus (HIV)-negative cavitary and noncavitary tuberculosis (TB) patients' sera recognized 126 and 59 fractions, respectively. Sera from HIV patients coinfected with TB recognized 20 fractions of which five overlapped with those recognized by non-HIV TB patients' sera and 15 were unique to the HIV+TB+ disease state. Identification of antigens within the reactive fractions yielded 11 products recognized by both cavitary and noncavitary TB patients' sera and four proteins (HspX, MPT64, PstS1, and TrxC) specific to cavitary TB patients. Moreover four novel B cell antigens (BfrB, LppZ, SodC, and TrxC) of human tuberculosis were identified.     

Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles

Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.     

Differential Levels of Alpha-2-Macroglobulin,Haptoglobin and Sero-Transferrin as Adjunct Markers for TB Diagnosis and Disease Progression in the Malnourished Tribal Population of Melghat,India

Lack of diagnostic capacity has been a crucial barrier preventing an effective response to the challenges of malnutrition and tuberculosis (TB). Point-of-care diagnostic tests for TB in immuno-incompetent, malnourished population are thus needed to ensure rapid and accurate detection. The aim of the study was to identify potential biomarkers specific for TB infection and progression to overt disease in the malnourished population of Melghat. A prospective cohort study was conducted in the year 2009 through 2011 in six villages of the Melghat region. 275 participants consisting of malnourished cases with a) active TB (n = 32), b) latent TB infection (n = 90), c) with no clinical or bacteriological signs of active or latent TB (n = 130) and healthy control subjects (n = 23) were recruited for the study. The proteome changes of the host serum in response to Mycobacterium tuberculosis (M.tb) infection were investigated using one dimensional electrophoresis in combination with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Three most differentially expressed proteins; alpha-2-macroglobulin (A-2-M), sero-transferrin and haptoglobin were identified by MALDI-TOF MS analysis, which were up-regulated in the malnourished patients with active TB and down-regulated in the malnourished patients compared with the healthy controls. Additionally, follow-up studies indicated that the expression of these proteins increased to nearly two folds in patients who developed active disease from latent state. Our preliminary results suggest that A-2-M, sero-transferrin and haptoglobin may be clinically relevant host biomarkers for TB diagnosis and disease progression in the malnourished population. This study provides preliminary framework for an in-depth analysis of the biomarkers in larger well-characterized cohorts. Evaluation of these biomarkers in follow-up cases may further aid in improving TB diagnosis.     

Identification and functional analysis of spermatogenesis-associated gene modules in azoospermia by weighted gene coexpression network analysis

Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein–protein interaction network revealed that spermatogenesis- and spermatid development–associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

Up‐regulated SAMD9L modulated by TLR2 and HIF‐1α as a promising biomarker in tuberculosis

The aim of this study was to identify potential biomarkers of TB in blood and determine their function in Mtb‐infected macrophages. First of all, WGCNA was used to analyse 9451 genes with significant changes in TB patients’ whole blood. The 220 interferon‐γ‐related genes were identified, and then 30 key genes were screened using Cytoscape. Then, the AUC values of key genes were calculated to further narrow the gene range. Finally, we identified 9 genes from {"type":"entrez-geo","attrs":{"text":"GSE19444","term_id":"19444"}}GSE19444. ROC analysis showed that SAMD9L, among 9 genes, had a high diagnostic value (AUC = 0.925) and a differential diagnostic value (AUC>0.865). To further narrow down the range of DEGs, the top 10 hub‐connecting genes were screened from monocytes ({"type":"entrez-geo","attrs":{"text":"GSE19443","term_id":"19443"}}GSE19443). Finally, we obtained 4 genes (SAMD9L, GBP1, GBP5 and STAT1) by intersections of genes from monocytes and whole blood. Among them, it was found that the function of SAMD9L was unknown after data review, so this paper studied this gene. Our results showed that SAMD9L is up‐regulated and suppresses cell necrosis, and might be regulated by TLR2 and HIF‐1α during Mtb infection. In addition, miR‐181b‐5p is significantly up‐regulated in the peripheral blood plasma of tuberculosis patients, which has a high diagnostic value (AUC = 0.969).     

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.     

Immune-related chemotactic factors were found in acute coronary syndromes by bioinformatics

DNA microarray data for thrombus-related leukocyte from patients with acute coronary syndrome (ACS) was analyzed to acquire key genes associated with ACS. Microarray data set GSE19339, including four ACS patients’ samples and four normal samples, were downloaded from Gene Expression Omnibus database. Raw data was pre-processed and differentially expressed genes (DEGs) were identified by Affy packages of R. The interaction network was established with STRING. DrugBank was retrieved to obtain relevant small molecules. A total of 487 differentially expressed genes were identified as DEGs between normal and disease samples. Among which, ten up-regulated genes belonging to chemokine family (CCL2, CCR1, CXCL3, CXCL2, CCL8, CXCL11, CCL7, IL10, CCL22 and CCL20) were related to inflammatory response. In addition, two inhibitors of CCL2 (L-Mimosine) were retrieved from the DrugBank database. Considering the roles of inflammatory response in the progression of ACS and the functions of the ten up-regulated genes, we speculated that these genes might be related to ACS. Moreover, the inhibitors could provide guidelines for future drug design acting on these genes.     

Deregulated microRNAs in CD4+ T cells from individuals with latent tuberculosis versus active tuberculosis

The mechanisms of latent tuberculosis (TB) infection remain elusive. Roles of microRNA (miRNA) have been highlighted in pathogen–host interactions recently. To identify miRNAs involved in the immune response to TB, expression profiles of miRNAs in CD4⁺ T cells from patients with latent TB, active TB and healthy controls were investigated by microarray assay and validated by RT‐qPCR. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyse the significant functions and involvement in signalling pathways of the differentially expressed miRNAs. To identify potential target genes for miR‐29, interferon‐γ (IFN‐γ) mRNA expression was measured by RT‐qPCR. Our results showed that 27 miRNAs were deregulated among the three groups. RT‐qPCR results were generally consistent with the microarray data. We observed an inverse correlation between miR‐29 level and IFN‐γ mRNA expression in CD4⁺ T cells. GO and KEGG pathway analysis showed that the possible target genes of deregulated miRNAs were significantly enriched in mitogen‐activated protein kinase signalling pathway, focal adhesion and extracellular matrix receptor interaction, which might be involved in the transition from latent to active TB. In all, for the first time, our study revealed that some miRNAs in CD4⁺ T cells were altered in latent and active TB. Function and pathway analysis highlighted the possible involvement of miRNA‐deregulated mRNAs in TB. The study might help to improve understanding of the relationship between miRNAs in CD4⁺ T cells and TB, and laid an important foundation for further identification of the underlying mechanisms of latent TB infection and its reactivation.     

椎间盘退变纤维环及髓核异常表达基因的比较及生物信息学分析

目的:通过对已公开发表的基因芯片表达谱数据进行研究,探究椎间盘退变过程中纤维环与髓核组织的基因表达差异,并采用生物信息学方法对差异进行分析。方法:经GEO数据库选取两组椎间盘退变相关的基因芯片表达谱数据GSE23130及GSE67567,GSE23130所研究标本来源于正常及退变纤维环组织,GSE67567标本来源于正常及退变髓核组织。对上述数据系列进行质量分析,GSE23130及GSE67567各有10例样本数据被纳入实验。采用Gene Spring 13.0软件对GSE23130正常及退变纤维环间差异表达基因及GSE67567正常及退变髓核间差异表达基因分别进行筛选,利用KEGG PATHWAY和DAVID功能注释簇集分析分别对GSE23130及GSE67567上调及下调基因进行生物信息学分析。结果:GSE23130及GSE67567各筛选出差异表达基因3182个和3017个,其中135个基因在上述两个基因表达谱数据中均存在差异表达。针对两组数据进行的KEGG PATHWAY分析发现TGF-beta signaling pathway和regulation of apoptosis等数个相同的生物学通路及DAVID功能注释簇集;此外,还发现了数个与GSE23130及GSE67567单独相关的DAVID功能注释簇集。结论:椎间盘退变过程中纤维环及髓核组织内基因表达情况存在差异,两种组织内发生的生物过程不尽相同。某些生物学过程在两种组织内均出现异常改变,这些生物学过程中的异常变化可能是椎间盘退变的关键环节,值得进行深入研究。     

Novel serological biomarker panel using protein microarray can distinguish active TB from latent TB infection

BackgroundRapid laboratory technologies which can effectively distinguish active tuberculosis (ATB) from controls and latent tuberculosis infection (LTBI) are lacked.The objective of this study is to explore MTB biomarkers in serum that can distinguish ATB from LTBI.MethodsWe constructed a tuberculosis protein microarray containing 64 MTB associated antigens. We then used this microarray to screen 180 serum samples, from patients with ATB and LTBI, and healthy volunteer controls. Both SAM (Significance analysis of microarrays) and ROC curve analysis were used to identify the differentially recognized biomarkers between groups. Extra 300 serum samples from patients with ATB and LTBI, and healthy volunteer controls were employed to validate the identified biomarkers using ELISA-based method.ResultsAccording to the results, the best biomarker combinations of 4 proteins (Rv1860, RV3881c, Rv2031c and Rv3803c) were selected. The biomarker panel containing these 4 proteins has reached a sensitivity of 93.3% and specificity of 97.7% for distinguishing ATB from LTBI, and a sensitivity of 86% and specificity of 97.6% for distinguishing ATB from HC.ConclusionThe biomarker combination in this study has high sensitivity and specificity in distinguishing ATB from LTBI, suggesting it is worthy for further validation in more clinical samples.     

GSEA在全基因组表达谱芯片数据分析中的应用

GSEA是一个可下载后免费使用的全基因组表达谱芯片数据分析工具。它根据已有的对基因的定位、性质、功能、生物学意义等知识的基础上,首先构建了一个分子标签数据库,数据库中包含了多个功能基因集。通过分析一组处于两个生物学状态的基因表达谱杂交数据,它们在特定的功能基因集中的表达状况,以及这种表达状况是否存在某种统计学显著性。GSEA是从另一个角度来诠释生物信息,可进一步完善我们对相关生物学事件的认识。