BLR1 and FCGR1A transcripts in peripheral blood associate with the extent of intrathoracic tuberculosis in children and predict treatment outcome

Decoding differentially expressed genes to identify potential immunity associated biomarkers in Tuberculosis: An integrative bioinformatics approach

Tuberculosis (TB) poses a significant threat to the Indian population, with India accounting for 20 % of the global TB cases. The current study aims to identify molecular biomarkers for better diagnostics by comparing the transcriptome signatures of healthy individuals against TB-affected individuals. Next-generation sequencing (NGS) tools were used to identify critical differentially expressed genes (DEGs). 302 DEGs were identified based on a logFC threshold of |3| and adjusted p-value < 0.05. STRING database was used to plot the interactions amongst the 302 DEGs. The DEGs were functionally annotated, highlighting numerous physiological functions affected due to the dysregulation of the identified hub genes. TLR4, FCGR1A, ITGAM, LTF, and CXCR2 were the hub genes identified and observed to dysregulate crucial physiological functions. TLR4 has been implicated in the progression of TB in various populations, and the findings of this study will enable researchers to improve the current landscape of diagnostics for TB.

Cathepsin S Is More Abundant in Serum of Mycobacterium avium subsp. paratuberculosis-Infected Dairy Cows

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of bovine paratuberculosis, a chronic granulomatous enteritis leading to economic losses and posing a risk to human health due to its zoonotic potential. The pathogen cannot reliably be detected by standard methods, and immunological procedures during the infection are not well understood. Therefore, the aim of our study was to explore host-pathogen interactions in MAP-infected dairy cows and to improve diagnostic tests. Serum proteomics analysis using quantitative label-free LC-MS/MS revealed 60 differentially abundant proteins in MAP-infected dairy cows compared to healthy controls from the same infected herd and 90 differentially abundant proteins in comparison to another control group from an uninfected herd. Pathway enrichment analysis provided new insights into the immune response to MAP and susceptibility to the infection. Furthermore, we found a higher abundance of Cathepsin S (CTSS) in the serum of MAP-infected dairy cows, which is involved in multiple enriched pathways associated with the immune system. Confirmed with Western blotting, we identified CTSS as a potential biomarker for bovine paratuberculosis. This study enabled a better understanding of procedures in the host-pathogen response to MAP and improved detection of paratuberculosis-diseased cattle.

Comprehensive analysis of a pyroptosis-related gene signature of clinical and biological values in spinal cord injury

Background

Since some of the clinical examinations are not suitable for patients with severe spinal cord injury (SCI), blood biomarkers have been reported to reflect the severity of SCI. The objective of this study was to screen out the potential biomarkers associated with the diagnosis of SCI by bioinformatics analysis.

Methods

The microarray expression profiles of SCI were obtained from the Gene Expression Omnibus (GEO) database. Core genes correlated to pyroptosis were obtained by crossing the differential genes, and module genes were obtained by WGCNA analysis and lasso regression. The immune infiltration analysis and GSEA analysis revealed the essential effect of immune cells in the progression of SCI. In addition, the accuracy of the biomarkers in diagnosing SCI was subsequently evaluated and verified using the receiver operating characteristic curve (ROC) and qRT-PCR.

Results

A total of 423 DEGs were identified, among which 319 genes were upregulated and 104 genes were downregulated. Based on the WGCNA analysis, six potential biomarkers were screened out, including LIN7A, FCGR1A, FGD4, GPR27, BLOC1S1, and GALNT4. The results of ROC curves demonstrated the accurate value of biomarkers related to SCI. The immune infiltration analysis and GSEA analysis revealed the essential effect of immune cells in the progression of SCI, including macrophages, natural killer cells, and neutrophils. The qRT-PCR results verified that FGD4, FCAR1A, LIN7A, BLOC1S1, and GPR27 were significantly upregulated in SCI patients.

Conclusion

In this study, we identified and verified five immune pyroptosis-related hub genes by WGCNA and biological experiments. It is expected that the five identified potential biomarkers in peripheral white blood cells may provide a novel strategy for early diagnosis.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

Objective

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

Methods

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

Results

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

Conclusion

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Screening of potential biomarkers for distinguishing between latent and active tuberculosis in children using bioinformatics analysis

Tuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis.

Combining host-derived biomarkers with patient characteristics improves signature performance in predicting tuberculosis treatment outcomes

Tuberculosis (TB) is a global health concern. Treatment is prolonged, and patients on anti-TB therapy (ATT) often experience treatment failure for various reasons. There is an urgent need to identify signatures for early detection of failure and initiation of a treatment switch.

We investigated how gene biomarkers and/or basic patient characteristics could be used to define signatures for treatment outcomes in Indian adult pulmonary-TB patients treated with standard ATT. Using blood samples at baseline, a 12-gene signature combined with information on gender, previously-diagnosed TB, severe thinness, smoking and alcohol consumption was highly predictive of treatment failure at 6 months. Likewise a 4-protein biomarker signature combined with the same patient characteristics was almost as highly predictive of treatment failure. Combining biomarkers and basic patient characteristics may be useful for predicting and hence identification of treatment failure at an early stage of TB therapy.

Sivakumaran et al. show that a 12-gene signature combined with gender, previously diagnosed tuberculosis (TB), severe thinness, smoking, and alcohol consumption predict the treatment outcome at 6 months. This study suggests that the combination of biomarkers and basic patient characteristics may better predict the treatment failure at an early stage of TB therapy.

Development and validation of an integrative methylation signature and nomogram for predicting survival in clear cell renal cell carcinoma

Background

Growing evidence has shown that genetic or epigenetic alterations are highly involved in the initiation and progression of renal cell carcinoma (RCC). This study aimed to find prognostic methylation markers in clear cell RCC (ccRCC).

Methods

In this study, we developed and confirmed an integrated and comprehensive methylation signature by integrating DNA methylation, gene expression, and The Cancer Genome Atlas (TCGA) survival data. First, the methylation signature was found and checked based on data analysis of published datasets. Then, independent predictive factors were selected using the Cox proportional model and incorporated into the nomogram. Finally, the predictive nomogram was derived and validated using a concordance index and calibration plots.

Results

A series of differentially expressed and methylated genes were identified. After intersection analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) analysis, and correlation analysis, FCGR1A, F2, and NOD2 were established as a predictive signature. According to the Kaplan-Meier survival analysis, the risk score system based on the predictive signature was able to stratify the patients into high- and low-risk groups with significantly different overall survival. The receiver operating characteristic (ROC) analysis further showed that the predictive signature yielded high sensitivity and specificity in predicting the prognosis outcome of ccRCC patients. Moreover, univariate and multivariate Cox regression analysis confirmed that the three-gene methylation signature was an independent prognostic factor in ccRCC. Finally, a nomogram comprising the predictive signature and several independent variables were constructed and proved to effectively predict ccRCC patient survival.

Conclusions

The three-gene methylation signature was revealed to be a potential novel and independent adverse predictor of prognosis for ccRCC patients and may serve as a promising marker for treatment management and survival outcome improvement. However, substantial validation experiments are required to characterize the molecular background of the predictive signature.

M. Ottenhoff T, M. Cliff J, C. Haks M. Gene expression profiles classifying clinical stages of tuberculosis and monitoring treatment responses in Ethiopian HIV-negative and HIV-positive cohorts

Background

Validation of previously identified candidate biomarkers and identification of additional candidate gene expression profiles to facilitate diagnosis of tuberculosis (TB) disease and monitoring treatment responses in the Ethiopian context is vital for improving TB control in the future.

Methods

Expression levels of 105 immune-related genes were determined in the blood of 80 HIV-negative study participants composed of 40 active TB cases, 20 latent TB infected individuals with positive tuberculin skin test (TST+), and 20 healthy controls with no Mycobacterium tuberculosis (Mtb) infection (TST-), using focused gene expression profiling by dual-color Reverse-Transcription Multiplex Ligation-dependent Probe Amplification assay. Gene expression levels were also measured six months after anti-TB treatment (ATT) and follow-up in 38 TB patients.

Results

The expression of 15 host genes in TB patients could accurately discriminate between TB cases versus both TST+ and TST- controls at baseline and thus holds promise as biomarker signature to classify active TB disease versus latent TB infection in an Ethiopian setting. Interestingly, the expression levels of most genes that markedly discriminated between TB cases versus TST+ or TST- controls did not normalize following completion of ATT therapy at 6 months (except for PTPRCv1, FCGR1A, GZMB, CASP8 and GNLY) but had only fully normalized at the 18 months follow-up time point. Of note, network analysis comparing TB-associated host genes identified in the current HIV-negative TB cohort to TB-associated genes identified in our previously published Ethiopian HIV-positive TB cohort, revealed an over-representation of pattern recognition receptors including TLR2 and TLR4 in the HIV-positive cohort which was not seen in the HIV-negative cohort. Moreover, using ROC cutoff ≥ 0.80, FCGR1A was the only marker with classifying potential between TB infection and TB disease regardless of HIV status.

Conclusions

Our data indicate that complex gene expression signatures are required to measure blood transcriptomic responses during and after successful ATT to fully diagnose TB disease and characterise drug-induced relapse-free cure, combining genes which resolve completely during the 6-months treatment phase of therapy with genes that only fully return to normal levels during the post-treatment resolution phase.

Genome-Wide Association Study in Mexican Holstein Cattle Reveals Novel Quantitative Trait Loci Regions and Confirms Mapped Loci for Resistance to Bovine Tuberculosis

Bovine tuberculosis (bTB) is a disease of cattle that represents a risk to public health and causes severe economic losses to the livestock industry. Recently, genetic studies, like genome-wide association studies (GWAS) have greatly improved the investigation of complex diseases identifying thousands of disease-associated genomic variants. Here, we present evidence of genetic variants associated with resistance to TB in Mexican dairy cattle using a case-control approach with a selective DNA pooling experimental design. A total of 154 QTLRs (quantitative trait loci regions) at 10% PFP (proportion of false positives), 42 at 5% PFP and 5 at 1% PFP have been identified, which harbored 172 annotated genes. On BTA13, five new QTLRs were identified in the MACROD2 and KIF16B genes, supporting their involvement in resistance to bTB. Six QTLRs harbor seven annotated genes that have been previously reported as involved in immune response against Mycobacterium spp: BTA (Bos taurus autosome) 1 (CD80), BTA3 (CTSS), BTA 3 (FCGR1A), BTA 23 (HFE), BTA 25 (IL21R), and BTA 29 (ANO9 and SIGIRR). We identified novel QTLRs harboring genes involved in Mycobacterium spp. immune response. This is a first screening for resistance to TB infection on Mexican dairy cattle based on a dense SNP (Single Nucleotide Polymorphism) chip.

Evaluation of the transcriptional immune biomarkers in peripheral blood from Warao indigenous associate with the infection by Mycobacterium tuberculosis

INTRODUCTION

Biomarkers are critical tools for finding new approaches for controlling the spread of tuberculosis (TB), including for predicting the development of TB therapeutics, vaccines, and diagnostic tools.

METHODS

Expression of immune biomarkers was analyzed in peripheral blood cells stimulated and non-stimulated with M. tuberculosis antigens ESAT-6, CFP10 and TB7.7. in Warao indigenous individuals. These biomarkers may be able to differentiate TB states, such as active tuberculosis (ATB) cases and latent tuberculosis infection (LTBI) from non-infected controls (NIC). A real-time reverse transcription polymerase chain reaction (RT-qPCR) assay was performed on 100 blood samples under non-stimulation or direct ex vivo conditions (NS=50) and stimulation conditions (S=50).

RESULTS

The findings are shown as the median and interquartile range (IQR) of relative gene expression levels of IFN-γ, CD14, MMP9, CCR5, CCL11, CXCL9/MIG, and uPAR/PLAUR immune biomarkers. MMP9 levels were significantly higher in the LTBI-NS and LTBI-S groups compared with the NIC-NS and NIC-S groups. However, CCR5 levels were significantly lower in the LTBI-S group compared with both NIC-NS and NIC-S groups. CCL11 levels were significantly lower in the LTBI-S group compared with the NIC-NS group.

CONCLUSIONS

Preliminary findings showed that MMP9 immune biomarkers separated LTBI indigenous individuals from NIC indigenous individuals, while CCR5, CCL11, CD14, and IFN-γ did not differentiate TB states from NIC. MMP9 may be useful as a potential biomarker for LTBI and new infected case detection among Warao indigenous individuals at high risk of developing the disease. It may also be used to halt the epidemic, which will require further validation in larger studies.