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

Gene Expression Dysregulation in Whole Blood of Patients with Clostridioides difficile Infection

Clostridioides difficile (C. difficile) is a global threat and has significant implications for individuals and health care systems. Little is known about host molecular mechanisms and transcriptional changes in peripheral immune cells. This is the first gene expression study in whole blood from patients with C. difficile infection. We took blood and stool samples from patients with toxigenic C. difficile infection (CDI), non-toxigenic C. difficile infection (GDH), inflammatory bowel disease (IBD), diarrhea from other causes (DC), and healthy controls (HC). We performed transcriptome-wide RNA profiling on peripheral blood to identify diarrhea common and CDI unique gene sets. Diarrhea groups upregulated innate immune responses with neutrophils at the epicenter. The common signature associated with diarrhea was non-specific and shared by various other inflammatory conditions. CDI had a unique 45 gene set reflecting the downregulation of humoral and T cell memory functions. Dysregulation of immunometabolic genes was also abundant and linked to immune cell fate during differentiation. Whole transcriptome analysis of white cells in blood from patients with toxigenic C. difficile infection showed that there is an impairment of adaptive immunity and immunometabolism.

Development of a Nomogram Based on Transcriptional Signatures, IFN-γ Response and Neutrophils for Diagnosis of Tuberculosis

PURPOSE

Tuberculosis (TB) is a major global health threat and its diagnosis remains challenging. This study aimed to develop a nomogram that incorporated peripheral blood transcriptional signatures and other blood tests for the diagnosis of tuberculosis.

PATIENTS AND METHODS

Patients with TB, patients with other definite pulmonary diseases (OPD), individuals with latent tuberculosis infection (LTBI), and healthy controls (HC) were retrospectively enrolled between May 2017 and April 2018. The results of the interferon-γ release assay (IGRA) and blood counts were obtained from medical records, and the transcripts of 10 genes were detected using reverse transcription polymerase chain reaction (RT-PCR). Variable selection was performed using least absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression was performed for the optimal prediction model with backward direction. The model was displayed as a nomogram, and its performance was evaluated for discrimination ability, calibration ability, and clinical usefulness. Internal validation of the prediction model was conducted using bootstrap resampling.

RESULTS

A total of 185 participants were enrolled, including 84 patients with TB and 101 controls. A prediction nomogram composed of IGRA, percentage of neutrophils, and expression levels of CD64, granzyme A (GZMA), and PR/SET domain 1 (PRDM1) was established. The nomogram demonstrated good discrimination, with an unadjusted area under the curve (AUC) of 0.914 (95% CI: 0.875-0.954) and a bootstrap-corrected AUC of 0.914 (95% CI: 0.874-0.947). With a cutoff value of 0.519, the sensitivity and specificity for discriminating PTB from controls were 0.81 and 0.871, respectively. The nomogram also showed good calibration with the Hosmer-Lemeshow test (P=0.58) and good clinical practicality displayed by the decision curve analysis.

CONCLUSION

A nomogram composed of IGRA, percentage of neutrophils, and expression of CD64, GZMA, and PRDM1 was established. The nomogram demonstrated a sensitivity and specificity of 81% and 87%, respectively, for differentiating TB from controls.

Signature Proteins in Small Extracellular Vesicles of Granulocytes and CD4+ T-Cell Subpopulations Identified by Comparative Proteomic Analysis

Several studies have described the proteomic profile of different immune cell types, but only a few have also analysed the content of their delivered small extracellular vesicles (sEVs). The aim of the present study was to compare the protein signature of sEVs delivered from granulocytes (i.e., neutrophils and eosinophils) and CD4+ T cells (i.e., TH1, TH2, and TH17) to identify potential biomarkers of the inflammatory profile in chronic inflammatory diseases. Qualitative (DDA) and quantitative (DIA-SWATH) analyses of in vitro-produced sEVs revealed proteome variations depending on the cell source. The main differences were found between granulocyte- and TH cell-derived sEVs, with a higher abundance of antimicrobial proteins (e.g., LCN2, LTF, MPO) in granulocyte-derived sEVs and an enrichment of ribosomal proteins (RPL and RPS proteins) in TH-derived sEVs. Additionally, we found differentially abundant proteins between neutrophil and eosinophil sEVs (e.g., ILF2, LTF, LCN2) and between sEVs from different TH subsets (e.g., ISG15, ITGA4, ITGB2, or NAMPT). A "proof-of-concept" assay was also performed, with TH2 biomarkers ITGA4 and ITGB2 displaying a differential abundance in sEVs from T2high and T2low asthma patients. Thus, our findings highlight the potential use of these sEVs as a source of biomarkers for diseases where the different immune cell subsets studied participate, particularly chronic inflammatory pathologies such as asthma or chronic obstructive pulmonary disease (COPD).

TB diagnostic insights, progress made on point of care diagnostics and bioinformatics as an additional tool for improvement

Despite major efforts made to control tuberculosis disease (TB), this disease continues to present a major global health challenge and drug resistance is continuously growing. TB is caused by Mycobacterium tuberculosis and spreads exclusively via human-to-human contact transmission. Therefore, early detection and diagnosis for proper treatment with active TB have a great impact on public health. Regardless, most people in developing countries with TB or TB-associated symptoms do not have access to an adequate initial diagnosis. Available bacteriologic-based techniques are either inefficient or may require a longer turnaround time from the laboratory. Contemporarily, non-bacteriologic based methods have both questionable sensitivity and specificity and while others cannot distinguish between active and latent TB. Thus, additional efforts have been made to find accurate diagnostic tests for TB. Herein, we review the available methods used for TB diagnosis, and in addition, we explore point of care (POC) diagnostics as an alternative way to develop TB diagnostic tests and further evaluate whether bioinformatics can be used as an additional screening tool for identification of possible TB biomarkers for the development of POC TB diagnostics, which is part of our research focus.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Genome-wide scan for selective sweeps identifies novel loci associated with resistance to mastitis in German Holstein cattle

Domestication and selection significantly changed phenotypic and behavioural traits in modern domestic animals. In this study, to identify the genomic regions associated with mastitis, genomic data of German Holstein dairy cattle were analysed. The samples were genotyped using the Bovine 50 K SNP chip. For each defined healthy and sick group, 133 samples from 13,276 genotyped dairy cows were selected based on mastitis random residual effects. Grouping was done to infer selection signatures based on XP-EHH statistic. The results revealed that for the top 0.01 percentile of the obtained XP-EHH values, five genomic regions on chromosomes 8, 11, 12, 14 and 26 of the control group, and four regions on chromosomes 3, 4 (two regions) and 22 of the case group, have been under selection. Also, consideration of the top 0.1 percentile of the XP-EHH values, clarified 21 and 15 selective sweeps in the control and case group, respectively. This study identified some genomic regions containing potential candidate genes associated with resistance and susceptibility to mastitis, immune system and inflammation, milk traits, udder morphology and different types of cancers. In addition, these regions overlap with some quantitative trait loci linked to clinical mastitis, immunoglobulin levels, somatic cell score, udder traits, milk fat and protein, milk yield, milking speed and veterinary treatments. It is noteworthy that we found two regions in the healthy group (on chromosomes 12 and 14) with strong signals, which were not described previously. It is likely that future research could link these identified genomic regions to mastitis. The results of the current study contribute to the identification of causal mutations, genomic regions and genes affecting mastitis incidence in dairy cows.

Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB

Bacterial culture of M. tuberculosis (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change > 1.2; p < 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.