Meta-analysis of host response networks identifies a common core in tuberculosis

Inhibition of leukotriene-B4 signalling-mediated host response to tuberculosis is a potential mode of adjunctive host-directed therapy

Treatment of tuberculosis (TB) is faced with several challenges including the long treatment duration, drug toxicity and tissue pathology. Host-directed therapy provides promising avenues to find compounds for adjunctively assisting antimycobacterials in the TB treatment regimen, by promoting pathogen eradication or limiting tissue destruction. Eicosanoids are a class of lipid molecules that are potent mediators of inflammation and have been implicated in aspects of the host response against TB. Here, we have explored the blood transcriptome of pulmonary TB patients to understand the activity of leukotriene B4, a pro-inflammatory eicosanoid. Our study shows a significant upregulation in the leukotriene B4 signalling pathway in active TB patients, which is reversed with TB treatment. We have further utilized our in-house network analysis algorithm, ResponseNet, to identify potential downstream signal effectors of leukotriene B4 in TB patients including STAT1/2 and NADPH oxidase at a systemic as well as local level, followed by experimental validation of the same. Finally, we show the potential of inhibiting leukotriene B4 signalling as a mode of adjunctive host-directed therapy against TB. This study provides a new mode of TB treatment along with mechanistic insights which can be further explored in pre-clinical trials.

Integrated sequence-based genomic, transcriptomic, and methylation characterization of the susceptibility to tuberculosis in monozygous twins

Background

Tuberculosis (TB) is a complex disease with a spectrum of outcomes for more than six decades; however, the genomic and epigenetic mechanisms underlying the highly heritable susceptibility to TB remain unclear.

Methods

Integrated sequence-based genomic, transcriptomic, and methylation analyses were conducted to identity the genetic factors associated with susceptibility to TB in two pairs of Mongolian monozygous twins. In this study, whole-genome sequencing was employed to analyze single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), and copy number variations (CNVs). Gene expression was assessed through RNA sequencing, and methylation patterns were examined using the Illumina Infinium Methylation EPIC BeadChip. The gene-gene interaction network was analyzed using differentially expressed genes.

Results

Our study revealed no significant difference in SNP and InDel profiles between participants with and without TB. Genes with CNVs were involved in human immunity (human leukocyte antigen [HLA] family and interferon [IFN] pathway) and the inflammatory response. Different DNA methylation patterns and mRNA expression profiles were observed in genes participating in immunity (HLA family) and inflammatory responses (IFNA, interleukin 10 receptor [IL-10R], IL-12B, Toll-like receptor, and IL-1B).

Conclusions

The results of this study suggested that susceptibility to TB is associated with transcriptional and epigenetic alternations of genes involved in immune and inflammatory responses. The genes in the HLA family (HLA-A, HLA-B, and HLA-DRB1) and IFN pathway (IFN-α and IFN-γ) may play major roles in susceptibility to TB.

Reanalysis and validation of the transcriptional pleural fluid signature in pleural tuberculosis

Introduction

Pleural tuberculosis (PlTB), the most common site of extrapulmonary TB, is characterized by a paucibacillary nature and a compartmentalized inflammatory response in the pleural cavity, both of which make diagnosis and management extremely challenging. Although transcriptional signatures for pulmonary TB have already been described, data obtained by using this approach for extrapulmonary tuberculosis and, specifically, for pleural tuberculosis are scarce and heterogeneous. In the present study, a set of candidate genes previously described in pulmonary TB was evaluated to identify and validate a transcriptional signature in clinical samples from a Brazilian cohort of PlTB patients and those with other exudative causes of pleural effusion.

Methods

As a first step, target genes were selected by a random forest algorithm with recursive feature elimination (RFE) from public microarray datasets. Then, peripheral blood (PB) and pleural fluid (PF) samples from recruited patients presenting exudative pleural effusion were collected during the thoracentesis procedure. Transcriptional analysis of the selected top 10 genes was performed by quantitative RT-PCR (RT-qPCR).

Results

Reanalysis of the public datasets identified a set of candidate genes (CARD17, BHLHE40, FCGR1A, BATF2, STAT1, BTN3A1, ANKRD22, C1QB, GBP2, and SEPTIN4) that demonstrated a global accuracy of 89.5% in discriminating pulmonary TB cases from other respiratory diseases. Our validation cohort consisted of PlTB (n = 35) patients and non-TB (n = 34) ones. The gene expressions of CARD17, GBP2, and C1QB in PF at diagnosis were significantly different between the two (PlTB and non-TB) groups (p < 0.0001). It was observed that the gene expressions of CARD17 and GBP2 were higher in PlTB PF than in non-TB patients. C1QB showed the opposite behavior, being higher in the non-TB PF. After anti-TB therapy, however, GBP2 gene expression was significantly reduced in PlTB patients (p < 0.001). Finally, the accuracy of the three above-cited highlighted genes in the PF was analyzed, showing AUCs of 91%, 90%, and 85%, respectively. GBP2 was above 80% (sensitivity = 0.89/specificity = 0.81), and CARD17 showed significant specificity (Se = 0.69/Sp = 0.95) in its capacity to discriminate the groups.

Conclusion

CARD17, GBP2, and C1QB showed promise in discriminating PlTB from other causes of exudative pleural effusion by providing accurate diagnoses, thus accelerating the initiation of anti-TB therapy.

Systems-level profiling of early peripheral host-response landscape variations across COVID-19 severity states in an Indian cohort

Host immune response to COVID-19 plays a significant role in regulating disease severity. Although big data analysis has provided significant insights into the host biology of COVID-19 across the world, very few such studies have been performed in the Indian population. This study utilizes a transcriptome-integrated network analysis approach to compare the immune responses between asymptomatic or mild and moderate-severe COVID-19 patients in an Indian cohort. An immune suppression phenotype is observed in the early stages of moderate-severe COVID-19 manifestation. A number of pathways are identified that play crucial roles in the host control of the disease such as the type I interferon response and classical complement pathway which show different activity levels across the severity spectrum. This study also identifies two transcription factors, IRF7 and ESR1, to be important in regulating the severity of COVID-19. Overall this study provides a deep understanding of the peripheral immune landscape in the COVID-19 severity spectrum in the Indian genetic background and opens up future research avenues to compare immune responses across global populations.

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

OBJECTIVE

This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs.

METHODS

In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment.

RESULTS

The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals.

CONCLUSION

PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB.

Identification of Probucol as a candidate for combination therapy with Metformin for Type 2 diabetes

Type 2 Diabetes (T2D) is often managed with metformin as the drug of choice. While it is effective overall, many patients progress to exhibit complications. Strategic drug combinations to tackle this problem would be useful. We constructed a genome-wide protein-protein interaction network capturing a global perspective of perturbations in diabetes by integrating T2D subjects' transcriptomic data. We computed a 'frequently perturbed subnetwork' in T2D that captures common perturbations across tissue types and mapped the possible effects of Metformin onto it. We then identified a set of remaining T2D perturbations and potential drug targets among them, related to oxidative stress and hypercholesterolemia. We then identified Probucol as the potential co-drug for adjunct therapy with Metformin and evaluated the efficacy of the combination in a rat model of diabetes. We find Metformin-Probucol at 5:0.5 mg/kg effective in restoring near-normal serum glucose, lipid, and cholesterol levels.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

An overview of current strategies and future prospects in drug repurposing in tuberculosis

A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.

Pathogenesis of Human Immunodeficiency Virus and Mycobacterium tuberculosis Infection as Revealed by Transcriptome and Interactome Data

Tuberculosis (TB) among patients with human immunodeficiency virus (HIV) is a major global health burden and contributes to a high mortality rate due to HIV-mediated immunosuppression and subsequent susceptibility to TB. It is imperative to understand the pathogenesis of the association between HIV and TB for therapeutic innovation and preventive medicine. In the present study, we employed transcriptomic and bioinformatic analyses of differential gene expression data obtained from Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The expression data of Mycobacterium tuberculosis-infected macrophages and blood samples from TB patients (GSE54992, GSE52819, and GSE19435) and blood samples from HIV patients (GSE30310) were accessed for identification of differentially expressed genes (DEGs). Data from 20 healthy subjects and 19 patients with TB and 16 healthy subjects and 16 patients with HIV were analyzed. We report here the DEGs shared by HIV and TB infection. Moreover, HIV and TB host-pathogen interaction data were collected from BIOGRID, v 4.4.210, for identifying significantly modulated genes' targets and their interactions with the host. Host targets, including PLSCR1 (phospholipid scramblase 1), STAT1 (signal transducer and activator of transcription-1 alpha/beta), FBXO6 (F-box only protein 6), ITGAL (integrin alpha-L), and APP (amyloid beta precursor protein), are commonly modulated in both diseases. The function of these targets was screened from and reconciled with the literature to understand their role in the pathogenesis of HIV and TB. Overall, the study results suggest that these targets may potentially be important contributors to the pathogenesis of this comorbidity. Further experimental work is needed for evaluating these new observations, with a view to future therapeutic innovation for patients with HIV and TB.

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.

Mycobacterium tuberculosis infection drives a type I IFN signature in lung lymphocytes

Mycobacterium tuberculosis (Mtb) infects 25% of the world's population and causes tuberculosis (TB), which is a leading cause of death globally. A clear understanding of the dynamics of immune response at the cellular level is crucial to design better strategies to control TB. We use the single-cell RNA sequencing approach on lung lymphocytes derived from healthy and Mtb-infected mice. Our results show the enrichment of the type I IFN signature among the lymphoid cell clusters, as well as heat shock responses in natural killer (NK) cells from Mtb-infected mice lungs. We identify Ly6A as a lymphoid cell activation marker and validate its upregulation in activated lymphoid cells following infection. The cross-analysis of the type I IFN signature in human TB-infected peripheral blood samples further validates our results. These findings contribute toward understanding and characterizing the transcriptional parameters at a single-cell depth in a highly relevant and reproducible mouse model of TB.

A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R9-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R9-score can determine treatment effectiveness, making a case for designing a larger clinical study.

[Signatures of selection and candidate genes for adaptation to extreme environmental factors in the genomes of Turano-Mongolian cattle breeds]

Changes in the environment force populations of organisms to adapt to new conditions, either through phenotypic plasticity or through genetic or epigenetic changes. Signatures of selection, such as specific changes in the frequency of alleles and haplotypes, as well as the reduction or increase in genetic diversity, help to identify changes in the cattle genome in response to natural and artificial selection, as well as loci and genetic variants directly affecting adaptive and economically important traits. Advances in genetics and biotechnology enable a rapid transfer of unique genetic variants that have originated in local cattle breeds in the process of adaptation to local environments into the genomes of cosmopolitan high-performance breeds, in order to preserve their outstanding performance in new environments. It is also possible to use genomic selection approach to increase the frequency of already present adaptive alleles in cosmopolitan breeds. The review examines recent work on the origin and evolution of Turano-Mongolian cattle breeds, adaptation of Turano-Mongolian cattle to extreme environments, and summarizes available information on potential candidate genes for climate adaptation of Turano-Mongolian breeds, including cold resistance genes, immune response genes, and high-altitude adaptation genes. The authors conclude that the current literature data do not provide preference to one of the two possible scenarios of Turano-Mongolian breed origins: as a result of the domestication of a wild aurochs at East Asia or as a result of the migration of taurine proto-population from the Middle East. Turano-Mongolian breeds show a high degree of adaptation to extreme climatic conditions (cold, heat, lack of oxygen in the highlands) and parasites (mosquitoes, ticks, bacterial and viral infections). As a result of high-density genotyping and sequencing of genomes and transcriptomes, prospective candidate genes and genetic variants involved in adaptation to environmental factors have recently been identified.

Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.

Multi-country evaluation of RISK6, a 6-gene blood transcriptomic signature, for tuberculosis diagnosis and treatment monitoring

There is a crucial need for non-sputum-based TB tests. Here, we evaluate the performance of RISK6, a human-blood transcriptomic signature, for TB screening, triage and treatment monitoring. RISK6 performance was also compared to that of two IGRAs: one based on RD1 antigens (QuantiFERON-TB Gold Plus, QFT-P, Qiagen) and one on recombinant M. tuberculosis HBHA expressed in Mycobacterium smegmatis (IGRA-rmsHBHA). In this multicenter prospective nested case-control study conducted in Bangladesh, Georgia, Lebanon and Madagascar, adult non-immunocompromised patients with bacteriologically confirmed active pulmonary TB (ATB), latent TB infection (LTBI) and healthy donors (HD) were enrolled. ATB patients were followed-up during and after treatment. Blood RISK6 scores were assessed using quantitative real-time PCR and evaluated by area under the receiver-operating characteristic curve (ROC AUC). RISK6 performance to discriminate ATB from HD reached an AUC of 0.94 (95% CI 0.89-0.99), with 90.9% sensitivity and 87.8% specificity, thus achieving the minimal WHO target product profile for a non-sputum-based TB screening test. Besides, RISK6 yielded an AUC of 0.93 (95% CI 0.85-1) with 90.9% sensitivity and 88.5% specificity for discriminating ATB from LTBI. Moreover, RISK6 showed higher performance (AUC 0.90, 95% CI 0.85-0.94) than IGRA-rmsHBHA (AUC 0.75, 95% CI 0.69-0.82) to differentiate TB infection stages. Finally, RISK6 signature scores significantly decreased after 2 months of TB treatment and continued to decrease gradually until the end of treatment reaching scores obtained in HD. We confirmed the performance of RISK6 signature as a triage TB test and its utility for treatment monitoring.

PathExt: a general framework for path-based mining of omics-integrated biological networks

MOTIVATION

Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs.

RESULTS

We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls.

AVAILABILITYAND IMPLEMENTATION

The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

BACKGROUND

Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.

METHODS

We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.

FINDING

We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.

INTERPRETATION

Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

FUNDING

Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Immune Subtyping in Latent Tuberculosis

Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.

Validation of Differentially Expressed Immune Biomarkers in Latent and Active Tuberculosis by Real-Time PCR

Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB), pulmonary (PTB)) and latent TB (LTBI) infection remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. Whole blood mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new TB diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs).

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

Effects of Mycobacterium vaccae vaccine in a mouse model of tuberculosis: protective action and differentially expressed genes

BACKGROUND

Tuberculosis is a leading cause of death worldwide. BCG is an effective vaccine, but not widely used in many parts of the world due to a variety of issues. Mycobacterium vaccae (M. vaccae) is another vaccine used in human subjects to prevent tuberculosis. In the current study, we investigated the potential mechanisms of M. vaccae vaccination by determining differentially expressed genes in mice infected with M. tuberculosis before and after M. vaccae vaccination.

METHODS

Three days after exposure to M. tuberculosis H37Rv strain (5 × 10⁵ CFU), adult BALB/c mice randomly received either M. vaccae vaccine (22.5 μg) or vehicle via intramuscular injection (n = 8). Booster immunization was conducted 14 and 28 days after the primary immunization. Differentially expressed genes were identified by microarray followed by standard bioinformatics analysis.

RESULTS

M. vaccae vaccination provided protection against M. tuberculosis infection (most prominent in the lungs). We identified 2326 upregulated and 2221 downregulated genes in vaccinated mice. These changes could be mapped to a total of 123 signaling pathways (68 upregulated and 55 downregulated). Further analysis pinpointed to the MyD88-dependent TLR signaling pathway and PI3K-Akt signaling pathway as most likely to be functional.

CONCLUSIONS

M. vaccae vaccine provided good protection in mice against M. tuberculosis infection, via a highly complex set of molecular changes. Our findings may provide clue to guide development of more effective vaccine against tuberculosis.

RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response

Improved tuberculosis diagnostics and tools for monitoring treatment response are urgently needed. We developed a robust and simple, PCR-based host-blood transcriptomic signature, RISK6, for multiple applications: identifying individuals at risk of incident disease, as a screening test for subclinical or clinical tuberculosis, and for monitoring tuberculosis treatment. RISK6 utility was validated by blind prediction using quantitative real-time (qRT) PCR in seven independent cohorts. Prognostic performance significantly exceeded that of previous signatures discovered in the same cohort. Performance for diagnosing subclinical and clinical disease in HIV-uninfected and HIV-infected persons, assessed by area under the receiver-operating characteristic curve, exceeded 85%. As a screening test for tuberculosis, the sensitivity at 90% specificity met or approached the benchmarks set out in World Health Organization target product profiles for non-sputum-based tests. RISK6 scores correlated with lung immunopathology activity, measured by positron emission tomography, and tracked treatment response, demonstrating utility as treatment response biomarker, while predicting treatment failure prior to treatment initiation. Performance of the test in capillary blood samples collected by finger-prick was noninferior to venous blood collected in PAXgene tubes. These results support incorporation of RISK6 into rapid, capillary blood-based point-of-care PCR devices for prospective assessment in field studies.

Genome-wide association study of Buruli ulcer in rural Benin highlights role of two LncRNAs and the autophagy pathway

Buruli ulcer, caused by Mycobacterium ulcerans and characterized by devastating necrotizing skin lesions, is the third mycobacterial disease worldwide. The role of host genetics in susceptibility to Buruli ulcer has long been suggested. We conduct the first genome-wide association study of Buruli ulcer on a sample of 1524 well characterized patients and controls from rural Benin. Two-stage analyses identify two variants located within LncRNA genes: rs9814705 in ENSG00000240095.1 (P = 2.85 × 10⁻⁷; odds ratio = 1.80 [1.43–2.27]), and rs76647377 in LINC01622 (P = 9.85 × 10⁻⁸; hazard ratio = 0.41 [0.28–0.60]). Furthermore, we replicate the protective effect of allele G of a missense variant located in ATG16L1, previously shown to decrease bacterial autophagy (rs2241880, P = 0.003; odds ratio = 0.31 [0.14–0.68]). Our results suggest LncRNAs and the autophagy pathway as critical factors in the development of Buruli ulcer.

Jeremy Manry, Quentin Vincent et al. report a genome-wide association study for susceptibility to Buruli ulcer in a rural population from the West African country of Benin. They identify two independently associated variants within LncRNA genes and confirm the protective effect of a missense variant in the bacterial autophagy gene ATG16L1.

Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks

Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.

Extracellular vesicles released by J774A.1 macrophages reduce the bacterial load in macrophages and in an experimental mouse model of tuberculosis

Background

Tuberculosis is the leading cause of death by an infectious microorganism worldwide. Conventional treatment lasts at least six months and has adverse effects; therefore, it is important to find therapeutic alternatives that reduce the bacterial load and may reduce the treatment duration. The immune response against tuberculosis can be modulated by several mechanisms, including extracellular vesicles (EVs), which are nano-sized membrane-bound structures that constitute an efficient communication mechanism among immune cells.

Methods

The EVs released by the J774A.1 mouse macrophage cell line, both spontaneously (S-EV) and after infection with Mycobacterium tuberculosis H37Rv (Mtb-EV), were purified by ultra-centrifugation and size-exclusion chromatography. The size distribution and chemical composition of these EVs were evaluated, and their effect on the bacterial load and the production of cytokines was determined in both in vitro and in vivo models of M. tuberculosis infection.

Results

Mtb-EV are larger than S-EV, they contain M. tuberculosis-specific antigens (not detected in EVs released from M. fortuitum-infected J774A.1 cells) and are rich in phosphatidylserine, present in their outer membrane layer. S-EV, but not Mtb-EV, reduced the bacterial load and the production of MCP-1 and TNF-α in M. tuberculosis-infected macrophages, and these effects were reversed when phosphatidylserine was blocked with annexin V. Both S-EV and Mtb-EV significantly reduced the lung bacterial load in mice infected with M. tuberculosis after 60 days of treatment, but they had no effect on survival or on the lung pneumonic area of these mice.

Conclusion

J774A.1 macrophages infected with M. tuberculosis H37Rv released EVs that differed in size and phosphatidylserine content from spontaneously released EVs, and these EVs also had different biological effects: S-EV reduced the mycobacterial load and the cytokine production in vitro (through a phosphatidylserine-dependent mechanism), while both EVs reduced the lung bacterial load in vivo. These results are the basis for further experiments to evaluate whether EVs improve the efficiency of the conventional treatment for tuberculosis.

Impact of the Polymorphism of the PACRG and CD80 Genes on the Development of the Different Stages of Tuberculosis Infection

BACKGROUND

Tuberculosis (TB) is one of the most significant health-care problems worldwide. The host's genetics play an important role in the development of TB in humans. The disease progresses through several stages, each of which can be under the control of different genes. The precise genes influencing the different stages of the disease are not yet identified. The aim of the current study was to determine the associations between primary and secondary TB and the polymorphisms of novel candidate genes for TB susceptibility, namely CD79A, HCST, CXCR4, CD4, CD80, CP, PACRG, and CD69.

METHODS

A total of 357 patients with TB (130 cases with primary TB and 227 cases with secondary TB) from the Siberian region of Russia as well as 445 healthy controls were studied. The study was performed at the Research Institute of Medical Genetics, Tomsk NRMC, Tomsk, Russia, between July 2015 and November 2016. Genotyping was carried out using MALDI-TOF mass spectrometry and PCR-RFLP. The associations between the single-nucleotide polymorphisms and TB were assessed using logistic regression adjusting for covariates (age and gender). Multiple testing was addressed via the experiment-wise permutation approach. The statistical significance threshold was a P value less than 0.05 for the permutation P values. The analyses were done in R 3.2 statistical software.

RESULTS

An association was established between the rs1880661 variant of the CD80 gene and secondary TB and the rs10945890 variant of the PACRG gene and both primary and secondary TB. However, the same allele of PACRG appeared to be both a risk factor for reactivation (secondary TB) and a protector against primary infection.

CONCLUSION

The results suggested that the CD80 and PACRG genes were associated with susceptibility to different forms of TB infection in the Russian population.

The value of transcriptomics in advancing knowledge of the immune response and diagnosis in tuberculosis

Blood transcriptomics analysis of tuberculosis has revealed an interferon-inducible gene signature that diminishes in expression after successful treatment; this promises improved diagnostics and treatment monitoring, which are essential for the eradication of tuberculosis. Sensitive radiography revealing lung abnormalities and blood transcriptomics have demonstrated heterogeneity in patients with active tuberculosis and exposed asymptomatic people with latent tuberculosis, suggestive of a continuum of infection and immune states. Here we describe the immune response to infection with Mycobacterium tuberculosis revealed through the use of transcriptomics, as well as differences among clinical phenotypes of infection that might provide information on temporal changes in host immunity associated with evolving infection. We also review the diverse blood transcriptional signatures, composed of small sets of genes, that have been proposed for the diagnosis of tuberculosis and the identification of at-risk asymptomatic people and suggest novel approaches for the development of such biomarkers for clinical use.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Meta-Analysis Identification of Highly Robust and Differential Immune-Metabolic Signatures of Systemic Host Response to Acute and Latent Tuberculosis in Children and Adults

Background: Whole blood expression profiling is a mainstay for delineating differential diagnostic signatures of infection yet is subject to high variability that reduces power and complicates clinical usefulness. To date, confirmatory high confidence expression profiling signatures for clinical use remain uncertain. Here we have sought to evaluate the reproducibility and confirmatory nature of differential expression signatures, comprising molecular and cellular pathways, across multiple international clinical observational studies investigating children and adult whole blood transcriptome responses to tuberculosis (TB). Methods and findings: A systematic search and quality control assessment of gene expression repositories for human TB using whole blood resulted in 11 datasets with a total of 1073 patients from Africa, Europe, and South America. A non-parametric estimation of percentage of false prediction was used for meta-analysis of high confidence differential expression analysis. Deconvolution analysis was applied to infer changes in immune cell proportions and enrichment tests applied using pathway database resources. Meta-analysis identified high confidence differentially expressed genes, comprising 372 in adult active-TB versus latent-TB (LTBI), 332 in adult active-TB versus controls (CON), five in LTBI versus CON, and 415 in childhood active-TB versus LTBI. Notably, these confirmatory markers have low representation in published signatures for diagnosing TB. Pathway biology analysis of high confidence gene sets revealed dominant metabolic and innate-immune pathway signatures while suppressed signatures were enriched with adaptive signaling pathways and reduced proportions of T and B cells. Childhood TB showed uniquely strong inflammasome antagonist signature (IL1RN and ILR2), while adult TB patients exhibit a significant preponderance type I and type II IFN markers. Key limitations of the study include the paucity of data on potential confounders. Conclusion: Meta-analysis identified high confidence confirmatory immune-metabolic and cellular expression signatures across studies regardless of the population resource setting, HIV status and circulating endemic pathogens. Notably, previously identified diagnostic signature markers for TB show limited concordance with the confirmatory meta-analysis. Overall, our results support the use of the confirmatory expression signatures for guiding optimized diagnostic, prognostic, and therapeutic monitoring modalities in TB.

Addressing diversity in tuberculosis using multidimensional approaches

Tuberculosis is a complex disease, which can affect many organs other than the lungs. Initial infection may be cleared without inducing immunological memory, or progress directly to primary disease. Alternatively, the infection may be controlled as latent TB infection, that may progress to active tuberculosis at a later stage. There is now a greater understanding that these infection states are part of a continuum, and studies using PET/CT imaging have shown that individual lung granulomas may respond to infection independently, in an un-synchronized manner. In addition, the Mycobacterium tuberculosis organisms themselves can exist in different states: as nonculturable forms, as 'persisters', as rapidly growing bacteria and a biofilm-forming cording phenotype. The 'omics' approaches of transcriptomics, metabolomics and proteomics can help reveal the mechanisms underlying these different infection states in the host, and identify biosignatures with diagnostic potential, that can predict the development of disease, in 'progressors' as early as 12-18 months before it can be detected clinically, or that can monitor the success of anti-TB therapy. Further insights can be obtained from studies of BCG vaccination and new TB vaccines. For example, epigenetic changes associated with trained immunity and a stronger immune responses following BCG vaccination can be identified. These omics approaches may be particularly valuable when linked to studies of mycobacterial growth inhibition, as a direct read-out of the ability to control mycobacterial growth. The second generation of omics studies is identifying much smaller signatures based on as few as 3 or 4 genes. Thus, narrowing down omics-derived biosignatures to a manageable set of markers now opens the way to field-friendly point of care assays.

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Type I interferons in tuberculosis: Foe and occasionally friend

Tuberculosis remains one of the leading causes of mortality worldwide, and, despite its clinical significance, there are still significant gaps in our understanding of pathogenic and protective mechanisms triggered by Mycobacterium tuberculosis infection. Type I interferons (IFN) regulate a broad family of genes that either stimulate or inhibit immune function, having both host-protective and detrimental effects, and exhibit well-characterized antiviral activity. Transcriptional studies have uncovered a potential deleterious role for type I IFN in active tuberculosis. Since then, additional studies in human tuberculosis and experimental mouse models of M. tuberculosis infection support the concept that type I IFN promotes both bacterial expansion and disease pathogenesis. More recently, studies in a different setting have suggested a putative protective role for type I IFN. In this study, we discuss the mechanistic and contextual factors that determine the detrimental versus beneficial outcomes of type I IFN induction during M. tuberculosis infection, from human disease to experimental mouse models of tuberculosis.

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10-4) alone remained predictive after adjusting for clinical predictors.

Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.