Transcriptomics: the key to biomarker discovery during tuberculosis?

CeRNA network identified hsa-miR-17-5p, hsa-miR-106a-5p and hsa-miR-2355-5p as potential diagnostic biomarkers for tuberculosis

This study aims to analyze the regulatory non-coding RNAs in the pathological process of tuberculosis (TB), and identify novel diagnostic biomarkers. A longitudinal study was conducted in 5 newly diagnosed pulmonary tuberculosis patients, peripheral blood samples were collected before and after anti-TB treatment for 6 months, separately. After whole transcriptome sequencing, the differentially expressed RNAs (DE RNAs) were filtrated with |log2 (fold change) | > log2(1.5) and P value < .05 as screening criteria. Then functional annotation was actualized by gene ontology enrichment analysis, and enrichment pathway analysis was conducted by Kyoto Encyclopedia of Genes and Genomes database. And finally, the competitive endogenous RNA (ceRNA) regulatory network was established according to the interaction of ceRNA pairs and miRNA-mRNA pairs. Five young women were recruited and completed this study. Based on the differential expression analysis, a total of 1469 mRNAs, 996 long non-coding RNAs, 468 circular RNAs, and 86 miRNAs were filtrated as DE RNAs. Functional annotation demonstrated that those DE-mRNAs were strongly involved in the cellular process (n = 624), metabolic process (n = 513), single-organism process (n = 505), cell (n = 651), cell part (n = 650), organelle (n = 569), and binding (n = 629). Enrichment pathway analysis revealed that the differentially expressed genes were mainly enriched in HTLV-l infection, T cell receptor signaling pathway, glycosaminoglycan biosynthesis-heparan sulfate/heparin, and Hippo signaling pathway. CeRNA networks revealed that hsa-miR-17-5p, hsa-miR-106a-5p and hsa-miR-2355-5p might be regarded as potential diagnostic biomarkers for TB. Immunomodulation-related genes are differentially expressed in TB patients, and hsa-miR-106a-5p, hsa-miR-17-5p, hsa-miR-2355-5p might serve as potential diagnostic biomarkers.

Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection

Globally, it is estimated that one-quarter of the world's population is latently infected with Mycobacterium tuberculosis (Mtb), also known as latent tuberculosis infection (LTBI). Recently, this condition has been referred to as tuberculosis infection (TBI), considering the dynamic spectrum of the infection, as 5-10% of the latently infected population will develop active TB (ATB). The chances of TBI development increase due to close contact with index TB patients. The emergence of multidrug-resistant TB (MDR-TB) and the risk of development of latent MDR-TB has further complicated the situation. Detection of TBI is challenging as the infected individual does not present symptoms. Currently, there is no gold standard for TBI diagnosis, and the only screening tests are tuberculin skin test (TST) and interferon gamma release assays (IGRAs). However, these tests have several limitations, including the inability to differentiate between ATB and TBI, false-positive results in BCG-vaccinated individuals (only for TST), false-negative results in children, elderly, and immunocompromised patients, and the inability to predict the progression to ATB, among others. Thus, new host markers and Mtb-specific antigens are being tested to develop new diagnostic methods. Besides screening, TBI therapy is a key intervention for TB control. However, the long-course treatment and associated side effects result in non-adherence to the treatment. Additionally, the latent MDR strains are not susceptible to the current TBI treatments, which add an additional challenge. This review discusses the current situation of TBI, as well as the challenges and efforts involved in its control.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Identification of Hub Genes in Tuberculosis via Bioinformatics Analysis

Background

Tuberculosis (TB) is a serious chronic bacterial infection caused by Mycobacterium tuberculosis (MTB). It is one of the deadliest diseases in the world and a heavy burden for people all over the world. However, the hub genes involved in the host response remain largely unclear.

Methods

The data set GSE11199 was studied to clarify the potential gene network and signal transduction pathway in TB. The subjects were divided into latent tuberculosis and pulmonary tuberculosis, and the distribution of differentially expressed genes (DEGs) was analyzed between them using GEO2R. We verified the enriched process and pathway of DEGs by making use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The construction of protein-protein interaction (PPI) network of DEGs was achieved through making use of the Search Tool for the Retrieval of Interacting Genes (STRING), aiming at identifying hub genes. Then, the hub gene expression level in latent and pulmonary tuberculosis was verified by a boxplot. Finally, through making use of Gene Set Enrichment Analysis (GSEA), we further analyzed the pathways related to DEGs in the data set GSE11199 to show the changing pattern between latent and pulmonary tuberculosis.

Results

We identified 98 DEGs in total in the data set GSE11199, 91 genes upregulated and 7 genes downregulated included. The enrichment of GO and KEGG pathways demonstrated that upregulated DEGs were mainly abundant in cytokine-mediated signaling pathway, response to interferon-gamma, endoplasmic reticulum lumen, beta-galactosidase activity, measles, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, etc. Based on the PPI network, we obtained 4 hub genes with a higher degree, namely, CTLA4, GZMB, GZMA, and PRF1. The box plot showed that these 4 hub gene expression levels in the pulmonary tuberculosis group were higher than those in the latent group. Finally, through Gene Set Enrichment Analysis (GSEA), it was concluded that DEGs were largely associated with proteasome and primary immunodeficiency.

Conclusions

This study reveals the coordination of pathogenic genes during TB infection and offers the diagnosis of TB a promising genome. These hub genes also provide new directions for the development of latent molecular targets for TB treatment.

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.

Phage Display Technology as a Powerful Platform for Antibody Drug Discovery

Antibody drugs with a high affinity and specificity are effective and safe for intractable diseases, such as cancers and autoimmune diseases. Furthermore, they have played a central role in drug discovery, currently accounting for eight of the top 20 pharmaceutical products worldwide by sales. Forty years ago, clinical trials on antibody drugs that were thought to be a magic bullet failed, partly due to the immunogenicity of monoclonal antibodies produced in mice. The recent breakthrough in antibody drugs is largely because of the contribution of phage display technology. Here, we reviewed the importance of phage display technology as a powerful platform for antibody drug discovery from various perspectives, such as the development of human monoclonal antibodies, affinity enhancement of monoclonal antibodies, and the identification of therapeutic targets for antibody drugs.

Application of Transcriptomics to Enhance Early Diagnostics of Mycobacterial Infections, with an Emphasis on Mycobacterium avium ssp. paratuberculosis

Mycobacteria cause a wide variety of disease in human and animals. Species that infect ruminants include M. bovis and M. avium ssp. paratuberculosis (MAP). MAP is the causative agent of Johne’s disease in ruminants, which is a chronic granulomatous enteric infection that leads to severe economic losses worldwide. Characteristic of MAP infection is the long, latent phase in which intermittent shedding can take place, while diagnostic tests are unable to reliably detect an infection in this stage. This leads to unnoticed dissemination within herds and the presence of many undetected, silent carriers, which makes the eradication of Johne’s disease difficult. To improve the control of MAP infection, research is aimed at improving early diagnosis. Transcriptomic approaches can be applied to characterize host-pathogen interactions during infection, and to develop novel biomarkers using transcriptional profiles. Studies have focused on the identification of specific RNAs that are expressed in different infection stages, which will assist in the development and clinical implementation of early diagnostic tests.

Integrative omics to detect bacteremia in patients with febrile neutropenia

BACKGROUND

Cancer chemotherapy-associated febrile neutropenia (FN) is a common condition that is deadly when bacteremia is present. Detection of bacteremia depends on culture, which takes days, and no accurate predictive tools applicable to the initial evaluation are available. We utilized metabolomics and transcriptomics to develop multivariable predictors of bacteremia among FN patients.

METHODS

We classified emergency department patients with FN and no apparent infection at presentation as bacteremic (cases) or not (controls), according to blood culture results. We assessed relative metabolite abundance in plasma, and relative expression of 2,560 immunology and cancer-related genes in whole blood. We used logistic regression to identify multivariable predictors of bacteremia, and report test characteristics of the derived predictors.

RESULTS

For metabolomics, 14 bacteremic cases and 25 non-bacteremic controls were available for analysis; for transcriptomics we had 7 and 22 respectively. A 5-predictor metabolomic model had an area under the receiver operating characteristic curve of 0.991 (95%CI: 0.972,1.000), 100% sensitivity, and 96% specificity for identifying bacteremia. Pregnenolone steroids were more abundant in cases and carnitine metabolites were more abundant in controls. A 3-predictor gene expression model had corresponding results of 0.961 (95%CI: 0.896,1.000), 100%, and 86%. Genes involved in innate immunity were differentially expressed.

CONCLUSIONS

Classifiers derived from metabolomic and gene expression data hold promise as objective and accurate predictors of bacteremia among FN patients without apparent infection at presentation, and can provide insights into the underlying biology. Our findings should be considered illustrative, but may lay the groundwork for future biomarker development.

Successful TB treatment induces B-cells expressing FASL and IL5RA mRNA

Activated B-cells increase T-cell behaviour during autoimmune disease and other infections by means of cytokine production and antigen-presentation. Functional studies in experimental autoimmune encephalomyelitis (EAE) indicate that B-cell deficiencies, and a lack of IL10 and IL35 leads to a poor prognosis. We hypothesised that B-cells play a role during tuberculosis. We evaluated B-cell mRNA expression using real-time PCR from healthy community controls, individuals with other lung diseases and newly diagnosed untreated pulmonary TB patients at three different time points (diagnosis, month 2 and 6 of treatment).

We show that FASLG, IL5RA, CD38 and IL4 expression was lower in B-cells from TB cases compared to healthy controls. The changes in expression levels of CD38 may be due to a reduced activation of B-cells from TB cases at diagnosis. By month 2 of treatment, there was a significant increase in the expression of APRIL and IL5RA in TB cases. Furthermore, after 6 months of treatment, APRIL, FASLG, IL5RA and CD19 were upregulated in B-cells from TB cases. The increase in the expression of APRIL and CD19 suggests that there may be restored activation of B-cells following anti-TB treatment. The upregulation of FASLG and IL5RA indicates that B-cells expressing regulatory genes may play an important role in the protective immunity against M.tb infection. Our results show that increased activation of B-cells is present following successful TB treatment, and that the expression of FASLG and IL5RA could potentially be utilised as a signature to monitor treatment response.

Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis

New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (P < 10⁻²⁰) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.