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

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

Advancing personalized medicine for tuberculosis through the application of immune profiling

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

Comparative transcriptomic analysis of whole blood mycobacterial growth assays and tuberculosis patients' blood RNA profiles

In vitro whole blood infection models are used for elucidating the immune response to Mycobacterium tuberculosis (Mtb). They exhibit commonalities but also differences, to the in vivo blood transcriptional response during natural human Mtb disease. Here, we present a description of concordant and discordant components of the immune response in blood, quantified through transcriptional profiling in an in vitro whole blood infection model compared to whole blood from patients with tuberculosis disease. We identified concordantly and discordantly expressed gene modules and performed in silico cell deconvolution. A high degree of concordance of gene expression between both adult and paediatric in vivo-in vitro tuberculosis infection was identified. Concordance in paediatric in vivo vs in vitro comparison is largely characterised by immune suppression, while in adults the comparison is marked by concordant immune activation, particularly that of inflammation, chemokine, and interferon signalling. Discordance between in vitro and in vivo increases over time and is driven by T-cell regulation and monocyte-related gene expression, likely due to apoptotic depletion of monocytes and increasing relative fraction of longer-lived cell types, such as T and B cells. Our approach facilitates a more informed use of the whole blood in vitro model, while also accounting for its limitations.

Transcriptomics for child and adolescent tuberculosis

Tuberculosis (TB) in humans is caused by Mycobacterium tuberculosis (Mtb). It is estimated that 70 million children (<15 years) are currently infected with Mtb, with 1.2 million each year progressing to disease. Of these, a quarter die. The risk of progression from Mtb infection to disease and from disease to death is dependent on multiple pathogen and host factors. Age is a central component in all these transitions. The natural history of TB in children and adolescents is different to adults, leading to unique challenges in the development of diagnostics, therapeutics, and vaccines. The quantification of RNA transcripts in specific cells or in the peripheral blood, using high-throughput methods, such as microarray analysis or RNA-Sequencing, can shed light into the host immune response to Mtb during infection and disease, as well as understanding treatment response, disease severity, and vaccination, in a global hypothesis-free manner. Additionally, gene expression profiling can be used for biomarker discovery, to diagnose disease, predict future disease progression and to monitor response to treatment. Here, we review the role of transcriptomics in children and adolescents, focused mainly on work done in blood, to understand disease biology, and to discriminate disease states to assist clinical decision-making. In recent years, studies with a specific pediatric and adolescent focus have identified blood gene expression markers with diagnostic or prognostic potential that meet or exceed the current sensitivity and specificity targets for diagnostic tools. Diagnostic and prognostic gene expression signatures identified through high-throughput methods are currently being translated into diagnostic tests.

Global health systems' data science approach for precision diagnosis of sepsis in early life

Neonates and children in low-income and middle-income countries (LMICs) contribute to the highest number of sepsis-associated deaths globally. Interventions to prevent sepsis mortality are hampered by a lack of comprehensive epidemiological data and pathophysiological understanding of biological pathways. In this review, we discuss the challenges faced by LMICs in diagnosing sepsis in these age groups. We highlight a role for multi-omics and health care data to improve diagnostic accuracy of clinical algorithms, arguing that health-care systems urgently need precision medicine to avoid the pitfalls of missed diagnoses, misdiagnoses, and overdiagnoses, and associated antimicrobial resistance. We discuss ethical, regulatory, and systemic barriers related to the collection and use of big data in LMICs. Technologies such as cloud computing, artificial intelligence, and medical tricorders might help, but they require collaboration with local communities. Co-partnering (joint equal development of technology between producer and end-users) could facilitate integration of these technologies as part of future care-delivery systems, offering a chance to transform the global management and prevention of sepsis for neonates and children.

Novel functional sequences uncovered through a bovine multiassembly graph

Many genomic analyses start by aligning sequencing reads to a linear reference genome. However, linear reference genomes are imperfect, lacking millions of bases of unknown relevance and are unable to reflect the genetic diversity of populations. This makes reference-guided methods susceptible to reference-allele bias. To overcome such limitations, we build a pangenome from six reference-quality assemblies from taurine and indicine cattle as well as yak. The pangenome contains an additional 70,329,827 bases compared to the Bos taurus reference genome. Our multiassembly approach reveals 30 and 10.1 million bases private to yak and indicine cattle, respectively, and between 3.3 and 4.4 million bases unique to each taurine assembly. Utilizing transcriptomes from 56 cattle, we show that these nonreference sequences encode transcripts that hitherto remained undetected from the B. taurus reference genome. We uncover genes, primarily encoding proteins contributing to immune response and pathogen-mediated immunomodulation, differentially expressed between Mycobacterium bovis-infected and noninfected cattle that are also undetectable in the B. taurus reference genome. Using whole-genome sequencing data of cattle from five breeds, we show that reads which were previously misaligned against the Bos taurus reference genome now align accurately to the pangenome sequences. This enables us to discover 83,250 polymorphic sites that segregate within and between breeds of cattle and capture genetic differentiation across breeds. Our work makes a so-far unused source of variation amenable to genetic investigations and provides methods and a framework for establishing and exploiting a more diverse reference genome.

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.

Alzheimer's Disease: Protective Effects of Mycobacterium vaccae, a Soil-Derived Mycobacterium with Anti-Inflammatory and Anti-Tubercular Properties, on the Proteomic Profiles of Plasma and Cerebrospinal Fluid in Rats

BACKGROUND

Alzheimer's disease (AD) is an inflammatory neurodegenerative disease that may be associated with prior bacterial infections. Microbial "old friends" can suppress exaggerated inflammation in response to disease-causing infections or increase clearance of pathogens such as Mycobacterium tuberculosis, which causes tuberculosis (TB). One such "old friend" is Mycobacterium vaccae NCTC 11659, a soil-derived bacterium that has been proposed either as a vaccine for prevention of TB, or as immunotherapy for the treatment of TB when used alongside first line anti-TB drug treatment.

OBJECTIVE

The goal of this study was to use a hypothesis generating approach to explore the effects of M. vaccae on physiological changes in the plasma and cerebrospinal fluid (CSF).

METHODS

Liquid chromatography-tandem mass spectrometry-based proteomics were performed in plasma and CSF of adult male rats after immunization with a heat-killed preparation of M. vaccae NCTC 11659 or borate-buffered saline vehicle. Gene enrichment analysis and analysis of protein-protein interactions were performed to integrate physiological network changes in plasma and CSF. We used RT-qPCR to assess immune and metabolic gene expression changes in the hippocampus.

RESULTS

In both plasma and CSF, immunization with M. vaccae increased proteins associated with immune activation and downregulated proteins corresponding to lipid (including phospholipid and cholesterol) metabolism. Immunization with M. vaccae also increased hippocampal expression of interleukin-4 (IL-4) mRNA, implicating anti-inflammatory effects in the central nervous system.

CONCLUSION

M. vaccae alters host immune activity and lipid metabolism. These data are consistent with the hypothesis that microbe-host interactions may protect against possible infection-induced, inflammation-related cognitive impairments.

A blood RNA transcript signature for TB exposure in household contacts

Background

Current tools for diagnosing latent TB infection (LTBI) detect immunological memory of past exposure but are unable to determine whether exposure is recent. We sought to identify a whole-blood transcriptome signature of recent TB exposure.

Methods

We studied household contacts of TB patients; healthy volunteers without recent history of TB exposure; and patients with active TB. We performed whole-blood RNA sequencing (in all), an interferon gamma release assay (IGRA; in contacts and healthy controls) and PET/MRI lung scans (in contacts only). We evaluated differentially-expressed genes in household contacts (log2 fold change ≥1 versus healthy controls; false-discovery rate < 0.05); compared these to differentially-expressed genes seen in the active TB group; and assessed the association of a composite gene expression score to independent exposure/treatment/immunological variables.

Results

There were 186 differentially-expressed genes in household contacts (n = 26, age 22–66, 46% male) compared with healthy controls (n = 5, age 29–38, 100% male). Of these genes, 141 (76%) were also differentially expressed in active TB (n = 14, age 27–69, 71% male). The exposure signature included genes from inflammatory response, type I interferon signalling and neutrophil-mediated immunity pathways; and genes such as BATF2 and SCARF1 known to be associated with incipient TB. The composite gene-expression score was higher in IGRA-positive contacts (P = 0.04) but not related to time from exposure, isoniazid prophylaxis, or abnormalities on PET/MRI (all P > 0.19).

Conclusions

Transcriptomics can detect TB exposure and, with further development, may be an approach of value for epidemiological research and targeting public health interventions.

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.

Identification of differentially expressed genes in primary Sjögren's syndrome

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease that affects exocrine glands. To study the molecular mechanism and identify crucial genes/pathways in pSS pathogenesis, the microarray-based whole-genome gene expression profiles from salivary glands of patients with pSS and non-sicca controls were retrieved. After normalization and subsequent batch effect adjustment, significance analysis of microarrays method was applied to five available datasets, and 379 differentially expressed genes (DEGs) were identified. The 300 upregulated DEGs were enriched in Gene Ontology terms of immune and inflammatory responses, including antigen processing and presentation, interferon-mediated signaling pathway, and chemotaxis. Previously reported pSS-associated genes, including HLA-DRA, TAP2, PRDM1, and IFI16, were found to be significantly upregulated. The downregulated DEGs were enriched in pathways of salivary secretion, carbohydrate digestion and absorption, and starch and sucrose metabolism, implying dysfunction of salivary glands during pathogenesis. Next, a protein-protein interaction network was constructed, and B2M, an upregulated DEG, was shown to be a hub, suggesting its potential involvement in pSS development. In summary, we found the activation of pSS-associated genes in pathogenesis, and provide clues for salivary glands dysfunction. Experimental investigation on the identified DEGs in this study will deepen our understanding on pSS.

Tuberculosis and HIV-An Update on the "Cursed Duet" in Children

HIV and tuberculosis (TB) often occur together with each exacerbating the other. Improvements in vertical transmission prevention has reduced the number of HIV-infected children being born and early antiretroviral therapy (ART) protects against tuberculosis. However, with delayed HIV diagnosis, HIV-infected infants often present with tuberculosis co-infection. The number of HIV exposed uninfected children has increased and these infants have high exposure to TB and may be more immunologically vulnerable due to HIV exposure in utero. Bacillus Calmette-Guérin (BCG) immunization shortly after birth is essential for preventing severe TB in infancy. With early infant HIV diagnosis and ART, disseminated BCG is no longer an issue. TB prevention therapy should be implemented for contacts of a source case and for all HIV-infected individuals over a year of age. Although infection can be identified through skin tests or interferon gamma release assays, the non-availability of these tests should not preclude prevention therapy, once active TB has been excluded. Therapeutic options have moved from isoniazid only for 6-9 months to shorter regimens. Prevention therapy after exposure to a source case with resistant TB should also be implemented, but should not prevent pivotal prevention trials already under way. A microbiological diagnosis for TB remains the gold standard because of increasing drug resistance. Antiretroviral therapy for rifampicin co-treatment requires adaptation for those on lopinavir-ritonavir, which requires super-boosting with additional ritonavir. For those with drug resistant TB, the main problems are identification and overlapping toxicity between antiretroviral and anti-TB therapy. In spite of renewed focus and improved interventions, infants are still vulnerable to TB.