Comorbidities and Coinfections in Latent TB

Overview

Approximately 2 billion people worldwide are infected with Mycobacterium tuberculosis (TB), with 90% of individuals having latent infection (LTBI). The control of TB requires clearly delineated helper T cell (Th) 1 responses and, to a lesser extent, Th17 responses, which both play important roles in the induction and maintenance of protective immune responses in mouse models of TB infection and in the prevention of active disease, as seen in LTBI. During latency, M. tuberculosis is contained in localized granulomas. Mycobacteria specific T cells mediate delayed type hypersensitivity reactions to purified protein derivative (PPD), and this reaction is generally considered to indicate an LTBI status in the absence of demonstrable active infection. Among the various risk factors that are known to play a role in promoting active TB, HIV is the most well studied and described. However, in low-HIV-endemic countries like India, other risk factors might play a more prominent role in active TB pathogenesis. These include malnutrition, diabetes mellitus (DM), and helminth infections. LTBI individuals with these comorbidities or coinfections could be at a higher risk for developing active TB than their "healthy" LTBI counterparts without these comorbidities. Thus, it is imperative to study the pathogenesis of TB infection and disease in these "at risk" populations. In this study, we will estimate the prevalence of severe to moderate malnutrition, uncontrolled DM, and helminth infections in LTBI-positive individuals. We will collect samples from a cohort of individuals with LTBI, those with LTBI and coexistent malnutrition, DM, or helminth coinfection, and those without any of these conditions. Individual participation may last up to 6 months. The main objective of the study is to estimate the prevalence of malnutrition, DM, and helminth infections in LTBI individuals. Simultaneously, we will perform transcriptomic, proteomic, and metabolomic assays, including profiles in serum and urine, to determine the biosignature portfolio of these individuals. In addition, immunological assays examining cytokine/chemokine signatures as well as other immune parameters related to innate and adaptive responses will be performed to enhance the understanding of the immunological cross talk between LTBI and malnutrition, DM, and helminth infections.

Study Design: This is a cross-sectional study to identify individuals with LTBI and coinfections/comorbidities: malnutrition; DM; and helminth infections. Individuals will first be evaluated clinically for symptoms of active TB. Individuals with symptoms of active TB will be excluded from the study and referred for treatment. Individuals who are asymptomatic for active TB will be screened for LTBI by interferon gamma (IFNγ) release assay (IGRA) and clinically assessed for malnutrition (by body mass index \[BMI\]), evaluated for DM status (by hemoglobin A1c \[HbA1c\] levels), and evaluated for helminth infection (by serology and stool quantitative polymerase chain reaction \[qPCR\]). Individuals who are eligible will be assigned to one of six study groups based on LTBI status and presence of coinfections/comorbidities. Participants will have an additional study visit within 6 months of screening for clinical assessment and provide blood (30 ml), urine, and stool samples for experimental studies and storage for future research. Key research evaluations will include gene expression analyses and immunophenotyping on blood samples. Sample Size: Primary Objective: N=5000 Secondary Objective: N=300; n=50 for each of the following groups: 1. Latent tuberculosis (TB) infection (LTBI) and malnourished; 2. LTBI with uncontrolled diabetes mellitus (DM); 3. LTBI with helminth infection; 4. LTBI with multiple comorbidities; 5. LTBI with no comorbidities (LTBI+ healthy controls); and 6. LTBI negative healthy controls Study Population: Adults and adolescents (14-65 years of age) with or without LTBI. Primary Objective: To estimate the prevalence of malnutrition, DM and helminth infections in LTBI individuals. Secondary Objective: To determine the effect of coinfections/comorbidities on biosignatures of LTBI using RNA sequencing (RNA-seq), proteomics, metabolomics, and immunological assays. Endpoints: Prevalence of malnutrition, DM and helminth infections in LTBI individuals and their effects on biosignatures. Enrollment will continue until both the total screening sample and the 6 study groups are fully enrolled. We will increase the screening sample size to 6000 if the required study group sample sizes are not achieved by screening 5000 participants. Recruitment Plan: The screening phase of this study will be a community-based study in South India. Participants will be recruited from villages in the Kancheepuram District, where approximately 50% of the adult population tests positive for LTBI by IGRA based on our previous study (unpublished data). We also anticipate based on our previous study that the percentage of the adult population positive for malnutrition is 35%, for DM is 20%, and for helminth infection is 20% (unpublished data). The census in villages in the Kancheepuram District is updated annually by local health workers employed by the Department of Public Health and the field teams of the NIRT in Chennai, India. The villages will be chosen in consultation with the Department of Public Health in Tamil Nadu. NIRT field teams will distribute pamphlets about the study to spread awareness. The pamphlet will be approved by the Institutional Ethics Committee (IEC) prior to use. Clinical Evaluations: Physical examination and history: Review of medical history and regular physical examination (including height, weight, vital signs) will be performed at the screening phase. During the study phase, a complete medical history will be taken, as well as a regular physical exam with vital signs and anthropometric measurements, including height, weight, BMI Z scores, weight for height Z scores, mid upper arm circumference, waist and abdominal circumference, ratio of waist/hip circumference, skinfold thickness, and grip strength, and bioelectrical impedance analysis. For the bioelectrical impedance analysis, a multifrequency body composition analyser (Bodystat Quadscan 4000) will be used to derive body composition data. The measurements will be taken with light clothing and according to standard recommendations for conduct of bioelectrical impedance measurement (e.g., consistent time of the day, voiding urine before measurement, avoiding measurements soon after a major meal or exercise). The body composition data derived from the source data will be body fat mass, fat free mass, and body cell mass. The visceral adiposity index will be calculated based on sex, BMI, triglycerides, and high-density lipoprotein (HDL) cholesterol. Additionally, questionnaires for smoking and drug and alcohol use, including estimating Alcohol Use Disorder Identification Test (AUDIT) scores, will also be administered. Blood draw: A total of 10 ml of blood will be initially collected from each participant via venipuncture in the screening phase. The study phase will involve an additional 30 ml blood draw. Blood will be used for laboratory evaluations. Urine collection: Urine samples will be collected and stored and evaluated. Stool collection: Stool samples will be collected in specialized containers and will be used for DNA extraction and storage. At-home stool/urine collection: Participants who cannot provide urine and/or stool at a study visit may provide it within 3 days. The study team will provide instructions for the collection and storage of the samples. Laboratory Evaluations: Blood collected at the screening phase will be used for the following evaluations. 1\. Hematology: Complete blood count with differential and hematocrit levels. 2. IGRA (QuantiFERON-TB Plus Gold In-Tube; Qiagen) to confirm LTBI status. 3. Biochemistry: HbA1c, random blood glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine. 4\. ELISA to identify and quantify infection with Wuchereria bancrofti. 5. Storage for future research. Blood collected in the study phase will be used for the following evaluations. 1. Fasting glucose, HbA1c, and other biochemical parameters. 2. Macro- and micronutrient levels, including serum albumin, C-reactive protein, cholesterol (total, HDL, low-density lipoprotein, triglycerides), vitamins A, B6, B12, C, D, and E, selenium, and zinc. 3. For LTBI-negative individuals, repeat IGRA to confirm LTBI status. If the test is positive, the remaining blood will be discarded and the individual will be withdrawn from the LTBI negative cohort. 4. Tempus or PAXgene tube blood collection for DNA and RNA isolation for experimental studies and storage for future research. No human genetic testing will be performed under this protocol. 5. Peripheral blood mononuclear cell (PBMC) isolation for experimental studies and storage for future research. 6. Serum will also be collected for experimental studies and storage for future research. Stool samples will be used for the following evaluations. 1. At screening, stool DNA for qPCR diagnostics to detect hookworms, Ascaris, Strongyloides, and Trichuris. 2. Storage for future research. Additionally, urine samples collected in the study phase will be used for the following evaluations. 1. Proteomic and metabolomic investigations. 2. Storage for future research. Results of clinical evaluations will be returned to participants. Experimental Studies: Transcriptomics: We will perform RNA seq analysis on 50 individuals in each group to examine the transcriptomic signature. RNA will be extracted from Tempus or PAXgene tubes, coded, and analyzed by RNA seq. The data obtained will then be evaluated for RNA expression patterns and alterations. Proteomics: Blood and urine proteomics is quickly becoming a major tool in advancing biomarker discovery, validation, diagnostics, and other fields. We will use serum and urine proteomics from a subset of individuals (n=30) in each group to perform proteomics by liquid chromatography with tandem mass spectrometry. Bioinformatic analysis of protein expression in these groups would provide useful information on the protein signatures of LTBI in high risk populations. Metabolomics: We will use non-targeted capillary electrophoresis time of flight mass spectrometry to analyze the serum metabolic profiles of a subset of individuals (n=30) in each group. Metabolomics will complement the data obtained from proteomics. Immunological assays: 1. Define circulating immune cell population frequencies by flow cytometry Our standardized flow cytometry panels, described below, will be applied to a subset of individuals (n=30) in each group (Table 1). We will measure frequencies of all major immune cell subsets: monocytes, NK cells, B cells, and T cells, including CD56+ T cells. T cells will further be divided into conventional T cells (CD4- and CD8 expressing cells) and non conventional T cells (mucosal associated invariant T cells, NK T cells, and gamma delta T cells). For conventional T cells, we will also capture memory phenotype and, for CD4+ T cells, the Th subsets. We will perform quality control using repeat runs of samples from a control donation to validate sample integrity and control for the sometimes drastic technical variability observed in cytometry experiments. To minimize technical variability, we will apply a centralized gating method where all results will be analysed by the same individual. 2. Define the immune signatures of PBMCs and memory CD4+ and CD8+ T cells Cells will be sorted on a fluorescence activated cell sorting (FACS) Aria cytometer from a subset of individuals (n=20) in each group. A minimum of 50,000 cells each of PBMCs and memory CD4+ and CD8+ T cells will be sorted into TRIzol LS. Based on our experience, at least 7% of PBMCs are memory CD4+ T cells and 4% are memory CD8+ T cells. Gene expression profiles of whole PBMCs and sorted memory T cells will be obtained by RNA-seq using the genome wide expression platform from Illumina, providing the expression levels for \>50,000 identified genes in the human genome (including non-protein coding RNA species). Sample generation, library preparation, sequencing, and mapping will be performed under well-defined and standardized protocols, with quality control checks included at each major step to ensure high quality data generation. For gene expression analysis, where a huge number of variables is tested simultaneously, we will use the DESeq Benjamini Hochberg corrected p-adjusted values of \<0.05 to identify differentially expressed genes between groups. We will perform pathway analysis of genes significantly upregulated between the different comparisons and investigate the associated gene modules. For this purpose, we will use the web tool Gene Set Enrichment Analysis (GSEA) to determine which pathways are significantly represented. We plan to use the Ingenuity Pathway Analysis (IPA) software to determine in more detail the directionality of the overrepresented functions and the common upstream regulators for the given set of genes. A complementary approach will follow a modular analysis that identifies clusters of genes that share a similar expression profile. In particular, we plan to use the weighted gene co-expression network analysis (WGCNA) algorithm. By simultaneously monitoring the PBMC immune cell composition, determining the overall PBMC gene expression, and determining the profile of memory CD4+ and CD8+ T cells, we can detect disease specific signatures in PBMCs in general and identify the contribution of T cell subsets to these dysregulations in particular. 3. Define antigen-reactive cellular immune responses by flow cytometry Antigen reactive cellular immune responses will be measured in a subset of individuals (n=20) in each group. PBMCs will be stimulated with PPD and M. tuberculosis whole cell lysate for 24 hours and antigen stimulated cellular immune responses will be studied. We plan to examine a panel of activation markers (HLA-DR, CD38, OX-40 and CD153), cytokines (IL-2, IFNγ, TNFα, IL-17), and cytotoxic markers (perforin, granzyme B, granulysin, CD107a) on all major conventional and non-conventional T cell subsets and NK cells. Return of Research Results The experimental studies are not expected to reveal individual clinical results or medically actionable incidental findings. Therefore, no research results will be returned to participants.