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Anaerobe-enriched gut microbiota predicts pro-inflammatory responses in pulmonary tuberculosis

Background The relationship between tuberculosis (TB), one of the leading infectious causes of death worldwide, and the microbiome, which is critical for health, is poorly understood. Methods To identify potential microbiome-host interactions, profiling of the oral, sputum and stool microbiota [n = 58 cases, n = 47 culture-negative symptomatic controls (SCs)] and whole blood transcriptome were done in pre-treatment presumptive pulmonary TB patients. This was a cross-sectional study. Microbiota were also characterised in close contacts of cases (CCCs, n = 73) and close contacts of SCs (CCSCs, n = 82) without active TB. Findings Cases and SCs each had similar α- and β-diversities in oral washes and sputum, however, β-diversity differed in stool (PERMANOVA p = 0•035). Cases were enriched with anaerobes in oral washes, sputum (Paludibacter, Lautropia in both) and stool (Erysipelotrichaceae, Blautia, Anaerostipes) and their stools enriched in microbial genes annotated as amino acid and carbohydrate metabolic pathways. In pairwise comparisons with their CCCs, cases had Megasphaera-enriched oral and sputum microbiota and Bifidobacterium-, Roseburia-, and Dorea-depleted stools. Compared to their CCSCs, SCs had reduced α-diversities and many differential taxa per specimen type. Cases differed transcriptionally from SCs in peripheral blood (PERMANOVA p = 0•001). A co-occurrence network analysis showed stool taxa, Erysipelotrichaceae and Blautia, to negatively co-correlate with enriched “death receptor” and “EIF2 signalling” pathways whereas Anaerostipes positively correlated with enriched “interferon signalling”, “Nur77 signalling” and “inflammasome” pathways; all of which are host pathways associated with disease severity. In contrast, none of the taxa enriched in SCs correlated with host pathways. Interpretation TB-specific microbial relationships were identified in oral washes, induced sputum, and stool from cases before the confounding effects of antibiotics. Specific anaerobes in cases’ stool predict upregulation of pro-inflammatory immunological pathways, supporting the gut microbiota's role in TB. Funding European & Developing Countries Clinical Trials Partnership, South African-Medical Research Council, National Institute of Allergy and Infectious Diseases.


Introduction
Tuberculosis is one of the world's leading causes of death, with 1.4 million fatalities reported in 2019 alone [1]. Understanding the determinants of tuberculosis (TB) pathogenesis is a global research priority that is required to identify novel vaccination and therapeutic strategies [1]. The human microbiome is a key modulator of host immune responses and has wide-ranging effects on health [2]. The microbiota may be causatively associated with disease. For example, microbial airway commensals protect mice against influenza-mediated death [3] and, in HIV-positive pneumonia patients, pulmonary enrichment with Prevotellaceae independently predicts mortality. The microbiota may also influence immunity at distal sites, such as the lungs [4]. For instance, early-life reduction in Faecalibacterium, Lachnospira, Rothia, and Veillonella in the gut is associated with an increased risk for asthma in humans [5].
Importantly, data supporting a role of the microbiome in TB disease are emerging. Aerosol challenge with Mycobacterium tuberculosis modifies murine gut microbial communities six days postexposure and macaques have increased microbial diversity and more oral taxa in the lung one-month after infection with M. tuberculosis [6,7]. Distinct sputum and gut microbial communities are found in cases compared to healthy controls, [8,9] however, most studies do not include TB patients before confounding effects of antibiotics [10À12].
Key knowledge gaps remain that preclude interventions that modify the microbiome to limit TB infection, progression, disease, and/or relapse [8]. First, prior work primarily compared cases to healthy rather than symptomatic controls (SCs; defined as patients with symptoms requiring investigation for TB who later had TB ruled out), which is not representative of clinical scenarios as healthy controls do not routinely present to clinics. Second, the extent to which the composition of microbial communities in TB cases are therefore specific to TB, rather than poor lung health or respiratory infections in general, has not been evaluated. Third, aside from one study, [13] there is no comprehensive analysis of the microbiota across different anatomical sites within the same patients, including respiratory secretions (which contain microbes from the site-of-disease) and the gut (the body's most microbially-rich compartment). Critically, any such evaluations involving TB cases and SCs can identify potential mechanisms by which taxa exert immunomodulatory effects on the host.
Blood transcriptional profiling is one approach to evaluating host immune responses and is an innovative method to derive testable hypotheses by which the microbiome and host influence each other and potentially TB clinical outcomes. Such hypotheses could mirror what is known for other lung diseases. For example, Veillonella and Megasphaera spp. enriched in the lower airways of lung cancer patients are associated with upregulated host inflammatory pathways ("extracellular signalÀregulated kinase", "phosphoinositide 3kinase") [14]. In healthy HIV-infected individuals who later progress to active TB, [15] enriched pulmonary anaerobes (Prevotella, Veillonella, Haemophilus) inversely correlate with CD4+ lymphocytes counts, and in active TB patients, interferon (IFN)-inducible (type I and II), myeloid and inflammatory genes are consistently upregulated [16].
We therefore sought to comprehensively characterise the microbiome and host immune system in TB patients from our high burden setting. We evaluated oral, sputum and gut microbiota in TB cases, comparing them to symptomatic controls, and close contacts. We targeted cases before the confounding effects of TB treatment and specifically analysed the relationship between the microbiome and host to elucidate potential mechanisms of interaction to inform the development of interventions.

Ethics
The study was approved by the Stellenbosch University Human Research Ethics Committee (N14/10/136). Participants provided written informed consent.

Patient recruitment, consent, and data collection
Presumptive TB patients: Patients with signs and symptoms of pulmonary TB disease (defined as presumptive TB patients) were classified as cases or symptomatic controls (SCs) in an observational TB diagnostic study [17]. Briefly, adult presumptive TB patients ( 18 years) self-presenting to a primary healthcare facility in Cape Town, South Africa (12 April 2016À24 July 2017) for investigation and not on TB treatment within the last two months were eligible. Demographic and clinical data [including HIV status and TB symptom score (TBscoreII)] [18] were recorded. Cigarette and alcohol consumption were assessed using the Fagerstr€ om Test for Nicotine Dependence (FTND) [19] and Alcohol Use Disorders Identification Test (AUDIT) [20] questionnaires, respectively.

Research in context
Evidence before this study The microbiome is crucial for human health and a growing body of research suggests it may have a role in TB. We did a PubMed search to identify studies published before November 2020, using the terms "microbiome", "microbiota" and "tuberculosis". Briefly, these studies documented few microbiome differences according to TB status in oral and upper airway specimens such as sputum, whereas the gut microbiome of active tuberculosis cases was reported as perturbed compared to healthy controls. We noted significant limitations in the literature, including how patients often had different treatment statuses, few comparisons of different specimen types were in the same patients, a paucity of data on the gut, comparisons were often between TB cases and healthy controls rather than symptomatic patients without TB, and no published studies linked the microbiome to the host immune system in TB, which is crucial to uncovering putative mechanistic linkages.

Added value of this study
To address these knowledge gaps, we concomitantly analysed the oral, sputum, and gut microbiota and peripheral blood transcriptome in pre-treatment cases compared to symptomatic controls, as well as contacts. Our findings indicate that cases are specifically enriched in anaerobes hitherto unidentified in this context in oral washes and induced sputum (Paludibacter, Lautropia), and have a distinct stool microbiome characterised by enriched anaerobes (Anaerostipes, Blautia, Erysipelotrichaceae) and precursor inferred pathways (amino acid and carbohydrate metabolic pathways) of short chain fatty acids, which are well known immune system modulators. Furthermore, these enriched gut anaerobes significantly predicted the upregulation of pro-inflammatory pathways associated with disease severity, and these same gut microbiome-transcriptome network interactions were absent in SCs; suggesting they are unique to TB.

Implications of all the available evidence
Collective evidence suggests specific gut anaerobes may modulate the host likely via short chain fatty acids, however, more mechanistic studies are needed. This may represent a new non-Mycobacterium tuberculosis-specific microbial target for both therapeutic and diagnostic interventions.
Close contacts: Two adult close contacts cases (CCCs) or close contacts of SCs (CCSCs) who had not taken any antibiotics within three months and preferably from the same household were identified. Contacts with TB were excluded.

Specimen collection and processing
Each participant provided an oral wash (after gargling with 20 ml sterile water for~10 s). An aliquot of the sterile water used for the oral wash was stored as a background DNA sampling control in the same type of collection container. Similarly, 20 ml sterile saline (Ysterplaat Medical Supplies, Cape Town, South Africa) was passed through the nebulisation equipment (Ultrasonic Hospital Grade WH-802, Hitech Therapy, Johannesburg, South Africa) and stored before sputum induction (Ysterplaat Medical Supplies). Sputum was induced with 5% saline for 7À10 min. Three sputa were collected for microbiome and TB testing. Stool collection was done at home by participants using an EasySampler (ALPCO, Salem, USA) in a receptacle containing DNA stabilization buffer (Stratec Biomedical, Birkenfeld, Germany). Oral washes, induced sputum and background controls were decontaminated with N-acetyl-L-cysteine (NALC) on the day of collection, pelleted (3217 x g), and resuspended in 2 mL phosphate buffer (pH 6.8; BD, Johannesburg, South Africa) before biobanking at À80°C. Stool samples were typically received the day after collection and frozen at À20°C. Whole blood was collected in Tempus tubes (Applied Biosystems, Foster City, USA) and stored at À20°C .

Mycobacterium tuberculosis complex testing
Induced sputum from presumptive TB patients was tested using Xpert MTB/RIF (Xpert; version G4) or Xpert MTB/RIF Ultra (Ultra; version 2) per the manufacturer's instructions. Sputa for culture were first decontaminated with Mycoprep (BD) and inoculated into a Mycobacteria Growth Indicator Tube 960 (MGIT960; BD). M. tuberculosis complex-confirmation was done with MTBDRplus (Hain Lifesciences, Nehren, Germany). Case definitions are described in the Supplement.

Nucleic acid extractions
Microbial DNA was isolated from oral washes, induced sputum, and background controls (one specimen-control pair selected for every 10 patients) using the Purelink DNA Microbiome Kit (Invitrogen, Carlsbad, USA) and from stool using the PSP Spin Stool DNA Plus Kit (Stratec Biomedical, Birkenfeld, Germany). Human RNA was isolated from blood using the Tempus Spin RNA Isolation kit (Applied Biosystems). Contaminating genomic DNA was removed using the TURBO DNA-free kit (Ambion, Austin, USA), globin mRNA depleted using the GLOBINclear kit (Ambion, Austin, USA), RNA concentrated using the RNeasy MinElute Cleanup kit (QIAGEN, Hilden, Germany) and its quality assessed using a Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, USA).

16S rRNA gene sequencing and analysis
Bacterial 16S rRNA gene sequencing (V4 region, 150 bp read length, paired-end) was performed on the Illumina MiSeq and resulting sequences pre-processed using Quantitative Insights into Microbial Ecology (QIIME 1.9) as previously described [15]. Sequences are available in the Sequence Read Archive (PRJNA664352). Samples with < 1000 total reads were excluded prior to downstream analyses in R (v.3.5.1). a-diversity (measures the count and abundance of different taxa within a sample) and b-diversity (compares microbial composition between samples) [21] was calculated using vegan, [ [24] was used for microbial metagenomic inference from stool 16S rRNA sequences and comparisons made with DESeq2.

Statistics
Non-parametric statistical tests (Mann-Whitney, Kruskal-Wallis; alpha 005) were used to compare a-diversity and relative abundances, and permutational multivariate analysis of variance (PERMA-NOVA) was used for b-diversity.

RNA sequencing and analysis
Our whole blood RNA sequencing was described previously in detail [25]. These data are on Array Express (accession number E-MTAB-8290). Differential gene expression was assessed using DESeq2, where q-values < 001 were considered statistically significant. Ingenuity pathway analysis (IPA) was used to identify canonical pathways different in cases compared to SCs, with inputted genes having q-values < 005.

Microbiome and transcriptome co-occurrence network analysis
This was done within each patient group and sample type by first constructing a compositionally-robust co-occurrence network between genus-level taxa using our previously-established SparCC pipeline [26]. P-values were obtained via bootstrapping using 500 iterations. Edges with a bootstrapped p-value 005 and correlation strength 015 were retained. After constructing the inter-taxa cooccurrence network, correlations between taxa and transcripts were superimposed upon the SparCC network and significant correlations (Spearman p < 005) were added. The combined network was visualized in Cytoscape (v.3.8.0).

Role of funding source
Funders had no role in study design, data collection, data analyses, interpretation, or writing of the manuscript.

Study population
We recruited 105 presumptive TB patients (58 cases, 47 SCs) and 155 close contacts (73 CCCs, 82 CCSCs, Fig. 1). Presumptive TB patients were more likely to be male, HIV-positive, have a selfreported co-morbidity, and had a previous TB episode compared to their close contacts (Table 1). Compared to SCs, cases were more likely to be anaemic, of black ethnicity, and have more severe symptoms.

Background DNA sampling controls show minimal crosscontamination of oral washes and sputum
We compared microbial communities in sampling controls to confirm that taxa in low biomass specimens were likely not contaminants. Water and saline controls had lower a-diversity compared to paired oral wash and induced sputa, respectively (Shannon diversity, p < 0001 for both comparisons). Controls did not cluster in principal coordinate analysis (PCoA; weighted UniFrac PERMANOVA p = 0001 for both comparisons) nor unsupervised hierarchical clustering heatmaps (Figs. S1 and S2).  3.3. Cases have an oral microbiota enriched with anaerobes compared to SCs, and diversity in each group differs compared to their close contacts Cases vs SCs: No a-diversity (p = 0674) or b-diversity (PERMA-NOVA p = 0861) differences were found between cases and SCs in oral washes (Fig. 2a,b). However, cases were enriched with anaerobic bacteria (Paludibacter, Lautropia, Selenomonas) and depleted of Alloscardovia (Fig. 2c,d).
When b-diversity was analysed within households containing a case or SC, the magnitude of b-diversity differences between patientcontact pairs (cases and their CCCs or SCs and their CCSCs) did not differ (PERMANOVA p = 0935; Fig. 2g). In summary, cases and SCs have few oral taxonomic differences and compared to their contacts, each have a similar magnitude of differences; suggesting that b-diversity differences compared to contacts likely reflect a general illness-induced state of dysbiosis in the oral microbiome and is not a TB-specific phenomenon. Nevertheless, we identify specific oral taxa that differ.

Like oral washes, cases' sputum microbiota is enriched with anaerobes compared to SCs and overall diversity in each group differs compared to their close contacts
Sputum is readily available from most presumptive TB patients, however, comparative data on the sputum microbiome from SCs are limited, making the specificity of TB-associated microbial communities unclear.
Cases vs SCs: Both a-diversity (p = 0180; Fig. 3a) and b-diversity (PERMANOVA p = 0412, Fig. 3b) did not differ in sputum. Compared to SCs, cases were enriched in Alloiococcus and oral anaerobes Peptoniphilus, Paludibacter, and Lautropia (latter two also enriched in oral washes) and depleted of Pseudomonas and Leptotrichia (Fig. 3c,d). These observations suggest that, as in oral washes, cases' sputum microbial communities are enriched with oral anaerobes.
Paired weighted UniFrac distances between cases and CCCs did not differ from those between SCs and CCSCs (PERMANOVA p = 0841; Fig. 3g). As observed in oral washes, distinct sputum microbiota in sick patients are therefore not TB-specific but likely reflect a general shift away from a healthy state.

Overlap in oral and sputum microbiota
If the oral microbiota were like the sputum microbiota, study of the respiratory microbiome in presumptive TB patients would be facilitated as sputum is difficult to obtain and typically consumed for diagnostic testing. Pairwise comparisons of oral washes and induced sputum within each patient group (cases, SCs, CCCs, CCSCs) revealed no a-diversity differences ( Figure S3a). Likewise, weighted UniFrac distances between paired oral washes and induced sputa in each patient group were similar (PERMANOVA p = 0851; Figure S3b). These specimen types showed broad similarity in microbial community membership ( Figure S3c) with Veillonella (14% of total reads in oral washes vs 15% in sputum), Prevotella (21% vs 25%) and Streptococcus (26% vs 17%) representing the three most abundant taxa in both specimens for each patient group. Moreover, the top two differential genera identified in cases' oral washes (Paludibacter, Lautropia) were also in sputum (although not similarly ranked) (Figs. 2d and 3d). Oral washes communities were therefore like sputum.
3.6. Active TB is associated with specific gut microbiota perturbations despite the absence of mycobacterium in stool It remains unclear whether differences in the gut microbiota reflect generalised illness or are TB-specific [12,27,28].
Cases vs SCs: a-diversity in stool was similar in cases and SCs (p = 0470; Fig. 4a), however, b-diversity differed (PERMANOVA p = 0035; Fig. 4b). Cases were enriched in the anaerobes Anaerostipes, Blautia, and Erysipelotrichaceae (Fig. 4c,d) and, unlike in oral and sputum microbiota, this enrichment was extensive enough to result in overall b-diversity differing relative to SCs.
We next determined whether cases' and SCs' stool microbiota were more dissimilar to each other (within each group) than their corresponding contacts. Based on weighted UniFrac distances, microbiomes of cases were more dissimilar to each other than their CCCs were to each other (PERMANOVA p < 0001, Fig. S4). Similarly, SCs were more dissimilar to each other than CCSCs (PERMANOVA p < 0001). The degree of dissimilarity within cases did not differ to that in SCs (p = 0419).

HIV infection does not affect oral, sputum, and gut microbiomes in presumptive TB patients
Reduced diversity and enriched pathobionts have been reported in the gut microbiomes of HIV-infected individuals [29], however, HIV did not impact microbial diversity, composition, or relative abundances in any fluid (Figs. S5ÀS10) apart from stool comparisons within CCCs (HIV-infected vs. HIV-uninfected PERMANOVA p = 0025; Fig.  S10b).

Cases' stools are inferred to have enrichment of microbial genes annotated as part of amino acid and carbohydrate metabolic pathways
Functional profiles of the gut microbiota in cases and SCs were inferred using PICRUSt [24]. Stool OTUs were normalised by 16S copy number and metagenomes predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [30]. 34 KEGG pathways differed significantly between cases and SCs. Cases were enriched in metabolism for amino acids (arginine and proline, histidine, lysine), carbohydrates (starch and sucrose, pentose phosphate pathway, fructose, and mannose), and glycerolipids, and depleted of capacities for arachidonic acid metabolism, lipopolysaccharide biosynthesis and cofactors and vitamins metabolism (ubiquinone and other terpenoidquinone biosynthesis, folate, riboflavin; Fig. 5).
Microbiome and transcriptome clustering: A co-occurrence analysis found similar node clustering coefficients between networks of microbiome and transcriptome data in cases and SCs for both oral washes and induced sputa (p = 0490, p = 0070) but differences in stool (t-test p < 0010). In stool, the strength of the correlation between clustering coefficient and degree of clustering was also different between cases and SCs, further demonstrating a distinct network structure (Fisher's r-to-z transformation p = 00035; Fig. S12).
Gut microbiome-transcriptome network analysis: None of the taxa we identified as enriched in SCs correlated with transcriptional networks (Fig. 6B). In contrast, gut anaerobes identified as enriched in cases correlated significantly with specific immune pathways in the same individuals: Erysipelotrichaceae and Blautia co-occur (correlation coefficient of 043 for both) and negatively correlate with "death receptor" (À045, À035) and "EIF2 signalling" (À045, À035) pathways (Fig. 6c). Furthermore, Anaerostipes positivity in cases correlates with "interferon signalling" (033), "Nur77 signalling in T lymphocytes" (033) and "inflammasome" (043) pathways. Overall, these results confirm the presence of a distinct host-microbe phenotype in TB disease and the co-occurrence of specific taxa and host pathways in cases.

Discussion
We comprehensively described oral, sputum and gut microbiota in pre-treatment presumptive TB patients and their close contacts, including the host transcriptome in cases. We demonstrate that: (1) Fig. 5. Cases' stools have increased inferred amino acid and carbohydrate metabolic pathway microbial genes. PICRUSt was used to infer the bacterial metagenome using 16S rRNA sequence data from stool. Thereafter, DESeq2 analysis was done to compare differentially abundant pathways in cases vs. symptomatic controls (SCs). Significantly more discriminatory pathways appear closer to the left or right, and higher above the threshold (red dotted line, FDR=02). Relative gene abundance is indicated by circle size. Key pathways of interest are bolded including precursor pathways for short chain fatty acid production i.e. carbohydrate (starch and sucrose, pentose phosphate pathway, fructose and mannose) and amino acid (arginine and proline, histidine, lysine) metabolism. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
cases have a distinct gut microbiome compared to SCs and to CCCs, with the anaerobes Anaerostipes, Blautia, and Erysipelotrichaceae being highly discriminatory and enriched with amino acid and carbohydrate metabolism-related genes; (2) these taxa correlate with specific upregulated pro-inflammatory pathways in cases; (3) the oral wash microbiota in TB approximates that of sputum; and (4) the microbiota of cases and SCs (oral wash or sputum) differ more vs. their respective contacts than to each other suggesting that, unlike what is observed with stool, taxonomic changes at these sites are not TB-specific but rather reflective of poor respiratory health.
Stool taxa differentiating cases from SCs are members of the Lachnospiraceae (including Anaerostipes and Blautia) and Erysipelotrichaceae families. Anaerostipes has not been described in TB before, however, Blautia was previously reported to be enriched in stool versus healthy household contacts [27]. We now show that these taxa are specifically associated with TB disease compared to symptomatic patients without TB, despite Mycobacterium not being detectable in stool. These stool compositional differences were not accounted for by taxonomic variation associated with risk factors for TB (i.e. HIV) or factors described elsewhere [31,32].
Certain gut anaerobes ferment substrates (carbohydrates and proteins) thereby producing short chain fatty acids (SCFAs) like butyrate, propionate, and acetate, which influence host immunity [33]. Taxa enriched in cases are butyrate-producers [34À36]. Although we were unable to infer enrichment of microbial genes encoding butyrate metabolism, we did detect an enrichment of genes in cases' stool (vs. SCs) involved in key butyrate precursor pathways (amino acid and carbohydrate metabolism). Butyrate facilitates communication between the microbiota and the immune system [37]. By inhibiting histone deacetylases, butyrate regulates cytokine expression in T cells and macrophages [38À40] and regulatory T cell induction [41]. However, elevated concentrations of butyrate may be detrimental in TB as Fig. 6. Unique host transcriptome in cases and co-occurrence network analysis showing certain gut anaerobic taxa that are enriched in cases (red lines, vs. sick controls) correlate with distinct inflammation-modulating pathways: Blautia and Erysipelotrichaceae co-occur and negatively correlate with "death receptor signalling" and "EIF2 signalling pathways" whereas Anaerostipes positively correlates with "interferon signalling", "Nur77 signalling in T lymphocytes" and "inflammasome" pathways. (a) Transcriptomic differences between cases (n = 38) and SCs (n = 30) based on Bray-Curtis dissimilarity index. (b) In symptomatic controls, although several taxa were significantly correlated with specific pathways, none of these taxa or pathways were enriched vs. cases. (c) In cases, distinct co-occurrence nodes involving "death receptor" and "EIF2 signalling" pathways and Blautia and Erysipelotrichaceae was observed (left red lines) and "interferon signalling", "Nur77 signalling in T lymphocytes" and "inflammasome" pathways and Anaerostipes (right red lines). Blue square nodes correspond to transcriptional pathways, while yellow circular nodes correspond to taxa. Edge length is inversely proportional to correlation strength, and the overall structure was obtained using a spring-embedded configuration. For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. it inhibits the proinflammatory responses (TNF-a, IL-1b, IL-17) induced by Mtb antigens and subsequent Mtb kill [42]. Mechanistic studies are needed to confirm if SCFAs made by the enriched gut anaerobes identified here regulate peripheral immune function and we intend to do such work.
Cases were enriched in many oral anaerobes (Paludibacter, Lautropia, Alloiococcus, Peptoniphilus) in oral washes and induced sputum. Previously, in a South African cohort of latently-infected HIV-positive patients, oral anaerobe-enriched lower airways were associated with elevated SCFAs that predicted the later development of active TB [15]. We have now shown such anaerobes have likely proliferated by the time of presentation for active TB disease.
An important strength of this study is the contemporaneous collection of peripheral blood. Critically, the differences between cases and SCs observed independently in the analysis of microbiome and transcriptome data were corroborated by co-occurrence network analysis. For instance, the gut microbiome-transcriptome network differed in cases compared to SCs, as shown by the significantly lower clustering coefficient, which is indicative of a less modular structure and is, in principle, associated with diseased states in general [43]. Further, we observed significant correlations in cases -but not in SCs -between Anaerostipes, Blautia and Erysipelotrichaceae and defined TB-related host peripheral blood pro-inflammatory responses (interferon signalling, inflammasome), [44,45] cell death signalling, [46] and antibacterial response [47]. This is an important finding in the field of TB and agrees with previous studies which demonstrate immunomodulatory properties for these taxa in other diseases (e.g. Blautia and IL-17 [48] and Erysipelotrichaceae and IL-1b) [29]. These findings merit further validation and raise the question of whether targeting the gut anaerobes identified here which have inflammatory effects (e.g., using narrow-spectrum antibiotics), would reduce TB pathology.
Another study strength included the pairing of presumptive TB patients and their close contacts. Although a greater resemblance in microbiome signatures exists within households, [49] in our cohort, presumptive TB patients had distinct oral, sputum and gut microbial compositions compared to their close contacts, which is likely as a result of the former group's illness. In cases compared to their CCCs, this difference (in all three compartments) was mainly driven by changes in Megasphaera which is considered a biomarker for lung cancer [14,26]. Cases were also depleted of immunomodulatory gut commensals like Bifidobacterium, [50] corroborating previous studies [27].
In SCs and their CCSCs, many taxonomic differences were apparent across specimen types (unlike in cases vs CCCs) and likely reflect heterogeneous respiratory illnesses amongst SCs. Furthermore, reduced diversity in comparisons involving SCs vs CCSCs and not in cases vs CCCs (or SCs) suggest that M. tuberculosis disease results in fewer microbial perturbations than other bacteria (e.g. Haemophilus or Moraxella) which predominate during respiratory infections [51]. This agrees with our documentation of M. tuberculosis not having high abundance in sputum despite it being the causative pathogen of disease.
Our study has strengths and limitations. Although the lung microbiome is best sampled by bronchoalveolar lavage, [52] this is not feasible in primary healthcare or at large scale. We therefore collected induced sputum, meaning that presumptive TB patients and their contacts who could not expectorate could be included. Mycobacteria could have been underestimated for technical reasons [53] or because sputum contains microbes not just from the pulmonary site of disease (Mycobacterium was not a discriminatory taxon in comparisons). Follow up studies using shotgun metagenomics, rather than inferred metagenomics, are warranted and will increase taxonomic and functional resolution of bacteria and other types of microbial populations [54]. Some patients with respiratory symptoms may have received antibiotic treatment (with drugs other than for TB) prior to TB diagnosis and, although this is relatively rare in our setting, we did not collect these data in all presumptive TB patients. An improved understanding of the underlying causes of the spectrum of disease in SCs is especially important to not only the TB field in general [55,56] but especially for microbiome research. Lastly, our objective was to comprehensively characterise the microbiome pretreatment (before the confounding effects of antibiotics), however, future research on the long-term effects of TB treatment on the microbiome is important, including its link with recurrence.
In summary, TB-specific microbial signatures in oral washes (Paludibacter, Lautropia), induced sputum (Alloiococcus, Peptoniphilus) and, most notably, the gut (Anaerostipes, Blautia, Erysipelotrichaceae) were evident prior to treatment. In the gut, enriched anaerobes correlated with pro-inflammatory host immune pathways known to be associated with TB disease severity. We therefore postulate that the gut microbiota has a role in TB pathogenesis and may regulate lung immunity, however, more research is needed on the types of metabolites produced by these taxa and the mechanisms by which they may influence host immunity. This work therefore lays the foundation for further exploration of the gut microbiome as a potential diagnostic or prognostic marker for TB.

Data sharing
De-identified patient data, the study protocol, informed consent, and datasets generated in this study may be requested from the corresponding author.

Declaration of Competing Interest
GT received in-kind donations (GeneXpert cartridges and machines) from Cepheid and FIND. Cepheid had no role in study design or interpretation of results. BWPR received travel support from Cepheid to attend a conference and present unrelated data. All other authors declare no competing interests.