Frontiers in Immunology

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EDITED BY

Abhijit Maji,
University of Texas Southwestern Medical
Center, United States

REVIEWED BY

H. Syed Iqbal,
YR Gaitonde Centre for AIDS Research and
Education, India
Prakash Babu Narasimhan,
Sri Balaji Vidyapeeth University, India
Eugenia Silva-Herzog,
National Institute of Genomic Medicine
(INMEGEN), Mexico

*CORRESPONDENCE

Mamoudou Maiga

mamoudou.maiga@northwestern.edu

Dramane Diallo

dramanediallo@icermali.org

RECEIVED 15 January 2025

ACCEPTED 23 April 2025
PUBLISHED 14 May 2025

CITATION

Diallo D, Sun S, Somboro AM, Baya B, Koné A,
Diarra B, Nantoumé M, Koloma I, Diakite M,
Holl J, Maiga AI, Seydi M, Theron G, Hou L,
Fodor A and Maiga M (2025) Metabolic and
immune consequences of antibiotic related
microbiome alterations during first-line
tuberculosis treatment in Bamako, Mali.
Front. Immunol. 16:1561459.
doi: 10.3389/fimmu.2025.1561459

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© 2025 Diallo, Sun, Somboro, Baya, Koné,
Diarra, Nantoumé, Koloma, Diakite, Holl, Maiga,
Seydi, Theron, Hou, Fodor and Maiga. This is an
open-access article distributed under the terms
of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
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original author(s) and the copyright owner(s)
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which does not comply with these terms.

TYPE Original Research

PUBLISHED 14 May 2025

DOI 10.3389/fimmu.2025.1561459

Metabolic and immune
consequences of antibiotic
related microbiome alterations
during first-line tuberculosis
treatment in Bamako, Mali
Dramane Diallo1*, Shan Sun2, Anou M. Somboro3,4, Bocar Baya1,
Amadou Koné1,4, Bassirou Diarra1,4, Mohamed Nantoumé1,
Isaac Koloma1, Mahamadou Diakite1,4, Jane Holl5,
Almoustapha Issiaka Maiga1, Moussa Seydi6, Grant Theron7,
Lifang Hou8, Anthony Fodor2 and Mamoudou Maiga4,8*

1University Clinical Research Center (UCRC), Bamako, Mali, 2Department of Bioinformatics and
Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States, 3Antimicrobial
Research Unit, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa,
4University of Sciences, Techniques, and Technologies of Bamako (USTTB), Bamako, Mali,
5Department of Neurology and Center for Healthcare Delivery Science and Innovation, University of
Chicago, Chicago, IL, United States, 6Service des Maladies Infectieuses et Tropicales, Fann University
Hospital Center, Dakar, Senegal, 7Centre of Excellence for Biomedical Tuberculosis Research, South
African Medical Research Council Centre for Tuberculosis, Stellenbosch University, Cape Town, South
Africa, 8Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

Background: Individuals with a history of tuberculosis (TB) treatment are at a

higher risk of experiencing a recurrent episode of the disease. Previous cross-

sectional studies identified a connection between dysbiosis (alterations) in the

gut microbiota composition and the administration of first-line TB antibiotics.

However, these studies have not successfully elucidated this dysbiosis’s resulting

metabolic and immune consequences.

Methods: In a longitudinal assessment, we studied the antituberculosis drug-

related changes in the gut microbiota’s composition and the resulting functional

consequences. Sputum for TB culture, peripheral blood for metabolomics and

cytokines analysis, and stool for shotgun metagenomics were collected from TB

participants at Month-0, Month-2, Month-6 of treatment, and 9 Months after

treatment (Month-15). Healthy controls were sampled at Month-0 and Month-6.

Findings: We found notable differences in gut microbiota between individuals

with TB and healthy controls. While gut microbiota tended to resemble healthy

controls at the end of TB treatment, significant differences for many taxa

persisted up to Month-15. Concurrently, disturbances in plasma metabolites,

including tryptophan, tricarboxylic acids, and cytokine levels were observed.

Certain fatty acids associated with inflammation pathways negatively correlated

with the abundance of several taxa.

Conclusion: We observed alterations in the gut microbiota composition and

function during treatment and at Month-15. Numerous changes in bacterial taxa

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Diallo et al. 10.3389/fimmu.2025.1561459

Frontiers in Immunology

abundances and inflammation-linked metabolites did not reverse at Month-15.

This study suggests potential influences of anti-TB drugs and the gut microbiome

on the disease outcome, response to treatment, and resistance to future

TB infections.

KEYWORDS

tuberculosis, gutmicrobiome alterations, metabolic and immune response, tuberculosis
treatment, dysbiosis, Mali

Background

Tuberculosis (TB) remains ranked as one of the leading causes

of death from a single pathogen,Mycobacterium tuberculosis (Mtb),

with 1.13 million deaths among HIV-negative patients and 167,000

deaths among HIV-positive patients reported only in 2022 (1).

One-fourth of the world’s population has been infected with Mtb,

and more than 10 million people develop the disease yearly (1).

Multiple underlying environmental conditions and immune and

host genetic predisposing factors have been associated with TB

infection (2, 3). Furthermore, treatment duration, adverse events

with antibiotics, and drug resistance are important factors for

disease outcomes. Standard first-line TB treatment requires six

months of combination treatment with isoniazid, rifampicin,

ethambutol, and pyrazinamide (4). However, treated and cured

individuals are at least 8 times more likely to experience a new

episode of TB disease than the general population (5–7).

Disruption of gut microbiota, which includes bacteria, archaea,

and fungi, is a key factor potentially contributing to TB recurrence

(8–10). Previous studies, many of which are cross-sectional, have

found that TB and its treatment cause long-lasting dysbiosis up to

two years after treatment. However, the metabolic consequences

remain unexplored (5, 11). In addition, the microbiome-linked

functional changes have not been studied longitudinally during

TB treatment.

The Gut microbiota produces metabolites, such as short-chain

fatty acids (SCFAs), that contribute to the host’s overall metabolic

function, including defense against pathogens and drug metabolism

(12). Anaerobic commensals produce enzymes that degrade dietary

fibers into SCFAs (such as acetate, propionate, and butyrate), which

regulate host immune-inflammatory response.

We conducted a longitudinal clinical study to investigate

changes in gut microbiota profiles in TB patients before, during,

and after treatment and in healthy controls, using shotgun

metagenomics, metabolomics, and human Th1/Th2/Th17

cytometric bead array (CBA) for cytokines measurement. This

study is one of the first of its kind to use shotgun metagenomics

to understand the effect of anti-TB treatment on gut microbiota

over time. This study provides new knowledge about potential

strategies to improve TB treatment efficacy using host microbiota-

directed therapies (10).

02

Methods

Study design and setting

We conducted a longitudinal study from February 2016 to

August 2020, enrolling newly diagnosed TB cases based on sputum

smear-positive with AFB (acid fast bacilli) from TB referral health

centers in Bamako, the capital city of Mali, West Africa, and then

later confirmed by culture at the University Clinical Research

Center, University of Sciences Techniques and Technologies of

Bamako (USTTB), Mali. In this longitudinal cohort study, a total of

155 TB patients were enrolled. However, were included in this final

analysis only 30 TB participants, who were infected with Mtb

complex strains confirmed by phenotypic (Mtb culture) and

genotypic (Spoligotyping) identification methods.

Study subjects and samples processing

Participants in this study included a group of TB patients and

healthy individuals. Mtb-infected participants had four study visits

for clinical investigation and sample collection as follows: before TB

treatment initiation (TB_M0), two months (TB_M2), and six

months (TB_M6) during anti-tuberculosis therapy, and then nine

months after treatment completion (TB_M15). Healthy controls

were sampled at Month-0 and Month-6 during the recruitment

periods of TB patients. Samples collected include sputum, plasma,

and stool from each participant. All the participants were 18 years

and above who were newly diagnosed microscopically with

pulmonary tuberculosis (TB group and later confirmed with

sputum culture and molecular identification) and healthy control

volunteers (Control group), with no TB disease or no latent TB

infection as confirmed by the QuantiFERON-TB Gold assay (QFT-

Plus; Qiagen, Hilden, Germany). All the participants were

confirmed HIV seronegative and without any prior anti-

tuberculosis therapy or antibiotics in the past 4 weeks prior to

enrollment. Of all the TB patients included, 23 (76.6%) completed

the study in Month-15, while 26 healthy controls provided the

requested samples for the study. All the participants (TB and

Healthy cases) were from the same geographical region (Bamako,

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Diallo et al. 10.3389/fimmu.2025.1561459

the capital city of Mali in West Africa). A written and signed

informed consent form was obtained from each participant before

being enrolled. The confirmed cases of the newly diagnosed TB

patients were followed before and after starting a first-line anti-

tuberculosis treatment regimen comprising two months of isoniazid

(H), rifampin (R), pyrazinamide (Z), ethambutol (E), followed by

four months of rifampin (R), and isoniazid (H) (2HRZE/4RH).

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review

Board (IRB) of Northwestern University (approval number:

STU00094500) of Chicago (USA) and the Ethics committee of the

University of Sciences Techniques and Technologies of Bamako

(USTTB), Mali (approval number: 2014/04 CE/FMPOS). A consent

form was signed by each participant before inclusion in the study.

Mycobacterial identifications

Early morning sputum specimens collected from presumptive

TB patients were tested for TB using the standard N-acetyl-L-

cysteine/4% sodium hydroxide solution for sputum digestion and

decontamination; thereafter, the sample was concentrated by high-

speed centrifugation. The pellets were used to inoculate liquid

medium (Mycobacterium Growth Incubator Tube (MGIT™)
[BD, Sparks, MD, USA], and solid medium (Middlebrook 7H11

agar and selective 7H11 agar) to isolate pure mycobacteria colonies,

as previously described (13, 14).

Confirmed Mtb isolates underwent Spoligotyping to determine

the mycobacterial strains. Therefore, DNA from pure isolates of

Mtb were extracted, and Spoligotying was performed according to

the manufacturer’s instructions using commercial kits (Isogen

Bioscience BV, Maarssen, The Netherlands). A detailed

description of the Spoligotyping methodology can be found in the

supplementary data.

Cytokine measurements

Cytokine levels were monitored longitudinally to assess the

immune response from the pre- to post-treatment stages. Per the

manufacturer’s instructions, we used the Human Th1/Th2/Th17 kit

(BD Biosciences, San Diego, USA) to perform the cytokine

measurement assays. The BD CBA assessed Interleukin-2 (IL2),

IL4, IL6, IL10, TNF, IFN-g, and IL17A levels in plasma samples.
Samples were thawed at 4-8°C before the assay, then prepared and

measured following instructions using an LSR II flow cytometer at

the University Clinical Research Center, Mali (UCRC). Analysis

used FCAP Array TM software v3.0.1 and the detection limits were

IL-2 (2.6 pg/mL), IL-4 (4.9 pg/mL), IL-6 (2.4 pg/mL), IL-10 (4.5 pg/

mL), TNF (3.8 pg/mL), IFN-g (3.7 pg/mL), and IL-17A (18.9
pg/mL).

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DNA extraction from stool samples

DNA was extracted from stool samples using the QIAmp

DNA Stool Mini Kit (Qiagen, Hilden, Germany). Frozen stool

samples were placed and thawed on ice before the extraction

starts. The stool samples were first weighed (180–220 mg of

thawed stool), thereafter, extraction was performed according to

the manufacturer’s instructions (QIAmp DNA Stool Mini Kit

(Qiagen, Hilden, Germany)). The extracted DNA was then

measured using the Nanodrop Onec (Thermofisher Scientific,

Verona Rd, Madison, USA) to assess the DNA concentration and

purity before sequencing.

Shotgun metagenomics of the gut
microbiome and bioinformatics analysis

DNA from stool samples was sequenced at the University of

Illinois at Chicago (UIC) Research Genome Core Laboratory

employing Illumina HiSeq 4000 using 150 bp paired-end reads.

The taxonomic composition was profiled using Kraken2, and the

functional pathways (stratified and unstratified) were characterized

using HUMAnN2 per developers’ instructions. The sequencing

reads ranged from 496,978 to 31,329,790 across 122 samples, with

an average 20,311,846 reads per sample. The human reads were

detected by alignment to human genomes and removed with

KneadData from bioBakery. The proportion of reads aligned to

human genomes ranged from 0.001% to 6.33%, with an average of

0.27%. On average, 6,681,981 reads/sample were classified to

bacteria, 114,108 reads/sample were classified to archaea, 14,876

reads/sample were classified to fungi, 1,480 reads/sample were

classified to non-fungal Eukaryota and 410,915 reads/sample were

classified to virus.

The statistical analysis of taxonomic composition and pathway

abundances were performed with R. The Principal Coordinate

Analysis (PCoA) was calculated based on the Bray-Curtis

dissimilarity using ‘capscale’ function in the R package ‘vegan’.

Alpha diversity was measured using Shannon index while beta

dispersion between groups was analyzed using TukeyHSD. The

differential taxa and pathways between healthy controls and TB

patients at each time point were analyzed with non-parametric

Wilcoxon test and linear regression models adjusted for age. The

change of taxa and pathways with time in TB patients were analyzed

with linear mixed-effects models with continuous time as main effect

and subject ID as random effects. P values were adjusted using the

Benjamini-Hochberg method for multiple hypotheses testing. False

Discovery Rate (FDR) <0.1 was considered as statistically significant.

Metabolomics analysis of plasma samples

Metabolomic analysis was performed on the plasma samples

from TB patients only at three time points (TB_M0, TB_M6, and

TB_M15) at Metabolon (Morrisville, NC, USA). Samples were

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prepared using the automated MicroLab STAR® system from

Hamilton Company. The extracts were analyzed with reverse

phase (RP)/UPLC-MS/MS in positive ion mode electrospray

ionization (ESI), RP/UPLC-MS/MS with negative ion mode ESI

and HILIC/UPLC-MS/MS with negative ion mode ESI. All methods

utilized a Waters ACQUITY ultra-performance liquid

chromatography (UPLC) and a Thermo Scientific Q-Exactive

high resolution/accurate mass spectrometer interfaced with a

heated electrospray ionization (HESI-II) source and Orbitrap

mass analyzer operated at 35,000 mass resolution. Raw data was

extracted, peak-identified and QC processed using Metabolon’s

equipment and software with peaks quantified by area-under-the-

curve. The metabolomics data were mapped to Metabolon’s

biochemical pathways to analyze the changes of functional

pathways. Detailed methods can be found in supplementary

information. Principal components analysis (PCA) was used to

visualize the differences in metabolome profiles between time-

points. Hierarchical Clustering Analysis (HCA) was used to

cluster samples based on the Euclidean distance. ANOVA with

repeated measures analyzed differential metabolites across groups

with contrasts revealing significant differences between each pair.

The association between taxonomic abundance and metabolites

were analyzed with Spearman’s correlation. P-values were adjusted

using the Benjamini-Hochberg method to correct for multiple

testing. GraphPad prism version 8.0.1 was used for patients’

social characteristics analyses and cytokines measurement analysis.

Results

Participants’ socio-demographic and
clinical characteristics

The longitudinal cohort study involved thirty (30) confirmed

TB individuals and twenty-six (26) healthy controls (TB and HIV

negative). The TB-infected participants were followed up for over

15 months, including during six months of the treatment and then

nine months after the treatment completion. The age, sex, and

smoking status of study participants are shown in Table 1. Age was

significantly different between healthy controls and TB patients,

while sex and smoking status were not significantly different

(Table 1). A total of 19/30 (63.33%) of the TB patients were

infected with the modern Euro-American lineage 4, which

showed a high amount of persistence of sputum smear positivity

at month-2 of TB treatment 10/13 (76.92%). The other circulating

lineages in Mali, lineage 1, lineage 2, and lineage 3, were more

responsive to the anti-TB drugs, 8/11 (72.72%) at this time point.

Longitudinal metagenomics analysis of
microbial diversity in the gut of TB patients
during drug treatment

PCoA at the genus level showed the separation of gut

microbial communities by study groups (Figure 1A). For

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longitudinal analysis, Mtb-infected individuals were shown by

time-points: TB_M0, TB_M2, TB_M6, and TB_M15, as described

above. The gut microbiota of TB_M0 and TB_M2 were distinct

from healthy controls. While TB_M6 and TB_M15 microbiota

became more similar to healthy controls but are still significantly

different (PERMANOVA test: TB_M6 vs Healthy: R²=0.065,

P=0.002; TB_M15 vs Healthy: R²=0.087, P=0.001). The

differences between TB patients and healthy controls remained

significant after adjusting for age in the PERMANOVA model

(P=0.001). Shannon diversity increased in patients’ gut microbiota

over time, with TB_M0 and TB_M2 significantly different from

healthy controls and TB_M6 and TB_M15 insignificant

(TukeyHSD, ANOVA) (Figure 1B). We tested the association of

microbial community and metadata, including Case/Control, time

points for patients, age, sex, smoking status, and subject ID using a

univariate PERMANOVA test. We found that Case/Control, time

points for patients, and smoking status were significantly

associated with the microbial community in this cohort

(P<0.05), while age, sex, and subject ID were not significantly

associated (Figure 1C). Among the significant associated

metadata, time points in patients have the largest effect size

(R2). The beta-diversity of TB_M0 and TB_M6 were

significantly higher than that of healthy controls and TB_M15,

while the beta-diversity of TB_M15 is similar to that of healthy

controls (Figure 1D).

In addition to PERMANOVA tests, which examine beta

diversity metrics on the entire microbial community, we analyzed

the differences in relative abundance for taxonomic composition

between patients and controls using the Wilcoxon test and linear

regression models adjusted for age. We also analyzed the changes in

treatment time with mixed effects linear models with time as the

main effect and subject ID as the random effect in patients. At the

phylum level, Firmicutes increased significantly with time after

treatment, while Proteobacteria decreased significantly

(Figure 1E), (linear mixed effect models, FDR<0.1). In patients,

411 taxa across taxonomic levels from phylum to species increased

with time, and nine decreased with time (linear mixed effect models,

FDR<0.1, (Supplementary Table S1)). At M15, Lactobacillus and

Lactobacillaceae were still significantly lower than that of healthy

controls (Wilcoxon test, FDR<0.1, (Supplementary Table S1)). In

patients, 815 taxa were significantly lower than healthy controls at

all four-time points, and two taxa were significantly higher than

healthy controls (Supplementary Table S1). After adjusting for age,

1234 taxa were significantly less abundant at TB_0 than healthy

TABLE 1 Characteristics of study participants.

Participants
Characteristics

Case Control P value1

Number 30 26

Age (y), mean (SD) 32.5 (11.8) 25.8 (5.7) 0.012

Female, % 16.7 34.6 0.14

Non-smoking % 66.7 76.9 0.55

fr

1For age, p-value derived from Wilcoxon test. For sex and smoking, p-value derived from
Fisher’s exact test.

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controls, among which 767 were still less abundant at TB_2.

However, none of these were still significant at TB_6 and TB_15

after age adjustment (Supplementary Table S2). At TB_0, 32 taxa

were significantly more abundant than healthy controls, but none of

Frontiers in Immunology 05

these taxa were still significant after treatment at TB_2

(Supplementary Table S2).

We analyzed the differences in functional pathway abundance

between TB patients and healthy controls with Wilcoxon test and

FIGURE 1

Gut microbiota diversity and taxonomic composition of TB patients and healthy controls. (A) Principal Coordinates Analysis (PCoA) of the microbial
communities at the genus level. Cases are colored by groups, and time points and ellipses indicate 95% confidence limits. (B) Shannon diversity of
TB patients and healthy controls. Each point refers to an individual sample. (C) PERMANOVA R2 of the association between microbial taxonomic
composition and metadata. (D) Beta-diversity (distance to centroid) of TB patients and healthy controls. Each point refers to an individual sample.
(E) Bar plot of average phylum composition of TB patients at each time-point and healthy individuals. * statistically significant.

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Diallo et al. 10.3389/fimmu.2025.1561459

linear regression models adjusted for age and the changes in

functional pathways with treatment time with mixed effects linear

models with time as the main effect and subject ID as the random

effect. (Figure 2) In TB patients, 75 pathways increased with time,

and 11 decreased with time (linear mixed effect models, FDR<0.1,

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Supplementary Table S3). Compared with healthy controls, 11

pathways were less abundant at TB_M0, and 212 pathways were

more abundant. Among these pathways, four were still less

abundant, and 15 were still more abundant at TB_M2. None of

these pathways were significantly different between TB_M6 and

FIGURE 2

Differences in gut microbial functional pathways between TB patients and healthy subjects. (A) Heatmap shows select pathways significantly different
between healthy controls and TB patients before treatment with the Wilcoxon test adjusted for multiple hypotheses testing. Pathways present in
<25% of samples are excluded from the analyses. Keys indicate z-scores of relative abundances. (B) Box plots showed the changes in eight
significant functional pathways. * statistically significant.

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controls, while three became significantly more abundant at

TB_M15 again. After adjusting for covariates, 207 pathways were

significantly more abundant at TB_M0 than healthy controls, and

two were significantly less abundant, among which none were

significant at TB_M2 (Supplementary Table S4).

Metabolomics analysis

Three groups were compared to evaluate changes in blood

metabolites: TB_M0, TB_M6, and TB_M15. By using ANOVA

with repeated measures, 470 metabolites were found to be

statistically significant across the three groups (FDR<0.1). Next,

we compared each pair of groups with ANOVA contrast and found

that 361 metabolites were significantly different between TB_M0

and TB_M6, 103 metabolites were significantly different between

TB_M6 and TB_M15 and 428 metabolites were significantly

different between TB_M0 and TB_M15.

Principal Components Analysis (PCA) plots showed that

TB_M0 is distinct and separated from TB_M6 and TB_M15,

indicating that the treatment effects on metabolites were

maintained at least nine months after treatment completion

(Figures 3A, B).

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The overlap between TB_M6 and TB_M15 suggests their

metabolic profiles are similar and different from the pre-

treatment/infection stage. Using the Hierarchical Clustering

Analysis (HCA), we found five groupings of 15 subjects’ samples

of metabolic profiles (Figure 3C). A cluster of nine pre-treatment

samples also suggests good similarities among many pre-treatment

samples. The lack of such high clustering at the end of the standard

treatment and nine months after treatment suggested that the

variance from these two-time points is smaller than the variance

across the study subjects.

Our data showed major changes between TB time points in four

main pathways, as reported in (Figure 4), such as tryptophan

metabolism, fatty acid metabolism, and energy pathways.

Correlation between genus and pathways
abundance and produced microbial
metabolites

We found significant correlations between genus/pathway and

metabolite abundance in the gut during the six-month TB antibiotic

regimen. We compared the metabolites’ production with the taxa’s

relative abundance TB_M6 and TB_M15 (Figure 5A). The whole

FIGURE 3

Principal component analysis showing separation in metabolomics profile of the different groups. Data are displayed in two dimensions: (A), three
dimensions (B, C) Hierarchical clustering analysis of metabolite pathways from plasma.

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correlated taxa from gut microbiota to peripheral metabolites are

listed in supplemental data 1. The taxa correlated with metabolite

production belong to the bacteria phyla of Proteobacteria,

Actinobacteria, and Firmicutes, and Euryarchaeota from the

archaea domain. The fatty acids such as quinoline and

arachidonate levels that are important for the inflammatory

balance/pathway decreased with the increase of these taxa. In

addition, we found correlations between the pathways’ alterations

and produced metabolites (Figure 5B). A negative correlation was

found between quinolinate and both Guanosine and

methylerythritol phosphate pathway I. The ornithine was also

found to negatively correlate with the alteration of glycolysis,

tricarboxylic acid cycle, and glyoxylate bypass. In addition,

citrulline and kynurenate were found to be positively correlated

with the alteration of histidine, purine, and pyrimidine biosynthesis

(kynurenate), aromatic amino acid biosynthesis, and starch

degradation V pathway (citrulline) in the samples of TB patients.

Cytokines analysis with CBA

The immunological profile of participants was analyzed using

the cytokines levels that are relevant from the literature for both TB

disease and the microbiome (Figure 6). Despite the concentrations

below the analysis’s detection limit, we compare the trend of

cytokines from patients during the study time points without

healthy individuals. We found that the mean cytokines levels

were high for the major inflammatory players that are important

Frontiers in Immunology 08

for TB disease, such as IL-4, IL-6, IL-10, and IFN-g TB-M0, but the
levels continued to decrease slowly during and after treatment

completion. In contrast, IL-17A, known to have a strong link

with the gut, was highly expressed during the treatment period,

and the trend was maintained a long time after completion

(TB-M15).

Discussion

This longitudinal cohort study looked at the gut microbiota

profiles dynamics of TB patients before, during, and after treatment

and thereafter compared to healthy individuals, this includes the

microbial and metabolite changes within TB patients at different

treatment stages and between TB and healthy participants. The

study revealed that the gut microbiome of TB patients before and

after treatment was distinct from that of healthy individuals, and the

altered microbial community in the gut environment persisted for

at least nine months after treatment completion. Gut microbiota is

involved in the biological homeostasis of the host by its implication

in the production of molecules that interact with the host cells. The

dysregulation likely due to the Mtb infection tends to become

normal after treatment (5, 15, 16). However, individuals with

successful TB treatment outcomes remain at risk of developing

the disease again (recurrent TB), either from the initial Mtb strain

(relapse) or from a new strain (reinfection). Our study showed that

the use of anti-mycobacterial drugs is also associated with human

gut microbes disruption leading to gut microbiome dysbiosis.

FIGURE 4

Main biochemical pathway changes during treatment of TB disease. Metabolomics data showed changes in the scaled intensity of biochemicals.

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Therefore, the gut microbiome dysbiosis induced by anti-

tuberculosis treatment may contribute to the risk of recurrent TB.

Previous studies supported this hypothesis regarding treated TB-

infected individuals having higher chances of developing a new TB

episode (7, 17, 18), and this could be explained by the long-lasting

damage from TB drugs on the gut microbiota and the resulting

impact of its homeostatic role in inflammation and other metabolic

functions that are essential for resistance against Mtb.

Recent studies showed significant relationships between

changes in the gut microbiota and many human disease

outcomes, including tuberculosis (19–21). The gender of men

represents the majority of TB patients in our study, and that was

also reported in several other studies (11, 22, 23). The mean age for

TB patients was 32.5, and the smoking status represented 33%.

Studies related to TB infection revealed its impact on young men

and its association with cigarette use (14, 24, 25).

Because of the high percentage of bacteria in gut microbiota

composition, other microbes, such as archaea, viruses, and fungi,

are also being impacted during TB treatment but are generally less

investigated compared to bacteria. Our study findings showed

significant differences in the Euryarchaeota phylum from the

archaea domain. One of the two major known archaeal phyla,

Frontiers in Immunology 09

Euryarchaeota, decreased from the gut microbiota of TB_M0 and at

TB_M2 (with isoniazid and rifampicin regimen), then the level

increased at TB_M6 before being finally decreased at TB_M15.

As reported by Negi S et al. in the mice model, the use of broad-

spectrum TB antibiotics, such as rifampicin active on Gram-positive

bacteria, could contribute to changes in the gut microbiota diversity

and composition (26). In our study, the persistence of the alteration

lasted nine months after treatment completion, and the data showed a

negative correlation between the genus Actinosynnema, Megasphaera,

and Roseburia relative abundances and the quinolinate metabolite.

Quinolinate (quinolinic acid) is known as a marker for kynurenine in

the tryptophan pathway (27). Similarly, Shibata et al. also reported

disturbing effects after administration of antituberculosis drugs

(mainly pyrazinamide and isoniazid) on the metabolic pathways of

quinolinic acid (28). Furthermore, the kynurenine/tryptophan ratio is

reported to be a great biomarker for pulmonary tuberculosis (29).

Antibiotics during TB treatment potentially caused dysbiosis in the

mentioned genus, leading to alterations in the plasma tryptophan

pathway. Furthermore, our study found that arachidonic acid, a

polyunsaturated fatty acid, negatively correlates with the abundance

of Actinosynnema. This acid, used byMtb via biosynthesis in infected

macrophages, impairs their inflammatory and antimicrobial activities;

FIGURE 5

Correlation between microbial-produced metabolites and both pathways and taxa abundance. (A) Quinolinate was negatively correlated with the
abundance of genera Megasphaera, Roseburia and the species Alpha proteobacterium HIMB5 from the gut microbiota. Arachidonate was negatively
correlated with the abundance of genus Actinosynnema. (B) Citrulline and kynurenate were found to be positively correlated with starch degradation
V and histidine, purine and pyrimidine biosynthesis pathways respectively, while the quinolinate was negatively correlated with methylerythritol
phosphate pathway I.

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Diallo et al. 10.3389/fimmu.2025.1561459

its persistence nine months after TB treatment may explain the higher

vulnerability of cured TB patients to new TB episodes, up to eight

times more than the general population (30).

We further measured the level of seven cytokines from the

plasma of TB patients, performed a comparative analysis between

time points, and observed that IL-6 and IFN-g were the most
significantly higher levels of cytokines, which play an important

role in the acute phase response against Mtb, while later they may

potentiate tissue damage and induce other pathological pathways

(31, 32). During TB treatment, we observed a decrease in their levels

before the total bacterial clearance predicting positive TB treatment

outcomes. Moreover, the gut microbiome also influences cytokines

production, both directly via metabolites and indirectly by

modulating host immune cells, which has an impact on immune

homeostasis and disease susceptibility (33).

This study has some limitations: The sample size could be

bigger to establish a stronger statement regarding this study’s

conclusion, although the size used here is in line with other

similar conducted studies (11, 15). Future studies must include

various populations with a bigger sample size to generalize the

findings. Participants were not matched by age and gender.

However, when we analyzed the differences in these two

parameters between case and control, we found that age was

significantly different. We, therefore, adjusted for age in some of

Frontiers in Immunology 10

our statistical models. In addition, the healthy group was sampled at

a different time than the TB patients and some of the differences

between the cohorts may be explained by batch effects associated

with these differences.

Another limitation of our study is that while we have control data

from the microbiome, we did not collect metabolome or cytokines

information for healthy controls. Finally, the effects of the TB drugs

were measured collectively for multiple TB drugs and not

individually, as it is not ethical to treat patients with a single drug.

However, our studies in animal models in the past have found that

rifampin, a large spectrum antibiotic, was responsible for the majority

of dysbiosis seen with this drug regimen (5). Despite these limitations,

and based on recent reports, this study is the most comprehensive

analysis of the consequences of TB drug-related dysbiosis in

participants during and after treatment and will advance the field

of TB microbiome and our understanding of involved mechanisms.

In conclusion, the gut microbiota dysbiosis caused by

antituberculosis drugs persists up to nine months after treatment

completion. It shows putative links between microbiota-related

metabolites and their pathways, which may contribute to

weakening the inflammatory balance in TB-treated participants,

and this could potentially make them more vulnerable to another

TB episode. It is essential to characterize the dynamics of the gut

microbiome and its metabolites during TB treatment first to

FIGURE 6

Cytokines production at study time points for TB groups. To measure the kinetics of cytokines during the disease in patients, we measured their
concentrations longitudinally in plasma (TB_M0, TB_M2, TB_M6, and TB_M15). We observed a degradation of cytokines when comparing the values
to other studies, which does not change the trend of the kinetics. IL-6 and IFN-g were statistically significant (P<0.0001 and P<0.05, respectively).
For IL-4 and IL-10, a reduction in their levels was observed after two months of treatment.

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Diallo et al. 10.3389/fimmu.2025.1561459

improve treatment efficacy using host microbiota-directed therapies

and, second, to prevent recurrent tuberculosis.

Data availability statement

Sequences from this study are available on NCBI under the

accession number PRJNA1002577 (https://www.ncbi.nlm.nih.gov/

bioproject/PRJNA1002577).

Ethics statement

The studies involving humans were approved by University of

Sciences Techniques and Technologies of Bamako (USTTB), Mali

(approval number: 2014/04 CE/FMPOS. The studies were

conducted in accordance with the local legislation and

institutional requirements. The participants provided their written

informed consent to participate in this study.

Author contributions

DD: Conceptualization, Writing – original draft, Writing –

review & editing, Data curation, Formal analysis, Investigation,

Methodology, Resources, Software. SS: Writing – review & editing,

Data curation, Formal analysis. AS: Conceptualization, Funding

acquisition, Supervision, Validation, Visualization, Writing –

original draft, Writing – review & editing. BB: Investigation,

Supervision, Validation, Visualization, Writing – review &

editing. AK: Conceptualization, Supervision, Validation,

Visualization, Writing – review & editing. BD: Conceptualization,

Supervision, Validation, Visualization, Writing – review & editing.

MN: Data curation, Investigation, Methodology, Visualization,

Writing – review & editing. IK: Investigation, Methodology,

Visualization, Writing – review & editing. MD: Supervision,

Validation, Visualization, Writing – review & editing. JH:

Supervision, Validation, Visualization, Writing – review &

editing. AM: Funding acquisition, Project administration,

Supervision, Validation, Visualization, Writing – review &

editing. MS: Supervision, Validation, Visualization, Writing –

review & editing. GT: Supervision, Validation, Visualization,

Writing – review & editing. LH: Funding acquisition, Supervision,

Validation, Visualization, Writing – review & editing. AF:

Conceptualization, Funding acquisition, Supervision, Validation,

Visualization, Writing – review & editing. MM: Conceptualization,

Funding acquisition, Investigation, Project administration, Resources,

Software, Supervision, Validation, Visualization, Writing – review

& editing.

Frontiers in Immunology 11

Funding

The author(s) declare that financial support was received for the

research and/or publication of this article. This work was supported

by Northwestern University’s Havey Institute for Global Health

(Havey IGH) Catalyzer, the National Institutes of Health grants

(R21AI148033, D43TW010543, D43CA260658, D43 TW010543).

The content is solely the responsibility of the authors and does not

necessarily represent the official views of the National Institutes of

Health or the Havey IGH.

Acknowledgments

The authors are grateful to the study participants and

acknowledge the laboratory and clinical staff of UCRC and the

Teaching Hospital of Point-G, who contributed to the recruitment

of participants, sample processing, and data collection. We want to

thank the funders for making this study possible.

Conflict of interest

The authors declare that the research was conducted in the

absence of any commercial or financial relationships that could be

construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the

creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the

authors and do not necessarily represent those of their affiliated

organizations, or those of the publisher, the editors and the

reviewers. Any product that may be evaluated in this article, or

claim that may be made by its manufacturer, is not guaranteed or

endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online

at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.

1561459/full#supplementary-material

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Diallo et al. 10.3389/fimmu.2025.1561459

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