
==== Front
Nat Commun
Nat Commun
Nature Communications
2041-1723
Nature Publishing Group UK London

39223134
51464
10.1038/s41467-024-51464-w
Article
Gut microbiota patterns associated with duration of diarrhea in children under five years of age in Ethiopia
http://orcid.org/0000-0002-0344-784X
Tesfaw Getnet gettesfaw2@gmail.com

12
Siraj Dawd S. 3
http://orcid.org/0000-0003-3798-5037
Abdissa Alemseged 24
http://orcid.org/0000-0001-7416-1358
Jakobsen Rasmus Riemer 1
Johansen Øystein H. 567
http://orcid.org/0000-0002-6709-4502
Zangenberg Mike 89
http://orcid.org/0000-0002-1466-2326
Hanevik Kurt 510
Mekonnen Zeleke 2
http://orcid.org/0000-0003-0278-1616
Langeland Nina 510
http://orcid.org/0009-0000-1827-8164
Bjørang Ola 6
Safdar Nasia 3
http://orcid.org/0000-0002-0757-6702
Mapes Abigail C. 3
http://orcid.org/0000-0003-3843-0615
Kates Ashley 3
Krych Lukasz 1
Castro-Mejía Josué L. 1
http://orcid.org/0000-0001-8121-1114
Nielsen Dennis S. dn@food.ku.dk

1
1 https://ror.org/035b05819 grid.5254.6 0000 0001 0674 042X Department of Food Science, University of Copenhagen, Copenhagen, Denmark
2 https://ror.org/05eer8g02 grid.411903.e 0000 0001 2034 9160 School of Medical Laboratory Sciences, Jimma University, Jimma, Ethiopia
3 https://ror.org/01y2jtd41 grid.14003.36 0000 0001 2167 3675 Department of Medicine, University of Wisconsin, Madison, WI USA
4 https://ror.org/05mfff588 grid.418720.8 0000 0000 4319 4715 Armauer Hansen Research Institute, Addis Ababa, Ethiopia
5 https://ror.org/03zga2b32 grid.7914.b 0000 0004 1936 7443 Department of Clinical Science, University of Bergen, Bergen, Norway
6 https://ror.org/04a0aep16 grid.417292.b 0000 0004 0627 3659 Department of Microbiology, Vestfold Hospital Trust, Tønsberg, Norway
7 https://ror.org/02fjtnt35 grid.487411.f Microbiology Laboratory, Southern Health and Social Care Trust, Portadown, Northern Ireland
8 https://ror.org/051dzw862 grid.411646.0 0000 0004 0646 7402 Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre, Denmark
9 https://ror.org/035b05819 grid.5254.6 0000 0001 0674 042X Department of Immunology and Microbiology, Centre for Medical Parasitology, University of Copenhagen, Copenhagen, Denmark
10 https://ror.org/03np4e098 grid.412008.f 0000 0000 9753 1393 National Center for Tropical Infectious Diseases, Department of Medicine, Haukeland University Hospital, Bergen, Norway
2 9 2024
2 9 2024
2024
15 753228 10 2023
6 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Diarrhea claims >500,000 lives annually among children under five years of age in low- and middle-income countries. Mortality due to acute diarrhea (<7 days’ duration) is decreasing, but prolonged (7-13 days) and persistent (≥14 days of duration) diarrhea remains a massive challenge. Here, we use a case-control study to decipher if fecal gut microbiota compositional differences between Ethiopian children with acute (n=554) or prolonged/persistent (n=95) diarrhea and frequency-matched non-diarrheal controls (n=663) are linked to diarrheal etiology. We show that diarrhea cases are associated with lower bacterial diversity and enriched in Escherichia spp., Campylobacter spp., and Streptococcus spp. Further, diarrhea cases are depleted in gut commensals such as Prevotella copri, Faecalibacterium prausnitzii, and Dialister succinatiphilus, with depletion being most pronounced in prolonged/persistent cases, suggesting that prolonged duration of diarrhea is accompanied by depletion of gut commensals and that re-establishing these via e.g., microbiota-directed food supplements offer a potential treatment strategy.

Here, the authors profile the gut microbiome of Ethiopian children suffering from acute and prolonged diarrhea, showing the latter group to exhibit a higher degree of microbial imbalance, characterized by a reduction of gut commensals and an enrichment of potential pathogens.

Subject terms

Microbiome
Dysbiosis
https://doi.org/10.13039/501100005068 Jimma University (JU) https://doi.org/10.13039/100008028 UW | School of Medicine and Public Health, University of Wisconsin-Madison (University of Wisconsin School of Medicine and Public Health) https://doi.org/10.13039/100000865 Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) grant OPP1153139 grant OPP1153139 Johansen Øystein H. Langeland Nina Norwegian Research Council GLOBVAC fund (grant 255571), Vestfold Hospital Trust, and the Norwegian Society for Medical Microbiologyhttps://doi.org/10.13039/501100001734 Københavns Universitet (University of Copenhagen) https://doi.org/10.13039/501100005036 Universitetet i Bergen (University of Bergen) Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Diarrhea is a global problem and causes about 500,000 deaths annually in children below 5 years of age, primarily (90%) in Sub-Saharan Africa and South Asia1,2. In addition to death attributed to diarrhea, recurrent enteric infections have far reaching consequences such as growth faltering, which is associated with decreased cognitive development and increased risk of death from other infectious diseases3,4.

The etiologies of diarrhea are diverse including pathogenic bacteria, vira, and protozoa1,5. In the Global Enteric Multicenter Study (GEMS), rotavirus, Shigella, enterotoxigenic Escherichia coli, and Cryptosporidium were identified as the leading causes of moderate-to-severe diarrhea across all seven low- and middle-income countries (LMICs) involved in the study6. However, pathogen detection was common even in asymptomatic children7, suggesting a more complex explanation for the development of diarrhea in many children.

The majority of diarrhea cases are acute diarrhea (AD), i.e., lasting less than 7 days8 and considerable progress had been made in decreasing childhood mortality due to AD9. However, childhood diarrhea remains a serious healthcare burden in LMICs due to the growing multidrug resistance of diarrheal pathogens10, the emergence of new pathogens11 and the absence of well-established management for prolonged diarrhea (ProD; 7–13 days of duration), and persistent diarrhea (PD; ≥14 days of duration), referred to as ProPD, when referring to the two combined12. ProPD is responsible for 36% to 54% of all diarrhea-related mortality13–15.

The gut microbiota (GM) is established during the first years of life16. This development is influenced by a range of factors, such as mode of birth and nutrition (breast milk vs. formula, timing, and composition of weaning foods)17. A balanced and diverse “eubiotic” GM18 is important for human health19,20 via the metabolization of otherwise indigestible dietary compounds, synthesis of vitamins, immune system regulation, and host resistance against pathogens21. Consequently, GM imbalance may have deleterious effects on the host, such as malnutrition or susceptibility to gut pathogens11,19. In children, AD is associated with GM dysbiosis22, which is characterized by decreased intestinal microbiota diversity23 as confirmed by the large GEMS study that reported decreased bacterial diversity and decreased abundance of beneficial anaerobic bacteria in AD cases compared to non-diarrheal controls11,24. The decreased bacterial diversity was apparent even after recovery from diarrhea25.

Despite many years of effort, mortality remains high for ProPD8,15,26. The role of enteric pathogens as causes of ProPD remains unclear. While some studies have identified a similar range of enteric pathogens in AD and diarrhea of longer duration12,27–29, others failed to identify any specific enteric pathogens associated with ProPD30. It has been hypothesized that GM dysbiosis is an important factor in ProPD etiology14,29,31,32. Interestingly, even though the management of ProPD is complex, nutritional interventions have shown promising outcomes13,31,33,34.

In this work, we take advantage of the CRYPTO-POC study, a case-control study of diagnostic accuracy for cryptosporidiosis in children that recruited 2080 children aged 0–59 months with and without diarrhea in Ethiopia35. Large, well-powered studies examining links between diarrhea and GM are relatively scarce in LMICs and there have been no studies particularly on GM composition in children with ProPD. We report the analysis of 1321 fecal samples of children available for DNA extraction and sequencing with the aim of deciphering underlying GM characteristics of children suffering from AD and ProPD compared to healthy controls. We report the GM composition of 1313 Ethiopian children under 5 years of age, a period where significant GM changes occur. Of these, 650 children have diarrhea and 663 are frequency matched, non-diarrheal controls. We demonstrate that the GM of diarrhea cases is associated with lower bacterial diversity, an enrichment of putative pathogens, and a depletion of gut commensals compared to non-diarrheal controls. Prolonged and persistent diarrhea cases are characterized by a more pronounced depletion of gut commensals, relative to acute diarrhea.

Results

Study cohort characteristics

A total of 1321 Ethiopian children aged 0–59 months were included in the present study. Out of these, 8 (0.6%) were excluded due to insufficient sequencing depth, meaning that 1313 children (99.4%) were included in the analysis. Of these, 650 (49.5%) were diarrhea cases and 663 (50.5%) were frequency matched non-diarrheal controls. Among the diarrhea cases (one with missing data on the duration of diarrhea), 554 (85%) had AD and 95 (15%) had ProPD. Cohort details including clinical and anthropometric data have been published elsewhere35,36 and are summarized in Table 1.Table 1 Demographic/clinical factors associated with diarrhea and prolonged or persistent diarrhea in Ethiopian children aged 0–59 months

Characteristics	Diarrhea cases	Controls	Adjusted OR (95% CI)	p value	ProPD	AD	Adjusted OR (95% CI)	p value	
Number of children, n (%)	650 (49.5)	663 (50.5)			95 (15)	554 (85)			
Male sex, n (%)	386 (59.4)	337 (50.8)	1.3 (0.98, 1.64)	0.067	60 (63.2)	325 (58.7)	1.2 (0.69, 2.20)	0.475	
Age months, mean ± SD	17.2 ± 12.5	19.2 ± 12.8	1 (0.97, 1.00)	0.141	17.1 ± 12.2	17.2 ± 12.6	1 (0.98, 1.03)	0.492	
Born vaginally, n (%)	575 (88.6)	624 (94.3)	0.6 (0.35, 0.92)	0.023	84 (88.4)	490 (88.6)	1.7 (0.64, 6.07)	0.322	
Born prematurely, n (%)	39 (6.0)	10 (1.5)	3 (1.35, 7.1)	0.009	6 (6.4)	33 (5.9)	0.4 (0.08, 1.34)	0.185	
Not exclusive breastfeed for <6 months, n (%)	235 (34.5)	228 (36.8)	0.9 (0.68, 1.17)	0.423	42 (44.2)	193 (35.5)	1.3 (0.75, 2.33)	0.306	
Not breastfed now, n (%)	193 (24.7)	162 (24.3)	1.1 (0.75, 1.61)	0.631	25 (26.3)	136 (24.5)	1.1 (0.51, 2.35)	0.792	
Children’s caretaker not the mother, n (%)	53 (8.2)	18 (2.7)	2.8 (1.53, 5.27)	0.001	8 (8.4)	45 (8.1)	0.7 (0.22, 1.95)	0.555	
Mid-upper arm circumference ≤125 mm for >6 months, n (%, mean ± SD)	58 (10.5, 119.7 ± 5.6)	12 (2, 117.7 ± 7.3)	5.8 (3.01, 12.20)	6.5e-07	14 (17.9, 118 ± 4.5)	44 (9.2, 118.2 ± 6)	1.6 (0.68, 3.47)	0.270	
WAM index <0.5, n (%, mean ± SD)	502 (78.6, 0.27 ± 0.13)	604 (92.8, 0.27 ± 0.12)	0.3 (0.17, 0.40)	1.8e-09	73 (80.2, 0.28 ± 0.12)	428 (78.2, 0.26 ± 0.13)	1.9 (0.86, 4.39)	0.125	
Antibiotic use in previous week, n (%)	63 (10)	47 (7.1)	1.2 (0.72, 2.00)	0.474	22 (24.2)	41 (7.6)	1.8 (0.71, 4.41)	0.203	
Diarrhea in the previous month, n (%)	105 (16.3)	104 (15.7)	0.9 (0.61, 1.28)	0.538	27 (28.4)	78 (14.2)	1.3 (0.62, 2.54)	0.498	
No hospital visits in the previous month, n (%)	414 (63.8)	497 (74.9)	0.6 (0.47, 0.86)	0.003	52 (54.7)	361 (65.3)	0.8 (0.46, 1.57)	0.591	
Previous treatment for malnutrition, n (%)	10 (1.5)	7 (1.1)	0.7 (0.25, 2.31)	0.605	3 (3.2)	7 (1.3)	1.1 (0.18, 5.57)	0.896	
Previous hospital admission, n (%)	59 (9.1)	57 (8.6)	1 (0.61, 1.56)	0.944	10 (10.5)	49 (8.9)	0.6 (0.21, 1.67)	0.395	
Number of family living per room ≥2, n (%)	588 (90.9)	627 (94.9)	0.5 (0.33, 0.92)	0.024	86 (91.5)	501 (90.8)	0.9 (0.37, 2.67)	0.883	
Presence of animal in household, n (%)	327 (50.4)	339 (51.2)	1.0 (0.76, 1.36)	0.891	42 (44.2)	285 (51.5)	1.2 (0.65, 2.39)	0.502	
Oral rehydration use in the previous week, n (%)	NA	NA	NA	NA	25 (26.3)	38 (6.9)	3.2 (1.25, 8.08)	0.014	
Stunting*, n (%)	NA	NA	NA	NA	38 (28.6)	133 (20.2)	1.8 (0.95, 3.35)	0.06	
Dysentery, n (%)	NA	NA	NA	NA	11 (11.6)	49 (8.8)	1.3 (0.39, 3.55)	0.64	
Watery stool, n (%)	NA	NA	NA	NA	16 (17.6)	101 (18.9)	0.8 (0.37, 1.65)	0.58	
Logistic regression (two-sided Wald test) analysis adjusted for enrollment season and enrollment site; OR (Odds ratio) was reported with in 95% CI; unadjusted exact p values; born premature: born alive before 37 weeks of gestation; WAM index: water/sanitation, asset, and maternal education; one diarrhea case has missing information on the duration of diarrhea; children’s caretaker (mother or other); Stunting* data were available only for diarrheal cases and children aged 0–11 months among non-diarrheal children.

AD acute diarrhea, ProPD prolonged or persistent diarrhea, NA data not available.

Briefly, we found that a higher proportion of children in the diarrhea group were born by cesarean section (11.4% vs. 5.7%; p = 0.0004), were born prematurely (6% vs. 1.5%; p < 0.0001), suffered from malnutrition (19.5 vs. 4.5%; p < 0.0001) and cryptosporidiosis (6.6% vs. 0.6%; p < 0.0001) compared to non-diarrhea controls. There was no significant difference on the antibiotics use between diarrhea cases and non-diarrhea controls (10% vs. 7.1%; p = 0.08) during the last week before enrollment. From those having data on the type of antibiotics used 21 (3.2%), 22 (3.4%), 3 (0.5%) diarrhea cases, and 25 (3.8%), 14 (2.1%) and 0 (0%) non-diarrhea cases consumed amoxicillin, cotrimoxazole and metronidazole, respectively.

We further analyzed data by logistic regression, and we found that premature birth (OR 3, 95% CI, 1.37, 7.1), mid-upper arm circumference ≤125 mm (OR 5.8, 95% CI, 3.01, 12.2) and if the children’s caretaker is another person than the mother (OR 2.8, 95% CI, 1.53, 5.57) all were significantly associated with diarrhea (Table 1).

The study included children aged 0–59 months—a period where the GM undergoes profound changes. The number of observed species and Shannon diversity index increased with age (p < 0.0001) (Fig. 1a) in both diarrhea cases and non-diarrheal controls (Fig. 2a, c). Bray-Curtis dissimilarity metrics also demonstrated a clear progression with age (Fig. 1b, c) for both groups (Fig. 2b, d). For younger children (up to 1 year of age), the GM was characterized by high relative abundance of Escherichia/Shigella, Bifidobacterium, and Streptococcus spp., while obligate anaerobes such as Prevotella, Faecalibacterium, and Dialister dominated the GM at older ages. This was seen among all participants combined (Fig. 1d), as well as in diarrhea cases and non-diarrheal controls when analyzed separately (Supplementary Fig. 1).Fig. 1 Age has pronounced effect on gut microbiome diversity and composition of Ethiopian children aged 0–59 months (ntotal = 1313).

a Observed number of zOTUs and Shannon diversity index as influenced by age. Boxplot distributions include median, min, max, 25 and 75 percentiles, and outliers (more than 1.5 IQR); b PCoA plot based on Bray-Curtis dissimilarity metrics as influenced by age; c Pairwise PERMANOVA (two-sided) between different age groups based on Bray-Curtis dissimilarity metrics (false discovery rate (FDR) corrected q values); d Genus level relative abundance of top taxa (≥2.1 %) in different age groups. ****, ***, **, and * represents unadjusted p values < 0.0001, <0.001, <0.01, <0.05 while ns: not significant, respectively (two-sided Wilcoxon rank sum test).

Fig. 2 The gut microbiome of Ethiopian children aged 0–59 months undergo maturation with increasing age irrespective of their diarrhea status.

a, b (diarrhea cases); c, d (non-diarrheal controls). a, c observed number of zOTUs and Shannon diversity index increase with age. Boxplot distributions include median, min, max, 25 and 75 percentiles, and outliers (more than 1.5 IQR); b, d PCoA plot and pairwise PERMANOVA (two-sided) (false discovery rate (FDR) corrected q values) based on Bray-Curtis dissimilarity metrics of diarrhea cases and non-diarrheal controls stratified by age. ****, ***, **, and *, represents unadjusted p < 0.0001, <0.001, <0.01, <0.05, respectively, while ns: not significant (two-sided Wilcoxon rank sum test).

Diarrhea cases have lower gut microbial diversity than non-diarrheal controls

The GM of children with diarrhea combined (AD and ProPD) (Supplementary Fig. 2a) and cases with AD and ProPD separately (Fig. 3a), was characterized by having lower number of observed species as well as reduced Shannon diversity index compared to non-diarrheal controls (p < 0.0001). However, when comparing AD and ProPD cases we did not observe any significant differences in alpha diversity measures (p > 0.05) (Fig. 3a).Fig. 3 Diarrhea has pronounced effect on gut microbiome diversity and composition of Ethiopian children aged 0–59 months.

Gut microbiota characterization of children suffering from acute diarrhea (n = 554), prolonged/persistent diarrhea (n = 95), and non-diarrheal controls (n = 663). a Observed number of zOTUs and Shannon diversity index. Boxplot distributions include median, min, max, 25 and 75 percentiles, and outliers (more than 1.5 IQR); b Constrained distance-based Redundancy Analysis (db-RDA) PCoA plot based on Bray-Curtis dissimilarity metrics of diarrhea status conditioned for age in months, enrollment site, sex, enrollment season, WAM index, current breastfeeding or diarrhea in the previous month; c Pairwise PERMANOVA (two-sided) conditioned by age in months, enrollment site, sex, and enrollment season, WAM index, current breastfeeding or diarrhea in the previous month with false discovery rate (FDR) corrected q values; d Genus level relative abundance of top taxa (≥1.5 %) grouped by diarrhea status. ****, ***, **, and *, represents unadjusted p values < 0.0001, <0.001, <0.01, <0.05, respectively, while ns: not significant (two-sided Wilcoxon rank sum test).

The overall GM composition of diarrhea cases combined (Supplementary Fig. 2b, d) and cases with AD and ProPD separately (Supplementary Fig. 2c, d) differed from non-diarrheal controls (q < 0.001) as determined by pairwise permutation multivariate analysis of variance (PERMANOVA) of Bray-Curtis dissimilarity metrics. Interestingly, we also found that AD and ProPD differed significantly (q < 0.01) in GM composition (Supplementary Fig. 2c, d). Controlling for age, enrollment season, enrollment site, sex, WAM index, current breastfeeding status, and diarrhea in the previous month did not change the difference between cases (AD and ProPD) and non-diarrheal controls (q < 0.01) nor between AD and ProPD cases (q < 0.01) (Fig. 3b, c) as determined by distance-based Redundancy Analysis (db-RDA) of Bray-Curtis dissimilarity metrics.

Diarrhea cases are characterized by higher abundance of pathogens and loss of gut commensal bacteria

Differential abundance testing by DESeq2 adjusted for age group, enrollment season, enrollment site, sex, WAM index, current breastfeeding, and diarrhea in the previous month was used to determine taxa differing between children with diarrhea (AD and ProPD) and non-diarrheal controls. We found that diarrhea cases had a higher relative abundance of Escherichia spp. (16.3% vs. 8.2%; q < 0.0001), Campylobacter spp. (3.6% vs. 0.83%; q < 0.001), Streptococcus spp. (8.2% vs. 4.2%; q < 0.0001) and Veillonella dispar (6.3% vs. 3.2%; q < 0.0001). Furthermore, compared to non-diarrheal controls, the diarrhea cases were enriched in taxa associated with the oral cavity microbiota like Haemophilus parainfluenzae (1.5% vs. 1.2%; q < 0.05) (Fig. 4a, Fig. 5, Supplementary Table 1). This contrasted with the relatively lower abundance of obligate anaerobic bacterial taxa in diarrhea cases compared to non-diarrheal controls, in particular Prevotella copri (12.5% vs. 20.6%; q < 0.0001), Faecalibacterium prausnitzii (5.4% vs. 8.3%; q < 0.0001), Dialister succinatiphilus (2.0% vs. 4.5%; q < 0.0001), unclassified Lachnospiraceae (2.4% vs. 3.9%; q < 0.0001) and Bacteroides fragilis (3.6% vs. 5.1%; q < 0.0001) (Fig. 4a, Supplementary Table 1).Fig. 4 Relative abundance of bacterial taxa at species level selected by DESeq2 differential abundance testing in Ethiopian children aged 0–59 months.

a Diarrhea cases compared with non-diarrheal controls; b AD cases compared with non-diarrheal controls; c ProPD cases compared with non-diarrheal controls; d ProPD cases compared with AD cases. Boxplot distributions include median, min, max, 25 and 75 percentiles, and outliers (more than 1.5 IQR). ****, ***, **, and *, represents q values, <0.0001, <0.001, <0.01, <0.05, respectively, while ns: not significant and q value corrected by Benjamini-Hochberg method (two-sided Wilcoxon rank sum test). Black middle lines represent the median values. Summary statistics with exact q values are shown in Supplementary Table 1 (diarrhea cases vs. non-diarrheal controls), Supplementary Table 2 (AD vs. non-diarrheal controls), Supplementary Table 3 (ProPD vs. non-diarrheal controls), and Supplementary Table 4 (ProPD vs. AD).

Fig. 5 Specific gut microbiome profiles associated with diarrhea status of Ethiopian children aged 0–59 months.

Heatmap depiction of differentially abundant taxa between diarrhea cases and non-diarrheal controls as determined by DESeq2 (two-sided Wald test) differential abundance testing (q < 0.05 corrected for multiple testing by the Benjamini-Hochberg method) and adjusted for age group, enrollment season, enrollment site, sex, WAM index, current breastfeeding, and diarrhea in the previous month. Subsequently, differences were tested by two-sided Wilcoxon rank sum test and corrected by the Benjamini-Hochberg method resulting in the stated exact q values found in Supplementary Table 1. Clusters: de novo clustering of the diarrhea cases and non-diarrheal controls using Canberra distance metrics and the proportions of diarrhea cases and non-diarrheal controls are determined by Chi-square test (two-sided) in each cluster with unadjusted p values.

To understand whether specific GM signatures characterize diarrhea cases and non-diarrheal controls, we used de novo clustering analysis of taxa being found by DESeq2-based differential abundance analysis to significantly differ between groups. Four overall clusters were identified and of these, two were dominated by diarrhea cases (I: 56.1% diarrhea cases; p = 0.009; III: 69.3% diarrhea cases; p < 0.0001, respectively), while one cluster was dominated by non-diarrheal controls (II: 62.2% non-diarrheal controls; p < 0.0001) (Fig. 5). The clusters dominated by diarrhea cases were enriched in Escherichia spp., Streptococcus spp., unclassified Bifidobacterium (I and III) and Campylobacter spp. (I) and characterized by a low relative abundance of obligate anaerobic gut commensals (P. copri, F. prausnitzii and D. succinatiphilus) (Fig. 6). The de novo clustering was supported by sparse partial least square discriminatory analysis (sPLS-DA), which overall showed the same clustering (Supplementary Fig. 3).Fig. 6 Specific bacterial taxa that characterize gut microbiome clusters with a high fraction of diarrhea cases (I and III) or non-diarrhea controls (II), respectively.

See Fig. 5 for cluster details (diarrhea cases and non-diarrheal controls). Boxplot distributions include median, min, max, 25 and 75 percentiles, and outliers (more than 1.5 IQR). ****, ***, **, and *, represents unadjusted p values < 0.0001, <0.001, <0.01, <0.05, respectively, while ns: not significant (two-sided Wilcoxon rank sum test).

Microbial taxa differing in AD cases and ProPD cases compared to non-diarrheal controls

When subgrouping the diarrhea cases according to duration of diarrhea, we found that AD cases relative to the healthy controls were characterized by an increased abundance of facultative anaerobes such as Escherichia spp. (16.3% vs. 8.7%; q < 0.0001) and Campylobacter (3.5% vs. 0.83%; q < 0.01) and a decreased relative abundance of obligate anaerobic gut commensals like F. prausnitzii (5.5% vs. 8.3%; q < 0.0001) and Prevotella copri (12.3% vs. 20.6%; q < 0.0001) (Fig. 4b, Fig. 7, Supplementary Table 2). De novo clustering analysis of the AD cases and non-diarrheal controls identified three major clusters, with one of them having a large fraction of AD cases (II: 64%). This cluster was characterized by relatively high abundance of Escherichia, Campylobacter and Streptococcus spp., whereas the cluster with a low proportion of AD cases (I: 36.2%) was characterized by higher relative abundance of P. copri and F. prausnitzii (Fig. 7). Again, sPLS-DA confirmed these patterns (Supplementary Fig. 4).Fig. 7 Ethiopian children aged 0–59 months with AD are characterized by specific gut microbiome profiles when compared to non-diarrheal controls.

Heatmap depiction of differentially abundant taxa between AD cases and non-diarrheal controls as determined by DESeq2 (two-sided Wald test) differential abundance testing (q < 0.05 corrected for multiple testing by the Benjamini-Hochberg method) and adjusted for age group, enrollment season, enrollment site, sex, WAM index, current breastfeeding, and diarrhea in the previous month. Subsequently, differences were tested by two-sided Wilcoxon rank sum test and corrected by the Benjamini-Hochberg method resulting in the stated exact q values found in Supplementary Table 2. Clusters: de novo clustering of the AD cases and non-diarrheal controls using Canberra distances metrics and the proportions of AD cases and non-diarrheal controls are determined by Chi-square test (two-sided) in each cluster with unadjusted p values.

Further, ProPD cases were characterized by an increased relative abundance of Escherichia spp. (15.9% vs. 8.2%; q < 0.0001), Campylobacter spp. (3.9% vs. 0.83%; q < 0.01), and Streptococcus spp. (9.0% vs. 4.2%; q < 0.0001) compared to non-diarrheal controls (Fig. 4c, Fig. 8, Supplementary Table 3). These findings were confirmed by selecting features using sPLS-DA comparing ProPD and non-diarrheal controls (Supplementary Fig. 5).Fig. 8 Ethiopian children aged 0–59 months with ProPD are characterized by specific gut microbiome profiles when compared to non-diarrheal controls.

Heatmap depiction of differentially abundant taxa between ProPD cases and non-diarrheal controls as determined by DESeq2 (two-sided Wald test) differential abundance testing (q < 0.05 corrected for multiple testing by the Benjamini-Hochberg method) and adjusted for age group, enrollment season, enrollment site, sex, WAM index, current breastfeeding, and diarrhea in the previous month. Subsequently, differences were tested by two-sided Wilcoxon rank sum test and corrected by the Benjamini-Hochberg method resulting in the stated exact q values found in Supplementary Table 3. Clusters: de novo clustering of the ProPD cases and non-diarrheal controls using Canberra distances metrics and the proportions of ProPD cases and non-diarrheal controls are determined by Chi-square test (two-sided) in each cluster with unadjusted p values.

Microbial taxa differing between ProPD and AD cases

While there was no difference between AD and ProPD cases in alpha diversity measures (p > 0.05: Fig. 3a), the GM composition differed significantly between AD and ProPD cases (q < 0.01: Fig. 3b, c). Compared to AD cases, ProPD cases had a higher relative abundance of unclassified lactobacilli (0.28% vs. 0.13%; q < 0.05) and was further characterized by lower relative abundance of gut commensals such as F. prausnitzii (4.4% vs. 5.5%; q = 0.02), Anaerostipes hardum (0.27% vs. 0.49%; q < 0.01), unclassified Coriobacteriaceae (0.27% vs. 0.77%; q = 0.02) and unclassified Bacteroides (1.8% vs. 0.64%; q = 0.04) (Fig. 4d, Fig. 9, Supplementary Table 4). Again, these findings were confirmed by sPLS-DA that additionally identified taxa such as Akkermansia muciniphila (0.94% vs. 1.2%) and Anaerostipes hardum (0.27% vs. 0.49%) as having somewhat lower relative abundance in ProPD cases compared to AD cases (Supplementary Fig. 6). However, de novo clustering analysis of AD and ProPD cases based on taxa found to be differentially abundant between the two groups did not lead to clear clustering of AD and ProPD cases (Fig. 9). In addition to the analysis above, we furthermore did a one-to-one ProPD vs. AD cases comparison, which again showed decreased abundance of Anaerostipes hardum and increased abundance of Limisolactobacillus mucosae (0.06% vs. 0.43%; q < 0.05) in ProPD cases. Interestingly, F. prausnitzii remained significantly lower in abundance in ProPD cases compared to AD cases but only before correcting for multiple testing (p = 0.03; q = 0.10) (Supplementary Fig. 7).Fig. 9 Ethiopian children aged 0–59 months with ProPD showed differences in only a few gut commensals compared to AD cases, and all ProPD cases did not segregate into a single cluster.

Heatmap depiction of differentially abundant taxa between ProPD cases and AD cases as determined by DESeq2 (two-sided Wald test) differential abundance testing (q < 0.05 corrected for multiple testing by the Benjamini-Hochberg method) and adjusted for age group, enrollment season, enrollment site, sex, WAM index, current breastfeeding status, dysentery, and children’s caretaker. Subsequently, differences were tested by two-sided Wilcoxon rank sum test and corrected by the Benjamini-Hochberg method resulting in the stated exact q values found in Supplementary Table 4. Clusters: de novo clustering of the ProPD cases and AD cases using Canberra distances metrics and the proportions of ProPD cases and AD cases are determined by Chi-square test (two-sided) in each cluster with unadjusted p values.

Demographic and clinical variables associated with GM variation in children

Out of a total of 19 variables, five were significantly associated with GM variation (Table 2). Of these, age had the highest explanatory power, explaining 18.9% of the GM variation (q < 0.01), followed by diarrhea, explaining 4.7% (q < 0.01) of the GM variation. Furthermore, the WAM index (<0.5), current breastfeeding status, and diarrhea status in the previous month explained 1.3%, 0.63%, and 0.35% of the GM variation, respectively. All the significant variables combined explained 26% of the variation in GM in the entire cohort. A subgroup analysis of diarrhea cases also found that age had the highest explanatory power, explaining of 14.7% of the GM variation in diarrhea cases (q < 0.01). Furthermore, WAM index <0.5, dysentery, current breastfeeding status, duration of diarrhea, and children’s caretaker explained 1.8%, 1.6%, 1.0%, 0.77%, and 0.66% and all significantly contributed to GM variation (Table 2).Table 2 The gut microbiome structure of Ethiopian children aged 0–59 months is linked to demographic, clinical, and environmental characteristics as determined by distance-based redundancy analysis (db-RDA based on Bray-Curtis dissimilarity metrics) in the entire cohort (diarrhea cases and non-diarrheal controls combined) as well as in diarrhea cases

Characteristics	Entire cohort	Diarrhea cases	
q value	R2 value (%)	q value	R2 value (%)	
Age (months)	0.005	18.9	0.008	14.7	
Diarrhea	0.005	4.7	NA	NA	
WAM index <0.5	0.005	1.3	0.008	1.8	
Dysentery	NA	NA	0.008	1.6	
Stool consistency	NA	NA	0.11	0.82	
Breastfeeding now	0.005	0.63	0.012	1.01	
Diarrhea duration	NA	NA	0.035	0.77	
Children’s caretaker	0.19	0.16	0.041	0.66	
Diarrhea in previous month	0.02	0.35	0.58	0.19	
Exclusive breastfeeding for <6 months	0.11	0.21	0.48	0.26	
Previous malnutrition treatment	0.12	0.19	0.19	0.21	
Sex	0.24	0.13	0.54	0.23	
Previous hospital admission	1	0.03	0.58	0.20	
Presence of animal in the households	0.27	0.12	0.19	0.40	
Bipedal oedema	0.53	0.09	0.48	0.25	
Number of persons living per room	0.57	0.08	0.48	0.24	
Delivery mode	0.42	0.10	0.08	0.60	
Hospital visits in the previous month	0.20	0.16	0.43	0.29	
Antibiotics use in the previous week	1	0.04	0.72	0.17	
Stunting*	NA	NA	0.58	0.20	
Acute malnutrition	0.64	0.07	0.58	0.21	
Prematurity	1	0.04	0.43	0.29	
Oral rehydration solution taken	NA	NA	1	0.08	
The analysis was conditioned for enrollment site and enrollment season; WAM index: (water/sanitation, asset, and maternal education); prematurity born alive before 37 weeks of gestation; Stunting* data were available only for diarrheal cases and children aged 0–11 months among non-diarrheal children; NA (data not available); children’s caretaker (mother or other); q values corrected for multiple testing by the Benjamini-Hocheberg method from db-RDA model of Bray-Curtis dissimilarity metrics by the ANOVA-like Permutation Test.

Discussion

The aim of our study was to decipher what differentiates the GM of children with diarrhea (AD and ProPD) from healthy controls in a LMIC setting. We also explored the role of the GM in prolonged and persistent diarrhea (ProPD) compared to acute diarrhea (AD). This is the first large scale study on GM in children below 5 years of age with AD and ProPD (650 children with diarrhea) and non-diarrheal controls (663 children, frequency matched) in a low-income setting.

The GM diversity and composition in infants and children is influenced by multiple factors including age16,37, mode of birth, prematurity, breastfeeding, time of weaning, and antibiotics use38. In line with this, we observed strong associations between GM composition and ongoing breastfeeding, which is in agreement with other studies39. The GM composition of the enrolled children (cases and controls) was also linked with socio-economic factors, water and sanitation and maternal education as also seen in Malawian children40. Somewhat surprisingly, we found no association between antibiotics use and GM variations. However, this finding is consistent with studies from Vietnam41, Bangladesh42 and a longitudinal cohort study in Western countries39, although there are obviously also studies reporting associations between antibiotics use and GM composition37,43.

Furthermore, in agreement with previous studies16,37,44, the GM diversity and richness increased with age irrespective of diarrhea status in our study. The GM composition shifted from taxa associated with a milk-rich diet early in life such as Bifidobacterium spp45. to taxa associated with a solid food-diet with a high content of indigestible carbohydrates such as Prevotella46 and Faecalibacterium47.

Children with diarrhea had a lower bacterial diversity and richness compared to non-diarrheal controls, regardless of the duration of diarrhea. This agrees with previous studies on AD in LMICs11,25,48,49. Repeated flushing of the intestinal lumen leads to a depletion of the microbiota, resulting in reduced bacterial diversity50. This decrease in diversity has also been linked to Clostridiodes difficile-associated diarrhea in adults51,52. Importantly, GM diversity and richness did not differ by diarrhea duration indicating that it is not loss of overall GM diversity that differentiates AD from ProPD. Of note, a recent study in undernourished children in Peru did find that longer duration of diarrhea was linked with reduced bacterial diversity and richness25. It can be speculated that the discrepancy between the two studies reflects different dietary traditions in Peru and Ethiopia.

Diarrhea cases (AD and ProPD cases) were associated with higher relative abundance of facultative anaerobes and microaerophilic bacterial taxa such as Escherichia spp., Campylobacter spp., and Streptococcus spp. than non-diarrheal controls. Both Campylobacter and certain Escherichia coli pathotypes are known to cause diarrhea53–55. Campylobacter has also been associated with gut inflammation, increased gut permeability, and impaired linear growth53 and has been implicated in GM dysbiosis in children56. Diarrhea induces an oxygenated environment within the gut57, promoting the proliferation of facultative anaerobic Enterobacteriaceae, as observed both in our study and by others11,49. This proliferation is considered as a hallmark of GM dysbiosis19,58.

Further, the inhibition of enterocyte lactase activity due to diarrhea leaves free lactose in the intestinal lumen14, which is efficiently utilized by lactose fermenting streptococci, that often becomes predominant in the GM of children with AD11,24,49. The increased relative abundance of Streptococcus spp. in AD and its association with dysentery11, and identification of Streptococcus lutetiensis virulence genes in children with AD of unknown etiology59 may support the possible link between increased abundance of Streptococcus spp. and diarrhea. Another possibility, not mutually exclusive, is increased flow-through from the small intestine, which is rich in streptococci60. Furthermore, we found a proliferation of H. parainfluenzae in diarrhea cases. Other studies have also reported an expansion of H. parainfluenzae in GM of children with diarrhea61 and has been linked to causing acute gastroenteritis62.

Interestingly, the increased relative abundance of facultative anaerobes in diarrhea cases were coupled with the depletion of potent short-chain fatty acids (SCFAs) producing obligate anaerobes, such as P. copri, F. prausnitzii and D. succinatiphilus. This is an observation that is in line with previous studies on AD11,49,61,63. Similar to our study, non-diarrhea controls were enriched in B. fragilis11. However, in cases that developed into diarrheal dysentery B. fragilis was found to be enriched, relative to non-complicated diarrhea cases11. This might be explained by strain-to-strain variation11. Bacteroides fragilis likely plays a double-edged sword role in its interaction with host, where non-toxigenic B. fragilis has anti-inflammatory properties through the release of polysaccharide A (PSA) and production of SCFAs via metabolization of complex carbohydrates64, whereas enterotoxigenic B. fragilis subtypes are pathogenic and drive inflammation in host cells65.

Our findings are consistent with a double disease burden, where diarrhea cases suffer both from increased abundance of (or an outright infection due to) pathogens causing diarrhea53–55 and a loss of beneficial gut commensals that would otherwise help to stabilize the GM and ease transition back to an eubiosis state. These gut commensals are involved in the production of SCFAs (acetate, propionate, butyrate) from otherwise indigestible complex carbohydrates66. SCFAs have numerous functions, such as serving as energy source for the host (~10% of daily energy from colonic production of acetate), improve intestinal epithelial barrier function and influence endocrine and immune signaling67–69. Furthermore, SCFAs make the environment less favorable for most pathogens by lowering colonic pH thereby providing host resistance against pathogens70–72.

We also compared AD vs. ProPD cases, to investigate if specific GM patterns characterize ProPD cases relative to AD cases. We found that ProPD cases were characterized by low abundance of gut commensals normally associated with GM eubiosis such as F. prausnitzii, Anaerostipes hadrum, and Akkermansia muciniphila when compared to AD cases. Previous studies investigating the etiology of ProPD have identified E. coli and Campylobacter as potential causative agents, but also noted that the presence or absence of specific enteric pathogens were unlikely to fully explain why some children progresses to ProPD and others not27,28,30. Our findings indicate that further loss of gut commensals is associated with the progression of AD into ProPD. Treatment of ProPD is complex, but lactose-free therapeutic food, rice and chicken-based diets, dietary fibers and green banana have shown promising results13,31,33,34. Colonic commensal bacteria ferment fibers and resistant starches to produce SCFAs which have several effects that may hasten recovery from ProPD such as increased salt and water absorption and strengthening of the epithelium and as a direct source of energy to the host in addition to helping to restore overall gut eubiosis31,34,73.

The increase in difficult-to-treat multidrug resistant diarrheal pathogens in LMICs10 and low success rates of oral rehydration and zinc supplementation to treat ProPD cases15 could indicate the future potential of using F. prausnitzii74 or P. copri based probiotics to ameliorate diarrhea symptoms in children. Therefore, re-establishing healthy gut commensals, either by targeted dietary supplements to enhance the recovery of depleted gut commensals75 and/or by direct replenishment of obligate anaerobes in so-called “next generation probiotics”70 might be promising new treatments for AD and ProPD.

Our study has limitations to consider when interpreting our findings. First and foremost, the design of this study does not allow us to determine any causal relationship between GM and diarrheal subgroups. Even though we found that AD or ProPD was characterized by a depletion of gut commensals it is possible that this reflects the effect of diarrhea on GM composition rather than being a driving force in the development of AD or ProPD. Secondly, the study design did not include follow-up, and we were therefore unable to determine whether certain GM signatures were associated with development of disease in non-diarrheal controls or progression, or regression of severity of diseases in diarrhea cases. Prospective follow-up studies with GM characterization would be a logical next step, especially with respect to designing more efficient future treatments. Thirdly, our results were based on compositional data and reported as a relative abundance rather than absolute abundance. It remains to be investigated if some of the investigated groups (AD, ProPD, and non-diarrheal controls) differ with respect to changes in the absolute abundance of GM members, which may influence the interpretation of data. Fourth, we categorized children with AD and ProPD based on the number of days with diarrhea before enrollment from parental reports. Some AD cases may thus proceed to ProPD after being enrolled and delivering the fecal sample. This limits us to show only effects of diarrhea duration rather than capturing features indicating disease progression from AD to ProPD.

Lastly, we employed short read sequencing technology targeting only one hypervariable region of the 16S rRNA gene (V4) limiting the possibility for detailed taxonomic identification and functional characterization of bacteria.

In conclusion, the GM of Ethiopian children with AD and ProPD were enriched in putative pathogens as well as facultative anaerobes and were further characterized by lowered relative abundance of obligate anaerobes. The GM of children with ProPD was characterized by a reduction of obligate anaerobic gut commensals compared to AD, suggesting that gut dysbiosis could be a potential factor in development of prolonged or persistent diarrhea. Re-establishing the healthy gut commensals by microbiota-directed food supplements, or next generation probiotics containing obligate anaerobes might be worth exploring as treatment strategies for diarrhea. We recommend further longitudinal studies characterizing the GM of children before, during and after a diarrheal episode to identify GM signatures of children who are at risk of developing ProPD. We also suggest further studies to develop and evaluate the effect of treatment regimens with targeted dietary supplements and/or next generation probiotics containing obligate anaerobic bacteria for the treatment of diarrhea in LMICs.

Methods

Study design and participant recruitment

The study has been conducted according to all ethical standards required to conduct research in human participants. The study received ethical approval to conduct the study and publishing the results of the study from the Ethiopian National Ethics Review Committee, Ethiopia (Reference MoSHE/RD/14.1/7188/19), the Regional Committee for Medical and Health Research Ethics of Western Norway (2016/1096/REK Vest) and the Minimal Risk IRB (Health Sciences) University of Wisconsin, USA (ID: 2019-0842). Written informed consent was obtained from the children’s parents or legal guardians to let their children participate in the study.

The present study is a substudy of a prospective case-control study (CRYPTO-POC) described in detail elsewhere35. Briefly, participants were recruited from February 2017 until July 2018, from two health clinic facilities in Ethiopia, namely Jimma University Specialized Hospital (JUSH), a referral and teaching hospital in Jimma, and Serbo Health Centre (SHC), a health center located in a small town 20 km from Jimma with a more rural catchment area.

Participants recruitment

The detailed procedure for recruitment of the study participants has been published previously35,36. In brief, children 0–59 months old with diarrhea were recruited at JUSH and SHC. Non-diarrheal controls recruited from communities surrounding JUSH and SHC were children without diarrhea in the previous 48-h who were frequency matched in strata by residential district, age, and enrollment week. Diarrhea was defined as the passage of three or more watery or loose stools during the 24-h prior to presentation76. The exclusion criterion was inpatient admission for longer than 24-h before enrollment in the study. The duration of diarrhea was assessed by the parents or legal guardians recall and completed by well-trained research nurse. Acute diarrhea (AD) was defined as an episode that had lasted <7 days on presentation8, ProD as 7–13 days36, and PD as a duration of ≥14 days77.

In the present study, ProD and PD were combined into the category “Prolonged or persistent diarrhea” (ProPD; ≥7 days of duration) due to a limited number of PD cases. Demographic and clinical data were collected with standardized and contextualized case report forms completed by research nurses. Water/sanitation, Assets, and Maternal education (WAM) index was calculated as previously described based on access to “improved” or “unimproved” water and/or sanitation, the presence or absence of eight household assets, and maternal education78.

A single stool sample was collected from both diarrhea cases and non-diarrheal controls. Stool samples from children with diarrhea were collected with a nappy lined with plastic film, a single-use bedpan or a potty and transferred to a closed plastic container at JUSH or SHC. Stool samples from non-diarrheal controls were collected at the child’s home, except non-diarrheal controls aged 0–11 months who were enrolled from vaccination rooms in JUSH or SHC. Stool samples collected at JUSH were transported immediately to Jimma University Microbiology Research Laboratory and stored at −80 °C. Samples from SHC were kept at 2–8 °C before same-day transportation to Jimma University Microbiology Research Laboratory for storage at −80 °C. After the completion of study recruitment, samples were shipped on dry ice to Vestfold Hospital Trust, Tønsberg, Norway. DNA was extracted from 500 μL pre-treated stool-buffer suspension supernatant using a MagNA Pure 96 instrument and a MagNA Pure 96 DNA and Viral NA Large Volume Kit, and eluted in 100 μL as previously described35,79.

High throughput 16S rRNA gene amplicon sequencing

Sequencing was done at the University of Minnesota Genomics Center (UMGC) using the Illumina MiSeq sequencing platform by standard methods. Sequencing targeted the V4 hypervariable region of the 16S rRNA gene using a two step PCR double-indexing protocol80. The estimation of 16S rRNA gene copy numbers were verified with qPCR using an ABI7900 (ThermoFisher Scientific, MA, USA) platform, whereas reactions were performed using KAPA HiFi Polymerase (KAPA Biosystems, Woburn, MA, USA). Based on the qPCR results, samples were normalized ~167,000 molecules/μL and 3 μL of sample was used for the PCR1. After the first round of amplification, the PCR1 products were diluted 1:100, and 5 μL of PCR1 was used in the second PCR reaction. Finally, PCR2 products were normalized using SequalPrep Normalization plates. The pooled sample was denatured with NaOH, diluted to 6 pM in Illumina’s HT1 buffer, spiked with 15% PhiX, and heat denatured at 96 °C for 2 min immediately prior to loading. A MiSeq 600 cycle v3 kit was used to sequence the samples.

Statistics and reproducibility

We used available stool samples from a prospective case-control study (CRYPTO-POC) described in detail here35 and no statistical method was used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment. Raw sequencing reads were pre-processed using QIIME281. Overlapping sequences were compiled and low-quality reads were removed. Chimeric sequences were removed, and zero-radius Operational Taxonomic Units (zOTUs) constructed using the UNOISE 3 algorithm from Vsearch82. The analysis was based on zero-radius OTUs (zOTUs) using RDP-based taxonomic assignment at the lowest unambiguous taxonomic rank. Additionally, the taxonomy of all taxa that belonging to genus Lactobacillus has been updated for the new taxonomic description of the genus83. Samples with less than 5000 reads (8 samples in total) were removed and zOTUs found in ≤1% of the samples and with relative abundance less than 0.1% were purged from the zOTU table.

Data analysis was done using R version 4.1.2 (2021-11-01) using packages Vegan v2.5-784, Phyloseq v1.38.085, and DESeq2 v1.34.086. Logistic regression analysis adjusted for enrollment season and enrollment site in order to identify factors associated with diarrhea and between subcategories of diarrhea cases. Putative explanatory variables with a p value less than 0.05 were considered statistically significant.

Alpha diversity measures were based on the number of observed zOTUs and Shannon diversity metrics. Bray-Curtis dissimilarity metrics were determined after cumulative sum scaling (CSS) normalization. Unconstrained principal coordinate analysis (PCoA) and constrained PCoA plots conditioned for age, enrollment site, enrollment season, sex, WAM index, current breastfeeding, and diarrhea in the previous month were subsequently generated. Pairwise permutation multivariate analysis of variance (PERMANOVA) was used for pairwise comparisons based on Bray-Curtis dissimilarity metrics between groups (clinical/demographic data). DESeq2 was used for differential abundance testing between diarrhea cases and non-diarrheal controls and between AD and ProPD (illustrated in heatmaps). AD cases frequency matched by age group and sex with ProPD cases were randomly selected for one-to-one AD vs. ProPD comparison. DESeq2 analysis was controlled for variables that were contributing to the GM variations as determined by distance-based Redundancy Analysis (db-RDA) as well as variables that were known to affect GM from other studies40 including age, enrollment site, enrollment season, sex, WAM index, current breastfeeding, diarrhea in the previous month, dysentery and children’s caretaker. Subsequently, Wilcoxon rank sum test was carried out on the bacterial taxa that were found to differ significantly in abundance by DESeq2 (q < 0.05, with the q value corrected by the Benjamini-Hochberg method). All q values corrected by the Benjamini-Hochberg method reported in the differential abundance testing were from Wilcoxon rank sum test and relative abundances were presented as means. The DESeq2 results were augmented by sparse partial least square discriminatory analysis (sPLS-DA) with 10-fold cross validation used to determine the taxa that discriminates best between diarrhea case and non-diarrheal control status as well as AD vs. ProPD87. To further characterize the bacterial community clusters, we used de novo clustering based on community composition. Overall relationships between clinical and demographic parameters and GM composition in children were illustrated using distance-based Redundancy Analysis (db-RDA) conditioned for enrollment site and enrollment season based on Bray-Curtis dissimilarity metrics.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary information

Reporting Summary

Peer Review File

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-024-51464-w.

Acknowledgements

We would like to thank all the children and their caregivers who participated in the study. We thank Jimma University Clinical and Nutrition Research Center (JUCAN) staff for their valuable contribution in clinical data and stool sample collection, and University of Minnesota Genomics Center (UMGC) for conducting the sequencing. This study was funded by Norwegian Research Council GLOBVAC fund (grant 255571: N.L. and Ø.H.J.), The Bill & Melinda Gates Foundation (grant OPP1153139: N.L. and Ø.H.J.), Trond Mohn Foundation (grant TMS2020TMT11: N.L.), JPIAMR (grant NFR333432: N.L.), University of Wisconsin Department of Medicine Research Committee Pilot Funding Award (D.S.S.), University of Copenhagen (D.S.N. and M.Z.), Jimma University (G.T.), University of Bergen (K.H.), Vestfold Hospital Trust, and the Norwegian Society for Medical Microbiology (Ø.H.J.). The funders had no role in the design, data collection, data analysis, interpretation, or writing of the report.

Author contributions

G.T., A.A., M.Z., K.H., Ø.H.J., N.L., A.C.M., D.S.S., and D.S.N. conceptualized and designed the study. D.S.S., D.S.N., M.Z., G.T., K.H., N.L., and Ø.H.J. had obtained the funding. A.A. and Z.M. supervised the field data collection. A.C.M. contributed to DNA normalization and logistical arrangements for sequencing the samples. N.S. and A.K. supervised and facilitated the sequencing of the samples. O.B. did the DNA extraction. G.T., R.R.J., L.K., J.L.C., and D.S.N. did the bioinformatics and statistical analysis. G.T. wrote the first draft of the manuscript aided by M.Z. and D.S.N. All authors contributed to the revisions, interpretation of results, and completion of the final manuscript. All authors have read and approved the manuscript.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Data availability

The sequence data reported in this study published at Sequence Read Archive (SRA) https://www.ncbi.nlm.nih.gov/sra/PRJNA1036533 and clinical data used in this study cannot be made freely available to protect the privacy of the participants, in accordance with the Danish Data Protection Act and European Regulation 2016/679 of the European Parliament and of the Council (GDPR). However, data can be made available upon request after agreement reached with University of Copenhagen and other participating institutions via the corresponding authors Getnet Tesfaw (gettesfaw2@gmail.com) and Dennis Sandris Nielsen (dn@food.ku.dk), who aim at responding to any request within 2 weeks. Once an agreement has been reached between the parties the data will be available to any type of research covered by the ethical permission under which the original study was carried out. Access will be granted for as long as needed to reasonably carry out the envisioned analysis.

Code availability

We used publicly free available tools. R codes used to produce all tables and figures are available https://github.com/gettesfaw2/GutMicrobiome.

Competing interests

The authors declare no competing interests.

Inclusion and ethics statement

The study involved Ethiopian researchers in the design and implementation phases, and they retained ownership of the data. Any further processing of the data requires approval from these Ethiopian researchers. In the present manuscript, the first author and two co-authors are from Ethiopia. The relevance of the research was discussed with Ethiopian researchers, and a PhD student received training in microbiome and metagenomics research, areas where capacity is limited in Ethiopia. Fecal samples were shipped to Vestfold Hospital Trust in Tønsberg, Norway, for DNA extraction. The DNA extracts were then sent to the University of Wisconsin (UW), USA, following signature of an overall collaboration agreement involved Jimma University (JU) in Ethiopia, the University of Copenhagen (KU) in Denmark, the University of Bergen (UiB) in Norway, and the University of Wisconsin (UW), USA. The agreements govern the relationships between all parties, including the flow of materials and personal data (such as stool samples, clinical data, DNA extracts, sequence data, and results), project financing, the publication of foreground knowledge, and the responsibilities of each party. We have cited local and regional research relevant to our current study.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Troeger C Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016 Lancet Infect. Dis. 2018 18 1211 1228 10.1016/S1473-3099(18)30362-1 30243583
Troeger, C. et al. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Infect. Dis. 18, 1211–1228 (2018).30243583 10.1016/S1473-3099(18)30362-1
2. Fischer Walker CL Perin J Aryee MJ Boschi-Pinto C Black RE Diarrhea incidence in low-and middle-income countries in 1990 and 2010: a systematic review BMC Public Health 2012 12 1 7 10.1186/1471-2458-12-220 22214479
Fischer Walker, C. L., Perin, J., Aryee, M. J., Boschi-Pinto, C. & Black, R. E. Diarrhea incidence in low-and middle-income countries in 1990 and 2010: a systematic review. BMC Public Health 12, 1–7 (2012).22214479 10.1186/1471-2458-12-220
3. Guerrant RL DeBoer MD Moore SR Scharf RJ Lima AA The impoverished gut—a triple burden of diarrhoea, stunting and chronic disease Nat. Rev. Gastroenterol. Hepatol. 2013 10 220 229 10.1038/nrgastro.2012.239 23229327
Guerrant, R. L., DeBoer, M. D., Moore, S. R., Scharf, R. J. & Lima, A. A. The impoverished gut—a triple burden of diarrhoea, stunting and chronic disease. Nat. Rev. Gastroenterol. Hepatol. 10, 220–229 (2013).23229327 10.1038/nrgastro.2012.239
4. Black RE Maternal and child undernutrition: global and regional exposures and health consequences Lancet 2008 371 243 260 10.1016/S0140-6736(07)61690-0 18207566
Black, R. E. et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371, 243–260 (2008).18207566 10.1016/S0140-6736(07)61690-0
5. Ugboko HU Nwinyi OC Oranusi SU Oyewale JO Childhood diarrhoeal diseases in developing countries Heliyon 2020 6 e03690 10.1016/j.heliyon.2020.e03690 32322707
Ugboko, H. U., Nwinyi, O. C., Oranusi, S. U. & Oyewale, J. O. Childhood diarrhoeal diseases in developing countries. Heliyon 6, e03690 (2020).32322707 10.1016/j.heliyon.2020.e03690
6. Kotloff KL Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study Lancet 2013 382 209 222 10.1016/S0140-6736(13)60844-2 23680352
Kotloff, K. L. et al. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. Lancet 382, 209–222 (2013).23680352 10.1016/S0140-6736(13)60844-2
7. Platts-Mills JA Pathogen-specific burdens of community diarrhoea in developing countries: a multisite birth cohort study (MAL-ED) Lancet Glob. Health 2015 3 e564 e575 10.1016/S2214-109X(15)00151-5 26202075
Platts-Mills, J. A. et al. Pathogen-specific burdens of community diarrhoea in developing countries: a multisite birth cohort study (MAL-ED). Lancet Glob. Health 3, e564–e575 (2015).26202075 10.1016/S2214-109X(15)00151-5
8. WHO. The Treatment of Diarrhoea: A Manual for Physicians and Other Senior Health Workers (WHO, 2005).
9. Aghsaeifard Z Heidari G Alizadeh R Understanding the use of oral rehydration therapy: a narrative review from clinical practice to main recommendations Health Sci. Rep. 2022 5 e827 10.1002/hsr2.827 36110343
Aghsaeifard, Z., Heidari, G. & Alizadeh, R. Understanding the use of oral rehydration therapy: a narrative review from clinical practice to main recommendations. Health Sci. Rep. 5, e827 (2022).36110343 10.1002/hsr2.827
10. Neupane, R. et al. Antibiotic resistance trends for common bacterial aetiologies of childhood diarrhoea in low-and middle-income countries: a systematic review. J. Glob. Health 13, 04060 (2023).
11. Pop M Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition Genome biol. 2014 15 1 12 10.1186/gb-2014-15-6-r76
Pop, M. et al. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome biol. 15, 1–12 (2014).10.1186/gb-2014-15-6-r76
12. Moore SR Prolonged episodes of acute diarrhea reduce growth and increase risk of persistent diarrhea in children Gastroenterology 2010 139 1156 1164 10.1053/j.gastro.2010.05.076 20638937
Moore, S. R. et al. Prolonged episodes of acute diarrhea reduce growth and increase risk of persistent diarrhea in children. Gastroenterology 139, 1156–1164 (2010).20638937 10.1053/j.gastro.2010.05.076
13. Ashraf, H. et al. Evaluation of an algorithm for the treatment of persistent diarrhoea: a multicentre study. Bull. World Health Organ. 74, 479–489 (1996).
14. Sarker SA Ahmed T Brüssow H Persistent diarrhea: a persistent infection with enteropathogens or a gut commensal dysbiosis? Environ. Microbiol. 2017 19 3789 3801 10.1111/1462-2920.13873 28752952
Sarker, S. A., Ahmed, T. & Brüssow, H. Persistent diarrhea: a persistent infection with enteropathogens or a gut commensal dysbiosis? Environ. Microbiol. 19, 3789–3801 (2017).28752952 10.1111/1462-2920.13873
15. Rahman AE Childhood diarrhoeal deaths in seven low-and middle-income countries Bull. World Health Organ. 2014 92 664 671 10.2471/BLT.13.134809 25378757
Rahman, A. E. et al. Childhood diarrhoeal deaths in seven low-and middle-income countries. Bull. World Health Organ. 92, 664–671 (2014).25378757 10.2471/BLT.13.134809
16. Wernroth M-L Development of gut microbiota during the first 2 years of life Sci. Rep. 2022 12 9080 10.1038/s41598-022-13009-3 35641542
Wernroth, M.-L. et al. Development of gut microbiota during the first 2 years of life. Sci. Rep. 12, 9080 (2022).35641542 10.1038/s41598-022-13009-3
17. Laursen MF Gut microbiota development: influence of diet from infancy to toddlerhood Ann. Nutr. Metab. 2021 77 21 34 10.1159/000517912 33906194
Laursen, M. F. Gut microbiota development: influence of diet from infancy to toddlerhood. Ann. Nutr. Metab. 77, 21–34 (2021).33906194 10.1159/000517912
18. Iebba V Eubiosis and dysbiosis: the two sides of the microbiota New Microbiol. 2016 39 1 12 26922981
Iebba, V. et al. Eubiosis and dysbiosis: the two sides of the microbiota. New Microbiol. 39, 1–12 (2016).26922981
19. Walker AW Lawley TD Therapeutic modulation of intestinal dysbiosis Pharm. Res. 2013 69 75 86 10.1016/j.phrs.2012.09.008
Walker, A. W. & Lawley, T. D. Therapeutic modulation of intestinal dysbiosis. Pharm. Res. 69, 75–86 (2013).10.1016/j.phrs.2012.09.008
20. Icaza-Chávez M Gut microbiota in health and disease Rev. Gastroenterol. Mex. 2013 78 240 248 24290319
Icaza-Chávez, M. Gut microbiota in health and disease. Rev. Gastroenterol. Mex. 78, 240–248 (2013).24290319
21. Shreiner AB Kao JY Young VB The gut microbiome in health and in disease Curr. Opin. Gastroenterol. 2015 31 69 10.1097/MOG.0000000000000139 25394236
Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69 (2015).25394236 10.1097/MOG.0000000000000139
22. Hooks KB O’Malley MA Dysbiosis and its discontents mBio 2017 8 e01492 01417 10.1128/mBio.01492-17 29018121
Hooks, K. B. & O’Malley, M. A. Dysbiosis and its discontents. mBio 8, e01492–01417 (2017).29018121 10.1128/mBio.01492-17
23. Bik EM Relman DA Unrest at home: diarrheal disease and microbiota disturbance Genome Biol. 2014 15 1 3 10.1186/gb4182
Bik, E. M. & Relman, D. A. Unrest at home: diarrheal disease and microbiota disturbance. Genome Biol. 15, 1–3 (2014).10.1186/gb4182
24. Kieser S Bangladeshi children with acute diarrhoea show faecal microbiomes with increased Streptococcus abundance, irrespective of diarrhoea aetiology Environ. Microbiol. 2018 20 2256 2269 10.1111/1462-2920.14274 29786169
Kieser, S. et al. Bangladeshi children with acute diarrhoea show faecal microbiomes with increased Streptococcus abundance, irrespective of diarrhoea aetiology. Environ. Microbiol. 20, 2256–2269 (2018).29786169 10.1111/1462-2920.14274
25. Rouhani S Diarrhea as a potential cause and consequence of reduced gut microbial diversity among undernourished children in Peru Clin. Infect. Dis. 2020 71 989 999 10.1093/cid/ciz905 31773127
Rouhani, S. et al. Diarrhea as a potential cause and consequence of reduced gut microbial diversity among undernourished children in Peru. Clin. Infect. Dis. 71, 989–999 (2020).31773127 10.1093/cid/ciz905
26. Schilling KA Factors associated with the duration of moderate-to-severe diarrhea among children in rural western Kenya enrolled in the global enteric multicenter study, 2008–2012 Am. J. Trop. Med. Hyg. 2017 97 248 10.4269/ajtmh.16-0898 28719331
Schilling, K. A. et al. Factors associated with the duration of moderate-to-severe diarrhea among children in rural western Kenya enrolled in the global enteric multicenter study, 2008–2012. Am. J. Trop. Med. Hyg. 97, 248 (2017).28719331 10.4269/ajtmh.16-0898
27. Schorling JB A prospective study of persistent diarrhea among children in an urban Brazilian slum Am. J. Epidemiol. 1990 132 144 156 10.1093/oxfordjournals.aje.a115626 2192547
Schorling, J. B. et al. A prospective study of persistent diarrhea among children in an urban Brazilian slum. Am. J. Epidemiol. 132, 144–156 (1990).2192547 10.1093/oxfordjournals.aje.a115626
28. Arthur JD Bodhidatta L Echeverria P Phuphaisan S Paul S Diarrheal disease in Cambodian children at a camp in Thailand Am. J. Epidemiol. 1992 135 541 551 10.1093/oxfordjournals.aje.a116321 1570820
Arthur, J. D., Bodhidatta, L., Echeverria, P., Phuphaisan, S. & Paul, S. Diarrheal disease in Cambodian children at a camp in Thailand. Am. J. Epidemiol. 135, 541–551 (1992).1570820 10.1093/oxfordjournals.aje.a116321
29. Giannattasio, A., Guarino, A. & Vecchio, A. L. Management of children with prolonged diarrhea. F1000Res. 5, F1000 Faculty Rev-206 (2016).
30. Lanata CF Etiologic agents in acute vs persistent diarrhea in children under three years of age in peri‐urban Lima, Perú Acta Paediatr. Suppl. 1992 81 32 38 10.1111/j.1651-2227.1992.tb12369.x
Lanata, C. F. et al. Etiologic agents in acute vs persistent diarrhea in children under three years of age in peri‐urban Lima, Perú. Acta Paediatr. Suppl. 81, 32–38 (1992).10.1111/j.1651-2227.1992.tb12369.x
31. Rabbani G Larson C Islam R Saha U Kabir A Green banana‐supplemented diet in the home management of acute and prolonged diarrhoea in children: a community‐based trial in rural Bangladesh Trop. Med. Int. Health 2010 15 1132 1139 10.1111/j.1365-3156.2010.02608.x 20831671
Rabbani, G., Larson, C., Islam, R., Saha, U. & Kabir, A. Green banana‐supplemented diet in the home management of acute and prolonged diarrhoea in children: a community‐based trial in rural Bangladesh. Trop. Med. Int. Health 15, 1132–1139 (2010).20831671 10.1111/j.1365-3156.2010.02608.x
32. Bhutta ZA Recent advances and evidence gaps in persistent diarrhea J. Pediatr. Gastroenterol. Nutr. 2008 47 260 265 10.1097/MPG.0b013e318181b334 18664885
Bhutta, Z. A. et al. Recent advances and evidence gaps in persistent diarrhea. J. Pediatr. Gastroenterol. Nutr. 47, 260–265 (2008).18664885 10.1097/MPG.0b013e318181b334
33. Bhutta Z Dietary management of persistent diarrhea: comparison of a traditional rice-lentil based diet with soy formula Pediatrics 1991 88 1010 1018 10.1542/peds.88.5.1010 1945604
Bhutta, Z. et al. Dietary management of persistent diarrhea: comparison of a traditional rice-lentil based diet with soy formula. Pediatrics 88, 1010–1018 (1991).1945604 10.1542/peds.88.5.1010
34. Rabbani GH Clinical studies in persistent diarrhea: dietary management with green banana or pectin in Bangladeshi children Gastroenterology 2001 121 554 560 10.1053/gast.2001.27178 11522739
Rabbani, G. H. et al. Clinical studies in persistent diarrhea: dietary management with green banana or pectin in Bangladeshi children. Gastroenterology 121, 554–560 (2001).11522739 10.1053/gast.2001.27178
35. Johansen ØH Performance and operational feasibility of two diagnostic tests for cryptosporidiosis in children (CRYPTO-POC): a clinical, prospective, diagnostic accuracy study Lancet Infect. Dis. 2021 21 722 730 10.1016/S1473-3099(20)30556-9 33278916
Johansen, Ø. H. et al. Performance and operational feasibility of two diagnostic tests for cryptosporidiosis in children (CRYPTO-POC): a clinical, prospective, diagnostic accuracy study. Lancet Infect. Dis. 21, 722–730 (2021).33278916 10.1016/S1473-3099(20)30556-9
36. Zangenberg M Prolonged and persistent diarrhoea is not restricted to children with acute malnutrition: an observational study in Ethiopia Trop. Med. Int. Health 2019 24 1088 1097 10.1111/tmi.13291 31325406
Zangenberg, M. et al. Prolonged and persistent diarrhoea is not restricted to children with acute malnutrition: an observational study in Ethiopia. Trop. Med. Int. Health 24, 1088–1097 (2019).31325406 10.1111/tmi.13291
37. Bokulich NA Antibiotics, birth mode, and diet shape microbiome maturation during early life Sci. Transl. Med. 2016 8 343ra382 343ra382 10.1126/scitranslmed.aad7121
Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra382–343ra382 (2016).10.1126/scitranslmed.aad7121
38. Jeong S Factors influencing development of the infant microbiota: from prenatal period to early infancy Clin. Exp. Pediatr. 2022 65 438 10.3345/cep.2021.00955
Jeong, S. Factors influencing development of the infant microbiota: from prenatal period to early infancy. Clin. Exp. Pediatr. 65, 438 (2022).10.3345/cep.2021.00955
39. Stewart CJ Temporal development of the gut microbiome in early childhood from the TEDDY study Nature 2018 562 583 588 10.1038/s41586-018-0617-x 30356187
Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).30356187 10.1038/s41586-018-0617-x
40. Kortekangas E Environmental exposures and child and maternal gut microbiota in rural Malawi Paediatr. Perinat. Epidemiol. 2020 34 161 170 10.1111/ppe.12623 32011017
Kortekangas, E. et al. Environmental exposures and child and maternal gut microbiota in rural Malawi. Paediatr. Perinat. Epidemiol. 34, 161–170 (2020).32011017 10.1111/ppe.12623
41. Bich VTN Moderate and transient impact of antibiotic use on the gut microbiota in a rural Vietnamese cohort Sci. Rep. 2022 12 20189 10.1038/s41598-022-24488-9 36424459
Bich, V. T. N. et al. Moderate and transient impact of antibiotic use on the gut microbiota in a rural Vietnamese cohort. Sci. Rep. 12, 20189 (2022).36424459 10.1038/s41598-022-24488-9
42. Subramanian S Persistent gut microbiota immaturity in malnourished Bangladeshi children Nature 2014 510 417 421 10.1038/nature13421 24896187
Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).24896187 10.1038/nature13421
43. McDonnell L Association between antibiotics and gut microbiome dysbiosis in children: systematic review and meta-analysis Gut Microbes 2021 13 1870402 10.1080/19490976.2020.1870402 33651651
McDonnell, L. et al. Association between antibiotics and gut microbiome dysbiosis in children: systematic review and meta-analysis. Gut Microbes 13, 1870402 (2021).33651651 10.1080/19490976.2020.1870402
44. Yatsunenko T Human gut microbiome viewed across age and geography Nature 2012 486 222 227 10.1038/nature11053 22699611
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).22699611 10.1038/nature11053
45. Robertson RC Manges AR Finlay BB Prendergast AJ The human microbiome and child growth–first 1000 days and beyond Trends Microbiol. 2019 27 131 147 10.1016/j.tim.2018.09.008 30529020
Robertson, R. C., Manges, A. R., Finlay, B. B. & Prendergast, A. J. The human microbiome and child growth–first 1000 days and beyond. Trends Microbiol. 27, 131–147 (2019).30529020 10.1016/j.tim.2018.09.008
46. De Filippo C Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa Proc. Natl Acad. Sci. USA 2010 107 14691 14696 10.1073/pnas.1005963107 20679230
De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010).20679230 10.1073/pnas.1005963107
47. Álvarez-Mercado AI Plaza-Diaz J Dietary polysaccharides as modulators of the gut microbiota ecosystem: an update on their impact on health Nutrients 2022 14 4116 10.3390/nu14194116 36235768
Álvarez-Mercado, A. I. & Plaza-Diaz, J. Dietary polysaccharides as modulators of the gut microbiota ecosystem: an update on their impact on health. Nutrients 14, 4116 (2022).36235768 10.3390/nu14194116
48. Becker-Dreps S Gut microbiome composition in young Nicaraguan children during diarrhea episodes and recovery Am. J. Trop. Med. Hyg. 2015 93 1187 10.4269/ajtmh.15-0322 26350452
Becker-Dreps, S. et al. Gut microbiome composition in young Nicaraguan children during diarrhea episodes and recovery. Am. J. Trop. Med. Hyg. 93, 1187 (2015).26350452 10.4269/ajtmh.15-0322
49. The HC Assessing gut microbiota perturbations during the early phase of infectious diarrhea in Vietnamese children Gut Microbes 2018 9 38 54 10.1080/19490976.2017.1361093 28767339
The, H. C. et al. Assessing gut microbiota perturbations during the early phase of infectious diarrhea in Vietnamese children. Gut Microbes 9, 38–54 (2018).28767339 10.1080/19490976.2017.1361093
50. The HC Le S-NH Dynamic of the human gut microbiome under infectious diarrhea Curr. Opin. Microbiol. 2022 66 79 85 10.1016/j.mib.2022.01.006 35121284
The, H. C. & Le, S.-N. H. Dynamic of the human gut microbiome under infectious diarrhea. Curr. Opin. Microbiol. 66, 79–85 (2022).35121284 10.1016/j.mib.2022.01.006
51. Chang JY Decreased diversity of the fecal microbiome in recurrent Clostridium difficile—associated diarrhea J. Infect. Dis. 2008 197 435 438 10.1086/525047 18199029
Chang, J. Y. et al. Decreased diversity of the fecal microbiome in recurrent Clostridium difficile—associated diarrhea. J. Infect. Dis. 197, 435–438 (2008).18199029 10.1086/525047
52. Young VB Schmidt TM Antibiotic-associated diarrhea accompanied by large-scale alterations in the composition of the fecal microbiota J. Clin. Microbiol. 2004 42 1203 1206 10.1128/JCM.42.3.1203-1206.2004 15004076
Young, V. B. & Schmidt, T. M. Antibiotic-associated diarrhea accompanied by large-scale alterations in the composition of the fecal microbiota. J. Clin. Microbiol. 42, 1203–1206 (2004).15004076 10.1128/JCM.42.3.1203-1206.2004
53. Lee G Symptomatic and asymptomatic Campylobacter infections associated with reduced growth in Peruvian children PLoS Negl. Trop. Dis. 2013 7 e2036 10.1371/journal.pntd.0002036 23383356
Lee, G. et al. Symptomatic and asymptomatic Campylobacter infections associated with reduced growth in Peruvian children. PLoS Negl. Trop. Dis. 7, e2036 (2013).23383356 10.1371/journal.pntd.0002036
54. Kaakoush NO Castaño-Rodríguez N Mitchell HM Man SM Global epidemiology of Campylobacter infection Clin. Microbiol Rev. 2015 28 687 720 10.1128/CMR.00006-15 26062576
Kaakoush, N. O., Castaño-Rodríguez, N., Mitchell, H. M. & Man, S. M. Global epidemiology of Campylobacter infection. Clin. Microbiol Rev. 28, 687–720 (2015).26062576 10.1128/CMR.00006-15
55. Kaper JB Nataro JP Mobley HL Pathogenic escherichia coli Nat. Rev. Microbiol. 2004 2 123 140 10.1038/nrmicro818 15040260
Kaper, J. B., Nataro, J. P. & Mobley, H. L. Pathogenic escherichia coli. Nat. Rev. Microbiol. 2, 123–140 (2004).15040260 10.1038/nrmicro818
56. Rouhani S Gut microbiota features associated with Campylobacter burden and postnatal linear growth deficits in a Peruvian birth cohort Clin. Infect. Dis. 2020 71 1000 1007 10.1093/cid/ciz906 31773126
Rouhani, S. et al. Gut microbiota features associated with Campylobacter burden and postnatal linear growth deficits in a Peruvian birth cohort. Clin. Infect. Dis. 71, 1000–1007 (2020).31773126 10.1093/cid/ciz906
57. David LA Gut microbial succession follows acute secretory diarrhea in humans mBio 2015 6 00381 00315 10.1128/mBio.00381-15
David, L. A. et al. Gut microbial succession follows acute secretory diarrhea in humans. mBio 6, 00381–00315 (2015). 10.1128/mbio.10.1128/mBio.00381-15
58. Shin N-R Whon TW Bae J-W Proteobacteria: microbial signature of dysbiosis in gut microbiota Trends Biotechnol. 2015 33 496 503 10.1016/j.tibtech.2015.06.011 26210164
Shin, N.-R., Whon, T. W. & Bae, J.-W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 33, 496–503 (2015).26210164 10.1016/j.tibtech.2015.06.011
59. Jin D Dynamics of fecal microbial communities in children with diarrhea of unknown etiology and genomic analysis of associated Streptococcus lutetiensis BMC Microbiol. 2013 13 1 12 10.1186/1471-2180-13-141 23286760
Jin, D. et al. Dynamics of fecal microbial communities in children with diarrhea of unknown etiology and genomic analysis of associated Streptococcus lutetiensis. BMC Microbiol. 13, 1–12 (2013).23286760 10.1186/1471-2180-13-141
60. Villmones HC Investigating the human jejunal microbiota Sci. Rep. 2022 12 1 11 10.1038/s41598-022-05723-9 34992227
Villmones, H. C. et al. Investigating the human jejunal microbiota. Sci. Rep. 12, 1–11 (2022).34992227 10.1038/s41598-022-05723-9
61. Mathew S Mixed viral-bacterial infections and their effects on gut microbiota and clinical illnesses in children Sci. Rep. 2019 9 1 12 10.1038/s41598-018-37162-w 30626917
Mathew, S. et al. Mixed viral-bacterial infections and their effects on gut microbiota and clinical illnesses in children. Sci. Rep. 9, 1–12 (2019).30626917 10.1038/s41598-018-37162-w
62. Olivart M Galera E Falguera M Acute gastroenteritis and Haemophilus parainfluenzae: an unreported but predictable association Gastroenterol. Hepatol. 2016 40 23 24 10.1016/j.gastrohep.2015.10.010 26774675
Olivart, M., Galera, E. & Falguera, M. Acute gastroenteritis and Haemophilus parainfluenzae: an unreported but predictable association. Gastroenterol. Hepatol. 40, 23–24 (2016).26774675 10.1016/j.gastrohep.2015.10.010
63. Balamurugan R Molecular studies of fecal anaerobic commensal bacteria in acute diarrhea in children J. Pediatr. Gastroenterol. Nutr. 2008 46 514 519 10.1097/MPG.0b013e31815ce599 18493205
Balamurugan, R. et al. Molecular studies of fecal anaerobic commensal bacteria in acute diarrhea in children. J. Pediatr. Gastroenterol. Nutr. 46, 514–519 (2008).18493205 10.1097/MPG.0b013e31815ce599
64. Sun F A potential species of next-generation probiotics? The dark and light sides of Bacteroides fragilis in health Food Res. Int. 2019 126 108590 10.1016/j.foodres.2019.108590 31732047
Sun, F. et al. A potential species of next-generation probiotics? The dark and light sides of Bacteroides fragilis in health. Food Res. Int. 126, 108590 (2019).31732047 10.1016/j.foodres.2019.108590
65. Carrow HC Batachari LE Chu H Strain diversity in the microbiome: lessons from Bacteroides fragilis PLoS Pathog. 2020 16 e1009056 10.1371/journal.ppat.1009056 33301530
Carrow, H. C., Batachari, L. E. & Chu, H. Strain diversity in the microbiome: lessons from Bacteroides fragilis. PLoS Pathog. 16, e1009056 (2020).33301530 10.1371/journal.ppat.1009056
66. Morrison DJ Preston T Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism Gut Microbes 2016 7 189 200 10.1080/19490976.2015.1134082 26963409
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).26963409 10.1080/19490976.2015.1134082
67. Bergman E Energy contributions of volatile fatty acids from the gastrointestinal tract in various species Physiol. Rev. 1990 70 567 590 10.1152/physrev.1990.70.2.567 2181501
Bergman, E. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).2181501 10.1152/physrev.1990.70.2.567
68. Bose S Ramesh V Locasale JW Acetate metabolism in physiology, cancer, and beyond Trends Cell Biol. 2019 29 695 703 10.1016/j.tcb.2019.05.005 31160120
Bose, S., Ramesh, V. & Locasale, J. W. Acetate metabolism in physiology, cancer, and beyond. Trends Cell Biol. 29, 695–703 (2019).31160120 10.1016/j.tcb.2019.05.005
69. Silva YP Bernardi A Frozza RL The role of short-chain fatty acids from gut microbiota in gut-brain communication Front. Endocrinol. 2020 11 25 10.3389/fendo.2020.00025
Silva, Y. P., Bernardi, A. & Frozza, R. L. The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front. Endocrinol. 11, 25 (2020).10.3389/fendo.2020.00025
70. He, X., Zhao, S. & Li, Y. Faecalibacterium prausnitzii: a next-generation probiotic in gut disease improvement. Can. J. Infect. Dis. Med. Microbiol. 2021, 6666114 (2021).
71. Chang C-J Next generation probiotics in disease amelioration J. Food Drug Anal. 2019 27 615 622 10.1016/j.jfda.2018.12.011 31324278
Chang, C.-J. et al. Next generation probiotics in disease amelioration. J. Food Drug Anal. 27, 615–622 (2019).31324278 10.1016/j.jfda.2018.12.011
72. Shin R Suzuki M Morishita Y Influence of intestinal anaerobes and organic acids on the growth of enterohaemorrhagic Escherichia coli O157: H7 J. Med. Microbiol. 2002 51 201 206 10.1099/0022-1317-51-3-201 11871614
Shin, R., Suzuki, M. & Morishita, Y. Influence of intestinal anaerobes and organic acids on the growth of enterohaemorrhagic Escherichia coli O157: H7. J. Med. Microbiol. 51, 201–206 (2002).11871614 10.1099/0022-1317-51-3-201
73. Maier E Anderson RC Roy NC Understanding how commensal obligate anaerobic bacteria regulate immune functions in the large intestine Nutrients 2014 7 45 73 10.3390/nu7010045 25545102
Maier, E., Anderson, R. C. & Roy, N. C. Understanding how commensal obligate anaerobic bacteria regulate immune functions in the large intestine. Nutrients 7, 45–73 (2014).25545102 10.3390/nu7010045
74. Khan MT Synergy and oxygen adaptation for development of next-generation probiotics Nature 2023 620 381 385 10.1038/s41586-023-06378-w 37532933
Khan, M. T. et al. Synergy and oxygen adaptation for development of next-generation probiotics. Nature 620, 381–385 (2023).37532933 10.1038/s41586-023-06378-w
75. Chen RY A microbiota-directed food intervention for undernourished children N. Engl. J. Med. 2021 384 1517 1528 10.1056/NEJMoa2023294 33826814
Chen, R. Y. et al. A microbiota-directed food intervention for undernourished children. N. Engl. J. Med. 384, 1517–1528 (2021).33826814 10.1056/NEJMoa2023294
76. Abba K Sinfield R Hart CA Garner P Pathogens associated with persistent diarrhoea in children in low and middle income countries: systematic review BMC Infect. Dis. 2009 9 1 15 19144106
Abba, K., Sinfield, R., Hart, C. A. & Garner, P. Pathogens associated with persistent diarrhoea in children in low and middle income countries: systematic review. BMC Infect. Dis. 9, 1–15 (2009).19144106
77. Aponte, G. B., Mancilla, C. A. B., Carreazo, N. Y. & Galarza, R. A. R. Probiotics for treating persistent diarrhoea in children. Cochrane Database Syst. Rev. 11, CD007401 (2010).
78. Psaki SR Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study Popul. Health Metr. 2014 12 1 11 10.1186/1478-7954-12-8 24479861
Psaki, S. R. et al. Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study. Popul. Health Metr. 12, 1–11 (2014).24479861 10.1186/1478-7954-12-8
79. Roche Diagnostics. MagNA Pure 96 System Operator’s Guide, Version 2.0. (2010).
80. Gohl DM Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies Nat. Biotechnol. 2016 34 942 949 10.1038/nbt.3601 27454739
Gohl, D. M. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949 (2016).27454739 10.1038/nbt.3601
81. Bolyen E Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 Nat. Biotechnol. 2019 37 852 857 10.1038/s41587-019-0209-9 31341288
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).31341288 10.1038/s41587-019-0209-9
82. Rognes T Flouri T Nichols B Quince C Mahé F VSEARCH: a versatile open source tool for metagenomics PeerJ 2016 4 e2584 10.7717/peerj.2584 27781170
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).27781170 10.7717/peerj.2584
83. Zheng J A taxonomic note on the genus Lactobacillus: description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae Int J. Syst. Evol. Microbiol. 2020 70 2782 2858 10.1099/ijsem.0.004107 32293557
Zheng, J. et al. A taxonomic note on the genus Lactobacillus: description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. Int J. Syst. Evol. Microbiol. 70, 2782–2858 (2020).32293557 10.1099/ijsem.0.004107
84. Oksanen J The vegan package Community Ecol. Package 2007 10 719
Oksanen, J. et al. The vegan package. Community Ecol. Package 10, 719 (2007).
85. McMurdie PJ Holmes S phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data PloS ONE 2013 8 e61217 10.1371/journal.pone.0061217 23630581
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS ONE 8, e61217 (2013).23630581 10.1371/journal.pone.0061217
86. Love M Anders S Huber W Differential analysis of count data–the DESeq2 package Genome Biol. 2014 15 10.1186
Love, M., Anders, S. & Huber, W. Differential analysis of count data–the DESeq2 package. Genome Biol. 15, 10.1186 (2014).
87. Lê Cao K-A Boitard S Besse P Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems BMC Bioinform. 2011 12 1 17 10.1186/1471-2105-12-253
Lê Cao, K.-A., Boitard, S., Besse, P. & Sparse, P. L. S. discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinform. 12, 1–17 (2011).10.1186/1471-2105-12-253
