Fecal microbiota imbalance in mexican children with type 1 diabetes

ABSTRACT Dysbiosis of the intestinal microbiota affecting the gut barrier could be triggering Type 1 Diabetes (T1D), the second most frequent autoimmune disease in childhood. This study

compared the structure of the fecal microbiota in 29 mestizo children aged 7–18 years, including 8 T1D at onset, 13 T1D after 2 years treatment and 8 healthy controls. Clinical information

was collected, predisposing haplotypes were determined; the fecal DNA was extracted, the V4 region of the 16S rRNA gene amplified and 454-pyrosequenced. The newly diagnosed T1D cases had

high levels of the genus _Bacteroides_ (p < 0.004), whereas the control group had a gut microbiota dominated by _Prevotella_. Children with T1D treated for ≥2 years had levels of

_Bacteroides_ and _Prevotella_ compared to those of the control group. The gut microbiota of newly diagnosed T1D cases is altered, but whether it is involved in disease causation or is a

consequence of host selection remains unclear. SIMILAR CONTENT BEING VIEWED BY OTHERS FUNCTIONAL AND METABOLIC ALTERATIONS OF GUT MICROBIOTA IN CHILDREN WITH NEW-ONSET TYPE 1 DIABETES

Article Open access 26 October 2022 CHARACTERIZATION OF THE GUT BACTERIAL AND VIRAL MICROBIOTA IN LATENT AUTOIMMUNE DIABETES IN ADULTS Article Open access 09 April 2024 ISLET AUTOANTIBODY

SEROCONVERSION IN TYPE-1 DIABETES IS ASSOCIATED WITH METAGENOME-ASSEMBLED GENOMES IN INFANT GUT MICROBIOMES Article Open access 21 June 2022 INTRODUCTION Type 1 Diabetes (T1D) is a

pro-inflammatory autoimmune disorder that results in β-cells destruction, leading to insulin deficiency1. T1D is one of the most frequent autoimmune disorders in childhood, with a worldwide

prevalence of 1:200 to 1:300, which is higher than that reported a few decades ago2. The increasing incidence cannot be explained only by host genetic factors (HLA DR4-DQ8 and DR3-DQ2)3 and

could instead reflect changes in early childhood exposure to risk factors4, such as lack of breastfeeding5 and viral infections6. Reported protection factors include microbial exposure7,8

and selective antibiotic treatments in early life7,9. Associations between the intestinal microbiota alterations and B-cell autoimmunity have been described in HLA-DR/DQ genetically

predisposed humans with positive GAD/IA2 autoantibodies10,11 or with T1D diagnosis12. Autoimmunity can be triggered after the translocation of microbes through the intestinal epithelium as a

result of increased permeability13. Interestingly, the pancreas and gut endoderm share a common embryonic origin, which may create an immune link between both organs14. The aim of this

study was to evaluate the structure of the gut microbiota in genetically predisposed Mexican children with T1D at onset and after treatment for more than 2 years. RESULTS STUDY POPULATION

All subjects were Sonoran by birth, with at least 90% of the parents and grandparents born in northwest Mexico. A total of 21 T1D cases and 8 healthy controls were included. Eight cases were

newly diagnosed T1D cases (less than 2 months evolution) and 13 were controlled with insulin treatment (with more than 2 years evolution [T1D treated for ≥2 years]) and HbA1C levels <

8%. Four subjects in the control group were siblings of T1D cases. The subjects' mean age for the 3 groups was statistically similar (Table S1). CLINICAL INFORMATION The number of

antibiotic treatments per year, previous to diagnosis, was significantly higher (_p_ = 0.043) in the T1D cases when compared with the antibiotic frequency in the same period in the controls

(Table S1). The mean of antibiotic cycles per year in the newly diagnosed T1D cases was 4 cycles, while the T1D treated for more >2 year treatment group and controls had 6 and 3 cycles

per year, respectively. At the time of the study, all subjects were on antibiotic-free scheme for at least 3 months prior to sampling. Other variables such as delivery mode, breastfeeding

time, lactation regimes and infections per year, were not statistically different between controls and T1D cases (_p_ ≥ 0.05). HLA TYPES All of the T1D cases (Table S1) had at least one HLA

risk allele. The HLA DR3-DQ2 (DRB1*03/DQA1*0501/DQB1*0201) haplotype was in 19% of the cases, followed by 14.3% HLA DR4-DQ8 (DRB1*04/DQA1*0301/DQB1*0302/3) (Table S1). In addition, another

9.52% were heterozygous for the HLA DQ2-DQ8 haplotype. Furthermore, 75% of HLA DR3-DQ2 cases of T1D, were carriers of a DQ8 allele. Regarding DR alleles, 95% of the T1D children presented

the DR4 variant (DRB1*04), while 66.6% were carriers of the DR3 allele (DRB1*03). In the control group, 12.5% were HLA DQ2. The rest of the control subjects were negative to DQ2 or DQ8;

however, 37.5% presented isolated alleles associated with DQ8. All the control children were negatives for the DR alleles. GUT MICROBIOTA A total of 245,134 rRNA16S reads were obtained from

the 29 samples, averaging 8,452 sequences per sample. The bacterial diversity was not significantly different between controls and the two T1D groups (Figures 1A, S1–2). However, beta

diversity analysis using principal coordinates indicate a separate clustering of the fecal communities in subjects with T1D at onset compared to controls (Figure 1B). The group of T1D cases

treated for 2 years did not show clustering and were spread along PC1 axis (Figures 1, 2, S3). Weighted UniFrac distances within groups were significantly smaller than between groups (_p_ =

0.000001) thus indicating group clustering (Figure 2). The major taxa explaining this clustering were not reflected at the phyla but at the genus level (Figure 3). Control children had a

fecal community dominated by _Prevotella_, while children with T1D at onset had higher representation of _Bacteroides_ (_p_ = 0.0037) showing substantially reduced proportions of

_Prevotella_ (_p_ = 0.0003), _Megamonas_ (_p_ = 0.0161) and _Acidaminococcus_ (_p_ = 0.0214), in comparison to controls. Children with T1D treated for 2 years showed an intermediate relative

abundance of these genera between the controls and T1D new cases (Figure 3, S4, S5). Dominant phyla were Bacteroidetes and Firmicutes, followed by Proteobacteria and Tenericutes, with no

differences between groups (_p_ > 0.05) and thus the ratio Bacteroidetes/Firmicutes was not significantly different between our groups. DISCUSSION T1D occurs typically in childhood or

adolescence and has been associated to host genetic factors with major histocompatibility complex (MHC) region harboring genes that contribute more than 50% of the risk, mediated by

HLADR3-DQ2 or DR4-DQ815. Our results show that these in T1D Mexican children had higher proportions of HLA DR4 (95%) and DR3 (66.6%) than Mexican-American T1D children (46% and 27%

respectively)16. In addition to genetic factors, environmental factors have also been epidemiologically associated to T1D. These include early life factors such as C-section17, lack of

breastfeeding5 and early infections and antibiotic exposure7, all factors that are known to alter microbial communities18,19. The higher frequency of antibiotic treatments prior to diagnosis

in T1D patients could influence the shift of the communities away from the controls; however, T1D at onset group had received fewer courses than the group of T1D treated for 2 years and

yet, had the most distinct community. There was higher dispersion in the structure of the gut microbiota in T1D cases, than in controls (Figure 2). This finding is consistent with those

found in previous studies related to autoimmunity and T1D10,12, suggesting instability of the microbiome associated with this condition. The fecal microbiota in Mexican healthy children of

our study had a similar composition to that reported in people from developing countries with high levels of _Prevotella_, in contrast with the _Bacteroides_-dominated microbiota of US

children and adults20,21. These differences are likely to be dietary, since _Bacteroides_ has been associated with high protein and fat diets, while carbohydrate rich diets, increase

_Prevotella_22. It is not clear if high dominance of _Bacteroides_ in the US is associated with higher risk of TD1, which is indeed higher in US than in Mexican children (11 to 17 vs 1.5 per

100,000, respectively)23. The incidence of T1D in the Sonora region -near the US-Mexican border- has doubled in the last 10 years24, suggesting that the factors underlying risks are

environmental and could be related to diet or lifestyle changes. _Bacteroides_-dominant gut communities were also observed in prediabetic Finish children who also showed decreased levels of

_Prevotella_ when compared to healthy controls25. Insulin treatment of T1D partially normalizes the microbiota profile towards _Prevotella_-dominant profile, indicating that physiological

changes related to T1D are driving the gut microbiota structure in T1D. It has been suggested that _Bacteroides_ activity in mucine-synthesis and degradation might be contributing to T1D

development by thinning of the mucus layer, leading to increased gut permeability and inflammation26. Lactate conversion to butyrate in the gut induces synthesis of mucins and consequently

favors a healthy epithelium. _Bacteroides_ would transform lactate into short chain fatty acids, such as propionate, acetate and succinate, which contribute to decrease mucin synthesis and

therefore the tight junctions could be affected25. Thus, the lactate model seems to be critical to maintain intestinal health and explain the way to autoimmunity in T1D. Bacterial antigens

and microorganisms toxins can be sensed by molecules related to epithelial cells' tight junctions like zonulin, claudin and occludin, altering their activity and consequently increasing

gut permeability and bacterial translocation27. In this context, some _Bacteroides sp._ with pathogen activity, such as _Bacteroides fragilis_, disrupts the tight junctions by proteolytic

degradation due to metalloprotease enterotoxins (e.g. fragilysin) increasing paracellular permeability with local inflammation, cell damage and loss of microvilli28. This process could lead

to loss of self-tolerance, causing aberrant immune responses, with an homeostatic imbalance of T-cells, gut inflammation and extra intestinal inflammatory infiltrate «insulitis» associated

with T1D29. The microbiota profile associated to T1D supports the idea of the existence of an “autoimmune” microbiome associated with diabetes10. However, this association does not

necessarily imply causality. Intervention studies where the gut microbiota structure could be normalized in children at high risk for T1D via fecal transplant, have not yet been performed,

but it could demonstrate an etiologic role of dysbiosis in T1D. METHODS STUDY POPULATION A cross-sectional case-control study was conducted in the Mexican Sonora state, between 2010 and

2012, recruiting children T1D patients at the Children's Hospital of the State of Sonora (HIES). The criteria for inclusion comprised being of 7 to 18 years of age, a T1D diagnose as

established according to the American Diabetes Association criteria1, a positive anti-GAD and/or anti-IA-2 auto-antibodies result, HbA1C levels < 8% and an antibiotic-free scheme for at

least 3 months prior to the sampling. Subjects presenting chronic and inflammatory gastrointestinal diseases were excluded from the study. In T1D patients, the insulin dose is currently

carefully calculated based on the carbohydrate content of the diet and the physical activity. In general, a high-fiber diet with low glycemic index is recommended30. All patients'

parents were informed about the nature of the study and were asked to provide written informed consent for their child's participation. All patients had nutritional counseling.

Information about birth delivery mode, breastfeeding, complementary feeding patterns in the first year of life, infections and antibiotic treatments prior to T1D diagnosis was collected in

an interview with the parent. Clinical records were revised and the protocol was approved by the Ethics Committee of the Centre for Food Research and Development and the HIES Learning and

Research Board. SAMPLE COLLECTION Peripheral blood (2 mL) was collected from all patients and stored in EDTA tubes at 4°C for haplotype analysis. Stool samples were obtained from every

subject and immediately placed on ice for transportation to the laboratory. The DNA extraction was performed within the first 3 h after collection. GDNA EXTRACTION Genomic DNA was extracted

from whole blood samples using the QIAamp DNA Blood Mini Kit (QIAGEN®), following manufacturer's instructions. For stool samples the QIAamp DNA Stool Mini Kit (QIAGEN®) was used. The

concentration and purity were evaluated using a Nanodrop ® spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Extracted DNA was stored at −20°C. HLA TYPING The HLA DR3-DQ2 and

DR4-DQ8 typing was performed from blood gDNA through polymerase chain reaction (PCR) using sequence specific primers31 for DRB1*03, DRB1*04, DQA1*0501, DQA1*0301, DQB1*0201 and DQB1*0302/3

alleles (IDT-Integrated DNA Technologies, Tucson-AZ, USA). The amplicons obtained were resolved on 1.8% agarose gel, stained with GelRed™ (Biotium Inc., Hayward, CA, USA) and visualized

under ultraviolet light (Molecular Imager®Bio-Rad, Hercules, CA, USA). The allele HLA frequencies in both clinical groups were calculated using the direct counting method. MICROBIOTA

DETERMINATION The V4 region of the bacterial 16S rRNA gene was amplified from fecal gDNA with 357F/926R primers containing the A and B adaptors from 454 Life Sciences for pyrosequencing and

a unique 12 base pair error correcting Golay barcode, allowing multiplexing of samples in one single run. Replicate amplicons were pooled and purified using the UltraClean –htp 96 well PCR

clean up kit (MO BIO). The concentration of samples was measured and samples were combined in equimolar ratios into a single tube. Pyrosequencing was carried out on a 454 Life Sciences

Genome Sequencer FLX instrument (Roche) by the Human Genome Sequencing Center at Baylor College of Medicine, in Houston, Texas, USA, using their standard protocol. BIOINFORMATICS AND

COMMUNITY COMPARISONS The sequence and microbial communities diversity analysis was performed with the QIIME software package (Quantitative Insights Into Microbial Ecology)32. Sequences were

removed from the analysis if they were <200 nt, had a quality score < 25, contained ambiguous characters, contained an uncorrectable barcode, or did not contain the primer sequence.

Sequences were assigned to samples by examining their individual 12-nt barcode. Sequences were aligned using PyNAST, clustered into OTUs and taxonomy assigned using the Ribosomal Database

Project (RDP) as a reference base. The phylogenetic tree was built with FastTree. QIIME and UniFrac were used for analyses of bacterial communities and group comparisons33. Differences

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comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS The work had partial support from the

Mexican Council for Science and Technology (CONACYT), grant S0008-2009-01-115212. Authors are grateful to Dr. V. Mata Haro, from CIAD for her advice on the study design and MD. G. Garcia

Galvan, Pediatric Endocrinologist from HIES, for her support during subjects' recruitment. Thanks also to S.M. Rodriguez Rivera from University of Puerto Rico Department of Biology, for

her assistance in sequence analysis and to E. Hyde from Baylor College of Medicine Alkek Center for Metagenomics and Microbiome Research, for her comments to the manuscript. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Nutrition and Metabolism, Centro de Investigación en Alimentación y Desarrollo, A.C., Hermosillo, Sonora, México María Esther Mejía-León 

& Ana María Calderón de la Barca * Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas,

USA Joseph F. Petrosino & Nadim Jose Ajami * Laboratory of Microbial Ecology, Department of Biology, University of Puerto Rico, San Juan, Puerto Rico María Gloria Domínguez-Bello *

Department of Medicine, New York University School of Medicine, New York, NY, USA María Gloria Domínguez-Bello Authors * María Esther Mejía-León View author publications You can also search

for this author inPubMed Google Scholar * Joseph F. Petrosino View author publications You can also search for this author inPubMed Google Scholar * Nadim Jose Ajami View author publications

You can also search for this author inPubMed Google Scholar * María Gloria Domínguez-Bello View author publications You can also search for this author inPubMed Google Scholar * Ana María

Calderón de la Barca View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.E.M. and A.M.C. designed and conducted the study, collected samples,

analyzed haplotypes, extracted DNA, analyzed data and wrote the main manuscript text, J.F.P. and N.J.A. run the microbiota pyrosequence and suggested data analyses methods and M.D.B.

supervised the analysis data and wrote the main manuscript text. All authors reviewed the manuscript. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial

interests. ELECTRONIC SUPPLEMENTARY MATERIAL SUPPLEMENTARY INFORMATION Supplementary Information RIGHTS AND PERMISSIONS This work is licensed under a Creative Commons Attribution 3.0

Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Mejía-León, M., Petrosino,

J., Ajami, N. _et al._ Fecal microbiota imbalance in Mexican children with type 1 diabetes. _Sci Rep_ 4, 3814 (2014). https://doi.org/10.1038/srep03814 Download citation * Received: 23

October 2013 * Accepted: 02 January 2014 * Published: 22 January 2014 * DOI: https://doi.org/10.1038/srep03814 SHARE THIS ARTICLE Anyone you share the following link with will be able to

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