- Open Access
16S gut community of the Cameron County Hispanic Cohort
© Ross et al.; licensee BioMed Central. 2015
- Received: 21 October 2014
- Accepted: 3 February 2015
- Published: 6 March 2015
Obesity and type 2 diabetes (T2D) are major public health concerns worldwide, and their prevalence has only increased in recent years. Mexican Americans are disproportionately afflicted by obesity and T2D, and rates are even higher in the United States-Mexico border region. To determine the factors associated with the increased risk of T2D, obesity, and other diseases in this population, the Cameron County Hispanic Cohort was established in 2004.
In this study, we characterized the 16S gut community of a subset of 63 subjects from this unique cohort. We found that these communities, when compared to Human Microbiome Project subjects, exhibit community shifts often observed in obese and T2D individuals in published studies. We also examined microbial network relationships between operational taxonomic units (OTUs) in the Cameron County Hispanic Cohort (CCHC) and three additional datasets. We identified a group of seven genera that form a tightly interconnected network present in all four tested datasets, dominated by butyrate producers, which are often increased in obese individuals while being depleted in T2D patients.
Through a combination of increased disease prevalence and relatively high gut microbial homogeneity in the subset of CCHC members we examined, we believe that the CCHC may represent an ideal community to dissect mechanisms underlying the role of the gut microbiome in human health and disease. The lack of CCHC subject gut community segregation based on all tested metadata suggests that the community structure we observe in the CCHC likely occurs early in life, and endures. This persistent ‘disease’-related gut microbial community in CCHC subjects may enhance existing genetic or lifestyle predispositions to the prevalent diseases of the CCHC, leading to increased attack rates of obesity, T2D, non-alcoholic fatty liver disease, and others.
- Irritable Bowel Syndrome
- Chronic Fatigue Syndrome
- Human Microbiome Project
- Bacteroidetes Ratio
- Colorectal Cancer Rank
Obesity is a major public health problem worldwide and the number one risk factor associated with multiple diseases, including type 2 diabetes (T2D). T2D is currently the most prevalent endocrine disease in the world and is estimated to afflict 430+ million people by 2030 . T2D is a multifactorial disorder, with pathogenic contributions from genetics, the environment, and lifestyle [2,3]. Accumulating evidence shows metabolic diseases like T2D develop because of chronic, low grade, systemic inflammation that leads to disruption of the normal gut microbiota . Recent studies have revealed gut microbiome signatures associated with obesity and T2D patients [5,6]. Two independent studies in European and Chinese populations revealed increased abundances of opportunistically pathogenic Clostridium species and decreased abundances of butyrate-producing Roseburia, Faecalibacterium, and Eubacterium species associated with T2D patients [7,8]. Karlsson et al.  also found increased abundances of Lactobacillus gasseri and Streptococcus mutans predictive of insulin resistance while Qin et al.  found enrichment in Escherichia coli associated with current T2D patients. There is mounting evidence that the structure of the gut microbial community has significant implications for health and disease, and therapeutic manipulations of these communities can have immediate effects [9-14]. For example, in mice, deficiency of Toll-like receptor 5 (TLR-5), an innate immune sensor of flagellin, results in mice that mimic the symptoms of metabolic syndrome including hyperlipidemia, hypertension, and insulin resistance . These symptoms were induced in wild-type germ-free mice upon fecal transfer from the TLR-5 deficient donors. Likewise, there is evidence that direct alteration of the gut microbiome through fecal transplantation can temporarily reverse many of the symptoms associated with metabolic syndrome and other diseases including ulcerative colitis, irritable bowel syndrome, and chronic fatigue syndrome [12,14,16]. Diet can have a major impact on the composition of the gut microbial community and has been implicated in the establishment of gut enterotypes, which are distinct microbial community signatures driven by the differential abundance of certain key taxa . However, once established, enterotype-defining taxa appear to be resistant to modification through dietary intervention , suggesting that events early in life establish enduring signatures in the gut microbial composition . However, studies in animals and humans implementing dietary interventions aimed at improving metabolic markers have noted non-enterotype-defining shifts in the gut microbiome associated with improved health [13,20-23]. For example, Akkermansia muciniphila, a closely adherent mucin-degrading bacterium, is depleted after administration of a high-fat diet and is strongly negatively associated with obesity and T2D . Dietary supplementation of mice with A. muciniphila reversed the high-fat-diet effects, such as inflammation and insulin resistance, presumably through improved gut barrier function. As a result, there is mounting enthusiasm for altering the microbiome through changes in lifestyle, diet and/or probiotics to help prevent and alleviate many of the disease risks associated with obesity and T2D.
Americans of Mexican descent (MAm) are at an increased risk of obesity and T2D compared to all Americans (Am) nationally (MAm 39.1% BMI ≥30, T2D 12.8%) (Am 35.7% BMI ≥30, T2D 8.3%) [25-27]. Diabetes in Hispanics occurs earlier (mean age of diagnosis 49.4 years for Hispanics, 53.8 years for all Americans), manifests with higher complication rates, and attacks at nearly twice the rate of non-Hispanic whites (WAm) (T2D 12.8% MAm, T2D 7.6% WAm ). Mexican Americans living along the United States (US)-Mexico border are at even greater risk of developing T2D compared to Mexican Americans nationally (15.7% along the border vs. 12.8% nationally) [29-33]. This likely has roots in the genetic makeup of this population, lifestyle, diet, and socioeconomic status among other factors.
16S rRNA gene sequencing
16S rRNA gene profiling of 63 CCHC subjects revealed that no taxa is significantly associated with any clinical measure evaluated in this study (after correcting for multiple comparisons). These included BMI, age, cholesterol, waist-to-hip ratio, diabetes status, triglycerides, sex, fasting glucose, and others (complete metadata included in Additional file 1). We found this apparent high level of homogeneity across subjects surprising, as the variance between subjects of the CCHC was about one third of that observed between HMP subjects.
16S rRNA relative abundance comparison between CCHC and HMP subject stool samples
OTU network analysis
The gut microbial environment is influenced by host genetics, host diet, and other factors and may influence the way the gut microbiota interact with each other and the host [20,62]. We sought to determine whether OTUs exhibited correlative associations across individuals in the CCHC, possibly revealing biologically relevant relationships between taxa not discernable from other types of analysis. Among other applications, this information can prove useful for in vitro manipulation of organisms currently recalcitrant to culture [63,64] and will likely be indispensible as probiotics and personalized medical treatments are developed.
Negative correlations were less abundant and no OTU pairs were negatively correlated across all four datasets; however, there were nine pairs that were negatively correlated in two or three of the datasets. Many studies have noted a strong co-exclusive relationship between Prevotella and Bacteroides; however, these were only strongly negatively correlated in two of the four datasets (CCHC and T1D). In the two datasets in which there was little correlation between Prevotella and Bacteroides (HMP and elderly), Prevotella was present at very low abundance across subjects. This suggests that the host environment, such as differences imparted by genetic factors or by diet, may tip the balance in a competition between species as has been suggested for Prevotella. In both the T1D (children) and elderly cohorts, we found that strong OTU correlations, both positive and negative, were much less abundant. This may be explained by the increased flux in the microbiota in the very young and the very old as well as extreme interpersonal variability in these age groups [65,67].
Within the CCHC, there are no significant gut microbiome shifts associated with age, sex, disease status, or any other available measure. This suggests that the often-reported ‘disease’ associated gut community shifts we found in the CCHC manifest early and likely persist for life. The youngest CCHC member of this analysis set is 28 years old, thus sampling this population at earlier time points would provide insight into the age at which these observed signatures become apparent. Whether causal or an effect of outside influences, this persistent community structure might compound other predisposing genetic or lifestyle factors, leading to the higher rates of obesity, T2D, NAFLD, and other diseases observed in the CCHC. However, this gut community structure may contribute to the low rates of colorectal cancer observed in the CCHC through increased production of butyrate, suggesting that a particular gut microbiome composition can be both predisposing and protective of different diseases simultaneously. This highlights the need for a more complete understanding of host/microbe relationships when implementing targeted manipulation of the human gut community.
Among other observations, we identified a core group of taxa that appear to be tightly correlated across many populations. A large portion of these taxa belong to the butyrate-producing family Lachnospiraceae and were found to be increased in relative abundance in the CCHC compared to the HMP. This correlated group of taxa is also often depleted in T2D patients, suggesting a possible link between the metabolic functions of these taxa and progression of T2D. Information about microbial codependence will be indispensable in the creation of synthetic communities for probiotics or other purposes.
Manipulation of the intestinal microbiome shows promise as a therapy for many diseases; nonetheless, a comprehensive understanding of the mechanisms of action remains to be deciphered. Members of the CCHC exhibit increased prevalence of obesity and T2D, which make them an interesting population to utilize for unraveling related host/microbiome relationships. However, this was a cross-sectional study and thus lacks the resolution necessary to elucidate how the microbiome signatures in the CCHC specifically impact these diseases.
Additionally, we found a higher level of gut microbiome homogeneity within the CCHC compared with other sampled populations such as the HMP (average variance between CCHC subjects across phyla was more than threefold lower than HMP subjects), perhaps making the CCHC an ideal community to test prebiotic or probiotic interventions. Future directions will include longitudinal sampling of CCHC members so that we may assess the low variability and overall stability of the gut microbiome signatures observed in this cohort and its involvement in disease states.
A subset of participants from the original CCHC study  were re-solicited for a follow-up visit and asked to provide a stool sample. This follow-up visit occurred approximately 5 years after the original study visit. Stools were self-collected at home, delivered to the clinic, aliquoted into 50 mL tubes, mixed to a 50% suspension in RNAlater (Invitrogen, Carlsbad, CA, USA) and stored at −20°C within 24 h of collection. A total of 69 stool samples were delivered to Baylor College of Medicine for extraction and sequencing, and sequence data was generated for 63 of them. Samples for both the HMP and CCHC studies were processed via the same protocols  and, in most cases, by the same personnel. The samples were thawed, vortexed, and a wide-bore tip was used to transfer approximately 750 μL of the slurry to a MoBio PowerSoil garnet bead tube (Mo Bio Laboratories, Carlsbad, CA, USA). The stool was processed according to the kit instructions and eluted into 50 μL. 16S rRNA sequencing was performed on the V1 to V3 region (primers 27f and 534r) and sequenced via Roche 454 pyrosequencing (454 Life Sciences, Branford, CT, USA) (average 12,900 reads/sample). Raw fastq files have been deposited into SRA (project accession SRP053023). Sequences were processed using QIIME software . Reads were quality trimmed using default settings and normalized to 3,100 reads/sample. All CCHC clinical metadata are included in Additional file 1. OTUs were picked closed-reference using uclust-ref against the Silva database release 111 at 97%. OTU network correlation analysis was performed using 16S rRNA relative abundance data to identify clusters of highly correlated OTUs. Iterations of the sparse correlations for compositional data (SparCC) algorithm  were used to generate correlation matrices in this analysis. This algorithm is specifically designed to account for compositional effects often present in genomic survey data, such as 16S rRNA sequence data. We chose the HMP dataset to compare against, as this represents a standard gut microbiome of a healthy population [39-41]. For our analysis, we used all HMP stool samples that had 16S rRNA V1 to V3 region data generated and also contained at least 3,100 reads, which amounted to 213 samples.
We thank our cohort recruitment team, particularly Rocio Uribe, Jodi Rodriguez, and Julie Ramirez. We also thank Marcela Montemayor and laboratory staff for their contribution, Pablo Sanchez for our database management, and Christina Villarreal for administrative support. We thank Valley Baptist Medical Center, Brownsville for providing us space for our Center for Clinical and Translational Science Clinical Research Unit. We finally thank the community of Brownsville and the participants who so willingly participated in this study in their city. This work was supported by MD000170 P20 funded from the National Institute on Minority Health and Health disparities (NIMHD), and the Centers for Clinical and Translational Science Award 1U54RR023417-01 from the National Center for Research Resources (NCRR).
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