Oral microbiota reveals signs of acculturation in Mexican American women

The intersection of social determinants of health, the microbiome, and health outcomes in immigrants: A scoping review

In the present scoping review, we explore whether existing evidence supports the premise that social determinants of health (SDoH) affect immigrant health outcomes through their effects on the microbiome. We adapt the National Institute on Minority Health and Health Disparities' research framework to propose a conceptual model that considers the intersection of SDoH, the microbiome, and health outcomes in immigrants. We use this conceptual model as a lens through which to explore recent research about SDoH, biological factors associated with changes to immigrants' microbiomes, and long-term health outcomes. In the 17 articles reviewed, dietary acculturation, physical activity, ethnicity, birthplace, age at migration and length of time in the host country, socioeconomic status, and social/linguistic acculturation were important determinants of postmigration microbiome-related transformations. These factors are associated with progressive shifts in microbiome profile with time in host country, increasing the risks for cardiometabolic, mental, immune, and inflammatory disorders and antibiotic resistance. The evidence thus supports the premise that SDoH influence immigrants' health postmigration, at least in part, through their effects on the microbiome. Omission of important postmigration social-ecological variables (e.g., stress, racism, social/family relationships, and environment), limited research among minoritized subgroups of immigrants, complexity and inter- and intra-individual differences in the microbiome, and limited interdisciplinary and biosocial collaboration restrict our understanding of this area of study. To identify potential microbiome-based interventions and promote immigrants' well-being, more research is necessary to understand the intersections of immigrant health with factors from the biological, behavioral/psychosocial, physical/built environment, and sociocultural environment domains at all social-ecological levels.

Migration Spurs Changes in the Human Microbiome: a Review

International migration often results in major changes in living environments and lifestyles, and these changes may lead to the observed increases in obesity and diabetes among foreign-born people after resettling in higher-income countries. A possible mechanism linking changes in living environments to the onset of health conditions may be changes in the microbiome. Previous research has shown that unfavorable changes in the composition of the microbiome can increase disposition to diseases such as diabetes, obesity, kidney disease, and inflammatory bowel disease. We investigated the relationship between human migration and microbiome composition through a review using microbiome- and migration-related search terms in PubMed and Web of Science. We included articles examining the gut, oral, or oropharyngeal microbiome in people who migrated internationally. Nine articles met eligibility criteria. All but one examined migration from a non-Western to a Western country. Four of these found a difference in the microbiome of migrants compared with non-migrating residents of their country of birth, seven found differences in the microbiome of migrants compared with the native-born population in the country of resettlement, and five found microbiome differences associated with duration of stay in the country of resettlement. Microbiome composition varies with country of birth, age at migration, time since immigration, and country of resettlement. The results suggest that migration may lead to changes in the microbiome; thus, microbiome characteristics are a plausible pathway to examine changes in health after resettlement in a new country.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

The role of the oral microbiome in smoking-related cardiovascular risk: a review of the literature exploring mechanisms and pathways

Cardiovascular disease is a leading cause of morbidity and mortality. Oral health is associated with smoking and cardiovascular outcomes, but there are gaps in knowledge of many mechanisms connecting smoking to cardiovascular risk. Therefore, the aim of this review is to synthesize literature on smoking and the oral microbiome, and smoking and cardiovascular risk/disease, respectively. A secondary aim is to identify common associations between the oral microbiome and cardiovascular risk/disease to smoking, respectively, to identify potential shared oral microbiome-associated mechanisms. We identified several oral bacteria across varying studies that were associated with smoking. Atopobium, Gemella, Megasphaera, Mycoplasma, Porphyromonas, Prevotella, Rothia, Treponema, and Veillonella were increased, while Bergeyella, Haemophilus, Lautropia, and Neisseria were decreased in the oral microbiome of smokers versus non-smokers. Several bacteria that were increased in the oral microbiome of smokers were also positively associated with cardiovascular outcomes including Porphyromonas, Prevotella, Treponema, and Veillonella. We review possible mechanisms that may link the oral microbiome to smoking and cardiovascular risk including inflammation, modulation of amino acids and lipids, and nitric oxide modulation. Our hope is this review will inform future research targeting the microbiome and smoking-related cardiovascular disease so possible microbial targets for cardiovascular risk reduction can be identified.

Discrimination of Bacterial Community Structures among Healthy, Gingivitis, and Periodontitis Statuses through Integrated Metatranscriptomic and Network Analyses

Periodontal disease is an inflammatory condition caused by polymicrobial infection. The inflammation is initiated at the gingiva (gingivitis) and then extends to the alveolar bone, leading to tooth loss (periodontitis). Previous studies have shown differences in bacterial composition between periodontal healthy and diseased sites. However, bacterial metabolic activities during the health-to-periodontitis microbiome shift are still inadequately understood. This study was performed to investigate the bacterial characteristics of healthy, gingivitis, and periodontitis statuses through metatranscriptomic analysis. Subgingival plaque samples of healthy, gingivitis, and periodontitis sites in the same oral cavity were collected from 21 patients. Bacterial compositions were then determined based on 16S rRNA reads; taxonomic and functional profiles derived from genes based on mRNA reads were estimated. The results showed clear differences in bacterial compositions and functional profiles between healthy and periodontitis sites. Co-occurrence networks were constructed for each group by connecting two bacterial species if their mRNA abundances were positively correlated. The clustering coefficient values were 0.536 for healthy, 0.600 for gingivitis, and 0.371 for periodontitis sites; thus, network complexity increased during gingivitis development, whereas it decreased during progression to periodontitis. Taxa, including Eubacterium nodatum, Eubacterium saphenum, Filifactor alocis, and Fretibacterium fastidiosum, showed greater transcriptional activities than those of red complex bacteria, in conjunction with disease progression. These taxa were associated with periodontal disease progression, and the health-to-periodontitis microbiome shift was accompanied by alterations in bacterial network structure and complexity.

IMPORTANCE The characteristics of the periodontal microbiome influence clinical periodontal status. Gingivitis involves reversible gingival inflammation without alveolar bone resorption. In contrast, periodontitis is an irreversible disease characterized by inflammatory destruction in both soft and hard tissues. An imbalance of the microbiome is present in both gingivitis and periodontitis. However, differences in microbiomes and their functional activities in the healthy, gingivitis, and periodontitis statuses are still inadequately understood. Furthermore, some inflamed gingival statuses do not consistently cause attachment loss. In this study, metatranscriptomic analyses were used to investigate the specific bacterial composition and gene expression patterns of the microbiomes of the healthy, gingivitis, and periodontitis statuses. In addition, co-occurrence network analysis revealed that the gingivitis site included features of networks observed in both the healthy and periodontitis sites. These results provide transcriptomic evidence to support gingivitis as an intermediate state between the healthy and periodontitis statuses.

Bacterial Diversity Correlates with Overall Survival in Cancers of the Head and Neck, Liver, and Stomach

One in five cancers is attributed to infectious agents, and the extent of the impact on the initiation, progression, and disease outcomes may be underestimated. Infection-associated cancers are commonly attributed to viral, and to a lesser extent, parasitic and bacterial etiologies. There is growing evidence that microbial community variation rather than a single agent can influence cancer development, progression, response to therapy, and outcome. We evaluated microbial sequences from a subset of infection-associated cancers-namely, head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and stomach adenocarcinoma (STAD) from The Cancer Genome Atlas (TCGA). A total of 470 paired tumor and adjacent normal samples were analyzed. In STAD, concurrent presence of EBV and Selemonas sputigena with a high diversity index were associated with poorer survival (HR: 2.23, 95% CI 1.26-3.94, p = 0.006 and HR: 2.31, 95% CI 1.1-4.9, p = 0.03, respectively). In LIHC, lower microbial diversity was associated with poorer overall survival (HR: 2.57, 95% CI: 1.2, 5.5, p = 0.14). Bacterial within-sample diversity correlates with overall survival in infection-associated cancers in a subset of TCGA cohorts.

Tobacco Smoking and the Fecal Microbiome in a Large, Multi-ethnic Cohort

BACKGROUND

Increasing evidence suggests that tobacco smoking, a well-known driver of carcinogenesis, influences the gut microbiome; however, these relationships remain understudied in diverse populations. Thus, we performed an analysis of smoking and the gut microbiome in a subset of 803 adults from the multi-ethnic NYU FAMiLI study.

METHODS

We assessed fecal microbiota using 16S rRNA gene sequencing, and clustered samples into Amplicon Sequence Variants using QIIME2. We evaluated inferred microbial pathway abundance using PICRUSt. We compared population β-diversity, and relative taxonomic and functional pathway abundance, between never smokers, former smokers, and current smokers.

RESULTS

We found that the overall composition of the fecal microbiome in former and current smokers differs significantly from that of never smokers. The taxa Prevotella and Veillonellaceae were enriched in current and former smokers, whereas the taxa Lachnospira and Tenericutes were depleted, relative to never smokers. These shifts were consistent across racial and ethnic subgroups. Relative to never smokers, the abundance of taxa enriched in current smokers were positively correlated with the imputed abundance of pathways involving smoking-associated toxin breakdown and response to reactive oxygen species (ROS).

CONCLUSIONS

Our findings suggest common mechanisms of smoking associated microbial change across racial subgroups, regardless of initial microbiome composition. The correlation of these differentials with ROS exposure pathways may suggest a role for these taxa in the known association between smoking, ROS and carcinogenesis.

IMPACT

Smoking shifts in the microbiome may be independent of initial composition, stimulating further studies on the microbiome in carcinogenesis and cancer prevention.

Fructan-sensitive children with irritable bowel syndrome have distinct gut microbiome signatures

BACKGROUND

Dietary fructans may worsen gastrointestinal symptoms in children with irritable bowel syndrome (IBS).

AIM

To determine whether gut microbiome composition and function are associated with childhood IBS fructan-induced symptoms.

METHODS

Faecal samples were collected from 38 children aged 7-17 years with paediatric Rome III IBS, who previously completied a double-blind, randomised, placebo-controlled crossover (fructan vs maltodextrin) trial. Fructan sensitivity was defined as an increase of ≥30% in abdominal pain frequency during the fructan diet. Gut microbial composition was determined via 16Sv4 rDNA sequencing. LEfSe evaluated taxonomic composition differences. Tax4Fun2 predicted microbial fructan metabolic pathways.

RESULTS

At baseline, 17 fructan-sensitive (vs 21 fructan-tolerant) subjects had lower alpha diversity (q < 0.05) and were enriched in the genus Holdermania. In contrast, fructan-tolerant subjects were enriched in 14 genera from the class Clostridia. During the fructan diet, fructan-sensitive (vs tolerant) subjects were enriched in both Agathobacter (P = 0.02) and Cyanobacteria (P = 0.0001). In contrast, fructan-tolerant subjects were enriched in three genera from the Clostridia class. Comparing the fructan vs maltodextrin diet, fructan-sensitive subjects had a significantly increased relative abundance of Bifidobacterium (P = 0.02) while fructan-tolerant subjects had increased Anaerostipes (P = 0.03) during the fructan diet. Only fructan-sensitive subjects had a trend towards increased predicted β-fructofuranosidase during the fructan vs maltodextrin diet.

CONCLUSIONS

Fructan-sensitive children with IBS have distinct gut microbiome signatures. These microbiome signatures differ both at baseline and in response to a fructan challenge.

Optimizing the quality of clinical studies on oral microbiome: A practical guide for planning, performing, and reporting

With this review, we aim to increase the quality standards for clinical studies with microbiome as an output parameter. We critically address the existing body of evidence for good quality practices in oral microbiome studies based on 16S rRNA gene amplicon sequencing. First, we discuss the usefulness of microbiome profile analyses. Is a microbiome study actually the best approach for answering the research question? This is followed by addressing the criteria for the most appropriate study design, sample size, and the necessary data (study metadata) that should be collected. Next, we evaluate the available evidence for best practices in sample collection, transport, storage, and DNA isolation. Finally, an overview of possible sequencing options (eg, 16S rRNA gene hypervariable regions, sequencing platforms), processing and data interpretation approaches, as well as requirements for meaningful data storage, sharing, and reporting are provided.

Baseline Oral Microbiome and All-cancer Incidence in a Cohort of Nonsmoking Mexican American Women

Given the increasing evidence that the oral microbiome is involved in obesity, diabetes, and cancer risk, we investigated baseline oral microbiota profiles in relation to all-cancer incidence among nonsmoking women enrolled in a Texas cohort of first- and second-generation immigrants of Mexican origin. We characterized the 16Sv4 rDNA microbiome in oral mouthwash samples collected at baseline from a representative subset of 305 nonsmoking women, ages 20-75 years. We evaluated within- (alpha) and between-sample (beta) diversity by incident cancer status and applied linear discriminant analysis (LDA) effect size analysis to assess differentially abundant taxa. Diversity and candidate taxa in relation to all-cancer incidence were evaluated in multivariable-adjusted Cox regression models. Over 8.8 median years of follow-up, 31 incident cancer cases were identified and verified. Advanced age, greater acculturation, and cardiometabolic risk factors were associated with all-cancer incidence. Higher alpha diversity (age-adjusted P _difference < 0.01) and distinct biological communities (P _difference = 0.002) were observed by incident cancer status. Each unit increase in the Shannon diversity index yielded >8-fold increase in all-cancer and obesity-related cancer risk [multivariable-adjusted HR (95% confidence interval), 8.11 (3.14-20.94) and 10.72 (3.30-34.84), respectively] with similar findings for the inverse Simpson index. Streptococcus was enriched among women who did not develop cancer, while Fusobacterium, Prevotella, Mogibacterium, Campylobacter, Lachnoanaerobaculum, Dialister, and Atopobium were higher among women who developed cancer (LDA score ≥ 3; q-value < 0.01). This initial study of oral microbiota and overall cancer risk in nonsmoking Mexican American women suggests the readily accessible oral microbiota as a promising biomarker. PREVENTION RELEVANCE: Mexican American women suffer a disproportionate burden of chronic health conditions that increase cancer risk. Few investigations of the microbiome, a key determinant of host health, have been conducted among this group. Oral microbiota profiles may provide early and accessible cancer biomarker data on invasive bacteria or community disruptions.

Salivary microbiome composition changes after bariatric surgery

Recent studies show that the salivary microbiome in subjects with obesity differ from those without obesity, but the mechanism of interaction between the salivary microbiome composition and body weight is unclear. Herein we investigate this relation by analyzing saliva samples from 35 adult patients with obesity undergoing bariatric surgery. Our aim was to describe salivary microbiome changes during body weight loss on an individual-specific level, and to elucidate the effect of bariatric surgery on the salivary microbiome which has not been studied before. Analysis of samples collected before and 1 day after surgery, as well as 3 and 12 months after surgery, showed that the salivary microbiome changed in all study participants, but these changes were heterogeneous. In the majority of participants proportions of Gemella species, Granulicatella elegans, Porphyromonas pasteri, Prevotella nanceiensis and Streptococcus oralis decreased, while Veillonella species, Megasphaera micronuciformis and Prevotella saliva increased. Nevertheless, we found participants deviating from this general trend which suggests that a variety of individual-specific factors influence the salivary microbiome composition more effectively than the body weight dynamics alone. The observed microbiome alternations could be related to dietary changes. Therefore, further studies should focus on association with altered taste preferences and potential oral health consequences.

Dietary inflammatory potential in relation to the gut microbiome: results from a cross-sectional study

Diet has direct and indirect effects on health through inflammation and the gut microbiome. We investigated total dietary inflammatory potential via the literature-derived index (Dietary Inflammatory Index (DII®)) with gut microbiota diversity, composition and function. In cancer-free patient volunteers initially approached at colonoscopy and healthy volunteers recruited from the medical centre community, we assessed 16S ribosomal DNA in all subjects who provided dietary assessments and stool samples (n 101) and the gut metagenome in a subset of patients with residual fasting blood samples (n 34). Associations of energy-adjusted DII scores with microbial diversity and composition were examined using linear regression, permutational multivariate ANOVA and linear discriminant analysis. Spearman correlation was used to evaluate associations of species and pathways with DII and circulating inflammatory markers. Across DII levels, α- and β-diversity did not significantly differ; however, Ruminococcus torques, Eubacterium nodatum, Acidaminococcus intestini and Clostridium leptum were more abundant in the most pro-inflammatory diet group, while Akkermansia muciniphila was enriched in the most anti-inflammatory diet group. With adjustment for age and BMI, R. torques, E. nodatum and A. intestini remained significantly associated with a more pro-inflammatory diet. In the metagenomic and fasting blood subset, A. intestini was correlated with circulating plasminogen activator inhibitor-1, a pro-inflammatory marker (rho = 0·40), but no associations remained significant upon correction for multiple testing. An index reflecting overall inflammatory potential of the diet was associated with specific microbes, but not overall diversity of the gut microbiome in our study. Findings from this preliminary study warrant further research in larger samples and prospective cohorts.

Neighborhoods to Nucleotides - Advances and gaps for an obesity disparities systems epidemiology model

Purpose of Review

Disparities in obesity rates in the US continue to increase. Here we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that include at least two or more of these domains, and then provide a real world example of data collection efforts that reflect these domains.

Recent Findings

Research into DNA methylation related to discrimination and microbiome relating to eating behaviors and food content is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep.

Composition and diversity of the subgingival microbiome and its relationship with age in postmenopausal women: an epidemiologic investigation

BACKGROUND

The extent to which the composition and diversity of the oral microbiome varies with age is not clearly understood.

METHODS

The 16S rRNA gene of subgingival plaque in 1219 women, aged 53-81 years, was sequenced and its taxonomy annotated against the Human Oral Microbiome Database (v.14.5). Composition of the subgingival microbiome was described in terms of centered log(2)-ratio (CLR) transformed OTU values, relative abundance, and prevalence. Correlations between microbiota abundance and age were evelauted using Pearson Product Moment correlations. P-values were corrected for multiple testing using the Bonferroni method.

RESULTS

Of the 267 species identified overall, Veillonella dispar was the most abundant bacteria when described by CLR OTU (mean 8.3) or relative abundance (mean 8.9%); whereas Streptococcus oralis, Veillonella dispar and Veillonella parvula were most prevalent (100%, all) when described as being present at any amount. Linear correlations between age and several CLR OTUs (Pearson r = - 0.18 to 0.18), of which 82 (31%) achieved statistical significance (P < 0.05). The correlations lost significance following Bonferroni correction. Twelve species that differed across age groups (each corrected P < 0.05); 5 (42%) were higher in women ages 50-59 compared to ≥70 (corrected P < 0.05), and 7 (48%) were higher in women 70 years and older.

CONCLUSIONS

We identified associations between several bacterial species and age across the age range of postmenopausal women studied. Understanding the functions of these bacteria could identify intervention targets to enhance oral health in later life.

Microbiomic differences at cancer-prone oral mucosa sites with marijuana usage

Marijuana smoke contains cannabinoids, immunosuppressants, and a mixture of potentially-mutagenic chemicals. In addition to systemic disease, it is thought to contribute to oral disease, such as tooth loss, tissue changes in the gums and throat, and possibly oral pharyngeal cancer. We used a cross-sectional study of 20 marijuana users and 19 control non-users, to determine if chronic inhalation-based exposure to marijuana was associated with a distinct oral microbiota at the two most common sites of head and neck squamous cell carcinoma (HNSCC), the lateral border of the tongue and the oral pharynx. At the tongue site, genera earlier shown to be enriched on HNSCC mucosa, Capnocytophaga, Fusobacterium, and Porphyromonas, were at low levels in marijuana users, while Rothia, which is found at depressed levels on HNSCC mucosa, was high. At the oral pharynx site, differences in bacteria were distinct, with higher levels of Selenomonas and lower levels of Streptococcus which is what is seen in HNSCC. No evidence was seen for a contribution of marijuana product contaminating bacteria to these differences. This study revealed differences in the surface oral mucosal microbiota with frequent smoking of marijuana.

Review: modulation of the oral microbiome by the host to promote ecological balance

The indivisible relationship between the human host and its oral microbiome has been shaped throughout the millennia, by facing various changes that have forced the adaptation of oral microorganisms to new environmental conditions. In this constant crosstalk between the human host and its microbiome, a bidirectional relationship has been established. The microorganisms provide the host with functions it cannot perform on its own and at the same time the host provides its microbes with a suitable environment for their growth and development. These host factors can positively affect the microbiome, promoting diversity and balance between different species, resulting in a state of symbiosis and absence of pathology. In contrast, other host factors can negatively influence the composition of the oral microbiome and drive the interaction towards a dysbiotic state, where the balance tilts towards a harmful relationship between the host and its microbiome. The aim of this review is to describe the role host factors play in cultivating and maintaining a healthy oral ecology and discuss mechanisms that can prevent its drift towards dysbiosis.

System for Quality-Assured Data Analysis: Flexible, reproducible scientific workflows

The reproducibility of scientific processes is one of the paramount problems of bioinformatics, an engineering problem that must be addressed to perform good research. The System for Quality-Assured Data Analysis (SyQADA), described here, seeks to address reproducibility by managing many of the details of procedural bookkeeping in bioinformatics in as simple and transparent a manner as possible. SyQADA has been used by persons with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse bioinformatics workflows on tens of thousands of samples, consuming over 80 CPU-months of computing on over 300,000 individual tasks of scores of projects on laptops, computer servers, and computing clusters. SyQADA is especially well-suited for paired-sample analyses found in cancer tumor-normal studies. SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab is available from http://scheet.org/software.html.