Oral microbial signatures associated with age and frailty in Canadian adults.

This study aimed to assess the association between the oral microbiome, age, and frailty. Data and saliva samples were obtained from male and female participants aged 35-70 years (n = 1357). Saliva samples were analysed by 16S rRNA gene sequencing and differences in microbial diversity and community compositions were examined in relation to chronological age and the frailty index (FI). Most alpha diversity measures (Richness, Shannon Diversity, Faith's Phylogenetic Diversity) showed an inverse association with frailty, whereas a positive association was observed with age and Shannon Diversity and Evenness. A further sex-stratified analysis revealed differences in measures of microbial diversity and composition. Multiple genera were detected as significantly differentially abundant with increasing frailty and age by at least two methods. With age, the relative abundance of Veillonella was reduced in both males and females, whereas increases in Corynebacterium appeared specific to males and Aggregatibacter, Fusobacterium, Neisseria, Stomatobaculum, and Porphyromonas specific to females. Beta diversity was significantly associated with multiple mental health components of the FI. This study shows age and frailty are differentially associated with measures of microbial diversity and composition, suggesting the oral microbiome may be a useful indicator of increased risk of frailty or a potential target for improving health in ageing adults.

The Effect of Oral Iron Supplementation on Gut Microbial Composition: a Secondary Analysis of a Double-Blind, Randomized Controlled Trial among Cambodian Women of Reproductive Age.

The World Health Organization recommends untargeted iron supplementation for women of reproductive age (WRA) in countries where anemia prevalence is greater than 40%, such as Cambodia. Iron supplements, however, often have poor bioavailability, so the majority remains unabsorbed in the colon. The gut houses many iron-dependent bacterial enteropathogens; thus, providing iron to individuals may be more harmful than helpful. We examined the effects of two oral iron supplements with differing bioavailability on the gut microbiomes in Cambodian WRA. This study is a secondary analysis of a double-blind, randomized controlled trial of oral iron supplementation in Cambodian WRA. For 12 weeks, participants received ferrous sulfate, ferrous bisglycinate, or placebo. Participants provided stool samples at baseline and 12 weeks. A subset of stool samples (n = 172), representing the three groups, were randomly selected for gut microbial analysis by 16S rRNA gene sequencing and targeted real-time PCR (qPCR). At baseline, 1% of women had iron-deficiency anemia. The most abundant gut phyla were Bacteroidota (45.7%) and Firmicutes (42.1%). Iron supplementation did not alter gut microbial diversity. Ferrous bisglycinate increased the relative abundance of Enterobacteriaceae, and there was a trend towards an increase in the relative abundance of Escherichia-Shigella. qPCR detected an increase in the enteropathogenic Escherichia coli (EPEC) virulence gene, bfpA, in the group that received ferrous sulfate. Thus, iron supplementation did not affect overall gut bacterial diversity in predominantly iron-replete Cambodian WRA, however, evidence does suggest an increase in relative abundance within the broad family Enterobacteriaceae associated with ferrous bisglycinate use. IMPORTANCE To the best of our knowledge, this is the first published study to characterize the effects of oral iron supplementation on the gut microbiomes of Cambodian WRA. Our study found that iron supplementation with ferrous bisglycinate increases the relative abundance of Enterobacteriaceae, which is a family of bacteria that includes many Gram-negative enteric pathogens like Salmonella, Shigella, and Escherichia coli. Using qPCR for additional analysis, we were able to detect genes associated with enteropathogenic E. coli, a type of diarrheagenic E. coli known to be present around the world, including water systems in Cambodia. The current WHO guidelines recommend blanket (untargeted) iron supplementation for Cambodian WRA despite a lack of studies in this population examining iron's effect on the gut microbiome. This study can facilitate future research that may inform evidence-based global practice and policy.

Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer.

The human microbiome has been proposed as a potentially useful biomarker for several cancers. To examine this, we made use of salivary samples from the Atlantic Partnership for Tomorrow's Health (PATH) project and Alberta's Tomorrow Project (ATP). Sample selection was divided into both a retrospective and prospective case control design examining prostate, breast, and colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing. We found no significant differences in alpha diversity. All beta diversity measures were insignificant except for unweighted UniFrac profiles in retrospective breast cancer cases and weighted UniFrac, Bray-Curtis and Robust Atchinson's distances in colon cancer after testing with age and sex adjusted MiRKAT models. Differential abundance (DA) analysis showed several taxa that were associated with previous cancer in all three groupings. Only one genus (Clostridia UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one DA tool. In prospective cases three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. Random Forest classification showed low levels of signal in both study designs in breast and prostate cancer. Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.737) and in one of two prospective cohorts (AUC: 0.717). Our results indicate that it is unlikely that reliable microbial oral biomarkers for breast and prostate cancer exist.. However, further research into the oral microbiome and colon cancer could be fruitful.

Plant-Based Diets and Cancer Risk: What is the Evidence?

PURPOSE OF REVIEW: The purpose of this review is to summarize the recent (past 5 years) available evidence regarding the association between plant-based diets on cancer risk from clinical trials and observational studies. Biological mechanisms and gaps in the current literature will also be discussed. RECENT FINDINGS: There is a lack of intervention studies but there are abundant observational studies assessing the association between plant-based diets and cancer risk, including multiple longitudinal cohort studies and similar data from case-control studies that demonstrate a decreased overall cancer risk with plant-based diets. Case-control studies support a decreased risk of colorectal and breast cancers with plant-based diets, but results for specific cancers remain inconsistent in cohort studies. Current evidence from observational studies indicates an inverse association between plant-based diets and overall cancer risk. Future research should include intervention studies, address inconsistencies in dietary assessment methods and provide greater detail on underrepresented groups.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Investigation of the impact of commonly used medications on the oral microbiome of individuals living without major chronic conditions.

BACKGROUND: Commonly used medications produce changes in the gut microbiota, however, the impact of these medications on the composition of the oral microbiota is understudied. METHODS: Saliva samples were obtained from 846 females and 368 males aged 35-69 years from a Canadian population cohort, the Atlantic Partnership for Tomorrow's Health (PATH). Samples were analyzed by 16S rRNA gene sequencing and differences in microbial community compositions between nonusers, single-, and multi-drug users as well as the 3 most commonly used medications (thyroid hormones, statins, and proton pump inhibitors (PPI)) were examined. RESULTS: Twenty-six percent of participants were taking 1 medication and 21% were reported taking 2 or more medications. Alpha diversity indices of Shannon diversity, Evenness, Richness, and Faith's phylogenetic diversity were similar among groups, likewise beta diversity as measured by Bray-Curtis dissimilarity (R2 = 0.0029, P = 0.053) and weighted UniFrac distances (R2 = 0.0028, P = 0.161) were non-significant although close to our alpha value threshold (P = 0.05). After controlling for covariates (sex, age, BMI), six genera (Saprospiraceae uncultured, Bacillus, Johnsonella, Actinobacillus, Stenotrophomonas, and Mycoplasma) were significantly different from non-medication users. Thyroid hormones, HMG-CoA reductase inhibitors (statins) and PPI were the most reported medications. Shannon diversity differed significantly among those taking no medication and those taking only thyroid hormones, however, there were no significant difference in other measures of alpha- or beta diversity with single thyroid hormone, statin, or PPI use. Compared to participants taking no medications, the relative abundance of eight genera differed significantly in participants taking thyroid hormones, six genera differed in participants taking statins, and no significant differences were observed with participants taking PPI. CONCLUSION: The results from this study show negligible effect of commonly used medications on microbial diversity and small differences in the relative abundance of specific taxa, suggesting a minimal influence of commonly used medication on the salivary microbiome of individuals living without major chronic conditions.

Identifying biases and their potential solutions in human microbiome studies.

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

An in vivo genome-wide shRNA screen identifies BCL6 as a targetable biomarker of paclitaxel resistance in breast cancer.

Paclitaxel is a common breast cancer drug; however, some tumors are resistant. The identification of biomarkers for paclitaxel resistance or sensitivity would enable the development of strategies to improve treatment efficacy. A genome-wide in vivo shRNA screen was performed on paclitaxel-treated mice with MDA-MB-231 tumors to identify genes associated with paclitaxel sensitivity or resistance. Gene expression of the top screen hits was associated with tumor response (resistance or sensitivity) among patients who received neoadjuvant chemotherapy containing paclitaxel. We focused our validation on screen hit B-cell lymphoma 6 (BCL6), which is a therapeutic target in cancer but for which no effects on drug response have been reported. Knockdown of BCL6 resulted in increased tumor regression in mice treated with paclitaxel. Similarly, inhibiting BCL6 using a small molecule inhibitor enhanced paclitaxel treatment efficacy both in vitro and in vivo in breast cancer models. Mechanism studies revealed that reduced BCL6 enhances the efficacy of paclitaxel by inducing sustained G1/S arrest, concurrent with increased apoptosis and expression of target gene cyclin-dependent kinase inhibitor 1A. In summary, the genome-wide shRNA knockdown screen has identified BCL6 as a potential targetable resistance biomarker of paclitaxel response in breast cancer.

Harnessing the plant microbiome to promote the growth of agricultural crops.

The rhizosphere microbiome is composed of diverse microbial organisms, including archaea, viruses, fungi, bacteria as well as eukaryotic microorganisms, which occupy a narrow region of soil directly associated with plant roots. The interactions between these microorganisms and the plant can be commensal, beneficial or pathogenic. These microorganisms can also interact with each other, either competitively or synergistically. Promoting plant growth by harnessing the soil microbiome holds tremendous potential for providing an environmentally friendly solution to the increasing food demands of the world's rapidly growing population, while also helping to alleviate the associated environmental and societal issues of large-scale food production. There recently have been many studies on the disease suppression and plant growth promoting abilities of the rhizosphere microbiome; however, these findings largely have not been translated into the field. Therefore, additional research into the dynamic interactions between crop plants, the rhizosphere microbiome and the environment are necessary to better guide the harnessing of the microbiome to increase crop yield and quality. This review explores the biotic and abiotic interactions that occur within the plant's rhizosphere as well as current agricultural practices, and how these biotic and abiotic factors, as well as human practices, impact the plant microbiome. Additionally, some limitations, safety considerations, and future directions to the study of the plant microbiome are discussed.

Investigating the gut microbial community and genes in children with differing levels of change in serum asparaginase activity during pegaspargase treatment for acute lymphoblastic leukemia.

Asparaginase (ASNase) is an effective treatment of pediatric acute lymphoblastic leukemia (ALL). Changes in ASNase activity may lead to suboptimal treatment and poorer outcomes. The gut microbiome produces metabolites that could impact ASNase therapy, however, remains uninvestigated. We examined gut-microbial community and microbial-ASNase and asparagine synthetase (ASNS) genes using 16SrRNA and metagenomic sequence data from stool samples of pediatric ALL patients. Comparing ASNase activity between consecutive ASNase-doses, we found microbial communities differed between decreased- and increased-activity samples. Escherichia predominated in the decreased-activity community while Bacteroides and Streptococcus predominated in the increased-activity community. In addition microbial ASNS was significantly (p=.004) negatively correlated with change in serum ASNase activity. These preliminary findings suggest microbial communities prior to treatment could affect serum ASNase levels, although the mechanism is unknown. Replication in an independent cohort is needed, and future research on manipulation of these communities and genes could prove useful in optimizing ASNase therapy.

Assessing the Variation within the Oral Microbiome of Healthy Adults.

More than 1,000 different species of microbes have been found to live within the human oral cavity, where they play important roles in maintaining both oral and systemic health. Several studies have identified the core members of this microbial community; however, the factors that determine oral microbiome composition are not well understood. In this study, we exam the salivary oral microbiome of 1,049 Atlantic Canadians using 16S rRNA gene sequencing to determine which dietary, lifestyle, and anthropometric features play a role in shaping microbial community composition. Features that were identified as being significantly associated with overall composition then were additionally examined for genera, amplicon sequence variants, and predicted pathway abundances that were associated with these features. Several associations were replicated in an additional secondary validation data set. Overall, we found that several anthropometric measurements, including waist-hip ratio (WHR), height, and fat-free mass, as well as age and sex, were associated with overall oral microbiome structure in both our exploratory and validation data sets. We were unable to validate any dietary impacts on overall taxonomic oral microbiome composition but did find evidence to suggest potential contributions from factors such as the number of vegetable and refined grain servings an individual consumes. Interestingly, each one of these factors on its own was associated with only minor shifts in the overall taxonomic composition of the oral microbiome, suggesting that future biomarker identification for several diseases associated with the oral microbiome can be undertaken without the worry of confounding factors obscuring biological signals.IMPORTANCE The human oral cavity is inhabited by a diverse community of microbes, known as the human oral microbiome. These microbes play a role in maintaining both oral and systemic health and, as such, have been proposed to be useful biomarkers of disease. However, to identify these biomarkers, we first need to determine the composition and variation of the healthy oral microbiome. In this report, we investigate the oral microbiome of 1,049 healthy individuals to determine which genera and amplicon sequence variants are commonly found between individual oral microbiomes. We then further investigate how lifestyle, anthropometric, and dietary choices impact overall microbiome composition. Interestingly, the results from this investigation showed that while many features were significantly associated with oral microbiome composition, no single biological factor explained a variation larger than 2%. These results indicate that future work on biomarker detection may be encouraged by the lack of strong confounding factors.

Metagenomic Functional Shifts to Plant Induced Environmental Changes.

The Vaccinium angustifolium (wild blueberry) agricultural system involves transformation of the environment surrounding the plant to intensify plant propagation and to improve fruit yield, and therefore is an advantageous model to study the interaction between soil microorganisms and plant-host interactions. We studied this system to address the question of a trade-off between microbial adaptation to a plant-influenced environment and its general metabolic capabilities. We found that many basic metabolic functions were similarly represented in bulk soil and rhizosphere microbiomes overall. However, we identified a niche-specific difference in functions potentially beneficial for microbial survival in the rhizosphere but that might also reduce the ability of microbes to withstand stresses in bulk soils. These functions could provide the microbiome with additional capabilities to respond to environmental fluctuations in the rhizosphere triggered by changes in the composition of root exudates. Based on our analysis we hypothesize that the rhizosphere-specific pathways involved in xenobiotics biodegradation could provide the microbiome with functional flexibility to respond to plant stress status.

Infectious Complications Are Associated With Alterations in the Gut Microbiome in Pediatric Patients With Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia is the most common pediatric cancer. Fortunately, survival rates exceed 90%, however, infectious complications remain a significant issue that can cause reductions in the quality of life and prognosis of patients. Recently, numerous studies have linked shifts in the gut microbiome composition to infection events in various hematological malignances including acute lymphoblastic leukemia (ALL). These studies have been limited to observing broad taxonomic changes using 16S rRNA gene profiling, while missing possible differences within microbial functions encoded by individual species. In this study we present the first combined 16S rRNA gene and metagenomic shotgun sequencing study on the gut microbiome of an independent pediatric ALL cohort during treatment. In this study we found distinctive differences in alpha diversity and beta diversity in samples from patients with infectious complications in the first 6 months of therapy. We were also able to find specific species and functional pathways that were significantly different in relative abundance between samples that came from patients with infectious complications. Finally, machine learning models based on patient metadata and bacterial species were able to classify samples with high accuracy (84.09%), with bacterial species being the most important classifying features. This study strengthens our understanding of the association between infection and pediatric acute lymphoblastic leukemia treatment and warrants further investigation in the future.

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches.

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel "denoising" pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray-Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.