Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure

Association between physical activity and the prevalence of tumorigenic bacteria in the gut microbiota of Japanese adults: a cross-sectional study

Escherichia coli harboring polyketide synthase (pks+ E. coli) has been suggested to contribute to colorectal cancer development. Physical activity is strongly associated with lower colorectal cancer risks, but its effects on pks+ E. coli remain unclear. The aim of this study was to investigate the association between pks+ E. coli prevalence and physical activity. A cross-sectional study was conducted on 222 Japanese adults (27-79-years-old, 73.9% female). Triaxial accelerometers were used to measure light-intensity physical activity, moderate-to-vigorous intensity physical activity, the physical activity level, step-count, and time spent inactive. Fecal samples collected from participants were used to determine the prevalence of pks+ E. coli. Multivariate logistic regression analysis and restricted cubic spline curves were used to examine the association between pks+ E. coli prevalence and physical activity. The prevalence of pks+ E. coli was 26.6% (59/222 participants). The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the highest tertile with reference to the lowest tertile of physical activity variables were as follows: light-intensity physical activity (OR 0.63; 95% CI 0.26-1.5), moderate-to-vigorous intensity physical activity (OR 0.85; 95% CI 0.39-1.87), physical activity level (OR 0.69; 95% CI 0.32-1.51), step-count (OR 0.92; 95% CI 0.42-2.00) and time spent inactive (OR 1.30; 95% CI 0.58-2.93). No significant dose-response relationship was found between all physical activity variables and pks+ E. coli prevalence. Our findings did not suggest that physical activity has beneficial effects on the prevalence of pks+ E. coli. Longitudinal studies targeting a large population are needed to clarify this association.

Targeting gut-liver axis by dietary lignans ameliorate obesity: evidences and mechanisms

An imbalance between energy consumption and energy expenditure causes obesity. It is characterized by increased adipose accumulation and accompanied by chronic low-grade inflammation. Many studies have suggested that the gut microbiota of the host mediates the relationship between high-fat diet consumption and the development of obesity. Diet and nutrition of the body are heavily influenced by gut microbiota. The alterations in the microbiota in the gut may have effects on the homeostasis of the host's energy levels, systemic inflammation, lipid metabolism, and insulin sensitivity. The liver is an important organ for fat metabolism and gut-liver axis play important role in the fat metabolism. Gut-liver axis is a bidirectional relationship between the gut and its microbiota and the liver. As essential plant components, lignans have been shown to have different biological functions. Accumulating evidences have suggested that lignans may have lipid-lowering properties. Lignans can regulate the level of the gut microbiota and their metabolites in the host, thereby affecting signaling pathways related to fat synthesis and metabolism. These signaling pathways can make a difference in inhibiting fat accumulation, accelerating energy metabolism, affecting appetite, and inhibiting chronic inflammation. It will provide the groundwork for future studies on the lipid-lowering impact of lignans and the creation of functional meals based on those findings.

Fecal short chain fatty acids and urinary 3-indoxyl sulfate do not discriminate between patients with Crohn´s disease and ulcerative colitis and are not of diagnostic utility for predicting disease severity

BACKGROUND

Urinary 3-indoxyl sulfate levels as well as fecal short chain fatty acid (SCFA) concentrations are surrogate markers for gut microbiota diversity. Patients with inflammatory bowel diseases (IBDs) and patients with primary sclerosing cholangitis (PSC), a disease closely associated with IBD, have decreased microbiome diversity. In this paper, the fecal SCFAs propionate, acetate, butyrate and isobutyrate of patients with IBD and patients with PSC-IBD and urinary 3-indoxyl sulfate of IBD patients were determined to study associations with disease etiology and severity.

METHODS

SCFA levels in feces of 64 IBD patients and 20 PSC-IBD patients were quantified by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Urinary 3-indoxyl sulfate levels of 45 of these IBD patients were analysed by means of reversed-phase liquid chromatography-electrospray ionization-tandem mass spectrometry. Feces of 17 healthy controls and urine of 13 of these controls were analyzed in parallel. These cohorts had comparable sex distribution and age.

RESULTS

Urinary 3-indoxyl sulfate concentrations (normalized to urinary creatinine levels) was increased (P = 0.030) and fecal isobutyrate levels (normalized to dry weight of the stool sample) of IBD patients were decreased (P = 0.035) in comparison to healthy controls. None of the analyzed metabolites differed between patients with Crohn´s disease (CD) and patients with ulcerative colitis (UC). Fecal acetate and butyrate positively correlated with fecal calprotectin (P = 0.040 and P = 0.005, respectively) and serum C-reactive protein (P = 0.024 and P = 0.025, respectively) in UC but not CD patients. UC patients with fecal calprotectin levels above 150 µg/g, indicating intestinal inflammatory activity, had higher fecal acetate (P = 0.016), butyrate (P = 0.007) and propionate (P = 0.046) in comparison to patients with fecal calprotectin levels < 50 µg/g. Fecal SCFA levels of PSC-IBD and IBD patients were comparable.

CONCLUSIONS

Current findings suggest that analysis of urinary 3-indoxyl-sulfate as well as fecal SCFAs has no diagnostic value for IBD and PSC-IBD diagnosis or monitoring of disease severity.

Microbiota-accessible carbohydrates (MACs) as novel gut microbiome modulators in noncommunicable diseases

The gut microbiota has a significant role in human health and is affected by many factors. Diet and dietary components have profound impacts on the composition of the gut microbiome and largely contribute to the change in bacterial flora. A high-fiber diet increased dietary fiber (DF) fermentation and the production of short-chain fatty acids (SCFAs), which increased the number of microorganisms. Microbiota-accessible carbohydrates (MACs), a subgroup of fermentable carbohydrates such as DF, are defined as indigestible carbohydrates metabolized by microbes. These carbohydrates are important components to sustain the microbial environment of the complicated digestive tract and avoid intestinal dysbiosis. Each MAC has a unique property and can therefore be used as a sensitive output microbiota modulator to support host homeostasis and modulate health. In addition to the overall health-developing effects, MACs are thought to have a promising effect on the prevention of non-communicable diseases (NCDs), which are major health problems worldwide. The aim of the manuscript was to describe microbiota-accessible carbohydrates and summarize their effects on gut modulation and NCDs.

The gut microbiome, short chain fatty acids, and related metabolites in cystic fibrosis patients with and without colonic adenomas

BACKGROUND

Adults with cystic fibrosis (CF) are at increased risk for colon cancer. CF patients have reductions in intestinal bacteria that produce short chain fatty acids (SCFAs), although it is unclear whether this corresponds with intestinal SCFA levels and the presence of colonic neoplasia. The aim of this study was to compare gut microbiome and SCFA composition in patients with and without CF, and to assess associations with colonic adenomas.

METHODS

Colonic aspirates were obtained from adults with and without CF undergoing colon cancer screening or surveillance colonoscopy. Microbiome characterization was performed by 16S rRNA V3-V4 sequencing. Targeted profiling of SCFAs and related metabolites was performed by LC-MS.

RESULTS

42 patients (21 CF, 21 control) were enrolled. CF patients had significantly reduced alpha diversity and decreased relative abundance of many SCFA-producing taxa. There were no significant differences in SCFA levels in CF patients, although there were reduced levels of branched chain fatty acids (BCFAs) and related metabolites. CF patients with adenomas, but not controls with adenomas, had significantly increased relative abundance of Bacteroides fragilis. CF microbiome composition was significantly associated with isovalerate concentration and the presence of adenomas.

CONCLUSIONS

CF patients have marked disturbances in the gut microbiome, and CF patients with adenomas had notably increased relative abundance of B. fragilis, a pathogen known to promote colon cancer. Reductions in BCFAs but not SCFAs were found in CF. Further studies are warranted to evaluate the role of B. fragilis as well the biological significance of reductions in BCFAs in CF.

Progress of gut microbiome and its metabolomics in early screening of colorectal cancer

Gut microbes are widely considered to be closely associated with colorectal cancer (CRC) development. The microbiota is regarded as a potential identifier of CRC, as several studies have found great significant changes in CRC patients' microbiota and metabolic groups. Changes in microbiota, like Fusobacterium nucleatum and Bacteroides fragilis, also alter the metabolic activity of the host, promoting CRC development. In contrast, the metabolome is an intuitive discriminative biomarker as a small molecular bridge to distinguish CRC from healthy individuals due to the direct action of microbes on the host. More diagnostic microbial markers have been found, and the potential discriminatory power of microorganisms in CRC has been investigated through the combined use of biomic genomic metabolomics, bringing new ideas for screening fecal microbial markers. In this paper, we discuss the potential of microorganisms and their metabolites as biomarkers in CRC screening, hoping to provide thoughts and references for non-invasive screening of CRC.

Genetic Predictors for Fecal Propionate and Butyrate-Producing Microbiome Pathway Are Not Associated with Colorectal Cancer Risk: A Mendelian Randomization Analysis

BACKGROUND

Mechanistic data indicate the benefit of short-chain fatty acids (SCFA) produced by gut microbial fermentation of fiber on colorectal cancer, but direct epidemiologic evidence is limited. A recent study identified SNPs for two SCFA traits (fecal propionate and butyrate-producing microbiome pathway PWY-5022) in Europeans and showed metabolic benefits.

METHODS

We conducted a two-sample Mendelian randomization analysis of the genetic instruments for the two SCFA traits (three SNPs for fecal propionate and nine for PWY-5022) in relation to colorectal cancer risk in three large European genetic consortia of 58,131 colorectal cancer cases and 67,347 controls. We estimated the risk of overall colorectal cancer and conducted subgroup analyses by sex, age, and anatomic subsites of colorectal cancer.

RESULTS

We did not observe strong evidence for an association of the genetic predictors for fecal propionate levels and the abundance of PWY-5022 with the risk of overall colorectal cancer, colorectal cancer by sex, or early-onset colorectal cancer (diagnosed at <50 years), with no evidence of heterogeneity or pleiotropy. When assessed by tumor subsites, we found weak evidence for an association between PWY-5022 and risk of rectal cancer (OR per 1-SD, 0.95; 95% confidence intervals, 0.91-0.99; P = 0.03) but it did not surpass multiple testing of subgroup analysis.

CONCLUSIONS

Genetic instruments for fecal propionate levels and the abundance of PWY-5022 were not associated with colorectal cancer risk.

IMPACT

Fecal propionate and PWY-5022 may not have a substantial influence on colorectal cancer risk. Future research is warranted to comprehensively investigate the effects of SCFA-producing bacteria and SCFAs on colorectal cancer risk.

The Modulatory Effects of Fatty Acids on Cancer Progression

Cancer is the second leading cause of death worldwide and the global cancer burden rises rapidly. The risk factors for cancer development can often be attributed to lifestyle factors, of which an unhealthy diet is a major contributor. Dietary fat is an important macronutrient and therefore a crucial part of a well-balanced and healthy diet, but it is still unclear which specific fatty acids contribute to a healthy and well-balanced diet in the context of cancer risk and prognosis. In this review, we describe epidemiological evidence on the associations between the intake of different classes of fatty acids and the risk of developing cancer, and we provide preclinical evidence on how specific fatty acids can act on tumor cells, thereby modulating tumor progression and metastasis. Moreover, the pro- and anti-inflammatory effects of each of the different groups of fatty acids will be discussed specifically in the context of inflammation-induced cancer progression and we will highlight challenges as well as opportunities for successful application of fatty acid tailored nutritional interventions in the clinic.

Ecological dynamics of the gut microbiome in response to dietary fiber

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual's response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.

Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure

Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection process facilitated the identification of taxons that are most predictive of nitrification and to compare habitats. We found that Nitrosomonas and Nitrospirae were more frequently identified as important predictors of nitrification in conventional systems, whereas Thaumarchaeota were more important predictors in diversified systems. Our results suggest that model performance was not substantively improved by incorporating additional time-consuming and expensive qPCR data on amoA gene abundance. We also identified several clades of nitrifiers important for nitrification in different cropping systems, though we were unable to detect system- or rhizosphere-specific patterns in OTU-level biomarkers for nitrification. Finally, our results highlight the inherent risk of combining data from disparate habitats with the goal of increasing sample size to avoid overfitting models. This study represents a step toward developing machine learning approaches for microbiome research to identify nitrifier ecotypes that may be important for distinguishing ecotypes with defining roles in different habitats.

Emerging role of human microbiome in cancer development and response to therapy: special focus on intestinal microflora

In recent years, there has been a greater emphasis on the impact of microbial populations inhabiting the gastrointestinal tract on human health and disease. According to the involvement of microbiota in modulating physiological processes (such as immune system development, vitamins synthesis, pathogen displacement, and nutrient uptake), any alteration in its composition and diversity (i.e., dysbiosis) has been linked to a variety of pathologies, including cancer. In this bidirectional relationship, colonization with various bacterial species is correlated with a reduced or elevated risk of certain cancers. Notably, the gut microflora could potentially play a direct or indirect role in tumor initiation and progression by inducing chronic inflammation and producing toxins and metabolites. Therefore, identifying the bacterial species involved and their mechanism of action could be beneficial in preventing the onset of tumors or controlling their advancement. Likewise, the microbial community affects anti-cancer approaches’ therapeutic potential and adverse effects (such as immunotherapy and chemotherapy). Hence, their efficiency should be evaluated in the context of the microbiome, underlining the importance of personalized medicine. In this review, we summarized the evidence revealing the microbiota's involvement in cancer and its mechanism. We also delineated how microbiota could predict colon carcinoma development or response to current treatments to improve clinical outcomes.

The ketogenic diet: its impact on human gut microbiota and potential consequent health outcomes: a systematic literature review

Aim

This systematic review examined the diet's impact on the human gut microbiota to identify potential consequent health outcomes.

Background

The extreme macronutrient profile of the ketogenic diet (KD) instigates compositional shifts in the gut's microbial community.

Methods

In this systematic literature review, an evidence-based and methodical approach was undertaken, which involved systematic searches of the Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, generating a total of 263 relevant research papers. Following the application of inclusion and exclusion criteria, eight papers were deemed suitable for inclusion. These papers were critically appraised using a checklist tool adapted from the National Institute of Care and Excellence (NICE). The findings were analysed using a simplified thematic analysis.

Results

The results provide strong evidence for a persistent reduction in Bifidobacterium abundance following KD adherence. A reduced abundance of key Firmicutes butyrate-producing bacteria was found to be a likely impact, although two studies with extended intervention periods indicate this may be time-limited. Studies investigating short-chain fatty acids (SCFA's) indicate KD reduces total faecal SCFA's, acetate, and butyrate.

Conclusion

Changes to microbial communities resulting from KD adherence are potentially detrimental to colonic health. The persistent reduction in Bifidobacterium abundance was concerning, with obesity, type-2 diabetes, and depression highlighted as potential consequent risks. For nutrition and healthcare professionals, the findings emphasize the importance of considering KDs microbial effects and resulting health implications at an individual level.

Faecal microbial biomarkers in early diagnosis of colorectal cancer

Colorectal cancer (CRC) is ranked as the second most common cause of cancer deaths and the third most common cancer globally. It has been described as a 'silent disease' which is often easily treatable if detected early-before progression to carcinoma. Colonoscopy, which is the gold standard for diagnosis is not only expensive but is also an invasive diagnostic procedure, thus, effective and non-invasive diagnostic methods are urgently needed. Unfortunately, the current methods are not sensitive and specific enough in detecting adenomas and early colorectal neoplasia, hampering treatment and consequently, survival rates. Studies have shown that imbalances in such a relationship which renders the gut microbiota in a dysbiotic state are implicated in the development of adenomas ultimately resulting in CRC. The differences found in the makeup and diversity of the gut microbiota of healthy individuals relative to CRC patients have in recent times gained attention as potential biomarkers in early non-invasive diagnosis of CRC, with promising sensitivity, specificity and even cost-effectiveness. This review summarizes recent studies in the application of these microbiota biomarkers in early CRC diagnosis, limitations encountered in the area of the faecal microbiota studies as biomarkers for CRC, and future research exploits that address these limitations.

Acetate Promotes a Differential Energy Metabolic Response in Human HCT 116 and COLO 205 Colon Cancer Cells Impacting Cancer Cell Growth and Invasiveness

Under dysbiosis, a gut metabolic disorder, short-chain carboxylic acids (SCCAs) are secreted to the lumen, affecting colorectal cancer (CRC) development. Butyrate and propionate act as CRC growth inhibitors, but they might also serve as carbon source. In turn, the roles of acetate as metabolic fuel and protein acetylation promoter have not been clearly elucidated. To assess whether acetate favors CRC growth through active mitochondrial catabolism, a systematic study evaluating acetate thiokinase (AcK), energy metabolism, cell proliferation, and invasiveness was performed in two CRC cell lines incubated with physiological SCCAs concentrations. In COLO 205, acetate (+glucose) increased the cell density (50%), mitochondrial protein content (3–10 times), 2-OGDH acetylation, and oxidative phosphorylation (OxPhos) flux (36%), whereas glycolysis remained unchanged vs. glucose-cultured cells; the acetate-induced OxPhos activation correlated with a high AcK activity, content, and acetylation (1.5–6-fold). In contrast, acetate showed no effect on HCT116 cell growth, OxPhos, AcK activity, protein content, and acetylation. However, a substantial increment in the HIF-1α content, HIF-1α-glycolytic protein targets (1–2.3 times), and glycolytic flux (64%) was observed. Butyrate and propionate decreased the growth of both CRC cells by impairing OxPhos flux through mitophagy and mitochondrial fragmentation activation. It is described, for the first time, the role of acetate as metabolic fuel for ATP supply in CRC COLO 205 cells to sustain proliferation, aside from its well-known role as protein epigenetic regulator. The level of AcK determined in COLO 205 cells was similar to that found in human CRC biopsies, showing its potential role as metabolic marker.

Reproducibility, Temporal Variability, and Concordance of Serum and Fecal Bile Acids and Short Chain Fatty Acids in a Population-Based Study

BACKGROUND

Bile acid (BA) and short chain fatty acid (SCFA) production is affected by diet and microbial metabolism. These metabolites may play important roles in human carcinogenesis.

METHODS

We used a fully quantitative targeted LC-MS/MS system to measure serum and fecal BA and SCFA concentrations in 136 Costa Rican adults at study baseline and 6-months. We randomly selected 50 participants and measured their baseline samples in duplicate. Our objective was to evaluate: Technical reproducibility; 6-month temporal variability; and concordance between sample type collected from the same individual at approximately the same time.

RESULTS

Technical reproducibility was excellent, with intraclass correlation coefficients (ICC) ≥0.83 for all BAs except serum tauroursodeoxycholic acid (ICC = 0.72) and fecal glycolithocholic acid (ICC = 0.66) and ICCs ≥0.81 for all SCFAs except serum 2-methylbutyric acid (ICC = 0.56) and serum isobutyric acid (ICC = 0.64). Temporal variability ICCs were generally low, but several BAs (i.e., deoxycholic, glycoursodeoxycholic, lithocholic, taurocholic, and tauroursodeoxycholic acid) and SCFAs (i.e., 2-methylbutyric, butyric, propionic, and valeric acid) had 6-month ICCs ≥0.44. The highest degree of concordance was observed for secondary and tertiary BAs.

CONCLUSIONS

Serum and fecal BAs and SCFAs were reproducibly measured. However, 6-month ICCs were generally low, indicating that serial biospecimen collections would increase statistical power in etiologic studies. The low concordance for most serum and fecal metabolites suggests that consideration should be paid to treating these as proxies.

IMPACT

Our findings will inform the design and interpretation of future human studies on associations of BAs, SCFAs, and potentially other microbial metabolites, with disease risk.

Recent advancements in the exploitation of the gut microbiome in the diagnosis and treatment of colorectal cancer

Over the last few decades it has been established that the complex interaction between the host and the multitude of organisms that compose the intestinal microbiota plays an important role in human metabolic health and disease. Whilst there is no defined consensus on the composition of a healthy microbiome due to confounding factors such as ethnicity, geographical locations, age and sex, there are undoubtably populations of microbes that are consistently dysregulated in gut diseases including colorectal cancer (CRC). In this review, we discuss the most recent advances in the application of the gut microbiota, not just bacteria, and derived microbial compounds in the diagnosis of CRC and the potential to exploit microbes as novel agents in the management and treatment of CRC. We highlight examples of the microbiota, and their derivatives, that have the potential to become standalone diagnostic tools or be used in combination with current screening techniques to improve sensitivity and specificity for earlier CRC diagnoses and provide a perspective on their potential as biotherapeutics with translatability to clinical trials.

Ecological Adaptation and Succession of Human Fecal Microbial Communities in an Automated In Vitro Fermentation System

Longitudinal studies of gut microbiota following specific interventions are vital for understanding how they influence host health. However, robust longitudinal sampling of gut microbiota is a major challenge, which can be addressed using in vitro fermentors hosting complex microbial communities. Here, by employing 16S rRNA gene amplicon sequencing, we investigated the adaptation and succession of human fecal microbial communities in an automated multistage fermentor. We performed two independent experiments using different human donor fecal samples, one configured with two units of three colon compartments each studied for 22 days and another with one unit of two colon compartments studied for 31 days. The fermentor maintained a trend of increasing microbial alpha diversity along colon compartments. Within each experiment, microbial compositions followed compartment-specific trajectories and reached independent stable configurations. While compositions were highly similar between replicate units, they were clearly separated between different experiments, showing that they maintained the individuality of fecal inoculum rather than converging on a fermentor-specific composition. While some fecal amplicon sequence variants (ASVs) were undetected in the fermentor, many ASVs undetected in the fecal samples flourished in vitro. These bloomer ASVs accounted for significant proportions of the population and included prominent health-associated microbes such as Bacteroides fragilis and Akkermansia muciniphila. Turnover in community compositions is likely explained by feed composition and pH, suggesting that these communities can be easily modulated. Our results suggest that in vitro fermentors are promising tools to study complex microbial communities harboring important members of human gut microbiota. IMPORTANCE In vitro fermentors that can host complex gut microbial communities are promising tools to investigate the dynamics of human gut microbiota. In this work, using an automated in vitro gut fermentor consisting of different colon compartments, we investigated the adaptation dynamics of two different human fecal microbial communities over 22 and 31 days. By observing the temporal trends of different community members, we found that many dominant members of the fecal microbiota failed to maintain their dominance in vitro, and some of the low-abundance microbes undetected in the fecal microbiota successfully grew in the in vitro communities. Microbiome compositional changes and blooming could largely be explained by feed composition and pH, suggesting that these communities can be modulated to desired compositions via optimizing culture conditions. Thus, our results open up the possibility of modulating in vitro microbial communities to predefined compositions by optimizing feed composition and culture conditions.

High-Fiber, Whole-Food Dietary Intervention Alters the Human Gut Microbiome but Not Fecal Short-Chain Fatty Acids

Dietary shifts can have a direct impact on the gut microbiome by preferentially selecting for microbes capable of utilizing the various dietary nutrients. The intake of dietary fiber has decreased precipitously in the last century, while consumption of processed foods has increased. Fiber, or microbiota-accessible carbohydrates (MACs), persist in the digestive tract and can be metabolized by specific bacteria encoding fiber-degrading enzymes. The digestion of MACs results in the accumulation of short-chain fatty acids (SCFAs) and other metabolic by-products that are critical to human health. Here, we implemented a 2-week dietary fiber intervention aiming for 40 to 50 g of fiber per day within the context of a course-based undergraduate research experience (CURE) (n = 20). By coupling shotgun metagenomic sequencing and targeted gas chromatography-mass spectrometry (GC-MS), we found that the dietary intervention significantly altered the composition of individual gut microbiomes, accounting for 8.3% of the longitudinal variability within subjects. Notably, microbial taxa that increased in relative abundance as a result of the diet change included known MAC degraders (i.e., Bifidobacterium and Lactobacillus). We further assessed the genetic diversity within Bifidobacterium, assayed by amplification of the groEL gene. Concomitant with microbial composition changes, we show an increase in the abundance of genes involved in inositol degradation. Despite these changes in gut microbiome composition, we did not detect a consistent shift in SCFA abundance. Collectively, our results demonstrate that on a short-term timescale of 2 weeks, increased fiber intake can induce compositional changes of the gut microbiome, including an increase in MAC-degrading bacteria.

IMPORTANCE A profound decrease in the consumption of dietary fiber in many parts of the world in the last century may be associated with the increasing prevalence of type II diabetes, colon cancer, and other health problems. A typical U.S. diet includes about ∼15 g of fiber per day, far less fiber than the daily recommended allowance. Changes in dietary fiber intake affect human health not only through the uptake of nutrients directly but also indirectly through changes in the microbial community and their associated metabolism. Here, we conducted a 2-week diet intervention in healthy young adults to investigate the impact of fiber consumption on the gut microbiome. Participants increased their average fiber consumption by 25 g/day on average for 2 weeks. The high-fiber diet intervention altered the gut microbiome of the study participants, including increases in known fiber-degrading microbes, such as Bifidobacterium and Lactobacillus.

Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life

Infant gut microbiota development affects the host physiology throughout life, and short-chain fatty acids (SCFAs) are promising key metabolites mediating microbiota-host relationships. Here, we investigated dense longitudinally collected faecal samples from 12 subjects during the first 2 years (n = 1048) to identify early life gut SCFA patterns and their relationships with the microbiota. Our results revealed three distinct phases of progression in the SCFA profiles: early phase characterised by low acetate and high succinate, middle-phase characterised by high lactate and formate and late-phase characterised by high propionate and butyrate. Assessment of the SCFA-microbiota relationships revealed that faecal butyrate is associated with increased Clostridiales and breastfeeding cessation, and that diverse and personalised assemblage of Clostridiales species possessing the acetyl-CoA pathway play major roles in gut butyrate production. We also found an association between gut formate and some infant-type bifidobacterial species, and that human milk oligosaccharides (HMO)-derived fucose is the substrate for formate production during breastfeeding. We identified genes upregulated in fucose and fucosylated HMO utilisation in infant-type bifidobacteria. Notably, bifidobacteria showed interspecific and intraspecific variation in the gene repertoires, and cross-feeding of fucose contributed to gut formate production. This study provides an insight into early life SCFA-microbiota relationships, which is an important step for developing strategies for modulating lifelong health.

Microbial Metabolites in Colorectal Cancer: Basic and Clinical Implications

Colorectal cancer (CRC) is one of the leading cancers that cause cancer-related deaths worldwide. The gut microbiota has been proved to show relevance with colorectal tumorigenesis through microbial metabolites. By decomposing various dietary residues in the intestinal tract, gut microbiota harvest energy and produce a variety of metabolites to affect the host physiology. However, some of these metabolites are oncogenic factors for CRC. With the advent of metabolomics technology, studies profiling microbiota-derived metabolites have greatly accelerated the progress in our understanding of the host-microbiota metabolism interactions in CRC. In this review, we briefly summarize the present metabolomics techniques in microbial metabolites researches and the mechanisms of microbial metabolites in CRC pathogenesis, furthermore, we discuss the potential clinical applications of microbial metabolites in cancer diagnosis and treatment.

Biomarkers of human gut microbiota diversity and dysbiosis

The association of gut microbiota dysbiosis with various human diseases is being substantiated with increasing evidence. Metabolites derived from both, microbiota and the human host play a central role in disease susceptibility and disease progression by extensively modulating host physiology and metabolism. Several of these metabolites have the potential to serve as diagnostic biomarkers for monitoring disease states in conjunction with intestinal microbiota dysbiosis. In this narrative review we evaluate the potential of trimethylamine-N-oxide, short-chain fatty acids, 3-indoxyl sulfate, p-cresyl sulfate, secondary bile acids, hippurate, human β-defensin-2, chromogranin A, secreted immunoglobulins and zonulin to serve as biomarkers for metabolite profiling and diagnostic suitability for dysbiosis and disease.

Diet and cancer risk reduction: The role of diet-microbiota interactions and microbial metabolites

According to recent estimates, over one third of the human population will be diagnosed with cancer at some point in their lifetime. While genetic factors play a large part in cancer risk, as much as 50 % of cancers may be preventable through various lifestyle modifications. Nutrition is a major modifiable risk factor, both through its impacts on obesity as well as through dietary chemical exposures that can either increase or decrease cancer risk. However, specific associations and mechanistic links between diet and cancer risk are either inconsistent or elusive. New insights regarding the reciprocal interactions between diet and the gut microbiota, the trillions of organisms that reside in our intestines, may help clarify how diet impacts cancer. The gut microbiota is largely shaped by an individual's diet and has far-reaching effects on metabolism, the immune system, and inflammation- important factors in the development and progression of various cancers. Likewise, the microbiota modifies dietary components, and consequently, exposure to metabolites that can influence cancer. This review explores some of these diet-microbiota interactions in the context of their potential impacts on cancer prevention.

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability.IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

IPCO: Inference of Pathways from Co-variance analysis

Background

Key aspects of microbiome research are the accurate identification of taxa and the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundance of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools that can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition are essential. At present, the applicability of these tools is limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset.

Results

IPCO outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological associations between shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools showed significantly lower performances with predicted functionality showing little to no correlation with paired shotgun features across samples.

Conclusions

IPCO can infer functionality from 16S datasets and significantly outperforms existing tools. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.

Inulin alleviates inflammation of alcoholic liver disease via SCFAs-inducing suppression of M1 and facilitation of M2 macrophages in mice

BACKGROUND

Alcoholic liver disease (ALD) presents one of the leading causes of cirrhosis worldwide. We have demonstrated that inulin alleviates ALD in mice. However, the exact role of hepatic macrophages in effects of inulin on ALD remains largely unclear.

METHODS

In vivo, mice were divided into 4 groups: pair-fed (PF) group (PF/CON), alcohol-fed (AF) group (AF/CON), PF with inulin (INU) group (PF/INU) and AF with INU group (AF/INU). Each group was fed modified Lieber-DeCarli liquid diet with or without alcohol. In vitro, RAW264.7 cell lines were polarized to M1 macrophage (Mψ) or M2 Mψ subsets with lipopolysaccharide (LPS) or interleukin-4 (IL-4) stimulation, respectively. The effects of propionate, butyrate and valeric on macrophage M1/M2 were investigated.

RESULTS

The contents of propionate, butyrate and valeric were significantly increased in AF/INU group compared with that in the AF/CON group. M1 Mψ, inducible nitric oxide synthase (iNOS) and tumor necrosis factor-α (TNF-α) in AF/INU group were significantly lower than those in AF/CON group. In contrast, M2 Mψ, arginase-1 (Arg-1), and interleukin-10 (IL-10) were notably increased in AF/INU group. In vitro, sodium propionate, sodium butyrate and sodium valerate can suppress M1 Mψ and increase M2 Mψ polarization.

CONCLUSION

In ALD, inulin ameliorates the inflammation via SCFAs-inducing suppression of M1 and facilitation of M2 Mψ, which may potentially contribute to the control of the disease.