Gene expression profiling gut microbiota in different races of humans

Comparison of gut microbiota profiles between patients suffering from elderly frailty syndrome and non-frail elderly individuals

BACKGROUND

Frailty syndrome is a state of increased vulnerability to stressors, marked by lowered physical strength and increased dependence on others. The well-established changes in gut microbiota associated with old age suggest a probable relationship between gut microbiota and frailty.

METHODS AND RESULTS

This study was aimed at finding the relationship between gut microbiota and frailty syndrome, by comparing the sociodemographic data and the gut microbiota profiles of 23 non-frail and 14 frail elderly individuals. We used the quantitative polymerase chain reaction method (qPCR) to determine the bacterial loads of Bifidobacteria, Lactobacillus, Bacteroidetes, Prevotella, and Escherichia coli in stool samples from test subjects. We discovered a significant increase in the bacterial load of Prevotella in frail elderly individuals aged 70 or above. Other bacterial loads and ratios were not significantly different between the two groups.

CONCLUSIONS

More comprehensive studies with larger sample sizes and encompassing a wider range of inflammation-related bacteria need to be performed to discover the existence and exact nature of these relations.

Effect of potassium-competitive acid blockers on human gut microbiota: a systematic review and meta-analysis

Background: Vonoprazan has been reported to exert more potent and long-lasting gastric acid inhibition than proton pump inhibitors, potentially leading to a greater impact on the gut microbiota. This study aimed to clarify changes in microbial diversity and bacterial composition after VPZ treatments. Methods: We searched from PubMed, Embase, WOS, Scopus, Cochrane Library, and ClinicalTrials.gov (all years up to May 2023). The primary outcomes were alpha and beta diversity, as well as differences in gut microbiota composition between before and after VPZ treatments. We performed a meta-analysis to uncover the potential changes in human gut microbiota among VPZ users by pooled mean difference (MD) with a 95% confidence interval (CI). The risk of bias was assessed using the ROBINS-I tool. Results: A total of 12 studies were included to compare differences before and after VPZ treatments. Compared with baseline, alpha diversity was significantly reduced after VPZ treatments and gradually returned to baseline with longer follow-up. At the phylum level, there was a decrease in the relative abundance of Firmicutes and Actinobacteria, while Bacteroidetes increased compared with baseline. At the genus level, we found a significant decrease in the relative abundance of Coprococcus and Bifidobacterium and a significant increase in the relative abundance of Bacteroides compared with those before treatment. In subgroup analyses according to country and participants, we found differences in microbial changes after VPZ treatments. Conclusion: Vonoprazan can affect the changes of gut microbiota, which may be potentially associated with its strong ability of acid inhibition. However, due to the large heterogeneity, further studies are required to validate these findings. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023412265.

Association between Mild Cognitive Impairment and Gut Microbiota in Elderly Korean Patients

Recent studies have confirmed that gut microbiota differs according to race or country in many diseases, including mild cognitive impairment (MCI) and Alzheimer's disease. However, no study has analyzed the characteristics of Korean MCI patients. This study was performed to observe the association between gut microbiota and MCI in the Korean elderly and to identify potential markers for Korean MCI patients. For this purpose, we collected fecal samples from Korean subjects who were divided into an MCI group (n = 40) and control group (n = 40) for 16S rRNA gene amplicon sequencing. Although no significant difference was observed in the overall microbial community profile, the relative abundance of several genera, including Bacteroides, Prevotella, and Akkermansia, showed significant differences between the two groups. In addition, the relative abundance of Prevotella was negatively correlated with that of Bacteroides (r = 0.733). This study may provide Korean-specific basic data for comparing the characteristics of the gut microbiota between Korean and non-Korean MCI patients.

Emerging Relationship between the Gut Microbiome and Prostate Cancer

The human gut microbiota changes under the influence of environmental and genetic factors, affecting human health. Extensive studies have revealed that the gut microbiome is closely associated with many non-intestinal diseases. Among these, the influence of the gut microbiome on cancer biology and the efficacy of cancer therapy has attracted much attention. Prostate cancer cells are affected by direct contact with the microbiota of local tissues and urine, and a relationship between prostate cancer cells and the gut microbiota has been suggested. In the human gut microbiota, bacterial composition differs depending on prostate cancer characteristics, such as histological grade and castration resistance. Moreover, the involvement of several intestinal bacteria in testosterone metabolism has been demonstrated, suggesting that they may affect prostate cancer progression and treatment through this mechanism. Basic research indicates that the gut microbiome also plays an important role in the underlying biology of prostate cancer through multiple mechanisms owing to the activity of microbial-derived metabolites and components. In this review, we describe the evidence surrounding the emerging relationship between the gut microbiome and prostate cancer, termed the "gut-prostate axis."

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Association of Habitual Intake of Probiotic Supplements and Yogurt with Characteristics of the Gut Microbiome in the Multiethnic Cohort Adiposity Phenotype Study

Consumption of probiotics and/or yogurt could be a solution for restoring the balance of the gut microbiota. This study examined associations of regular intake of probiotic supplements or yogurt with the gut microbiota among a diverse population of older adults (N=1,861; 60-72 years). Fecal microbial composition was obtained from 16S rRNA gene sequencing (V1-V3 region). General Linear Models were used to estimate the associations of probiotic supplement or yogurt intake with microbiome measures adjusting for covariates. Compared to non-yogurt consumers (N=1,023), regular yogurt consumers (≥once/week, N=818) had greater Streptococcus (β=0.29, P=0.0003) and lower Odoribacter (β=-0.33, P<0.0001) abundance. The directions of the above associations were consistent across the five ethnic groups but stronger among Japanese Americans (Streptococcus: β=0.56, P=0.0009; Odoribacter: β=-0.62, P=0.0005). Regular intake of probiotic supplements (N=175) was not associated with microbial characteristics (i.e., alpha diversity and the abundance of 152 bacteria genera). Streptococcus is one of the predominant bacteria genera in yogurt products, which may explain the positive association between yogurt consumption and Streptococcus abundance. Our analyses suggest that changes in Odoribacter were independent of changes in Streptococcus abundance. Future studies may investigate whether these microbial genera and their sub-level species mediate potential pathways between yogurt consumption and health.

Identifying microbial signatures for patients with postmenopausal osteoporosis using gut microbiota analyses and feature selection approaches

Osteoporosis (OP) is a metabolic bone disorder characterized by low bone mass and deterioration of micro-architectural bone tissue. The most common type of OP is postmenopausal osteoporosis (PMOP), with fragility fractures becoming a global burden for women. Recently, the gut microbiota has been connected to bone metabolism. The aim of this study was to characterize the gut microbiota signatures in PMOP patients and controls. Fecal samples from 21 PMOP patients and 37 controls were collected and analyzed using amplicon sequencing of the V3-V4 regions of the 16S rRNA gene. The bone mineral density (BMD) measurement and laboratory biochemical test were performed on all participants. Two feature selection algorithms, maximal information coefficient (MIC) and XGBoost, were employed to identify the PMOP-related microbial features. Results showed that the composition of gut microbiota changed in PMOP patients, and microbial abundances were more correlated with total hip BMD/T-score than lumbar spine BMD/T-score. Using the MIC and XGBoost methods, we identified a set of PMOP-related microbes; a logistic regression model revealed that two microbial markers (Fusobacteria and Lactobacillaceae) had significant abilities in disease classification between the PMOP and control groups. Taken together, the findings of this study provide new insights into the etiology of OP/PMOP, as well as modulating gut microbiota as a therapeutic target in the diseases. We also highlight the application of feature selection approaches in biological data mining and data analysis, which may improve the research in medical and life sciences.

Effects of Silage Diet on Meat Quality through Shaping Gut Microbiota in Finishing Pigs

With increasing demand for high-quality pork, development of green and healthy feed for finishing pigs is urgently needed. In this study, the effects and mechanisms of mulberry and paper mulberry silages on growth performance, meat quality, and intestinal health of finishing pigs were explored. Intestinal microbiota were profiled, and microbially produced short-chain fatty acids (SCFAs) were measured. The average daily gain (ADG) and feed conversion rate (FCR) with mulberry and paper mulberry silages were not significantly different from those of the control. Meat quality as measured by pork marbling and fatty acids in the longissimus dorsi was better with mulberry silage. The highest concentration of SCFAs was also with mulberry silage. According to 16S rRNA sequencing, Clostridium_sensu_stricto_1, Terrisporobacter, and Lachnospiraceae, which are important in SCFA production, were biomarkers of mulberry silage. PICRUSt functional analysis of intestinal microbes indicated that galactose metabolism, starch and sucrose metabolism, and carbohydrate digestion and absorption decreased significantly in silage treatments but increased in the control. Correlations between intestinal microbes and SCFAs and fatty acids indicated Clostridium_sensu_stricto_1, Terrisporobacter, and Lachnospiraceae were closely associated with SCFA and fatty acid contents. The results indicated that mulberry silage could increase SCFA content through shaping intestinal microbes to affect the deposition of fatty acids, which laid a solid theoretical foundation for improving pork quality. IMPORTANCE To avoid competition between people and animals for food, it is essential to develop nontraditional feeds. In this study, the effects of the silages of the unconventional feed resources mulberry and paper mulberry on meat quality of finishing pigs were examined. With mulberry silage in the diet, meat quality improved as indicated by meat color, marbling score, and beneficial fatty acids in the longissimus dorsi muscle. Pigs fed mulberry silage had the highest concentrations of short-chain fatty acids (SCFAs), and 16S rRNA sequencing identified Clostridium_sensu_stricto_1, Terrisporobacter, and Lachnospiraceae as biomarkers, which are important in SCFA production. Functions of intestinal microbes in the two silage groups primarily involved amino acid metabolism and SCFA production. Correlations between intestinal microbes and SCFAs and fatty acids indicated that Clostridium_sensu_stricto-1, Terrisporobacter, and Lachnospiraceae were closely associated with SCFA contents in the intestine and fatty acids in the longissimus dorsi.

Trait-Based Method of Quantitative Assessment of Ecological Functional Groups in the Human Intestinal Microbiome

We propose the trait-based method for quantifying the activity of functional groups in the human gut microbiome based on metatranscriptomic data. It allows one to assess structural changes in the microbial community comprised of the following functional groups: butyrate-producers, acetogens, sulfate-reducers, and mucin-decomposing bacteria. It is another way to perform a functional analysis of metatranscriptomic data by focusing on the ecological level of the community under study. To develop the method, we used published data obtained in a carefully controlled environment and from a synthetic microbial community, where the problem of ambiguity between functionality and taxonomy is absent. The developed method was validated using RNA-seq data and sequencing data of the 16S rRNA amplicon on a simplified community. Consequently, the successful verification provides prospects for the application of this method for analyzing natural communities of the human intestinal microbiota.

Proton pump inhibitor usage associates with higher risk of first episodes of pneumonia and peritonitis in peritoneal dialysis patients

Background

A large number of studies have shown that proton pump inhibitors (PPIs) are associated with infection events. Therefore, we retrospectively evaluated the association of PPI therapy with the occurrence of first pneumonia and peritoneal dialysis(PD)-related peritonitis events in the maintenance PD patients.

Methods

We collected PD patients in two large hospitals from January 1, 2012 to December 31, 2016, and divided them into the PPI group and the non-PPI group. Multivariate Cox proportional hazards models were applied to evaluate the cumulative incidence and hazard ratios (HRs). Inverse probability of treatment weight (IPTW) method was used to adjust for covariate imbalance between the two groups and further confirm our findings.

Results

Finally, 656 PD patients were included for data analysis, and the results showed that PPI usage was associated with an increased risk of pneumonia [HR 1.71; 95% CI 1.06-2.76; p = 0.027] and peritonitis [HR 1.73; 95% CI 1.24-2.40; p = 0.001]. IPTW-adjusted HRs for the association of PPIs with pneumonia and peritonitis were 1.58 (95% CI:1.18-2.12; p = 0.002) and 2.33 (95% CI:1.91-2.85; p < 0.001), respectively. Moreover, the competitive risk model proved that under the conditions of competition for other events(including transfer to hemodialysis therapy, kidney transplant, transfer from our research center, loss to follow-up, and death), the differences in endpoints events between the two groups were still statistically significant (p = 0.009, p < 0.001, respectively).

Conclusions

PPIs was associated with an increased risk of first pneumonia and PD-related peritonitis events in PD patients, which reminds clinicians to be cautious when prescribing acid-suppressing drugs for PD patients.

Characteristics of the Gut Bacterial Composition in People of Different Nationalities and Religions

High-throughput sequencing has made it possible to extensively study the human gut microbiota. The links between the human gut microbiome and ethnicity, religion, and race remain rather poorly understood. In this review, data on the relationship between gut microbiota composition and the nationality of people and their religion were generalized. The unique gut microbiome of a healthy European (including Slavic nationality) is characterized by the dominance of the phyla Firmicutes, Bacteroidota, Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia. Among the African population, the typical members of the microbiota are Bacteroides and Prevotella. The gut microbiome of Asians is very diverse and rich in members of the genera Prevotella, Bacteroides Lactobacillus, Faecalibacterium, Ruminococcus, Subdoligranulum, Coprococcus, Collinsella, Megasphaera, Bifidobacterium, and Phascolarctobacterium. Among Buddhists and Muslims, the Prevotella enterotype is characteristic of the gut microbiome, while other representatives of religions, including Christians, have the Bacteroides enterotype. Most likely, the gut microbiota of people of different nationalities and religions are influenced by food preferences. The review also considers the influences of pathologies such as obesity, Crohn’s disease, cancer, diabetes, etc., on the bacterial composition of the guts of people of different nationalities.

Gut microbiota alteration and modulation in hepatitis B virus-related fibrosis and complications: Molecular mechanisms and therapeutic inventions

Hepatitis B virus (HBV) has posed a threat to public health, mainly resulting in liver damage. With long-term accumulation of extracellular matrix, patients with chronic hepatitis B are at high risk of developing into liver fibrosis and cirrhosis and even life-threatening hepatic carcinoma. The occurrence of complications such as spontaneous bacterial peritonitis and hepatic encephalopathy greatly increases disability and mortality. With deeper understanding of the bidirectional interaction between the liver and the gut (gut-liver axis), there is a growing consensus that the human health closely relates to the gut microbiota. Supported by animal and human studies, the gut microbiota alters as the HBV-related liver fibrosis initials and progresses, characterized as the decrease of the ratio between “good” and “potentially pathogenic” microbes. When the primary disease is controlled via antiviral treatment, the gut microbiota dysfunction tends to be improved. Conversely, the recovery of gut microbiota can promote the regression of liver fibrosis. Therapeutic strategies targeted on gut microbiota (rifaximin, probiotics, engineered probiotics and fecal microbiota transplantation) have been applied to animal models and patients, obtaining satisfactory results.

Phenylacetylglutamine as a risk factor and prognostic indicator of heart failure

AIMS

To explore the associations between serum phenylacetylglutamine (PAGln) and chronic heart failure (HF).

METHODS AND RESULTS

Totally 956 subjects were enrolled consecutively from the Department of Cardiovascular Medicine, Ruijin Hospital. Baseline data were obtained from all participants, and 471 stable chronic HF subjects were followed up. Serum PAGln was analysed by liquid chromatography-tandem mass spectrometry. The association between PAGln and basic renal indicators was assessed by simple correlation analysis. Logistic regression analysis was conducted to measure the association between PAGln and HF risk. Event-free survival was determined by Kaplan-Meier curves, and differences in survival were assessed using log-rank tests. Cox proportional hazards analysis was used to assess the prognostic value of PAGln in HF. Serum PAGln levels were increased in patients with chronic HF (3.322 ± 8.220 μM vs. 1.249 ± 1.168 μM, P < 0.001) and were associated with HF after full adjustment [odds ratio (OR), 1.507; 95% confidence interval (CI): 1.213-1.873; P < 0.001]. PAGln levels were correlated with the levels of basic renal indicators. High PAGln levels indicated a high risk of renal dysfunction in HF (OR: 1.853; 95% CI: 1.344-2.556; P < 0.001), and elevated PAGln levels were associated with a high risk of cardiovascular death in patients with chronic HF (HR: 2.049; 95% CI: 1.042-4.029; P = 0.038).

CONCLUSIONS

Elevated PAGln levels are an independent risk factor for HF and are associated with a higher risk of cardiovascular death. High PAGln levels could indicate renal dysfunction in HF patients. PAGln can be a valuable indicator of HF.

Gut Dysbiosis Associated with Antibiotics and Disease Severity and Its Relation to Mortality in Critically Ill Patients

BACKGROUND

The gut microbiota are reported to be altered in critical illness. The pattern and impact of dysbiosis on prognosis has not been thoroughly investigated in the ICU setting.

AIMS

We aimed to evaluate changes in the gut microbiota of ICU patients via 16S rRNA gene deep sequencing, assess the association of the changes with antibiotics use or disease severity, and explore the association of gut microbiota changes with ICU patient prognosis.

METHODS

Seventy-one mechanically ventilated patients were included. Fecal samples were collected serially on days 1-2, 3-4, 5-7, 8-14, and thereafter when suitable. Microorganisms of the fecal samples were profiled by 16S rRNA gene deep sequencing.

RESULTS

Proportions of the five major phyla in the feces were diverse in each patient at admission. Those of Bacteroidetes and Firmicutes especially converged and stabilized within the first week from admission with a reduction in α-diversity (p < 0.001). Significant differences occurred in the proportional change of Actinobacteria between the carbapenem and non-carbapenem groups (p = 0.030) and that of Actinobacteria according to initial SOFA score and changes in the SOFA score (p < 0.001). An imbalance in the ratio of Bacteroidetes to Firmicutes within seven days from admission was associated with higher mortality when the ratio was > 8 or < 1/8 (odds ratio: 5.54, 95% CI: 1.39-22.18, p = 0.015).

CONCLUSIONS

Broad-spectrum antibiotics and disease severity may be associated with gut dysbiosis in the ICU. A progression of dysbiosis occurring in the gut of ICU patients might be associated with mortality.

Gut microbiota as prognosis markers for patients with HBV-related acute-on-chronic liver failure

The gut microbiota in the hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF) is poorly defined. We aim to uncover the characteristics of the gut microbiota in HBV-ACLF and in other HBV associated pathologies. We analyzed the gut microbiome in patients with HBV-ACLF or other HBV associated pathologies and healthy individuals by 16S rRNA sequencing and metagenomic sequencing of fecal samples. 212 patients with HBV-ACLF, 252 with chronic hepatitis B (CHB), 162 with HBV-associated cirrhosis (HBV-LC) and 877 healthy individuals were recruited for the study. CHB and HBV-LC patients are grouped as HBV-Other. We discovered striking differences in the microbiome diversity between the HBV-ACLF, HBV-Other and healthy groups using 16S rRNA sequencing. The ratio of cocci to bacilli was significantly elevated in the HBV-ACLF group compared with healthy group. Further analysis within the HBV-ACLF group identified 52 genera showing distinct richness within the group where Enterococcus was enriched in the progression group whilst Faecalibacterium was enriched in the regression group. Metagenomic sequencing validated these findings and further uncovered an enrichment of Lactobacillus casei paracasei in progression group, while Alistipes senegalensis, Faecalibacterium prausnitzii and Parabacteroides merdae dominated the regression group. Importantly, our analysis revealed that there was a rapid increase of Enterococcus faecium during the progression of HBV-ACLF. The gut microbiota displayed distinct composition at different phases of HBV-ACLF. High abundance of Enterococcus is associated with progression while that of Faecalibacterium is associated with regression of HBV-ACLF. Therefore, the microbiota features hold promising potential as prognostic markers for HBV-ACLF.

Reporting guidelines for human microbiome research: the STORMS checklist

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Spinal cord injury in mice impacts central and peripheral pathology in a severity-dependent manner

ABSTRACT

Chronic pain is a common medical complication experienced by those living with spinal cord injury (SCI) and leads to worsened quality of life. The pathophysiology of SCI pain is poorly understood, hampering the development of safe and efficacious therapeutics. We therefore sought to develop a clinically relevant model of SCI with a strong pain phenotype and characterize the central and peripheral pathology after injury. A contusion (50 kdyn) injury, with and without sustained compression (60 seconds) of the spinal cord, was carried out on female C57BL/6J mice. Mice with compression of the spinal cord exhibited significantly greater heat and mechanical hypersensitivity starting at 7 days post-injury, concomitant with reduced locomotor function, compared to those without compression. Immunohistochemical analysis of spinal cord tissue revealed significantly less myelin sparing and increased macrophage activation in mice with compression compared to those without. As measured by flow cytometry, immune cell infiltration and activation were significantly greater in the spinal cord (phagocytic myeloid cells and microglia) and dorsal root ganglia (Ly6C+ monocytes) following compression injury. We also decided to investigate the gastrointestinal microbiome, as it has been shown to be altered in SCI patients and has recently been shown to play a role in immune system maturation and pain. We found increased dysbiosis of the gastrointestinal microbiome in an injury severity-dependent manner. The use of this contusion-compression model of SCI may help advance the preclinical assessment of acute and chronic SCI pain and lead to a better understanding of mechanisms contributing to this pain.

The gut microbiota associated with high-Gleason prostate cancer

We have found that intestinal bacteria and their metabolites, short-chain fatty acids (SCFAs), promote cancer growth in prostate cancer (PCa) mouse models. To clarify the association between gut microbiota and PCa in humans, we analyzed the gut microbiota profiles of men with suspected PCa. One hundred and fifty-two Japanese men undergoing prostate biopsies (96 with cancer and 56 without cancer) were included in the study and randomly divided into two cohorts: a discovery cohort (114 samples) and a test cohort (38 samples). The gut microbiota was compared between two groups, a high-risk group (men with Grade group 2 or higher PCa) and a negative + low-risk group (men with negative biopsy or Grade group 1 PCa), using 16S rRNA gene sequencing. The relative abundances of Rikenellaceae, Alistipes, and Lachnospira, all SCFA-producing bacteria, were significantly increased in high-risk group. In receiver operating characteristic curve analysis, the index calculated from the abundance of 18 bacterial genera which were selected by least absolute shrinkage and selection operator regression detected high-risk PCa in the discovery cohort with higher accuracy than the prostate specific antigen test (area under the curve [AUC] = 0.85 vs 0.74). Validation of the index in the test cohort showed similar results (AUC = 0.81 vs 0.67). The specific bacterial taxa were associated with high-risk PCa. The gut microbiota profile could be a novel useful marker for the detection of high-risk PCa and could contribute to the carcinogenesis of PCa.

Ectopic gut colonization: a metagenomic study of the oral and gut microbiome in Crohn's disease

BACKGROUND

This study aims to characterize, the gut and oral microbiome in Asian subjects with Crohn's disease (CD) using whole genome shotgun sequencing, thereby allowing for strain-level comparison.

METHODS

A case-control study with age, sex and ethnicity matched healthy controls was conducted. CD subjects were limited to well-controlled patients without oral manifestations. Fecal and saliva samples were collected for characterization of gut and oral microbiome respectively. Microbial DNA were extracted, libraries prepared and sequenced reads profiled. Taxonomic diversity, taxonomic association, strain typing and microbial gene pathway analyses were conducted.

RESULTS

The study recruited 25 subjects with CD and 25 healthy controls. The oral microbe Streptococcus salivarius was found to be enriched and of concordant strains in the gut and oral microbiome of Crohn's disease subjects. This was more likely in CD subjects with higher Crohn's Disease Activity Index (184.3 ± 2.9 vs 67.1 ± 82.5, p = 0.012) and active disease status (Diarrhoea/abdominal pain/blood-in-stool/fever and fatigue) (p = 0.016). Gut species found to be significantly depleted in CD compared to control (Relative abundance: Median[Range]) include: Faecalibacterium prausnitzii (0.03[0.00-4.56] vs 13.69[5.32-18.71], p = 0.010), Roseburia inulinivorans (0.00[0.00-0.03] vs 0.21[0.01-0.53], p = 0.010) and Alistipes senegalensis (0.00[0.00-0.00] vs 0.00[0.00-0.02], p = 0.029). While Clostridium nexile (0.00[0.00-0.12] vs 0.00[0.00-0.00], p = 0.038) and Ruminococcus gnavus (0.43[0.02-0.33] vs 0.00[0.00-0.13], p = 0.043) were found to be enriched. C. nexile enrichment was not found in CD subjects of European descent. Microbial arginine (Linear-discriminant-analysis: 3.162, p = 0.001) and isoprene (Linear-discriminant-analysis: 3.058, p < 0.001) pathways were found at a higher relative abundance level in gut microbiome of Crohn's disease.

CONCLUSIONS

There was evidence of ectopic gut colonization by oral bacteria, especially during the active phase of CD. Previously studied gut microbial differences were detected, in addition to novel associations which could have resulted from geographical/ethnic differences to subjects of European descent. Differences in microbial pathways provide possible targets for microbiome modification.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Differences in the gut microbiota between young and elderly persons in Korea

The gut microbiota differs among countries owing to the prevailing diet composition. For the characterization of the gut microbiota of Koreans at different ages in future studies, e.g., in an in vitro human digestion model, we tried to investigate whether the gut microbiota differs between the young and elderly in Korea. Two hundred fecal samples were collected: 100 from elderly people (over 65 years old) at geriatric nursing hospitals and 100 from young people (university students, 20-25 years old) in Gyeonggi province, Korea. The composition of the gut microbiota in these fecal samples was analyzed by next-generation sequencing methods. There were significant differences in the taxonomic composition of the microbiota (the top 10 most abundant taxa) between the young and elderly people in Korea, especially in terms of relative abundance levels of bacteria in phyla Firmicutes, Proteobacteria, Tenericutes, and Fusobacteria (P < 001). The gut microbiota of young people contained higher relative abundance of Lactobacillus than did the microbiota of elderly people, while the microbiota of elderly people manifested higher relative abundance of Escherichia. Even though the sample size may not be large enough for this study to be representative of the entire population of Korea, the study still provides data that are suggestive of differences in the gut microbiota between young and elderly people in Korea. Furthermore, our findings may be applied to develop an improved age-based in vitro model of digestion of Koreans for future research.

The gut-brain axis and beyond: Microbiome control of spinal cord injury pain in humans and rodents

Spinal cord injury (SCI) is a devastating injury to the central nervous system in which 60 to 80% of patients experience chronic pain. Unfortunately, this pain is notoriously difficult to treat, with few effective options currently available. Patients are also commonly faced with various compounding injuries and medical challenges, often requiring frequent hospitalization and antibiotic treatment. Change in the gut microbiome from the "normal" state to one of imbalance, referred to as gut dysbiosis, has been found in both patients and rodent models following SCI. Similarities exist in the bacterial changes observed after SCI and other diseases with chronic pain as an outcome. These changes cause a shift in the regulation of inflammation, causing immune cell activation and secretion of inflammatory mediators that likely contribute to the generation/maintenance of SCI pain. Therefore, correcting gut dysbiosis may be used as a tool towards providing patients with effective pain management and improved quality of life.

Characterization of the gut microbiota in frail elderly patients

BACKGROUND

The change in the composition of gut microbiota has been reported in the elderly and in the frail individuals; however, studies on gut microbiota in frail elderly are limited.

AIMS

This study aimed to investigate the gut microbiota of the frail elderly.

METHODS

From September 2017 to February 2018, 27 elderly patients hospitalized in the Department of Geriatrics of our hospital were enrolled and divided into the frailty group (n = 15) and the control group (n = 12) based on the cutoff of 0.25 for the frailty index. The fecal samples were collected for 16S rRNA-amplicon sequencing to analyze the composition and richness of gut microbiota. Operational taxonomic unit (OTU) clustering was performed using Usearch software. Intra-sample diversity (alpha-diversity) analysis and inter-sample diversity (beta-diversity) analyses were performed. The community richness was compared between the two groups at family and genus levels.

RESULTS

There were 1903 and 1880 OTUs identified in the control and frailty groups, respectively, with 1282 OTUs overlap between the two groups. The alpha diversity of microbiota community was similar between the two groups, whereas the frailty group had larger beta diversity than the control group. The top-10 taxonomy categories and abundances of gut microbiota between the two groups were similar. As for the gut microbiota composition, 4 families and 17 genera were significantly different between the two groups (p < 0.05).

CONCLUSION

These results suggested that frailty can affect gut microbiota diversity and compositions in late elderly hospitalized patients.

Diet and gut microbiome interactions in gynecologic cancer

Over the last decade, there has been a dramatic surge in research exploring the human gut microbiome and its role in health and disease. It is now widely accepted that commensal microorganisms coexist within the human gastrointestinal tract and other organs, including those of the reproductive tract. These microorganisms, which are collectively known as the "microbiome", contribute to maintaining host physiology and to the development of pathology. Next generation sequencing and multi-'omics' technology has enriched our understanding of the complex and interdependent relationship that exists between the host and microbiome. Global changes in the microbiome are known to be influenced by dietary, genetic, lifestyle, and environmental factors. Accumulating data have shown that alterations in the gut microbiome contribute to the development, prognosis and treatment of many disease states including cancer primarily through interactions with the immune system. However, there are large gaps in knowledge regarding the association between the gut microbiome and gynecologic cancers, and research characterizing the reproductive tract microbiome is insufficient. Herein, we explore the mechanisms by which alterations in the gut and reproductive tract microbiome contribute to carcinogenesis focusing on obesity, hyperestrogenism, inflammation and altered tumor metabolism. The impact of the gut microbiome on response to anti-cancer therapy is highlighted with an emphasis on immune checkpoint inhibitor efficacy in gynecologic cancers. We discuss dietary interventions that are likely to modulate the metabolic and immunologic milieu as well as tumor microenvironment through the gut microbiome including intermittent fasting/ketogenic diet, high fiber diet, use of probiotics and the metabolic management of obesity. We conclude that enhanced understanding of the microbiome in gynecologic cancers coupled with thorough evaluation of metabolic and metagenomic analyses would enable us to integrate novel preventative strategies and adjunctive interventions into the care of women with gynecologic cancers.

Changes in the faecal microbiota of horses and ponies during a two-year body weight gain programme

Obesity is a major health concern in many domesticated equids animals since it is related to metabolic abnormalities such as insulin dysregulation, hyperlipidaemia or laminitis. Ponies especially are known as "easy keepers" and are often affected by obesity and its related metabolic disorders. Research in the last decade indicated that the intestinal microbiota may play an important role in the development of obesity, at least in humans. Therefore, the objective of our study was to characterize changes in the faecal microbiota during a two-year weight gain programme which compared ponies and warmblood horses. For this purpose, 10 Shetland ponies and ten warmblood horses were fed a ration which provided 200% of their maintenance energy requirement over two years. Feed intake, body weight, body condition and cresty neck score were recorded weekly. At three standardized time points faecal samples were collected to characterize the faecal microbiota and its fermentation products such as short chain fatty acids and lactate. Next generation sequencing was used for the analysis of the faecal microbiota. During body weight gain the richness of the faecal microbiota decreased in ponies. Besides changes in the phylum Firmicutes in ponies that were already described in human studies, we found a decrease of the phylum Fibrobacteres in horses and an increase of the phylum Actinobacteria. We were also able to show that the phylum Fibrobacteres is more common in the microbiota of horses than in the microbiota of ponies. Therefore, the fibrolytic phylum Fibrobacteres seems to be an interesting phylum in the equine microbiota that should receive more attention in future studies.

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11–16% of lung adenocarcinomas (p.G12C accounts for 45–50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

Proton pump inhibitor use increases the risk of peritonitis in peritoneal dialysis patients

Peritonitis is a major and the most significant complication of peritoneal dialysis (PD). Although some predictors of peritonitis in PD patients are known, the association between proton pump inhibitor (PPI) use and peritonitis has not been characterized. Here, we examined whether PPI use is a risk factor for the development of peritonitis, based on a single-center retrospective analysis of 230 consecutive Japanese PD patients at Narita Memorial Hospital. We assessed the association between PPI use and subsequent first episode of peritonitis using multivariate Cox proportional hazards models, following adjustment for clinically relevant factors. The median follow-up period was 36 months (interquartile range, 19–57 months). In total, 86 patients (37.4%) developed peritonitis. Analysis with multivariate Cox proportional hazards models revealed the following significant predictors of peritonitis: PPI use (adjusted hazard ratio [HR] = 1.72, 95% confidence interval [CI]: 1.11–2.66; P = 0.016) and low serum albumin level (per g/dl adjusted HR = 0.59, 95% CI: 0.39–0.90; P = 0.014). Thus, PPI use was independently associated with PD-related peritonitis. The results suggest that nephrology physicians should exercise caution when prescribing PPIs for PD patients.

Alteration of gut microbiota by a Westernized lifestyle and its correlation with insulin resistance in non-diabetic Japanese men

AIMS/INTRODUCTION

The severity of insulin resistance is higher in Japanese-American people with American lifestyles than in native Japanese people with Japanese lifestyles. Recently, the role of gut microbiota in the control of host metabolic homeostasis and organ physiology has been recognized. In addition, gut microbiota alterations have been suggested to contribute to pathogenesis of insulin resistance. The principle aim of the present study was to evaluate the impact of a Westernized lifestyle on the gut microbiota of Japanese-Americans versus native Japanese, and its correlation with insulin resistance.

MATERIALS AND METHODS

A total of 14 native Japanese men living in Hiroshima, Japan, and 14 Japanese-American men living in Los Angeles, USA, were included. A 75-g oral glucose tolerance test was carried out for all participants to assess their glucose tolerance, and normal glucose tolerance was observed. We compared the insulin response with oral glucose load, the Matsuda Index, and the composition of the gut microbiota between the native Japanese and Japanese-American men.

RESULTS

Japanese-American men showed higher area under the curve values for serum insulin concentrations during the oral glucose tolerance test and lower Matsuda Index than native Japanese men. Gut microbiota composition of the Japanese-American men was different; in particular, they showed a relatively lower abundance of Odoribacter than native Japanese men. The ratio between relative abundance of Odoribacter and Matsuda Index was positively correlated between the two groups.

CONCLUSIONS

Our findings suggest that Westernized lifestyles alter gut microbiota, and its alteration might induce insulin resistance in non-diabetic Japanese men.

Race, the microbiome and colorectal cancer

In the past decade, more cancer researchers have begun to understand the significance of cancer prevention, which has prompted a shift in the increasing body of scientific literature. An area of fascination and great potential is the human microbiome. Recent studies suggest that the gut microbiota has significant roles in an individual's ability to avoid cancer, with considerable focus on the gut microbiome and colorectal cancer. That in mind, racial disparities with regard to colorectal cancer treatment and prevention are generally understudied despite higher incidence and mortality rates among Non-Hispanic Blacks compared to other racial and ethnic groups in the United States. A comprehension of ethnic differences with relation to colorectal cancer, dietary habits and the microbiome is a meritorious area of investigation. This review highlights literature that identifies and bridges the gap in understanding the role of the human microbiome in racial disparities across colorectal cancer. Herein, we explore the differences in the gut microbiota, common short chain fatty acids produced in abundance by microbes, and their association with racial differences in cancer acquisition.

The Gut Microbiome and Metabolome of Two Riparian Communities in the Amazon

During the last decades it has become increasingly clear that the microbes that live on and in humans are critical for health. The communities they form, termed microbiomes, are involved in fundamental processes such as the maturation and constant regulation of the immune system. Additionally, they constitute a strong defense barrier to invading pathogens, and are also intricately linked to nutrition. The parameters that affect the establishment and maintenance of these microbial communities are diverse, and include the genetic background, mode of birth, nutrition, hygiene, and host lifestyle in general. Here, we describe the characterization of the gut microbiome of individuals living in the Amazon, and the comparison of these microbial communities to those found in individuals from an urban, industrialized setting. Our results showed striking differences in microbial communities from these two types of populations. Additionally, we used high-throughput metabolomics to study the chemical ecology of the gut environment and found significant metabolic changes between the two populations. Although we cannot point out a single cause for the microbial and metabolic changes observed between Amazonian and urban individuals, they are likely to include dietary differences as well as diverse patterns of environmental exposure. To our knowledge, this is the first description of gut microbial and metabolic profiles in Amazonian populations, and it provides a starting point for thorough characterizations of the impact of individual environmental conditions on the human microbiome and metabolome.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC⁺ and LSC^- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC⁺ and LSC^- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

An Increased Abundance of Clostridiaceae Characterizes Arthritis in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Cross-sectional Study

BACKGROUND

Inflammatory bowel diseases (IBDs) are a group of heterogeneous inflammatory conditions affecting the gastrointestinal tract. Although there is considerable evidence linking the gut microbiota to intestinal inflammation, there is limited knowledge on its potential role in the development of extraintestinal manifestations of IBD.

METHODS

Four groups of patients were included: IBD-associated arthropathy (IBD-A); IBD without arthropathy (IBD-N); rheumatoid arthritis (RA); and non-IBD, nonarthritis controls. DNA from stool samples was isolated and sequenced using the Illumina platform. Paired-end reads were quality-controlled using SHI7 and processed with SHOGUN. Abundance and diversity analyses were performed using QIIME, and compositional biomarker identification was performed using LEfSe.

RESULTS

One hundred eighty patients were included in the analysis. IBD-A was associated with an increased abundance of microbial tyrosine degradation pathways when compared with IBD-N (P = 0.02), whereas IBD-A and RA patients both shared an increased abundance of Clostridiaceae when compared with controls (P = 0.045). We found that history of bowel surgery was a significant source of variability (P = 0.001) among all IBD patients and was associated with decreased alpha diversity and increased abundance of Enterobacteriaceae (P = 0.004).

CONCLUSIONS

An increased abundance of gut microbial tyrosine degradation pathways was associated with IBD-A. An increased abundance of Clostridiaceae was shared by both IBD-A and RA patients and suggests a potentially common microbial link for inflammatory arthritis. The increased abundance of Enterobacteriaceae, previously reported in IBD, may be due to the effects of previous bowel surgery and highlights the importance of controlling for this variable in future studies.

Computational Method for the Identification of Molecular Metabolites Involved in Cereal Hull Color Variations

Background:

Cereal hull color is an important quality specification characteristic. Many studies were conducted to identify genetic changes underlying cereal hull color diversity. However, these studies mainly focused on the gene level. Recent studies have suggested that metabolomics can accurately reflect the integrated and real-time cell processes that contribute to the formation of different cereal colors.

Methods:

In this study, we exploited published metabolomics databases and applied several advanced computational methods, such as minimum redundancy maximum relevance (mRMR), incremental forward search (IFS), random forest (RF) to investigate cereal hull color at the metabolic level. First, the mRMR was applied to analyze cereal hull samples represented by metabolite features, yielding a feature list. Then, the IFS and RF were used to test several feature sets, constructed according to the aforementioned feature list. Finally, the optimal feature sets and RF classifier were accessed based on the testing results.

Results and Conclusion:

A total of 158 key metabolites were found to be useful in distinguishing white cereal hulls from colorful cereal hulls. A prediction model constructed with these metabolites and a random forest algorithm generated a high Matthews coefficient correlation value of 0.701. Furthermore, 24 of these metabolites were previously found to be relevant to cereal color. Our study can provide new insights into the molecular basis of cereal hull color formation.

Prediction of Drug Combinations with a Network Embedding Method

Aim and Objective:

There are several diseases having a complicated mechanism. For such complicated diseases, a single drug cannot treat them very well because these diseases always involve several targets and single targeted drugs cannot modulate these targets simultaneously. Drug combination is an effective way to treat such diseases. However, determination of effective drug combinations is time- and cost-consuming via traditional methods. It is urgent to build quick and cheap methods in this regard. Designing effective computational methods incorporating advanced computational techniques to predict drug combinations is an alternative and feasible way.

Method:

In this study, we proposed a novel network embedding method, which can extract topological features of each drug combination from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. These topological features were combined with individual features of drug combination reported in one previous study. Several advanced computational methods were employed to construct an effective prediction model, such as synthetic minority oversampling technique (SMOTE) that was used to tackle imbalanced dataset, minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS) methods that were adopted to analyze features and extract optimal features for building an optimal support machine vector (SVM) classifier.

Results and Conclusion:

The constructed optimal SVM classifier yielded an MCC of 0.806, which is superior to the classifier only using individual features with or without SMOTE. The performance of the classifier can be improved by combining the topological features and essential features of a drug combination.

Metagenomic analyses of the gut microbiota associated with colorectal adenoma

Recent studies have suggested an association between certain members of the Fusobacterium genus, especially F. nucleatum, and the progression of advanced colorectal carcinoma (CRC). We assessed such an association of the gut microbiota in Japanese patients with colorectal adenoma (CRA) or intramucosal CRC using colonoscopy aspirates. We analyzed samples from 81 Japanese patients, including 47 CRA and 24 intramucosal CRC patients, and 10 healthy subjects. Metagenomic analysis of the V3-V4 region of the 16S ribosomal RNA gene was performed. The linear discriminant analysis (LDA) effect size (LEfSe) method was used to examine microbial dysbiosis, revealing significant differences in bacterial abundances between the healthy controls and CRA or intramucosal CRC patients. In particular, F. varium was statistically more abundant in patients with CRA and intramucosal CRC than in healthy subjects. Here, we present the metagenomic profile of CRA and intramucosal CRC and demonstrate that F. varium is at least partially involved in the pathogenesis of CRA and intramucosal CRC.

Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores

AIM AND OBJECTIVE

Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia.

METHODS

In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways.

RESULTS

Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia.

CONCLUSION

This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.

Intestinal bacteria detected in cancer and adjacent tissue from patients with colorectal cancer

Intestinal bacteria are symbiotic microbiota within the human gut and are implicated in the occurrence and development of colorectal cancer (CRC). The current study investigated the changes in bacterial composition prior to and following surgery, as well as the differences in the bacterial community structure between cancer tissue and adjacent normal tissue. The diversity of the bacterial community and the composition of the bacteria were assessed. In addition, phylogenetic analysis and principle component analysis (PCA) were performed. The results revealed that cancer tissue and adjacent normal tissue exhibited similar bacterial compositions. However, a significant difference was identified in the composition of intestinal bacteria in stool samples collected from patients following surgery compared with stool samples collected prior to surgery. Each patient had their own unique intestinal bacterial community, likely due to a number of factors, including diet, genetic factors and health status. In addition, phylogenetic trees revealed that the most abundant operational taxonomic unit, 0001, was associated with Escherichia coli in all samples. Finally, PCA suggested that the bacterial community structure in all patient stools was similar following surgery. The current study provides information regarding the diversity of the intestinal bacterial community of patients with CRC and provides a basis for postoperative intestinal assessments.

Identification of the Gene Expression Rules That Define the Subtypes in Glioma

As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as IGFBP2, IGF2BP3, PRDX1, NOV, NEFL, HOXA10, GNG12, SPRY4, and BCL11A, were identified. In addition, the underlying rules of classifying the three subtypes were produced by Johnson reducer algorithm. We found that in diffuse astrocytoma, PRDX1 is highly expressed, and in glioblastoma, the expression level of PRDX1 is low. These rules revealed the difference among the three subtypes, and how they are formed and transformed. These genes are not only biomarkers for glioma subtypes, but also drug targets that may switch the clinical features or even reverse the tumor progression.

Identification of the copy number variant biomarkers for breast cancer subtypes

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew's correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying "quantitatively tissue-specific expressed genes" capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.

Study of Microbiomes in Aseptically Collected Samples of Human Breast Tissue Using Needle Biopsy and the Potential Role of in situ Tissue Microbiomes for Promoting Malignancy

Mounting evidence suggests that changes in microbiome are linked to development of cancer and its aggressiveness. Microbiome profiles in human breast tissue previously presumed to be sterile, have recently been characterized using high-throughput technologies. Recent findings of microbiome variation between benign and malignant disease provides a rationale for exploring microbiomes associated with cancer during tumor progression. We assessed microbiomes of aseptically collected human breast tissue samples in this study, using needle biopsy from patients with benign and malignant tumors of different histological grading, using 16S rRNA gene amplicon sequencing. This is consistent with previous studies, and our results identified distinct microbiome profiles in breast tissues from women with cancer as compared to women with benign breast disease in Chinese cohorts. The enriched microbial biomarkers in malignant tissue included genus Propionicimonas and families Micrococcaceae, Caulobacteraceae, Rhodobacteraceae, Nocardioidaceae, Methylobacteriaceae, which appeared to be ethno-specific. Further, we compared microbiome profiles in malignant tissues of three different histological grades. The relative abundance of family Bacteroidaceae decreased and that of genus Agrococcus increased with the development of malignancy. KEGG pathways inferred by PICRUSt showed that biotin and glycerophospholipid metabolism had significant differences in all three grades. Glycerophospholipid and ribosome biogenesis increased in grade III tissue as compared to grades I and II. Flavonoid biosynthesis significantly decreased in grade III tissue. The specific correlation of these potential microbial biomarkers and indicated pathways with advanced disease could have broad implications in the diagnosis and staging of breast cancer. Further large-cohort investigation of the breast cancer microbiome and its potential mechanism in breast cancer development are essential.

Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we proposed a computational method to identify novel cancer-related lncRNAs GO terms and KEGG pathways. By using existing lncRNA database and Max-relevance Min-redundancy (mRMR) method, GO terms and KEGG pathways were evaluated based on their importance on distinguishing cancer-related and non-cancer-related lncRNAs. Finally, GO terms and KEGG pathways with high importance were presented and analyzed. Our literature reviewing showed that the top 10 ranked GO terms and pathways were really related to interpretable tumorigenesis according to recent publications.

The interaction between invariant Natural Killer T cells and the mucosal microbiota

The surface of mammalian bodies is colonized by a multitude of microbial organisms, which under normal conditions support the host and are considered beneficial commensals. This requires, however, that the composition of the commensal microbiota is tightly controlled and regulated. The host immune system plays an important role in the maintenance of this microbiota composition. Here we focus on the contribution of one particular immune cell type, invariant Natural Killer T (iNKT) cells, in this process. The iNKT cells are a unique subset of T cells characterized by two main features. First, they express an invariant T-cell receptor that recognizes glycolipid antigens presented by CD1d, a non-polymorphic major histocompatibility complex class I-like molecule. Second, iNKT cells develop as effector/memory cells and swiftly exert effector functions, like cytokine production and cytotoxicity, after activation. We outline the influence that the mucosal microbiota can have on iNKT cells, and how iNKT cells contribute to the maintenance of the microbiota composition.

Frailty and the gut

Frailty, which is a syndrome that encompasses losses in physical, psychological and social domains, is responsible for enhanced vulnerability to endogenous and/or exogenous stressors. Frailty is a public health problem for an ageing society; however, it is poorly understood and often under-recognised in clinical settings. In particular, the impact of frailty on either intestinal functions, i.e. immune response, permeability, and absorption, or gut microbiota composition is as yet mostly unexplored. A better comprehension of the intestinal dysfunction occurring in the elderly would help in clarifying the mechanisms predisposing frail patients to a higher risk of infectious or inflammatory events. Moreover, recent evidence suggests that senescence-induced perturbations of the gut-brain axis are involved in the neuroinflammation process, thus raising the hypothesis that preserving gut permeability and preventing frailty-related changes in the microbiota composition might reduce the susceptibility to develop neurodegenerative disorders. In this review, we highlight the current insights concerning the relationship between frailty, intestinal functions, microbiota, and gut-brain axis.

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.