scholarly journals Altered Fecal Small RNA Profiles in Colorectal Cancer Reflect Gut Microbiome Composition in Stool Samples

mSystems ◽  
2019 ◽  
Vol 4 (5) ◽  
Author(s):  
Sonia Tarallo ◽  
Giulio Ferrero ◽  
Gaetano Gallo ◽  
Antonio Francavilla ◽  
Giuseppe Clerico ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Gut Microbiota ◽  
Small Rna ◽  
Gut Microbiome ◽  
Small Rnas ◽  
Area Under The Curve ◽  
Small Rna Sequencing ◽  
Predictive Tool ◽  
Cross Sectional ◽  
Stool Samples

ABSTRACT Dysbiotic configurations of the human gut microbiota have been linked to colorectal cancer (CRC). Human small noncoding RNAs are also implicated in CRC, and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis, but their role has been less extensively explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens from patients with CRC or with adenomas and from healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We observed considerable overlap and a correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. We identified a combined predictive signature composed of 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC samples separately from healthy and adenoma samples (area under the curve [AUC] = 0.87). In the present study, we report evidence that host-microbiome dysbiosis in CRC can also be observed by examination of altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more-accurate tools for diagnostic purposes. IMPORTANCE The characteristics of microbial small RNA transcription are largely unknown, while it is of primary importance for a better identification of molecules with functional activities in the gut niche under both healthy and disease conditions. By performing combined analyses of metagenomic and small RNA sequencing (sRNA-Seq) data, we characterized both the human and microbial small RNA contents of stool samples from healthy individuals and from patients with colorectal carcinoma or adenoma. With the integrative analyses of metagenomic and sRNA-Seq data, we identified a human and microbial small RNA signature which can be used to improve diagnosis of the disease. Our analysis of human and gut microbiome small RNA expression is relevant to generation of the first hypotheses about the potential molecular interactions occurring in the gut of CRC patients, and it can be the basis for further mechanistic studies and clinical tests.

scholarly journals Postoperative Complications Are Associated with Long-Term Changes in the Gut Microbiota Following Colorectal Cancer Surgery

Life ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 246
Author(s):  
Felix C.F. Schmitt ◽  
Martin Schneider ◽  
William Mathejczyk ◽  
Markus A. Weigand ◽  
Jane C. Figueiredo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Postoperative Complications ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Tumor Resection ◽  
Microbial Composition ◽  
Colorectal Cancer Surgery ◽  
Long Term Changes ◽  
Stool Samples

Changes in the gut microbiome have already been associated with postoperative complications in major abdominal surgery. However, it is still unclear whether these changes are transient or a long-lasting effect. Therefore, the aim of this prospective clinical pilot study was to examine long-term changes in the gut microbiota and to correlate these changes with the clinical course of the patient. Methods: In total, stool samples of 62 newly diagnosed colorectal cancer patients undergoing primary tumor resection were analyzed by 16S-rDNA next-generation sequencing. Stool samples were collected preoperatively in order to determine the gut microbiome at baseline as well as at 6, 12, and 24 months thereafter to observe longitudinal changes. Postoperatively, the study patients were separated into two groups—patients who suffered from postoperative complications (n = 30) and those without complication (n = 32). Patients with postoperative complications showed a significantly stronger reduction in the alpha diversity starting 6 months after operation, which does not resolve, even after 24 months. The structure of the microbiome was also significantly altered from baseline at six-month follow-up in patients with complications (p = 0.006). This was associated with a long-lasting decrease of a large number of species in the gut microbiota indicating an impact in the commensal microbiota and a long-lasting increase of Fusobacterium ulcerans. The microbial composition of the gut microbiome shows significant changes in patients with postoperative complications up to 24 months after surgery.

scholarly journals Correction for Tarallo et al., “Altered Fecal Small RNA Profiles in Colorectal Cancer Reflect Gut Microbiome Composition in Stool Samples”

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sonia Tarallo ◽  
Giulio Ferrero ◽  
Gaetano Gallo ◽  
Antonio Francavilla ◽  
Giuseppe Clerico ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Small Rna ◽  
Gut Microbiome ◽  
Microbiome Composition ◽  
Stool Samples

scholarly journals Circulating let-7e-5p, miR-106a-5p, miR-28-3p, and miR-542-5p as a Promising microRNA Signature for the Detection of Colorectal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1493
Author(s):  
Camila Meirelles S. Silva ◽  
Mateus C. Barros-Filho ◽  
Deysi Viviana T. Wong ◽  
Julia Bette H. Mello ◽  
Livia Maria S. Nobre ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Unmet Need ◽  
Area Under The Curve ◽  
Discrimination Performance ◽  
Colorectal Carcinogenesis ◽  
The Cancer Genome Atlas ◽  
Colon Perforation ◽  
Data Series ◽  
Predictive Tool ◽  
Incidence And Mortality

Colorectal cancer (CRC) is a disease with high incidence and mortality. Colonoscopy is a gold standard among tests used for CRC traceability. However, serious complications, such as colon perforation, may occur. Non-invasive diagnostic procedures are an unmet need. We aimed to identify a plasma microRNA (miRNA) signature for CRC detection. Plasma samples were obtained from subjects (n = 109) at different stages of colorectal carcinogenesis. The patients were stratified into a non-cancer (27 healthy volunteers, 17 patients with hyperplastic polyps, 24 with adenomas), and a cancer group (20 CRC and 21 metastatic CRC). miRNAs (381) were screened by TaqMan Low-Density Array. A classifier based on four differentially expressed miRNAs (miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p) was able to discriminate cancer versus non-cancer cases. The overexpression of these miRNAs was confirmed by RT-qPCR, and a cross-study validation step was implemented using eight data series retrieved from Gene Expression Omnibus (GEO). In addition, another external data validation using CRC surgical specimens from The Cancer Genome Atlas (TCGA) was carried out. The predictive model’s performance in the validation set was 76.5% accuracy, 59.4% sensitivity, and 86.8% specificity (area under the curve, AUC = 0.716). The employment of our model in the independent publicly available datasets confirmed a good discrimination performance in five of eight datasets (median AUC = 0.823). Applying this algorithm to the TCGA cohort, we found 99.5% accuracy, 99.7% sensitivity, and 90.9% specificity (AUC = 0.998) when the model was applied to solid colorectal tissues. Overall, we suggest a novel signature of four circulating miRNAs, i.e., miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p, as a predictive tool for the detection of CRC.

scholarly journals Interaction between Host MicroRNAs and the Gut Microbiota in Colorectal Cancer

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Ce Yuan ◽  
Michael B. Burns ◽  
Subbaya Subramanian ◽  
Ran Blekhman
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gut Microbiota ◽  
Mirna Expression ◽  
Gut Microbiome ◽  
Noncoding Rna ◽  
Microbial Community Composition ◽  
Host Cells ◽  
Microbiome Composition ◽  
Small Noncoding Rna

ABSTRACT Although variation in gut microbiome composition has been linked with colorectal cancer (CRC), the factors that mediate the interactions between CRC tumors and the microbiome are poorly understood. MicroRNAs (miRNAs) are known to regulate CRC progression and are associated with patient survival outcomes. In addition, recent studies suggested that host miRNAs can also regulate bacterial growth and influence the composition of the gut microbiome. Here, we investigated the association between miRNA expression and microbiome composition in human CRC tumor and normal tissues. We identified 76 miRNAs as differentially expressed (DE) in tissue from CRC tumors and normal tissue, including the known oncogenic miRNAs miR-182, miR-503, and mir-17~92 cluster. These DE miRNAs were correlated with the relative abundances of several bacterial taxa, including Firmicutes , Bacteroidetes , and Proteobacteria . Bacteria correlated with DE miRNAs were enriched with distinct predicted metabolic categories. Additionally, we found that miRNAs that correlated with CRC-associated bacteria are predicted to regulate targets that are relevant for host-microbiome interactions and highlight a possible role for miRNA-driven glycan production in the recruitment of pathogenic microbial taxa. Our work characterized a global relationship between microbial community composition and miRNA expression in human CRC tissues. IMPORTANCE Recent studies have found an association between colorectal cancer (CRC) and the gut microbiota. One potential mechanism by which the microbiota can influence host physiology is through affecting gene expression in host cells. MicroRNAs (miRNAs) are small noncoding RNA molecules that can regulate gene expression and have important roles in cancer development. Here, we investigated the link between the gut microbiota and the expression of miRNA in CRC. We found that dozens of miRNAs are differentially regulated in CRC tumors and adjacent normal colon and that these miRNAs are correlated with the abundance of microbes in the tumor microenvironment. Moreover, we found that microbes that have been previously associated with CRC are correlated with miRNAs that regulate genes related to interactions with microbes. Notably, these miRNAs likely regulate glycan production, which is important for the recruitment of pathogenic microbial taxa to the tumor. This work provides a first systems-level map of the association between microbes and host miRNAs in the context of CRC and provides targets for further experimental validation and potential interventions.

Relationship between the Japanese-style diet, gut microbiota, and dementia: a cross-sectional study

2021 ◽  
Author(s):  
Naoki Saji ◽  
Tsuyoshi Tsuduki ◽  
Kenta Murotani ◽  
Takayoshi Hisada ◽  
Taiki Sugimoto ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Gut Microbiota ◽  
Cognitive Decline ◽  
Gut Microbiome ◽  
Cross Sectional Study ◽  
Dietary Composition ◽  
Microbial Metabolites ◽  
Cross Sectional ◽  
Fish And Shellfish ◽  
Japanese Diet

Abstract Background Previous studies have shown associations between the gut microbiota, microbial metabolites, and cognitive decline. However, the effect of the dietary composition on such associations has not been fully investigated. Methods We performed a cross-sectional sub-analysis of data from our prospective hospital-based cohort study (the Gimlet study) to evaluate the relationships between dietary composition, cognitive decline, and the gut microbiota. All the participants of the Gimlet study had been provided with information regarding this sub-study in 2018. Patients were excluded if they were unable to provide sufficient data in the questionnaire regarding their dietary composition. We assessed their demographics, dietary composition, risk factors, cognitive function, results of brain imaging, gut microbiome, and microbial metabolites. On the basis of previous studies, a nine-component traditional Japanese diet index (JDI9), a 12-component modern JDI (JDI12), and a 12-component revised JDI (rJDI12), were defined. Higher JDI scores indicated greater conformity to the traditional Japanese diet. We then evaluated the relationships between the JDI scores, cognitive function, and the gut microbiome and microbial metabolites using multivariable logistic regression analyses. Results We analyzed data from 85 eligible patients (61% women; mean age: 74.6 ± 7.4 years; mean Mini-Mental State Examination score: 24 ± 5). Compared with participants with dementia, those without dementia were more likely to consume foods in the JDI12, including fish and shellfish (64.5% vs. 39.1%, P = 0.048), mushrooms (61.3% vs. 30.4%, P = 0.015), soybeans and soybean-derived foods (62.9% vs. 30.4%, P = 0.013), and coffee (71.0% vs. 43.5%, P = 0.024). There were non-significant trends towards lower fecal concentrations of gut microbial metabolites in participants with a more traditional Japanese diet. Participants with dementia had lower JDI9, JDI12, and rJDI12 scores than participants without dementia (dementia vs. non-dementia, median JDI9 score: 5 vs. 7, P = 0.049; JDI12: 7 vs. 8, P = 0.017; and rJDI12: 7 vs. 9, P = 0.006, respectively). Conclusions Adherence to a traditional Japanese diet was found to be inversely associated with cognitive decline and tended to be associated with lower concentrations of gut microbial metabolites. Trial registration: UMIN000031851.

scholarly journals Small RNAs in parasitic nematodes – forms and functions

Parasitology ◽  
2019 ◽  
Vol 147 (8) ◽  
pp. 855-864
Author(s):  
Collette Britton ◽  
Roz Laing ◽  
Eileen Devaney
Keyword(s):  
Gene Expression ◽  
Small Rna ◽  
Small Rnas ◽  
Target Genes ◽  
Parasitic Nematodes ◽  
Small Rna Sequencing ◽  
Small Interfering Rnas ◽  
Rna Sequences ◽  
C Elegans

AbstractSmall RNAs are important regulators of gene expression. They were first identified in Caenorhabditis elegans, but it is now apparent that the main small RNA silencing pathways are functionally conserved across diverse organisms. Availability of genome data for an increasing number of parasitic nematodes has enabled bioinformatic identification of small RNA sequences. Expression of these in different lifecycle stages is revealed by small RNA sequencing and microarray analysis. In this review we describe what is known of the three main small RNA classes in parasitic nematodes – microRNAs (miRNAs), Piwi-interacting RNAs (piRNAs) and small interfering RNAs (siRNAs) – and their proposed functions. miRNAs regulate development in C. elegans and the temporal expression of parasitic nematode miRNAs suggest modulation of target gene levels as parasites develop within the host. miRNAs are also present in extracellular vesicles released by nematodes in vitro, and in plasma from infected hosts, suggesting potential regulation of host gene expression. Roles of piRNAs and siRNAs in suppressing target genes, including transposable elements, are also reviewed. Recent successes in RNAi-mediated gene silencing, and application of small RNA inhibitors and mimics will continue to advance understanding of small RNA functions within the parasite and at the host–parasite interface.

scholarly journals Comparative Analysis of Fecal Microbiota Composition Between Rheumatoid Arthritis and Osteoarthritis Patients

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 748 ◽  
Author(s):  
Jin-Young Lee ◽  
Mohamed Mannaa ◽  
Yunkyung Kim ◽  
Jehun Kim ◽  
Geun-Tae Kim ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Amplicon Sequencing ◽  
Fecal Microbiota ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Stool Samples ◽  
Gut Microbiota Composition

The aim of this study was to investigate differences between the gut microbiota composition in patients with rheumatoid arthritis (RA) and those with osteoarthritis (OA). Stool samples from nine RA patients and nine OA patients were collected, and DNA was extracted. The gut microbiome was assessed using 16S rRNA gene amplicon sequencing. The structures and differences in the gut microbiome between RA and OA were analyzed. The analysis of diversity revealed no differences in the complexity of samples. The RA group had a lower Bacteroidetes: Firmicutes ratio than did the OA group. Lactobacilli and Prevotella, particularly Prevotella copri, were more abundant in the RA than in the OA group, although these differences were not statistically significant. The relative abundance of Bacteroides and Bifidobacterium was lower in the RA group. At the species level, the abundance of certain bacterial species was significantly lower in the RA group, such as Fusicatenibacter saccharivorans, Dialister invisus, Clostridium leptum, Ruthenibacterium lactatiformans, Anaerotruncus colihominis, Bacteroides faecichinchillae, Harryflintia acetispora, Bacteroides acidifaciens, and Christensenella minuta. The microbial properties of the gut differed between RA and OA patients, and the RA dysbiosis revealed results similar to those of other autoimmune diseases, suggesting that a specific gut microbiota pattern is related to autoimmunity.

scholarly journals The Role of the Gut Microbiome in Colorectal Cancer

2018 ◽  
Vol 31 (03) ◽  
pp. 192-198 ◽  
Author(s):  
Grace Chen
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Microbial Community ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Intestinal Health ◽  
Genotoxic Effects ◽  
Bacterial Populations ◽  
Health And Disease

AbstractThere is increasing evidence that the gut microbiome, which consists of trillions of microbes representing over 1,000 species of bacteria with over 3 million genes, significantly impacts intestinal health and disease. The gut microbiota not only is capable of promoting intestinal homeostasis and antitumor responses but can also contribute to chronic dysregulated inflammation as well as have genotoxic effects that lead to carcinogenesis. Whether the gut microbiota maintains health or promotes colon cancer may ultimately depend on the composition of the gut microbiome and the balance within the microbial community of protective and detrimental bacterial populations. Disturbances in the normal balanced state of a healthful microbiome, known as dysbiosis, have been observed in patients with colorectal cancer (CRC); however, whether these alterations precede and cause CRC remains to be determined. Nonetheless, studies in mice strongly suggest that the gut microbiota can modulate susceptibility to CRC, and therefore may serve as both biomarkers and therapeutic targets.

scholarly journals Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza

2020 ◽  
Vol 71 (10) ◽  
pp. 2669-2678 ◽  
Author(s):  
Silan Gu ◽  
Yanfei Chen ◽  
Zhengjie Wu ◽  
Yunbo Chen ◽  
Hainv Gao ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Relative Abundance ◽  
Influenza A ◽  
Gastrointestinal Symptoms ◽  
Area Under The Curve ◽  
H1n1 Influenza ◽  
Cross Sectional Study ◽  
Microbial Composition ◽  
Cross Sectional ◽  
Influenza A H1n1

Abstract Background Coronavirus disease 2019 (COVID-19) is an emerging serious global health problem. Gastrointestinal symptoms are common in COVID-19 patients, and severe acute respiratory syndrome coronavirus 2 RNA has been detected in stool specimens. However, the relationship between the gut microbiome and disease remains to be established. Methods We conducted a cross-sectional study of 30 patients with COVID-19, 24 patients with influenza A(H1N1), and 30 matched healthy controls (HCs) to identify differences in the gut microbiota by 16S ribosomal RNA gene V3–V4 region sequencing. Results Compared with HCs, COVID-19 patients had significantly reduced bacterial diversity; a significantly higher relative abundance of opportunistic pathogens, such as Streptococcus, Rothia, Veillonella, and Actinomyces; and a lower relative abundance of beneficial symbionts. Five biomarkers showed high accuracy for distinguishing COVID-19 patients from HCs with an area under the curve (AUC) up to 0.89. Patients with H1N1 displayed lower diversity and different overall microbial composition compared with COVID-19 patients. Seven biomarkers were selected to distinguish the 2 cohorts (AUC = 0.94). Conclusions The gut microbial signature of patients with COVID-19 was different from that of H1N1 patients and HCs. Our study suggests the potential value of the gut microbiota as a diagnostic biomarker and therapeutic target for COVID-19, but further validation is needed.

scholarly journals Massively differential bias between two widely used Illumina library preparation methods for small RNA sequencing

2013 ◽  
Author(s):  
Jeanette Baran-Gale ◽  
Michael R Erdos ◽  
Christina Sison ◽  
Alice Young ◽  
Emily E Fannin ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Small Rna Sequencing ◽  
Library Preparation ◽  
Sequencing Technology ◽  
Preparation Methods ◽  
Sequencing Platforms ◽  
Small Rna Library ◽  
Illumina Library

Recent advances in sequencing technology have helped unveil the unexpected complexity and diversity of small RNAs. A critical step in small RNA library preparation for sequencing is the ligation of adapter sequences to both the 5’ and 3’ ends of small RNAs. Two widely used protocols for small RNA library preparation, Illumina v1.5 and Illumina TruSeq, use different pairs of adapter sequences. In this study, we compare the results of small RNA-sequencing between v1.5 and TruSeq and observe a striking differential bias. Nearly 100 highly expressed microRNAs (miRNAs) are >5-fold differentially detected and 48 miRNAs are >10-fold differentially detected between the two methods of library preparation. In fact, some miRNAs, such as miR-24-3p, are over 30-fold differentially detected. The results are reproducible across different sequencing centers (NIH and UNC) and both major Illumina sequencing platforms, GAIIx and HiSeq. While some level of bias in library preparation is not surprising, the apparent massive differential bias between these two widely used adapter sets is not well appreciated. As increasingly more laboratories transition to the newer TruSeq-based library preparation for small RNAs, researchers should be aware of the extent to which the results may differ from previously published results using v1.5.

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