Data mining analysis of human gut microbiota links Fusobacterium spp. with colorectal cancer onset

Construction of pan-cancer regulatory networks based on causal inference

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC).

Influence of neo-adjuvant radiotherapy on the intestinal microbiota of rectal cancer patients

PURPOSE

Neo-adjuvant radiotherapy (NART) is a widely used pre-surgery radiotherapy for rectal cancer patients. Although NART is effective in reducing tumor burden before surgery, it may cause dysbiosis of intestinal microbiota. The intestinal microbiota shapes tumor inflammatory environment and influences cancer progression. However, how NART remodels the microbiota and how the microbiota affects therapeutic efficacy has been largely elusive. This study aimed to reveal the details of how NART affects the intestinal microbiota in patients with rectal cancer.

METHODS

Rectal cancer patients who received NART were recruited into the study, and their healthy family members on the same diet served as controls. Stool samples from five rectal cancer patients (28 in total) and five healthy individuals (16 in total) were collected for intestinal microbiota analysis by 16S rRNA gene amplicon sequencing. Samples from patients were divided into earlier- and later-NART according to the number of NART.

RESULTS

NART did not significantly affect the α diversity of intestinal microbiota. However, the abundance of bacterial genera associated with cancer progression tended to decrease in later-NART patients. More importantly, a variety of oral pathogenic bacteria were enriched in the intestine of later-NART patients. NART also affected functional pathways associated with the microbiota in DNA repair, metabolism, and bacterial infection.

CONCLUSION

NART significantly altered the microbiota composition and function in rectal cancer patients, and some oral pathogens were found to translocate to the intestine. This is the first report to study the effect of NART on intestinal microbiota in patients with rectal cancer, exploring the importance of intestinal microbiota during the process of NART.

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

Host microbiomes in tumor precision medicine: how far are we?

The human gut microbiome has received a crescendo of attention in recent years, due to the countless influences on human pathophysiology, including cancer. Research on cancer and anticancer therapy is constantly looking for new hints to improve the response to therapy while reducing the risk of relapse. In this scenario, the gut microbiome and the plethora of microbial-derived metabolites are considered a new opening in the development of innovative anticancer treatments for a better prognosis. This narrative review summarizes the current knowledge on the role of the gut microbiome in the onset and progression of cancer, as well as in response to chemo-immunotherapy. Recent findings regarding the tumor microbiome and its implications for clinical practice are also commented on. Current microbiome-based intervention strategies (i.e., prebiotics, probiotics, live biotherapeutics and fecal microbiota transplantation) are then discussed, along with key shortcomings, including a lack of long-term safety information in patients who are already severely compromised by standard treatments. The implementation of bioinformatic tools applied to microbiomics and other omics data, such as machine learning, has an enormous potential to push research in the field, enabling the prediction of health risk and therapeutic outcomes, for a truly personalized precision medicine.

Telomere Signaling and Maintenance Pathways in Spermatozoa of Infertile Men Treated With Antioxidants: An in silico Approach Using Bioinformatic Analysis

Telomere shortening is considered as a marker of cellular senescence and it is regulated by various signaling pathways. Sperm telomere appears to play important role in its longevity and function. Antioxidant intake has been known to prevent the shortening of telomere. In the management of male infertility, antioxidants are commonly used to counterbalance the seminal oxidative stress. It is important to understand how antioxidants treatment may modulate telomere signaling in sperm. In the current study, we have identified 377 sperm proteins regulated by antioxidants based on data mining of published literature. Bioinformatic analysis revealed involvement of 399 upstream regulators and 806 master regulators associated with differentially expressed sperm proteins. Furthermore, upstream regulator analysis indicated activation of kinases (EGFR and MAPK3) and transcription factors (CCNE1, H2AX, MYC, RB1, and TP53). Hence, it is evident that antioxidant supplementation activates molecules associated with telomere function in sperm. The outcome of this in silico study suggests that antioxidant therapy has beneficial effects on certain transcription factors and kinases associated with sperm telomere maintenance and associated signaling pathways that may play an important role in the management of male factor infertility.

The Role of Microbiota in Gastrointestinal Cancer and Cancer Treatment: Chance or Curse?

The gastrointestinal (GI) tract is home to a complex and dynamic community of microorganisms, comprising bacteria, archaea, viruses, yeast, and fungi. It is widely accepted that human health is shaped by these microbes and their collective microbial genome. This so-called second genome plays an important role in normal functioning of the host, contributing to processes involved in metabolism and immune modulation. Furthermore, the gut microbiota also is capable of generating energy and nutrients (eg, short-chain fatty acids and vitamins) that are otherwise inaccessible to the host and are essential for mucosal barrier homeostasis. In recent years, numerous studies have pointed toward microbial dysbiosis as a key driver in many GI conditions, including cancers. However, comprehensive mechanistic insights on how collectively gut microbes influence carcinogenesis remain limited. In addition to their role in carcinogenesis, the gut microbiota now has been shown to play a key role in influencing clinical outcomes to cancer immunotherapy, making them valuable targets in the treatment of cancer. It also is becoming apparent that, besides the gut microbiota's impact on therapeutic outcomes, cancer treatment may in turn influence GI microbiota composition. This review provides a comprehensive overview of microbial dysbiosis in GI cancers, specifically esophageal, gastric, and colorectal cancers, potential mechanisms of microbiota in carcinogenesis, and their implications in diagnostics and cancer treatment.

Caecal microbiota could effectively increase chicken growth performance by regulating fat metabolism

It has been established that gut microbiota influences chicken growth performance and fat metabolism. However, whether gut microbiota affects chicken growth performance by regulating fat metabolism remains unclear. Therefore, seven‐week‐old chickens with high or low body weight were used in the present study. There were significant differences in body weight, breast and leg muscle indices, and cross‐sectional area of muscle cells, suggesting different growth performance. The relative abundance of gut microbiota in the caecal contents at the genus level was compared by 16S rRNA gene sequencing. The results of LEfSe indicated that high body weight chickens contained Microbacterium and Sphingomonas more abundantly (P < 0.05). In contrast, low body weight chickens contained Slackia more abundantly (P < 0.05). The results of H & E, qPCR, IHC, WB and blood analysis suggested significantly different fat metabolism level in serum, liver, abdominal adipose, breast and leg muscles between high and low body weight chickens. Spearman correlation analysis revealed that fat metabolism positively correlated with the relative abundance of Microbacterium and Sphingomonas while negatively correlated with the abundance of Slackia. Furthermore, faecal microbiota transplantation was performed, which verified that transferring faecal microbiota from adult chickens with high body weight into one‐day‐old chickens improved growth performance and fat metabolism in liver by remodelling the gut microbiota. Overall, these results suggested that gut microbiota could affect chicken growth performance by regulating fat metabolism.

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Background: Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.

Lytic Bacteriophage EFA1 Modulates HCT116 Colon Cancer Cell Growth and Upregulates ROS Production in an Enterococcus faecalis Co-culture System

Enterococcus faecalis is an opportunistic pathogen in the gut microbiota that’s associated with a range of difficult to treat nosocomial infections. It is also known to be associated with some colorectal cancers. Its resistance to a range of antibiotics and capacity to form biofilms increase its virulence. Unlike antibiotics, bacteriophages are capable of disrupting biofilms which are key in the pathogenesis of diseases such as UTIs and some cancers. In this study, bacteriophage EFA1, lytic against E. faecalis, was isolated and its genome fully sequenced and analyzed in silico. Electron microscopy images revealed EFA1 to be a Siphovirus. The bacteriophage was functionally assessed and shown to disrupt E. faecalis biofilms as well as modulate the growth stimulatory effects of E. faecalis in a HCT116 colon cancer cell co-culture system, possibly via the effects of ROS. The potential exists for further testing of bacteriophage EFA1 in these systems as well as in vivo models.

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.

Gut Microbiota Profiling in Patients With HER2-Negative Metastatic Breast Cancer Receiving Metronomic Chemotherapy of Capecitabine Compared to Those Under Conventional Dosage

Purpose: Low-dose metronomic chemotherapy can achieve disease control with reduced toxicity compared to conventional chemotherapy in maximum tolerated dose. Characterizing the gut microbiota of cancer patients under different dosage regimens may describe a new role of gut microbiota associated with drug efficacy. Therefore, we evaluated the composition and the function of gut microbiome associated with metronomic capecitabine compared to conventional dosage. Methods: The fecal samples of HER2-negative metastatic breast cancer patients treated with capecitabine as maintenance chemotherapy were collected and analyzed by 16S ribosome RNA gene sequencing. Results: A total of 15 patients treated with metronomic capecitabine were compared to 16 patients under a conventional dose. The unweighted-unifrac index of the metronomic group was statistically significantly lower than that of the routine group (P = 0.025). Besides that, the Bray-Curtis distance-based redundancy analysis illustrated that the microbial genera between the two groups can be separated partly. Nine Kyoto Encyclopedia of Genes and Genomes (KEGG) modules were enriched in the metronomic group, while no KEGG modules were significantly enriched in the routine group. Moreover, univariate and multivariate analyses suggested that the median progression-free survival (PFS) was significantly shorter in patients with the gut microbial composition of Slackia (9.2 vs. 32.7 months, P = 0.004), while the patients with Blautia obeum had a significantly prolonged PFS than those without (32.7 vs. 12.9 months, P = 0.013). Conclusions: The proof-of-principle study suggested that the gut microbiota of patients receiving metronomic chemotherapy was different in terms of diversity, composition, and function from those under conventional chemotherapy, and the presence of specific bacterial species may act as microbial markers associated with drug resistance monitoring and prognostic evaluation.

Targeting Programmed Fusobacterium nucleatum Fap2 for Colorectal Cancer Therapy

Colorectal patients generally have the maximum counts of Fusobacterium nucleatum (F. nucleatum) in tumors and elevate colorectal adenomas and carcinomas, which show the lowest rate of human survival. Hence, F. nucleatum is a diagnostic marker of colorectal cancer (CRC). Studies demonstrated that targeting fusobacterial Fap2 or polysaccharide of the host epithelium may decrease fusobacteria count in the CRC. Attenuated F. nucleatum-Fap2 prevents transmembrane signals and inhibits tumorigenesis inducing mechanisms. Hence, in this review, we hypothesized that application of genetically programmed fusobacterium can be skillful and thus reduce fusobacterium in the CRC. Genetically programmed F. nucleatum is a promising antitumor strategy.