A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Interferon-induced transmembrane protein 2 is a prognostic marker in colorectal cancer and promotes its progression by activating the PI3K/AKT pathway

BACKGROUND

Interferon-induced transmembrane protein 2 (IFITM2) is involved in repressing viral infection. This study aim to investigate the expression of IFITM2 in colorectal cancer (CRC) and explore its effect on cell proliferation, migration, and invasion.

METHODS

We analyzed The Cancer Genome Atlas (TCGA) database for IFITM2 expression in colorectal cancer and used western blots to detect IFITM2 protein in specimens and cell lines of colorectal cancers. To assess the association between IFITM2 and clinical features, both univariate and multivariate cox regression analysis were conducted. Kaplan-Meier plots were used in the TCGA database to assess IFITM2 gene expression's prognostic significance. Silencing IFITM2 in SW480 and HCT116 cells was achieved by transient transfection with siRNA. Proliferation of CRCs was examined using Cell Counting Kit-8. The effect of IFITM2 on the migration and invasion of CRC cells was studied using wound healing and transwell assays. Gene set enrichment analysis (GSEA) was used to examine IFITM2-associated pathways and Western blotting was used to confirm it.

RESULTS

IFITM2 was over-expressed in the CRC tissues and cells, with high IFITM2 expression related to the tumor N, M, and pathologic stages. The presence of IFITM2 significantly impacted patient survival in CRC. The proliferation of SW480 and HCT116 cells was suppressed when IFITM2 was silenced, resulting in weakened migration and invasion of CRC cells. GSEA analysis showed that IFITM2 was positively related to the phosphoinositide 3-kinase (PI3K)/AKT pathway, and western blot results confirmed that IFITM2 activated it.

CONCLUSIONS

IFITM2 was over-expressed in CRC and modulated the PI3K/AKT pathway to promote CRC cells proliferation and metastasis.

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model

Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing on stool samples from 94 CRC patients and employed the linear discriminant analysis effect size algorithm to identify distinct gut microbiota between KRAS mutant and KRAS wild-type CRC patients. Transcriptome sequencing data from nine CRC patients were transformed into a matrix of immune infiltrating cells, which was then utilized to explore KRAS mutation-associated biological functions, including Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways. Subsequently, we analyzed the correlations among these KRAS mutation-associated gut microbiota, host immunity, and KRAS mutation-associated biological functions. At last, we developed a predictive random forest (RF) machine learning model to predict the KRAS mutation status in CRC patients, based on the gut microbiota associated with KRAS mutation. We identified a total of 26 differential gut microbiota between both groups. Intriguingly, a significant positive correlation was observed between Bifidobacterium spp. and mast cells, as well as between Bifidobacterium longum and chemokine receptor CX3CR1. Additionally, we also observed a notable negative correlation between Bifidobacterium and GOMF:proteasome binding. The RF model constructed using the KRAS mutation-associated gut microbiota demonstrated qualified efficacy in predicting the KRAS phenotype in CRC. Our study ascertained the presence of 26 KRAS mutation-associated gut microbiota in CRC and speculated that Bifidobacterium may exert an essential role in preventing CRC progression, which appeared to correlate with the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:proteasome binding. Furthermore, the RF model constructed on the basis of KRAS mutation-associated gut microbiota exhibited substantial potential in predicting KRAS mutation status in CRC patients.IMPORTANCEGut microbiota has emerged as an essential player in the onset and development of colorectal cancer (CRC). However, the relationship between gut microbiota and KRAS mutation in CRC remains elusive. Our study not only identified a total of 26 gut microbiota associated with KRAS mutation in CRC but also unveiled their significant correlations with tumor-infiltrating immune cells, immune-related genes, and biological pathways (Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways). We speculated that Bifidobacterium may play a crucial role in impeding CRC progression, potentially linked to the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:Proteasome binding. Furthermore, based on the KRAS mutation-associated gut microbiota, the RF model exhibited promising potential in the prediction of KRAS mutation status for CRC patients. Overall, the findings of our study offered fresh insights into microbiological research and clinical prediction of KRAS mutation status for CRC patients.

Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests

Objective

The recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.

Methods

We employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.

Results

The XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.

Conclusion

The XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.

Case Report: The impact of severe cryptosporidiosis on the gut microbiota of a pediatric patient with CD40L immunodeficiency

Cryptosporidium parvum is a protozoan parasite and one of the leading causes of gastroenteritis in the world, primarily affecting very young children and immunocompromised patients. While infection is usually self-limiting, it can become chronic and even lethal in these vulnerable populations, in whom Cryptosporidium treatments are generally ineffective, due to their acting in concert with a functioning immune system. Here, we describe a case of chronic cryptosporidiosis in a European child with severe CD40L immunodeficiency infected with Cryptosporidium parvum of the IIa20G1 subgenotype, a lineage which has thus far only ever been described in the Middle East. After years of on-off treatment with conventional and non-conventional anti-parasitic drugs failed to clear parasitosis, we performed targeted metagenomics to observe the bacterial composition of the patient's gut microbiota (GM), and to evaluate fecal microbiota transplantation (FMT) as a potential treatment option. We found that C. parvum infection led to significant shifts in GM bacterial composition in our patient, with consequent shifts in predicted intestinal functional signatures consistent with a state of persistent inflammation. This, combined with the patient's poor prognosis and increasing parasitic burden despite many rounds of anti-parasitic drug treatments, made the patient a potential candidate for an experimental FMT procedure. Unfortunately, given the many comorbidities that were precipitated by the patient's immunodeficiency and chronic C. parvum infection, FMT was postponed in favor of more urgently necessary liver and bone marrow transplants. Tragically, after the first liver transplant failed, the patient lost his life before undergoing FMT and a second liver transplant. With this case report, we present the first description of how cryptosporidiosis can shape the gut microbiota of a pediatric patient with severe immunodeficiency. Finally, we discuss how both our results and the current scientific literature suggest that GM modulations, either by probiotics or FMT, can become novel treatment options for chronic Cryptosporidium infection and its consequent complications, especially in those patients who do not respond to the currently available anti-parasitic therapies.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead

Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.

Connecting Gut Microbial Diversity with Plasma Metabolome and Fecal Bile Acid Changes Induced by the Antibiotics Tobramycin and Colistin Sulfate

The diversity of microbial species in the gut has a strong influence on health and development of the host. Further, there are indications that the variation in expression of gut bacterial metabolic enzymes is less diverse than the taxonomic profile, underlying the importance of microbiome functionality, particularly from a toxicological perspective. To address these relationships, the gut bacterial composition of Wistar rats was altered by a 28 day oral treatment with the antibiotics tobramycin or colistin sulfate. On the basis of 16S marker gene sequencing data, tobramycin was found to cause a strong reduction in the diversity and relative abundance of the microbiome, whereas colistin sulfate had only a marginal impact. Associated plasma and fecal metabolomes were characterized by targeted mass spectrometry-based profiling. The fecal metabolome of tobramycin-treated animals had a high number of significant alterations in metabolite levels compared to controls, particularly in amino acids, lipids, bile acids (BAs), carbohydrates, and energy metabolites. The accumulation of primary BAs and significant reduction of secondary BAs in the feces indicated that the microbial alterations induced by tobramycin inhibit bacterial deconjugation reactions. The plasma metabolome showed less, but still many alterations in the same metabolite groups, including reductions in indole derivatives and hippuric acid, and furthermore, despite marginal effects of colistin sulfate treatment, there were nonetheless systemic alterations also in BAs. Aside from these treatment-based differences, we also uncovered interindividual differences particularly centering on the loss of Verrucomicrobiaceae in the microbiome, but with no apparent associated metabolite alterations. Finally, by comparing the data set from this study with metabolome alterations in the MetaMapTox database, key metabolite alterations were identified as plasma biomarkers indicative of altered gut microbiomes resulting from a wide activity spectrum of antibiotics.

Sex Differences in Fatty Acid Metabolism and Blood Pressure Response to Dietary Salt in Humans

Salt sensitivity is a trait in which high dietary sodium (Na+) intake causes an increase in blood pressure (BP). We previously demonstrated that in the gut, elevated dietary Na+ causes dysbiosis. The mechanistic interplay between excess dietary Na+-induced alteration in the gut microbiome and sex differences is less understood. The goal of this study was to identify novel metabolites in sex differences and blood pressure in response to a high dietary Na+ intake. We performed stool and plasma metabolomics analysis and measured the BP of human volunteers with salt intake above or below the American Heart Association recommendations. We also performed RNA sequencing on human monocytes treated with high salt in vitro. The relationship between BP and dietary Na+ intake was different in women and men. Network analysis revealed that fatty acids as top subnetworks differentially changed with salt intake. We found that women with high dietary Na+ intake have high levels of arachidonic acid related metabolism, suggesting a role in sex differences of the blood pressure response to Na+. The exposure of monocytes to high salt in vitro upregulates the transcription of fatty acid receptors and arachidonic acid-related genes. These findings provide potentially novel insights into metabolic changes underlying gut dysbiosis and inflammation in salt sensitivity of BP.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

Chromatographic-Based Platforms as New Avenues for Scientific Progress and Sustainability

Chromatography was born approximately one century ago and has undergone outstanding technological improvements in innovation, research, and development since then that has made it fundamental to advances in knowledge at different levels, with a relevant impact on the well-being and health of individuals. Chromatography boosted a comprehensive and deeper understanding of the complexity and diversity of human–environment interactions and systems, how these interactions affect our life, and the several societal challenges we are currently facing, namely those related to the sustainability of our planet and the future generations. From the life sciences, which allowed us to identify endogenous metabolites relevant to disease mechanisms, to the OMICS field, nanotechnology, clinical and forensic analysis, drug discovery, environment, and “foodprint”, among others, the wide range of applications of today’s chromatographic techniques is impressive. This is fueled by a great variability of powerful chromatographic instruments currently available, with very high sensitivity, resolution, and identification capacity, that provide a strong basis for an analytical platform able to support the challenging demands of the postgenomic and post COVID-19 eras. Within this context, this review aims to address the great utility of chromatography in helping to cope with several societal-based challenges, such as the characterization of disease and/or physiological status, and the response to current agri-food industry challenges of food safety and sustainability, or the monitoring of environmental contamination. These are increasingly important challenges considering the climate changes, the tons of food waste produced every day, and the exponential growth of the human population. In this context, the principles governing the separation mechanisms in chromatography as well the different types and chromatographic techniques will be described. In addition, the major achievements and the most important technological advances will be also highlighted. Finally, a set of studies was selected in order to evince the importance of different chromatographic analyses to understand processes or create fundamental information in the response to current societal challenges.