LC-MS-based serum metabolomics analysis for the screening and monitoring of colorectal cancer

Mitochondrial medicine: "from bench to bedside" 3PM-guided concept

Mitochondria are the primary sites for aerobic respiration and play a vital role in maintaining physiologic function at the cellular and organismal levels. Physiologic mitochondrial homeostasis, functions, health, and any kind of mitochondrial impairments are associated with systemic effects that are linked to the human health and pathologies. Contextually, mitochondria are acting as a natural vital biosensor in humans controlling status of physical and mental health in a holistic manner. So far, no any disorder is known as happening to humans independently from a compromised mitochondrial health as the cause (primary mitochondrial dysfunction) or a target of collateral damage (secondary mitochondrial injury). This certainty makes mitochondrial medicine be the superior instrument to reach highly ambitious objectives of predictive, preventive, and personalized medicine (PPPM/3PM). 3PM effectively implements the paradigm change from the economically ineffective reactive medical services to a predictive approach, targeted prevention and treatments tailored to individualized patient profiles in primary (protection against health-to-disease transition) and secondary (protection against disease progression) healthcare. Mitochondrial DNA (mtDNA) properties differ significantly from those of nuclear DNA (nDNA). For example, mtDNA as the cell-free DNA molecule is much more stable compared to nDNA, which makes mtDNA be an attractive diagnostic target circulating in human body fluids such as blood and tear fluid. Further, genetic variations in mtDNA contribute to substantial individual differences in disease susceptibility and treatment response. To this end, the current gene editing technologies, such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas, are still immature in mtDNA modification, and cannot be effectively applied in clinical practice posing a challenge for mtDNA-based therapies. In contrast, comprehensive multiomics technologies offer new insights into mitochondrial homeostasis, health, and functions, which enables to develop more effective multi-level diagnostics and targeted treatment strategies. This review article highlights health- and disease-relevant mitochondrial particularities and assesses involvement of mitochondrial medicine into implementing the 3PM objectives. By discussing the interrelationship between 3PM and mitochondrial medicine, we aim to provide a foundation for advancing early and predictive diagnostics, cost-effective targeted prevention in primary and secondary care, and exemplify personalized treatments creating proof-of-concept approaches for 3PM-guided clinical applications.

Association analysis of gut microbiota with LDL-C metabolism and microbial pathogenicity in colorectal cancer patients

BACKGROUND

Colorectal cancer (CRC) is the most common gastrointestinal malignancy worldwide, with obesity-induced lipid metabolism disorders playing a crucial role in its progression. A complex connection exists between gut microbiota and the development of intestinal tumors through the microbiota metabolite pathway. Metabolic disorders frequently alter the gut microbiome, impairing immune and cellular functions and hastening cancer progression.

METHODS

This study thoroughly examined the gut microbiota through 16S rRNA sequencing of fecal samples from 181 CRC patients, integrating preoperative Low-density lipoprotein cholesterol (LDL-C) levels and RNA sequencing data. The study includes a comparison of microbial diversity, differential microbiological analysis, exploration of the associations between microbiota, tumor microenvironment immune cells, and immune genes, enrichment analysis of potential biological functions of microbe-related host genes, and the prediction of LDL-C status through microorganisms.

RESULTS

The analysis revealed that differences in α and β diversity indices of intestinal microbiota in CRC patients were not statistically significant across different LDL-C metabolic states. Patients exhibited varying LDL-C metabolic conditions, leading to a bifurcation of their gut microbiota into two distinct clusters. Patients with LDL-C metabolic irregularities had higher concentrations of twelve gut microbiota, which were linked to various immune cells and immune-related genes, influencing tumor immunity. Under normal LDL-C metabolic conditions, the protective microorganism Anaerostipes_caccae was significantly negatively correlated with the GO Biological Process pathway involved in the negative regulation of the unfolded protein response in the endoplasmic reticulum. Both XGBoost and MLP models, developed using differential gut microbiota, could forecast LDL-C levels in CRC patients biologically.

CONCLUSIONS

The intestinal microbiota in CRC patients influences the LDL-C metabolic status. With elevated LDL-C levels, gut microbiota can regulate the function of immune cells and gene expression within the tumor microenvironment, affecting cancer-related pathways and promoting CRC progression. LDL-C and its associated gut microbiota could provide non-invasive markers for clinical evaluation and treatment of CRC patients.

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n = 380) and colorectal adenomas (CRA, n = 110) against generally healthy participants (n = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies.

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study

Background and Aims

Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC).

Methods

Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination.

Results

A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs.

Conclusion

The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.