Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

A novel alternative strategy for monitoring and insight into liver fibrosis progression: The combination of surface-enhanced Raman spectroscopy (SERS) and gut microbiota

Nowadays, the studies on the interaction and relationship between the intestinal microorganisms and liver diseases are increasing. However, it is still a huge challenge for the in-depth investigation and dynamic monitoring of such a complex network. Herein, a significant discovery was made. A strong association between gut microbial structural and functional genomics and SERS spectra of hepatocytes were revealed. Based on the study of gut microbes and SERS spectra, complementary information could be provided for the mechanism analysis of related diseases. Liver fibrosis, a chronic liver disease that lack specific cure was thus comprehensive studied. Liver targeting gold nanoparticle dimers were prepared as the SERS tags, and abundant SERS peak signals were acquired. Meanwhile, the gut microbiomes were also comparative studied. The changes of carbohydrates and lipids in liver cells were observed at the early stages of liver fibrosis, and TLR4 (toll-like receptors 4) was activated to elicit immune responses. Then again, oxidative stress, endotoxin and serum inflammatory factors were the major observations at the late stages. The SERS signals and the microbiome analysis were well confirmed and complemented each other, which suggested that the detection strategy could be another valuable method for the "gut-liver axis" study.

Biomarker discovery and application-An opportunity to resolve the challenge of liver cancer diagnosis and treatment

Liver cancer is one of the most common malignancies, with severe morbidity and mortality. While considerable progress has been made in liver cancer treatment, the 5-year overall survival (OS) of patients has not improved significantly. Reasons include the inadequate capability of early screening and diagnosis, a high incidence of recurrence and metastasis, a high degree of tumor heterogeneity, and an immunosuppressive tumor microenvironment. Therefore, the identification and validation of specific and robust liver cancer biomarkers are of major importance for early screening, timely diagnosis, accurate prognosis, and the prevention of tumor progression. In this review, we highlight some of the latest research progress and potential applications of liver cancer biomarkers, describing hotspots and prospective directions in biomarker discovery.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.

Kernel-based hierarchical structural component models for pathway analysis

MOTIVATION

Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex.

RESULTS

To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Application of High-Throughput Technologies for the Study of Microbiome and Cancer

Human gut microbiome research, especially gut microbiome, has been developing at a considerable pace over the last decades, driven by a rapid technological advancement. The emergence of high-throughput technologies, such as genomics, transcriptomics, and others, has afforded the generation of large volumes of data, and in relation to specific pathologies such as different cancer types. The current review identifies high-throughput technologies as they have been implemented in the study of microbiome and cancer. Four main thematic areas have emerged: the characterization of microbial diversity and composition, microbial functional analyses, biomarker prediction, and, lastly, potential therapeutic applications. The majority of studies identified focus on the microbiome diversity characterization, which is reaching technological maturity, while the remaining three thematic areas could be described as emerging.