The metaRbolomics Toolbox in Bioconductor and beyond

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

A Network Pharmacology Approach to Estimate the Active Ingredients and Potential Targets of Cuscutae semen in the Treatment of Osteoporosis

Background

Osteoporosis is a metabolic osteopathy characterized by abnormal bone mass and microstructure that has become a public health problem worldwide. Cuscutae semen (CS) is a traditional Chinese medicine (TCM) that has a positive effect on the prevention and treatment of osteoporosis. However, the mechanism of CS is unclear. Therefore, this study aimed to reveal the possible molecular mechanism involved in the effects of CS on osteoporosis based on a network pharmacology approach.

Material/Methods

The inactive and active ingredients of CS were identified by searching the pharmacology analysis platform of the Chinese medicine system (TCMSP), and the targets of osteoporosis were screened in the relevant databases, such as GeneCards, PubMed, and the Comparative Toxicogenomics Database (CTD). The network of “medicine-ingredients-disease-targets (M-I-D-T)” was established by means of network pharmacology, and the key targets and core pathways were determined by R analysis. Molecular docking methods were used to evaluate the binding activity between the target and the active ingredients of CS.

Results

Eleven active ingredients were identified in CS, and 175 potential targets of the active ingredients were also identified from the TCMSP. Moreover, we revealed 22 539 targets related to osteoporosis in the 3 well-established databases, and we determined the intersection of the disease targets and the potential targets of the active ingredients; 107 common targets were identified and used in further analysis. Additionally, biological processes and signaling pathways involved in target action, such as fluid shear stress, atherosclerosis, cancer pathways, and the TNF signaling pathway, were determined. Finally, we chose the top 5 common targets, CCND1, EGFR, IL6, MAPK8, and VEGFA, for molecular docking with the 11 active ingredients of CS.

Conclusions

This study suggested that CS has multiple ingredients and multiple targets relevant to the treatment of osteoporosis. We determined that the active ingredient, sesamin, may be the most crucial ingredient of CS for the treatment of osteoporosis. Additionally, the network pharmacology method provided a novel research approach to analyze the function of complex ingredients.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.