Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Multi-omics in thoracic aortic aneurysm: the complex road to the simplification

BACKGROUND

Thoracic aortic aneurysm (TAA) is a serious condition that affects the aorta, characterized by the dilation of its first segment. The causes of TAA (e.g., age, hypertension, genetic syndromes) are heterogeneous and contribute to the weakening of the aortic wall. This complexity makes treating this life-threatening aortopathy challenging, as there are currently no etiological therapy available, and pharmacological strategies, aimed at avoiding surgical aortic replacement, are merely palliative. Recent studies on novel therapies for TAA have focused on identifying biological targets and etiological mechanisms of the disease by using advanced -omics techniques, including epigenomics, transcriptomics, proteomics, and metabolomics approaches.

METHODS

This review presents the latest findings from -omics approaches and underscores the importance of integrating multi-omics data to gain more comprehensive understanding of TAA.

RESULTS

Literature suggests that the alterations in TAA mediators frequently involve members of pro-fibrotic process (i.e., TGF-β signaling pathways) or proteins associated with cell/extracellular structures (e.g., aggrecans). Further analyses often reported the importance in TAA of processes as inflammation (PCR, CD3, leukotriene compounds), oxidative stress (chromatin OXPHOS, fatty acids), mitochondrial respiration and glycolysis/gluconeogenesis (e.g., PPARs and HIF1a). Of note, more recent metabolomics studies added novel molecular markers to the list of TAA-specific detrimental mediators (proteoglycans).

CONCLUSION

It is increasingly clear that integrating data from different -omics branches, along with clinical data, is essential as well as complicated both to reveal hidden relevant information and to address complex diseases such as TAA. Importantly, recent progresses in metabolomics highlighted novel potential and unprecedented marks in TAA diagnosis and therapy.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.