The power of OMICs

Myocardial infarction complexity: A multi-omics approach

Myocardial infarction (MI), a prevalent cardiovascular disease, is fundamentally precipitated by thrombus formation in the coronary arteries, which subsequently decreases myocardial perfusion and leads to cellular necrosis. The intricacy of MI pathogenesis necessitates extensive research to elucidate the disease's root cause, thereby addressing the limitations present in its diagnosis and prognosis. With the continuous advancement of genomics technology, genomics, proteomics, metabolomics and transcriptomics are widely used in the study of MI, which provides an excellent way to identify new biomarkers that elucidate the complex mechanisms of MI. This paper provides a detailed review of various genomics studies of MI, including genomics, proteomics, transcriptomics, metabolomics and multi-omics studies. The metabolites and proteins involved in the pathogenesis of MI are investigated through integrated protein-protein interactions and multi-omics analysis by STRING and Metascape platforms. In conclusion, the future of omics research in myocardial infarction offers significant promise.

Induction of reproductive injury by bisphenol A and the protective effects of cyanidin-3-O-glucoside and protocatechuic acid in rats

Bisphenol A (BPA) has attracted growing attention as a well-known environmental pollutant due to its high risk of male reproductive toxicity. In this study, transcriptomics profiling combined with metabolomic techniques was applied to explore the intervention effects of BPA-induced male reproductive toxicity. We demonstrated that cyanidin-3-O-glucoside (C3G) and its main metabolite protocatechuic acid (PCA) significantly increased testosterone and luteinizing hormone (LH) levels in the serum of rats, and improved sperm quality. Furthermore, we identified and screened differentially expressed genes (DEGs) and metabolites (DMs) that functionally enriched in the steroidogenesis-related pathways. Next, the validated results found that C3G and PCA significantly up-regulated the gene expressions of Star, Cyp11a1, Cyp17a1, Cyp19a1, Cyp7a1, Hsd3b1, Hsd3b2, Hsd17b3, Scrab1, and Ass1 in testicular. In Leydig cells, C3G and PCA dramatically alleviated apoptosis, ROS accumulation, and cell cycle arrest caused by BPA. In addition, molecular docking and simulation results implied that C3G and PCA competitively with BPA bind to the estrogen receptors α and β (ERα and ERβ) and shared common key amino acids. The main interaction modes between small molecules and estrogen receptors included π-π stacking, salt bridges, hydrogen bonds, and hydrophobic interactions. Therefore, our study sheds light on C3G and PCA supplementation can protect male reproduction from BPA-induced injury.

PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping

Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances' mode-of-action is crucial to develop alternative test strategies. Omics methods are promising tools to achieve this goal. Until now, most attention was focused on transcriptomics, while proteomics is not yet routinely applied in toxicology despite the large number of datasets available in public repositories. Exploiting the full potential of these datasets is hampered by differences in measurement procedures and follow-up data processing. Here we present the tool PROTEOMAS, which allows meta-analysis of proteomic data from public origin. The workflow was designed for analyzing proteomic studies in a harmonized way and to ensure transparency in the analysis of proteomic data for regulatory purposes. It agrees with the Omics Reporting Framework guidelines of the OECD with the intention to integrate proteomics to other omic methods in regulatory toxicology. The overarching aim is to contribute to the development of AOPs and to understand the mode of action of substances. To demonstrate the robustness and reliability of our workflow we compared our results to those of the original studies. As a case study, we performed a meta-analysis of 25 proteomic datasets to investigate the toxicological effects of nanomaterials at the lung level. PROTEOMAS is an important contribution to the development of alternative test strategies enabling robust meta-analysis of proteomic data. This workflow commits to the FAIR principles (Findable, Accessible, Interoperable and Reusable) of computational protocols.

Multi-omics & pathway analysis identify potential roles for tumor N-acetyl aspartate accumulation in murine models of castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) is incurable and remains a significant worldwide challenge (Oakes and Papa, 2015). Matched untargeted multi-level omic datasets may reveal biological changes driving CRPC, identifying novel biomarkers and/or therapeutic targets. Untargeted RNA sequencing, proteomics, and metabolomics were performed on xenografts derived from three independent sets of hormone naive and matched CRPC human cell line models of local, lymph node, and bone metastasis grown as murine orthografts. Collectively, we tested the feasibility of muti-omics analysis on models of CRPC in revealing pathways of interest for future validation investigation. Untargeted metabolomics revealed NAA and NAAG commonly accumulating in CRPC across three independent models and proteomics showed upregulation of related enzymes, namely N-acetylated alpha-linked acidic dipeptidases (FOLH1/NAALADL2). Based on pathway analysis integrating multiple omic levels, we hypothesize that increased NAA in CRPC may be due to upregulation of NAAG hydrolysis via NAALADLases providing a pool of acetyl Co-A for upregulated sphingolipid metabolism and a pool of glutamate and aspartate for nucleotide synthesis during tumor growth.

Linking Multi-Omics to Wheat Resistance Types to Fusarium Head Blight to Reveal the Underlying Mechanisms

Fusarium head blight (FHB) caused by Fusarium graminearum is a worldwide disease which has destructive effects on wheat production, resulting in severe yield reduction and quality deterioration, while FHB-infected wheat grains are toxic to people and animals due to accumulation of fungal toxins. Although impressive progress towards understanding host resistance has been achieved, our knowledge of the mechanism underlying host resistance is still quite limited due to the complexity of wheat-pathogen interactions. In recent years, disease epidemics, the resistance germplasms and components, the genetic mechanism of FHB, and disease management and control, etc., have been well reviewed. However, the resistance mechanism of FHB is quite complex with Type I, II to V resistances. In this review, we focus on the potential resistance mechanisms by linking different resistance types to multi-omics and emphasize the pathways or genes that may play significant roles in the different types of resistance. Deciphering the complicated mechanism of FHB resistance types in wheat at the integral levels based on multi-omics may help discover the genes or pathways that are critical for different FHB resistance, which could then be utilized and manipulated to improve FHB resistance in wheat breeding programs by using transgenic approaches, gene editing, or marker assisted selection strategies.

Combined transcriptomic and lipidomic analysis reveals aberrant lipid metabolism in central nervous system hemangioblastomas

Peritumoral cysts are commonly detected in the central nervous system tumors, especially hemangioblastomas (HBs). However, the molecular mechanisms driving their formation and propagation are still unknown. We conducted an integrated lipidomics and transcriptomics analysis on solid and cystic HB samples in order to elucidate the changes in the lipid profile and expression of lipid metabolism-related genes during cyst formation. Transcriptomic analysis revealed differential expression of several genes between the solid and cystic HBs, and those associated with lipid metabolism, such as ADCY4, MGLL, ACOT2, DGKG, SHC1 and LPAR2, were markedly dysregulated in the cystic HBs. The lipidomic analysis further showed a significant reduction in the abundance of triacylglycerol, ceramide, lysophosphatidylcholine and lysophosphatidylethanolamine, and an increase in phosphatidylcholine and phosphatidylethanolamine levels in the cystic HBs. Furthermore, bioinformatics analysis revealed altered lipid biosynthesis, glycerophospholipid metabolism and phospholipase activity in the cystic HBs. Taken together, our findings indicate that cyst formation in HBs is related with aberrant lipid metabolism.

Split Intein-Mediated Protein Ligation for detecting protein-protein interactions and their inhibition

Here, to overcome many limitations accompanying current available methods to detect protein-protein interactions (PPIs), we develop a live cell method called Split Intein-Mediated Protein Ligation (SIMPL). In this approach, bait and prey proteins are respectively fused to an intein N-terminal fragment (IN) and C-terminal fragment (IC) derived from a re-engineered split intein GP41-1. The bait/prey binding reconstitutes the intein, which splices the bait and prey peptides into a single intact protein that can be detected by regular protein detection methods such as Western blot analysis and ELISA, serving as readouts of PPIs. The method is robust and can be applied not only in mammalian cell lines but in animal models such as C. elegans. SIMPL demonstrates high sensitivity and specificity, and enables exploration of PPIs in different cellular compartments and tracking of kinetic interactions. Additionally, we establish a SIMPL ELISA platform that enables high-throughput screening of PPIs and their inhibitors.

Protein-protein interactions are fundamental to the regulation of protein activity and cellular phyisology. Here the authors present Split Intein-Mediated Protein Ligation, which uses bait and prey proteins fused to intein fragments to generate single intact proteins upon interaction.

Next generation sequencing and proteomics in plant virology: how is Colombia doing?

Crop production and trade are two of the most economically important activities in Colombia, and viral diseases cause a high negative impact to agricultural sector. Therefore, the detection, diagnosis, control, and management of viral diseases are crucial. Currently, Next-Generation Sequencing (NGS) and ‘Omic’ technologies constitute a right-hand tool for the discovery of novel viruses and for studying virus-plant interactions. This knowledge allows the development of new viral diagnostic methods and the discovery of key components of infectious processes, which could be used to generate plants resistant to viral infections. Globally, crop sciences are advancing in this direction. In this review, advancements in ‘omic’ technologies and their different applications in plant virology in Colombia are discussed. In addition, bioinformatics pipelines and resources for omics data analyses are presented. Due to their decreasing prices, NGS technologies are becoming an affordable and promising means to explore many phytopathologies affecting a wide variety of Colombian crops so as to improve their trade potential.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Rooting of eucalypt cuttings as a problem-solving oriented model in plant biology

Species of Eucalyptus are some of the most planted trees in the world, providing fiber, cellulose, energy, and wood for construction and furniture in renewable fashion, with the added advantage of fixing large amounts of atmospheric carbon. The efficiency of eucalypts in forestry relies mostly on the clonal propagation of selected genotypes both as pure species and interspecific hybrids. The formation of new roots from cambium tissues at the base of cuttings, referred to as adventitious rooting (AR), is essential for accomplishing clonal propagation successfully. AR is a highly complex, multi-level regulated developmental process, affected by a number of endogenous and environmental factors. In several cases, highly desirable genotypes from an industrial point of view carry along the undesirable trait of difficulty-to-root (recalcitrance). Understanding the bases of this phenotype is needed to identify ways to overcome recalcitrance and allow efficient clonal propagation. Herein, an overview of the state-of-the-art on the basis of AR recalcitrance in eucalypts addressed at various levels of regulation (transcript, protein, metabolite and phenotype), and OMICs techniques is presented. In addition, a focus is also provided on the gaps that need to be filled in order to advance in this strategic biological problem for global forestry industry relying on eucalypts.

Integrative lipidomic and transcriptomic analysis of X-linked adrenoleukodystrophy reveals distinct lipidome signatures between adrenomyeloneuropathy and childhood cerebral adrenoleukodystrophy

Precise pathophysiology with respect to the phenotypic variations and severity of X-ALD, specifically between adrenomyeloneuropathy (AMN) and childhood cerebral adrenoleukodystrophy (CCALD), has not been fully discovered. Herein, a systematic analysis using multi-layered lipidomics and transcriptomics was conducted to elucidate distinctive metabolic biosignatures among healthy control, AMN, and CCALD. Significant alterations regarding the accumulation of very long chain fatty acids were found in various lipid species such as phospholipids, glycerolipids, and sphingolipids. Remarkably, TG and CER that are physiologically essential were markedly down-regulated in CCALD than AMN. Transcriptomic analysis further supported the robustness of our findings by providing valuable information on the gene expressions of the regulatory factors. For instance, regulators of sphingolipid catabolism (SMPD1, CERK, and SPHK1) and TG anabolism (GPAM, GPAT2, and MBOAT2) were more up-regulated in AMN than in CCALD. These observations, among others, were in line with the recognized alterations of the associated lipidomes. In conclusion, the homeostatic imbalance of the complex lipid networks may be pathogenically important in X-ALD and the particular dysregulations of TG and CER may further influence the severity of CCALD among X-ALD patients.

A power law distribution of metabolite abundance levels in mice regardless of the time and spatial scale of analysis

Biomolecule abundance levels change with the environment and enable a living system to adapt to the new conditions. Although, the living system maintains at least some characteristics, e.g. homeostasis. One of the characteristics maintained by a living system is a power law distribution of biomolecule abundance levels. Previous studies have pointed to a universal characteristic of biochemical reaction networks, with data obtained from lysates of multiple cells. As a result, the spatial scale of the data related to the power law distribution of biomolecule abundance levels is not clear. In this study, we researched the scaling law of metabolites in mouse tissue with a spatial scale of quantification that was changed stepwise between a whole-tissue section and a single-point analysis (25 μm). As a result, metabolites in mouse tissues were found to follow the power law distribution independently of the spatial scale of analysis. Additionally, we tested the temporal changes by comparing data from younger and older mice. Both followed similar power law distributions, indicating that metabolite composition is not diversified by aging to disrupt the power law distribution. The power law distribution of metabolite abundance is thus a robust characteristic of a living system regardless of time and space.

Culturomics: A New Kid on the Block of OMICS to Enable Personalized Medicine

This innovation analysis highlights the underestimated and versatile potential of the new field of culturomics and examines its relation to other OMICS system sciences such as infectiomics, metabolomics, phenomics, and pharmacomicrobiomics. The advent of molecular biology, followed by the emergence of various disciplines of the genomics, and most importantly metagenomics, brought about the sharp decline of conventional microbiology methods. Emergence of culturomics has a natural synergy with therapeutic and clinical genomic approaches so as to realize personalized medicine. Notably, the concept of culturomics expands on that of phenomics and allows a reintroduction of the culture-based phenotypic characterization into the 21st century research repertoire, bolstered by robust technology for automated and massive execution, but its potential is largely unappreciated at present; the few available references show unenthusiastic pursuit and in narrow applications. This has not to be so: depending on the specific brand of culturomics, the scope of applications may extend to medicine, agriculture, environmental sciences, pharmacomicrobiomics, and biotechnology innovation. Moreover, culturomics may produce Big Data. This calls for a new generation of data scientists and innovative ways of harnessing and valorizing Big Data beyond classical genomics. Much more detailed and objective classification and identification of microbiota may soon be at hand through culturomics, thus enabling precision diagnosis toward truly personalized medicine. Culturomics may both widen the scope of microbiology and improve its contributions to diagnostics and personalized medicine, characterizing microbes and determining their associations with health and disease dynamics.