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At least 613 records · Page 34Linked to original sources

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗

Drug-induced liver injury: summary of a single topic clinical research conference.

Idiosyncratic drug induced liver injury (DILI) remains poorly understood. It is assumed that the affected individuals possess a rare combination of genetic and non genetic factors that, if identified, would greatly improve understanding of the underlying mechanisms. This single topic conference brought together basic scientists, translational investigators, and clinicians with an interest in DILI. The goal was to define high priority areas of investigation that will soon be made possible by The Drug-Induced Liver Injury Network (DILIN). Since 2004 DILIN has been collecting clinical data, genomic DNA and some tissues from patients who have experienced bone fide DILI. The presentations spanned many different areas of DILI, and included novel data concerning mechanisms of hepatotoxicity, new "omics" approaches, and the challenges of improving causation assessment.

Acetaminophen↗

Immune Markers and Risk of Pancreatic Cancer in the European EPIC Cohort.

The immune system is a major driver in pancreatic cancer development. Several prospective cohort studies have found associations for single immune system-derived proteins such as IL6 or CRP, but results are inconclusive, and Omics-based research is scarce. Hence, we aimed to investigate associations of a comprehensive protein panel with the risk of pancreatic cancer. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, 92 immune proteins were measured in baseline blood samples of 406 incident pancreatic cancer cases and 406 sex- and age-matched controls, using the Olink Immuno-Oncology panel. Multivariable adjusted conditional logistic regression was used to estimate odds ratios (OR, 95% CI) for protein levels in association with pancreatic cancer risk. Eight biomarkers were associated with pancreatic cancer risk (MMP12, LAMP3, CD28, IL-6, IL-12, FASLG, PD-L2, and PDCD1) but only MMP12 was significantly associated after multivariable adjustments for confounders and the seven proteins, with OR = 1.56 (95% CI: 1.20-2.03) for a doubling in protein concentration. After correction for multiple testing, none of the proteins were associated with risk. Restricting analyses to cases diagnosed within the first 4 years and 4-8 years after recruitment resulted in OR of 1.89 (95% CI: 1.28-2.80) and 1.37 (95% CI: 1.01-1.86) for MMP12, respectively. Higher levels of MMP12 were associated with pancreatic cancer risk specifically in those diagnosed shortly after recruitment, while other immune-related factors were not associated with risk. Further cohort studies are needed to confirm our initial findings.

Humans↗

Dynamic Molecular Changes in Brain, Lung, and Heart of Hamsters Infected With SARS-CoV-2: Insights From a Severe and Recovery Phase Model.

The Global pandemic of coronavirus disease 2019 was initiated by the emergence of severe acute respiratory syndrome coronavirus 2. In addition to conventional pulmonary lesions, a range of neurological injury symptoms have been identified in clinical practice, but the aetiology of neurological disorders linked to SARS-CoV-2 infection remains poorly understood. Syrian hamsters, which are highly susceptible to SARS-CoV-2 infection, exhibit a disease phenotype similar to that observed in human COVID-19 patients. In this study, a hamster model of COVID-19 infection was used to analyze molecular changes in different tissues at various time points post infection with distinct strains using proteomic and phosphoproteomic approaches. Multi-omics analysis showed that SARS-COV-2 infection triggers sustained downregulation of the abundance and phosphorylation levels of neuronal and synapse-associated proteins in the brain, suggesting that neuronal damage persists even during the recovery period. Additionally, infections with SARS-CoV-2 may contribute to the onset of long-term symptoms of COVID-19 by impacting energy metabolism, neurotransmitter release, and synaptic transmission pathways. This study provides a comprehensive molecular profile of hamsters infected with different SARS-CoV-2 strains in different tissues, offering foundational insights into the pathogenic mechanisms of COVID-19.

Animals↗

Proteomics and regulomics: the yin and yang of functional genomics.

Protein analysis is a field of research with a long history. Recently, the development of a series of proteomics approaches, i.e., simultaneous analyses on all or a majority of proteins in a cell at a given state, has reinvigorated protein analyses. Mass Spectrometry also developed into one of the most versatile technical tools supporting or even enabling many proteomics-oriented approaches, providing a convenient link between experimental protein analysis and the corresponding amino acid sequences. Thus direct links to the genomic sequence can be established, which opens the door for a synergistic combination with genomic sequence analysis. This review focuses especially on aspects of genome-wide transcription control, regulomics in analogy to all the other -omics, and how a combination of MS-based proteomics with in silico regulomics analyses can produce synergistic effects in the quest to understand how cells function. This is illustrated on a real life example showing how the MS-analysis and in silico promoter analysis can extend the list of candidates for signaling pathways, here the MAP kinase pathway.

Energy Metabolism↗

The role of mass spectrometry in plant systems biology.

Large-scale analyses of proteins and metabolites are intimately bound to advancements in MS technologies. The aim of these non-targeted "omic" technologies is to extend our understanding beyond the analysis of only parts of the system. Here, metabolomics and proteomics emerged in parallel with the development of novel mass analyzers and hyphenated techniques such as gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and multidimensional liquid chromatography coupled to mass spectrometry (LC-MS). The analysis of (i) proteins (ii) phosphoproteins, and (iii) metabolites is discussed in the context of plant physiology and environment and with a focus on novel method developments. Recently published studies measuring dynamic (quantitative) behavior at these levels are summarized; for these works, the completely sequenced plants Arabidopsis thaliana and Oryza sativa (rice) have been the primary models of choice. Particular emphasis is given to key physiological processes such as metabolism, development, stress, and defense. Moreover, attempts to combine spatial, tissue-specific resolution with systematic profiling are described. Finally, we summarize the initial steps to characterize the molecular plant phenotype as a corollary of environment and genotype.

Mass Spectrometry↗

Impact of microarray technology in nutrition and food research.

Microarrays have become standard tools for gene expression profiling as the mRNA levels of a large number of genes can be measured in a single assay. Many technical aspects concerning microarray production and laboratory usage have been addressed in great detail, but it remains still crucial to establish this technology in new research fields such as human nutrition and food-related areas. The correlation between diet and inter-individual variation in gene expression is an important and relatively unexplored issue in human nutrition. Therefore, nutritionists changed their research field dramatically from epidemiology and physiology towards the "omics" sciences. Nutrigenomics as a field of research is based on the complete knowledge of the human genome and refers to the entire spectrum of human genes that determine the interactions of nutrition with the organism. Nutrigenetics is based on the inter-individual, genetically determined differences in metabolism. Nutrigenomics and nutrigenetics carry the hope that individualized diet can improve human health and prevent nutrition-related diseases. In this article we give an overview of current DNA and protein microarray techniques (including fabrication, experimental procedure and data analysis), we describe their applications to nutrition and food research and point out the limitations, problems and pitfalls of microarray experiments.

Confidentiality↗

Pterostilbene Targets Hallmarks of Aging in the Gene Expression Landscape in Blood of Healthy Rats.

SCOPE: Polyphenols from the phytoestrogen group, including pterostilbene (PTS), are known for their antioxidant, anti-inflammatory, and anti-cancer effects. In recent reports, phytoestrogens attenuate age-related diseases; however, their pro-longevity effects in healthy models in mammals remain unknown. As longevity research demonstrates age-related transcriptomic signatures in human blood, the current study hypothesizes that phytoestrogen-supplemented diet may induce changes in gene expression that ultimately confer pro-longevity benefits. METHODS AND RESULTS: In the present study, RNA sequencing is conducted to determine transcriptome-wide changes in gene expression in whole blood of healthy rats consuming diets supplemented with phytoestrogens. Ortholog cell deconvolution is applied to analyze the omics data. The study discovered that PTS leads to changes in the gene expression landscape and PTS-target genes are associated with functions counteracting hallmarks of aging, including genomic instability, epigenetic alterations, compromised autophagy, mitochondrial dysfunction, deregulated nutrient sensing, altered intercellular interaction, and loss of proteostasis. These functions bridge together under anti-inflammatory effects through multiple pathways, including immunometabolism, where changes in cellular metabolism (e.g., ribosome biogenesis) impact the immune system. CONCLUSION: The findings provide a rationale for pre-clinical and clinical longevity studies and encourage investigations on PTS in maintaining cellular homeostasis, decelerating the process of aging, and improving conditions with chronic inflammation.

Animals↗

Stem cells and cancer: an intimate relationship.

Tumour-wide 'omics' approaches have long held sway as the approach to identifying useful therapeutic targets. This view is changing with the realization that many, if not all, cancers contain a minority population of self-renewing stem cells, the cancer stem cells, which are entirely responsible for sustaining the tumour as well as giving rise to proliferating but progressively differentiating cells that are responsible for much of the cellular heterogeneity that is so familiar to histopathologists. Moreover, although many tumours probably have their origins in normal stem cells, persuasive evidence from the haematopoietic system suggests that genetic alterations in more committed progenitor cells can reactivate the self-renewal machinery, resulting in a further source of cancer stem cells. Thus, the bulk of the tumour is not the problem, and so the identification of cancer stem cells and the factors that regulate their behaviour are likely to have an enormous bearing on the way that we treat neoplastic disease in the future.

Bone Marrow Cells↗

Sociomics! Using the IssueCrawler to map, monitor and engage with the global proteomics research network.

We invite comment upon an experiment to locate proteomics on the WWW using a software tool called the IssueCrawler. We call our research "sociomics" because, like the bioscience omics, it is a semi-automated, computerised approach to the global analysis of data, whose computerised results can be integrated towards the development of a new "systems sociology" approach to the study of society. Our findings are that proteomics on the web is a scale-free network whose nodes display considerable "dynamic range".

Cluster Analysis↗

Advances in plant proteomics.

With the avalanche of genomic information and improvements in analytical technology, proteomics is becoming increasingly important for the study of many different aspects of plant functions. Since proteins serve as important components of major signaling and biochemical pathways, studies at protein levels are essential to reveal molecular mechanisms underlying plant growth, development, and interactions with the environment. The plant proteome is highly complex and dynamic. Although great strides need to be taken towards the ultimate goal of characterizing all the proteins in a proteome, current technologies have provided immense opportunities for high-throughput proteomic studies that have gone beyond simple protein identification to analyzing various functional aspects, such as quantification, PTM, subcellular localization, and protein-protein interactions. In this review of plant proteomics, advances in protein fractionation, separation, and MS will be outlined. Focus will be on recent development in functional analysis of plant proteins, which paves the way towards the comprehensive integration with transcriptomics, metabolomics, and other large scale "-omics" into systems biology.

Algorithms↗

Label-free detection methods for protein microarrays.

With the growth of the "-omics" such as functional genomics and proteomics, one of the foremost challenges in biotechnologies has become the development of novel methods to monitor biological process and acquire the information of biomolecular interactions in a systematic manner. To fully understand the roles of newly discovered genes or proteins, it is necessary to elucidate the functions of these molecules in their interaction network. Microarray technology is becoming the method of choice for such a task. Although protein microarray can provide a high throughput analytical platform for protein profiling and protein-protein interaction, most of the current reports are limited to labeled detection using fluorescence or radioisotope techniques. These limitations deflate the potential of the method and prevent the technology from being adapted in a broader range of proteomics applications. In recent years, label-free analytical approaches have gone through intensified development and have been coupled successfully with protein microarray. In many examples of label-free study, the microarray has not only offered the high throughput detection in real time, but also provided kinetics information as well as in situ identification. This article reviews the most significant label-free detection methods for microarray technology, including surface plasmon resonance imaging, atomic force microscope, electrochemical impedance spectroscopy and MS and their applications in proteomics research.

Animals↗

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p&#x2009;<&#x2009;0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8&#x2009;+&#x2009;T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

What it takes to get a herbicide's mode of action. Physionomics, a classical approach in a new complexion.

Discovering new herbicides with novel modes of action is a priority assignment in plant protection research. However, for active compounds identified in greenhouse screens, the crucial point is to tread the most efficient path in determining a herbicide's target site, regarding chance of success, time and research costs. Today, in the literature, molecular (functional genomics, transcriptomics), biochemical (proteomics) and analytical (metabolomics) approaches are particularly discussed. So far, less attention has been focused on the comprehensive physiological profiling of the complex plant system as a procedure which enables new herbicides, with an unknown target site for their mode of action, to be screened rapidly. Here, the concept of an array of 'functional' bioassays is presented which has ultimately been developed from the classical tool of mode of action diagnosis by symptoms. These bioassays are designed to differentiate between the distinct responses of the multiple organization units (plant, tissue, meristematic cell, organelle), developmental stages, types of metabolism (phototrophic, heterotrophic) and physiological processes in the plant organism. The response pattern to a herbicide can be viewed as the end result of changes induced in the molecular and biochemical process chain and should be diagnostic of its physiological mode of action. The results can be interpreted directly or a fingerprint database for all known modes of action to be screened for analogy. The term 'physionomics' is proposed for this comprehensive physiological profiling of the plant system, following the parallel terminology of the molecular and biochemical 'omics' technologies. Physionomics procedures provide a first clue to the mode of action of a new herbicide that can direct more time-consuming and costly molecular, biochemical, histochemical or analytical studies to identify a target site more efficiently.

Biological Assay↗

Quantile Tensor Regression for Integrative Genomic Analysis of Oesophageal Carcinoma.

Recent integrative genomic studies have increasingly exploited the tensor structure of multi-omics data to develop statistical methods that jointly model the relationship between clinical outcomes and multiple genomes. However, genomic measurements and clinical outcomes are frequently contaminated by outliers or heavy-tailed noise, necessitating robust tensor-based inference approaches. In this paper, we investigate the quantile tensor regression with an emphasis on the region selection problem. We introduce a novel estimator that integrates quantile regression for robustness with a nonconvex penalty to encourage sparsity in the tensor coefficient, thereby enabling the identification of localized genomic regions that significantly influence the clinical response. To solve the resulting optimization problem, we devise an effective algorithm tailored to the nonconvex objective and tensor architecture. We establish the asymptotic properties of the proposed nonconvex penalized estimator. Extensive simulations demonstrate the excellent finite-sample performance of the proposed estimator. We further illustrate the practical utility of the proposed estimator through an application to esophageal carcinoma data, providing empirical validation.

Humans↗

BMDx2: A Tool for Integrating Toxicogenomics-Based Dose-Dependency Analysis and AOP-Based Mechanistic Insights.

Despite the advent of mechanistic toxicology using omics data to link molecular perturbations with systemic outcomes, regulatory toxicology still lacks the application of mechanism-anchored metrics from such data. This is partially because traditional gene-centric analysis often falls short of linking molecular changes to adverse outcomes. To address this gap, BMDx2, an open-source tool that transforms multi-dose toxicogenomics datasets into quantitative, mechanistic evidence for human chemical safety assessment is developed. BMDx2 couples benchmark-dose modeling with Adverse Outcome Pathway (AOP) enrichment to derive transcriptomic-based points of departure, enabling potency ranking, chemical prioritization, and mechanistically anchored explanations of the effect of chemical exposures. BMDx2 can process a broad range of data, including DNA microarray and RNA sequencing studies. Here, case studies are used to illustrate the versatility of BMDx2 in characterizing the mechanism of action of chemicals. An initial case study on carbon nanotubes exposure applies integrative analysis of transcriptomics and genome-wide DNA methylation data, uncovering cellular reprogramming processes underlying fibrosis. A second case study on bleomycin exposure demonstrate how transcriptomic data alone can be mapped to fibrosis-related AOPs in a standardized, regulatory appropriate manner. Together, these examples show how BMDx2 supports the regulatory application of toxicogenomics and accelerates mechanism-based chemical safety evaluation.

Toxicogenetics↗

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗

DNA Methylation Analysis by Bisulfite Pyrosequencing of Mouse Embryonic Fibroblasts with Reprogramming Enhanced by Thyroid Hormones.

DNA methylation is a widely studied epigenetic mark which in mammals involves the incorporation of a methyl group to the fifth carbon of cytosines, mainly those belonging to CpG dinucleotides. It has been linked to context-dependent regulatory functions ranging from gene and repetitive DNA silencing to gene body transcriptional activity. Because of its important roles during embryonic development and cell differentiation, DNA methylation can be used to track cell reprogramming by measuring the methylation levels of pluripotency-associated factors. In this scenario, bisulfite pyrosequencing is a simple, robust, and widely used technique which allows for the quantification of DNA methylation levels at small, specific regions of the genome. It involves the amplification and biotin tagging of bisulfite-converted DNA. Single amplified strands are then purified using streptavidin and finally pyrosequenced using a sequencing primer. Thus, it is an ideal method for the quantitative profiling of specific genomic regions, with applications ranging from biomarker discovery and epigenetic clock tracking to omic validation studies.

Animals↗