Search

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered - omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different -omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner. [ABSTRACT FROM AUTHOR]

Herbal medicines were widely used in ancient and modern societies as remedies for human ailments. Notably, the Papaveraceae family includes well-known species, such as Papaver somniferum and Chelidonium majus , which possess medicinal properties due to their latex content. Latex-bearing plants are a rich source of diverse bioactive compounds, with applications ranging from narcotics to analgesics and relaxants. With the advent of high-throughput technologies and advancements in sequencing tools, an opportunity exists to bridge the knowledge gap between the genetic information of herbs and the regulatory networks underlying their medicinal activities. This emerging discipline, known as herbgenomics, combines genomic information with other -omics studies to unravel the genetic foundations, including essential gene functions and secondary metabolite biosynthesis pathways. Furthermore, exploring the genomes of various medicinal plants enables the utilization of modern genetic manipulation techniques, such as Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR/Cas9) or RNA interference. This technological revolution has facilitated systematic studies of model herbs, targeted breeding of medicinal plants, the establishment of gene banks and the adoption of synthetic biology approaches. In this article, we provide a comprehensive overview of the recent advances in genomic, transcriptomic, proteomic and metabolomic research on species within the Papaveraceae family. Additionally, it briefly explores the potential applications and key opportunities offered by the -omics perspective in the pharmaceutical industry and the agrobiotechnology field. [ABSTRACT FROM AUTHOR]

Multiomics approaches have significantly aided the identification of molecular signatures in complex neuropsychiatric disorders. Lipidomics, one of the newest additions in the -omics family, sheds light on lipid profiles and is an emerging methodological tool to study schizophrenia pathobiology, as lipid dysregulation has been repeatedly observed in schizophrenia. In this review, we performed a detailed literature search for lipidomics studies in schizophrenia. Following elaborate inclusion/exclusion criteria, we focused on human studies in schizophrenia and schizophrenia-related diagnoses in brain and blood specimens, including serum plasma, platelets and red blood cells. Eighteen studies fulfilled our inclusion criteria, of which five were conducted in the brain, 12 in peripheral material and one in both. Here, we first provide background on lipidomics and the main lipid categories addressed, review in detail the included literature and look for common lipidomics patterns in brain and the periphery that emerge from these studies. Furthermore, we highlight current limitations in schizophrenia lipidomics research and underline the need for following up on lipidomics results with complementary molecular approaches. [ABSTRACT FROM AUTHOR]

We sought to investigate differential metabolism in patients with systemic sclerosis (SSc) who develop pulmonary arterial hypertension (PAH) versus those who do not, as a method of identifying potential disease biomarkers. In a nested case-control design, serum metabolites were assayed in SSc subjects who developed right heart catheterization-confirmed PAH (n ¼ 22) while under surveillance in a longitudinal cohort from Johns Hopkins, then compared with metabolites assayed in matched SSc patients who did not develop PAH (n ¼ 22). Serum samples were collected at “proximate” (within 12 months) and “distant” (within 1–5 yr) time points relative to PAH diagnosis. Metabolites were identified using liquid chromatography-mass spectroscopy (LC-MS). An LC-MS dataset from SSc subjects with either mildly elevated pulmonary pressures or overt PAH from the University of Michigan was compared. Differentially abundant metabolites were tested as predictors of PAH in two additional validation SSc cohorts. Longchain fatty acid metabolism (LCFA) consistently differed in SSc-PAH versus SSc without PH. LCFA metabolites discriminated SScPAH patients with mildly elevated pressures in the Michigan cohort and predicted SSc-PAH up to 2 yr before clinical diagnosis in the Hopkins cohort. Acylcholines containing LCFA residues and linoleic acid metabolites were most important for discriminating SSc-PAH. Combinations of acylcholines and linoleic acid metabolites provided good discrimination of SSc-PAH across cohorts. Aberrant lipid metabolism is observed throughout the evolution of PAH in SSc. Lipidomic signatures of abnormal LCFA metabolism distinguish SSc-PAH patients from those without PH, including before clinical diagnosis and in mild disease. NEW & NOTEWORTHY Abnormal lipid metabolism is evident across time in the development of SSc-PAH, and dysregulated long-chain fatty acid metabolism predicts overt PAH. [ABSTRACT FROM AUTHOR]

Cell death is an essential physiological process for the survival of multicellular organisms. Our understanding of programmed cell death in development, immune function maintenance, and adult tissue repair has significantly advanced over the past decade. However, there are still gaps in our knowledge about the induction, regulation, and checkpoints of this process due to the diverse forms of cellular suicide and the rapid nature of the process. Molecular advancements such as specific cell death sensors, RNA-seq, single-cell RNA-seq, and proteomics have allowed for identifying new factors and a better understanding of the molecular networks and pathways that regulate these processes. Programmed cell death also plays a role in cancer, both limiting and facilitating aspects of the malignant process, making its analysis and inhibition challenging. This review discusses the field’s advancements using the model organism Drosophila melanogaster, the types of cell death in development and adult tissues, the techniques for studying it, and its role in cancer.

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered –omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different –omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner.

The use of novel high‐throughput sequencing (HTS) technologies to examine the responses of natural multidomain microbial communities to scrubber effluent discharges to the marine environment is still limited. Thus, we applied metabarcoding sequencing targeting the planktonic unicellular eukaryotic and prokaryotic fraction (phytoplankton, bacterioplankton, and protozooplankton) in mesocosm experiments with natural microbial communities from a polluted and an unpolluted site. Furthermore, metagenomic analysis revealed changes in the taxonomic and functional dominance of multidomain marine microbial communities after scrubber effluent additions. The results indicated a clear shift in the microbial communities after such additions, which favored bacterial taxa with known oil and polycyclic aromatic hydrocarbons (PAHs) biodegradation capacities. These bacteria exhibited high connectedness with planktonic unicellular eukaryotes employing variable trophic strategies, suggesting that environmentally relevant bacteria can influence eukaryotic community structure. Furthermore, Clusters of Orthologous Genes associated with pathways of PAHs and monocyclic hydrocarbon degradation increased in numbers at treatments with high scrubber effluent additions acutely. These genes are known to express enzymes acting at various substrates including PAHs. These indications, in combination with the abrupt decrease in the most abundant PAHs in the scrubber effluent below the limit of detection—much faster than their known half‐lives—could point toward a bacterioplankton‐initiated rapid ultimate biodegradation of the most abundant toxic contaminants of the scrubber effluent. The implementation of HTS could be a valuable tool to develop multilevel biodiversity indicators of the scrubber effluent impacts on the marine environment, which could lead to improved impact assessment. Environ Toxicol Chem 2024;43:1012–1029. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

Summary: Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Findings: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54–2.93], PYY [OR = 1.87, 95% CI: 1.43–2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73–0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99–4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67–0.86] and TPM3 [OR = 0.47, 95% CI: 0.34–0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80–0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82–0.86] and early onset disease [OR = 0.71, 95% CI: 0.68–0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. Interpretation: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. Funding: This work was supported by Cancer Research UK (grant no. C8221/A29017).

In 2012, 20 key questions related to hazard and exposure assessment and environmental and health risks of pharmaceuticals and personal care products in the natural environment were identified. A decade later, this article examines the current level of knowledge around one of the lowest‐ranking questions at that time, number 19: "Can nonanimal testing methods be developed that will provide equivalent or better hazard data compared with current in vivo methods?" The inclusion of alternative methods that replace, reduce, or refine animal testing within the regulatory context of risk and hazard assessment of chemicals generally faces many hurdles, although this varies both by organism (human‐centric vs. other), sector, and geographical region or country. Focusing on the past 10 years, only works that might reasonably be considered to contribute to advancements in the field of aquatic environmental risk assessment are highlighted. Particular attention is paid to methods of contemporary interest and importance, representing progress in (1) the development of methods which provide equivalent or better data compared with current in vivo methods such as bioaccumulation, (2) weight of evidence, or (3) ‐omic‐based applications. Evolution and convergence of these risk assessment areas offer the basis for fundamental frameshifts in how data are collated and used for the protection of taxa across the breadth of the aquatic environment. Looking to the future, we are at a tipping point, with a need for a global and inclusive approach to establish consensus. Bringing together these methods (both new and old) for regulatory assessment and decision‐making will require a concerted effort and orchestration. Environ Toxicol Chem 2024;43:559–574. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

In the last decades, it has become evident that endothelial cells (ECs) in the microvasculature play an important role in the pathophysiology of sepsis-associated multiple organ dysfunction syndrome (MODS). Studies on how ECs orchestrate leukocyte recruitment, control microvascular integrity and permeability, and regulate the haemostatic balance have provided a wealth of knowledge and potential molecular targets that could be considered for pharmacological intervention in sepsis. Yet, this information has not been translated into effective treatments. As MODS affects specific vascular beds, (organotypic) endothelial heterogeneity may be an important contributing factor to this lack of success. On the other hand, given the involvement of ECs in sepsis, this heterogeneity could also be leveraged for therapeutic gain to target specific sites of the vasculature given its full accessibility to drugs. In this review, we describe current knowledge that defines heterogeneity of organ-specific microvascular ECs at the molecular level and elaborate on studies that have reported EC responses across organ systems in sepsis patients and animal models of sepsis. We discuss hypothesis-driven, single-molecule studies that have formed the basis of our understanding of endothelial cell engagement in sepsis pathophysiology, and include recent studies employing high-throughput technologies. The latter deliver comprehensive data sets to describe molecular signatures for organotypic ECs that could lead to new hypotheses and form the foundation for rational pharmacological intervention and biomarker panel development. Particularly results from single cell RNA sequencing and spatial transcriptomics studies are eagerly awaited as they are expected to unveil the full spatiotemporal signature of EC responses to sepsis. With increasing awareness of the existence of distinct sepsis subphenotypes, and the need to develop new drug regimen and companion diagnostics, a better understanding of the molecular pathways exploited by ECs in sepsis pathophysiology will be a cornerstone to halt the detrimental processes that lead to MODS.

Copyright of Immunology Quarterly / Immunológiai Szemle is the property of Medicina Konyvkiado Zrt. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil salinity is a major abiotic stress in global agricultural productivity with an estimated 50% of arable land predicted to become salinized by 2050. Since most domesticated crops are glycophytes, they cannot be cultivated on salt soils. The use of beneficial microorganisms inhabiting the rhizosphere (PGPR) is a promising tool to alleviate salt stress in various crops and represents a strategy to increase agricultural productivity in salt soils. Increasing evidence underlines that PGPR affect plant physiological, biochemical, and molecular responses to salt stress. The mechanisms behind these phenomena include osmotic adjustment, modulation of the plant antioxidant system, ion homeostasis, modulation of the phytohormonal balance, increase in nutrient uptake, and the formation of biofilms. This review focuses on the recent literature regarding the molecular mechanisms that PGPR use to improve plant growth under salinity. In addition, very recent -OMICs approaches were reported, dissecting the role of PGPR in modulating plant genomes and epigenomes, opening up the possibility of combining the high genetic variations of plants with the action of PGPR for the selection of useful plant traits to cope with salt stress conditions. [ABSTRACT FROM AUTHOR]

Abstract Although PAH is partially attributed to disordered metabolism, previous human studies have mostly examined circulating metabolites at a single time point, potentially overlooking crucial disease biology. Current knowledge gaps include an understanding of temporal changes that occur within and across relevant tissues, and whether observed metabolic changes might contribute to disease pathobiology. We utilized targeted tissue metabolomics in the Sugen hypoxia (SuHx) rodent model to investigate tissue‐specific metabolic relationships with pulmonary hypertensive features over time using regression modeling and time‐series analysis. Our hypotheses were that some metabolic changes would precede phenotypic changes, and that examining metabolic interactions across heart, lung, and liver tissues would yield insight into interconnected metabolic mechanisms. To support the relevance of our findings, we sought to establish links between SuHx tissue metabolomics and human PAH ‐omics data using bioinformatic predictions. Metabolic differences between and within tissue types were evident by Day 7 postinduction, demonstrating distinct tissue‐specific metabolism in experimental pulmonary hypertension. Various metabolites demonstrated significant tissue‐specific associations with hemodynamics and RV remodeling. Individual metabolite profiles were dynamic, and some metabolic shifts temporally preceded the emergence of overt pulmonary hypertension and RV remodeling. Metabolic interactions were observed such that abundance of several liver metabolites modulated lung and RV metabolite‐phenotype relationships. Taken all together, regression analyses, pathway analyses and time‐series analyses implicated aspartate and glutamate signaling and transport, glycine homeostasis, lung nucleotide abundance, and oxidative stress as relevant to early PAH pathobiology. These findings offer valuable insights into potential targets for early intervention in PAH.

Reproductive inefficiency is a major cause for economic loss in cattle operations. However, the mechanisms underlying their ethology are still unknown. Herein, we aimed to identify genes and genetic variants underlying the uterine function and their role in female fertility. To this end, we performed a multi-tiered approach to identify endometrial differentially expressed genes (DEGs) from beef heifers classified as fertile (n = 7) or sub-fertile (n = 5). Further, we used public RNASeq data from uterine luminal epithelial cells from beef cows (n = 43) to identify genetic variants affecting the expression of genes (expression quantitative trait loci - eQTLs) involved with pregnancy outcomes. We identified 798 DEGs underlying pathways important for pregnancy establishment, including inflammatory response and G-protein coupled receptor (GPCR) signaling (P-value ≤ 0.01). Based on the eQTL mapping analysis, we identified 4,676 cis and 7,682 trans eQTLs (FDR ≤ 0.05). Interestingly, some of the eQTLs overlapped with 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05), including daughter pregnancy rate and non-return rate. Additionally, these eQTLs were modulating the expression of transcription factors and DEGs previously associated with pregnancy outcomes in beef cows. The integration of gene lists from both studies resulted in 16 common genes, including F2 and P2RX5. The F2 gene encodes for a coagulation factor that plays a role in smooth muscle contraction and is modulated by a unique family of GPCRs. The expression of purinergic receptors, such as P2RX5, have been proposed to be major facilitators of attachment and implantation through calcium ions. Functional over-representation analysis highlighted metabolism, immune response, and hormone (estrogen and GnRH) signaling pathways affected by eQTL-regulated genes (P-value < 0.01). Immune cells and regulatory pregnancy hormones, such as interferon tau and progesterone, are tightly regulated by each other and work cooperatively. GPCR signaling involves key regulatory hormones of the estrous cycle, specifically GnRH, LH, and FSH. Improper immune cell signaling, cyclicity, hormonal signaling, and smooth muscle contractions all affect the endometrium function, with potential downstream effects on pregnancy establishment and fertility. Dysregulation of these signaling pathways due to genetic variants could have detrimental effects, ultimately leading to failed pregnancy. Our findings provide novel insights into the genetic basis of reproductive processes in beef cattle. The molecular features and over-represented pathways could be used for identification of potential biomarkers for predicting pregnancy success. However, the underlying causal mechanisms modulating the expression of uterine genes warrant further investigation. [ABSTRACT FROM AUTHOR]

Abstract Background Rapid development of high-throughput omics technologies generates an increasing interest in algorithms for cutoff point identification. Existing cutoff methods and tools identify cutoff points based on an association of continuous variables with another variable, such as phenotype, disease state, or treatment group. These approaches are not applicable for descriptive studies in which continuous variables are reported without known association with any biologically meaningful variables. Results The most common shape of the ranked distribution of continuous variables in high-throughput descriptive studies corresponds to a biphasic curve, where the first phase includes a big number of variables with values slowly growing with rank and the second phase includes a smaller number of variables rapidly growing with rank. This study describes an easy algorithm to identify the boundary between these phases to be used as a cutoff point. Discussion The major assumption of that approach is that a small number of variables with high values dominate the biological system and determine its major processes and functions. This approach was tested on three different datasets: human genes and their expression values in the human cerebral cortex, mammalian genes and their values of sensitivity to chemical exposures, and human proteins and their expression values in the human heart. In every case, the described cutoff identification method produced shortlists of variables (genes, proteins) highly relevant for dominant functions/pathways of the analyzed biological systems. Conclusions The described method for cutoff identification may be used to prioritize variables in descriptive omics studies for a focused functional analysis, in situations where other methods of dichotomization of data are inaccessible.

Lichens are mutually symbiotic systems consisting of fungal and algal symbionts. While diverse lichen-forming fungal species are known, limited species of algae form lichens. Plasticity in the combination of fungal and algal species with different eco-physiological properties may contribute to the worldwide distribution of lichens, even in extreme habitats. Lichens have been studied systematically for more than 200 years; however, plasticity in fungal–algal/cyanobacterial symbiotic combinations is still unclear. In addition, the association between non-cyanobacterial bacteria and lichens has attracted attention in recent years. The types, diversity, and functions of lichen-associated bacteria have been studied using both culture-based and culture-independent methods. This review summarizes the history of systematic research on lichens and lichen-associated bacteria and provides insights into the current status of research in this field. [ABSTRACT FROM AUTHOR]

The approval of new heart failure (HF) therapies has slowed over the past two decades in part due to the high costs of conducting large randomized clinical trials that are needed to adequately power major clinical endpoint studies. Several biomarkers have been identified reflecting different elements of HF pathophysiology, with possible applications in diagnosis, risk stratification, treatment monitoring, and even in the design of clinical trials. Biomarkers could potentially be used to refine study inclusion criteria to enable enrolment of patients who are more likely to respond to a therapeutic intervention, despite being at sufficient risk to meet pre‐determined study endpoint rates. When there is a close relationship between biomarker levels and clinical endpoints, changes in biomarker levels after a given treatment can act as a surrogate endpoint, potentially reducing the duration and cost of a clinical trial. Natriuretic peptides have been widely used in clinical trials with a variable amount of added value, which such variation being probably due to the absence of a close pathophysiological connection to the study drug. Notable exceptions to this include sacubitril/valsartan and vericiguat. Future studies should seek to adopt unbiased approaches for discovery of true companion diagnostics; with ‐omics‐based tools, biomarkers might be more precisely selected for use in clinical trials to identify responses that closely reflect the biological effects of the drug under investigation. Finally, biomarkers associated with cardiac damage and remodelling, such as cardiac troponin, could be employed as safety endpoints provided that standardization between different assays is achieved. [ABSTRACT FROM AUTHOR]

Introduced in 2010, the sub-discipline of gerontologic biostatistics was conceptualized to address the specific challenges of analyzing data from clinical research studies involving older adults. Since then, the evolving technological landscape has led to a proliferation of advancements in biostatistics and other data sciences that have significantly influenced the practice of gerontologic research, including studies beyond the clinic. Data science is the field at the intersection of statistics and computer science, and although the term "data science" was not widely used in 2010, the field has quickly made palpable impacts on gerontologic research. In this Review in Depth, we describe multiple advancements of biostatistics and data science that have been particularly impactful. Moreover, we propose the sub-discipline of "gerontologic biostatistics and data science", or GBDS, which subsumes gerontologic biostatistics into a more encompassing practice. Prominent GBDS advancements that we discuss herein include cutting-edge methods in experimental design and causal inference, adaptations of machine learning, the rigorous quantification of deep phenotypic measurement, and analysis of high-dimensional -omics data. We additionally describe the need for integration of information from multiple studies and propose strategies to foster reproducibility, replicability, and open science. Lastly, we provide information on software resources for GBDS practitioners to apply these approaches to their own work and propose areas where further advancement is needed. The methodological topics reviewed here aim to enhance data-rich research on aging and foster the next generation of gerontologic researchers., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Abstract Background Primary prevention trials have demonstrated that the traditional Mediterranean diet is associated with a reduction in cardiovascular mortality and morbidity. However, this benefit has not been proven for secondary prevention after an acute coronary syndrome (ACS). We hypothesized that a high-intensity Mediterranean diet intervention after an ACS decreases the vulnerability of atherosclerotic plaques by complex interactions between anti-inflammatory effects, microbiota changes and modulation of gene expression. Methods The MEDIMACS project is an academically funded, prospective, randomized, controlled and mechanistic clinical trial designed to address the effects of an active randomized intervention with the Mediterranean diet on atherosclerotic plaque vulnerability, coronary endothelial dysfunction and other mechanistic endpoints. One hundred patients with ACS are randomized 1:1 to a monitored high-intensity Mediterranean diet intervention or to a standard-of-care arm. Adherence to diet is assessed in both arms using food frequency questionnaires and biomarkers of compliance. The primary endpoint is the change (from baseline to 12 months) in the thickness of the fibrous cap of a non-significant atherosclerotic plaque in a non-culprit vessel, as assessed by repeated optical coherence tomography intracoronary imaging. Indices of coronary vascular physiology and changes in gastrointestinal microbiota, immunological status and protein and metabolite profiles will be evaluated as secondary endpoints. Discussion The results of this trial will address the key effects of dietary habits on atherosclerotic risk and will provide initial data on the complex interplay of immunological, microbiome-, proteome- and metabolome-related mechanisms by which non-pharmacological factors may impact the progression of coronary atherosclerosis after an ACS. Trial registration ClinicalTrials.gov NCT03842319 . Registered on 13 May 2019

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions. [ABSTRACT FROM AUTHOR]

Commercial interest in biostimulants as a tool for sustainable green economics and agriculture concepts is on a steep rise, being followed by increasing demand to employ efficient scientific methods to develop new products and understand their mechanisms of action. Biostimulants represent a highly diverse group of agents derived from various natural sources. Regardless of their nutrition content and composition, they are classified by their ability to improve crop performance through enhanced nutrient use efficiency, abiotic stress tolerance, and quality of crops. Numerous reports have described modern, non-invasive sensor-based phenotyping methods in plant research. This review focuses on applying phenotyping approaches in biostimulant research and development, and maps the evolution of interaction of these two intensively growing domains. How phenotyping served to identify new biostimulants, the description of their biological activity, and the mechanism/mode of action are summarized. Special attention is dedicated to the indoor high-throughput methods using model plants suitable for biostimulant screening and developmental pipelines, and high-precision approaches used to determine biostimulant activity. The need for a complex method of testing biostimulants as multicomponent products through integrating other -omic approaches followed by advanced statistical/mathematical tools is emphasized. [ABSTRACT FROM AUTHOR]

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.

Soil salinity is a major abiotic stress in global agricultural productivity with an estimated 50% of arable land predicted to become salinized by 2050. Since most domesticated crops are glycophytes, they cannot be cultivated on salt soils. The use of beneficial microorganisms inhabiting the rhizosphere (PGPR) is a promising tool to alleviate salt stress in various crops and represents a strategy to increase agricultural productivity in salt soils. Increasing evidence underlines that PGPR affect plant physiological, biochemical, and molecular responses to salt stress. The mechanisms behind these phenomena include osmotic adjustment, modulation of the plant antioxidant system, ion homeostasis, modulation of the phytohormonal balance, increase in nutrient uptake, and the formation of biofilms. This review focuses on the recent literature regarding the molecular mechanisms that PGPR use to improve plant growth under salinity. In addition, very recent -OMICs approaches were reported, dissecting the role of PGPR in modulating plant genomes and epigenomes, opening up the possibility of combining the high genetic variations of plants with the action of PGPR for the selection of useful plant traits to cope with salt stress conditions.

Background: Rapid development of high-throughput omics technologies generates an increasing interest in algorithms for cutoff point identification. Existing cutoff methods and tools identify cutoff points based on an association of continuous variables with another variable, such as phenotype, disease state, or treatment group. These approaches are not applicable for descriptive studies in which continuous variables are reported without known association with any biologically meaningful variables. Results: The most common shape of the ranked distribution of continuous variables in high-throughput descriptive studies corresponds to a biphasic curve, where the first phase includes a big number of variables with values slowly growing with rank and the second phase includes a smaller number of variables rapidly growing with rank. This study describes an easy algorithm to identify the boundary between these phases to be used as a cutoff point. Discussion: The major assumption of that approach is that a small number of variables with high values dominate the biological system and determine its major processes and functions. This approach was tested on three different datasets: human genes and their expression values in the human cerebral cortex, mammalian genes and their values of sensitivity to chemical exposures, and human proteins and their expression values in the human heart. In every case, the described cutoff identification method produced shortlists of variables (genes, proteins) highly relevant for dominant functions/pathways of the analyzed biological systems. Conclusions: The described method for cutoff identification may be used to prioritize variables in descriptive omics studies for a focused functional analysis, in situations where other methods of dichotomization of data are inaccessible. [ABSTRACT FROM AUTHOR]

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infection (UTI), a widespread infectious disease of great impact on human health. This is further emphasized by the rapidly increase in antimicrobial resistance in UPEC, which compromises UTI treatment. UPEC biology is highly complex since uropathogens must adopt extracellular and intracellular lifestyles and adapt to different niches in the host. In this context, the implementation of forefront 'omics' technologies has provided substantial insight into the understanding of UPEC pathogenesis, which has opened the doors for new therapeutics and prophylactics discovery programs. Thus, 'omics' technologies applied to studies of UPEC during UTI, or in models of UTI, have revealed extensive lists of factors that are important for the ability of UPEC to cause disease. The multitude of large 'omics' datasets that have been generated calls for scrutinized analysis of specific factors that may be of interest for further development of novel treatment strategies. In this review, we describe main UPEC determinants involved in UTI as estimated by 'omics' studies, and we compare prediction of factors across the different 'omics' technologies, with a focus on those that have been confirmed to be relevant under UTI-related conditions. We also discuss current challenges and future perspectives regarding analysis of data to provide an overview and better understanding of UPEC mechanisms involved in pathogenesis which should assist in the selection of target sites for future prophylaxis and treatment. [ABSTRACT FROM AUTHOR]

Atherosclerosis still is the primary cause of death worldwide. Our characterization of the atherosclerotic lesion is mainly rooted in definitions based on pathological descriptions. We often speak in absolutes regarding plaque phenotypes: vulnerable vs. stable plaques or plaque rupture vs. plaque erosion. By focusing on these concepts, we may have oversimplified the atherosclerotic disease and its mechanisms. The widely used definitions of pathology-based plaque phenotypes can be fine-tuned with observations made with various -omics techniques. Recent advancements in single-cell transcriptomics provide the opportunity to characterize the cellular composition of the atherosclerotic plaque. This additional layer of information facilitates the in-depth characterization of the atherosclerotic plaque. In this review, we discuss the impact that single-cell transcriptomics may exert on our current understanding of atherosclerosis.

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infection (UTI), a widespread infectious disease of great impact on human health. This is further emphasized by the rapidly increase in antimicrobial resistance in UPEC, which compromises UTI treatment. UPEC biology is highly complex since uropathogens must adopt extracellular and intracellular lifestyles and adapt to different niches in the host. In this context, the implementation of forefront ‘omics’ technologies has provided substantial insight into the understanding of UPEC pathogenesis, which has opened the doors for new therapeutics and prophylactics discovery programs. Thus, ‘omics’ technologies applied to studies of UPEC during UTI, or in models of UTI, have revealed extensive lists of factors that are important for the ability of UPEC to cause disease. The multitude of large ‘omics’ datasets that have been generated calls for scrutinized analysis of specific factors that may be of interest for further development of novel treatment strategies. In this review, we describe main UPEC determinants involved in UTI as estimated by ‘omics’ studies, and we compare prediction of factors across the different ‘omics’ technologies, with a focus on those that have been confirmed to be relevant under UTI-related conditions. We also discuss current challenges and future perspectives regarding analysis of data to provide an overview and better understanding of UPEC mechanisms involved in pathogenesis which should assist in the selection of target sites for future prophylaxis and treatment.

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention., Methods: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets., Findings: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions., Interpretation: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer., Funding: This work was supported by Cancer Research UK (grant no. C8221/A29017)., Competing Interests: Declaration of interests This work was supported by Cancer Research UK (grant no. C8221/A29017). Anders Mälarstig, Åsa Hedman, and Marios Dimitriou are employees of Pfizer Inc. Anders Mälarstig declares stock options for Pfizer Inc. Alastair D. Lamb is Section Editor for Prostate Cancer and Web, British Journal of Urology International., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Primary prevention trials have demonstrated that the traditional Mediterranean diet is associated with a reduction in cardiovascular mortality and morbidity. However, this benefit has not been proven for secondary prevention after an acute coronary syndrome (ACS). We hypothesized that a high-intensity Mediterranean diet intervention after an ACS decreases the vulnerability of atherosclerotic plaques by complex interactions between anti-inflammatory effects, microbiota changes and modulation of gene expression.Methods: The MEDIMACS project is an academically funded, prospective, randomized, controlled and mechanistic clinical trial designed to address the effects of an active randomized intervention with the Mediterranean diet on atherosclerotic plaque vulnerability, coronary endothelial dysfunction and other mechanistic endpoints. One hundred patients with ACS are randomized 1:1 to a monitored high-intensity Mediterranean diet intervention or to a standard-of-care arm. Adherence to diet is assessed in both arms using food frequency questionnaires and biomarkers of compliance. The primary endpoint is the change (from baseline to 12 months) in the thickness of the fibrous cap of a non-significant atherosclerotic plaque in a non-culprit vessel, as assessed by repeated optical coherence tomography intracoronary imaging. Indices of coronary vascular physiology and changes in gastrointestinal microbiota, immunological status and protein and metabolite profiles will be evaluated as secondary endpoints.Discussion: The results of this trial will address the key effects of dietary habits on atherosclerotic risk and will provide initial data on the complex interplay of immunological, microbiome-, proteome- and metabolome-related mechanisms by which non-pharmacological factors may impact the progression of coronary atherosclerosis after an ACS.Trial Registration: ClinicalTrials.gov NCT03842319 . Registered on 13 May 2019. [ABSTRACT FROM AUTHOR]

Lichens are mutually symbiotic systems consisting of fungal and algal symbionts. While diverse lichen-forming fungal species are known, limited species of algae form lichens. Plasticity in the combination of fungal and algal species with different eco-physiological properties may contribute to the worldwide distribution of lichens, even in extreme habitats. Lichens have been studied systematically for more than 200 years; however, plasticity in fungal–algal/cyanobacterial symbiotic combinations is still unclear. In addition, the association between non-cyanobacterial bacteria and lichens has attracted attention in recent years. The types, diversity, and functions of lichen-associated bacteria have been studied using both culture-based and culture-independent methods. This review summarizes the history of systematic research on lichens and lichen-associated bacteria and provides insights into the current status of research in this field.

There is increasing evidence that the metabolic status of T cells and macrophages is associated with severe phenotypes of chronic inflammation, including allergic inflammation. Metabolic changes in immune cells have a crucial role in their inflammatory or regulatory responses. This notion is reinforced by metabolic diseases influencing global energy metabolism, such as diabetes or obesity, which are known risk factors of severity in inflammatory conditions, due to the metabolic-associated inflammation present in these patients. Since several metabolic pathways are closely tied to T cell and macrophage differentiation, a better understanding of metabolic alterations in immune disorders could help to restore and modulate immune cell functions. This link between energy metabolism and inflammation can be studied employing animal, human or cellular models. Analytical approaches rank from classic immunological studies to integrated analysis of metabolomics, transcriptomics, and proteomics. This review summarizes the main metabolic pathways of the cells involved in the allergic reaction with a focus on T cells and macrophages and describes different models and platforms of analysis used to study the immune system and its relationship with metabolism.

The first 1000 days of life, from conception to 2 years, are a critical window for the influence of environmental exposures on the assembly of the oral microbiome, which is the precursor to dental caries (decay), one of the most prevalent microbially induced disorders worldwide. While it is known that the human microbiome is susceptible to environmental exposures, there is limited understanding of the impact of prenatal and early childhood exposures on the oral microbiome trajectory and oral health. A barrier has been the lack of technology to directly measure the foetal "exposome", which includes nutritional and toxic exposures crossing the placenta. Another barrier has been the lack of statistical methods to account for the high dimensional data generated by‐omic assays. Through identifying which early life exposures influence the oral microbiome and modify oral health, these findings can be translated into interventions to reduce dental decay prevalence. [ABSTRACT FROM AUTHOR]

Background: The multifactorial nature of non-alcoholic fatty liver disease cannot be explained solely by genetic factors. Recent evidence revealed that DNA methylation changes take place at proximal promoters within susceptibility genes. This emphasizes the need for integrating multiple data types to provide a better understanding of the disease's pathogenesis. One such candidate gene is paraoxonase-1 (PON1). Substantial interindividual differences in PON1 are apparent and could influence disease risk later in life. The aim of this study was therefore to determine the different regulatory aspects of PON1 variability and to examine them in relation to the predisposition to obesity-associated fatty liver disease. Results: A targeted multi-omics approach was applied to investigate the interplay between PON1 genetic variants, promoter methylation, expression profile and enzymatic activity in an adult patient cohort with extensive metabolic and hepatic characterisation including liver biopsy. Alterations in PON1 status were shown to correlate with waist-to-hip ratio and relevant features of liver pathology. Particularly, the regulatory polymorphism rs705379:C > T was strongly associated with more severe liver disease. Multivariable data analysis furthermore indicated a significant association of combined genetic and epigenetic PON1 regulation. This identified relationship postulates a role for DNA methylation as a mediator between PON1 genetics and expression, which is believed to further influence liver disease progression via modifications in PON1 catalytic efficiency. Conclusions: Our findings demonstrate that vertical data-integration of genetic and epigenetic regulatory mechanisms generated a more in-depth understanding of the molecular basis underlying the development of obesity-associated fatty liver disease. We gained novel insights into how NAFLD classification and outcome are orchestrated, which could not have been obtained by exclusively considering genetic variation. [ABSTRACT FROM AUTHOR]

There is increasing evidence that the metabolic status of T cells and macrophages is associated with severe phenotypes of chronic inflammation, including allergic inflammation. Metabolic changes in immune cells have a crucial role in their inflammatory or regulatory responses. This notion is reinforced by metabolic diseases influencing global energy metabolism, such as diabetes or obesity, which are known risk factors of severity in inflammatory conditions, due to the metabolic-associated inflammation present in these patients. Since several metabolic pathways are closely tied to T cell and macrophage differentiation, a better understanding of metabolic alterations in immune disorders could help to restore and modulate immune cell functions. This link between energy metabolism and inflammation can be studied employing animal, human or cellular models. Analytical approaches rank from classic immunological studies to integrated analysis of metabolomics, transcriptomics, and proteomics. This review summarizes the main metabolic pathways of the cells involved in the allergic reaction with a focus on T cells and macrophages and describes different models and platforms of analysis used to study the immune system and its relationship with metabolism. [ABSTRACT FROM AUTHOR]

Sex and gender differences in lung health and disease are imperative to consider and study if precision pulmonary medicine is to be achieved. The development of reliable COPD biomarkers has been elusive, and the translation of biomarkers to clinical care has been limited. Useful and effective biomarkers must be developed with attention to clinical heterogeneity of COPD; inherent heterogeneity exists related to grouping women and men together in the studies of COPD. Considering sex and gender differences and influences related to -omics may represent progress in susceptibility, diagnostic, prognostic, and therapeutic biomarker development and clinical innovation to improve the lung health of men and women. [ABSTRACT FROM AUTHOR]

Abstract Background Translational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary. Methods We developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers. Results We applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063–1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results. Conclusions We developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

The structured life-course modeling approach (SLCMA) is a theory-driven analytical method that empirically compares multiple prespecified life-course hypotheses characterizing time-dependent exposure-outcome relationships to determine which theory best fits the observed data. In this study, we performed simulations and empirical analyses to evaluate the performance of the SLCMA when applied to genomewide DNA methylation (DNAm). Using simulations (n  = 700), we compared 5 statistical inference tests used with SLCMA, assessing the familywise error rate, statistical power, and confidence interval coverage to determine whether inference based on these tests was valid in the presence of substantial multiple testing and small effects—2 hallmark challenges of inference from -omics data. In the empirical analyses (n  = 703), we evaluated the time-dependent relationship between childhood abuse and genomewide DNAm. In simulations, selective inference and the max-| t |-test performed best: Both controlled the familywise error rate and yielded moderate statistical power. Empirical analyses using SLCMA revealed time-dependent effects of childhood abuse on DNAm. Our findings show that SLCMA, applied and interpreted appropriately, can be used in high-throughput settings to examine time-dependent effects underlying exposure-outcome relationships over the life course. We provide recommendations for applying the SLCMA in -omics settings and encourage researchers to move beyond analyses of exposed versus unexposed individuals. [ABSTRACT FROM AUTHOR]

Toxicology aims to comprehend and envisage the adverse outcomes of chemicals and xenobiotics on the biological system. Predicting toxicity on the biological system is a complex and immense process. In-vivo, in-vitro, pre-, clinical and –omics level experimental approaches have been utilized to describe the toxicological impact of these chemicals and this has generated a vast wealth of data. Hence, there now exist a need for a system that can interrelate and provide accurate and robust extrapolation of these data across various systems. Therefore, it is essential to re-shift our notion from empirical, animal-based testing to a mechanistic understanding. Systems biology is one such system that can extrapolate and interrelate these vast biological system data. Systems biology is a computational and mathematical modelling approach developed to understand interrelationships between networks of biological systems. The use of systems biology to answer toxicology-related questions is termed as systems toxicology. In this review we will look at the standard and classical toxicology experimentations and how we can use mechanistic data (systems biology) to answer toxicology-related questions using systems toxicology and what are the future opportunities in systems toxicology. The advancement of systems toxicology heralds new dawn of technologies that will aid in our quest to better comprehend and envisage the adverse outcomes of chemicals and xenobiotics on the biological system.