Opportunities and challenges in omics

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Developing the 'omic toolkit of comparative physiologists

Typical 'omic analyses reduce complex biological systems to simple lists of supposedly independent variables, failing to account for changes in the wider transcriptional landscape. In this commentary, we discuss the utility of network approaches for incorporating this wider context into the study of physiological phenomena. We highlight opportunities to build on traditional network tools by utilising cutting-edge techniques to account for higher order interactions (i.e. beyond pairwise associations) within datasets, allowing for more accurate models of complex 'omic systems. Finally, we show examples of previous works utilising network approaches to gain additional insight into their organisms of interest. As 'omics grow in both their popularity and breadth of application, so does the requirement for flexible analytical tools capable of interpreting and synthesising complex datasets.

Omics technology draws a comprehensive heavy metal resistance strategy in bacteria

The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Influence of chemical dose and exposure duration on protein synthesis in green sea turtle primary cells

Understanding the impacts of chemical exposure in marine wildlife is challenging, due to practical and ethical constraints that preclude traditional toxicology research on these animals. This study addressed some of these limitations by presenting an ethical and high throughput cell-based approach to elucidate molecular-level effects of contaminants on sea turtles. The experimental design addressed basic questions of cell-based toxicology, including chemical dose and exposure time. Primary green turtle skin cells were exposed to polychlorinated biphenyl (PCB) 153 and perfluorononanoic acid (PFNA) for 24 and 48 h, at three sub-lethal, environmentally relevant concentrations (1, 10 and 100 μg/L). Sequential window acquisition of all theoretical mass spectra (SWATH-MS) identified over 1000 differentially abundant proteins within the 1% false discovery rate (FDR) threshold. The 24 h exposure resulted in a greater number of differentially abundant proteins, compared to 48 h exposure, for both contaminants. However, there were no statistically significant dose-response relationships for the number of differentially synthesised proteins, nor differences in the proportion of increased vs decreased proteins between or within exposure times. Known in vivo markers of contaminant exposure, superoxide dismutase and glutathione S-transferase, were differentially abundant following exposure to PCB153 and PFNA. SIGNIFICANCE: Cell-based (in vitro) proteomics provides an ethical and high throughput approach to understanding the impacts of chemical contamination on sea turtles. Through investigating effects of chemical dose and exposure duration on unique protein abundance in vitro, this study provides an optimised framework for conducting cell-based studies in wildlife proteomics, and highlights that proteins detected in vitro could act as biomarkers of chemical exposure and effect in vivo.

OMICS and Other Advanced Technologies in Mycological Applications

Fungi play many roles in different ecosystems. The precise identification of fungi is important in different aspects. Historically, they were identified based on morphological characteristics, but technological advancements such as polymerase chain reaction (PCR) and DNA sequencing now enable more accurate identification and taxonomy, and higher-level classifications. However, some species, referred to as "dark taxa", lack distinct physical features that makes their identification challenging. High-throughput sequencing and metagenomics of environmental samples provide a solution to identifying new lineages of fungi. This paper discusses different approaches to taxonomy, including PCR amplification and sequencing of rDNA, multi-loci phylogenetic analyses, and the importance of various omics (large-scale molecular) techniques for understanding fungal applications. The use of proteomics, transcriptomics, metatranscriptomics, metabolomics, and interactomics provides a comprehensive understanding of fungi. These advanced technologies are critical for expanding the knowledge of the Kingdom of Fungi, including its impact on food safety and security, edible mushrooms foodomics, fungal secondary metabolites, mycotoxin-producing fungi, and biomedical and therapeutic applications, including antifungal drugs and drug resistance, and fungal omics data for novel drug development. The paper also highlights the importance of exploring fungi from extreme environments and understudied areas to identify novel lineages in the fungal dark taxa.

Considerations for Use of Pharmacodynamic Biomarkers to Support Biosimilar Development - (III) A Randomized Trial with Interferon Beta-1a Products

The US Food and Drug Administration (FDA) has taken steps to bring efficiency to the development of biosimilars, including establishing guidance for the use of pharmacokinetic and pharmacodynamic (PD) similarity study data without a comparative clinical study with efficacy end point(s). To better understand the potential role for PD biomarkers in biosimilar development and inform best practices for biomarker selection and analysis, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants. Eighty-four healthy participants (n = 12 per dose arm) received either placebo or one of three doses of either interferon β-1a (7.5-30 μg) or pegylated interferon β-1a (31.25-125 μg) to evaluate the maximum change from baseline and the baseline-adjusted area under the effect curve for the biomarkers neopterin in serum and myxovirus resistance protein 1 in blood. Both PD biomarkers increased following product administration with clear separation from baseline (neopterin: 3.4-fold and 3.9-fold increase for interferon β-1a and pegylated interferon β-1a, respectively; myxovirus resistance protein 1: 19.0-fold and 47.2-fold increase for interferon β-1a and pegylated interferon β-1a, respectively). The dose-response curves support that therapeutic doses were adequately sensitive to detect differences in both PD biomarkers for consideration in a PD similarity study design. Because baseline levels of both biomarkers are low compared with on-treatment values, there was little difference in using PD measures adjusted to baseline compared with the results without baseline adjustment. This study illustrates potential methodologies for evaluating PD biomarkers and an approach to address information gaps when limited information is publicly available for one or more PD biomarkers.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The "Omics" field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method.

Evaluating the Utility of Proteomics for the Identification of Circulating Pharmacodynamic Biomarkers of IFNβ-1a Biologics

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon-beta 1a products (IFNβ-1a) or pegylated-IFNβ-1a (pegIFNβ-1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNβ-1a and pegIFNβ-1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty-one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4-fold maximal change from baseline. We identified previously reported candidates, beta-2microglobulin and interferon-induced GTP-binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I-TAC, C1QC, and IP-10) with CVs ranging from 26%-129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNβ1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers.

Keeping an eye on circadian time in clinical research and medicine

BACKGROUND

Daily rhythms are observed in humans and almost all other organisms. Most of these observed rhythms reflect both underlying endogenous circadian rhythms and evoked responses from behaviours such as sleep/wake, eating/fasting, rest/activity, posture changes and exercise. For many research and clinical purposes, it is important to understand the contribution of the endogenous circadian component to these observed rhythms.

CONTENT

The goal of this manuscript is to provide guidance on best practices in measuring metrics of endogenous circadian rhythms in humans and promote the inclusion of circadian rhythms assessments in studies of health and disease. Circadian rhythms affect all aspects of physiology. By specifying minimal experimental conditions for studies, we aim to improve the quality, reliability and interpretability of research into circadian and daily (i.e., time-of-day) rhythms and facilitate the interpretation of clinical and translational findings within the context of human circadian rhythms. We describe protocols, variables and analyses commonly used for studying human daily rhythms, including how to assess the relative contributions of the endogenous circadian system and other daily patterns in behaviours or the environment. We conclude with recommendations for protocols, variables, analyses, definitions and examples of circadian terminology.

CONCLUSION

Although circadian rhythms and daily effects on health outcomes can be challenging to distinguish in practice, this distinction may be important in many clinical settings. Identifying and targeting the appropriate underlying (patho)physiology is a medical goal. This review provides methods for identifying circadian effects to aid in the interpretation of published work and the inclusion of circadian factors in clinical research and practice.

Praegnatio Perturbatio-Impact of Endocrine-Disrupting Chemicals

The burden of adverse pregnancy outcomes such as preterm birth and low birth weight is considerable across the world. Several risk factors for adverse pregnancy outcomes have been identified. One risk factor for adverse pregnancy outcomes receiving considerable attention in recent years is gestational exposure to endocrine-disrupting chemicals (EDCs). Humans are exposed to a multitude of environmental chemicals with known endocrine-disrupting properties, and evidence suggests exposure to these EDCs have the potential to disrupt the maternal-fetal environment culminating in adverse pregnancy and birth outcomes. This review addresses the impact of maternal and fetal exposure to environmental EDCs of natural and man-made chemicals in disrupting the maternal-fetal milieu in human leading to adverse pregnancy and birth outcomes-a risk factor for adult-onset noncommunicable diseases, the role lifestyle and environmental factors play in mitigating or amplifying the effects of EDCs, the underlying mechanisms and mediators involved, and the research directions on which to focus future investigations to help alleviate the adverse effects of EDC exposure.

Impact of gestational exposure to endocrine disrupting chemicals on pregnancy and birth outcomes

With the advent of industrialization, humans are exposed to a wide range of environmental chemicals, many with endocrine disrupting potential. As successful maintenance of pregnancy and fetal development are under tight hormonal control, the gestational exposure to environmental endocrine disrupting chemicals (EDC) have the potential to adversely affect the maternal milieu and support to the fetus, fetal developmental trajectory and birth outcomes. This chapter summarizes the impact of exposure to EDCs both individually and as mixtures during pregnancy, the immediate and long-term consequences of such exposures on the mother and fetus, the direct and indirect mechanisms through which they elicit their effects, factors that modify their action, and the research directions to focus future investigations.

HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis

To facilitate biomedical studies of disease mechanisms, a high-quality interactome that connects functionally related genes is needed to help investigators formulate pathway hypotheses and to interpret the biological logic of a phenotype at the biological process level. Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes (23.2% functional interactions of HIR V2 are in common with the previous version of HIR), representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at http://human.biomedtzc.cn.

Characterization of the intestinal mucosal proteome in cats with inflammatory bowel disease and alimentary small cell lymphoma

BACKGROUND

Current tests for diagnosis and differentiation of lymphoplasmacytic enteritis (LPE) and small cell lymphoma (SCL) in cats are expensive, invasive, and lack specificity. The identification of less invasive, more reliable biomarkers would facilitate diagnosis.

OBJECTIVES

To characterize the mucosal proteome in endoscopically obtained, small intestinal tissue biopsy specimens. We hypothesized that differentially expressed proteins could be identified and serve as biomarker candidates for the differentiation of LPE and SCL in cats.

ANIMALS

Six healthy control cats, 6 cats with LPE, and 8 cats with SCL.

METHODS

The mucosal proteome was analyzed using 2-dimensional fluorescence difference gel electrophoresis (2D DIGE) and nanoflow liquid chromatography tandem mass spectrometry. For 5 proteins, results were verified by Western blot analysis.

RESULTS

A total of 2349 spots were identified, of which 9 were differentially expressed with a ≥2-fold change between healthy cats and cats with LPE and SCL (.01 < P < .001). Eight of these 9 spots were also differentially expressed between cats with LPE and cats with SCL (P .001 < P < .04). However, Western blot analysis for malate dehydrogenase-1, malate dehydrogenase-2, apolipoprotein, annexin IV, and annexin V did not confirm significant differential protein expression for any of the 5 proteins assessed.

CONCLUSIONS AND CLINICAL IMPORTANCE

Two-D DIGE did not identify potential biomarker candidates in the intestinal mucosa of cats with LPE and SCL. Future studies should focus on different techniques to identify biomarker candidates for cats with chronic enteropathies (CE).

Help, there are ‘omics’ in my comparative physiology!

‘Omics’ methods, such as transcriptomics, proteomics, lipidomics or metabolomics, yield simultaneous measurements of many related molecules in a sample. These approaches have opened new opportunities to generate and test hypotheses about the mechanisms underlying biochemical and physiological phenotypes. In this Commentary, we discuss general approaches and considerations for successfully integrating omics into comparative physiology. The choice of omics approach will be guided by the availability of existing resources and the time scale of the process being studied. We discuss the use of whole-organism extracts (common in omics experiments on small invertebrates) because such an approach may mask underlying physiological mechanisms, and we consider the advantages and disadvantages of pooling samples within biological replicates. These methods can bring analytical challenges, so we describe the most easily analyzed omics experimental designs. We address the propensity of omics studies to digress into ‘fishing expeditions’ and show how omics can be used within the hypothetico-deductive framework. With this Commentary, we hope to provide a roadmap that will help newcomers approach omics in comparative physiology while avoiding some of the potential pitfalls, which include ambiguous experiments, long lists of candidate molecules and vague conclusions.

Individualized metabolomics: opportunities and challenges

The goal of advancing science in health care is to provide high quality treatment and therapeutic opportunities to patients in need. This is especially true in precision medicine, wherein the ultimate goal is to link disease phenotypes to targeted treatments and novel therapeutics at the scale of an individual. With the advent of -omics technologies, such as genomics, proteomics, microbiome, among others, the metabolome is of wider and immediate interest for its important role in metabolic regulation. The metabolome, of course, comes with its own questions regarding technological challenges. In this opinion article, I attempt to interrogate some of the main challenges associated with individualized metabolomics, and available opportunities in the context of its clinical application. Some questions this article addresses and attempts to find answers for are: Can a personal metabolome (n = 1) be inexpensive, affordable and informative enough (i.e. provide predictive yet validated biomarkers) to represent the entirety of a population? How can a personal metabolome complement advances in other -omics areas and the use of monitoring devices, which occupy our personal space?

From Analysis of Ischemic Mouse Brain Proteome to Identification of Human Serum Clusterin as a Potential Biomarker for Severity of Acute Ischemic Stroke

Ischemic stroke is a devastating neurological disease that can cause permanent brain damage, but to date, few biomarkers are available to reliably assess the severity of injury during acute onset. In this study, quantitative proteomic analysis of ischemic mouse brain detected the increase in expression levels of clusterin (CLU) and cystatin C (CST3). Since CLU is a secretary protein, serum samples (n = 70) were obtained from acute ischemic stroke (AIS) patients within 24 h of stroke onset and together with 70 matched health controls. Analysis of CLU levels indicated significantly higher levels in AIS patients than healthy controls (14.91 ± 4.03 vs. 12.79 ± 2.22 ng/L; P = 0.0004). Analysis of serum CST3 also showed significant increase in AIS patients as compared with healthy controls (0.90 ± 0.19 vs. 0.84 ± 0.12 ng/L; P = 0.0064). The serum values of CLU were also positively correlated with the NIH Stroke Scale (NIHSS) scores, the time interval after stroke onset, as well as major stroke risk factors associated with lipid profile. These data demonstrate that elevated levels of serum CLU and CST3 are independently associated with AIS and may serve as peripheral biomarkers to aid clinical assessment of AIS and its severity. This pilot study thus contributes to progress toward preclinical proteomic screening by using animal models and allows translation of results from bench to bedside.

Augmentation of crop productivity through interventions of omics technologies in India: challenges and opportunities

With the continuous increase in the population of developing countries and decline of natural resources, there is an urgent need to qualitatively and quantitatively augment crop productivity by using new tools and technologies for improvement of agriculturally important traits. The new scientific and technological omics-based approaches have enabled us to deal with several issues and challenges faced by modern agricultural system and provided us novel opportunities for ensuring food and nutritional security. Recent developments in sequencing techniques have made available huge amount of genomic and transcriptomic data on model and cultivated crop plants including Arabidopsis thaliana, Oryza sativa, Triticum aestivum etc. The sequencing data along with other data generated through several omics platforms have significantly influenced the disciplines of crop sciences. Gene discovery and expression profiling-based technologies are offering enormous opportunities to the scientific community which can now apply marker-assisted selection technology to assess and enhance diversity in their collected germplasm, introgress essential traits from new sources and investigate genes that control key traits of crop plants. Utilization of omics science and technologies for crop productivity, protection and management has recently been receiving a lot of attention; the majority of the efforts have been put into signifying the possible applications of various omics technologies in crop plant sciences. This article highlights the background of challenges and opportunities for augmentation of crop productivity through interventions of omics technologies in India.

Transcriptomics, proteomics and metabolomics driven biomarker discovery in COPD: an update

INTRODUCTION

Diagnosis of chronic obstructive pulmonary disease (COPD), characterized by progressive irreversible airflow limitation, remains a challenge. Lack of sensitive diagnostic markers and alternative treatments have limited patients' survival rate. Herein, we provide for clinicians and scientists a comprehensive review on the various omics platforms used to investigate COPD.

AREAS COVERED

This review consists of articles from PubMed (2009-2016) as well as views of the contributing authors. The review highlights the need for COPD biomarker identification and also provides an update on promising candidate markers identified in various biological fluids using omics technologies. Expert commentary: The multi-omics approach holds promise for the development of robust early stage COPD diagnostic markers, screening of high-risk population, and also improved prognosis which could lead to personalized medicine in future. Various factors regulating an omics study including sample size, control selection, disease phenotyping, usage of complementary techniques and result replication in omics-based research are outlined.

Methodological issues in human studies of endocrine disrupting chemicals

Possible harm from endocrine disrupting chemicals (EDCs) in humans is speculated based on two types of evidence; 1) increasing trends of suspected diseases in ecological studies of populations and 2) findings from traditional epidemiological studies of individuals. However, ecological findings are not regarded as direct human evidence of the relation between EDCs and disease, while the evidence among epidemiological studies of individuals is often inconsistent. Thus, a criticism is that linking EDCs and health in human is naively presumed without solid evidence. However, human studies of EDCs are methodologically complex and understanding methodological issues will help to interpret findings from existing human studies and to properly design optimal human studies. The key issues are low reliability of exposure assessment of EDCs with short half-lives, EDC mixtures, possibility of non-monotonic dose-response relationships, non-existence of an unexposed group, difficulties in measuring exposure during critical periods, and interactions with established risk factors. Furthermore, EDC mixtures may affect human health through other mechanisms than traditional endocrine disruption, for example glutathione depletion or mitochondrial dysfunction. Given this complexity, the most plausible scenario in humans is that exposure to EDC mixtures leads to increasing risk of related diseases at the ecological level, but inconsistent associations would be expected in traditional epidemiological studies. Although epidemiologists have long relied on Bradford Hill's criteria to objectively evaluate whether associations observed in epidemiology can be interpreted as causal, there are challenges to use these criteria for EDCs, particularly concerning consistency across studies and the findings of linear dose-response relationships. At the individual level, compared to EDCs with short half-lives, epidemiological studies of EDCs with long half-lives among populations with a relatively low exposure dose range of exposure can likely produce relatively more reliable results, because the measurement of EDCs with long half-lives likely represents typical long-term exposure and populations with exposure in the low range of doses are likely to have a reference group closer to non-exposure.

Pharmaco-proteomics opportunities for individualizing neurovascular treatment

Neurovascular disease often involves multi-organ system injury. For example, patent foramen ovale (PFO) related ischemic strokes involve not just the brain, but also the heart, the lung, and the peripheral vascular circulation. For higher-risk but high-reward systemic therapy (e.g., thrombolytics, therapeutic hypothermia (TH), PFO closure) to be implemented safely, very careful patient selection and close monitoring of disease progression and therapeutic efficacy are imperative. For example, more than a decade after the approval of therapeutic hypothermic and intravenous thrombolysis treatments, they both remain extremely under-utilized, in part due to lack of clinical tools for patient selection or to follow therapeutic efficacy. Therefore, in understanding the complexity of the global effects of clinical neurovascular diseases and their therapies, a systemic approach may offer a unique perspective and provide tools with clinical utility. Clinical proteomic approaches may be promising to monitor systemic changes in complex multi-organ diseases - especially where the disease process can be 'sampled' in clinically accessible fluids such as blood, urine, and CSF. Here, we describe a 'pharmaco-proteomic' approach to three major challenges in translational neurovascular research directly at bedside - in order to better stratify risk, widen therapeutic windows, and explore novel targets to be validated at the bench - (i) thrombolytic treatment for ischemic stroke, (ii) therapeutic hypothermia for post-cardiac arrest syndrome, and (iii) treatment for PFO related paradoxical embolic stroke. In the future, this clinical proteomics approach may help to improve patient selection, ensure more precise clinical phenotyping for clinical trials, and individualize patient treatment.

Omics strategies for revealing Yersinia pestis virulence

Omics has remarkably changed the way we investigate and understand life. Omics differs from traditional hypothesis-driven research because it is a discovery-driven approach. Mass datasets produced from omics-based studies require experts from different fields to reveal the salient features behind these data. In this review, we summarize omics-driven studies to reveal the virulence features of Yersinia pestis through genomics, trascriptomics, proteomics, interactomics, etc. These studies serve as foundations for further hypothesis-driven research and help us gain insight into Y. pestis pathogenesis.

Application of proteomics to cerebrovascular disease

While neurovascular diseases such as ischemic and hemorrhagic stroke are the leading causes of disability in the world, the repertoire of therapeutic interventions has remained remarkably limited. There is a dire need to develop new diagnostic, prognostic, and therapeutic options. The study of proteomics is particularly enticing for cerebrovascular diseases such as stroke, which most likely involve multiple gene interactions resulting in a wide range of clinical phenotypes. Currently, rapidly progressing neuroproteomic techniques have been employed in clinical and translational research to help identify biologically relevant pathways, to understand cerebrovascular pathophysiology, and to develop novel therapeutics and diagnostics. Future integration of proteomic with genomic, transcriptomic, and metabolomic studies will add new perspectives to better understand the complexities of neurovascular injury. Here, we review cerebrovascular proteomics research in both preclinical (animal, cell culture) and clinical (blood, urine, cerebrospinal fluid, microdialyates, tissue) studies. We will also discuss the rewards, challenges, and future directions for the application of proteomics technology to the study of various disease phenotypes. To capture the dynamic range of cerebrovascular injury and repair with a translational targeted and discovery approach, we emphasize the importance of complementing innovative proteomic technology with existing molecular biology models in preclinical studies, and the need to advance pharmacoproteomics to directly probe clinical physiology and gauge therapeutic efficacy at the bedside.

Injury and repair in the neurovascular unit

The neurovascular unit provides a conceptual framework for investigating the pathophysiology of how brain cells die after stroke, brain injury, and neurodegeneration. Emerging data now suggest that this concept can be further extended. Cell-cell signaling between neuronal, glial, and vascular elements in the brain not only mediates the mechanisms of acute injury, but integrated responses in these same elements may also be required for recovery as the entire neurovascular unit attempts to reorganize and remodel. Understanding the common signals and substrates of this transition between acute injury and delayed repair in the neurovascular unit may reveal useful paradigms for augmenting neuronal, glial, and vascular plasticity in damaged and diseased brain.