Marine Microbiota Responses to Shipping Scrubber Effluent Assessed at Community Structure and Function Endpoints

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;00:1-18. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain)

We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

Herbgenomics meets Papaveraceae: a promising -omics perspective on medicinal plant research

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.

Organotypic heterogeneity in microvascular endothelial cell responses in sepsis-a molecular treasure trove and pharmacological Gordian knot

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.

Chronicle of Research into Lichen-Associated Bacteria

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.

Presence and future of plant phenotyping approaches in biostimulant research and development

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.

Simple method for cutoff point identification in descriptive high-throughput biological studies

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08427-6.

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

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.

The Importance of Metabolism for Immune Homeostasis in Allergic Diseases

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.

How does the early life environment influence the oral microbiome and determine oral health outcomes in childhood?

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.

A Structured Approach to Evaluating Life-Course Hypotheses: Moving Beyond Analyses of Exposed Versus Unexposed in the -Omics Context

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.

Developing novel drug candidates and repurposed drugs for prostate cancer based on molecular profiles

Prostate cancer (PCa) carries a growing burden on society. Lack of curative treatment and poor prognosis among patients with advanced PCa implies an urgent need for novel and improved drug identification. This is hampered by the disease's high molecular heterogeneity and complex molecular pathophysiology, resulting in drugs being efficient in few patients and cancer developing resistance to treatment. De novo drug discovery has proven to be complex and challenging. Along with technological advancements (mainly linked to -omics approaches) that allow for comprehensive characterization of the molecular changes underlying disease, and considering respective developments in bioinformatics, computational drug repurposing has emerged as a promising approach to shorten the way from discovery to clinical application and address the disease molecular complexity. With this article, we aimed at reviewing recent studies in which drugs/ compounds for PCa were defined through the investigation of molecular profiling (-omics) data and application of drug repurposing strategies. A brief overview of the technical requirements and associated challenges with the latter are also provided. For that purpose, a literature search was conducted using the PubMed database. Numerous drugs/ compounds have been proposed as potential PCa therapeutics, mostly based on the investigation of genomics and transcriptomics data. In most cases, further assessment in disease models is required. Since ultimately proteins are targeted by drugs, expanding on the use of proteomics profiling data (alone or in combination with other -omics) is expected to advance further defining new/repurposed drugs for PCa.

Sex and Gender Omic Biomarkers in Men and Women With COPD: Considerations for Precision Medicine

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.

Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO2], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype

Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure to make progress is attributed to the extreme heterogeneity of this condition, enormous complexity of the human brain, limitations of extant research paradigms, and inadequacy of traditional statistical methods to integrate or interpret increasingly large amounts of multidimensional information relevant to unravelling brain function. Fortunately, the rapidly developing science of machine learning appears to provide tools capable of addressing each of these impediments. Enthusiasm about the potential of machine learning methods to break the current impasse is reflected in the steep increase in the number of scientific publication about the application of machine learning to the study of schizophrenia. Machine learning approaches are, however, poorly understood by schizophrenia researchers and clinicians alike. In this paper, we provide a simple description of the nature and techniques of machine learning and their application to the study of schizophrenia. We then summarize its potential and constraints with illustrations from six studies of machine learning in schizophrenia and address some common misconceptions about machine learning. We suggest some guidelines for researchers, readers, science editors and reviewers of the burgeoning machine learning literature in schizophrenia. In order to realize its enormous promise, we suggest the need for the disciplined application of machine learning methods to the study of schizophrenia with a clear recognition of its capability and challenges accompanied by a concurrent effort to improve machine learning literacy among neuroscientists and mental health professionals.

Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking

Simple Summary

The European Cooperation in Science and Technology (COST) Action GroupHouseNet aims to provide synergy among scientists to prevent damaging behavior in group-housed pigs and laying hens. One goal of this network is to determine how genetic and genomic tools can be used to breed animals that are less likely to perform damaging behavior on their pen-mates. In this review, the focus is on feather-pecking behavior in laying hens. Reducing feather pecking in large groups of hens is a challenge, because it is difficult to identify and monitor individual birds. However, current developments in sensor technologies and animal breeding have the potential to identify individual animals, monitor individual behavior, and link this information back to the underlying genotype. We describe a combination of sensor technologies and “-omics” approaches that could be used to select against feather-pecking behavior in laying hens.

Abstract

Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of “-omics” approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

Post-Genomic Methodologies and Preclinical Animal Models: Chances for the Translation of Cardioprotection to the Clinic

Although many cardioprotective strategies have demonstrated benefits in animal models of myocardial infarction, they have failed to demonstrate cardioprotection in the clinical setting highlighting that new therapeutic target and treatment strategies aimed at reducing infarct size are urgently needed. Completion of the Human Genome Project in 2001 fostered the post-genomic research era with the consequent development of high-throughput “omics” platforms including transcriptomics, proteomics, and metabolomics. Implementation of these holistic approaches within the field of cardioprotection has enlarged our understanding of ischemia/reperfusion injury with each approach capturing a different angle of the global picture of the disease. It has also contributed to identify potential prognostic/diagnostic biomarkers and discover novel molecular therapeutic targets. In this latter regard, “omic” data analysis in the setting of ischemic conditioning has allowed depicting potential therapeutic candidates, including non-coding RNAs and molecular chaperones, amenable to pharmacological development. Such discoveries must be tested and validated in a relevant and reliable myocardial infarction animal model before moving towards the clinical setting. Moreover, efforts should also focus on integrating all “omic” datasets rather than working exclusively on a single “omic” approach. In the following manuscript, we will discuss the power of implementing “omic” approaches in preclinical animal models to identify novel molecular targets for cardioprotection of interest for drug development.

Precision Cardio-Oncology

Modern oncologic therapies and care have resulted in a growing population of cancer survivors with comorbid, chronic health conditions. As an example, many survivors have an increased risk of cardiovascular complications secondary to cardiotoxic systemic and radiation therapies. In response, the field of cardio-oncology has emerged as an integral component of oncologic patient care, committed to the early diagnosis and treatment of adverse cardiac events. However, as current clinical management of cancer therapy-related cardiovascular disease remains limited by a lack of phenotypic data, implementation of precision medicine approaches has become a focal point for deep phenotyping strategies. In particular, -omics approaches (a field of study in biology ending in -omic, such as genomics, proteomics, or metabolomics) have shown enormous potential in identifying sensitive biomarkers of cardiovascular disease, applying sophisticated, pattern-revealing technologies to growing databases of biologic molecules. Moreover, the use of -omics to inform radiologic strategies may add a dimension to future clinical practices. In this review, we present a paradigm for a precision medicine approach to the care of cardiotoxin-exposed cancer patients. We discuss the role of current imaging techniques; demonstrate how -omics can advance our understanding of disease phenotypes; and describe how molecular imaging can be integrated to personalize surveillance and therapeutics, ultimately reducing cardiovascular morbidity and mortality in cancer patients and survivors.

Genome analysis and -omics approaches provide new insights into the biodegradation potential of Rhodococcus

The past few years observed a breakthrough of genome sequences of bacteria of Rhodococcus genus with significant biodegradation abilities. Invaluable knowledge from genome data and their functional analysis can be applied to develop and design strategies for attenuating damages caused by hydrocarbon contamination. With the advent of high-throughput -omic technologies, it is currently possible to utilize the functional properties of diverse catabolic genes, analyze an entire system at the level of molecule (DNA, RNA, protein, and metabolite), simultaneously predict and construct catabolic degradation pathways. In this review, the genes involved in the biodegradation of hydrocarbons and several emerging plasticizer compounds in Rhodococcus strains are described in detail (aliphatic, aromatics, PAH, phthalate, polyethylene, and polyisoprene). The metabolic biodegradation networks predicted from omics-derived data along with the catabolic enzymes exploited in diverse biotechnological and bioremediation applications are characterized.

Agrobiotechnology Goes Wild: Ancient Local Varieties as Sources of Bioactives

The identification and use of species that have best adapted to their growth territory is of paramount importance to preserve biodiversity while promoting sustainable agricultural practices. Parameters including resistance to natural conditions (biotic and abiotic risk factors), biomass and fruit productivity, and phytochemical content with nutraceutical potential, could be used as quantitative markers of the adaptability of plants to wild environments characterized by minimal human impact. Ancient varieties, which are plant varieties growing in regional territories and not destined for market distribution, are a source of unique genetic characters derived from many years of adaptation to the original territory. These plants are often more resistant to biotic and abiotic stresses. In addition, these varieties have a high phytochemical (also known as bioactives) content considered health-beneficial. Notably, the content of these compounds is often lower in commercial cultivars. The use of selected territorial varieties according to the cultivation area represents an opportunity in the agricultural sector in terms of biodiversity preservation, environmental sustainability, and valorization of the final products. Our survey highlights the nutraceutical potential of ancient local varieties and stresses the importance of holistic studies (-omics) to investigate their physiology and secondary metabolism.

The Evolution of gene regulation research in Lactococcus lactis

Lactococcus lactis is a major microbe. This lactic acid bacterium (LAB) is used worldwide in the production of safe, healthy, tasteful and nutritious milk fermentation products. Its huge industrial importance has led to an explosion of research on the organism, particularly since the early 1970s. The upsurge in the research on L. lactis coincided not accidentally with the advent of recombinant DNA technology in these years. The development of methods to take out and re-introduce DNA in L. lactis, to clone genes and to mutate the chromosome in a targeted way, to control (over)expression of proteins and, ultimately, the availability of the nucleotide sequence of its genome and the use of that information in transcriptomics and proteomics research have enabled to peek deep into the functioning of the organism. Among many other things, this has provided an unprecedented view of the major gene regulatory pathways involved in nitrogen and carbon metabolism and their overlap, and has led to the blossoming of the field of L. lactis systems biology. All of these advances have made L. lactis the paradigm of the LAB. This review will deal with the exciting path along which the research on the genetics of and gene regulation in L. lactis has trodden.

Preprocessing Tools Applied to Improve the Assessment of Aldrin Effects on Prostate Cancer Cells Using Raman Spectroscopy

The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization-scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids.

Plant Systems Biology at the Single-Cell Level

Our understanding of plant biology is increasingly being built upon studies using 'omics and system biology approaches performed at the level of the entire plant, organ, or tissue. Although these approaches open new avenues to better understand plant biology, they suffer from the cellular complexity of the analyzed sample. Recent methodological advances now allow plant scientists to overcome this limitation and enable biological analyses of single-cells or single-cell-types. Coupled with the development of bioinformatics and functional genomics resources, these studies provide opportunities for high-resolution systems analyses of plant phenomena. In this review, we describe the recent advances, current challenges, and future directions in exploring the biology of single-cells and single-cell-types to enhance our understanding of plant biology as a system.

Liver fibrosis: a compilation on the biomarkers status and their significance during disease progression

Liver fibrosis occurs in response to different etiologies of chronic liver injury. Diagnosing degree of liver fibrosis is a crucial step in evaluation of severity of the disease. An invasive liver biopsy is the gold standard method associated with pain and complications. Biomarkers to detect liver fibrosis include direct markers of extracellular matrix turnover and indirect markers as a reflection of liver dysfunction. Although a single marker may not be useful for successful management, a mathematical equation combining tests might be effective. The main purpose of this review is to understand the diagnostic accuracy of biomarkers and scoring systems for liver fibrosis. Advances in -omics approach have generated clinically significant biomarker candidates for liver fibrosis that need further evaluation.

Liver fibrosis is a global health issue caused by various factors. Early diagnosis of the disease is important for better patient care. Liver biopsy is one of the diagnostic tools but comes with complications. Direct (involved in disease progression) and indirect markers are indicators for liver dysfunction that have easy applicability with less diagnostic value. Combination of these markers may give significant diagnosis but early detection is uncertain. Hence, the present review explains existing biomarkers and their relevance for the generation of ideal biomarkers for effective disease management by using advanced technology.

Multi-level and hybrid modelling approaches for systems biology

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

Phytoremediation: State-of-the-art and a key role for the plant microbiome in future trends and research prospects

Phytoremediation is increasingly adopted as a more sustainable approach for soil remediation. However, significant advances in efficiency are still necessary to attain higher levels of environmental and economic sustainability. Current interventions do not always give the expected outcomes in field settings due to an incomplete understanding of the multicomponent biological interactions. New advances in -omics are gradually implemented for studying microbial communities of polluted land in situ. This opens new perspectives for the discovery of biodegradative strains and provides us new ways of interfering with microbial communities to enhance bioremediation rates. This review presents retrospectives and future perspectives for plant microbiome studies relevant to phytoremediation, as well as some knowledge gaps in this promising research field. The implementation of phytoremediation in soil clean-up management systems is discussed, and an overview of the promoting factors that determine the growth of the phytoremediation market is given. Continuous growth is expected since elimination of contaminants from the environment is demanded. The evolution of scientific thought from a reductionist view to a more holistic approach will boost phytoremediation as an efficient and reliable phytotechnology. It is anticipated that phytoremediation will prove the most promising for organic contaminant degradation and bioenergy crop production on marginal land.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

Bioinformatic landscapes for plant transcription factor system research

Diverse bioinformatic resources have been developed for plant transcription factor (TF) research. This review presents the bioinformatic resources and methodologies for the elucidation of plant TF-mediated biological events. Such information is helpful to dissect the transcriptional regulatory systems in the three reference plants Arabidopsis , rice, and maize and translation to other plants. Transcription factors (TFs) orchestrate diverse biological programs by the modulation of spatiotemporal patterns of gene expression via binding cis-regulatory elements. Advanced sequencing platforms accompanied by emerging bioinformatic tools revolutionize the scope and extent of TF research. The system-level integration of bioinformatic resources is beneficial to the decoding of TF-involved networks. Herein, we first briefly introduce general and specialized databases for TF research in three reference plants Arabidopsis, rice, and maize. Then, as proof of concept, we identified and characterized heat shock transcription factor (HSF) members through the TF databases. Finally, we present how the integration of bioinformatic resources at -omics layers can aid the dissection of TF-mediated pathways. We also suggest ways forward to improve the bioinformatic resources of plant TFs. Leveraging these bioinformatic resources and methodologies opens new avenues for the elucidation of transcriptional regulatory systems in the three model systems and translation to other plants.

elegans Research

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

Normal cell phenotypes of breast epithelial cells provide the foundation of a breast cancer taxonomy

The current classification system for breast cancer is based on expression of empirical prognostic and predictive biomarkers. As an alternative, we propose a hypothesis-based ontological breast cancer classification modeled after the taxonomy of species in evolutionary biology. This approach uses normal breast epithelial cell types and differentiation lineages as the gold standard to classify tumors. We show that there are at least eleven previously undefined normal cell types in human breast epithelium and that each breast carcinoma is related to one of these normal cell types. We find that triple negative breast cancers do not have a 'basal-like' phenotype. Normal breast epithelial cells conform to four novel hormonal differentiation states and almost all human breast tumors duplicate one of these hormonal differentiation states which have significant survival differences. This ontological classification scheme provides actionable treatment strategies and provides an alternative approach for understanding tumor biology with wide-ranging implications for tumor taxonomy.

and -omics-based new diagnostic tools for schistosomiasis

Schistosomiasis, caused by dioecious flatworms in the genus Schistosoma, is torturing people from many developing countries nowadays and frequently leads to severe morbidity and mortality of the patients. Praziquantel based chemotherapy and morbidity control for this disease adopted currently necessitate viable and efficient diagnostic technologies. Fortunately, those “-omics” researches, which rely on high-throughput experimental technologies to produce massive amounts of informative data, have substantially contributed to the exploitation and innovation of diagnostic tools of schistosomiasis. In its first section, this review provides a concise conclusion on the progresses pertaining to schistosomal “-omics” researches to date, followed by a comprehensive section on the diagnostic methods of schistosomiasis, especially those innovative ones based on the detection of antibodies, antigens, nucleic acids, and metabolites with a focus on those achievements inspired by “-omics” researches. Finally, suggestions about the design of future diagnostic tools of schistosomiasis are proposed, in order to better harness those data produced by “-omics” studies.

Gender-specific transcriptional profiling of marine medaka (Oryzias melastigma) liver upon BDE-47 exposure

Marine medaka (Oryzias melastigma) were exposed to 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) to investigate the gender-specific transcriptional profiles of liver tissue in response to this flame retardant. A cDNA library of O. melastigma was constructed, and 2304 clones were amplified from the library to fabricate a cDNA microarray. Sequences of these genes were assembled into 1800 sequences using Geneious, a bioinformatics software. Corresponding expressed sequence tags were blasted against the National Centre for Biotechnology Information non-redundant database and further classified into various biological categories according to the Gene Ontology project. Male and female three-month-old were fed a diet of BDE-47 contaminated Artemia at low dosage (290.3±172.3ng BDE-47/day) and high dosage (580.5±344.6ng BDE-47/day) for 5 and 21 days, respectively. The transcriptional profiles of O. melastigma liver were then generated by the species-specific cDNA microrarray. The results from microarray analysis suggested very different gene expression patterns between males and females for both BDE-47 exposure-dose and exposure-time, with male livers having stronger gene regulatory responses than female livers. Importantly, our results revealed that in male O. melastigma only, BDE-47 exposure may activate phosphoinositide-3-kinase and mitogen-activated protein kinase, proteins that play importance roles in cell growth, proliferation and survival.

VANTED v2: a framework for systems biology applications

BACKGROUND

Experimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.

RESULTS

Here we present VANTED (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.

CONCLUSIONS

VANTED is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionary biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.

Integrating mechanistic and polymorphism data to characterize human genetic susceptibility for environmental chemical risk assessment in the 21st century

Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization of drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment.