Functional genomics: it's all how you read it

Data- and knowledge-derived functional landscape of human solute carriers

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

Genotype from Phenotype: Using CRISPR Screens to Dissect Lymphoma Biology

Genome-wide screens are a powerful technique to dissect the complex network of genes regulating diverse cellular phenotypes. The recent adaptation of the CRISPR-Cas9 system for genome engineering has revolutionized functional genomic screening. Here, we present protocols used to introduce Cas9 into human lymphoma cell lines, produce high-titer lentivirus of a genome-wide sgRNA library, transduce and culture cells during the screen, select cells with a specified phenotype, isolate genomic DNA, and prepare a custom library for next-generation sequencing. These protocols were tailored for loss-of-function CRISPR screens in human B-cell lymphoma cell lines but are highly amenable for other experimental purposes.

Culturomics: Taking the cross-scale, interdisciplinary science of culture into the next decade

Culture is a complex topic involving a comprehensive representation of human institutions, social customs, norms, and lifestyles. Over the past half-century, the methods of cultural studies have improved dramatically in the depth of the research questions posed. However, most contemporary research on cultural issues is conducted from a single perspective, which fails to account for the holistic and extensive nature of culture. The development of culture is influenced by various factors, encompassing not only the humanistic environment but also factors related to the natural environment and socio-economic conditions. Hence, culture involves multiple concepts with associated levels and dimensions, such as genes, molecules, brains, individuals, groups, institutions, societies, and political environments. Therefore, we propose the concept of Culturomics, a cross-level, interdisciplinary science that studies human behavior and cultural representation in high-order space. Under this concept, it is necessary to find new methods to compare multidimensional data from different levels directly. In this paper, we first review past cultural studies, then introduce the concept, research content, and methodology of Culturomics, and discuss future directions for this field.

Super-resolution ultrasound and microvasculomics: a consensus statement

This is a summary of a consensus statement on the introduction of "Ultrasound microvasculomics" produced by The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The evaluation of microvessels is a very important part for the assessment of diseases. Super-resolution ultrasound (SRUS) microvascular imaging surpasses traditional ultrasound imaging in the morphological and functional analysis of microcirculation. SRUS microvascular imaging relies on contrast microbubbles to gain sensitivity to microvessels and improves the spatial resolution of ultrasound blood flow imaging for a more detailed depiction of vascular structures and hemodynamics. This method has been applied in preclinical animal models and pilot clinical studies, involving areas including neurology, oncology, nephrology, and cardiology. However, the current quantitative parameters of SRUS images are not enough for precise evaluation of microvessels. Therefore, by employing omics methods, more quantification indicators can be obtained, enabling a more precise and personalized assessment of microvascular status. Ultrasound microvasculomics - a high-throughput extraction of image features from SRUS images - is one novel approach that holds great promise but needs further validation in both bench and clinical settings. CLINICAL RELEVANCE STATEMENT: Super-resolution Ultrasound (SRUS) blood flow imaging improves spatial resolution. Ultrasound microvasculomics is possible to acquire high-throughput information of features from SRUS images. It provides more precise and abundant micro-blood flow information in clinical medicine. KEY POINTS: This consensus statement reviews the development and application of super-resolution ultrasound (SRUS). The shortcomings of the current quantification indicators of SRUS and strengths of the omics methodology are addressed. "Ultrasound microvasculomics" is introduced for a high-throughput extraction of image features from SRUS images.

Functional genomics in inborn errors of immunity

Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Phenotypic screen and transcriptomics approach complement each other in functional genomics of defensive stink gland physiology

Background

Functional genomics uses unbiased systematic genome-wide gene disruption or analyzes natural variations such as gene expression profiles of different tissues from multicellular organisms to link gene functions to particular phenotypes. Functional genomics approaches are of particular importance to identify large sets of genes that are specifically important for a particular biological process beyond known candidate genes, or when the process has not been studied with genetic methods before.

Results

Here, we present a large set of genes whose disruption interferes with the function of the odoriferous defensive stink glands of the red flour beetle Tribolium castaneum. This gene set is the result of a large-scale systematic phenotypic screen using RNA interference applied in a genome-wide forward genetics manner. In this first-pass screen, 130 genes were identified, of which 69 genes could be confirmed to cause phenotypic changes in the glands upon knock-down, which vary from necrotic tissue and irregular reservoir size to irregular color or separation of the secreted gland compounds. Gene ontology analysis revealed that many of those genes are encoding enzymes (peptidases and cytochromes P450) as well as proteins involved in membrane trafficking with an enrichment in lysosome and mineral absorption pathways. The knock-down of 13 genes caused specifically a strong reduction of para-benzoquinones in the gland reservoirs, suggesting a specific function in the synthesis of these toxic compounds. Only 14 of the 69 confirmed gland genes are differentially overexpressed in stink gland tissue and thus could have been detected in a transcriptome-based analysis. However, only one out of eight genes identified by a transcriptomics approach known to cause phenotypic changes of the glands upon knock-down was recognized by this phenotypic screen, indicating the limitation of such a non-redundant first-pass screen.

Conclusion

Our results indicate the importance of combining diverse and independent methodologies to identify genes necessary for the function of a certain biological tissue, as the different approaches do not deliver redundant results but rather complement each other. The presented phenotypic screen together with a transcriptomics approach are now providing a set of close to hundred genes important for odoriferous defensive stink gland physiology in beetles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08822-z.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Machine learning applications for therapeutic tasks with genomics data

Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Origin, Evolution, Breeding, and Omics of Chayote, an Important Cucurbitaceae Vegetable Crop

Chayote (Sechium edule), a member of the Cucurbitaceae family, is cultivated throughout tropical and subtropical regions of the world and utilized in pharmaceutical, cosmetic and food industries because it is an excellent source of minerals, dietary fibers, protein, vitamins, carotenoids, polysaccharides, phenolic and flavonoid compounds, and other nutrients. Chayote extracts process various medicinal properties, such as anti-cardiovascular, antidiabetic, antiobesity, antiulcer, and anticancer properties. With the rapid advancements of molecular biology and sequencing technology, studies on chayote have been carried out. Research advances, including molecular makers, breeding, genomic research, chemical composition, and pests and diseases, regarding chayote are reviewed in this paper. Future exploration and application trends are briefly described. This review provides a reference for basic and applied research on chayote, an important Cucurbitaceae vegetable crop.

A multi-omics approach to solving problems in plant disease ecology

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.

Deep learning: new computational modelling techniques for genomics

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.

Transcription factors–DNA interactions in rice: identification and verification

The completion of the rice genome sequence paved the way for rice functional genomics research. Additionally, the functional characterization of transcription factors is currently a popular and crucial objective among researchers. Transcription factors are one of the groups of proteins that bind to either enhancer or promoter regions of genes to regulate expression. On the basis of several typical examples of transcription factor analyses, we herein summarize selected research strategies and methods and introduce their advantages and disadvantages. This review may provide some theoretical and technical guidelines for future investigations of transcription factors, which may be helpful to develop new rice varieties with ideal traits.

Yeast Systems Biology: The Continuing Challenge of Eukaryotic Complexity

Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.

Protocols for CRISPR-Cas9 Screening in Lymphoma Cell Lines

Genome-wide screens are a powerful technique to dissect the complex network of genes regulating diverse cellular phenotypes. The recent adaptation of the CRISPR-Cas9 system for genome engineering has revolutionized functional genomic screening. Here, we present protocols used to introduce Cas9 into human lymphoma cell lines, produce high-titer lentivirus of a genome-wide sgRNA library, transduce and culture cells during the screen, isolate genomic DNA, and prepare a custom library for next-generation sequencing. These protocols were tailored for loss-of-function CRISPR screens in human lymphoma cell lines but are highly amenable for other experimental purposes.

Improved SILAC method for double labeling of bacterial proteome

Stable isotope labeling with amino acids in cell culture (SILAC) is a robust proteomics method with advantages such as reproducibility and easy handling. This method is popular for the analysis of mammalian cells. However, amino acid conversion in bacteria decreases the labeling efficiency and quantification accuracy, limiting the application of SILAC in bacterial proteomics to auxotrophic bacteria or to single labeling with lysine. In this study, we found that adding high concentrations of isotope-labeled (heavy) and natural (light) amino acids into SILAC minimal medium can efficiently inhibit the complicated amino acid conversions. This simple and straightforward strategy facilitated complete incorporation of amino acids into the bacterial proteome with good accuracy. High labeling efficiency can be achieved in different bacteria by slightly modifying the supplementation of amino acids in culture media, promoting the widespread application of SILAC technique in bacterial proteomics. SIGNIFICANCE: Amino acid conversion in bacteria decreases labeling efficiency, limiting the application of Stable isotope labeling with amino acids in cell culture (SILAC) in bacterial proteomics to auxotrophic bacteria or single labeling with lysine. In this study, we found that high concentrations of isotope-labeled (heavy) and natural (light) amino acids facilitate full incorporation of amino acids into the bacterial proteome with good reproducibility. This improved double labeling SILAC technique using medium supplemented with high concentrations of amino acids is suitable for quantitative proteomics research on both gram-positive and -negative bacteria, facilitating the broad application of quantitative proteomics in bacterial studies.

"Super Gene Set" Causal Relationship Discovery from Functional Genomics Data

In this article, we present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships," we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures," or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

A Functional Genomics Approach to Henipavirus Research: The Role of Nuclear Proteins, MicroRNAs and Immune Regulators in Infection and Disease

Hendra and Nipah viruses (family Paramyxoviridae, genus Henipavirus) are zoonotic RNA viruses that cause lethal disease in humans and are designated as Biosafety Level 4 (BSL4) agents. Moreover, henipaviruses belong to the same group of viruses that cause disease more commonly in humans such as measles, mumps and respiratory syncytial virus. Due to the relatively recent emergence of the henipaviruses and the practical constraints of performing functional genomics studies at high levels of containment, our understanding of the henipavirus infection cycle is incomplete. In this chapter we describe recent loss-of-function (i.e. RNAi) functional genomics screens that shed light on the henipavirus-host interface at a genome-wide level. Further to this, we cross-reference RNAi results with studies probing host proteins targeted by henipavirus proteins, such as nuclear proteins and immune modulators. These functional genomics studies join a growing body of evidence demonstrating that nuclear and nucleolar host proteins play a crucial role in henipavirus infection. Furthermore these studies will underpin future efforts to define the role of nucleolar host-virus interactions in infection and disease.

Smog induces oxidative stress and microbiota disruption

Smog is created through the interactions between pollutants in the air, fog, and sunlight. Air pollutants, such as carbon monoxide, heavy metals, nitrogen oxides, ozone, sulfur dioxide, volatile organic vapors, and particulate matters, can induce oxidative stress in human directly or indirectly through the formation of reactive oxygen species. The outermost boundary of human skin and mucous layers are covered by a complex network of human-associated microbes. The relation between these microbial communities and their human host are mostly mutualistic. These microbes not only provide nutrients, vitamins, and protection against other pathogens, they also influence human's physical, immunological, nutritional, and mental developments. Elements in smog can induce oxidative stress to these microbes, leading to community collapse. Disruption of these mutualistic microbiota may introduce unexpected health risks, especially among the newborns and young children. Besides reducing the burning of fossil fuels as the ultimate solution of smog formation, advanced methods by using various physical, chemical, and biological means to reduce sulfur and nitrogen contains in fossil fuels could lower smog formation. Additionally, information on microbiota disruption, based on functional genomics, culturomics, and general ecological principles, should be included in the risk assessment of prolonged smog exposure to the health of human populations.

Overview on the Role of Advance Genomics in Conservation Biology of Endangered Species

In the recent era, due to tremendous advancement in industrialization, pollution and other anthropogenic activities have created a serious scenario for biota survival. It has been reported that present biota is entering a “sixth” mass extinction, because of chronic exposure to anthropogenic activities. Various ex situ and in situ measures have been adopted for conservation of threatened and endangered plants and animal species; however, these have been limited due to various discrepancies associated with them. Current advancement in molecular technologies, especially, genomics, is playing a very crucial role in biodiversity conservation. Advance genomics helps in identifying the segments of genome responsible for adaptation. It can also improve our understanding about microevolution through a better understanding of selection, mutation, assertive matting, and recombination. Advance genomics helps in identifying genes that are essential for fitness and ultimately for developing modern and fast monitoring tools for endangered biodiversity. This review article focuses on the applications of advanced genomics mainly demographic, adaptive genetic variations, inbreeding, hybridization and introgression, and disease susceptibilities, in the conservation of threatened biota. In short, it provides the fundamentals for novice readers and advancement in genomics for the experts working for the conservation of endangered plant and animal species.

Functional Toxicogenomic Assessment of Triclosan in Human HepG2 Cells Using Genome-Wide CRISPR-Cas9 Screening

There are thousands of chemicals used by humans and detected in the environment for which limited or no toxicological data are available. Rapid and cost-effective approaches for assessing the toxicological properties of chemicals are needed. We used CRISPR-Cas9 functional genomic screening to identify the potential molecular mechanism of a widely used antimicrobial triclosan (TCS) in HepG2 cells. Resistant genes at IC50 (the concentration causing a 50% reduction in cell viability) were significantly enriched in the adherens junction pathway, MAPK signaling pathway, and PPAR signaling pathway, suggesting a potential role in the molecular mechanism of TCS-induced cytotoxicity. Evaluation of the top-ranked resistant genes, FTO (encoding an mRNA demethylase) and MAP2K3 (a MAP kinase kinase family gene), revealed that their loss conferred resistance to TCS. In contrast, sensitive genes at IC10 and IC20 were specifically enriched in pathways involved with immune responses, which was concordant with transcriptomic profiling of TCS at concentrations of <IC10. It is suggested that the CRISPR-Cas9 fingerprint may reveal the patterns of TCS toxicity at low concentration levels. Moreover, we retrieved the potential connection between CRISPR-Cas9 fingerprint and disease terms, obesity, and breast cancer from an existing chemical-gene-disease database. Overall, CRISPR-Cas9 functional genomic screening offers an alternative approach for chemical toxicity testing.

Ascribing Functions to Genes: Journey Towards Genetic Improvement of Rice Via Functional Genomics

Rice, one of the most important cereal crops for mankind, feeds more than half the world population. Rice has been heralded as a model cereal owing to its small genome size, amenability to easy transformation, high synteny to other cereal crops and availability of complete genome sequence. Moreover, sequence wealth in rice is getting more refined and precise due to resequencing efforts. This humungous resource of sequence data has confronted research fraternity with a herculean challenge as well as an excellent opportunity to functionally validate expressed as well as regulatory portions of the genome. This will not only help us in understanding the genetic basis of plant architecture and physiology but would also steer us towards developing improved cultivars. No single technique can achieve such a mammoth task. Functional genomics through its diverse tools viz. loss and gain of function mutants, multifarious omics strategies like transcriptomics, proteomics, metabolomics and phenomics provide us with the necessary handle. A paradigm shift in technological advances in functional genomics strategies has been instrumental in generating considerable amount of information w.r.t functionality of rice genome. We now have several databases and online resources for functionally validated genes but despite that we are far from reaching the desired milestone of functionally characterizing each and every rice gene. There is an urgent need for a common platform, for information already available in rice, and collaborative efforts between researchers in a concerted manner as well as healthy public-private partnership, for genetic improvement of rice crop better able to handle the pressures of climate change and exponentially increasing population.

Functional genomic screening approaches in mechanistic toxicology and potential future applications of CRISPR-Cas9

Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Chemical toxicants act by many different mechanisms, however, and the genes involved in adverse outcome pathways (AOPs) and AOP networks are not yet characterized. Functional genomic approaches can reveal both toxicity pathways and susceptibility genes, through knockdown or knockout of all non-essential genes in a cell of interest, and identification of genes associated with a toxicity phenotype following toxicant exposure. Screening approaches in yeast and human near-haploid leukemic KBM7 cells have identified roles for genes and pathways involved in response to many toxicants but are limited by partial homology among yeast and human genes and limited relevance to normal diploid cells. RNA interference (RNAi) suppresses mRNA expression level but is limited by off-target effects (OTEs) and incomplete knockdown. The recently developed gene editing approach called clustered regularly interspaced short palindrome repeats-associated nuclease (CRISPR)-Cas9, can precisely knock-out most regions of the genome at the DNA level with fewer OTEs than RNAi, in multiple human cell types, thus overcoming the limitations of the other approaches. It has been used to identify genes involved in the response to chemical and microbial toxicants in several human cell types and could readily be extended to the systematic screening of large numbers of environmental chemicals. CRISPR-Cas9 can also repress and activate gene expression, including that of non-coding RNA, with near-saturation, thus offering the potential to more fully characterize AOPs and AOP networks. Finally, CRISPR-Cas9 can generate complex animal models in which to conduct preclinical toxicity testing at the level of individual genotypes or haplotypes. Therefore, CRISPR-Cas9 is a powerful and flexible functional genomic screening approach that can be harnessed to provide unprecedented mechanistic insight in the field of modern toxicology.

Application of Molecular Approaches for Understanding Foodborne Salmonella Establishment in Poultry Production

Salmonellosis in the United States is one of the most costly foodborne diseases. Given that Salmonella can originate from a wide variety of environments, reduction of this organism at all stages of poultry production is critical. Salmonella species can encounter various environmental stress conditions which can dramatically influence their survival and colonization. Current knowledge of Salmonella species metabolism and physiology in relation to colonization is traditionally based on studies conducted primarily with tissue culture and animal infection models. Consequently, while there is some information about environmental signals that control Salmonella growth and colonization, much still remains unknown. Genetic tools for comprehensive functional genomic analysis of Salmonella offer new opportunities for not only achieving a better understanding of Salmonella pathogens but also designing more effective intervention strategies. Now the function(s) of each single gene in the Salmonella genome can be directly assessed and previously unknown genetic factors that are required for Salmonella growth and survival in the poultry production cycle can be elucidated. In particular, delineating the host-pathogen relationships involving Salmonella is becoming very helpful for identifying optimal targeted gene mutagenesis strategies to generate improved vaccine strains. This represents an opportunity for development of novel vaccine approaches for limiting Salmonella establishment in early phases of poultry production. In this review, an overview of Salmonella issues in poultry, a general description of functional genomic technologies, and their specific application to poultry vaccine developments are discussed.

Towards a strategic approaches in alternative tests for pesticide safety

Pesticides have provided significant benefits including plant disease control and increased crop yields since people developed and utilized them. However, pesticide use is associated with many adverse effects, which necessitate precise toxicological tests and risk assessment. Most of these methods are based on animal studies, but considerations of animal welfare and ethics require the development of alternative methods for the evaluation of pesticide toxicity. Although the usage of laboratory animals is inevitable in scientific evaluation and alternative approaches have limitations in the whole coverage, continuous effort is necessary to minimize animal use and to develop reliable alternative tests for pesticide evaluation. This review discusses alternative approaches for pesticide toxicity tests and hazard evaluation that have been used in peer-reviewed reports and could be applied in future studies based on the critical animal research principles of reduction, replacement, and refinement.

Application of toxicogenomic profiling to evaluate effects of benzene and formaldehyde: from yeast to human

Genetic variation underlies a significant proportion of the individual variation in human susceptibility to toxicants. The primary current approaches to identify gene-environment (GxE) associations, genome-wide association studies and candidate gene association studies, require large exposed and control populations and an understanding of toxicity genes and pathways, respectively. This limits their application in the study of GxE associations for the leukemogens benzene and formaldehyde, whose toxicity has long been a focus of our research. As an alternative approach, our published work has applied innovative in vitro functional genomics testing systems, including unbiased functional screening assays in yeast and a near-haploid human bone marrow cell line. Through comparative genomic and computational analyses of the resulting data, human genes and pathways that may modulate susceptibility to benzene and formaldehyde were identified, and the roles of several genes in mammalian cell models were validated. In populations occupationally exposed to low levels of benzene, we applied peripheral blood mononuclear cell transcriptomics and chromosome-wide aneuploidy studies in lymphocytes. In this review, we describe our comprehensive toxicogenomic approach and the potential mechanisms of toxicity and susceptibility genes identified for benzene and formaldehyde, as well as related studies conducted by other researchers.

A history of microarrays in biomedicine

The fundamental strategy of the current postgenomic era or the era of functional genomics is to expand the scale of biologic research from studying single genes or proteins to studying all genes or proteins simultaneously using a systematic approach. As recently developed methods for obtaining genome-wide mRNA expression data, oligonucleotide and DNA microarrays are particularly powerful in the context of knowing the entire genome sequence and can provide a global view of changes in gene expression patterns in response to physiologic alterations or manipulation of transcriptional regulators. In biomedical research, such an approach will ultimately determine biologic behavior of both normal and diseased tissues, which may provide insights into disease mechanisms and identify novel markers and candidates for diagnostic, prognostic and therapeutic intervention. However, microarray technology is still in a continuous state of evolution and development, and it may take time to implement microarrays as a routine medical device. Many limitations exist and many challenges remain to be achieved to help inclusion of microarrays in clinical medicine. In this review, a brief history of microarrays in biomedical research is provided, including experimental overview, limitations, challenges and future developments.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

The glomerular proteome in a model of chronic kidney disease

Adequate kidney function is crucial in sustaining vertebrate homeostasis. Certain diseases can diminish renal function and lead to end-stage renal disease. Diabetes mellitus and hypertension are the main causes of glomerulosclerosis and albuminuria in adults. The molecular mechanisms that trigger these maladaptive changes are still unsatisfyingly described. We previously introduced 2-D DIGE in combination with focused tissue isolation methods to analyze protein expression in glomeruli. Glomeruli, the crucial compartments in albuminuric renal diseases, were extracted using magnetic particles from subtotally nephrectomized FVB mice (n = 6); this 5/6 nephrectomy in FVB mice is a model of chronic kidney disease. Analysis of protein expression levels from glomerular protein lysates was performed using 2-D DIGE and compared with glomerular protein lysates from mice that underwent sham surgery. The comparison of about 2100 detectable spots between both groups revealed 48 protein spots that showed significant differential expression. Of those, 33 proteins could be identified using nanoLC-ESI MS. The metalloproteinase meprin 1 alpha, the beta galactoside-binding-lectin galectin-1 and dimethylarginine dimethylaminohydrolase 1, a key enzyme in NO metabolism, were found to be differentially regulated, thus implying a role in the pathogenesis and pathophysiology of progressive kidney disease. In conclusion, 2-D DIGE protein analysis of smallest sample sizes from specific organ compartments provides focused protein expression results, which help in gaining an understanding of the molecular mechanisms of chronic kidney disease.

Complexity and the reductionism-holism debate in systems biology

Reductionism has largely influenced the development of science, culminating in its application to molecular biology. An increasing number of novel research findings have, however, shattered this view, showing how the molecular-reductionist approach cannot entirely handle the complexity of biological systems. Within this framework, the advent of systems biology as a new and more integrative field of research is described, along with the form which has taken on the debate of reductionism versus holism. Such an issue occupies a central position in systems biology, and nonetheless it is not always clearly delineated. This partly occurs because different dimensions (ontological, epistemological, methodological) are involved, and yet the concerned ones often remain unspecified. Besides, within systems biology different streams can be distinguished depending on the degree of commitment to embrace genuine systemic principles. Some useful insights into the future development of this discipline might be gained from the tradition of complexity and self-organization. This is especially true with regards the idea of self-reference, which incorporated into the organizational scheme is able to generate autonomy as an emergent property of the biological whole.

Accurate evaluation and analysis of functional genomics data and methods

The development of technology capable of inexpensively performing large-scale measurements of biological systems has generated a wealth of data. Integrative analysis of these data holds the promise of uncovering gene function, regulation, and, in the longer run, understanding complex disease. However, their analysis has proved very challenging, as it is difficult to quickly and effectively assess the relevance and accuracy of these data for individual biological questions. Here, we identify biases that present challenges for the assessment of functional genomics data and methods. We then discuss evaluation methods that, taken together, begin to address these issues. We also argue that the funding of systematic data-driven experiments and of high-quality curation efforts will further improve evaluation metrics so that they more-accurately assess functional genomics data and methods. Such metrics will allow researchers in the field of functional genomics to continue to answer important biological questions in a data-driven manner.

GENE CLASSIFICATION USING EXPRESSION PROFILES: A FEASIBILITY STUDY

As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus to functional genomics. Functional genomics represents the next phase, that expands the biological investigation to studying the functionality of genes of a single organism as well as studying and correlating the functionality of genes across many different organisms. Recently developed methods for monitoring genome-wide mRNA expression changes hold the promise of allowing us to inexpensively gain insights into the function of unknown genes. In this paper we focus on evaluating the feasibility of using supervised machine learning methods for determining the function of genes based solely on their expression profiles. We experimentally evaluate the performance of traditional classification algorithms such as support vector machines and k-nearest neighbors on the yeast genome, and present new approaches for classification that improve the overall recall with moderate reductions in precision. Our experiments show that the accuracies achieved for different classes varies dramatically. In analyzing these results we show that the achieved accuracy is highly dependent on whether or not the genes of that class were significantly active during the various experimental conditions, suggesting that gene expression profiles can become a viable alternative to sequence similarity searches provided that the genes are observed under a wide range of experimental conditions.

Cell functional enviromics: unravelling the function of environmental factors

Background

While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment interactions are relatively less developed. Understanding the function of environmental factors is, however, of paramount importance given the complex, interactive nature of environmental and genetic factors across multiple time scales.

Results

Here, we propose a systems biology framework, where the function of environmental factors is set at its core. We set forth a "reverse" functional analysis approach, whereby cellular functions are reconstructed from the analysis of dynamic envirome data. Our results show these data sets can be mapped to less than 20 core cellular functions in a typical mammalian cell culture, while explaining over 90% of flux data variance. A functional enviromics map can be created, which provides a template for manipulating the environmental factors to induce a desired phenotypic trait.

Conclusion

Our results support the feasibility of cellular function reconstruction guided by the analysis and manipulation of dynamic envirome data.

Systems biology: the next frontier for bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from 'omics platform technologies, in particular "downstream" technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.

Functional toxicogenomics: mechanism-centered toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21^st century.

Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy.

Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry

We sought to test the hypothesis that mutant bacterial strains could be discriminated from each other on the basis of the metabolites they secrete into the medium (their ‘metabolic footprint’), using two methods of ‘global’ metabolite analysis (FT–IR and direct injection electrospray mass spectrometry). The biological system used was based on a published study of Escherichia coli tryptophan mutants that had been analysed and discriminated by Yanofsky and colleagues using transcriptome analysis. Wild-type strains supplemented with tryptophan or analogues could be discriminated from controls using FT–IR of 24 h broths, as could each of the mutant strains in both minimal and supplemented media. Direct injection electrospray mass spectrometry with unit mass resolution could also be used to discriminate the strains from each other, and had the advantage that the discrimination required the use of just two or three masses in each case. These were determined via a genetic algorithm. Both methods are rapid, reagentless, reproducible and cheap, and might beneficially be extended to the analysis of gene knockout libraries.

A re-annotation of the Saccharomyces cerevisiae genome

Discrepancies in gene and orphan number indicated by previous analyses suggest that S. cerevisiae would benefit from a consistent re-annotation. In this analysis three new genes are identified and 46 alterations to gene coordinates are described. 370 ORFs are defined as totally spurious ORFs which should be disregarded. At least a further 193 genes could be described as very hypothetical, based on a number of criteria. It was found that disparate genes with sequence overlaps over ten amino acids (especially at the N-terminus) are rare in both S. cerevisiae and Sz. pombe. A new S. cerevisiae gene number estimate with an upper limit of 5804 is proposed, but after the removal of very hypothetical genes and pseudogenes this is reduced to 5570. Although this is likely to be closer to the true upper limit, it is still predicted to be an overestimate of gene number. A complete list of revised gene coordinates is available from the Sanger Centre (S. cerevisiae reannotation: ftp://ftp/pub/yeast/SCreannotation).

Reverse genetics for functional genomics of phytopathogenic fungi and oomycetes

Sequencing of over 40 fungal and oomycete genomes has been completed. The next major challenge in modern fungal/oomycete biology is now to translate this plethora of genome sequence information into biological functions. Reverse genetics has emerged as a seminal tool for functional genomics investigations. Techniques utilized for reverse genetics like targeted gene disruption/replacement, gene silencing, insertional mutagenesis, and targeting induced local lesions in genomes will contribute greatly to the understanding of gene function of fungal and oomycete pathogens. This paper provides an overview on high-throughput reverse genetics approaches to decode fungal/oomycete genomes.

The Differential Gene Expression Pattern of Mycobacterium tuberculosis in Response to Capreomycin and PA-824 versus First-Line TB Drugs Reveals Stress- and PE/PPE-Related Drug Targets

Tuberculosis is a leading infectious disease causing millions of deaths each year. How to eradicate mycobacterial persistence has become a central research focus for developing next-generation TB drugs. Yet, the knowledge in this area is fundamentally limited and only a few drugs, notably capreomycin and PA-824, have been shown to be active against non-replicating persistent TB bacilli. In this study, we performed a new bioinformatics analysis on microarray-based gene expression data obtained from the public domain to explore genes that were differentially induced by drugs between the group of capreomycin and PA-824 and the group of mainly the first-line TB drugs. Our study has identified 42 genes specifically induced by capreomycin and PA-824. Many of these genes are related to stress responses. In terms of the distribution of identified genes in a specific category relative to the whole genome, only the categories of PE/PPE and conserved hypotheticals have statistical significance. Six among the 42 genes identified in this study are on the list of the top 100 persistence targets selected by the TB Structural Genomics Consortium. Further biological elucidation of their roles in mycobacterial persistence is warranted.

Clone-based functional genomics

Annotated genomes have provided a wealth of information about gene structure and gene catalogs in a wide range of species. Taking advantage of these developments, novel techniques have been implemented to investigate systematically diverse aspects of gene and protein functions underpinning biology processes. Here, we review functional genomics applications that require the mass production of cloned sequence repertoires, including ORFeomes and silencing tag collections. We discuss the techniques employed in large-scale cloning projects and we provide an up-to-date overview of the clone resources available for model plant species and of the current applications that may be scaled up for systematic plant gene studies.

Genome sequencing and identification of gene function in rice

Rice is known to be one of the most important crops for human consumption. As the model cereal crop, large-scale sequencing of rice genome must play quite important roles both in theoretical research and practical application in rice breeding, which announces the opening of another new way to resolve the world food crisis. At present, the emphasis of rice genome research has been transferred from structure genomics to functional analysis. The discovery of new genes and annotation of gene function was believed to be an important issue in functional genomics research. In this article, the sequencing and functional research of the rice genome were reviewed. These results may provide some useful clues for rice genetic engineering and breeding practices.