The -omics era and its impact

From the reference human genome to human pangenome: Premise, promise and challenge

The Reference Human Genome remains the single most important resource for mapping genetic variations and assessing their impact. However, it is monophasic, incomplete and not representative of the variation that exists in the population. Given the extent of ethno-geographic diversity and the consequent diversity in clinical manifestations of these variations, population specific references were developed overtime. The dramatically plummeting cost of sequencing whole genomes and the advent of third generation long range sequencers allowing accurate, error free, telomere-to-telomere assemblies of human genomes present us with a unique and unprecedented opportunity to develop a more composite standard reference consisting of a collection of multiple genomes that capture the maximal variation existing in the population, with the deepest annotation possible, enabling a realistic, reliable and actionable estimation of clinical significance of specific variations. The Human Pangenome Project thus is a logical next step promising a more accurate and global representation of genomic variations. The pangenome effort must be reciprocally complemented with precise variant discovery tools and exhaustive annotation to ensure unambiguous clinical assessment of the variant in ethno-geographical context. Here we discuss a broad roadmap, the challenges and way forward in developing a universal pangenome reference including data visualization techniques and integration of prior knowledge base in the new graph based architecture and tools to submit, compare, query, annotate and retrieve relevant information from the pangenomes. The biggest challenge, however, will be the ethical, legal and social implications and the training of human resource to the new reference paradigm.

Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People's Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.

From Human Cytogenetics to Human Chromosomics

BACKGROUND

The concept of "chromosomics" was introduced by Prof. Uwe Claussen in 2005. Herein, the growing insights into human chromosome structure finally lead to a "chromosomic view" of the three-dimensional constitution and plasticity of genes in interphase nuclei are discussed. This review is dedicated to the memory of Prof. Uwe Claussen (30 April 1945⁻20 July 2008).

RECENT FINDINGS

Chromosomics is the study of chromosomes, their three-dimensional positioning in the interphase nucleus, the consequences from plasticity of chromosomal subregions and gene interactions, the influence of chromatin-modification-mediated events on cells, and even individuals, evolution, and disease. Progress achieved in recent years is summarized, including the detection of chromosome-chromosome-interactions which, if damaged, lead to malfunction and disease. However, chromosomics in the Human Genetics field is not progressing presently, as research interest has shifted from single cell to high throughput, genomic approaches.

CONCLUSION

Chromosomics and its impact were predicted correctly in 2005 by Prof. Claussen. Although some progress was achieved, present reconsiderations of the role of the chromosome and the single cell in Human Genetic research are urgently necessary.

A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.

Reconstructing the Ancestral Relationships Between Bacterial Pathogen Genomes

Following recent developments in DNA sequencing technology, it is now possible to sequence hundreds of whole genomes from bacterial isolates at relatively low cost. Analyzing this growing wealth of genomic data in terms of ancestral relationships can reveal many interesting aspects of the evolution, ecology, and epidemiology of bacterial pathogens. However, reconstructing the ancestry of a sample of bacteria remains challenging, especially for the majority of species where recombination is frequent. Here, we review and describe the computational techniques currently available to infer ancestral relationships, including phylogenetic methods that either ignore or account for the effect of recombination, as well as model-based and model-free phylogeny-independent approaches.

Laser capture microdissection: Big data from small samples

Any tissue is made up of a heterogeneous mix of spatially distributed cell types. In response to any (patho) physiological cue, responses of each cell type in any given tissue may be unique and cannot be homogenized across cell-types and spatial co-ordinates. For example, in response to myocardial infarction, on one hand myocytes and fibroblasts of the heart tissue respond differently. On the other hand, myocytes in the infarct core respond differently compared to those in the peri-infarct zone. Therefore, isolation of pure targeted cells is an important and essential step for the molecular analysis of cells involved in the progression of disease. Laser capture microdissection (LCM) is powerful to obtain a pure targeted cell subgroup, or even a single cell, quickly and precisely under the microscope, successfully tackling the problem of tissue heterogeneity in molecular analysis. This review presents an overview of LCM technology, the principles, advantages and limitations and its down-stream applications in the fields of proteomics, genomics and transcriptomics. With powerful technologies and appropriate applications, this technique provides unprecedented insights into cell biology from cells grown in their natural tissue habitat as opposed to those cultured in artificial petri dish conditions.

NCBI2RDF: enabling full RDF-based access to NCBI databases

RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over different NCBI repositories and presenting to users the query results in SPARQL results format, thus enabling this data to be integrated and/or stored with other RDF-compliant repositories. SPARQL queries are dynamically resolved, decomposed, and forwarded to the NCBI-provided E-utilities programmatic interface to access the NCBI data. Furthermore, we show how our approach increases the expressiveness of the native NCBI querying system, allowing several databases to be accessed simultaneously. This feature significantly boosts productivity when working with complex queries and saves time and effort to biomedical researchers. Our approach has been validated with a large number of SPARQL queries, thus proving its reliability and enhanced capabilities in biomedical environments.

Clinical trials of antioxidants as cancer prevention agents: past, present, and future

The purpose of this review is to summarize the most important human clinical trials of antioxidants as cancer prevention agents conducted to date, provide an overview of currently ongoing studies, and discuss future steps needed to advance research in this field. To date there have been several large (at least 7000 participants) trials testing the efficacy of antioxidant supplements in preventing cancer. The specific agents (diet-derived direct antioxidants and essential components of antioxidant enzymes) tested in those trials included β-carotene, vitamin E, vitamin C, selenium, retinol, zinc, riboflavin, and molybdenum. None of the completed trials produced convincing evidence to justify the use of traditional antioxidant-related vitamins or minerals for cancer prevention. Our search of ongoing trials identified six projects at various stages of completion. Five of those six trials use selenium as the intervention of interest delivered either alone or in combination with other agents. The lack of success to date can be explained by a variety of factors that need to be considered in the next generation research. These factors include lack of good biological rationale for selecting specific agents of interest; limited number of agents tested to date; use of pharmacological, rather than dietary, doses; and insufficient duration of intervention and follow-up. The latter consideration underscores the need for alternative endpoints that are associated with increased risk of neoplasia (i.e., biomarkers of risk), but are detectable prior to tumor occurrence. Although dietary antioxidants are a large and diverse group of compounds, only a small proportion of candidate agents have been tested. In summary, the strategy of focusing on large high-budget studies using cancer incidence as the endpoint and testing a relatively limited number of antioxidant agents has been largely unsuccessful. This lack of success in previous trials should not preclude us from seeking novel ways of preventing cancer by modulating oxidative balance. On the contrary, the well demonstrated mechanistic link between excessive oxidative stress and carcinogenesis underscores the need for new studies. It appears that future large-scale projects should be preceded by smaller, shorter, less expensive biomarker-based studies that can serve as a link from mechanistic and observational research to human cancer prevention trials. These relatively inexpensive studies would provide human experimental evidence for the likely efficacy, optimum dose, and long-term safety of the intervention of interest that would then guide the design of safe, more definitive large-scale trials.

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.

A proposed minimum skill set for university graduates to meet the informatics needs and challenges of the "-omics" era

BACKGROUND

The development of high throughput experimental technologies have given rise to the "-omics" era where terabyte-scale datasets for systems-level measurements of various cellular and molecular phenomena pose considerable challenges in data processing and extraction of biological meaning. Moreover, it has created an unmet need for the effective integration of these datasets to achieve insights into biological systems. While it has increased the demand for bioinformatics experts who can interface with biologists, it has also raised the requirement for biologists to possess a basic capability in bioinformatics and to communicate seamlessly with these experts. This may be achieved by embedding in their undergraduate and graduate life science education, basic training in bioinformatics geared towards acquiring a minimum skill set in computation and informatics.

RESULTS

Based on previous attempts to define curricula suitable for addressing the bioinformatics capability gap, an initiative was taken during the Workshops on Education in Bioinformatics and Computational Biology (WEBCB) in 2008 and 2009 to identify a minimum skill set for the training of future bioinformaticians and molecular biologists with informatics capabilities. The minimum skill set proposed is cross-disciplinary in nature, involving a combination of knowledge and proficiency from the fields of biology, computer science, mathematics and statistics, and can be tailored to the needs of the "-omics".

CONCLUSION

The proposed bioinformatics minimum skill set serves as a guideline for biology curriculum design and development in universities at both the undergraduate and graduate levels.

Systems biology in pharmacogenomic research: the way to personalized prescribing?

Response to pharmacotherapy can be highly variable amongst individuals. Pharmacogenomics may explain the interindividual variability in drug response due to genetic variation. However, besides genetics, many other factors can play a role in the response to pharmacotherapy, including disease severity, co-morbidity, environmental factors, therapy adherence and co-medication use. Better understanding of these factors and inter-relationships should bring about a much more effective approach to disease management. Systems biology that studies organisms as integrated and interacting networks of genes, proteins and biochemical reactions can contribute to this. Organisms are no longer studied part by part, but in a more integral manner. Integration of the genetic data with intermediate and end point phenotypic characterization may prove essential to define the inherent nature of drug effects. Therefore, in the future, a multidisciplinary systems-based approach will be necessary to deal with the bulk of the biological data that is available and, ultimately, to reach the goal of personalized prescribing.

A review of lipidomic technologies applicable to sphingolipidomics and their relevant applications

Sphingolipidomics, a branch of lipidomics, focuses on the large-scale study of the cellular sphingolipidomes. In the current review, two main approaches for the analysis of cellular sphingolipidomes (i.e. LC-MS- or LC-MS/MS-based approach and shotgun lipidomics-based approach) are briefly discussed. Their advantages, some considerations of these methods, and recent applications of these approaches are summarized. It is the authors' sincere hope that this review article will add to the readers understanding of the advantages and limitations of each developed method for the analysis of a cellular sphingolipidome.

Connecting chemosensitivity, gene expression and disease

Omics-based investigations offer potentially powerful readouts that might be useful for probing the underlying biology of normal and diseased states, identifying novel therapeutic targets and proposing relevant markers for designing treatment strategies. A vital component of these investigations involves a systematic analysis of gene expression and chemosensitivity data in the context of disease states and small molecule probes into the function of targets responsible for a disease phenotype. Systematic analysis of chemical and pharmacogenetics data offers a possible means to identify novel, small-molecule, potentially therapeutic, agents that affect the phenotype of a particular target. Elegantly simple in concept, the covariation of genetic and chemosensitivity readouts provide a hypothetical link for relating compounds through genomic expression profiles to underlying biology.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Biologic monitoring of exposure to environmental chemicals throughout the life stages: requirements and issues for consideration for the National Children's Study

Biomonitoring of exposure is a useful tool for assessing environmental exposures. The matrices available for analyses include blood, urine, breast milk, adipose tissue, and saliva, among others. The sampling can be staged to represent the particular time period of concern: preconceptionally from both parents, from a pregnant woman during each of the three trimesters, during and immediately after childbirth, from the mother postnatally, and from the child as it develops to 21 years of age. The appropriate sample for biomonitoring will depend upon matrix availability, the time period of concern for a particular exposure or health effect, and the different classes of environmental chemicals to be monitored. This article describes the matrices available for biomonitoring during the life stages being evaluated in the National Children's Study; the best biologic matrices for exposure assessment for each individual chemical class, including consideration of alternative matrices; the analytical methods used for analysis, including quality control procedures and less costly alternatives; the costs of analysis; optimal storage conditions; and chemical and matrix stability during long-term storage.