Minimum information about a microarray experiment (MIAME)-toward standards for microarray data

Integration of DNA Microarray with Clinical and Genomic Data

DNA microarrays have been widely employed to understand cancer development. This technology is able to measure expression levels of a large numbers of genes or to genotype multiple regions of a genome in a massively parallel experiment. In addition, the detection of methylation patterns and gene copy number variations are also performed. Clinicians began to apply these findings in personalized medicine for the selection of cancer therapy according to the individual's cancer genomic profile. Because cancer is a complex disease it is of great value to integrate microarray data with genomic and clinical data. Here, we presented an overview of DNA microarray technology and discuss about benefits and challenging of microarray data integration.

Experience: Automated Prediction of Experimental Metadata from Scientific Publications

While there exists an abundance of open biomedical data, the lack of high-quality metadata makes it challenging for others to find relevant datasets and to reuse them for another purpose. In particular, metadata are useful to understand the nature and provenance of the data. A common approach to improving the quality of metadata relies on expensive human curation, which itself is time-consuming and also prone to error. Towards improving the quality of metadata, we use scientific publications to automatically predict metadata key:value pairs. For prediction, we use a Convolutional Neural Network (CNN) and a Bidirectional Long-short term memory network (BiLSTM). We focus our attention on the NCBI Disease Corpus, which is used for training the CNN and BiLSTM. We perform two different kinds of experiments with these two architectures: (1) we predict the disease names by using their unique ID in the MeSH ontology and (2) we use the tree structures of MeSH ontology to move up in the hierarchy of these disease terms, which reduces the number of labels. We also perform various multi-label classification techniques for the above-mentioned experiments. We find that in both cases CNN achieves the best results in predicting the superclasses for disease with an accuracy of 83%.

Nucleic acid amplification-integrated single-molecule fluorescence imaging for in vitro and in vivo biosensing

Single-molecule fluorescence imaging is among the most advanced analytical technologies and has been widely adopted for biosensing due to its distinct advantages of simplicity, rapidity, high sensitivity, low sample consumption, and visualization capability. Recently, a variety of nucleic acid amplification approaches have been developed to provide a straightforward and highly efficient way for amplifying low abundance target signals. The integration of single-molecule fluorescence imaging with nucleic acid amplification has greatly facilitated the construction of various fluorescent biosensors for in vitro and in vivo detection of DNAs, RNAs, enzymes, and live cells with high sensitivity and good selectivity. Herein, we review the advances in the development of fluorescent biosensors by integrating single-molecule fluorescence imaging with nucleic acid amplification based on enzyme (e.g., DNA polymerase, RNA polymerase, exonuclease, and endonuclease)-assisted and enzyme-free (e.g., catalytic hairpin assembly, entropy-driven DNA amplification, ligation chain reaction, and hybridization chain reaction) strategies, and summarize the principles, features, and in vitro and in vivo applications of the emerging biosensors. Moreover, we discuss the remaining challenges and future directions in this area. This review may inspire the development of new signal-amplified single-molecule biosensors and promote their practical applications in fundamental and clinical research.

Hepatoprotective Screening of Seriphidium kurramense (Qazilb.) Y.R. Ling

Investigation on medicinal plants' therapeutic potential has gained substantial importance in the discovery of novel effective and safe therapeutic agents. The present study is aimed at investigating the hepatoprotective potential of Seriphidium kurramense methanolic extract (SKM) against carbon tetrachloride- (CCl4-) induced hepatotoxicity in rats. S. kurramense is one of the most imperative plants for its various pharmacological activities. Therefore, this study was aimed at evaluating the hepatoprotective potential against CCl4-induced liver toxicity. The serum samples were analyzed for alanine aminotransferase (ALT) and aspartate aminotransferase (AST) together with the oxidative stress mediator levels as nitric oxide (NO), malondialdehyde (MDA), glutathione (GSH), reduced glutathione (GSH), and superoxide dismutase (SOD) as well as peroxidation and H2O2 activity. CCl4 administration resulted in an elevated free radical generation, altered liver marker (AST and ALT) enzymes, reduced antioxidant enzyme, and increased DNA damage. Methanolic extract of S. kurramense decreased CCl4-induced hepatotoxicity by increasing the antioxidant status and reducing H2O2 and nitrate content generation as well as reducing DNA damage. Additionally, SKM reversed the morphological alterations induced by CCl4 in the SKM-treated groups. These results demonstrated that SKM displayed hepatoprotective activity against CCl4-induced hepatic damage in experimental rats.

REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.

FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example

BACKGROUND

The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts.

RESULTS

Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex.

CONCLUSIONS

We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.

Large-Scale Transcriptional Profiling of Molecular Perturbations Reveals Cell Line Specific Responses and Implications for Environmental Screening

Cell-based bioassays represent nearly half of all high-throughput screens currently conducted for risk assessment of environmental chemicals. However, there has long been a concern about the sensitivity and heterogeneity among cell lines, which were explored only in a limited manner. Here, we address this question by conducting a large-scale transcriptome analysis of the responses of discrete cell lines to specific molecules. We report the collections of >223 300 gene expression profiles from a wide array of cell lines exposed to 2243 compounds. Our results demonstrate distinct responses among cell lines at both the gene and the pathway levels. Temporal variations for a very large proportion of compounds occur as well. High sensitivity and/or heterogeneity is either cell line-specific or universal depending on the modes of action of the compounds. Among 12 representative pathways analyzed, distinct cell-chemical interactions exist. On one hand, lung carcinoma cells are always best suited for glucocorticoid receptor agonist identification, while on the other hand, high sensitivity and heterogenic features are universal for histone deacetylase inhibitors and ATPase inhibitors. Our data provide novel insights into the understanding of cell-specific responses and interactions between cells and xenobiotics. The findings have substantial implications for the design, execution, and interpretation of high-throughput screening assays in (eco)toxicology.

The importance of adherence to international standards for depositing open data in public repositories

There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.

Reporting Policies in Neurosurgical Journals: A Meta-Science Study of the Current State and Case for Standardization

BACKGROUND

Reporting quality within the neurosurgical literature is low, limiting the ability of journals to act as gatekeepers for evidence-based neurosurgical care. Journal policies during article submission aim to improve reporting quality. We conducted a meta-science study characterizing the reporting policies of neurosurgical journals and other related peer-reviewed publications.

METHODS

Journals were retrieved in 7 searches using Journal Citation Reports and Google Scholar. Characteristics, impact metrics, and submission policies were extracted.

RESULTS

Of 486 results, 54 journals were included, including 27 neurosurgical and 27 related topical journals. Thirty-eight (70.4%) adopted authorship guidelines and 20 (37.0%) disclosure standards of the International Council of Medical Journal Editors. Twenty-six (48.1%) required data availability statement and 33 (61.1%) clinical trials registration. Twenty-one (38.9%) required and 11 (20.4%) recommended adherence to reporting guidelines. Twenty (37.0%) endorsed EQUATOR network guidelines. PRISMA was mentioned by 30 (55.6%) journals, CONSORT by 28 (51.9%), and STROBE by 18 (33.3%). Among neurosurgical journals, factors associated with a requirement or recommendation to follow reporting guidelines among neurosurgical journals included impact factor (P = 0.0013), Article Influence Score (P = 0.0236), SCImago h-index (P = 0.0152), SCImago journal rank (P = 0.002), and CiteScore (P = 0.0023), as well as recommendations pertaining to International Council of Medical Journal Editors authorship guidelines (P = 0.0085), ORCID (P = 0.014), clinical trials registration (P = 0.0369), or data availability statement (P = 0.0047). CONSORT, PRISMA, or STROBE delineations were significantly associated with the mention of another guideline (P < 0.01).

CONCLUSIONS

Neurosurgical journal submission policies are inconsistent. Frameworks to improve reporting quality are uncommonly used. Increasing rigor and standardization of reporting policies across journals publishers may improve quality.

Nontargeted Analysis Study Reporting Tool: A Framework to Improve Research Transparency and Reproducibility

Non-targeted analysis (NTA) workflows using mass spectrometry are gaining popularity in many disciplines, but universally accepted reporting standards are nonexistent. Current guidance addresses limited elements of NTA reporting-most notably, identification confidence-and is insufficient to ensure scientific transparency and reproducibility given the complexity of these methods. This lack of reporting standards hinders researchers' development of thorough study protocols and reviewers' ability to efficiently assess grant and manuscript submissions. To overcome these challenges, we developed the NTA Study Reporting Tool (SRT), an easy-to-use, interdisciplinary framework for comprehensive NTA methods and results reporting. Eleven NTA practitioners reviewed eight published articles covering environmental, food, and health-based exposomic applications with the SRT. Overall, our analysis demonstrated that the SRT provides a valid structure to guide study design and manuscript writing, as well as to evaluate NTA reporting quality. Scores self-assigned by authors fell within the range of peer-reviewer scores, indicating that SRT use for self-evaluation will strengthen reporting practices. The results also highlighted NTA reporting areas that need immediate improvement, such as analytical sequence and quality assurance/quality control information. Although scores intentionally do not correspond to data/results quality, widespread implementation of the SRT could improve study design and standardize reporting practices, ultimately leading to broader use and acceptance of NTA data.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

BACKGROUND

Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks.

METHODS

The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology.

RESULTS

This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets.

CONCLUSIONS

The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations

Drafting and writing a data management plan (DMP) is increasingly seen as a key part of the academic research process. A DMP is a document that describes how a researcher will collect, document, describe, share, and preserve the data that will be generated as part of a research project. The DMP illustrates the importance of utilizing best practices through all stages of working with data while ensuring accessibility, quality, and longevity of the data. The benefits of writing a DMP include compliance with funder and institutional mandates; making research more transparent (for reproduction and validation purposes); and FAIR (findable, accessible, interoperable, reusable); protecting data subjects and compliance with the General Data Protection Regulation (GDPR) and/or local data protection policies. In this review, we highlight the importance of a DMP in modern biomedical research, explaining both the rationale and current best practices associated with DMPs. In addition, we outline various funders’ requirements concerning DMPs and discuss open-source tools that facilitate the development and implementation of a DMP. Finally, we discuss DMPs in the context of African research, and the considerations that need to be made in this regard.

The role of metadata in reproducible computational research

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana

Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

The SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) in myalgic encephalomyelitis/chronic fatigue syndrome: A meta-analysis of public DNA methylation and gene expression data

People with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) often report a high frequency of viral infections and flu-like symptoms during their disease course. Given that this reporting agrees with different immunological abnormalities and altered gene expression profiles observed in the disease, we aimed at answering whether the expression of the human angiotensin-converting enzyme 2 (ACE2), the major cell entry receptor for SARS-CoV-2, is also altered in these patients. In particular, a low expression of ACE2 could be indicative of a high risk of developing COVID-19. We then performed a meta-analysis of public data on CpG DNA methylation and gene expression of this enzyme and its homologous ACE protein in peripheral blood mononuclear cells and related subsets. We found that patients with ME/CFS have decreased methylation levels of four CpG probes in the ACE locus (cg09920557, cg19802564, cg21094739, and cg10468385) and of another probe in the promoter region of the ACE2 gene (cg08559914). We also found a decreased expression of ACE2 but not of ACE in patients when compared to healthy controls. Accordingly, in newly collected data, there was evidence for a significant higher proportion of samples with an ACE2 expression below the limit of detection in patients than healthy controls. Altogether, patients with ME/CFS can be at a higher COVID-19 risk and, if so, they should be considered a priority group for vaccination by public health authorities. To further support this conclusion, similar research is recommended for other human cell entry receptors and cell types, namely, those cells targeted by the virus.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

European Heart Journal quality standards

The aim of the European Heart Journal (EHJ) is to attract innovative, methodologically sound, and clinically relevant research manuscripts able to change clinical practice and/or substantially advance knowledge on cardiovascular diseases. As the reference journal in cardiovascular medicine, the EHJ is committed to publishing only the best cardiovascular science adhering to the highest ethical principles. EHJ uses highly rigorous peer-review, critical statistical review and the highest quality editorial process, to ensure the novelty, accuracy, quality, and relevance of all accepted manuscripts with the aim of inspiring the clinical practice of EHJ readers and reducing the global burden of cardiovascular diseases. This review article summarizes the quality standards pursued by the EHJ to fulfill its mission.

QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy

A modern day light microscope has evolved from a tool devoted to making primarily empirical observations to what is now a sophisticated , quantitative device that is an integral part of both physical and life science research. Nowadays, microscopes are found in nearly every experimental laboratory. However, despite their prevalent use in capturing and quantifying scientific phenomena, neither a thorough understanding of the principles underlying quantitative imaging techniques nor appropriate knowledge of how to calibrate, operate and maintain microscopes can be taken for granted. This is clearly demonstrated by the well-documented and widespread difficulties that are routinely encountered in evaluating acquired data and reproducing scientific experiments. Indeed, studies have shown that more than 70% of researchers have tried and failed to repeat another scientist's experiments, while more than half have even failed to reproduce their own experiments. One factor behind the reproducibility crisis of experiments published in scientific journals is the frequent underreporting of imaging methods caused by a lack of awareness and/or a lack of knowledge of the applied technique. Whereas quality control procedures for some methods used in biomedical research, such as genomics (e.g. DNA sequencing, RNA-seq) or cytometry, have been introduced (e.g. ENCODE), this issue has not been tackled for optical microscopy instrumentation and images. Although many calibration standards and protocols have been published, there is a lack of awareness and agreement on common standards and guidelines for quality assessment and reproducibility. In April 2020, the QUality Assessment and REProducibility for instruments and images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper (1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; (2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers and observers of such; (3) outlines the current actions of the QUAREP-LiMi initiative and (4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics.

An Arabidopsis lipid map reveals differences between tissues and dynamic changes throughout development

Mass spectrometry is the predominant analytical tool used in the field of plant lipidomics. However, there are many challenges associated with the mass spectrometric detection and identification of lipids because of the highly complex nature of plant lipids. Studies into lipid biosynthetic pathways, gene functions in lipid metabolism, lipid changes during plant growth and development, and the holistic examination of the role of plant lipids in environmental stress responses are often hindered. Here, we leveraged a robust pipeline that we previously established to extract and analyze lipid profiles of different tissues and developmental stages from the model plant Arabidopsis thaliana. We analyzed seven tissues at several different developmental stages and identified more than 200 lipids from each tissue analyzed. The data were used to create a web-accessible in silico lipid map that has been integrated into an electronic Fluorescent Pictograph (eFP) browser. This in silico library of Arabidopsis lipids allows the visualization and exploration of the distribution and changes of lipid levels across selected developmental stages. Furthermore, it provides information on the characteristic fragments of lipids and adducts observed in the mass spectrometer and their retention times, which can be used for lipid identification. The Arabidopsis tissue lipid map can be accessed at http://bar.utoronto.ca/efp_arabidopsis_lipid/cgi-bin/efpWeb.cgi.

CEDAR, an online resource for the reporting and exploration of complexome profiling data

Complexome profiling is an emerging 'omics' approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring mass spectrometry data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the "minimum information required for a complexome profiling experiment" (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.

Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care

The dengue virus (DENV) is a vector-borne flavivirus that infects around 390 million individuals each year with 2.5 billion being in danger. Having access to testing is paramount in preventing future infections and receiving adequate treatment. Currently, there are numerous conventional methods for DENV testing, such as NS1 based antigen testing, IgM/IgG antibody testing, and Polymerase Chain Reaction (PCR). In addition, novel methods are emerging that can cut both cost and time. Such methods can be effective in rural and low-income areas throughout the world. In this paper, we discuss the structural evolution of the virus followed by a comprehensive review of current dengue detection strategies and methods that are being developed or commercialized. We also discuss the state of art biosensing technologies, evaluated their performance and outline strategies to address challenges posed by the disease. Further, we outline future guidelines for the improved usage of diagnostic tools during recurrence or future outbreaks of DENV.

Exposure to TiO2 Nanostructured Aerosol Induces Specific Gene Expression Profile Modifications in the Lungs of Young and Elderly Rats

Although aging is associated with a higher risk of developing respiratory pathologies, very few studies have assessed the impact of age on the adverse effects of inhaled nanoparticles. Using conventional and transcriptomic approaches, this study aimed to compare in young (12–13-week-old) and elderly (19-month-old) fisher F344 rats the pulmonary toxicity of an inhaled nanostructured aerosol of titanium dioxide (TiO₂). Animals were nose-only exposed to this aerosol at a concentration of 10 mg/m³ for 6 h per day, 5 days per week for 4 weeks. Tissues were collected immediately (D0), and 28 days after exposure (D28). A pulmonary influx of neutrophilic granulocytes was observed in exposed rats at D0, but diminished with time while remaining significant until D28. Similarly, an increased expression of several genes involved in inflammation at the two post-exposure time-points was seen. Apart from an age-specific pulmonary influx of lymphocyte, only slight differences in physio-pathological responses following TiO₂ exposure between young and elderly animals were noticed. Conversely, marked age-related differences in gene expression profiles were observed making possible to establish lists of genes specific to each age group and post-exposure times. These results highlight different signaling pathways that were disrupted in rats according to their age.

Molecular omics resources should require sex annotation: a call for action

The most commonly-used omics databases are a compilation of results from primarily male-only and sex-agnostic studies. The pervasive use of these databases critically hinders progress towards fully accounting for the biology of sex differences.

Empagliflozin Inhibits IL-1β-Mediated Inflammatory Response in Human Proximal Tubular Cells

SGLT2 inhibitor-related nephroprotection is—at least partially—mediated by anti-inflammatory drug effects, as previously demonstrated in diabetic animal and human studies, as well as hyperglycemic cell culture models. We recently presented first evidence for anti-inflammatory potential of empagliflozin (Empa) under normoglycemic conditions in human proximal tubular cells (HPTC) by demonstrating Empa-mediated inhibition of IL-1β-induced MCP-1/CCL2 and ET-1 expression on the mRNA and protein level. We now add corroborating evidence on a genome-wide level by demonstrating that Empa attenuates the expression of several inflammatory response genes in IL-1β-induced (10 ng/mL) normoglycemic HPTCs. Using microarray-hybridization analysis, 19 inflammatory response genes out of >30.000 human genes presented a consistent expression pattern, that is, inhibition of IL-1β (10 ng/mL)-stimulated gene expression by Empa (500 nM), in both HK-2 and RPTEC/TERT1 cells. Pathway enrichment analysis demonstrated statistically significant clustering of annotated pathways (enrichment score 3.64). Our transcriptomic approach reveals novel genes such as CXCL8/IL8, LOX, NOV, PTX3, and SGK1 that might be causally involved in glycemia-independent nephroprotection by SGLT2i.

Differential root and shoot magnetoresponses in Arabidopsis thaliana

The geomagnetic field (GMF) is one of the environmental stimuli that plants experience continuously on Earth; however, the actions of the GMF on plants are poorly understood. Here, we carried out a time-course microarray experiment to identify genes that are differentially regulated by the GMF in shoot and roots. We also used qPCR to validate the activity of some genes selected from the microarray analysis in a dose-dependent magnetic field experiment. We found that the GMF regulated genes in both shoot and roots, suggesting that both organs can sense the GMF. However, 49% of the genes were regulated in a reverse direction in these organs, meaning that the resident signaling networks define the up- or downregulation of specific genes. The set of GMF-regulated genes strongly overlapped with various stress-responsive genes, implicating the involvement of one or more common signals, such as reactive oxygen species, in these responses. The biphasic dose response of GMF-responsive genes indicates a hormetic response of plants to the GMF. At present, no evidence exists to indicate any evolutionary advantage of plant adaptation to the GMF; however, plants can sense and respond to the GMF using the signaling networks involved in stress responses.

The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST)

Motivation

A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources.

Results

Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources.

Availability and implementation

The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST

Supplementary information

Supplementary data are available at Bioinformatics online.

Elevated expression of the adhesion GPCR ADGRL4/ELTD1 promotes endothelial sprouting angiogenesis without activating canonical GPCR signalling

ADGRL4/ELTD1 is an orphan adhesion GPCR (aGPCR) expressed in endothelial cells that regulates tumour angiogenesis. The majority of aGPCRs are orphan receptors. The Stachel Hypothesis proposes a mechanism for aGPCR activation, in which aGPCRs contain a tethered agonist (termed Stachel) C-terminal to the GPCR-proteolytic site (GPS) cleavage point which, when exposed, initiates canonical GPCR signalling. This has been shown in a growing number of aGPCRs. We tested this hypothesis on ADGRL4/ELTD1 by designing full length (FL) and C-terminal fragment (CTF) ADGRL4/ELTD1 constructs, and a range of potential Stachel peptides. Constructs were transfected into HEK293T cells and HTRF FRET, luciferase-reporter and Alphascreen GPCR signalling assays were performed. A stable ADGRL4/ELTD1 overexpressing HUVEC line was additionally generated and angiogenesis assays, signalling assays and transcriptional profiling were performed. ADGRL4/ELTD1 has the lowest GC content in the aGPCR family and codon optimisation significantly increased its expression. FL and CTF ADGRL4/ELTD1 constructs, as well as Stachel peptides, did not activate canonical GPCR signalling. Furthermore, stable overexpression of ADGRL4/ELTD1 in HUVECs induced sprouting angiogenesis, lowered in vitro anastomoses, and decreased proliferation, without activating canonical GPCR signalling or MAPK/ERK, PI3K/AKT, JNK, JAK/HIF-1α, beta catenin or STAT3 pathways. Overexpression upregulated ANTXR1, SLC39A6, HBB, CHRNA, ELMOD1, JAG1 and downregulated DLL4, KIT, CCL15, CYP26B1. ADGRL4/ELTD1 specifically regulates the endothelial tip-cell phenotype through yet undefined signalling pathways.

Quality Considerations When Using Tissue Samples for Biomarker Studies in Cancer Research

Tissue obtained from biobanks is frequently employed in biomarker studies. Biomarkers define objective, measurable characteristics of biological and biomedical procedures and have been used as indicators of clinical outcome. This article outlines some of the steps scientists should consider when embarking on biomarker research in cancer research using samples from biobanks and the importance and challenges of linking clinical data to biological samples.

The transcriptomic responses of Atlantic salmon (Salmo salar) to high temperature stress alone, and in combination with moderate hypoxia

BACKGROUND

Increases in ocean temperatures and in the frequency and severity of hypoxic events are expected with climate change, and may become a challenge for cultured Atlantic salmon and negatively affect their growth, immunology and welfare. Thus, we examined how an incremental temperature increase alone (Warm & Normoxic-WN: 12 → 20 °C; 1 °C week- 1), and in combination with moderate hypoxia (Warm & Hypoxic-WH: ~ 70% air saturation), impacted the salmon's hepatic transcriptome expr\ession compared to control fish (CT: 12 °C, normoxic) using 44 K microarrays and qPCR.

RESULTS

Overall, we identified 2894 differentially expressed probes (DEPs, FDR < 5%), that included 1111 shared DEPs, while 789 and 994 DEPs were specific to WN and WH fish, respectively. Pathway analysis indicated that the cellular mechanisms affected by the two experimental conditions were quite similar, with up-regulated genes functionally associated with the heat shock response, ER-stress, apoptosis and immune defence, while genes connected with general metabolic processes, proteolysis and oxidation-reduction were largely suppressed. The qPCR assessment of 41 microarray-identified genes validated that the heat shock response (hsp90aa1, serpinh1), apoptosis (casp8, jund, jak2) and immune responses (apod, c1ql2, epx) were up-regulated in WN and WH fish, while oxidative stress and hypoxia sensitive genes were down-regulated (cirbp, cyp1a1, egln2, gstt1, hif1α, prdx6, rraga, ucp2). However, the additional challenge of hypoxia resulted in more pronounced effects on heat shock and immune-related processes, including a stronger influence on the expression of 14 immune-related genes. Finally, robust correlations between the transcription of 19 genes and several phenotypic traits in WH fish suggest that changes in gene expression were related to impaired physiological and growth performance.

CONCLUSION

Increasing temperature to 20 °C alone, and in combination with hypoxia, resulted in the differential expression of genes involved in similar pathways in Atlantic salmon. However, the expression responses of heat shock and immune-relevant genes in fish exposed to 20 °C and hypoxia were more affected, and strongly related to phenotypic characteristics (e.g., growth). This study provides valuable information on how these two environmental challenges affect the expression of stress-, metabolic- and immune-related genes and pathways, and identifies potential biomarker genes for improving our understanding of fish health and welfare.

MicroRNA biomarkers of type 2 diabetes: A protocol for corroborating evidence by computational genomics and meta-analyses

Background

Few microRNAs were found consistently dysregulated in type 2 diabetes (T2D) that would gain confidence from Big Pharma to develop diagnostic or therapeutic biomarkers. This study aimed to corroborate evidence from eligible microRNAs-T2D association studies according to stringent quality criteria covering both biological and statistical significance in T2D for biomarker development.

Methods and analyses

Controlled microRNA expression profiling studies on human with T2D will be retrieved from PubMed, ScienceDirect, and Embase for selecting the statistically significant microRNAs according to pre-specified search strategies and inclusion criteria. Multiple meta-analyses with restricted maximum-likelihood estimation and empirical Bayes estimation under the random-effects model will be conducted by metafor package in R. Subgroup and sensitivity analyses further examine the microRNA candidates for their disease specificity, tissue specificity, blood fraction specificity, and statistical robustness of evidence. Biologically relevant microRNAs will then be selected through genomic database corroboration. Their association with T2D is further measured by area under the curve (AUC) of receive operating characteristic (ROC). Meta-analysis of AUC of potential biomarkers will also be conducted. Enrichment analysis on potential microRNA biomarkers and their target genes will be performed by iPathwayGuide and clusterProfiler, respectively. The corresponding reporting guidelines will be used to assess the quality of included studies according to their profiling methods (microarray, RT-PCR, and RNA-Seq).

Ethics and dissemination

No ethics approval is required since this study does not include identifiable personal patient data.

Protocol registration number

CRD42017081659.

The effect of calcium and magnesium on activity, immunogenicity, and efficacy of a recombinant N1/N2 neuraminidase vaccine

Despite the importance of immunity against neuraminidase (NA), NA content and immunogenicity are neglected in current influenza vaccines. To address this, a recombinant N1/N2 NA vaccine (NAV) was developed. Stability assays were used to determine optimal temperature and buffer conditions for vaccine storage. The effect of divalent cation-related enhancement of NA stability and activity on N1 and N2 immunogenicity and efficacy against viral challenge was assessed. Differences in activity between N1 and N2 and cation-related activity enhancement did not translate into differences in immunogenicity or efficacy. NAV-vaccinated mice showed robust antibody titers against N1 and N2, and after challenge with influenza A (H1N1) virus, decreased viral titers and decreased antiviral and inflammatory responses by transcriptomic analysis. These findings provide guidance for optimal storage and assessment of NA-based vaccines and confirm the importance of NA in influenza vaccination strategies in attenuating viral replication and limiting inflammatory responses necessary to clear infection.

Circulating miRNAs in bone health and disease

microRNAs have evolved as important regulators of multiple biological pathways essential for bone homeostasis, and microRNA research has furthered our understanding of the mechanisms underlying bone health and disease. This knowledge, together with the finding that active or passive release of microRNAs from cells into the extracellular space enables minimal-invasive detection in biofluids (circulating miRNAs), motivated researchers to explore microRNAs as biomarkers in several pathologic conditions, including bone diseases. Thus, exploratory studies in cohorts representing different types of bone diseases have been performed. In this review, we first summarize important molecular basics of microRNA function and release and provide recommendations for best (pre-)analytical practices and documentation standards for circulating microRNA research required for generating high quality data and ensuring reproducibility of results. Secondly, we review how the genesis of bone-derived circulating microRNAs via release from osteoblasts and osteoclasts could contribute to the communication between these cells. Lastly, we summarize evidence from clinical research studies that have investigated the clinical utility of microRNAs as biomarkers in musculoskeletal disorders. While previous reviews have mainly focused on diagnosis of primary osteoporosis, we have also included studies exploring the utility of circulating microRNAs in monitoring anti-osteoporotic treatment and for diagnosis of other types of bone diseases, such as diabetic osteopathy, bone degradation in inflammatory diseases, and monogenetic bone diseases.

Large-scale labeling and assessment of sex bias in publicly available expression data

BACKGROUND

Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio.

RESULTS

Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%).

CONCLUSIONS

Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

Human Inflammatory Neutrophils Express Genes Encoding Peptidase Inhibitors: Production of Elafin Mediated by NF-κB and CCAAT/Enhancer-Binding Protein β

The concept of plasticity of neutrophils is highlighted by studies showing their ability to transdifferentiate into APCs. In this regard, transdifferentiated neutrophils were found at inflammatory sites of autoimmune arthritis (AIA). Exposure of neutrophils to inflammatory stimuli prolongs their survival, thereby favoring the acquisition of pathophysiologically relevant phenotypes and functions. By using microarrays, quantitative RT-PCR, and ELISAs, we showed that long-lived (LL) neutrophils obtained after 48 h of culture in the presence of GM-CSF, TNF, and IL-4 differentially expressed genes related to apoptosis, MHC class II, immune response, and inflammation. The expression of anti-inflammatory genes mainly of peptidase inhibitor families is upregulated in LL neutrophils. Among these, the PI3 gene encoding elafin was the most highly expressed. The de novo production of elafin by LL neutrophils depended on a synergism between GM-CSF and TNF via the activation and cooperativity of C/EBPβ and NF-κB pathways, respectively. Elafin concentrations were higher in synovial fluids (SF) of patients with AIA than in SF of osteoarthritis. SF neutrophils produced more elafin than blood counterparts. These results are discussed with respect to implications of neutrophils in chronic inflammation and the potential influence of elafin in AIA.

A Minimal Information Model for Potential Drug-Drug Interactions

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Identification of Potential Therapeutic Genes and Pathways in Phytoestrogen Emodin Treated Breast Cancer Cell Lines via Network Biology Approaches

Phytoestrogens have been investigated for their potential anti-tumorigenic effects in various cancers including breast cancer. Emodin being a phytoestrogen shows anti-carcinogenic properties especially in estrogen receptor positive (ER+) breast cancers. The aim of this study is to identify the molecular mechanism and related biological pathways in both (ER+) MCF-7 and (ER-) MDA-MB-231 breast cancer cell lines upon Emodin treatment via microarray analysis in order to find out therapeutic biomarkers. In both cell lines, first differentially expressed genes were identified, then gene ontology and functional pathway enrichment analyses were performed. Genes regulated through multiple pathways were studied together with literature and a gene cluster was determined for each cell line. Further GeneMANIA and STRING databases were used to study the interactions within the related gene clusters. The results showed that, the genes which are related to cell cycle were significantly regulated in both cell lines. Also, Forkhead Box O1-related genes were found to be prominent in MCF-7 cells. In MDA-MB-231 cells, spindle attachment checkpoint mechanism-related genes were regulated, remarkably. As a result, novel gene regulations reported in this study in response to Emodin will give more information about its metabolism and antiproliferative effect, especially in ER + cells.

Adding Toxicological Context to Nanotoxicity Study Reporting Using the NanoTox Metadata List

This paper proposes a list of specifications (NanoTox metadata list) to be reported about nanotoxicity experiments (metadata) together with resultant data to add toxicological context to reported studies. In areas involving nanomaterials (NMs), existing metadata reporting standards include the reporting of experimental conditions and protocols (MIRIBEL) and material characteristics (MINChar and MIAN), as well as reporting focused on specific experiments (MINBE). NanoCRED is a similarly transparent and structured framework, however, it is developed to guide risk assessors in evaluating the reliability and relevance of NM ecotoxicity studies. There is no reporting standard which would include interpretation of the aims and outcomes of nanotoxicity studies beyond regulatory purposes. The proposed NanoTox metadata reporting checklist is elaborated to extend reporting toward describing nanotoxicological context and thus is a logical complement to technology/material-assay focused reporting checklists. It is further designed to allow for NM toxicity data and knowledge integration, reuse, and communication. Its ultimate goal is to adhere to the basic rules of toxicology when taking a stand on the toxicity of NMs and to limit speculations on safety. As nanotoxicology becomes more interdisciplinary with the advent of new tools and new materials to be tested, reporting standards will contribute to cross-disciplinary communication.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Curation of over 10 000 transcriptomic studies to enable data reuse

Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html.

Therapeutic Use of Mesenchymal Stromal Cells: The Need for Inclusive Characterization Guidelines to Accommodate All Tissue Sources and Species

Following their discovery over 50 years ago, mesenchymal stromal cells (MSCs) have become one of the most studied cellular therapeutic products by both academia and industry due to their regenerative potential and immunomodulatory properties. The promise of MSCs as a therapeutic modality has been demonstrated by preclinical data yet has not translated to consistent, successful clinical trial results in humans. Despite the disparities across the field, MSC shareholders are unified under one common goal-to use MSCs as a therapeutic modality to improve the quality of life for those suffering from a malady in which the standard of care is suboptimal or no longer effective. Currently, there is no Food and Drug Administration (FDA)-approved MSC therapy on the market in the United States although several MSC products have been granted regulatory approval in other countries. In this review, we intend to identify hurdles that are impeding therapeutic progress and discuss strategies that may aid in accomplishing this universal goal of widespread therapeutic use.

Caste-specific gene expression underlying the differential adult brain development in the honeybee Apis mellifera

Apis mellifera adult workers feature more developed key brain regions than queens, which allows them to cope with the broad range of duties they need to perform in a colony. However, at the end of larval development, the brain of queens is largely more developed than that of workers. Major morphogenetic changes take place after metamorphosis that shift caste-specific brain development. Here, we tested the hypothesis that this phenomenon is hormonally governed and involves differential gene expression. Our molecular screening approach revealed a set of differentially expressed genes in Pp (first pharate-adult phase) brains between castes mainly coding for tissue remodelling and energy-converting proteins (e.g. hex 70a and ATPsynβ). An in-depth qPCR analysis of the transcriptional behaviour during pupal and pharate-adult developmental stage in both castes and in response to artificially augmented hormone titres of 18 genes/variants revealed that: i. subtle differences in hormone titres between castes might be responsible for the differential expression of the EcR and insulin/insulin-like signalling (IIS) pathway genes; ii. the morphogenetic activity of the IIS in brain development must be mediated by ILP-2, iii. which together with the tum, mnb and caspase system, can constitute the molecular effectors of the caste-specific opposing brain developmental trajectories.

The International Collaboration for Cancer Classification and Research

Gaps in the translation of research findings to clinical management have been recognized for decades. They exist for the diagnosis as well as the management of cancer. The international standards for cancer diagnosis are contained within the World Health Organization (WHO) Classification of Tumours, published by the International Agency for Research on Cancer (IARC) and known worldwide as the WHO Blue Books. In addition to their relevance to individual patients, these volumes provide a valuable contribution to cancer research and surveillance, fulfilling an important role in scientific evidence synthesis and international standard setting. However, the multidimensional nature of cancer classification, the way in which the WHO Classification of Tumours is constructed, and the scientific information overload in the field pose important challenges for the translation of research findings to tumour classification and hence cancer diagnosis. To help address these challenges, we have established the International Collaboration for Cancer Classification and Research (IC3 R) to provide a forum for the coordination of efforts in evidence generation, standard setting and best practice recommendations in the field of tumour classification. The first IC3 R meeting, held in Lyon, France, in February 2019, gathered representatives of major institutions involved in tumour classification and related fields to identify and discuss translational challenges in data comparability, standard setting, quality management, evidence evaluation and copyright, as well as to develop a collaborative plan for addressing these challenges.