Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.

The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap’s successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. https://bitbucket.org/ibima/moldrugeffectsdb steffen.moeller@uni-rostock.de Supplementary data are available at Briefings in Bioinformatics online.

Archival collections contain an underutilized wealth of biodiversity data, encapsulated in government files and other historical documents. In 1845, the Bavarian government conducted a comprehensive national survey on the occurrence of 44 selected vertebrate species across the country. The detailed expert responses from 119 forestry offices, totalling 520 handwritten pages, have been preserved in the Bavarian State Archives. In this study, we digitized, annotated, geographically referenced, and published these historical records, making them widely available as data for research and conservation planning. Our dataset, openly accessible through the Global Biodiversity Information Facility (GBIF) and Zenodo, contains 5,467 species occurrence records from 1845. Besides the binary presence/absence data, we have also published the original textual survey responses, which contain rich qualitative information, such as species abundances, population trends, habitats, forest management practices, and human-nature relationships. This information can be further processed and interpreted to address a range of questions in historical and contemporary ecology.

The heterogeneity of lung tumor nodules is reflected in their phenotypic characteristics in radiological images. The radiogenomics field employs quantitative image features combined with transcriptome expression levels to understand tumor heterogeneity molecularly. Due to the different data acquisition techniques for imaging traits and genomic data, establishing meaningful connections poses a challenge. We analyzed 86 image features describing tumor characteristics (such as shape and texture) with the underlying transcriptome and post-transcriptome profiles of 22 lung cancer patients (median age 67.5 years, from 42 to 80 years) to unravel the molecular mechanisms behind tumor phenotypes. As a result, we were able to construct a radiogenomic association map (RAM) linking tumor morphology, shape, texture, and size with gene and miRNA signatures, as well as biological correlates of GO terms and pathways. These indicated possible dependencies between gene and miRNA expression and the evaluated image phenotypes. In particular, the gene ontology processes “regulation of signaling” and “cellular response to organic substance” were shown to be reflected in CT image phenotypes, exhibiting a distinct radiomic signature. Moreover, the gene regulatory networks involving the TFs TAL1, EZH2, and TGFBR2 could reflect how the texture of lung tumors is potentially formed. The combined visualization of transcriptomic and image features suggests that radiogenomic approaches could identify potential image biomarkers for underlying genetic variation, allowing a broader view of the heterogeneity of the tumors. Finally, the proposed methodology could also be adapted to other cancer types to expand our knowledge of the mechanistic interpretability of tumor phenotypes.

The well-being of farmed and companion animals is increasingly recognised as integral to sustainable agroecosystems, companionship, and the One Health approach, which emphasises the interconnected health of people, animals, and the environment. The ELIXIR Domestic Animals Genome and Phenome (DAGP) Community supports genome-to-phenome analyses for farmed and companion animal species. Its aim is to coordinate, discuss, and explore the potential of data technology solutions to address key issues in animal welfare, behaviour, health, infectious diseases, metabolism, nutritional efficiency, and the preservation of genetic diversity and the environment. Through consolidating efforts to develop data standards, coordination, workflows, and visualisation, it will enhance the science underpinning rapidly growing fields in domestic animal genomics, including genome-enabled breeding, population genomics, pangenome analysis, functional genomics, genome editing, paleogenomics, phenotyping, and bio-banking. These standards will adhere to the FAIR data principles and leverage established ontologies to promote best practices in data coordination and archiving. This white paper, prepared by the ELIXIR DAGP Focus Group, summarises the current data infrastructure, resources, and tools available for domestic animal genomics and phenomics, and presents community-led plans and priorities to be implemented to meet the requirements of ELIXIR services and the animal science community. We describe how ELIXIR services can be applied in the domestic animal genomics and phenomics fields, and how we can connect projects and infrastructures that are active in the animal sciences domain. We also discuss three key priority areas: i) expanding the FAANG Data Portal for phenotype data with ELIXIR Data Platforms; ii) supporting submissions of new data types across ELIXIR Core Data Resources , including proprietary data from industry partners; and iii) strengthening connections to existing ELIXIR Communities and international consortia. This article provides a set of priorities for a Domestic Animals Genome and Phenome Community in ELIXIR and outlines the next steps to engage across stakeholders and to consolidate data for domestic animal science in Europe.