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In the face of the biodiversity crisis, concerted efforts towards understanding the effects of climate change and habitat loss and fragmentation, both locally and globally, are urgently needed. These are often attempted by leveraging the advances of modern genomics and bioinformatics methodologies. Especially in biodiversity hotspots, the need to understand, monitor and mitigate the loss of biodiversity is pivotal. Greece is a country with especially high endemism. A large percentage of its endemic species is threatened by climate change and human activities. To this end, the national academic community in biodiversity genomics has established a corresponding network of scientists from various Greek research institutes and universities covering different disciplines of biodiversity research. The network aims to support and combine individual actions to establish a Task Force that will channel the flow of information amongst researchers, policy makers, stakeholders and the local society. Our overarching goal is to build a sustainable community and infrastructure for the efficient management of the entire molecular biodiversity data cycle (i.e., from production and storage to the analysis and modelling of data, development of computational tools, and knowledge extraction). Using national and European infrastructures, such as ELIXIR and LifeWatch, we envision to set the ground for studying biodiversity through the lens of biodiversity genomics and offer evidence-based knowledge to guide management of the habitats and the biodiversity they host, as well as the implementation of appropriate policies.

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Athena Research Center (ARC), in the context of activities undertaken betweenOpenAIRE National Open Access Desk,RDA National NodeandELIXIR-GR, organiseda new series of informative and educational webinars around COVID-19 research. Τhe new series of digital events follows thefirst webinar held in Aprilthat aimed at collecting and promoting all efforts and knowledge around managing the virus crisis to the Greek scientific and academic community. In this series, the purpose focuses, also, on knowledge exchange based on Open Science practices and on training in research data management and software development. The digital events were in collaboration with the following scientific institutions:National and Kapodistrian University of Athens,National Center for Research & Technology Hellas,Hellenic Pasteur Institute,Biomedical Sciences Research Center “Alexander Fleming”,Biomedical Research Foundation of the Academy of Athens,Foundation for Research and Technology Hellas,Hellenic Academic Libraries Link, and the research infrastructureInspired-RIs.

Brief update on Greek node participation in ELIXIR Converge, communities, focus groups and implementation studies. Update on progress for Greek local EGA instance.

The Galaxy Community and ELIXIR had organised a webinar available here series to demonstrate how open software and public research infrastructures can be used in analysing and publishing SARS-CoV2 data. Best practices for the analysis of this data were presented for Genomics, Proteomics, Evolution, and Cheminformatics. For the Greek life science community, we installed a local Galaxy instance at https://usegalaxy.elixir-greece.org that provides all tools used in these webinars as well as the standard tools of the reference Galaxy distribution. The instance operates on 3 servers with a total of 48 CPUs, 192 GB total memory and 3ΤΒ disk space, initially without quotas per user. Depending on user needs, additional resources (CPU, RAM, storage) and tools can be installed. Within the Genomics/Variant Calling workbench, the initial analysis of the SARS-CoV-2 genome, as published on bioRxiv can be reproduced and adapted to the users data. Comprehensive training material for different workflows in Galaxy is available at https://training.galaxyproject.org &nbsp

Supplementary figures S1–S9. Figure S1 Odorant-binding protein (OBP) genes phylogenetic tree. Figure S2 Ionotrophic receptor (IR) genes phylogenetic tree. Figure S3 Opsin genes phylogenetic tree. Figure S4 CYP gene clusters A and B. Figure S5 Glutathione S-transferase genes phylogenetic tree. Figure S6 cysLGIC genes superfamily phylogenetic tree. Figure S7 Cuticle protein gene clusters. Figure S8 CPLCA cuticle protein genes phylogenetic tree. Figure S9 Seminal fluid protein functional classes. (PDF 9425 kb)

Supplementary material A. C. capitata genome sequencing approaches, B. Automated annotations, C. Detection of bacterial sequence contamination and D. Screening the C. capitata genome sequence for potential horizontal gene transfer events. (DOCX 601 kb)

Supplementary material: C. capitata chemoreceptor genes. (DOCX 194 kb)

BACKGROUND: In mammalians, HIF is a master regulator of hypoxia gene expression through direct binding to DNA, while its role in microRNA expression regulation, critical in the hypoxia response, is not elucidated genome wide. Our aim is to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression. METHODS: MCF-7 cells were incubated at 1% Oxygen for 16, 32 and 48 h. SiRNA against HIF-1α and HIF-2α were performed as previously published. MicroRNA and mRNA expression were assessed using microRNA microarrays, small RNA sequencing, gene expression microarrays and Real time PCR. The Kraken pipeline was applied for microRNA-seq analysis along with Bioconductor packages. Microarray data was analysed using Limma (Bioconductor), ChIP-seq data were analysed using Gene Set Enrichment Analysis and multiple testing correction applied in all analyses. RESULTS: Hypoxia time course microRNA sequencing data analysis identified 41 microRNAs significantly up- and 28 down-regulated, including hsa-miR-4521, hsa-miR-145-3p and hsa-miR-222-5p reported in conjunction with hypoxia for the first time. Integration of HIF-1α and HIF-2α ChIP-seq data with expression data showed overall association between binding sites and microRNA up-regulation, with hsa-miR-210-3p and microRNAs of miR-27a/23a/24-2 and miR-30b/30d clusters as predominant examples. Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer. Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts involved in microRNA processing were found regulated by hypoxia, of which DICER (down-regulated) and AGO4 (up-regulated) were HIF dependent. DICER expression was found inversely correlated to hypoxia in breast cancer. CONCLUSIONS: Integrated analysis of microRNA, mRNA and ChIP-seq data in a model cell line supports the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level, with the presence of HIF binding sites at microRNA genomic loci associated with up-regulation. The identification of hypoxia and HIF regulated microRNAs relevant for breast cancer is important for our understanding of disease development and design of therapeutic interventions.

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANA-microT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA-gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANA-microT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.

Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population., {"references":["D. Treves, S. Manning, and J. Adams, \"Repeated evolution of an\nacetate-crossfeeding polymorphism in long-term populations of\nEscherichia coli,\" Mol. Biol. Evol., vol. 15, no. 7, pp. 789-797, 1998.","A. Varma, and B.O. Palsson, \"Stoichiometric flux balance models\nquantitatively predict growth and metabolic by-product secretion in\nwild-type Escherichia coli W3110,\" Appl. Environ. Microbiol., vol. 60,\nno. 10, pp. 3724-31, 1994.","E. Tzamali, and M. Reczko, \"The benefit of cooperation: Identifying\ngrowth efficient interacting strains of Escherichia coli using metabolic\nflux balance models,\" 8th IEEE International conference on\nbioinformatics and bioengineering, Greece, 2008.","J.L. Reed, et al., \"An expanded genome-scale model of Escherichia coli\nK-12 (iJR904 GSM/GPR),\" Genome Biol, vol. 4, no. 9, pp. R54, 2003.","Scott A Becker, Adam M Feist, Monica L Mo, Gregory Hannum,\nBernhard ├ÿ Palsson & Markus J Herrgard, \"Quantitative prediction of\ncellular metabolism with constraint-based models: the COBRA\nToolbox,\" Nature Protocols, vol. 2, pp. 727-738, 2007.","Janet M. Six, Ioannis G. Tollis, \"Effective Graph Visualization Via\nNode Grouping,\" infovis, pp.51-58, 2001 IEEE Symposium on\nInformation Visualization (InfoVis 2001), 2001","Paul Rainey, Angus Buckling, Rees Kassen and Michael Travisano,\n\"The emergence and maintenance of diversity: insights from\nexperimental bacterial populations,\" Tree, vol. 15, pp. 243-247, 2000.","R. F. Rosenzweig, R. R. Sharp, D. S. Treves, and J. Adams, \"Microbial\nEvolution in a Simple Unstructured Environment: Genetic\nDifferentiation in Escherichia Coli,\" Genetics, vol. 137(4), pp. 903-\n917, 1994","Patric R. J. Ostergard, \"A New Algorithm for the Maximum-Weight\nClique Problem,\" Electronic Notes in Discrete Mathematics, 6th Twente\nWorkshop on Graphs and Combinatorial Optimization, vol. 3, pp. 153-\n156, 1999."]}