BUSCADOR RECOLECTA

5 figures, 13 appendixes.-- Data availability: We used public data as described in the Methods section and Appendix 1-table 5.The code used for the analyses in the manuscript is available at https://github.com/functional-dark-side/functional-dark-side.github.io/tree/master/scripts. A list with the program versions can be found in https://github.com/functional-dark-side/functional-dark-side.github.io/blob/master/programs_and_versions.txt.The code to create the figures is available at https://github.com/functional-dark-side/vanni_et_al-figures, and the data for the figure can be downloaded from https://doi.org/10.6084/m9.figshare.12738476.v2. A reproducible version of the workflow is available at https://github.com/functional-dark-side/agnostos-wf.The data is publicly available at https://doi.org/10.6084/m9.figshare.12459056, Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data., The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2014-2-0085), the BMBF877 funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A537B, 031A533A, 031A538A, 031A533B, 031A535A, 031A537C, 031A534A, 031A532B), the University of Oxford Advanced Research Computing (http://dx.doi.org/10.5281/zenodo.22558) and the MARBITS bioinformatics core at ICM-CSIC.CV was supported by the Max Planck Society. AFG received funding from the European Union’s Horizon 2020 research and innovation program Blue Growth: Unlocking the potential of Seas and Oceans under grant agreement no. 634486 (project acronym INMARE). AM was supported by the Biotechnology and Biological Sciences Research Council [BB/M011755/1, BB/R015228/1] and RDF by the European Molecular Biology Laboratory core funds. EOC was supported by project INTERACTOMA RTI2018-101205-B-I00 from the Spanish Agency of Science MICIU/AEI. S 887 GA and PS received additional funding by the project MAGGY (CTM2017-87736-R) from the Spanish Ministry of Economy and Competitiveness. The Malaspina 2010 Expedition was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the Consolider-Ingenio program (ref. CSD2008-00077). The authors thank Johannes Söding and Alex Bateman for helpful discussions., Peer reviewed, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).

[Description of methods used for collection/generation of data] For Morphospecies_abundance.csv: Aerial sampling, Berlese sampling, cafeteria experiments, vacuuming sampling, shake sampling.

For zOTU_Abundance.csv: DNA extraction was carried out with 0.05-0.1 grams of root material using a Mobio PowerSoil DNA Isolation Kit (Mobio Laboratories). PCR and sequencing of the 16 rRNA gene was done with Illumina MiSeq (NGS) following the methods from the central genomic services of RTSF-MSU (Michigan State University, USA) (https://rtsf.natsci.msu.edu/). We analyzed the V4 variable region of the 16S rRNA gene (250 nucleotides) using primers F515 (5'-GTGCCAGCMGCCGCGGTAA-3') and R806 (5'-GGACTACHVGGGTWTCTAAT-3'). Raw rRNA gene sequences were processed using the UPARSE pipeline (Edgar, 2013) to identify zOTUS (zero-radius operational taxonomic units). Taxonomic assignment used the naive Bayes scikit-learn classifier implemented in QIIME2 (Caporaso et al., 2010) and the SILVA 132 database (Quast et al., 2012). Chloroplast, mitochondrial, and unclassified sequences were excluded from further analyses.

For PERDIVER_Alpha_diversity.csv: Multiple diversity metrics using R. For each community associated with each plant species: Species richnes, Shannon and Simpson diversity indices and standard effect size of the phylogenetic mean pairwise distance between species in the community (mpd.obs.z). Above ground phylogenetic distances come from Chesters, D. (2020), The phylogeny of insects in the data-driven era. Syst Entomol, 45: 540-551. Belowground distances from QIIME2 (Caporaso, J., Kuczynski, J., Stombaugh, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335–336 (2010).) and the SILVA 128 reference database (Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590–D596.).
2016, 2017 and 2018 during flowering season of host plants., Data from the PERDIVER and INTERACTOMA projects. The goal of the projects was to characterize the communities of species that interact with seven rare or endangered plants in Aragón and if those communities changed between isolated and non-isolated plant populations. The aboveground was surveyed in 2016, 2017, and 2018 during the flowering season of the host plant. All individual animals, visiting any part of flowering and non-flowering plants over an area covered visually by the observer were either visually identified or sampled, if needed to confirm identification. All animals trapped by the sticky leaves of pinlon were also identified. Sampling area and length of surveys varied among plant species, depending on size of plants and frequency of interactions, but ≥ 20 surveys were performed at each site, resulting in a total of > 9,000 minutes and 638 surveys. Sampling investment varied among plant species, because they differed in interaction detectability. However, sampling effort was always similar among patches of the same plant species. Other methods like Berlese funnnel traps, fruit sampling or cafeteria experiments were used to unveil interactions that are not easily visible, like those by small or camouflaged animals.
Below ground microbial communities were characterized for each plant population with three samples composed of root material from different individuals., PERDIVER Project (Fundación BBVA). INTERACTOMA Project RTI2018-101205-B-I00., Morphospecies_abundance.csv zOTU_Abundance.csv PERDIVER_Alpha_diversity.csv, Peer reviewed

We surveyed wild boar (Sus scrofa) populations using 16S rRNA gene analysis of the gut microbiota in fresh faeces taken from 88 animals hunted in 16 hunting estates. The wild boar is a very convenient model system to explore how environmental factors including game management, food availability, disease prevalence, and behaviour may affect different biological components of wild individuals with potential implications in management and conservation. We tested the hypotheses that diet (according to stable carbon isotopes analyses), gender (i.e., animal behaviour studying males and females), and both health (analyses of serum samples to detect exposure to several diseases) and form statutes (i.e., thoracic circumference in adults) are reflected in changes in the intestinal microbiota. We focused on a gut functional biomarker index combining Oscillospiraceae and Ruminococcaceae vs. Enterobacteriaceae. We found that gender and the estate (population) were explanatory variables (c.a. 28% of the variance), albeit a high degree of overlapping among individuals was observed. The individuals with higher abundance of Enterobacteriaceae showed a gut microbiota with low diversity, mostly in males. Significant statistical differences for thoracic circumference were not found between males and females. Interestingly, the thoracic circumference was significantly and inversely related to the relative abundance of Enterobacteriaceae in males. Overall, we found that diet, gender, and form status were major factors that could be related to the composition and diversity of the gut microbiota. A high variability was observed in the biomarker index for populations with natural diet (rich in C3 plants). Although, we noticed a marginally significant negative trend between the index (higher abundance of Enterobacteriaceae) and the continuous feeding of C4 plants (i.e., supplementary maize) in the diet of males. This result suggests that continuous artificial feeding in hunting estates could be one of the factors negatively influencing the gut microbiota and the form status of wild boars that deserves further investigations., XTM and EOC were supported by grant INTERACTOMA RTI2018-101205-B-I00 from the Spanish Agency of Research (AEI-MICINN) and European funding (ERDF)., Peer reviewed