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20 results for “ecosystem resistance”
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
CEE01 The Climate Extremes Experiment (CEE): Assessing ecosystem resistance and resilience to repeated climate extremes at Konza Prairie
Climate extremes, such as drought, are increasing in frequency and intensity, and the ecological consequences of these extreme events can be substantial and widespread. Yet, little is known about the factors that determine recovery (or resilience) of ecosystem function post-drought. Such knowledge is particularly important because post-drought recovery periods can be protracted depending on drought legacy effects (e.g., loss key plant populations, altered community structure and/or biogeochemical processes). These drought legacies may alter ecosystem function for many years post-drought and may impact future sensitivity (both resistance and resilience) to climate extremes. With forecasts of more frequent drought, there is an imperative to understand whether and how post-drought legacies will affect ecosystem response to future drought events. To address this knowledge gap, we experimentally imposed over an eight year period two extreme growing season droughts, each two years in duration followed by a two-year recovery period, in annually burned tallgrass prairie.
Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems
<p>Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli <em>et al.</em>, 2019) , metasub consortium (Danko <em>et al.</em>, 2020) and TARA ocean (Tully <em>et al.</em>, 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa <em>et al.</em>, 2020). </p> <p>If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li <em>et al.</em>, 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>).</p> <p>Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert <em>et al.</em>, 2021; Hennequin <em>et al.</em>, 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast <em>et al.</em>, 2013) and phylogenetic markers hosted by chromosomes (Wu <em>et al.</em>, 2013). The database created for this purpose is available at this address <a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-</a><a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">databases</a>. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk <em>et al.</em>, 2018) and PlasClass (Pellow <em>et al.</em>, 2020). Previously undetected viruses were removed by using ViralVerify (<a href="https://github.com/ablab/viralVerify">https://github.com/ablab/viralVerify</a>)(Antipov <em>et al.</em>, 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer <em>et al.</em> (Pfeifer <em>et al.</em>, 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (<em>i.e.</em> 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs).</p> <div> <p>The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv"</p> <ul> <li>fasta_id: fasta identification of the PLC</li> <li>ecosystem: ecosystem from which the PLC originates</li> <li>biome: biome of the ecosystem</li> <li>latitude, longitude: GPS coordinate of the ecosystem</li> <li>length: PLC length</li> <li>map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)</li> <li>map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)</li> <li>nb_genes: Number of genes detected by Prokka implemented in PlasSuite</li> <li>nb_args: ARGs detected by PlasSuite</li> <li>plascad: results from plascad (Che et al., 2021)</li> </ul> <p> </p> <p> </p> <p>Antipov, D., Raiko, M., Lapidus, A., and Pevzner, P.A. (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. <em>Bioinformatics</em> <strong>36</strong>: 4126–4129.</p> <p>Che, Y., Yang, Y., Xu, X., Břinda, K., Polz, M.F., Hanage, W.P., and Zhang, T. (2021) Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proceedings of the National Academy of Sciences 118: e2008731118.</p> <p>Corrêa, F.B., Saraiva, J.P., Stadler, P.F., and da Rocha, U.N. (2020) TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. <em>Nucleic Acids Res</em> <strong>48</strong>: D626–D632.</p> <p>Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D.J., et al. (2020) Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance. <em>bioRxiv</em> 724526.</p> <p>Hennequin, C., Forestier, C., Traore, O., Debroas, D., and Bricheux, G. (2022) Plasmidome analysis of a hospital effluent biofilm: Status of antibiotic resistance. <em>Plasmid</em> <strong>122</strong>: 102638.</p> <p>Hilpert, C., Bricheux, G., and Debroas, D. (2021) Reconstruction of plasmids by shotgun sequencing from environmental DNA: which bioinformatic workflow? <em>Briefings in Bioinformatics</em> <strong>22</strong>: bbaa059.</p> <p>Krawczyk, P.S., Lipinski, L., and Dziembowski, A. (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. <em>Nucleic Acids Res</em> <strong>46</strong>: e35.</p> <p>Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. <em>Bioinformatics</em> <strong>31</strong>: 1674–1676.</p> <p>Li, H. (2018) Minimap2: pairwise alignment for nucleotide sequences. <em>Bioinformatics</em> <strong>34</strong>: 3094–3100.</p> <p>Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., et al. (2019) Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. <em>Cell</em> <strong>176</strong>: 649-662.e20.</p> <p>Pellow, D., Mizrahi, I., and Shamir, R. (2020) PlasClass improves plasmid sequence classification. <em>PLOS Computational Biology</em> <strong>16</strong>: e1007781.</p> <p>Pfeifer, E., Moura de Sousa, J.A., Touchon, M., and Rocha, E.P.C. (2021) Bacteria have numerous distinctive groups of phage–plasmids with conserved phage and variable plasmid gene repertoires. <em>Nucleic Acids Res</em> <strong>49</strong>: 2655–2673.</p> <p>Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. <em>Nucleic Acids Res</em> <strong>41</strong>: D590–D596.</p> <p>Robertson, J. and Nash, J.H.E. (2018) MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. <em>Microbial Genomics</em> <strong>4</strong>:.</p> <p>Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. <em>Nature Biotechnology</em>.</p> <p>Tully, B.J., Graham, E.D., and Heidelberg, J.F. (2018) The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. <em>Scientific Data</em> <strong>5</strong>: 170203.</p> <p>Wu, D., Jospin, G., and Eisen, J.A. (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. <em>PLoS One</em> <strong>8</strong>:.</p> </div> <p> </p>
Africa's ecosystems exhibit a tradeoff between resistance and stability following disturbances
<p class="MsoNormal">Environmental disturbances may prevent ecosystems from consistently performing their critical ecological functions. Two important properties of ecosystems are their resistance and stability, which respectively reflect their capacities to withstand and recover from disturbance events (e.g., droughts, wildfires, pests, etc.). Theory suggests that resistant and stable ecosystems possess opposing characteristics, but this has seldom been established across diverse ecosystem attributes or broad spatial scales. Here, we compare the resistance and stability of >1,000 protected area ecosystems in Africa to disturbance-induced losses in primary productivity from 2000-2019. We quantitatively evaluated each ecosystem such that following disturbances, an ecosystem is more resistant if it experiences lower-magnitude losses in productivity, and more stable if it returns more rapidly to pre-disturbance productivity levels. To compare the characteristics of resistant versus stable ecosystems, we optimized random forest models that use ecosystem attributes (representing their climatic and environmental conditions, plant and faunal biodiversity, and exposure to human impacts) to predict their resistance and, separately, stability values. We visualized each attribute's relationship with resistance and stability after accounting for all other attributes in the model framework. Ecosystems that are more resistant to disturbances are less stable, and vice versa. The ecosystem attributes with the most predictive power in our models all exhibit contrasting relationships with resistance versus stability. Notably, highly resistant ecosystems are generally more arid and exhibit high habitat heterogeneity and mammalian biodiversity, while highly stable ecosystems are the opposite. We discuss the underlying mechanisms through which these attributes engender resistance or, conversely, stability. Our findings suggest that resistance and stability are fundamentally opposing phenomena. A balance between the two must be struck if ecosystems are to maintain their identity, structure, and function in the face of environmental change.</p>
Data from: Insect-microbe-fungus interplay in citrus agro-ecosystems: Cuticular symbionts mediate <em>Diaphorina citri</em> resistance to <em>Beauveria bassiana</em>
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Data from: Deep oxygen-depleted Red Sea coral reef depressions sustain resistant ecosystems
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Annual grass invasions and wildfire deplete ecosystem carbon storage by >50% to resistant base levels
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Africa’s ecosystems exhibit a tradeoff between resistance and stability following disturbances
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Plant diversity ameliorates the evolutionary development of fungicide resistance in an agricultural ecosystem
<p>1. Evolution of fungicide resistance in agricultural and natural ecosystems is associated with the biology of pathogens, the chemical property and a<span class="fontstyle01"><span>pplication strategies of the fungicides</span></span>. The influence of ecological factors such as host diversity on the evolution of fungicide resistance has been largely overlooked but is highly relevant to social and natural sustainability. In this study, we used an experimental evolution approach to understand how host population heterogeneity may affect the evolution of fungicide resistance in the associated pathogens.</p> <p>2. Potato populations with six levels of genetic heterogeneity were grown in the same field and naturally infected by <i>Phytophthora infestans.</i> Pathogen isolates (~1200) recovered from the field experiment were molecularly genotyped. Genetically distinct isolates were selected form each population and 142 isolates were assayed for their tolerance to two fungicides differing in the mode of action. Tolerance was determined by calculating the relative growth rate of the isolates in the presence and absence of fungicides and the effective concentration for 50% inhibition.</p> <p>3. The evolution of fungicide resistance in <i>P. infestans</i> was affected by the genetic variation of host populations. Higher potato diversification increased the sensitivity of <i>P. infestans</i> to both fungicides and reduced genetic variation of the pathogen available for the development of fungicide resistance. These mitigating effects are independent of biochemical properties of fungicides and are likely caused by host selection for pathogen strains differing in the ability of fungicide influxes, effluxes or detoxification rather than mutations in fungicide target genes.</p> <p>4. Synthesis and applications: Increased fungicide sensitivity and diminished evolutionary potential of fungicide resistance associated with higher host diversification reduce the fungicide dose and application frequency needed to achieve the same extent of disease control, relaxing the selection pressure acting on the pathogen populations and retarding the evolution of fungicide resistance. Together with benefits documented in other studies, our results indicate that host diversification is an eco-friendly approach that not only ameliorate fungicide resistance but also help achieve social and ecological sustainability by balancing the interaction among food security, socioeconomic development and ecological resilience.</p>
Community composition influences ecosystem resistance and productivity more than species richness or intraspecific diversity
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Plant diversity ameliorates the evolutionary development of fungicide resistance in an agricultural ecosystem
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Ecosystem resilience and pest resistance in Eucalyptus plantations is driven by understorey complexity due to forest management
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Data from: Density-dependent and species-specific effects on self-organization modulate the resistance of mussel bed ecosystems to hydrodynamic stress
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A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma [nCounter RNA]
GEO Series GSE317937. Homo sapiens. 46 samples. Type: Expression profiling by array.
A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma [nCounter protein]
GEO Series GSE317936. Homo sapiens. 46 samples. Type: Protein profiling by protein array.
In situ resistance, not immigration, supports invertebrate community resilience to drought intensification in a Neotropical ecosystem
<p class="MsoNoSpacing">While future climate scenarios predict declines in precipitations in many regions of the world, little is known of the mechanisms underlying community resilience to prolonged dry seasons, especially in "naïve" Neotropical rainforests. Predictions of community resilience to intensifying drought are complicated by the fact that the underlying mechanisms are mediated by species' tolerance and resistance traits<b>, </b>as well as rescue through dispersal from source patches.</p> <p class="MsoNoSpacing">We examined the contribution of <i>in situ</i> tolerance-resistance and immigration to community resilience, following drought events that ranged from the ambient norm to IPCC scenarios and extreme events.</p> <p class="MsoNoSpacing">We used rainshelters above rainwater-filled bromeliads of French Guiana to emulate a gradient of drought intensity (from 1 to 6 times the current number of consecutive days without rainfall), and we analyzed the post-drought dynamics of the taxonomic and functional community structure of aquatic invertebrates to these treatments when immigration is excluded (by netting bromeliads) or permitted (no nets).</p> <p class="MsoNoSpacing">Drought intensity negatively affected invertebrate community resistance, but had a positive influence on community recovery during the post-drought phase. After droughts of 1 to 1.4 times the current intensities, the overall invertebrate abundance recovered within invertebrate life cycle durations (up to 2 months). Shifts in taxonomic composition were more important after longer droughts, but overall, community composition showed recovery towards baseline states. The non-random patterns of changes in functional community structure indicated that deterministic processes like environmental filtering of traits drive community re-assembly patterns after a drought event. Community resilience mostly relied on <i>in situ </i>tolerance-resistance traits. A rescue effect of immigration after a drought event was weak and mostly apparent under extreme droughts.</p> <p class="MsoNoSpacing">Under climate change scenarios of drought intensification in Neotropical regions, community and ecosystem resilience could primarily depend on the persistence of suitable habitats and on the resistance traits of species, while metacommunity dynamics could make a minor contribution to ecosystem recovery. Climate change adaptation should thus aim at identifying and preserving local conditions that foster <i>in situ</i> resistance and the buffering effects of habitat features.</p>
A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma
GEO Series GSE317912. Homo sapiens. 38 samples. Type: Protein profiling by protein array.
A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma [DSP protein]
GEO Series GSE318248. Homo sapiens. 510 samples. Type: Protein profiling by protein array.
In situ resistance, not immigration, supports invertebrate community resilience to drought intensification in a Neotropical ecosystem
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A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma [SNV]
GEO Series GSE317918. Homo sapiens. 24 samples. Type: Genome variation profiling by array.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.