MARS
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ABSTRACT:
PROVIDER: PRJEB22274 | ENA |
REPOSITORIES: ENA
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Project description:Anthropogenic activities such as urbanization and agriculture can potentially pose a threat to neighboring freshwaters through nitrate and phosphorous contamination, which over time may lead to lake eutrophication. In such nitrogen-polluted environments, oxygen is depleted, and plants die and decompose. This enhances denitrifying microbes that respire under hypoxic/anoxic conditions by reducing nitrate instead of molecular oxygen and using plant remnants (lignocellulose) as carbon source. Microbial lignocellulose degradation has been well-studied for both aerobic- and anaerobic conditions; however, its degradation during denitrification remains largely unknown. Here we have applied a combination of gas kinetics and meta-omics techniques to enrich and analyze microbial communities from 10 eutrophic lakes to identify a set of core microbial metagenome-assembled genomes (MAGs) present in all the eutrophic lakes. We have further investigated their strategies and enzyme profiles for degrading lignocellulose under denitrifying conditions. We identified Pseudomonadota, Bacteroidota, Verrucomicrobiota, and Actinomycetota as the most abundant phyla and they were present in enrichments from all eutrophic lakes having a key role in denitrification and fermentation. Lignocellulose degradation was, however, dominated by species outside the core microbiome, i.e., there were differing key degraders between lakes, suggesting some level of lake-specialization. Among these we observed potential respiratory DNRA pathways, and they expressed a broad range of CAZymes targeting the various lignocellulose subfractions. Interestingly, many of the detected MAGs contained NO dismutases, enzymes postulated to convert NO to molecular oxygen and dinitrogen gas.
2026-04-30 | PXD069160 | Pride
Project description:This study used an emerging analytical technology (cDNA microarrays) to assess the potential effects of PFC exposure on largemouth bass in TCMA lakes. Microarrays simultaneously measure the expression of thousands of genes in various tissues from organisms exposed to different environmental conditions. From this large data set, biomarkers (i.e., genes that are expressed in response to an exposure to known stressors) and bioindicators (e.g., suites of genes that correspond to changes in organism health) can be simultaneously measured to clarify the relationship between contaminant exposure and organism health. Based on current scientific literature, we hypothesized that gene expression patterns would be altered in fish exposed to PFCs (as compared with fish from reference lakes), and that the magnitude of these changes would correspond to the concentrations of PFCs present throughout TCMA lakes. Patterns of gene expression in largemouth bass observed across the TCMA lakes corresponded closely with PFC concentration. Concentrations of PFCs in largemouth bass varied significantly across the sampled lakes, where the lowest concentrations were found in Steiger and Upper Prior Lakes and the highest concentrations were found in Calhoun and Twin Lakes. Patterns of gene expression were most different (relative to controls) in fish with the highest PFC tissue concentrations, where fish from Twin and Calhoun Lakes were observed to have between 5437 and 5936 differentially expressed genes in liver and gonad tissues. Although gene expression patterns demonstrated a high degree of correlation with PFC concentrations, microarray data also suggest there are likely additional factors influencing gene expression patterns in largemouth bass in TCMA lakes.
2014-07-31 | GSE57684 | GEO
Project description:Epigenetic variation has the potential to control environmentally dependent development and contribute to phenotypic responses to local environments. Environmental epigenetic studies of sexual organisms confirm the responsiveness of epigenetic variation, which should be even more important when genetic variation is lacking. A previous study of an asexual snail, Potamopyrgus antipodarum, demonstrated that different populations derived from a single clonal lineage differed in both shell phenotype and methylation signature when comparing lake versus river populations. Here, we examine methylation variation among lakes that differ in environmental disturbance and pollution histories. The differential DNA methylation regions (DMRs) identified among the different lake comparisons suggested a higher number of DMRs and variation between rural Lake 1 and one urban Lake 2 and between the two urban Lakes 2 and 3, but limited variation between the rural Lake 1 and urban Lake 3. DMR genomic characteristics and gene associations were investigated. Observations suggest there is no effect of geographic distance or any consistent pattern of DMRs between urban and rural lakes. Environmental factors may influence epigenetic response.
2019-09-27 | GSE133502 | GEO
Project description:scRNA-seq procedure and analysis. ScRNA-seq was performed using the Massively Parallel Single-Cell RNA-sequencing technology (MARS-seq) as described by Jaitin et al., 2014 (PMID: 24531970) EpiSCs were seeded at ~6 x 103 cells/cm2 in 6-well cell-culture plates pre-coated with 15 µg/ml human Fibronectin, and differentiated to APSD. At desired time-points, cells were dissociated using Accutase (Thermo Fisher Scientific, 00-4555-56) for 3 min at 37°C and counted. 500,000 cells per condition were washed with ice-cold FACS buffer (10% (v/v) FBS in PBS) and resuspended into 1 ml ice-cold FACS buffer containing 1 µg/ml DAPI. For in vivo experiments, E6.5/E7.0/E7.75 Tg(Eomes::GFP) BAC transgenic embryos were individually dissociated into 300 μl warmed Trypsin 0.05% (w/v) EDTA for 10 min at 37°C. Digestion was stopped by addition of 600 μl ice-cold FACS buffer followed by centrifugation at 350 x g for 3 min. Cells were washed with 500 μl ice-cold FACS buffer, centrifuged again at 350 x g for 3 min and resuspended into 250 μl ice-cold FACS buffer containing 1 µg/ml DAPI. Single-cells from either in vitro-differentiated cells or transgenic embryos were sorted into Eppendorf Polypropylene U-shaped 384-well Twin Tec PCR Microplates (Thermo Fisher Scientific, 10573035), containing 2 μl of lysis solution (0.2% (v/v) Triton X-100) supplemented with 0.4 U/μl RNasin Ribonuclease Inhibitor (Promega, N2515) and 400 nM indexed RT primer from group 1 (1-96 barcodes) or group 2 (97-192 barcodes), as described in Jaitin et al., 2014. Additionally, 71 WT EpiSCs were sorted into each plate, as spike-in control for batch-effect correction. Capture plates were prepared on the Bravo automated liquid handling robot station (Agilent) using 384-filtered tips (Axygen, 302-82-101). Index sorting was performed using either a FACS Aria III cell sorter (BD Biosciences) or a SONY SH800S cell sorter (Sony Biotechnology) at the DanStem/reNEW Flow Cytometry Platform (University of Copenhagen, Copenhagen, Denmark), gating in SSC-A versus FSC-A to collect live cells, and then in FSC-W versus FSC-A to sort only singlets. For in vivo experiments, only GFPpos cells were sorted to capture Eomes-expressing cell types. Immediately after sorting, plates were spun down, snap-frozen on dry ice, and stored at -80°C until further processing. Semi-automated library preparation was performed as described by Jaitin et al., 2014, using 10-12 cycles of PCR amplification and AMPure XP beads (Agencourt) for purification. DNA concentration was measured with a Qubit Fluorometer (Thermo Fisher Scientific, Q32854), and fragment size was determined with a Fragment analyzer (Advanced Analytical). Libraries were paired-end sequenced on a Next-Seq 500 Sequencer (Illumina) at the DanStem/reNEW Genomics Platform (University of Copenhagen, Copenhagen, Denmark). Between 1,146 and 1,528 cells were sequenced per lane. R1 and R2 fastq files were generated using bcl2fastq (v2.19.1), and the pooling and well information were extracted from the sequence using umis (v1.0.3) [https://github.com/vals/umis] into a unique fastq file. The reads were then filtered based on the pooling barcodes with 1 mismatch allowed. The poly-Ts at the end of the reads were trimmed using Cutadapt (v1.18). The reads were mapped to the mouse genome (GRCm38/mm10 together with ERCC92) using HISAT2 (v2.1.0), the alignments were processed with Samtools (v1.7)118, and the reads were counted with featureCounts (Subread (v1.5.3)) using Ensembl v93, and the UMIs using UMI_tools (v1.0.0). Expression data were analyzed using Seurat (v3.1). Data filtering, normalization, and scaling were performed using the standard pre-processing workflow. Integration of different datasets was performed as described (PMID: 31178118). Spiked-in EpiSCs were used as a reference to correct the batch effect between integrated datasets. Marker genes of each cell cluster were outputted for GO-term analysis to define the cell type. The Monocle package (v2.16.0) was used to perform pseudotime analyses.
2025-04-28 | E-MTAB-13660 | biostudies-arrayexpress
Project description:Warming and eutrophication interactively drive changes in the methane-oxidizing community of shallow lakes
| PRJEB43466 | ENA
Project description:Microbiome Changes during the Trans-Antarctic Winter Traverse
| PRJEB9027 | ENA