Data published here are various datasets used in the publication Algal (meta)proteomes uncover cellular adaptations to life on the Greenland Ice Sheet, by Feord et al., submitted for publication. Four datasets are presented in this data publication: i) amplicon sequencing (16S and 18S), ii) cell count and biovolumes of algae morphotypes quantified with a FlowCam, iii) raw and normalized metabolomic data (quantified with LC-MS and GC-MS), and iv) file containing a predicted protein database. The protein data used in Feord et al. (submitted), is available on ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD057047 (username: reviewer_pxd057047@ebi.ac.uk and password: kwg7a3NHfhwg).
All data except dataset iv originate from samples collected on the Greenland Ice Sheet in the Summer of 2021 during the DEEP PURPLE ERC ice camp (GR21). This field location (61°05’ N,46°50’ W) is described in Feord et al. (submitted). Datasets i-iii are three different analyses of the same two samples: one snow sample collected on the 24th July 2021 and one ice sample collected on the 7th August 2021. Both samples were high in algal biomass, with the snow sample being visibly red due to pigment-rich snow algae and the ice sample visible purple/brown due to pigment-rich glacier ice algae. All collection, extraction, and analyses methods are described and referenced Feord et al. (submitted).
Analysis and replication within the samples are:
i. Amplicon sequencing (for both 18S and 16S sequencing): SNOW one biological replicate sequenced = one sequencing reaction, and ICE: sequenced with three biological replicates (labelled a,bc) = three sequencing reactions. Raw sequencing data is provided as fastq.gz files and abundance tables as .txt files.
ii. Cell counts and biovolume with FlowCam: SNOW: one biological replicates measured in technical triplicates = three measurements (labelled 1,2,3) and ICE: three biological replicates (labelled a,b,c) measured in technical triplicate (labelled 1,2,3) = nine measurements. Data is provided as .txt files and .png files.
iii. Metabolomic analyses: SNOW: five biological replicates (labelled red_RS1-5) measured in three/four technical replicates (labelled F1-F4) = 19 measurements, and ICE: three biological replicates (labelled GIA_RS1-3) measured in technical triplicates (labelled F1-F3) = nine measurements. Raw data is provided as .mzML files and processed data and tables with sample explanation files are provided as .txt files.
Data iv) is a FASTA file (.fa) with the predicted protein database used to identity proteins from peptide data in Feord et al. (submitted). The database was built by translating open reading frames (ORFs) assembled from previously sequenced polyA-isolated metatranscriptomes from Greenland Ice Sheet samples published by Perini et al. (2024), using the samples MG3, MG5, MG6, MG7, MG8, MG11, MG12, MG14, MG19, MG22, MG23, MG24, MG25, MG26. MG27, MG28, MG30, MG31 from that paper. Assembly, identification of ORFs, and dereplication is described by Feord et al. (submitted)
Organisms in intertidal zones experience fluctuations in environmental stressors such as hypoxia and temperature. These stressors and their fluctuations often appear in combination. Combination of stressors can have different effects compared to single stressors. In this study, we investigate the physiological effects of intermittent hypoxia in combination with different temperature regimes on the Pacific Oyster Crassostrea (Magallana) gigas. The oysters were exposed to hypoxic cycles (12h hypoxia by emersion/12h submersion) at normal (15°C), elevated (30°C) or fluctuating (15°C submersion/30°C emersion) temperature for 10 days. After the last submersion phase, the gills and digestive gland were sampled. We measured markers for bioenergetics and redox-balance in the gills and digestive gland using colorimetric methods as well as a set of metabolites (predominantly amino acids, osmolytes, anaerobic end products and energetic metabolites) in the gills using LC-MS/MS. Oysters kept submerged for up to 10 days were used as controls.
Objective: The multidisciplinary research teams in this consortium have played lead roles in establishing that fetal and childhood periods are vulnerable to environmental disruption leading to common reproductive disorders. This proposal will investigate: (1) connections between normal/abnormal perinatal reproductive development and maturation of reproductive function at puberty and in adulthood; (2) systemic gene-environment interactions underlying reproductive disorders taking account of genetic susceptibility, multiple exposures (e.g. mixtures of environmental chemicals) and their timing (perinatal, peripubertal, adult); (3) connection between perinatal reproductive development and later obesity/metabolic disorders. To achieve this we will utilize large cohorts generated in previous EU projects and collect new data from these on reproductive maturation and adult function. Existing genomic and proteomics data, exposure data for greater than 100 potentially toxic environmental chemicals, lifestyle, dietary and medical history information will be analysed using integrative systems biology approaches to pinpoint critical (interacting) factors influencing development.
Objective: This project concerns the first large-scale application of the full range of omics technologies in a population study aiming at a) the discovery and validation of novel biomarkers predictive of increased risks of a number of chronic diseases, b) the exploration of the association of such biomarkers with environmental exposures, including high-priority pollutants and emerging exposures, and c) the discovery and validation of biomarkers of exposure to the above and other high-priority environmental exposures. The project will utilise three existing prospective cohorts. Cancer-related -omics biomarkers will be developed using a case-control study nested within 2 cohorts which contain biosamples collected prior to disease diagnosis, exposure and followup health information. Biomarkers will be compared in 600 breast cancer cases, 300 NHL cases and equal numbers of matched controls, to evaluate their risk predictivity. Biomarkers of chronic diseases which establish themselves in early childhood but persist into adult life will be evaluated using a mother-child cohort. Biosamples collected from 600 children at birth and at ages 2 and 4 years will be analysed and results compared with clinical indices obtained at age 4. Thanks to the availability of repeat samples, collected over a wide range of time intervals, the intra-individual variation of biomarkers and their relationship with disease progression will be evaluated. Biomarker search will utilize state-of-the-art metabonomics, epigenomics, proteomics and transcriptomics, in combination with advanced bioinformatics and systems biology tools. It will also include technical validation of -omics technology s utilisation with biobank samples. Exposure assessment will utilize exposure biomarkers, questionnaires, modelling and GIS technology. Additional data on exposure, biomarkers (including SNP data) and health indices, available through other projects, will be utilised, thus generating substantial added value.