A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the International Mouse Phenotyping Consortium
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus Musculus
TISSUE(S): Blood
PROVIDER: ST001154 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
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| mwtab | Other |
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Project description:Untargeted and targeted metabolomics datasets were acquired for blood plasma samples of 30 mouse knockouts within the International Mouse Phenotyping Consortium (IMPC). http://www.mousephenotype.org/. West Coast Metabolomics Center at UC Davis (https://metabolomics.ucdavis.edu/) conducted the metabolomics analyses.
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Project description:During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Project description:We used ATLAS-seq to comprehensively map the genomic location of LINE-1 elements belonging to the youngest and potentially polymorphic subfamily (L1HS-Ta). This was performed in a panel of 12 human primary or transformed cell lines (BJ, IMR90, MRC5, H1, K562, HCT116, HeLa S3, HepG2, MCF7, HEK-293, HEK-293T, 2102Ep). In brief, ATLAS-seq relies on the random mechanical fragmentation of the genomic DNA to ensure high-coverage, ligation of adapter sequences, suppression PCR-amplification of L1HS-Ta element junctions, and Ion Torrent sequencing using single-end 400 bp read chemistry. A notable aspect of ATLAS-seq is that we can obtain both L1 downstream and upstream junctions (3'- and 5'-ATLAS-seq libraries, respectively), for full-length L1 elements. Note that a 10-nt sample-specific barcode has been removed at the 5' end of the reads in the .fastq files upon demultiplexing. This was achieved using cutadapt v1.9.2.dev0 (with the following parameters: -e 0.1 -q 10 -m 25 -g <barcode_name>=^<barcode_sequence>)
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Project description:BackgroundPersistent symptoms after initial COVID-19 infection are common and are frequently referred to by the umbrella terms "post-COVID syndrome" and "long COVID". The sheer number of affected patients pose an increasing challenge to healthcare systems worldwide. To date, our understanding of the pathophysiology of the post-COVID syndrome remains poor and the extent to which persistent cardiopulmonary abnormalities contribute to the symptom complex is unclear. We sought to determine the presence and impact of cardiopulmonary sequelae after COVID-19 in longitudinal assessment.MethodsWe report on 71 patients who underwent comprehensive, longitudinal testing in regular intervals for up to 12 months after their initial COVID-19 diagnosis. Testing included pulmonary function testing, cardiopulmonary exercise testing, dedicated left and right heart echocardiography, lung ultrasonography, and cardiac MRI.ResultsOur results demonstrate that subjective quality of life after COVID-19 (EQ-5D visual acuity scale, VAS, 67.4 for patients treated as outpatient, 79.2 for patients admitted to the general floor, 71.8 for patients treated in an ICU) is not related to the severity of the initial infection. Maximal exercise capacity is also reduced (VO2max 79% predicted, SD ± 19%); however, this is driven in large parts by patients who had initially required ICU-level of care. The degree of objective reduction in exertion did not correlate with quality of life scores. Pulmonary function testing revealed mild and persistent reduction in DLCO over the first 12 months without significant restrictive or obstructive lung disease. Left and right heart function was intact with good RV function and intact RV/PA coupling, imaging findings suggestive of myocarditis were uncommon (7% of patients).ConclusionA reduction in exercise capacity after COVID-19 is common, but is most prominent in patients previously treated in the ICU and more likely related to deconditioning or fatigue than to cardiopulmonary impairment. Subjective quality of life scores are independent of the severity of initial infection and do not correlate with objective measures of cardiopulmonary function. In our cohort, persistent cardiopulmonary impairment after COVID-19 was uncommon. The post-COVID syndrome is unlikely to be the result of cardiopulmonary sequalae and may reflect a post-ICU syndrome in some. Trial registration Registered on clinicaltrials.gov (NCT04442789), Date: June 23, 2020.
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