Proteomic characterization of pilot scale hot-water extracts from the industrial carrageenan red seaweed Eucheuma denticulatum

Funding This work was supported by Innovation Fund Denmark (grant number 7045-00021B (PROVIDE) ) .Seaweeds have a long history as a resource for polysaccharides/hydrocolloids extraction for use in the food industry due to their functionality as stabilizing agents. In addition to the carbohydrate content, seaweeds also contains a significant amount of protein, which may find application in food and feed. Here, we present a novel combination of transcriptomics, proteomics, and bioinformatics to determine the protein composition in two pilot-scale extracts from Eucheuma denticilatum (Spinosum) obtained via hot-water extraction. Although the quality of extracted protein appeared quite poor based on SDS-PAGE analysis, extracts were characterized by qualitative and quantitative proteomics using LC-MS/MS and a de-novo transcriptome assembly for construction of a suitable protein database. A novel concept of length-normalization for relative quantification of sub-optimal protein extracts with partial, non-specific digestion is introduced and validated against conventional methods for relative quantification of proteins. Despite a limited number of protein identifications due to poor protein quality, our data suggest that the majority of quantified protein in the extracts (>75%) is constituted by merely three previously uncharacterized proteins. Putative subcellular localization for the quantified proteins was determined by bioinformatic prediction using several predictors, and by correlating with the expected copy number from the transcriptome analysis, we find that the extracts appear highly enriched in extracellular proteins. This implies that the extraction method used predominantly extracts extracellular proteins, and thus appear ineffective for cellular disruption and subsequent release of intracellular proteins. Nevertheless, the highly abundant proteins may be potential substrates for targeted hydrolysis and release of bioactive peptides. Ultimately, this study highlight the potential of quantitative proteomics for characterization of alternative protein sources intended for use in foods and evaluating protein extraction process efficiency through novel combinations with bioinformatic analysis.Innovation Fund Denmark 7045-00021

A Search for Infall Evidence in EGOs I: the Northern Sample

We report the first systematic survey of molecular lines (including HCO+ (1-0) and 12CO, 13CO, C18O (1-0) lines at 3 mm band) towards a new sample of 88 massive young stellar object (MYSO) candidates associated with ongoing outflows (known as extended green objects or EGOs) identified from the Spitzer GLIMPSE survey in the northern hemisphere with the PMO-13.7 m radio telescope. By analyzing the asymmetries of the optically thick line HCO+ for 69 of 72 EGOs with HCO+ detection, we found 29 sources with blue asymmetric profiles and 19 sources with red asymmetric profiles. This results in a blue excess of 0.14, seen as a signature of collapsing cores in the observed EGO sample. The relatively small blue excess measured in our full sample due to that the observed EGOs are mostly dominated by outflows and at an earlier evolutionary phase associated with IRDCs and 6.7 GHz methanol masers. The physical properties of clouds surrounding EGOs derived from CO lines are similar to those of massive clumps wherein the massive star forming cores associated with EGOs possibly embedded. The infall velocities and mass infall rates derived for 20 infall candidates are also consistent with the typical values found in MYSOs. Thus our observations further support the speculation of Cyganowski et al. (2008) that EGOs trace a population with ongoing outflow activity and active rapid accretion stage of massive protostellar evolution from a statistical view, although there maybe have limitations due to single-pointing survey with a large beam.Comment: 44 pages, 4 figures, accepted for publication in Ap

Asynchronous Probabilistic Couplings in Higher-Order Separation Logic

Probabilistic couplings are the foundation for many probabilistic relational program logics and arise when relating random sampling statements across two programs. In relational program logics, this manifests as dedicated coupling rules that, e.g., say we may reason as if two sampling statements return the same value. However, this approach fundamentally requires aligning or "synchronizing" the sampling statements of the two programs which is not always possible. In this paper, we develop Clutch, a higher-order probabilistic relational separation logic that addresses this issue by supporting asynchronous probabilistic couplings. We use Clutch to develop a logical step-indexed logical relational to reason about contextual refinement and equivalence of higher-order programs written in a rich language with higher-order local state and impredicative polymorphism. Finally, we demonstrate the usefulness of our approach on a number of case studies. All the results that appear in the paper have been formalized in the Coq proof assistant using the Coquelicot library and the Iris separation logic framework

Identification of Novel Associations and Localization of Signals in Idiopathic Inflammatory Myopathies Using Genome-Wide Imputation

Idiopathic inflammatory myopathies; GenomeMiopatías inflamatorias idiopáticas; GenomaMiopaties inflamatòries idiopàtiques; GenomaObjective The idiopathic inflammatory myopathies (IIMs) are heterogeneous diseases thought to be initiated by immune activation in genetically predisposed individuals. We imputed variants from the ImmunoChip array using a large reference panel to fine-map associations and identify novel associations in IIM. Methods We analyzed 2,565 Caucasian IIM patient samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically matched control samples. We imputed 1,648,116 variants from the ImmunoChip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM and clinical and serologic subgroups. Results The HLA locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, SDK2 and LINC00924 (both novel) and STAT4 in the whole IIM cohort, with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and for CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. Conclusion We found novel and strong associations in IIM and PM and localized signals to single genes and immune cell types.Supported by the Intramural Research Program, National Institute of Environmental Health Sciences, NIH. Dr. Lundberg's work was supported by grants from the Swedish Research Council and Stockholm Regional Council (ALF). Dr. Vencovsky's work was supported by the Czech Ministry of Health–Conceptual Development of Research Organization (award 00023728) (Institute of Rheumatology). Drs. Hanna and Machado's work were supported by the NIHR University College London Hospitals Biomedical Research Centre. Drs. De Bleecker and De Paepe's work were supported by the European Reference Network for Rare Neuromuscular Diseases (ERN EURO-NMD). Dr. Wedderburn's work was supported by Versus Arthritis (awards 21593 and 21552), the Wellcome trust (award 085860), Myositis UK, the Cure JM Foundation, the Remission Charity, and the NIHR Biomedical research Centre at GOSH. Dr. Chinoy's work was supported by the Medical Research Council UK (award MR/N003322/1), Myositis UK, and the NIHR Manchester Biomedical Research Centre Funding Scheme. Dr. Lamb's work was supported by the Medical Research Council UK (award MR/N003322/1) and Myositis UK

Exploring Approaches for Blended Learning in Life Sciences

Digital tools and platforms offer new solutions to design and conduct university teaching. This case illustrates how such digital solutions may be utilized in problem-based learning programmes within life science educations. Specifically, the case evaluated the use of live-streamed and recorded lectures, the incorporation of digital formative assessment in lectures, and the use of a digital platform to support experimental project work in a research laboratory. We find that digital solutions provide flexibility for both lecturers and students, advantageous options for collecting and sharing information, and for engaging students in their learning process. However, digital tools cannot replace all aspects of traditional in-person teaching, such as social interactions. Rather, when blended with in-person teaching, digital solutions have a large potential for supporting new forms of and approaches to both theoretical and experimental university teaching

Variability analysis of LC-MS experimental factors and their impact on machine learning

Abstract Background Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescoring. ML models need large-scale datasets for training and repurposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs. Results We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variability in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning. Conclusions Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it is important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pretrained model