Project description:The absorption of visible light in aquatic environments has led to the common assumption that aquatic organisms sense and adapt to penetrative blue/green light wavelengths, but show little or no response to the more attenuated red/far-red wavelengths. Here we show that two marine diatom species, Phaeodactylum tricornutum and Thalassiosira pseudonana, possess a bona fide red/far-red light sensing phytochrome (DPH) that uses biliverdin as a chromophore and displays accentuated red-shifted absorbance peaks compared to other characterized plant and algal phytochromes. Exposure to both red and far-red light causes changes in gene expression in P. tricornutum and the responses to far-red light disappear in DPH knockout cells, demonstrating that P. tricornutum DPH mediates far-red light signaling. The identification of DPH genes in diverse diatom species widely distributed along the water column further emphasizes the ecological significance of far-red light sensing, raising questions about the sources of far-red light. Our analyses indicate that, although far-red wavelengths from sunlight are only detectable at the ocean surface, chlorophyll fluorescence and Raman scattering can generate red/far-red photons in deeper layers. This study opens up novel perspectives on phytochrome-mediated far-red light signaling in the ocean and on the light sensing and adaptive capabilities of marine phototrophs.
Project description:Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.
Project description:Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.
Project description:We performed four small RNA sequencing for identification and characterization of microRNAs in Phalaenopsis aphrodite subsp. formosana. By comparing the low temperature-treated group with treated group, we concluded four miRNAs - miR156, miR162, miR528 and miR535 - as low temperature-induced miRNAs. In addition, tissue-specific expression of these miRNAs was investigated. The files contain the miRNAs analysis results in each group. Examination of low temperature-treated leaves and two other organs of Phalaenopsis orchid
Project description:Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.
Project description:The common octopus (Octopus vulgaris) is nowadays the most demanded cephalopod species for human consumption. This species was also postulated for aquaculture diversification to supply its increasing demand in the market worldwide, which only relies on continuously declining field captures. In addition, they serve as model species for biomedical and behavioral studies. Body parts of marine species are usually removed before reaching the final consumer as by-products in order to improve preservation, reduce shipping weight, and increase product quality. These by-products have recently attracted increasing attention due to the discovery of several relevant bioactive compounds. Particularly, the common octopus ink has been described as having antimicrobial and antioxidant properties, among others. In this study, the advanced proteomics discipline was applied to generate a common octopus reference proteome to screen potential bioactive peptides from fishing discards and by-products such as ink. A shotgun proteomics approach by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) using an Orbitrap Elite instrument was used to create a reference dataset from octopus ink. A total of 1432 different peptides belonging to 361 non-redundant annotated proteins were identified. The final proteome compilation was investigated by integrated in silico studies, including gene ontology (GO) term enrichment, pathways, and network studies. Different immune functioning proteins involved in the innate immune system, such as ferritin, catalase, proteasome, Cu/Zn superoxide dismutase, calreticulin, disulfide isomerase, heat shock protein, etc., were found in ink protein networks. Additionally, the potential of bioactive peptides from octopus ink was addressed. These bioactive peptides can exert beneficial health properties such as antimicrobial, antioxidant, antihypertensive, and antitumoral properties and are therefore considered lead compounds for developing pharmacological, functional foods or nutraceuticals.
Project description:Phosphorus (P) is a critical driver of phytoplankton growth and ecosystem structure and function in the ocean. Diatoms are an abundant and widespread functional group of phytoplankton that are responsible for significant amounts of primary production in the ocean, however there has not been a comprehensive study of diatom physiological responses to P deficiency. Here, we coupled deep sequencing of transcript tags and quantitative proteomic analysis from the diatom Thalassiosira pseudonana grown under P-replete and P-deficient conditions. The reads (tags) were mapped to the T. pseudonana genome sequence, confirming expression of 91% of the modeled gene set. A total of 318 genes were differentially regulated with a false discovery rate of p<0.05. A total of 1264 proteins were detected, and of those 136 were differentially expressed with a false discovery rate of p<0.05. Significant changes in the abundance of transcripts and proteins were observed and these changes were coordinated for glycolysis, translation, and multiple biochemical responses to P deficiency. These data demonstrate that diatom P deficiency results in changes in cellular P allocation through polyphosphate production, increased P transport, a switch to utilization of dissolved organic P (DOP) through increased production of alkaline phosphatase metalloenzymes and a diesterase, and a remodeling of the cell surface through production of sulfolipids. Together, these findings reveal that T. pseudonana has evolved a sophisticated response to P deficiency involving multiple biochemical strategies that are likely critical to its ability to rapidly respond to variations in environmental P availability.
Project description:Compare effects of salt in salt sensitive, salt resistant and congenic rat strains. There were 6 groups of animals: SHRSP salt-loaded, SHRSP, SP.WKYGla2a salt-loaded, SP.WKYGla2a, WKY salt-loaded, WKY, , The salt loaded animals had 1% salt added to their drinking water at 18 weeks of age. Non treated animals were not supplied with salt. All animals were sacrificed, by overdose of anaesthetic and exsaguination, at 21 weeks of age and tissues snap frozen in liquid N2. Tissues were removed to -70 degrees C for storage.
Project description:GLEAMS is a deep neural network to embed spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We have used GLEAMS as the basis for a large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide.
GLEAMS was used to embed 669 million spectra from the MassIVE-KB dataset, after which hierarchical clustering with average linkage was used to cluster the embeddings. Medoid spectra were extracted from clusters consisting of only unidentified spectra, resulting in 45 million medoid spectra representing 257 million clustered spectra. The medoid spectra were split into two groups based on cluster size (size two and size greater than two) and exported to two MGF files. ANN-SoLo was used for open modification searching, identifying 5.3 million peptide-spectrum matches.
We here present the originally unidentified cluster medoid spectra and the ANN-SoLo identification results as a community resource. This is a valuable dataset to further explore the dark proteome, by investigating spectra that are observed repeatedly across many experiments but consistently remain unidentified.