Identification and Functional Annotation of Long Intergenic Non-coding RNAs in the Brassicaceae

Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts with critical biological functions. Despite the wealth of RNA-seq data available, lincRNA identification lags in the plant lineage. In addition, there is a need for a harmonized identification and annotation effort to enable cross-species functional and genomic comparisons. In this study we processed >24 Tbp of RNA-seq data from >16,000 experiments to identify ~130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used Nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify functional motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our dataset with sequence or transcriptional evolutionary conservation, including lincRNAs transcribed adjacent to orthologous genes that display little sequence similarity and likely function as transcriptional regulators. Finally, we used guilt-by-association techniques to further classify these lincRNAs according to putative function. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, short ORFs, and whose expression is modulated by abiotic stress are a few of the annotations that will prioritize and guide future functional analyses.

Can we put Humpty Dumpty back together again? What does protein quantification mean in bottom-up proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. In bottom-up proteomics, protein parsimony and protein inference derived from these measured peptides are important for determining which protein coding genes are present. However, given the complexity of RNA splicing processes, and how proteins can be modified post-translationally, it is overly simplistic to assume that all peptides that map to a singular protein coding gene will demonstrate the same quantitative response. Accordingly, by assuming all peptides from a protein coding sequence are representative of the same protein we may be missing out on detecting important biological differences. To better account for the complexity of the proteome we need to think of new or better ways of handling peptide data.

The expression of YWHAZ and NDRG1 predicts aggressive outcome in human prostate cancer

Some prostate cancers (PCas) are histo-pathologically grouped within the same Gleason Grade (GG), but can differ significantly in outcome. Herein, we aimed at identifying molecular biomarkers that could improve risk prediction in PCa. LC ESI–MS/MS was performed on human PCa and benign prostatic hyperplasia (BPH) tissues and peptide data was integrated with omic analyses. We identified high YWHAZ and NDRG1 expression to be associated with poor PCa prognosis considering all Gleason scores (GS). YWHAZ and NDRG1 defined two subpopulations of PCa patients with high and intermediate risk of death. Multivariable analyses confirmed their independence from GS. ROC analysis unveiled that YWHAZ outperformed GS beyond 60 months post-diagnosis. The genomic analysis of PCa patients with YWHAZ amplification, or increased mRNA or protein levels, revealed significant alterations in key DNA repair genes. We hereby state the relevance of YWHAZ in PCa, showcasing its role as an independent strong predictor of aggressiveness.

GFOGER Peptide Modifies the Protein Content of Extracellular Vesicles and Inhibits Vascular Calcification

ObjectiveVascular calcification (VC) is an active process during which vascular smooth muscle cells (VSMCs) undergo an osteogenic switch and release extracellular vesicles (EVs). In turn, the EVs serve as calcification foci via interaction with type 1 collagen (COL1). We recently showed that a specific, six-amino-acid repeat (GFOGER) in the sequence of COL1 was involved in the latter’s interaction with integrins expressed on EVs. Our main objective was to test the GFOGER ability to inhibit VC.ApproachWe synthesized the GFOGER peptide and tested its ability to inhibit the inorganic phosphate (Pi)-induced calcification of VSMCs and aortic rings. Using mass spectrometry, we studied GFOGER’s effect on the protein composition of EVs released from Pi-treated VSMCs.ResultsCalcification of mouse VSMCs (MOVAS-1 cells), primary human VSMCs, and rat aortic rings was lower in the presence of GFOGER than with Pi alone (with relative decreases of 66, 58, and 91%, respectively; p < 0.001 for all) (no effect was observed with the scramble peptide GOERFG). A comparative proteomic analysis of EVs released from MOVAS-1 cells in the presence or absence of Pi highlighted significant differences in EVs’ protein content. Interestingly, the expression of some of the EVs’ proteins involved in the calcification process (such as osteogenic markers, TANK-binding kinase 1, and casein kinase II) was diminished in the presence of GFOGER peptide (data are available via ProteomeXchange with identifier PXD018169∗). The decrease of osteogenic marker expression observed in the presence of GFOGER was confirmed by q-RT-PCR analysis.ConclusionGFOGER peptide reduces vascular calcification by modifying the protein content of the subsequently released EVs, in particular by decreasing osteogenicswitching in VSMCs.

The Prospect of Bioactive Peptide Research: A Review on Databases and Tools

: Bioactive peptides (BPs) are peptides with hormonal or pharmacological properties. They play a key role in growth, metabolism, disease, aging and death by affecting digestion, endocrine, cardiovascular, immune and nervous systems. They show potential therapeutic effect on blood pressure-lowering (ACE inhibitory), anticancer, antithrombotic, antibacterial, anti-inflammatory, antioxidant, antiobesity, anti-genotoxic and immunomodulatory. Companied by the fast development and wide applications of DNA sequencing method, a wealth of bioactive peptide sequences accumulated through empirical and bioinformatics approaches or an integrated approach. To store and facilitate the usage of bioactive peptide data, a series of databases have been established that concerned about different aspects of BPs. A variety of information including sequence, source, biological activity, toxicity, physical-chemical property, and structure is stored in these databases. This review summarizes the latest development of BPs databases and briefly introduces the characteristics of different databases, as to help readers to retrieve the required information more easily. In addition, it also includes sequence analysis, structural simulation and activity prediction tools, which may be helpful for the design and discover of new bioactive peptides.