New protein-coding genes that arise de novo from non-coding DNA sequences contribute to protein diversity. However, de novo gene origination is challenging to study as it requires high-quality reference genomes for closely related species, evidence for ancestral non-coding sequences, and transcription and translation of the new genes. High-quality genomes of 13 closely related Oryza species provide unprecedented opportunities to understand de novo origination events. Here, we identify a large number of young de novo genes with discernible recent ancestral non-coding sequences and evidence of translation. Using pipelines examining the synteny relationship between genomes and reciprocal-best whole-genome alignments, we detected at least 175 de novo open reading frames in the focal species O. sativa subspecies japonica, which were all detected in RNA sequencing-based transcriptomes. Mass spectrometry-based targeted proteomics and ribosomal profiling show translational evidence for 57% of the de novo genes. In recent divergence of Oryza, an average of 51.5 de novo genes per million years were generated and retained. We observed evolutionary patterns in which excess indels and early transcription were favoured in origination with a stepwise formation of gene structure. These data reveal that de novo genes contribute to the rapid evolution of protein diversity under positive selection.

Summary: In eukaryotes, mechanisms such as alternative splicing (AS) and alternative translation initiation (ATI) contribute to organismal protein diversity. Specifically, splicing factors play crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short‐read RNA sequencing, single molecule long‐read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid (ABA) treatment. Combining the data from the two sequencing methods, approximately 83.4% of intron‐containing genes were alternatively spliced. Two AS types, which are referred to as alternative first exon (AFE) and alternative last exon (ALE), were more abundant than intron retention (IR); however, by contrast to AS events detected under normal conditions, differentially expressed AS isoforms were more likely to be translated. ABA extensively affects the AS pattern, indicated by the increasing number of non‐conventional splicing sites. This work also identified thousands of unannotated peptides and proteins by ATI based on mass spectrometry and a virtual peptide library deduced from both strands of coding regions within the Arabidopsis genome. The results enhance our understanding of AS and alternative translation mechanisms under normal conditions, and in response to ABA treatment.

Sepsis is the major cause of death for critically ill patients. Recent progress in proteomics permits a thorough characterization of the mechanisms associated with critical illness. The purpose of this study was to screen potential biomarkers for early prognostic assessment of patients with sepsis. For the discovery stage, 30 sepsis patients with different prognoses were selected. Urinary proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC-MS/MS. Mass spec instrument analysis were performed with Mascot software and the International Protein Index (IPI); bioinformatic analyses were used by the algorithm of set and the Gene Ontology (GO) Database. For the verification stage, the study involved another 54 sepsis-hospitalized patients, with equal numbers of patients in survivor and non-survivor groups based on 28-day survival. Differentially expressed proteins were verified by Western Blot. A total of 232 unique proteins were identified. Proteins that were differentially expressed were further analyzed based on the pathophysiology of sepsis and biomathematics. For sepsis prognosis, five proteins were significantly up-regulated: selenium binding protein-1, heparan sulfate proteoglycan-2, alpha-1-B glycoprotein, haptoglobin, and lipocalin; two proteins were significantly down-regulated: lysosome-associated membrane proteins-1 and dipeptidyl peptidase-4. Based on gene ontology clustering, these proteins were associated with the biological processes of lipid homeostasis, cartilage development, iron ion transport, and certain metabolic processes. Urinary LAMP-1 was down-regulated, consistent with the Western Blot validation. This study provides the proteomic analysis of urine to identify prognostic biomarkers of sepsis. The seven identified proteins provide insight into the mechanism of sepsis. Low urinary LAMP-1 levels may be useful for early prognostic assessment of sepsis. ClinicalTrial.gov NCT01493492

Background: Peptide identification based upon mass spectrometry (MS) is generally achieved by comparison of the experimental mass spectra with the theoretically digested peptides derived from a reference protein database. Obviously, this strategy could not identify peptide and protein sequences that are absent from a reference database. A customized protein database on the basis of RNA-Seq data is thus proposed to assist with and improve the identification of novel peptides. Correspondingly, development of a comprehensive pipeline, which provides an end-to-end solution for novel peptide detection with the customized protein database, is necessary. Results: A pipeline with an R package, assigned as a PGA utility, was developed that enables automated treatment to the tandem mass spectrometry (MS/MS) data acquired from different MS platforms and construction of customized protein databases based on RNA-Seq data with or without a reference genome guide. Hence, PGA can identify novel peptides and generate an HTML-based report with a visualized interface. On the basis of a published dataset, PGA was employed to identify peptides, resulting in 636 novel peptides, including 510 single amino acid polymorphism (SAP) peptides, 2 INDEL peptides, 49 splice junction peptides, and 75 novel transcript-derived peptides. The software is freely available from http://bioconductor.org/packages/PGA/, and the example reports are available at http://wenbostar.github.io/PGA/. Conclusions: The pipeline of PGA, aimed at being platform-independent and easy-to-use, was successfully developed and shown to be capable of identifying novel peptides by searching the customized protein database derived from RNA-Seq data.