bims-micpro Biomed News
on Discovery and characterization of microproteins
Issue of 2021‒01‒17
three papers selected by
Thomas Martinez
Salk Institute for Biological Studies


  1. Pain. 2021 Jan 11.
    Barragan-Iglesias P, Kunder N, Wanghzou A, Black B, Ray PR, Lou TF, Bryan de la Peña J, Atmaramani R, Shukla T, Pancrazio JJ, Price TJ, Campbell ZT.
      ABSTRACT: Translational regulation permeates neuronal function. Nociceptors are sensory neurons responsible for the detection of harmful stimuli. Changes in their activity, termed plasticity, are intimately linked to the persistence of pain. While inhibitors of protein synthesis robustly attenuate pain associated behavior, the underlying targets that support plasticity are largely unknown. Here, we examine the contribution of protein synthesis in regions of RNA annotated as non-coding. Based on analyses of previously reported ribosome profiling data, we provide evidence for widespread translation in non-coding transcripts and regulatory regions of mRNAs. We identify an increase in ribosome occupancy in the 5' untranslated regions of the calcitonin gene-related peptide (CGRP/Calca). We validate the existence of an upstream Open Reading Frame (uORF) using a series of reporter assays. Fusion of the uORF to a luciferase reporter revealed active translation in DRG neurons following nucleofection. Injection of the peptide corresponding to the CGRP encoded uORF resulted in pain associated behavioral responses in vivo and nociceptor sensitization in vitro. An inhibitor of heterotrimeric G protein signaling blocks both effects. Collectively, the data suggest pervasive translation in regions of the transcriptome annotated as non-coding in DRG neurons and identify a specific uORF encoded peptide that promotes pain sensitization through GPCR signaling.
    DOI:  https://doi.org/10.1097/j.pain.0000000000002191
  2. Mol Cell Proteomics. 2019 Jul;pii: S1535-9476(20)31546-2. [Epub ahead of print]18(7): 1382-1395
    Na CH, Sharma N, Madugundu AK, Chen R, Aksit MA, Rosson GD, Cutting GR, Pandey A.
      The eccrine sweat gland is an exocrine gland that is involved in the secretion of sweat for control of temperature. Malfunction of the sweat glands can result in disorders such as miliaria, hyperhidrosis and bromhidrosis. Understanding the transcriptome and proteome of sweat glands is important for understanding their physiology and role in diseases. However, no systematic transcriptome or proteome analysis of sweat glands has yet been reported. Here, we isolated eccrine sweat glands from human skin by microdissection and performed RNA-seq and proteome analysis. In total, ∼138,000 transcripts and ∼6,100 proteins were identified. Comparison of the RNA-seq data of eccrine sweat glands to other human tissues revealed the closest resemblance to the cortex region of kidneys. The proteome data showed enrichment of proteins involved in secretion, reabsorption, and wound healing. Importantly, protein level identification of the calcium ion channel TRPV4 suggests the importance of eccrine sweat glands in re-epithelialization of wounds and prevention of dehydration. We also identified 2 previously missing proteins from our analysis. Using a proteogenomic approach, we identified 7 peptides from 5 novel genes, which we validated using synthetic peptides. Most of the novel proteins were from short open reading frames (sORFs) suggesting that many sORFs still remain to be annotated in the human genome. This study presents the first integrated analysis of the transcriptome and proteome of the human eccrine sweat gland and would become a valuable resource for studying sweat glands in physiology and disease.
    Keywords:  Cystic fibrosis; Mass Spectrometry; Peptide Synthesis*; Proteogenomics; Proteomics; RNA SEQ; Sweat glands; Transcriptomics
    DOI:  https://doi.org/10.1074/mcp.RA118.001101
  3. Mol Cell Proteomics. 2019 Aug 09. pii: S1535-9476(20)32766-3. [Epub ahead of print]18(8S1): S126-S140
    Verbruggen S, Ndah E, Van Criekinge W, Gessulat S, Kuster B, Wilhelm M, Van Damme P, Menschaert G.
      PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.
    Keywords:  Chromatography; PROTEOFORMER; Prosit; Proteogenomics; Quality control and metrics; Ribosomes*; Tandem Mass Spectrometry; mQC; proteoform; ribosome profiling
    DOI:  https://doi.org/10.1074/mcp.RA118.001218