Tag-Count Analysis of Large-Scale Proteomic Data

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyze complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously.

Characterization of a small tRNA-binding protein that interacts with the archaeal proteasome complex

The proteasome system allows the elimination of functional or structurally impaired proteins. This includes the degradation of nascent peptides. In Archaea, how the proteasome complex interacts with the translational machinery remains to be described. Here, we characterised a small orphan protein, Q9UZY3 (Uniprot ID) conserved in Thermococcales. The protein was identified in native pull-down experiments using the proteasome regulatory complex (PAN) as bait. X-ray crystallography and SAXS experiments revealed that the protein is monomeric and adopts a β-barrel core structure with an Oligonucleotide/oligosaccharide-Binding (OB) fold, typically found in translation elongation factors. Mobility shift experiment showed that Q9UZY3 displays tRNA binding properties. Pull-downs, co-immunoprecipitation and ITC studies revealed that Q9UZY3 interacts in vitro with PAN. Native pull-downs and proteomic analysis using different versions of Q9UZY3 showed that the protein interacts with the assembled PAN-20S proteasome machinery in Pyrococcus abyssi cellular extracts. The protein was therefore named Pbp11, for Proteasome Binding Protein of 11 kDa. Interestingly, the interaction network of Pbp11 also includes ribosomal proteins, tRNA processing enzymes and exosome subunits dependent on Pbp11's N-terminal domain that was found to be essential for tRNA binding. Together these data suggest that Pbp11 participates in an interface between the proteasome and the translational machinery.

Multi-Omics Profiling Specifies Involvement of Alternative Ribosomal Proteins in Response to Zinc Limitation in Mycobacterium smegmatis

Zinc ion (Zn²⁺) is an essential micronutrient and a potent antioxidant. However, Zn²⁺ is often limited in the environment. Upon Zn²⁺ limitation, Mycolicibacterium (basonym: Mycobacterium) smegmatis (Msm) undergoes a morphogenesis, which relies on alternative ribosomal proteins (AltRPs); i.e., Zn²⁺-independent paralogues of Zn²⁺-dependent ribosomal proteins. However, the underlying physiological changes triggered by Zn²⁺ limitation and how AltRPs contribute to these changes were not known. In this study, we expand the knowledge of mechanisms utilized by Msm to endure Zn²⁺ limitation, by comparing the transcriptomes and proteomes of Zn²⁺-limited and Zn²⁺-replete Msm. We further compare, corroborate and contrast our results to those reported for the pathogenic mycobacterium, M. tuberculosis, which highlighted conservation of the upregulated oxidative stress response when Zn²⁺ is limited in both mycobacteria. By comparing the multi-omics analysis of a knockout mutant lacking AltRPs (ΔaltRP) to the Msm wild type strain, we specify the involvement of AltRPs in the response to Zn²⁺ limitation. Our results show that AltRP expression in Msm does not affect the conserved oxidative stress response during Zn²⁺ limitation observed in mycobacteria, but AltRPs do significantly impact expression patterns of numerous genes that may be involved in morphogenesis or other adaptive responses. We conclude that AltRPs are not only important as functional replacements for their Zn²⁺-dependent paralogues; they are also involved in the transcriptomic response to the Zn²⁺-limited environment.

Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

RNA processing machineries in Archaea: the 5'-3' exoribonuclease aRNase J of the β-CASP family is engaged specifically with the helicase ASH-Ski2 and the 3'-5' exoribonucleolytic RNA exosome machinery

A network of RNA helicases, endoribonucleases and exoribonucleases regulates the quantity and quality of cellular RNAs. To date, mechanistic studies focussed on bacterial and eukaryal systems due to the challenge of identifying the main drivers of RNA decay and processing in Archaea. Here, our data support that aRNase J, a 5'-3' exoribonuclease of the β-CASP family conserved in Euryarchaeota, engages specifically with a Ski2-like helicase and the RNA exosome to potentially exert control over RNA surveillance, at the vicinity of the ribosome. Proteomic landscapes and direct protein-protein interaction analyses, strengthened by comprehensive phylogenomic studies demonstrated that aRNase J interplay with ASH-Ski2 and a cap exosome subunit. Finally, Thermococcus barophilus whole-cell extract fractionation experiments provide evidences that an aRNase J/ASH-Ski2 complex might exist in vivo and hint at an association of aRNase J with the ribosome that is emphasised in absence of ASH-Ski2. Whilst aRNase J homologues are found among bacteria, the RNA exosome and the Ski2-like RNA helicase have eukaryotic homologues, underlining the mosaic aspect of archaeal RNA machines. Altogether, these results suggest a fundamental role of β-CASP RNase/helicase complex in archaeal RNA metabolism.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments requires a series of computational steps that identify and quantify LC-MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC-tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts' expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.