The potential clinical impact of the release of two drafts of the human proteome

Origins and Evolution of Human Tandem Duplicated Exon Substitution Events

The mutually exclusive splicing of tandem duplicated exons produces protein isoforms that are identical save for a homologous region that allows for the fine tuning of protein function. Tandem duplicated exon substitution events are rare, yet highly important alternative splicing events. Most events are ancient, their isoforms are highly expressed, and they have significantly more pathogenic mutations than other splice events. Here, we analyzed the physicochemical properties and functional roles of the homologous polypeptide regions produced by the 236 tandem duplicated exon substitutions annotated in the human gene set. We find that the most important structural and functional residues in these homologous regions are maintained, and that most changes are conservative rather than drastic. Three quarters of the isoforms produced from tandem duplicated exon substitution events are tissue-specific, particularly in nervous and cardiac tissues, and tandem duplicated exon substitution events are enriched in functional terms related to structures in the brain and skeletal muscle. We find considerable evidence for the convergent evolution of tandem duplicated exon substitution events in vertebrates, arthropods, and nematodes. Twelve human gene families have orthologues with tandem duplicated exon substitution events in both Drosophila melanogaster and Caenorhabditis elegans. Six of these gene families are ion transporters, suggesting that tandem exon duplication in genes that control the flow of ions into the cell has an adaptive benefit. The ancient origins, the strong indications of tissue-specific functions, and the evidence of convergent evolution suggest that these events may have played important roles in the evolution of animal tissues and organs.

APPRIS principal isoforms and MANE Select transcripts define reference splice variants

MOTIVATION

Selecting the splice variant that best represents a coding gene is a crucial first step in many experimental analyses, and vital for mapping clinically relevant variants. This study compares the longest isoforms, MANE Select transcripts, APPRIS principal isoforms, and expression data, and aims to determine which method is best for selecting biological important reference splice variants for large-scale analyses.

RESULTS

Proteomics analyses and human genetic variation data suggest that most coding genes have a single main protein isoform. We show that APPRIS principal isoforms and MANE Select transcripts best describe these main cellular isoforms, and find that using the longest splice variant as the representative is a poor strategy. Exons unique to the longest splice isoforms are not under selective pressure, and so are unlikely to be functionally relevant. Expression data are also a poor means of selecting the main splice variant. APPRIS principal and MANE Select exons are under purifying selection, while exons specific to alternative transcripts are not. There are MANE and APPRIS representatives for almost 95% of genes, and where they agree they are particularly effective, coinciding with the main proteomics isoform for over 98.2% of genes.

AVAILABILITY AND IMPLEMENTATION

APPRIS principal isoforms for human, mouse and other model species can be downloaded from the APPRIS database (https://appris.bioinfo.cnio.es), GENCODE genes (https://www.gencodegenes.org/) and the Ensembl website (https://www.ensembl.org). MANE Select transcripts for the human reference set are available from the Ensembl, GENCODE and RefSeq databases (https://www.ncbi.nlm.nih.gov/refseq/). Lists of splice variants where MANE and APPRIS coincide are available from the APPRIS database.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power

Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. We have proposed a validation method pValid before, but its scope of application is limited because two features used in pValid are related to open database search and sub-optimal peptide candidates for tandem mass spectra, and the performance on complex datasets still has room for improvement. In this study, we developed a more comprehensive validation method, pValid 2, to break these limitations by removing the two features and bringing in a new feature related to the retention time predicted by a deep learning-based method pPredRT. pValid 2 yielded an average false positive rate of 0.03% and an average false negative rate of 1.37% on three testing datasets, better than those of pValid, and flagged 8.47% to 11.31% more incorrect identifications than pValid on two complex datasets. Moreover, pValid 2 flagged almost all decoy identifications in validating the open-search datasets. In addition, the function of validating identifications given by MaxQuant and MS-GF+ was implemented in pValid 2, and the validation results showed that pValid 2 performed dramatically better than three metabolic labeling validation methods. Further considering its cost-effectiveness as a pure computational approach, pValid 2 has the potential to be a widely used validation tool for peptide identifications of any proteome search engines in shotgun proteomics. SIGNIFICANCE: Identification results given by shotgun proteomics are vital to life science research. The correctness of identifications deeply affects the precision of the subsequent studies about protein structures and functions, protein-protein interactions, pathogenic mechanism, and targeted drugs. Thus, validating the correctness of identifications is crucial and urgent. In 2019, we developed an identification credibility validation method named pValid, whose false positive rate (FPR) is 0.03% and false negative rate (FNR) is 1.79%, comparable to those of the gold standard, i.e., the Synthetic-peptide validation method. However, pValid can only be used for validating the results from pFind, and its validation performance on a few complex datasets still has room for improvement. So, in this submission, we proposed pValid 2, a more comprehensive computational validation method that can validate identifications from any proteome search engines with increased discriminating power.

The clinical importance of tandem exon duplication-derived substitutions

Most coding genes in the human genome are annotated with multiple alternative transcripts. However, clear evidence for the functional relevance of the protein isoforms produced by these alternative transcripts is often hard to find. Alternative isoforms generated from tandem exon duplication-derived substitutions are an exception. These splice events are rare, but have important functional consequences. Here, we have catalogued the 236 tandem exon duplication-derived substitutions annotated in the GENCODE human reference set. We find that more than 90% of the events have a last common ancestor in teleost fish, so are at least 425 million years old, and twenty-one can be traced back to the Bilateria clade. Alternative isoforms generated from tandem exon duplication-derived substitutions also have significantly more clinical impact than other alternative isoforms. Tandem exon duplication-derived substitutions have >25 times as many pathogenic and likely pathogenic mutations as other alternative events. Tandem exon duplication-derived substitutions appear to have vital functional roles in the cell and may have played a prominent part in metazoan evolution.

Assessing the functional relevance of splice isoforms

Alternative splicing of messenger RNA can generate an array of mature transcripts, but it is not clear how many go on to produce functionally relevant protein isoforms. There is only limited evidence for alternative proteins in proteomics analyses and data from population genetic variation studies indicate that most alternative exons are evolving neutrally. Determining which transcripts produce biologically important isoforms is key to understanding isoform function and to interpreting the real impact of somatic mutations and germline variations. Here we have developed a method, TRIFID, to classify the functional importance of splice isoforms. TRIFID was trained on isoforms detected in large-scale proteomics analyses and distinguishes these biologically important splice isoforms with high confidence. Isoforms predicted as functionally important by the algorithm had measurable cross species conservation and significantly fewer broken functional domains. Additionally, exons that code for these functionally important protein isoforms are under purifying selection, while exons from low scoring transcripts largely appear to be evolving neutrally. TRIFID has been developed for the human genome, but it could in principle be applied to other well-annotated species. We believe that this method will generate valuable insights into the cellular importance of alternative splicing.

MTFR2, A Potential Biomarker for Prognosis and Immune Infiltrates, Promotes Progression of Gastric Cancer Based on Bioinformatics Analysis and Experiments

Background: Mitochondrial fission regulator 2 (MTFR2) which can promote mitochondrial fission, has recently been reported to be involved in tumorigenesis. However, little is known about its expression levels and function in gastric cancer (GC). This study aims to clarify the role of MTFR2 in GC.

Methods:We firstly determined the expression level and prognostic value of MTFR2 in GC by integrated bioinformatics (Oncomine, GEPIA, Kaplan-Meier Plotter database) and experimental approaches (RT-qPCR, western blot, immunohistochemistry). After constructing stable down-regulated GC cells, the biological functions of MTFR2 in vitro and in vivo were studied through cell clone formation, wound healing, transwell and tumor formation experiments.To understand the reason for the high expression of MTFR2 in GC, copy number alternation, promoter methylation and mutation of MTFR2 were detected by UALCAN and cBioPortal. TargetScanHuman and PROMO databases were also used to explore the miRNAs and transcription factors of MTFR2, and the regulatory network was visualized by Cytoscape. LinkedOmics was used to detect the co-expression profile, and then these co-expressed genes were used for gene oncology function and pathway enrichment analysis to deepen the understanding of MTFR2 mechanism. The protein interaction network of MTFR2 was constructed by the GeneMANIA platform. Docking study of the binding mode was conducted by H DOCK webserver, and PYMOL is used for visualization, and analysis. TIMER database was used to explore the correlation between MTFR2 expression level and immune cells infiltration and gene markers of tumor infiltrating immune cells.

Results: We demonstrated that MTFR2 was up-regulated in GC, and its overexpression led to poorer prognosis. MTFR2 downregulation inhibited the proliferation, migration, and invasion of GC cells in vitro and in vivo. By bioinformatics analysis, we identified the possible factors in MTFR2 overexpression. Moreover, function and pathway enrichment analyses found that MTFR2 was involved in chromosome segregation, catalytic activity, cell cycle, and ribonucleic acid transport. A MTFR2-protein interaction network revealed a potential direct protein interaction between MTFR2 and protein kinase adenosine-monophosphate-activated catalytic subunit alpha 1 (PRKAA1), and their potential binding site was predicted in a molecular docking model. In addition, we also found that MTFR2 may be correlated with immune infiltration in GC.

Conclusions: Our study has effectively revealed the expression, prognostic value, potential functional networks, protein interactions and immune infiltration of MTFR2 in GC. Altogether, our data identify the possible underlying mechanisms of MTFR2 and suggest that MTFR2 may be a prognostic biomarker and therapeutic target in GC.

A Laboratory-Specific Scaling Factor to Predict the In Vivo Human Clearance of Aldehyde Oxidase Substrates

Aldehyde oxidase (AO) efficiently metabolizes a range of compounds with N-containing heterocyclic aromatic rings and/or aldehydes. The limited knowledge of AO activity and abundance (in vitro and in vivo) has led to poor prediction of in vivo systemic clearance (CL) using in vitro-to-in vivo extrapolation approaches, which for drugs in development can lead to their discontinuation. We aimed to identify appropriate scaling factors to predict AO CL of future new chemical entities (NCEs). The metabolism of six AO substrates was measured in human liver cytosol (HLC) and S9 fractions. Measured blood-to-plasma ratios and free fractions (in the in vitro system and in plasma) were used to develop physiologically based pharmacokinetic models for each compound. The impact of extrahepatic metabolism was explored, and the intrinsic clearance required to recover in vivo profiles was estimated and compared with in vitro measurements. Using HLC data and assuming only hepatic metabolism, a systematic underprediction of clearance was observed (average fold underprediction was 3.8). Adding extrahepatic metabolism improved the accuracy of the results (average fold error of 1.9). A workflow for predicting metabolism of an NCE by AO is proposed, and an empirical (laboratory-specific) scaling factor of three on the predicted intravenous CL allows a reasonable prediction of the available clinical data. Alternatively, considering also extrahepatic metabolism, an scaling factor of 6.5 applied on the intrinsic clearance could be used. Future research should focus on the impact of the in vitro study designs and the contribution of extrahepatic metabolism to AO-mediated clearance to understand the mechanisms behind the systematic underprediction. SIGNIFICANCE STATEMENT: This works describes the development of scaling factors to allow in vitro-in vivo extrapolation of the clearance of compounds by aldehyde oxidase metabolism in humans. In addition, physiologically based pharmacokinetic models were developed for each of the aldehyde oxidase substrate compounds investigated.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

Calculation of False Discovery Rate for Peptide and Protein Identification

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

Few SINEs of life: Alu elements have little evidence for biological relevance despite elevated translation

Transposable elements colonize genomes and with time may end up being incorporated into functional regions. SINE Alu elements, which appeared in the primate lineage, are ubiquitous in the human genome and more than a thousand overlap annotated coding exons. Although almost all Alu-derived coding exons appear to be in alternative transcripts, they have been incorporated into the main coding transcript in at least 11 genes. The extent to which Alu regions are incorporated into functional proteins is unclear, but we detected reliable peptide evidence to support the translation to protein of 33 Alu-derived exons. All but one of the Alu elements for which we detected peptides were frame-preserving and there was proportionally seven times more peptide evidence for Alu elements as for other primate exons. Despite this strong evidence for translation to protein we found no evidence of selection, either from cross species alignments or human population variation data, among these Alu-derived exons. Overall, our results confirm that SINE Alu elements have contributed to the expansion of the human proteome, and this contribution appears to be stronger than might be expected over such a relatively short evolutionary timeframe. Despite this, the biological relevance of these modifications remains open to question.

From Molecules to Mechanisms: Functional Proteomics and Its Application to Renal Tubule Physiology

Classical physiological studies using electrophysiological, biophysical, biochemical, and molecular techniques have created a detailed picture of molecular transport, bioenergetics, contractility and movement, and growth, as well as the regulation of these processes by external stimuli in cells and organisms. Newer systems biology approaches are beginning to provide deeper and broader understanding of these complex biological processes and their dynamic responses to a variety of environmental cues. In the past decade, advances in mass spectrometry-based proteomic technologies have provided invaluable tools to further elucidate these complex cellular processes, thereby confirming, complementing, and advancing common views of physiology. As one notable example, the application of proteomics to study the regulation of kidney function has yielded novel insights into the chemical and physical processes that tightly control body fluids, electrolytes, and metabolites to provide optimal microenvironments for various cellular and organ functions. Here, we systematically review, summarize, and discuss the most significant key findings from functional proteomic studies in renal epithelial physiology. We also identify further improvements in technological and bioinformatics methods that will be essential to advance precision medicine in nephrology.

Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine

We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.

Quality control in mass spectrometry-based proteomics

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

Towards a complete map of the human long non-coding RNA transcriptome

Gene maps, or annotations, enable us to navigate the functional landscape of our genome. They are a resource upon which virtually all studies depend, from single-gene to genome-wide scales and from basic molecular biology to medical genetics. Yet present-day annotations suffer from trade-offs between quality and size, with serious but often unappreciated consequences for downstream studies. This is particularly true for long non-coding RNAs (lncRNAs), which are poorly characterized compared to protein-coding genes. Long-read sequencing technologies promise to improve current annotations, paving the way towards a complete annotation of lncRNAs expressed throughout a human lifetime.

Probably less than one-tenth of the genes produce only the wild type protein without at least one additional protein isoform in some human cancer cell lines

To estimate how many genes produce multiple protein isoforms, we electrophoresed proteins from MCF7 and MDA-MB231 (MB231) human breast cancer cells in SDS-PAGE and excised narrow stripes of the gel at the 48kD, 55kD and 72kD. Proteins in these stripes were identified using liquid chromatography and tandem mass spectrometry. A total of 765, 750 and 679 proteins from MB231 cells, as well as 470, 390 and 490 proteins from MCF7 cells, were identified from the 48kD, 55kD and 72kD stripes, respectively. We arbitrarily allowed a 10% technical variation from the proteins' theoretical molecular mass (TMM) and considered those proteins with their TMMs within the 43-53 kD, 49-61 kD and 65-79 kD ranges as the wild type (WT) expected from the corresponding stripe, whereas those with a TMM above or below this range as a smaller- or larger-group, respectively. Only 263 (34.4%), 269 (35.9%) and 151 (22.2%) proteins from MB231 cells and 117 (24.9%), 135 (34.6%) and 130 (26.5%) proteins from MCF7 cells from the 48kD, 55kD and 72kD stripes, respectively, belonged to the WT, while the remaining majority belonged to the smaller- or larger-groups. Only about 3-16%, on average about 10% regardless of the stripe and cell line, of the proteins appeared in only one stripe and within the WT range, while the remaining preponderance appeared also in additional stripe(s) or had a larger or smaller TMM. We conclude that few (fewer than 10%) of the human genes produce only the WT protein without additional isoform(s).

The Challenge of Human Spermatozoa Proteome: A Systematic Review

Currently, there are 20,197 human protein-coding genes in the most expertly curated database (UniProtKB/Swiss-Pro). Big efforts have been made by the international consortium, the Chromosome-Centric Human Proteome Project (C-HPP) and independent researchers, to map human proteome. In brief, anno 2017 the human proteome was outlined. The male factor contributes to 50% of infertility in couples. However, there are limited human spermatozoa proteomic studies. Firstly, the development of the mapping of the human spermatozoa was analyzed. The human spermatozoa have been used as a model for missing proteins. It has been shown that human spermatozoa are excellent sources for finding missing proteins. Y chromosome proteome mapping is led by Iran. However, it seems that it is extremely challenging to map the human spermatozoa Y chromosome proteins based on current mass spectrometry-based proteomics technology. Post-translation modifications (PTMs) of human spermatozoa proteome are the most unexplored area and currently the exact role of PTMs in male infertility is unknown. Additionally, the clinical human spermatozoa proteomic analysis, anno 2017 was done in this study.

Alternative Splicing May Not Be the Key to Proteome Complexity

Alternative splicing is commonly believed to be a major source of cellular protein diversity. However, although many thousands of alternatively spliced transcripts are routinely detected in RNA-seq studies, reliable large-scale mass spectrometry-based proteomics analyses identify only a small fraction of annotated alternative isoforms. The clearest finding from proteomics experiments is that most human genes have a single main protein isoform, while those alternative isoforms that are identified tend to be the most biologically plausible: those with the most cross-species conservation and those that do not compromise functional domains. Indeed, most alternative exons do not seem to be under selective pressure, suggesting that a large majority of predicted alternative transcripts may not even be translated into proteins.