First Proteomic Exploration of Protein-Encoding Genes on Chromosome 1 in Human Liver, Stomach, and Colon

Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process

Background

The mass spectrometry based technical pipeline has provided a high-throughput, high-sensitivity and high-resolution platform for post-genomic biology. Varied models and algorithms are implemented by different tools to improve proteomics data analysis. The target-decoy searching strategy has become the most popular strategy to control false identification in peptide and protein identifications. While this strategy can estimate the false discovery rate (FDR) within a dataset, it cannot directly evaluate the false positive matches in target identifications.

Results

As a supplement to target-decoy strategy, the entrapment sequence method was introduced to assess the key steps of mass spectrometry data analysis process, database search engines and quality control methods. Using the entrapment sequences as the standard, we evaluated five database search engines for both the origanal scores and reprocessed scores, as well as four quality control methods in term of quantity and quality aspects. Our results showed that the latest developed search engine MS-GF+ and percolator-embeded quality control method PepDistiller performed best in all tools respectively. Combined with efficient quality control methods, the search engines can improve the low sensitivity of their original scores. Moreover, based on the entrapment sequence method, we proved that filtering the identifications separately could increase the number of identified peptides while improving the confidence level.

Conclusion

In this study, we have proved that the entrapment sequence method could be an useful strategy to assess the key steps of the mass spectrometry data analysis process. Its applications can be extended to all steps of the common workflow, such as the protein assembling methods and data integration methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3491-2) contains supplementary material, which is available to authorized users.

Mapping and analyzing the human liver proteome: progress and potential

INTRODUCTION

The liver is an important organ in humans. Hepatocellular carcinoma (HCC) is one of the deadliest cancers in the world. Progress in the Human Liver Proteome Project (HLPP) has improved understanding of the liver and the liver cancer proteome.

AREAS COVERED

Here, we summarize the recent progress in liver proteome modification profiles, proteomic studies in liver cancer, proteomic study in the search for novel liver cancer biomarkers and drug targets, and progress of the Chromosome Centric Human Proteome Project (CHPP) in the past five years in the Institutes of Biomedical Sciences (IBS) of Fudan University. Expert commentary: Recent advances and findings discussed here provide great promise of improving the outcome of patients with liver cancer.

Appraisal of the Missing Proteins Based on the mRNAs Bound to Ribosomes

Considering the technical limitations of mass spectrometry in protein identification, the mRNAs bound to ribosomes (RNC-mRNA) are assumed to reflect the mRNAs participating in the translational process. The RNC-mRNA data are reasoned to be useful for appraising the missing proteins. A set of the multiomics data including free-mRNAs, RNC-mRNAs, and proteomes was acquired from three liver cancer cell lines. On the basis of the missing proteins in neXtProt (release 2014-09-19), the bioinformatics analysis was carried out in three phases: (1) finding how many neXtProt missing proteins have or do not have RNA-seq and/or MS/MS evidence, (2) analyzing specific physicochemical and biological properties of the missing proteins that lack both RNA-seq and MS/MS evidence, and (3) analyzing the combined properties of these missing proteins. Total of 1501 missing proteins were found by neither RNC-mRNA nor MS/MS in the three liver cancer cell lines. For these missing proteins, some are expected higher hydrophobicity, unsuitable detection, or sensory functions as properties at the protein level, while some are predicted to have nonexpressing chromatin structures on the corresponding gene level. With further integrated analysis, we could attribute 93% of them (1391/1501) to these causal factors, which result in the expression products scarcely detected by RNA-seq or MS/MS.

Functional Proteomics Study Reveals SUMOylation of TFII-I is Involved in Liver Cancer Cell Proliferation

SUMOylation has emerged as a new regulatory mechanism for proteins involved in multiple physiological and pathological processes. However, the detailed function of SUMOylation in liver cancer is still elusive. This study reveals that the SUMOylation-activating enzyme UBA2 is highly expressed in liver cancer cells and clinical samples. Silencing of UBA2 expression could to some extent suppress cell proliferation. To elucidate the function of UBA2, we used a large scale proteomics strategy to identify SUMOylation targets in HepG2 cells. We characterized 827 potential SUMO1-modified proteins that were not present in the control samples. These proteins were enriched in gene expression processes. Twelve candidates were validated as SUMO1-modified proteins by immunoprecipitation-Western blotting. We further characterized SUMOylated protein TFII-I that was identified in this study and determined that TFII-I was modified by SUMO1 at K221 and K240. PIAS4 was an E3 ligase for TFII-I SUMOylation, and SENP2 was responsible for deSUMOylating TFII-I in HepG2 cells. SUMOylation reduced TFII-I binding to its repressor HDAC3 and thus promoted its transcriptional activity. We further show that SUMOylation is critical for TFII-I to promote cell proliferation and colony formation. Our findings contribute to understanding the role of SUMOylation in liver cancer development.

Utilising proteomic approaches to understand oncogenic human herpesviruses (Review)

The γ-herpesviruses Epstein-Barr virus and Kaposi's sarcoma-associated herpesvirus are successful pathogens, each infecting a large proportion of the human population. These viruses persist for the life of the host and may each contribute to a number of malignancies, for which there are currently no cures. Large-scale proteomic-based approaches provide an excellent means of increasing the collective understanding of the proteomes of these complex viruses and elucidating their numerous interactions within the infected host cell. These large-scale studies are important for the identification of the intricacies of viral infection and the development of novel therapeutics against these two important pathogens.

Screening of missing proteins in the human liver proteome by improved MRM-approach-based targeted proteomics

To completely annotate the human genome, the task of identifying and characterizing proteins that currently lack mass spectrometry (MS) evidence is inevitable and urgent. In this study, as the first effort to screen missing proteins in large scale, we developed an approach based on SDS-PAGE followed by liquid chromatography-multiple reaction monitoring (LC-MRM), for screening of those missing proteins with only a single peptide hit in the previous liver proteome data set. Proteins extracted from normal human liver were separated in SDS-PAGE and digested in split gel slice, and the resulting digests were then subjected to LC-schedule MRM analysis. The MRM assays were developed through synthesized crude peptides for target peptides. In total, the expressions of 57 target proteins were confirmed from 185 MRM assays in normal human liver tissues. Among the proved 57 one-hit wonders, 50 proteins are of the minimally redundant set in the PeptideAtlas database, 7 proteins even have none MS-based information previously in various biological processes. We conclude that our SDS-PAGE-MRM workflow can be a powerful approach to screen missing or poorly characterized proteins in different samples and to provide their quantity if detected. The MRM raw data have been uploaded to ISB/SRM Atlas/PASSEL (PXD000648).

MOPED enables discoveries through consistently processed proteomics data

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.

Chromosome-8-coded proteome of Chinese Chromosome Proteome Data set (CCPD) 2.0 with partial immunohistochemical verifications

We upgraded the preliminary CCPD 1.0 to CCPD 2.0 using the latest deep-profiling proteome (CCPD 2013) of three hepatocellular carcinoma (HCC) cell lines, namely, Hep3B, MHCC97H, and HCCLM3 (ProteomeXchange identifiers: PXD000529, PXD000533, and PXD000535). CCPD 2.0 totally covered 63.6% (438/689) of Chr. 8-coded proteins and 62.6% (439/701) of Chr. 8-coded protein-coding genes. Interestingly, we found that the missing proteins exhibited a tendency to form a cluster region in chromosomes, such as two β-defensins clusters in Chr. 8, caused perhaps by their inflammation-related features. For the 41 Chr. 8-coded proteins being weakly or barely identified previously, we have performed an immunohistochemical (IHC) verification in 30 pairs of carcinoma/para-carcinoma HCC and 20 noncancerous liver tissues and confirmed their expressional evidence and occurrence proportions in tissue samples. We also verified 13 Chr. 8-coded HCC tumorigenesis-associated depleting or deficient proteins reported in CCPD 1.0 using IHC and screened 16 positive and 24 negative HCC metastatic potential-correlated proteins from large-scale label-free proteome quantitation data of CCPD 2013. Our results suggest that the selection of proper samples and the methodology to look for targeted missing proteins should be carefully considered in further verifications for the remaining Chr. 8-coded proteins.

Omics evidence: single nucleotide variants transmissions on chromosome 20 in liver cancer cell lines

Cancer genomics unveils many cancer-related mutations, including some chromosome 20 (Chr.20) genes. The mutated messages have been found in the corresponding mRNAs; however, whether they could be translated to proteins still requires more evidence. Herein, we proposed a transomics strategy to profile the expression status of human Chr.20 genes (555 in Ensembl v72). The data of transcriptome and translatome (the mRNAs bound with ribosome, translating mRNAs) revealed that ∼80% of the coding genes on Chr.20 were detected with mRNA signals in three liver cancer cell lines, whereas of the proteome identified, only ∼45% of the Chr.20 coding genes were detected. The high amount of overlapping of identified genes in mRNA and RNC-mRNA (ribosome nascent-chain complex-bound mRNAs, translating mRNAs) and the consistent distribution of the abundance averages of mRNA and RNC-mRNA along the Chr.20 subregions in three liver cancer cell lines indicate that the mRNA information is efficiently transmitted from transcriptional to translational stage, qualitatively and quantitatively. Of the 457 genes identified in mRNAs and RNC-mRNA, 136 were found to contain SNVs with 213 sites, and >40% of these SNVs existed only in metastatic cell lines, suggesting them as the metastasis-related SNVs. Proteomics analysis showed that 16 genes with 20 SNV sites were detected with reliable MS/MS signals, and some SNVs were further validated by the MRM approach. With the integration of the omics data at the three expression phases, therefore, we are able to achieve the overall view of the gene expression of Chr.20, which is constructive in understanding the potential trend of encoding genes in a cell line and exploration of a new type of markers related to cancers.

Systematic analysis of missing proteins provides clues to help define all of the protein-coding genes on human chromosome 1

Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32-39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535.

Resolving chromosome-centric human proteome with translating mRNA analysis: a strategic demonstration

Chromosome-centric human proteome project (C-HPP) aims at differentiating chromosome-based and tissue-specific protein compositions in terms of protein expression, quantification, and modification. We previously found that the analysis of translating mRNA (mRNA attached to ribosome-nascent chain complex, RNC-mRNA) can explain over 94% of mRNA-protein abundance. Therefore, we propose here to use full-length RNC-mRNA information to illustrate protein expression both qualitatively and quantitatively. We performed RNA-seq on RNC-mRNA (RNC-seq) and detected 12,758 and 14,113 translating genes in human normal bronchial epithelial (HBE) cells and human colorectal adenocarcinoma Caco-2 cells, respectively. We found that most of these genes were mapped with >80% of coding sequence coverage. In Caco-2 cells, we provided translating evidence on 4180 significant single-nucleotide variations. While using RNC-mRNA data as a standard for proteomic data integration, both translating and protein evidence of 7876 genes can be acquired from four interlaboratory data sets with different MS platforms. In addition, we detected 1397 noncoding mRNAs that were attached to ribosomes, suggesting a potential source of new protein explorations. By comparing the two cell lines, a total of 677 differentially translated genes were found to be nonevenly distributed across chromosomes. In addition, 2105 genes in Caco-2 and 750 genes in HBE cells are expressed in a cell-specific manner. These genes are significantly and specifically clustered on multiple chromosomes, such as chromosome 19. We conclude that HPP/C-HPP investigations can be considerably improved by integrating RNC-mRNA analysis with MS, bioinformatics, and antibody-based verifications.