Combined Proteomic and Phosphoproteomic Characterization of the Molecular Regulators and Functional Modules During Pancreatic Progenitor Cell Development.

Differentiated multipotent pancreatic progenitors have major advantages for both modeling pancreas development and preventing or treating diabetes. Despite significant advancements in inducing the differentiation of human pluripotent stem cells into insulin-producing cells, the complete mechanism governing proliferation and differentiation remains poorly understood. This study used large-scale mass spectrometry to characterize molecular processes at various stages of human embryonic stem cell (hESC) differentiation toward pancreatic progenitors. hESCs were induced into pancreatic progenitor cells in a five-stage differentiation protocol. A high-performance liquid chromatography-mass spectrometry platform was used to undertake comprehensive proteome and phosphoproteome profiling of cells at different stages. A series of bioinformatic explorations, including coregulated modules, gene regulatory networks, and phosphosite enrichment analysis, were then conducted. A total of 27,077 unique phosphorylated sites and 8122 proteins were detected, including several cyclin-dependent kinases at the initial stage of cell differentiation. Furthermore, we discovered that ERK1, a member of the MAPK cascade, contributed to proliferation at an early stage. Finally, Western blotting confirmed that the phosphosites from SIRT1 and CHEK1 could inhibit the corresponding substrate abundance in the late stage. Thus, this study extends our understanding of the molecular mechanism during pancreatic cell development.

Light shift suppression with pulsed light detection in magnetic-state-selected cesium beam clocks.

Light detection is widely used in atomic clocks. The simple detecting structure induces the light shift which influences the clock's long-term stability. We introduce a new method to suppress light shift by using pulsed light instead of continuous light to detect atomic states. Under a suitable pulsed sequence, the part of the atoms which do not simultaneously interact with light and microwave field are detected. We demonstrate the validity of our approach in a magnetic-state-selected cesium beam clock. Using a well-tuned sequence, the light shift coefficient is reduced by a factor of about 10, in comparison with the continuous light detection scheme. In a clock stability test with extra light power noise, the result shows good immunity of the method to laser power fluctuations. We also analyze the sources of the clock short-term stability degradation, including the Dick effect and the fact that a reduced number of atoms is detected in the pulsed detection case.

Improvements to the Rice Genome Annotation Through Large-Scale Analysis of RNA-Seq and Proteomics Data Sets.

Rice (Oryza sativa) is one of the most important worldwide crops. The genome has been available for over 10 years and has undergone several rounds of annotation. We created a comprehensive database of transcripts from 29 public RNA sequencing data sets, officially predicted genes from Ensembl plants, and common contaminants in which to search for protein-level evidence. We re-analyzed nine publicly accessible rice proteomics data sets. In total, we identified 420K peptide spectrum matches from 47K peptides and 8,187 protein groups. 4168 peptides were initially classed as putative novel peptides (not matching official genes). Following a strict filtration scheme to rule out other possible explanations, we discovered 1,584 high confidence novel peptides. The novel peptides were clustered into 692 genomic loci where our results suggest annotation improvements. 80% of the novel peptides had an ortholog match in the curated protein sequence set from at least one other plant species. For the peptides clustering in intergenic regions (and thus potentially new genes), 101 loci were identified, for which 43 had a high-confidence hit for a protein domain. Our results can be displayed as tracks on the Ensembl genome or other browsers supporting Track Hubs, to support re-annotation of the rice genome.

Improving Gene Annotation of the Peanut Genome by Integrated Proteogenomics Workflow.

Peanut (Arachis hypogaea L.) is a staple crop in semiarid tropical and subtropical regions. Although the genome of peanut has been fully sequenced, the current gene annotations are still incomplete. New technologies in genomics and proteomics have resulted in the emergence of proteogenomics, which can integrate genomic, transcriptomic, and proteomic data for improving gene annotation. In the present study, we collected RNA-seq and proteomic data from multiple tissues such as seed, shell, and gynophore of peanut and utilized a proteogenomic approach to improve the gene annotation of peanut based on these data. A total of 1 935 655 904 RNA-seq reads and 7 490 280 MS/MS spectra were collected. Ultimately, 13 767 annotated genes were found with evidence at the protein level, and seven novel protein-coding genes were found with both RNA-seq and proteomics evidence. In addition, 35 gene models were updated based on proteomics data. Proteogenomic approaches improved the gene annotation in certain aspects by integrating both RNA-seq and proteomic data. We expect that these approaches could help improve existing genome annotations of other species.

PPIP: Automated Software for Identification of Bioactive Endogenous Peptides.

Endogenous peptides play an important role in multiple biological processes in many species. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is an important technique for detecting these peptides on a large scale. We present PPIP, which is a dedicated peptidogenomics software for identifying endogenous peptides based on peptidomics and RNA-Seq data. This software automates the de novo transcript assembly based on RNA-Seq data, construction of a protein reference database based on the de novo assembled transcripts, peptide identification, function analysis, and HTML-based report generation. Different function components are integrated using Docker technology. The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip , and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP . A user manual of PPIP is available at https://shawn-xu.github.io/PPIP .

Improvement of peptide identification with considering the abundance of mRNA and peptide.

BACKGROUND: Tandem mass spectrometry (MS/MS) followed by database search is a main approach to identify peptides/proteins in proteomic studies. A lot of effort has been devoted to improve the identification accuracy and sensitivity for peptides/proteins, such as developing advanced algorithms and expanding protein databases. RESULTS: Herein, we described a new strategy for enhancing the sensitivity of protein/peptide identification through combination of mRNA and peptide abundance in Percolator. In our strategy, a new workflow for peptide identification is established on the basis of the abundance of transcripts and potential novel transcripts derived from RNA-Seq and abundance of peptides towards the same life species. We demonstrate the utility of this strategy by two MS/MS datasets and the results indicate that about 5% ~ 8% improvement of peptide identification can be achieved with 1% FDR in peptide level by integrating the peptide abundance, the transcript abundance and potential novel transcripts from RNA-Seq data. Meanwhile, 181 and 154 novel peptides were identified in the two datasets, respectively. CONCLUSIONS: We have demonstrated that this strategy could enable improvement of peptide/protein identification and discovery of novel peptides, as compared with the traditional search methods.

Digging More Missing Proteins Using an Enrichment Approach with ProteoMiner.

Human Proteome Project (HPP) aims at mapping entire human proteins with a systematic effort upon all the emerging techniques, which would enhance understanding of human biology and lay a foundation for development of medical applications. Until now, 2563 missing proteins (MPs, PE2-4) are still undetected even using the most sensitive approach of protein detection. Herein, we propose that enrichment of low-abundance proteins benefits MPs finding. ProteoMiner is an equalizing technique by reducing high-abundance proteins and enriching low-abundance proteins in biological liquids. With triton X-100/TBS buffer extraction, ProteoMiner enrichment, and peptide fractionation, 20 MPs (at least two non-nested unique peptides with more than eight a.a. length) with 60 unique peptides were identified from four human tissues including eight membrane/secreted proteins and five nucleus proteins. Then 15 of them were confirmed with two non-nested unique peptides (≥9 a.a.) identified by matching well with their chemically synthetic peptides in PRM assay. Hence, these results demonstrated ProteoMiner as a powerful means in discovery of MPs.

PGA: an R/Bioconductor package for identification of novel peptides using a customized database derived from RNA-Seq.

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.

Drug Resistance in Colorectal Cancer Cell Lines is Partially Associated with Aneuploidy Status in Light of Profiling Gene Expression.

A priority in solving the problem of drug resistance is to understand the molecular mechanism of how a drug induces the resistance response within cells. Because many cancer cells exhibit chromosome aneuploidy, we explored whether changes of aneuploidy status result in drug resistance. Two typical colorectal cancer cells, HCT116 and LoVo, were cultured with the chemotherapeutic drugs irinotecan (SN38) or oxaliplatin (QxPt), and the non- and drug-resistant cell lines were selected. Whole exome sequencing (WES) was employed to evaluate the aneuploidy status of these cells, and RNAseq and LC-MS/MS were implemented to examine gene expression at both mRNA and protein level. The data of gene expression was well-matched with the genomic conclusion that HCT116 was a near diploid cell, whereas LoVo was an aneuploid cell with the increased abundance of mRNA and protein for these genes located at chromosomes 5, 7, 12, and 15. By comparing the genomic, transcriptomic, and proteomic data, the LoVo cells with SN38 tolerance showed an increased genome copy in chromosome 14, and the expression levels of the genes on this chromosome were also significantly increased. Thus, we first observed that SN38 could impact the aneuploidy status in cancer cells, which was partially associated with the acquired drug resistance.

IPeak: An open source tool to combine results from multiple MS/MS search engines.

Liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) is an important technique for detecting peptides in proteomics studies. Here, we present an open source software tool, termed IPeak, a peptide identification pipeline that is designed to combine the Percolator post-processing algorithm and multi-search strategy to enhance the sensitivity of peptide identifications without compromising accuracy. IPeak provides a graphical user interface (GUI) as well as a command-line interface, which is implemented in JAVA and can work on all three major operating system platforms: Windows, Linux/Unix and OS X. IPeak has been designed to work with the mzIdentML standard from the Proteomics Standards Initiative (PSI) as an input and output, and also been fully integrated into the associated mzidLibrary project, providing access to the overall pipeline, as well as modules for calling Percolator on individual search engine result files. The integration thus enables IPeak (and Percolator) to be used in conjunction with any software packages implementing the mzIdentML data standard. IPeak is freely available and can be downloaded under an Apache 2.0 license at https://code.google.com/p/mzidentml-lib/.

Assessing Transcription Regulatory Elements To Evaluate the Expression Status of Missing Protein Genes on Chromosomes 11 and 19.

During an investigation of missing proteins with the RNA-seq data acquired from three liver cancer cell lines, the majority of the missing protein coding genes (MPGs) located at chromosome 11 (chr11) had no corresponding mRNAs, while a high percentage of the MPGs on chr19 were detected at the mRNA level. The phenomenon, which was also observed in more than 40 cell lines, led to an inquiry of causation of the different transcriptional statuses of the MPGs in the two chromosomes. We hypothesized that the special chromatin structure was a key element to regulate MPG transcription. Upon a systematical comparison of the effects of DNase I hypersensitive sites (DHSs), transcription factors (TFs), and histone modifications toward these genes or MPGs with/without mRNA evidence in chr11 and 19, we attributed the poor transcription of the MPGs to the weak capacity of these transcription regulatory elements, regardless of which chromosome the MPGs were located. We further analyzed the gene contents in chr11 and found a number of genes related to sensory functions in the presence of chr11. We postulate that a high number of sensory-related genes, which are located within special chromatin structure, could bring a low detection rate of MPGs in chr11.

Biomarker Discovery and Verification of Esophageal Squamous Cell Carcinoma Using Integration of SWATH/MRM.

We propose an efficient integration of SWATH with MRM for biomarker discovery and verification when the corresponding ion library is well established. We strictly controlled the false positive rate associated with SWATH MS signals and carefully selected the target peptides coupled with SWATH and MRM. We collected 10 samples of esophageal squamous cell carcinoma (ESCC) tissues paired with tumors and adjacent regions and quantified 1758 unique proteins with FDR 1% at protein level using SWATH, in which 467 proteins were abundance-dependent with ESCC. After carefully evaluating the SWATH MS signals of the up-regulated proteins, we selected 120 proteins for MRM verification. MRM analysis of the pooled and individual esophageal tissues resulted in 116 proteins that exhibited similar abundance response modes to ESCC that were acquired with SWATH. Because the ESCC-related proteins consisted of a high percentile of secreted proteins, we conducted the MRM assay on patient sera that were collected from pre- and postoperation. Of the 116 target proteins, 42 were identified in the ESCC sera, including 11 with lowered abundances postoperation. Coupling SWATH and MRM is thus feasible and efficient for the discovery and verification of cancer-related protein biomarkers.

Insights from ENCODE on Missing Proteins: Why ß-Defensin Expression Is Scarcely Detected.

ß-Defensins (DEFBs) have a variety of functions. The majority of these proteins were not identified in a recent proteome survey. Neither protein detection nor the analysis of transcriptomic data based on RNA-seq data for three liver cancer cell lines identified any expression products. Extensive investigation into DEFB transcripts in over 70 cell lines offered similar results. This fact naturally begs the question­Why are DEFB genes scarcely expressed? After examining DEFB gene annotation and the physicochemical properties of its protein products, we postulated that regulatory elements could play a key role in the resultant poor transcription of DEFB genes. Four regions containing DEFB genes and six adjacent regions on chromosomes 6, 8, and 20 were carefully investigated using The Encyclopedia of DNA Elements (ENCODE) information, such as that of DNase I hypersensitive sites (DHSs), transcription factors (TFs), and histone modifications. The results revealed that the intensities of these ENCODE features were globally weaker than those in the adjacent regions. Impressively, DEFB-related regions on chromosomes 6 and 8 containing several non-DEFB genes had lower ENCODE feature intensities, indicating that the absence of DEFB mRNAs might not depend on the gene family but may be reliant upon gene location and chromatin structure.

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.

Tissue-Based Proteogenomics Reveals that Human Testis Endows Plentiful Missing Proteins.

Investigations of missing proteins (MPs) are being endorsed by many bioanalytical strategies. We proposed that proteogenomics of testis tissue was a feasible approach to identify more MPs because testis tissues have higher gene expression levels. Here we combined proteomics and transcriptomics to survey gene expression in human testis tissues from three post-mortem individuals. Proteins were extracted and separated with glycine- and tricine-SDS-PAGE. A total of 9597 protein groups were identified; of these, 166 protein groups were listed as MPs, including 138 groups (83.1%) with transcriptional evidence. A total of 2948 proteins are designated as MPs, and 5.6% of these were identified in this study. The high incidence of MPs in testis tissue indicates that this is a rich resource for MPs. Functional category analysis revealed that the biological processes that testis MPs are mainly involved in are sexual reproduction and spermatogenesis. Some of the MPs are potentially involved in tumorgenesis in other tissues. Therefore, this proteogenomics analysis of individual testis tissues provides convincing evidence of the discovery of MPs. All mass spectrometry data from this study have been deposited in the ProteomeXchange (data set identifier PXD002179).

sapFinder: an R/Bioconductor package for detection of variant peptides in shotgun proteomics experiments.

UNLABELLED: Single nucleotide variations (SNVs) located within a reading frame can result in single amino acid polymorphisms (SAPs), leading to alteration of the corresponding amino acid sequence as well as function of a protein. Accurate detection of SAPs is an important issue in proteomic analysis at the experimental and bioinformatic level. Herein, we present sapFinder, an R software package, for detection of the variant peptides based on tandem mass spectrometry (MS/MS)-based proteomics data. This package automates the construction of variation-associated databases from public SNV repositories or sample-specific next-generation sequencing (NGS) data and the identification of SAPs through database searching, post-processing and generation of HTML-based report with visualized interface. AVAILABILITY AND IMPLEMENTATION: sapFinder is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at http://bioconductor.org/packages/devel/bioc/html/sapFinder.html and are provided under a GPL-2 license.

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.

Expansion of the ion library for mining SWATH-MS data through fractionation proteomics.

The strategy of sequential window acquisition of all theoretical fragment ion spectra (SWATH) is emerging in the field of label-free proteomics. A critical consideration for the processing of SWATH data is the quality of the ion library (or mass spectrometric reference map). As the availability of open spectral libraries that can be used to process SWATH data is limited, most users currently create their libraries in-house. Herein, we propose an approach to construct an expanded ion library using the data-dependent acquisition (DDA) data generated by fractionation proteomics. We identified three critical elements for achieving a satisfactory ion library during the iterative process of our ion library expansion, including a correction of the retention times (RTs) gained from fractionation proteomics, appropriate integrations of the fractionated proteomics into an ion library, and assessments of the impact of the expanded ion libraries to data mining in SWATH. Using a bacterial lysate as an evaluation material, we employed sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to fractionate the lysate proteins and constructed the expanded ion library using the fractionation proteomics data. Compared with the ion library built from the unfractionated proteomics, approximately 20% more peptides were extracted from the expanded ion library. The extracted peptides, moreover, were acceptable for further quantitative analysis.

Proteomic analysis reveals LRPAP1 as a key player in the micropapillary pattern metastasis of lung adenocarcinoma.

Objectives: Lung adenocarcinomas have different prognoses depending on their histological growth patterns. Micropapillary growth within lung adenocarcinoma, particularly metastasis, is related to dismal prognostic outcome. Metastasis accounts for a major factor leading to mortality among lung cancer patients. Understanding the mechanisms underlying early stage metastasis can help develop novel treatments for improving patient survival. Methods: Here, quantitative mass spectrometry was conducted for comparing protein expression profiles among various histological subtypes, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma (including acinar and micropapillary [MIP] types). To determine the mechanism of MIP-associated metastasis, we identified a protein that was highly expressed in MIP. The expression of the selected highly expressed MIP protein was verified via immunohistochemical (IHC) analysis and its function was validated by an in vitro migration assay. Results: Proteomic data revealed that low-density lipoprotein receptor-related protein-associated protein 1 (LRPAP1) was highly expressed in MIP group, which was confirmed by IHC. The co-expressed proteins in this study, PSMD1 and HSP90AB1, have been reported to be highly expressed in different cancers and play an essential role in metastasis. We observed that LRPAP1 promoted lung cancer progression, including metastasis, invasion and proliferation in vitro and in vivo. Conclusion: LRPAP1 is necessary for MIP-associated metastasis and is the candidate novel anti-metastasis therapeutic target.