Zhe Ren

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.

Qualitative and Quantitative Expression Status of the Human Chromosome 20 Genes in Cancer Tissues and the Representative Cell Lines

Under the guidance of the Chromosome-centric Human Proteome Project (C-HPP), (1, 2) we conducted a systematic survey of the expression status of genes located at human chromosome 20 (Chr.20) in three cancer tissues, gastric, colon, and liver carcinoma, and their representative cell lines. We have globally profiled proteomes in these samples with combined technology of LC-MS/MS and acquired the corresponding mRNA information upon RNA-seq and RNAchip. In total, 323 unique proteins were identified, covering 60% of the coding genes (323/547) in Chr.20. With regards to qualitative information of proteomics, we overall evaluated the correlation of the identified Chr.20 proteins with target genes of transcription factors or of microRNA, conserved genes and cancer-related genes. As for quantitative information, the expression abundances of Chr.20 genes were found to be almost consistent in both tissues and cell lines of mRNA in all individual chromosome regions, whereas those of Chr.20 proteins in cells are different from tissues, especially in the region of 20q13.33. Furthermore, the abundances of Chr.20 proteins were hierarchically evaluated according to tissue- or cancer-related distribution. The analysis revealed several cancer-related proteins in Chr.20 are tissue- or cell-type dependent. With integration of all the acquired data, for the first time we established a solid database of the Chr.20 proteome.

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.

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/.

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

β-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 question—Why 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.

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.

Comparative qualitative phosphoproteomics analysis identifies shared phosphorylation motifs and associated biological processes in evolutionary divergent plants

Phosphorylation is one of the most prevalent post-translational modifications and plays a key role in regulating cellular processes. We carried out a bioinformatics analysis of pre-existing phosphoproteomics data, to profile two model species representing the largest subclasses in flowering plants the dicot Arabidopsis thaliana and the monocot Oryza sativa, to understand the extent to which phosphorylation signaling and function is conserved across evolutionary divergent plants. We identified 6537 phosphopeptides from 3189 phosphoproteins in Arabidopsis and 2307 phosphopeptides from 1613 phosphoproteins in rice. We identified phosphorylation motifs, finding nineteen pS motifs and two pT motifs shared in rice and Arabidopsis. The majority of shared motif-containing proteins were mapped to the same biological processes with similar patterns of fold enrichment, indicating high functional conservation. We also identified shared patterns of crosstalk between phosphoserines with enrichment for motifs pSXpS, pSXXpS and pSXXXpS, where X is any amino acid. Lastly, our results identified several pairs of motifs that are significantly enriched to co-occur in Arabidopsis proteins, indicating cross-talk between different sites, but this was not observed in rice. Significance Our results demonstrate that there are evolutionary conserved mechanisms of phosphorylation-mediated signaling in plants, via analysis of high-throughput phosphorylation proteomics data from key monocot and dicot species: rice and Arabidposis thaliana. The results also suggest that there is increased crosstalk between phosphorylation sites in A. thaliana compared with rice. The results are important for our general understanding of cell signaling in plants, and the ability to use A. thaliana as a general model for plant biology.

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning

The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this prediction. A new peptide RT prediction tool, DeepRT, was designed using a capsule network model, and the public data sets containing peptides separated by reverse-phase liquid chromatography were used to evaluate the DeepRT performance. Compared with other prevailing RT predictors, DeepRT attained overall improvement in the prediction of peptide RTs with an R2 of ∼0.994. Moreover, DeepRT was able to accommodate to the peptides that were separated by different types of LC, such as strong cation exchange (SCX) and hydrophilic interaction liquid chromatography (HILIC) and to reach the RT prediction with R2 values of ∼0.996 for SCX and ∼0.993 for HILIC, respectively. If a large peptide data set is available for one type of LC, DeepRT can be promoted to DeepRT(+) using transfer learning. Based on a large peptide data set gained from SWATH, DeepRT(+) further elevated the accuracy of RT prediction for peptides in a small data set and enabled a satisfactory prediction upon limited peptides approximating hundreds. Further, DeepRT automatically learns retention-related properties of amino acids under different separation mechanisms, which are well consistent with retention coefficients (Rc) of the amino acids. DeepRT was thus proven to be an improved RT predictor with high flexibility and efficiency. DeepRT is available at https://github.com/horsepurve/DeepRTplus .

Comparative Qualitative Phosphoproteomics Analysis Identifies Shared Phosphorylation Motifs and Associated Biological Processes in Flowering Plants

Phosphorylation is regarded as one of the most prevalent post-translational modifications and plays a key role in regulating cellular processes. In this work we carried out a comparative bioinformatics analysis of phosphoproteomics data, to profile two model species representing the largest subclasses in flowering plants the dicot Arabidopsis thaliana and the monocot Oryza sativa, to understand the extent to which phosphorylation signaling and function is conserved across evolutionary divergent plants. Using pre-existing mass spectrometry phosphoproteomics datasets and bioinformatic tools and resources, we identified 6,537 phosphopeptides from 3,189 phosphoproteins in Arabidopsis and 2,307 phosphopeptides from 1,613 phosphoproteins in rice. The relative abundance ratio of serine, threonine, and tyrosine phosphorylation sites in rice and Arabidopsis were highly similar: 88.3: 11.4: 0.4 and 86.7: 12.8: 0.5, respectively. Tyrosine phosphorylation shows features different from serine and threonine phosphorylation and was found to be more frequent in doubly-phosphorylated peptides in Arabidopsis. We identified phosphorylation sequence motifs in the two species to explore the similarities, finding nineteen pS motifs and two pT motifs that are shared in rice and Arabidopsis; among them are five novel motifs that have not previously been described in both species. The majority of shared motif-containing proteins were mapped to the same biological processes with similar patterns of fold enrichment, indicating high functional conservation. We also identified shared patterns of crosstalk between phosphoserines with motifs pSXpS, pSXXpS and pSXXXpS, where X is any amino acid, in both species indicating this is an evolutionary conserved signaling mechanism in flowering plants. However, our results are suggestive that there is greater co-occurrence of crosstalk between phosphorylation sites in Arabidopsis, and we were able to identify several pairs of motifs that are statistically significantly enriched to co-occur in Arabidopsis proteins, but not in rice.

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.