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

Enhanced single-cell metabolomics by capillary electrophoresis electrospray ionization-mass spectrometry with field amplified sample injection

Single-cell metabolomics provides information on the biochemical state of an individual cell and its relationship with the surrounding environment. Characterization of metabolic cellular heterogeneity is challenging, in part due to the small amounts of analytes and their wide dynamic concentration ranges within individual cells. CE-ESI-MS is well suited to single-cell assays because of its low sample-volume requirements and low detection limits. While the volume of a cell is in the picoliter range, after isolation, the typical volume of the lysed cell sample is on the order of a microliter; however, only nanoliters are injected into the CE system, with the volume mismatch limiting analytical performance. Here we developed an approach for the detection of intracellular metabolites from a single neuron using field amplified sample injection (FASI) CE-ESI-MS. Through the application of FASI, we achieved 100- to 300-fold detection limit enhancement compared to hydrodynamic injections. We further enhanced the analyte identification and quantification accuracy via introduction of two internal standards. As a result, the relative standard deviations of migration times were reduced to <5%, aiding identification. Finally, we successfully applied FASI CE-ESI-MS to the untargeted profiling of metabolites of Aplysia californica pleural sensory neurons with <50 μm diameter cell somata. As a result, twenty one neurotransmitters and metabolites have been quantified in these neurons.

Translatomics: The Global View of Translation

In all kingdoms of life, proteins are synthesized by ribosomes in a process referred to as translation. The amplitude of translational regulation exceeds the sum of transcription, mRNA degradation and protein degradation. Therefore, it is essential to investigate translation in a global scale. Like the other “omics”-methods, translatomics investigates the totality of the components in the translation process, including but not limited to translating mRNAs, ribosomes, tRNAs, regulatory RNAs and nascent polypeptide chains. Technical advances in recent years have brought breakthroughs in the investigation of these components at global scale, both for their composition and dynamics. These methods have been applied in a rapidly increasing number of studies to reveal multifaceted aspects of translation control. The process of translation is not restricted to the conversion of mRNA coding sequences into polypeptide chains, it also controls the composition of the proteome in a delicate and responsive way. Therefore, translatomics has extended its unique and innovative power to many fields including proteomics, cancer research, bacterial stress response, biological rhythmicity and plant biology. Rational design in translation can enhance recombinant protein production for thousands of times. This brief review summarizes the main state-of-the-art methods of translatomics, highlights recent discoveries made in this field and introduces applications of translatomics on basic biological and biomedical research.

Multi-Protease Strategy Identifies Three PE2 Missing Proteins in Human Testis Tissue

Although 5 years of the missing proteins (MPs) study have been completed, searching for MPs remains one of the core missions of the Chromosome-Centric Human Proteome Project (C-HPP). Following the next-50-MPs challenge of the C-HPP, we have focused on the testis-enriched MPs by various strategies since 2015. On the basis of the theoretical analysis of MPs (2017-01, neXtProt) using multiprotease digestion, we found that nonconventional proteases (e.g. LysargiNase, GluC) could improve the peptide diversity and sequence coverage compared with Trypsin. Therefore, a multiprotease strategy was used for searching more MPs in the same human testis tissues separated by 10% SDS-PAGE, followed by high resolution LC-MS/MS system (Q Exactive HF). A total of 7838 proteins were identified. Among them, three PE2 MPs in neXtProt 2017-01 have been identified: beta-defensin 123 ( Q8N688 , chr 20q), cancer/testis antigen family 45 member A10 ( P0DMU9 , chr Xq), and Histone H2A-Bbd type 2/3 ( P0C5Z0 , chr Xq). However, because only one unique peptide of ≥9 AA was identified in beta-defensin 123 and Histone H2A-Bbd type 2/3, respectively, further analysis indicates that each falls under the exceptions clause of the HPP Guidelines v2.1. After a spectrum quality check, isobaric PTM and single amino acid variant (SAAV) filtering, and verification with a synthesized peptide, and based on overlapping peptides from different proteases, these three MPs should be considered as exemplary examples of MPs found by exceptional criteria. Other MPs were considered as candidates but need further validation. All MS data sets have been deposited to the ProteomeXchange with identifier PXD006465.

Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications

The HUPO Human Proteome Project (HPP) has two overall goals: (1) stepwise completion of the protein parts list-the draft human proteome including confidently identifying and characterizing at least one protein product from each protein-coding gene, with increasing emphasis on sequence variants, post-translational modifications (PTMs), and splice isoforms of those proteins; and (2) making proteomics an integrated counterpart to genomics throughout the biomedical and life sciences community. PeptideAtlas and GPMDB reanalyze all major human mass spectrometry data sets available through ProteomeXchange with standardized protocols and stringent quality filters; neXtProt curates and integrates mass spectrometry and other findings to present the most up to date authorative compendium of the human proteome. The HPP Guidelines for Mass Spectrometry Data Interpretation version 2.1 were applied to manuscripts submitted for this 2016 C-HPP-led special issue [ www.thehpp.org/guidelines ]. The Human Proteome presented as neXtProt version 2016-02 has 16,518 confident protein identifications (Protein Existence [PE] Level 1), up from 13,664 at 2012-12, 15,646 at 2013-09, and 16,491 at 2014-10. There are 485 proteins that would have been PE1 under the Guidelines v1.0 from 2012 but now have insufficient evidence due to the agreed-upon more stringent Guidelines v2.0 to reduce false positives. neXtProt and PeptideAtlas now both require two non-nested, uniquely mapping (proteotypic) peptides of at least 9 aa in length. There are 2,949 missing proteins (PE2+3+4) as the baseline for submissions for this fourth annual C-HPP special issue of Journal of Proteome Research. PeptideAtlas has 14,629 canonical (plus 1187 uncertain and 1755 redundant) entries. GPMDB has 16,190 EC4 entries, and the Human Protein Atlas has 10,475 entries with supportive evidence. neXtProt, PeptideAtlas, and GPMDB are rich resources of information about post-translational modifications (PTMs), single amino acid variants (SAAVSs), and splice isoforms. Meanwhile, the Biology- and Disease-driven (B/D)-HPP has created comprehensive SRM resources, generated popular protein lists to guide targeted proteomics assays for specific diseases, and launched an Early Career Researchers initiative.

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .

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.

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.

Quantitative Proteomic Verification of Membrane Proteins as Potential Therapeutic Targets Located in the 11q13 Amplicon in Cancers

Tumor types can be defined cytologically by their regions of chromosomal amplification, which often results in the high expression of both mRNA and proteins of certain genes contained within the amplicon. An important strategy for defining therapeutically relevant targets in these situations is to ascertain which genes are amplified at the protein level and, concomitantly, are key drivers for tumor growth or maintenance. Furthermore, so-called passenger genes that are amplified with driver genes and a manifest on the cell surface can be attractive targets for an antibody-drug conjugate approach (ADC). We employed a tandem mass spectrometry proteomics approach using tumor cell lines to identify the cell surface proteins whose expression correlates with the 11q13 amplicon. The 11q13 amplicon is one of the most frequently amplified chromosomal regions in human cancer, being present in 45% of head and neck and oral squamous cell carcinoma (OSCC) and 13-21% of breast and liver carcinomas. Using a panel of tumor cell lines with defined 11q13 genomic amplification, we identified the membrane proteins that are differentially expressed in an 11q13 amplified cell line panel using membrane-enriched proteomic profiling. We found that DSG3, CD109, and CD14 were differentially overexpressed in head and neck and breast tumor cells with 11q13 amplification. The level of protein expression of each gene was confirmed by Western blot and FACS analysis. Because proteins with high cell surface expression on selected tumor cells could be potential antibody drug conjugate targets, we tested DSG3 and CD109 in antibody piggyback assays and validated that DSG3 and CD109 expression was sufficient to induce antibody internalization and cell killing in 11q13-amplified cell lines. Our results suggest that proteomic profiling using genetically stratified tumors can identify candidate antibody drug conjugate targets. Data are available via ProteomeXchange with the identifier PXD002486.

Leveraging the complementary nature of RNA-Seq and shotgun proteomics data

RNA sequencing (RNA-Seq) and MS-based shotgun proteomics are powerful high-throughput technologies for identifying and quantifying RNA transcripts and proteins, respectively. With the increasing affordability of these technologies, many projects have started to apply both to the same samples to achieve a more comprehensive understanding of biological systems. A major analytical challenge for such integrative projects is how to effectively leverage the complementary nature of RNA-Seq and shotgun proteomics data. RNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration. Sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in cell and tissue samples and thus improve protein identification. Meanwhile, proteomics data provide essential confirmation of the validity and functional relevance of novel findings from RNA-Seq data. At the quantitative level, mRNA and protein levels are only modestly correlated, suggesting strong involvement of posttranscriptional regulation in controlling gene expression. Here, we review recent studies at the interface of RNA-Seq and proteomics data. We discuss goals, accomplishments, and challenges in RNA-Seq-based proteogenomics. We also examine the current status and future potential of parallel transcriptome and proteome quantification in revealing posttranscriptional regulatory mechanisms.

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

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.