In the absence of an analog of PCR for proteins, the concentration detection limit (DL) becomes a real challenge. The problem may be solved by means of a combination of biospecific irreversible fishing with atomic force microscopy (AFM). AFM offers the ability to register individual molecules and their complexes, while biospecific fishing takes advantage of an affine interaction between analyte molecules spread over a large volume of biomaterial and ligand molecules immobilized on the chip surface. Fishing may be conducted in Kd-dependent reversible mode and in Kd-independent irreversible mode. In this study, the DLs of two previously applied proteomic approaches were determined and compared to the DL of a newly developed analytical method. The first approach, based on MS analysis of biomaterial after 2-DE or LC separation of proteins, attained a DL at the level of 10(-8)-10(-10) M. The second approach, based on the optical biosensor analysis of molecular interactions in the format of proteomic microarrays, had a DL of 10(-9)-10(-10) M. Our proposed method which combines biospecific fishing with AFM allowed us to attain DL values of 10(-11) M under reversible binding conditions and 10(-16) M under irreversible binding conditions.
This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.
Virtual and experimental 2DE coupled with ESI LC-MS/MS was introduced to obtain better representation of the information about human proteome. The proteins from HEPG2 cells and human blood plasma were run by 2DE. After staining and protein spot identification by MALDI-TOF MS, the protein maps were generated. The experimental physicochemical parameters (pI/Mw) of the proteoforms further detected by ESI LC-MS/MS in these spots were obtained. Next, the theoretical pI and Mw of identified proteins were calculated using program Compute pI/Mw (http://web.expasy.org/compute_pi/pi_tool-doc.html). Accordingly, the relationship between theoretical and experimental parameters was analyzed, and the correlation plots were built. Additionally, virtual/experimental information about different protein species/proteoforms from the same genes was extracted. As it was revealed from the plots, the major proteoforms detected in HepG2 cell line have pI/Mw parameters similar to theoretical values. In opposite, the minor protein species have mainly very different from theoretical pI and Mw parameters. A similar situation was observed in plasma in much higher degree. It means that minor protein species are heavily modified in cell and even more in plasma proteome.
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 (R ≈ 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 .
During the 2010 Human Proteome Organization Congress in Sydney, a gene-centric approach emerged as a feasible and tractable scaffold for assemblage of the Human Proteome Project. Bringing the gene-centric principle into practice, a roadmap for the 18th chromosome was drafted, postulating the limited sensitivity of analytical methods, as a serious bottleneck in proteomics. In the context of the sensitivity problem, we refer to the "copy number of protein molecules" as a measurable assessment of protein abundance. The roadmap is focused on the development of technology to attain the low- and ultralow -"copied" portion of the proteome. Roadmap merges the genomic, transcriptomic and proteomic levels to identify the majority of 285 proteins from 18th chromosome - master proteins. Master protein is the primary translation of the coding sequence and resembling at least one of the known isoforms, coded by the gene. The executive phase of the roadmap includes the expansion of the study of the master proteins with alternate splicing, single amino acid polymorphisms (SAPs) and post-translational modifications. In implementing the roadmap, Russian scientists are expecting to establish proteomic technologies for integrating MS and atomic force microscopy (AFM). These technologies are anticipated to unlock the value of new biomarkers at a detection limit of 10(-18) M, i.e. 1 protein copy per 1 μL of plasma. The roadmap plan is posted at www.proteome.ru/en/roadmap/ and a forum for discussion of the document is supported.
International audienceThe identification of target combinationswith synergistic effects on cancer is at the leading edge of modern cancer research, especially for the development of combined anticancer therapies. However, at present, the basis for selection of beneficial targetcombinations commonly relies on expert opinion without any systematic rationale. The development of high-throughput technologies has led to the availability of large-scale clinical gene expression data sets.2–4Mining of these data sets for identification of gene combinations with synergetic effects on survival outcome in cancer could provide a systematic rationale for the identification of target combinations with potential therapeutic synergy. ..
We propose an approach to detection of essential genes/proteins required for cancer cell survival. A gene is considered essential if a mutation with high impact upon the function of encoded protein causes death of the cancer cell. We draw an analogy between essential cancer proteins and well-known Abraham Wald’s work on estimating the plane critical areas using data on survivability of aircraft encountering enemy fire. Wald reasoned that parts with no bullet holes on the airplanes returned to the airbase from a combat flight are the most crucial ones for the airplane functioning: a hit in one of these parts downs an airplane, so it does not return back for the survey. We have envisaged that the airplane surface is a cancer genome and the bullets are somatic mutations with high impact upon protein function. Similarly we propose that genes specifically essential for tumor cell survival should carry less high-impact mutations in cancer cells compared to polymorphisms found in normal cells. We used data on mutations from the Cancer Genome Atlas and polymorphisms found in healthy humans (from 1000 Genomes Project) to predict 91 protein-coding genes essential for melanoma. These genes were selected according to several criteria, including negative selection, expression in melanocytes and decrease in the proportion of high-impact mutations in cancer compared with normal cells. The Gene Ontology analysis revealed enrichment of essential proteins related to membrane and cell periphery. We speculate that this could be a sign of immune system-driven negative selection of cancer neo-antigens. Another finding is the overrepresentation of semaphorin receptors, which can mediate distinctive signaling cascades and are involved in various aspects of tumor development. Cytokine receptors CCR5 and CXCR1 were also identified as cancer essential proteins and this is confirmed by other studies. Overall, our goal was to illustrate the idea of detecting proteins whose sequence integrity and functioning is important for cancer cell survival. Hopefully, this prediction of essential cancer proteins may point to new targets for anti-tumor therapies.
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