Protein chimerism: Novel source of protein diversity in humans adds complexity to bottom-up proteomics

Dietary protein sources, genetics, and cardiovascular disease incidence

BACKGROUND

To explore the potential correlation between the amount and source of dietary protein and cardiovascular disease (CVD), as well as the potential impact of genetic susceptibility on these connections.

METHODS

We performed a prospective analysis of 98,224 participants from the UK. We measured dietary protein intake using two 24-hour dietary recall interviews. To analyze the data, we used multivariable-adjusted Cox regression models and restricted cubic spline models. Additionally, we calculated weighted genetic risk scores.

RESULTS

A total of 8818 new cases of CVD were documented, which included 4076 cases of coronary artery disease (CAD) and 1126 cases of stroke. The study found a J-shaped association (p nonlinearity = 0.005) between CVD risk and the percentage of energy obtained from consuming plant protein. Higher intake of plant protein and whole protein was associated with a decreased risk of CVD. On the other hand, larger intakes of animal protein was linked to a higher occurrence of CAD. Additionally, increased intake of plant protein was also linked to a lower incidence of stroke. Replacing 5 % of animal protein-based energy intake with plant protein-based energy intake resulted in a 5 % decrease in CVD risk.

LIMITATIONS

There remains an effect of residual confounders.

CONCLUSION

The consumption of larger amounts of plant protein, whole protein, and nut protein was found to be associated with a lower risk of CVD events. Conversely, higher intakes of animal protein was associated with an increased risk of CAD events. Furthermore, replacing 5 % of energy intake from animal protein with energy intake from plant protein was found to reduce the risk of CVD by 5 %.

FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells

Fusion proteoforms are translation products derived from gene fusion. Although very rare, the fusion proteoforms play important roles in biomedical science. For example, fusion proteoforms influence the development of tumors by serving as cancer markers or cell cycle regulators. Although numerous studies have reported bioinformatics tools that can predict fusion transcripts, few proteogenomic tools are available that can predict and identify proteoforms. In this study, we develop a versatile proteogenomic tool "FusionPro," which facilitates the identification of fusion transcripts and their potential translatable peptides. FusionPro provides an independent gene fusion prediction module and can build sequence databases for annotated fusion proteoforms. FusionPro shows greater sensitivity than the available fusion finders when analyzing simulated or real RNA sequencing data sets. We use FusionPro to identify 18 fusion junction peptides and three potential fusion-derived peptides by MS/MS-based analysis of leukemia cell lines (Jurkat and K562) and ovarian cancer tissues from the Clinical Proteomic Tumor Analysis Consortium. Among the identified fusion proteins, we molecularly validate two fusion junction isoforms and a translation product of FAM133B:CDK6. Moreover, sequence analysis suggests that the fusion protein participates in the cell cycle progression. In addition, our prediction results indicate that fusion transcripts often have multiple fusion junctions and that these fusion junctions tend to be distributed in a nonrandom pattern at both the chromosome and gene levels. Thus, FusionPro allows users to detect various types of fusion translation products using a transcriptome-informed approach and to gain a comprehensive understanding of the formation and biological roles of fusion proteoforms.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

[Experimental estimation of proteome size for cells and human plasma]

Huge range of concentrations of different protein and insufficient sensitivity of methods for detection of proteins at a single molecule level does not yet allow obtaining the whole image of human proteome. In our investigations, we tried to evaluate the size of different proteomes (cells and plasma). The approach used is based on detection of protein spots in 2-DE after staining by protein dyes with different sensitivities. The function representing the dependence of the number of protein spots on sensitivity of protein dyes was generated. Next, by extrapolation of this function curve to theoretical point of the maximum sensitivity (detection of a single smallest polypeptide) it was calculated that a single human cell (HepG2) may contain minimum 70,000 proteoforms, and plasma--1.5 mln. Utilization of this approach to other, smaller proteomes showed the competency of this extrapolation. For instance, the size of mycoplas ma (Acholeplasma laidlawii) was estimated in 1100 proteoforms, yeast (Saccharomyces cerevisiae)--40,000, E. coli--6200, P. furiosus--3400. In hepatocytes, the amount of proteoforms was the same as in HepG2--70,000. Significance of obtained data is in possibilities to estimating the proteome organization and planning next steps in its study.

IgE recognition of chimeric isoforms of the honeybee (Apis mellifera) venom allergen Api m 10 evaluated by protein array technology

Api m 10 has recently been established as novel major allergen that is recognized by more than 60% of honeybee venom (HBV) allergic patients. Previous studies suggest Api m 10 protein heterogeneity which may have implications for diagnosis and immunotherapy of HBV allergy. In the present study, RT-PCR revealed the expression of at least nine additional Api m 10 transcript isoforms by the venom glands. Two distinct mechanisms are responsible for the generation of these isoforms: while the previously known variant 2 is produced by an alternative splicing event, novel identified isoforms are intragenic chimeric transcripts. To the best of our knowledge, this is the first report of the identification of chimeric transcripts generated by the honeybee. By a retrospective proteomic analysis we found evidence for the presence of several of these isoforms in the venom proteome. Additionally, we analyzed IgE reactivity to different isoforms by protein array technology using sera from HBV allergic patients, which revealed that IgE recognition of Api m 10 is both isoform- and patient-specific. While it was previously demonstrated that the majority of HBV allergic patients display IgE reactivity to variant 2, our study also shows that some patients lacking IgE antibodies for variant 2 display IgE reactivity to two of the novel identified Api m 10 variants, i.e. variants 3 and 4.

2DE-based approach for estimation of number of protein species in a cell

Insufficient sensitivity of methods for detection of proteins at a single molecule level does not yet allow obtaining the whole image of human proteome. But to go further, we need at least to know the proteome size, or how many different protein species compose this proteome. This is the task that could be at least partially realized by the method described in this article. The approach used in our study is based on detection of protein spots in 2DE after staining by protein dyes with various sensitivities. As the different protein spots contain different protein species, counting the spots opens a way for estimation of number of protein species. The function representing the dependence of the number of protein spots on sensitivity or LOD of protein dyes was generated. And extrapolation of this function curve to theoretical point of the maximum sensitivity (detection of a single smallest polypeptide) allowed to counting the number of different molecules (polypeptide species) at the concentration level of a single polypeptide per proteome. Using this approach, it was estimated that the minimal numbers of protein species for model objects, Escherichia coli and Pirococcus furiosus, are 6200 and 3400, respectively. We expect a single human cell (HepG2) to contain minimum 70 000 protein species.