Construction and assessment of individualized proteogenomic databases for large-scale analysis of nonsynonymous single nucleotide variants

Multi-omic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is gain-ofcysteine, which is the most frequently acquired amino acid due to missense variants in COSMIC. Acquired cysteines are both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain uncharacterized. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine acquisition. For both cancer and healthy genomes, we find that cysteine acquisition is a ubiquitous consequence of genetic variation that is further elevated in the context of decreased DNA repair. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized 2-stage false discovery rate (FDR) error controlled proteomic search, further enhanced with a user-friendly FragPipe interface. Integration of CADD predictions of deleteriousness revealed marked enrichment for likely damaging variants that result in acquisition of cysteine. By deploying chemoproteogenomics across eleven cell lines, we identify 116 gain-of-cysteines, of which 10 were liganded by electrophilic druglike molecules. Reference cysteines proximal to missense variants were also found to be pervasive, 791 in total, supporting heretofore untapped opportunities for proteoform-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

PrecisionProDB: improving the proteomics performance for precision medicine

SUMMARY

As the next-generation sequencing technology becomes broadly applied, genomics and transcriptomics are becoming more commonly used in both research and clinical settings. However, proteomics is still an obstacle to be conquered. For most peptide search programs in proteomics, a standard reference protein database is used. Because of the thousands of coding DNA variants in each individual, a standard reference database does not provide perfect match for many proteins/peptides of an individual. A personalized reference database can improve the detection power and accuracy for individual proteomics data. To connect genomics and proteomics, we designed a Python package PrecisionProDB that is specialized for generating a personized protein database for proteomics applications. PrecisionProDB supports multiple popular file formats and reference databases, and can generate a personized database in minutes. To demonstrate the application of PrecisionProDB, we generated human population-specific reference protein databases with PrecisionProDB, which improves the number of identified peptides by 0.34% on average. In addition, by incorporating cell line-specific variants into the protein database, we demonstrated a 0.71% improvement for peptide identification in the Jurkat cell line. With PrecisionProDB and these datasets, researchers and clinicians can improve their peptide search performance by adopting the more representative protein database or adding population and individual-specific proteins to the search database with minimum increase of efforts.

AVAILABILITY

PrecisionProDB and pre-calculated protein databases are freely available at https://github.com/ATPs/PrecisionProDB and https://github.com/ATPs/PrecisionProDB_references.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SIGNIFICANCE: We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Quality control of single amino acid variations detected by tandem mass spectrometry

Study of single amino acid variations (SAVs) of proteins, resulting from single nucleotide polymorphisms, is of great importance for understanding the relationships between genotype and phenotype. In mass spectrometry based shotgun proteomics, identification of peptides with SAVs often suffers from high error rates on the variant sites detected. These site errors are due to multiple reasons and can be confirmed by manual inspection or genomic sequencing. Here, we present a software tool, named SAVControl, for site-level quality control of variant peptide identifications. It mainly includes strict false discovery rate control of variant peptide identifications and variant site verification by unrestrictive mass shift relocalization. SAVControl was validated on three colorectal adenocarcinoma cell line datasets with genomic sequencing evidences and tested on a colorectal cancer dataset from The Cancer Genome Atlas. The results show that SAVControl can effectively remove false detections of SAVs.

SIGNIFICANCE

Protein sequence variations caused by single nucleotide polymorphisms (SNPs) are single amino acid variations (SAVs). The investigation of SAVs may provide a chance for understanding the relationships between genotype and phenotype. Mass spectrometry (MS) based proteomics provides a large-scale way to detect SAVs. However, using the current analysis strategy to detect SAVs may lead to high rate of false positives. The SAVControl we present here is a computational workflow and software tool for site-level quality control of SAVs detected by MS. It accesses the confidence of detected variant sites by relocating the mass shift responsible for an SAV to search for alternative interpretations. In addition, it uses a strict false discovery rate control method for variant peptide identifications. The advantages of SAVControl were demonstrated on three colorectal adenocarcinoma cell line datasets and a colorectal cancer dataset. We believe that SAVControl will be a powerful tool for computational proteomics and proteogenomics.

Proteogenomic Investigation of Strain Variation in Clinical Mycobacterium tuberculosis Isolates

Mycobacterium tuberculosis consists of a large number of different strains that display unique virulence characteristics. Whole-genome sequencing has revealed substantial genetic diversity among clinical M. tuberculosis isolates, and elucidating the phenotypic variation encoded by this genetic diversity will be of the utmost importance to fully understand M. tuberculosis biology and pathogenicity. In this study, we integrated whole-genome sequencing and mass spectrometry (GeLC-MS/MS) to reveal strain-specific characteristics in the proteomes of two clinical M. tuberculosis Latin American-Mediterranean isolates. Using this approach, we identified 59 peptides containing single amino acid variants, which covered ∼9% of all coding nonsynonymous single nucleotide variants detected by whole-genome sequencing. Furthermore, we identified 29 distinct peptides that mapped to a hypothetical protein not present in the M. tuberculosis H37Rv reference proteome. Here, we provide evidence for the expression of this protein in the clinical M. tuberculosis SAWC3651 isolate. The strain-specific databases enabled confirmation of genomic differences (i.e., large genomic regions of difference and nonsynonymous single nucleotide variants) in these two clinical M. tuberculosis isolates and allowed strain differentiation at the proteome level. Our results contribute to the growing field of clinical microbial proteogenomics and can improve our understanding of phenotypic variation in clinical M. tuberculosis isolates.

Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain

Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

Cancers are initiated and developed from a small population of stem-like cells termed cancer stem cells (CSCs). There is heterogeneity among this CSC population that leads to multiple subpopulations with their own distinct biological features and protein expression. The protein expression and function may be impacted by amino acid variants that can occur largely due to single nucleotide changes. We have thus performed proteomic analysis of breast CSC subpopulations by mass spectrometry to study the presence of single amino acid variants (SAAVs) and their relation to breast cancer. We have used CSC markers to isolate pure breast CSC subpopulation fractions (ALDH+ and CD44+/CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF-7 breast cancer cell line. By searching the Swiss-CanSAAVs database, 374 unique SAAVs were identified in total, where 27 are cancer-related SAAVs. 135 unique SAAVs were found in the CSC population compared with the mature luminal cells. The distribution of SAAVs detected in MCF-7 cells was compared with those predicted from the Swiss-CanSAAVs database, where we found distinct differences in the numbers of SAAVs detected relative to that expected from the Swiss-CanSAAVs database for several of the amino acids.

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.

MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms

Summary: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes.

Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/.

Contact:renardb@rki.de

Ecological venomics: How genomics, transcriptomics and proteomics can shed new light on the ecology and evolution of venom

Animal venom is a complex cocktail of bioactive chemicals that traditionally drew interest mostly from biochemists and pharmacologists. However, in recent years the evolutionary and ecological importance of venom is realized as this trait has direct and strong influence on interactions between species. Moreover, venom content can be modulated by environmental factors. Like many other fields of biology, venom research has been revolutionized in recent years by the introduction of systems biology approaches, i.e., genomics, transcriptomics and proteomics. The employment of these methods in venom research is known as 'venomics'. In this review we describe the history and recent advancements of venomics and discuss how they are employed in studying venom in general and in particular in the context of evolutionary ecology. We also discuss the pitfalls and challenges of venomics and what the future may hold for this emerging scientific field.