PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors.

METHODS

We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers.

RESULTS

We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors.

CONCLUSIONS

This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors

BACKGROUND

Molecular understanding of muscle-invasive (MIBC) and non-muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses.

OBJECTIVE

To conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes.

DESIGN, SETTING, AND PARTICIPANTS

Proteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions.

INTERVENTION

Recombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial-derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Proteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model.

RESULTS AND LIMITATIONS

Five uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant.

CONCLUSIONS

This proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation.

PATIENT SUMMARY

We integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling

BACKGROUND

Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics.

METHODS

Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor.

RESULTS

LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ).

CONCLUSIONS

Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

The Catabolic Network of Aromatoleum aromaticum EbN1T

The denitrifying betaproteobacterium Aromatoleum aromaticum EbN1T is a facultative anaerobic degradation specialist and belongs to the environmental bacteria studied best on the proteogenomic level. This review summarizes the current state of knowledge about the anaerobic and aerobic degradation (to CO2) of 47 organic growth substrates (23 aromatic, 21 aliphatic, and 3 amino acids) as well as the modes of respiratory energy conservation (denitrification vs. O2-respiration). The constructed catabolic network is comprised of 256 genes, which occupy ∼7.5% of the coding regions of the genome. In total, 219 encoded proteins have been identified by differential proteomics, yielding a proteome coverage of ∼74% of the network. Its degradation section is composed of 31 peripheral and 4 central pathways, with several peripheral modules (e.g., for 4-ethylphenol, 2-phenylethylamine, indoleacetate, and phenylpropanoids) discovered only after the complete genome [Arch Microbiol. 2005 Jan;183(1):27-36] and a first proteomic survey [Proteomics. 2007 Jun;7(13):2222-39] of A. aromaticum EbN1T were reported. The activation of recalcitrant aromatic compounds involves a suite of biochemically intriguing reactions ranging from C-H-bond activation (e.g., ethylbenzene dehydrogenase) via carboxylation (e.g., acetophenone carboxylase) to oxidative deamination (e.g., benzylamine), reductive dearomatization (benzoyl-CoA), and epoxide-forming oxygenases (e.g., phenylacetyl-CoA). The peripheral reaction sequences are substrate-specifically induced, mediated by specific transcriptional regulators with in vivo response thresholds in the nanomolar range. While lipophilic substrates (e.g., phenolics) enter the cells via passive diffusion, polar ones require active uptake that is driven by specific transporters. Next to the protein repertoire for canonical complexes I-III, denitrification, and O2-respiration (low- and high-affinity oxidases), the genome encodes an Ndh-II, a tetrathionate reductase, two ETF:quinone oxidoreductases, and two Rnf-type complexes, broadening the electron transfer flexibility of the strain. Taken together, the detailed catabolic network presented here forms a solid basis for future systems biology-level studies with A. aromaticum EbN1T.

Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics

Pheretima, also called "earthworms", is a well-known animal-derived traditional Chinese medicine that is extensively used in over 50 Chinese patent medicines (CPMs) in Chinese Pharmacopoeia (2020 edition). However, its zoological origin is unclear, both in the herbal market and CPMs. In this study, a strategy for integrating in-house annotated protein databases constructed from close evolutionary relationship-sourced RNA sequencing data from public archival resources and various sequencing algorithms (restricted search, open search, and de novo) was developed to characterize the phenotype of natural peptides of three major commercial species of Pheretima, including Pheretima aspergillum (PA), Pheretima vulgaris (PV), and Metaphire magna (MM). We identified 10,477 natural peptides in the PA, 7,451 in PV, and 5,896 in MM samples. Five specific signature peptides were screened and then validated using synthetic peptides; these demonstrated robust specificity for the authentication of PA, PV, and MM. Finally, all marker peptides were successfully applied to identify the zoological origins of Brain Heart capsules and Xiaohuoluo pills, revealing the inconsistent Pheretima species used in these CPMs. In conclusion, our integrated strategy could be used for the in-depth characterization of natural peptides of other animal-derived traditional Chinese medicines, especially non-model species with poorly annotated protein databases.

Mass-spectrometric analysis of APOB polymorphism rs1042031 (G/T) and its influence on serum proteome of coronary artery disease patients: genetic-derived proteomics consequences

Genetic polymorphisms of apolipoprotein B gene (APOB) may result into serum proteomic perturbance in Coronary Artery Disease (CAD). The current case-control cohort of Pakistani subjects was designed to analyze the genetic influence of APOB rs1042031, (G/T) genotype on serum proteome. Subjects were categorized into two groups: CAD patients (n = 480) and healthy individuals (n = 220). For genotyping, tetra ARMS-PCR was carried out and validated through sequencing, whereas LC/MS-based proteomic analysis of serum samples was performed through label-free quantification. In initial step of genotyping, the frequencies of each genotype GG, GT, and TT were 70%, 27%, and 30% in CAD patients, while in control group, the subjects were 52%, 43%, and 5%, respectively, in CAD patients. The genotypic frequencies in patients vs. control groups found significantly different (p = 0.004), and a strong association of dominant alleles GG with the CAD was observed in both dominant (OR: 2.4 (1.71-3.34), p = 0.001) and allelic genetic models (OR: 2.0 (1.45-2.86), p = 0.001). In second step of label-free quantitation, a total of 40 significant proteins were found with altered expression in CAD patients. The enriched Gene Ontology (GO) terms of molecular functions and pathways of these protein showed upregulated pathways as follows: chylomicron remodeling and assembly, complement cascade activation, plasma lipoprotein assembly, apolipoprotein-A receptor binding, and metabolism of fat-soluble vitamins in G allele carrier of rs1042031 (G > T) vs. mutant T-allele carriers. This study provides better understanding of CAD pathobiology by proteogenomics of APOB. It evidences the influence of APOB rs1042031-dominant (GG) genotype with CAD patients.

Assessing labelled carbon assimilation from poly butylene adipate-co-terephthalate (PBAT) monomers during thermophilic anaerobic digestion

PBAT (poly butylene adipate-co-terephthalate) is a widely used biodegradable plastic, but the knowledge about its metabolization in anaerobic environments is very limited. In this study, the anaerobic digester sludge from a municipal wastewater treatment plant was used as inoculum to investigate the biodegradability of PBAT monomers in thermophilic conditions. The research employs a combination of ¹³C-labelled monomers and proteogenomics to track the labelled carbon and identify the microorganisms involved. A total of 122 labelled peptides of interest were identified for adipic acid (AA) and 1,4-butanedio (BD). Through the time-dependent isotopic enrichment and isotopic profile distributions, Bacteroides, Ichthyobacterium, and Methanosarcina were proven to be directly involved in the metabolization of at least one monomer. This study provides a first insight into the identity and genomic potential of microorganisms responsible for biodegradability of PBAT monomers during anaerobic digestion under thermophilic conditions.

Functional impact of protein-RNA variation in clinical cancer analyses

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this manuscript we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4,500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using i) a protein database for Mycobacterium tuberculosis, ii) a M. avium genome six-frame translated database, and iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2,954 proteins. Further, proteogenomic analyses aided in identifying 1,301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

Improving the genome and proteome annotations of the marine model diatom Thalassiosira pseudonana using a proteogenomics strategy

Diatoms are unicellular eukaryotic phytoplankton that account for approximately 20% of global carbon fixation and 40% of marine primary productivity; thus, they are essential for global carbon biogeochemical cycling and climate. The availability of ten diatom genome sequences has facilitated evolutionary, biological and ecological research over the past decade; however, a complimentary map of the diatom proteome with direct measurements of proteins and peptides is still lacking. Here, we present a proteome map of the model marine diatom Thalassiosira pseudonana using high-resolution mass spectrometry combined with a proteogenomic strategy. In-depth proteomic profiling of three different growth phases and three nutrient-deficient samples identified 9526 proteins, accounting for ~ 81% of the predicted protein-coding genes. Proteogenomic analysis identified 1235 novel genes, 975 revised genes, 104 splice variants and 234 single amino acid variants. Furthermore, our quantitative proteomic analysis experimentally demonstrated that a considerable number of novel genes were differentially translated under different nutrient conditions. These findings substantially improve the genome annotation of T. pseudonana and provide insights into new biological functions of diatoms. This relatively comprehensive diatom proteome catalog will complement available diatom genome and transcriptome data to advance biological and ecological research of marine diatoms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00161-y.

Integrated In Silico Analysis of Proteogenomic and Drug Targets for Pancreatic Cancer Survival

Pancreatic cancer remains a major health concern, being among the deadliest forms of cancer with over 80% of the patients presenting with metastatic disease. According to the American Cancer Society, for all stages of pancreatic cancer combined, the 5-year survival rate is less than 10%. Genetic research on pancreatic cancer has generally been focused on familial pancreatic cancer, which is only 10% of all pancreatic cancer patients. This study focuses on finding genes that impact the survival of pancreatic cancer patients which can be used as biomarkers and potential targets to develop personalized treatment options. We used cBioPortal platform using NCI-initiated The Cancer Genome Atlas (TCGA) dataset to find genes that were altered differently in different ethnic groups which can serve as potential biomarkers and analyzed the genes' impact on patient survival. MD Anderson Cell Lines Project (MCLP) and genecards.org were also utilized to identify potential drug candidates that can target the proteins encoded by the genes. The results showed that there are unique genes that are associated with each race category which may influence the survival outcomes of patients, and their potential drug candidates were identified.

Identification of Pinelliae Rhizoma and its counterfeit species based on enzymatic signature peptides from toxic proteins

BACKGROUND

Pinelliae Rhizoma (PR), a toxic medication, with long history, is commonly used for eliminating phlegm. Due to the shortage of wild resources and the relative lacking of cultivation technology, it is often confused with its counterfeit species in the market, such as Typhonii Rhizoma (TR), Arisaematis Rhizoma (AR) and tubers of Typhonium flagelliforme (TF) and Pinellia pedatisecta (PP).

PURPOSE

It was aimed to screen signature enzymatic peptides from toxic proteins to identify PR and its four counterfeit species.

STUDY DESIGN

A comparative proteogenomics strategy based on open-source transcriptome data was applied for screening signature peptides from toxic proteins, which were applied for species authentication of PR and its counterfeit species.

METHODS

Firstly, the open-source transcriptome data was used for constructing the annotated protein database, which was used for peptides identification. Secondly, the toxicity of different fractions of PR were evaluated by the rat peritoneal inflammation model. Furthermore, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) were used to profile the main proteins bands of five species, whose sequences were identified based on the in-gel digestion experiment by using ultra-high-performance liquid chromatography/quadrupole-Orbitrap mass spectrometry. Finally, the label-free proteomic analysis was performed to character the proteins and screen the signature peptides of five species, which were validated in commercially available products by dynamic multi reaction monitor (DMRM).

RESULTS

The results in this study confirmed that protein was the main toxic components of PR. Both Pinellia ternata agglutinin (PTA) and trypsin inhibitor (TI) like proteins are the main proteins, which were characterized by proteomic analysis based on four annotated protein database. Meanwhile, seven signature peptides from toxic proteins were screened and validated with good repeatability and specificity in commercial products.

CONCLUSION

Seven signature enzymatic peptides from toxic protein screened by the comparative proteogenomics strategy based on open-source transcriptome data achieved good identification ability of PR and its four counterfeit species.

Proteogenomics identification of TBBPA degraders in anaerobic bioreactor

Tetrabromobisphenol A (TBBPA) is the most used flame retardant worldwide and has become a threat to aquatic ecosystems. Previous research into the degradation of this micropollutant in anaerobic bioreactors has suggested several identities of putative TBBPA degraders. However, the organisms actively degrading TBBPA under in situ conditions have so far not been identified. Protein-stable isotope probing (protein-SIP) has become a cutting-edge technique in microbial ecology for enabling the link between identity and function under in situ conditions. Therefore, it was hypothesized that combining protein-based stable isotope probing with metagenomics could be used to identify and provide genomic insight into the TBBPA-degrading organisms. The identified ¹³C-labelled peptides were found to belong to organisms affiliated to Phytobacter, Clostridium, Sporolactobacillus, and Klebsilla genera. The functional classification of identified labelled peptides revealed that TBBPA is not only transformed by cometabolic reactions, but also assimilated into the biomass. By application of the proteogenomics with labelled micropollutants (protein-SIP) and metagenome-assembled genomes, it was possible to extend the current perspective of the diversity of TBBPA degraders in wastewater and predict putative TBBPA degradation pathways. The study provides a link to the active TBBPA degraders and which organisms to favor for optimized biodegradation.

Integrated multi-omics characterization of KRAS mutant colorectal cancer

KRAS mutation is the most frequent oncogenic aberration in colorectal cancer (CRC). The molecular mechanism and clinical implications of KRAS mutation in CRC remain unclear and show high heterogeneity within these tumors. Methods: We harnessed the multi-omics data (genomic, transcriptomic, proteomic, and phosphoproteomic etc.) of KRAS-mutant CRC tumors and performed unsupervised clustering to identify proteomics-based subgroups and molecular characterization. Results: In-depth analysis of the tumor microenvironment by single-cell transcriptomic revealed the cellular landscape of KRAS-mutant CRC tumors and identified the specific cell subsets with KRAS mutation. Integrated multi-omics analyses separated the KRAS-mutant tumors into two distinct molecular subtypes, termed KRAS-M1 (KM1) and KRAS-M2 (KM2). The two subtypes had a similar distribution of mutated residues in KRAS (G12D/V/C etc.) but were characterized by distinct features in terms of prognosis, genetic alterations, microenvironment dysregulation, biological phenotype, and potential therapeutic approaches. Proteogenomic analyses revealed that the EMT, TGF-β and angiogenesis pathways were enriched in the KM2 subtype and that the KM2 subtype was associated with the mesenchymal phenotype-related CMS4 subtype, which indicated stromal invasion and worse prognosis. The KM1 subtype was characterized predominantly by activation of the cell cycle, E2F and RNA transcription and was associated with the chromosomal instability (CIN)-related ProS-E proteomic subtype, which suggested cyclin-dependent features and better survival outcomes. Moreover, drug sensitivity analyses based on three compound databases revealed subgroup-specific agents for KM1 and KM2 tumors. Conclusions: This study clarifies the molecular heterogeneity of KRAS-mutant CRC and reveals new biological subtypes and therapeutic possibilities for these tumors.

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics

Background

Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach.

Results

We establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible.

Conclusions

In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02701-2.

Identification and characterisation of sPEPs in Cryptococcus neoformans

Short open reading frame (sORF)-encoded peptides (sPEPs) have been found across a wide range of genomic locations in a variety of species. To date, their identification, validation, and characterisation in the human fungal pathogen Cryptococcus neoformans has been limited due to a lack of standardised protocols. We have developed an enrichment process that enables sPEP detection within a protein sample from this polysaccharide-encapsulated yeast, and implemented proteogenomics to provide insights into the validity of predicted and hypothetical sORFs annotated in the C. neoformans genome. Novel sORFs were discovered within the 5' and 3' UTRs of known transcripts as well as in "non-coding" RNAs. One novel candidate, dubbed NPB1, that resided in an RNA annotated as "non-coding", was chosen for characterisation. Through the creation of both specific point mutations and a full deletion allele, the function of the new sPEP, Npb1, was shown to resemble that of the bacterial trans-translation protein SmpB.

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

A short open reading frame (sORFs) constitutes ≤ 300 bases, encoding a microprotein or sORF-encoded protein (SEP) which comprises ≤ 100 amino acids. Traditionally dismissed by genome annotation pipelines as meaningless noise, sORFs were found to possess coding potential with ribosome profiling (RIBO-Seq), which unveiled sORF-based transcripts at various genome locations. Nonetheless, the existence of corresponding microproteins that are stable and functional was little substantiated by experimental evidence initially. With recent advancements in multi-omics, the identification, validation, and functional characterisation of sORFs and microproteins have become feasible. In this review, we discuss the history and development of an emerging research field of sORFs and microproteins. In particular, we focus on an array of bioinformatics and OMICS approaches used for predicting, sequencing, validating, and characterizing these recently discovered entities. These strategies include RIBO-Seq which detects sORF transcripts via ribosome footprints, and mass spectrometry (MS)-based proteomics for sequencing the resultant microproteins. Subsequently, our discussion extends to the functional characterisation of microproteins by incorporating CRISPR/Cas9 screen and protein–protein interaction (PPI) studies. Our review discusses not only detection methodologies, but we also highlight on the challenges and potential solutions in identifying and validating sORFs and their microproteins. The novelty of this review lies within its validation for the functional role of microproteins, which could contribute towards the future landscape of microproteomics.

Enhanced protein isoform characterization through long-read proteogenomics

BACKGROUND

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.

RESULTS

We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

CONCLUSIONS

Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

MaxQuant Module for the Identification of Genomic Variants Propagated into Peptides

Standard shotgun proteomics data analysis pipelines usually only identify peptides that are encoded in the reference genome. In many situations, it is desirable to identify peptides resulting from non-synonymous variations as well. Here, we present a new module in the MaxQuant software that takes both DNA and RNA based next-generation sequencing (NGS) data as well as raw proteomics data as input. This allows for the identification of variant peptides that are otherwise missed.

Peptide ancestry informative markers in uterine neoplasms from women of European, African, and Asian ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R² = 0.99). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues combined resulted in the quantitation of 62 pAIMs that correlate with patient race and genotype-confirmed ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. pAIMs will support generalized proteoancestry assessment as well as efforts investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

Accurate Prediction of Protein Sequences for Proteogenomics Data Integration

This book chapter discusses proteogenomics data integration and provides an overview into the different omics layer involved in defining the proteome of a living organism. Various aspects of genome variability affecting either the sequence or abundance level of proteins are discussed in this book chapter, such as the effect of single-nucleotide variants or larger genomic structural variants on the proteome. Next, various sequencing technologies are introduced and discussed from a proteogenomics data integration perspective such as those providing short- and long-read sequencing and listing their respective advantages and shortcomings for accurate protein variant prediction using genomic/transcriptomics sequencing data. Finally, the various bioinformatics tools used to process and analyze DNA/RNA sequencing data are discussed with the ultimate goal of obtaining accurately predicted sample-specific protein sequences that can be used as a drop-in replacement in existing approaches for peptide and protein identification using popular database search engines such as MSFragger, SearchGUI/PeptideShaker.

Small cell lung cancer; recent advances of its biology and therapeutic perspective

Lung cancer is historically divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). While the therapeutic efficacy of NSCLC has improved due to the development of molecular targeted therapy and immune checkpoint inhibitors (ICIs) treatment, there has been very slow progress in the therapeutic advances of SCLC. Since SCLC is a deadly disease with rapid progression and early metastasis and comprises approximately 10% of lung cancer cases, more attention should be given to the therapeutic strategy for SCLC. Most SCLC cases respond to cytotoxic drugs, cisplatin, and etoposide. The objective response rate to the standard regimen is reported to be approximately 70% that is sufficient as standard therapy. However, almost all tumors recur and become refractory to chemotherapy which is the most important problem of this deadly disease. Recently, for the first time in several decades, ICIs have changed the standard therapy for SCLC. It must be emphasized that although ICIs paved the new way for SCLC therapy, more precise and effective therapy for SCLC is desired. Unfortunately, precise molecular mechanisms of SCLC are yet to be understood. Recent elaborate studies on the cell biology of SCLC uncovered several important aspects of molecular mechanisms. Gene profiling of cancer cells can be done using modern technology like next-generation sequencing (NGS). In this minireview, we describe the advances of modern technology in SCLC research and consider future therapeutic strategies based on the molecular mechanisms of SCLC.

Proteogenomics analysis of CUG codon translation in the human pathogen Candida albicans

Background

Yeasts of the CTG-clade lineage, which includes the human-infecting Candida albicans, Candida parapsilosis and Candida tropicalis species, are characterized by an altered genetic code. Instead of translating CUG codons as leucine, as happens in most eukaryotes, these yeasts, whose ancestors are thought to have lost the relevant leucine-tRNA gene, translate CUG codons as serine using a serine-tRNA with a mutated anticodon, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer. Previously reported experiments have suggested that 3–5% of the CTG-clade CUG codons are mistranslated as leucine due to mischarging of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer. The mistranslation was suggested to result in variable surface proteins explaining fast host adaptation and pathogenicity.

Results

In this study, we reassess this potential mistranslation by high-resolution mass spectrometry-based proteogenomics of multiple CTG-clade yeasts, including various C. albicans strains, isolated from colonized and from infected human body sites, and C. albicans grown in yeast and hyphal forms. Our data do not support a bias towards CUG codon mistranslation as leucine. Instead, our data suggest that (i) CUG codons are mistranslated at a frequency corresponding to the normal extent of ribosomal mistranslation with no preference for specific amino acids, (ii) CUG codons are as unambiguous (or ambiguous) as the related CUU leucine and UCC serine codons, (iii) tRNA anticodon loop variation across the CTG-clade yeasts does not result in any difference of the mistranslation level, and (iv) CUG codon unambiguity is independent of C. albicans’ strain pathogenicity or growth form.

Conclusions

Our findings imply that C. albicans does not decode CUG ambiguously. This suggests that the proposed misleucylation of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer might be as prevalent as every other misacylation or mistranslation event and, if at all, be just one of many reasons causing phenotypic diversity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01197-9.

Effect of actinorhizal root exudates on the proteomes of Frankia soli NRRL B-16219, a strain colonizing the root tissues of its actinorhizal host via intercellular pathway

Frankia and actinorhizal plants exchange signals in the rhizosphere leading to specific mutual recognition of partners and nitrogen-fixing nodule organogenesis. Frankia soli strain NRRL B-16219, from the Elaeagnus specificity group, colonizes the root tissues of its actinorhizal host through direct intercellular penetration of root epidermis cells and cortex. Here, we studied the early proteogenomic response of strain NRRL B-16219 to treatment with root exudates from compatible Elaeagnus angustifolia, and incompatible Ceanothus thyrsiflorus and Coriaria myrtifolia, host plants grown in nitrogen depleted hydroponic medium. Next-generation proteomics was used to identify the main Frankia proteins differentially expressed in response to the root exudates. No products of the nod genes present in B-16219 were detected. Proteins specifically upregulated in presence of E. angustifolia root exudates include those connected to nitrogen fixation and assimilation (glutamate synthetase, hydrogenase and squalene synthesis), respiration (oxidative phosphorylation and citric acid cycle pathways), oxidative stress (catalase, superoxide dismutase, and peroxidase), proteolysis (proteasome, protease, and peptidase) and plant cell wall degrading proteins involved in the depolymerization of celluloses (endoglucanase, glycosyltransferase, beta-mannanases, glycoside hydrolase and glycosyl hydrolase). Proteomic data obtained in this study will help link signaling molecules/factors to their biosynthetic pathways once those factors have been fully characterized.

[Genomics and proteomics in the research of neuromuscular diseases]

Neurological diseases affect 3-5% of children and, apart from cardiovascular diseases and cancer, represent the most prominent cause of morbidity and mortality in adults and particularly in the aged population of western Europe. Neuromuscular disorders are a subgroup of neurological diseases and often have a genetic origin, which leads to familial clustering. Despite the enormous progress in the analysis of the genome, such as by sequence analysis of coding regions of deoxyribonucleic acid or even the entire deoxyribonucleic acid sequence, in approximately 50% of the patients suffering from rare forms of neurological diseases the genetic cause remains unsolved. The reasons for this limited detection rate are presented in this article. If a treatment concept is available, under certain conditions this can have an impact on the adequate and early treatment of these patients. Considering neuromuscular disorders as a paradigm, this article reports on the advantages of the inclusion of next generation sequencing analysis-based DNA investigations as an omics technology (genomics) and the advantage of the integration with protein analyses (proteomics). A special focus is on the combination of genomics and proteomics in the sense of a proteogenomic approach in the diagnostics and research of these diseases. Along this line, this article presents a proteogenomic approach in the context of a multidisciplinary project aiming towards improved diagnostic work-up and future treatment of patients with neuromuscular diseases; "NMD-GPS: gene and protein signatures as a global positioning system in patients suffering from neuromuscular diseases".

Protein-gene Expression Nexus: Comprehensive characterization of human cancer cell lines with proteogenomic analysis

Researchers have gained new therapeutic insights using multi-omics platform approaches to study DNA, RNA, and proteins of comprehensively characterized human cancer cell lines. To improve our understanding of the molecular features associated with oncogenic modulation in cancer, we proposed a proteogenomic database for human cancer cell lines, called Protein-gene Expression Nexus (PEN). We have expanded the characterization of cancer cell lines to include genetic, mRNA, and protein data of 145 cancer cell lines from various public studies. PEN contains proteomic and phosphoproteomic data on 4,129,728 peptides, 13,862 proteins, 7,138 phosphorylation site-associated genomic variations, 117 studies, and 12 cancer. We analyzed functional characterizations along with the integrated datasets, such as cis/trans association for copy number alteration (CNA), single amino acid variation for coding genes, post-translation modification site variation for Single Amino Acid Variation, and novel peptide expression for noncoding regions and fusion genes. PEN provides a user-friendly interface for searching, browsing, and downloading data and also supports the visualization of genome-wide association between CNA and expression, novel peptide landscape, mRNA-protein abundance, and functional annotation. Together, this dataset and PEN data portal provide a resource to accelerate cancer research using model cancer cell lines. PEN is freely accessible at http://combio.snu.ac.kr/pen.

A multi-OMIC characterisation of biodegradation and microbial community succession within the PET plastisphere

BACKGROUND

Plastics now pollute marine environments across the globe. On entering these environments, plastics are rapidly colonised by a diverse community of microorganisms termed the plastisphere. Members of the plastisphere have a myriad of diverse functions typically found in any biofilm but, additionally, a number of marine plastisphere studies have claimed the presence of plastic-biodegrading organisms, although with little mechanistic verification. Here, we obtained a microbial community from marine plastic debris and analysed the community succession across 6 weeks of incubation with different polyethylene terephthalate (PET) products as the sole carbon source, and further characterised the mechanisms involved in PET degradation by two bacterial isolates from the plastisphere.

RESULTS

We found that all communities differed significantly from the inoculum and were dominated by Gammaproteobacteria, i.e. Alteromonadaceae and Thalassospiraceae at early time points, Alcanivoraceae at later time points and Vibrionaceae throughout. The large number of encoded enzymes involved in PET degradation found in predicted metagenomes and the observation of polymer oxidation by FTIR analyses both suggested PET degradation was occurring. However, we were unable to detect intermediates of PET hydrolysis with metabolomic analyses, which may be attributed to their rapid depletion by the complex community. To further confirm the PET biodegrading potential within the plastisphere of marine plastic debris, we used a combined proteogenomic and metabolomic approach to characterise amorphous PET degradation by two novel marine isolates, Thioclava sp. BHET1 and Bacillus sp. BHET2. The identification of PET hydrolytic intermediates by metabolomics confirmed that both isolates were able to degrade PET. High-throughput proteomics revealed that whilst Thioclava sp. BHET1 used the degradation pathway identified in terrestrial environment counterparts, these were absent in Bacillus sp. BHET2, indicating that either the enzymes used by this bacterium share little homology with those characterised previously, or that this bacterium uses a novel pathway for PET degradation.

CONCLUSIONS

Overall, the results of our multi-OMIC characterisation of PET degradation provide a significant step forwards in our understanding of marine plastic degradation by bacterial isolates and communities and evidences the biodegrading potential extant in the plastisphere of marine plastic debris. Video abstract.

Co-expression network analysis identifies novel molecular pathways associated with cadmium and pyriproxyfen testicular toxicity in Gammarus fossarum

Omics approaches are continuously providing new clues on the mechanisms of action of contaminants in species of environmental relevance, contributing to the emergence of molecular ecotoxicology. Co-expression network approaches represent a suitable methodological framework for studying the rich content of omics datasets. This study aimed to find evidence of key pathways and proteins related to the testicular toxicity in the sentinel crustacean species Gammarus fossarum exposed to endocrine disruptors using a weighted protein co-expression network analysis. From a shotgun proteomics dataset of male gonads of G. fossarum organisms exposed to cadmium (Cd), pyriproxyfen (Pyr) and methoxyfenozide (Met) in laboratory conditions, four distinct modules were identified as significantly correlated to contaminants' exposure. Protein set enrichment analysis identified modules involved in cytoskeleton organization and oxidative stress response associated with the Cd exposure. The module associated with Pyr exposure was associated with endoplasmic reticulum stress (ER) response, and the module correlated with Met exposure was characterized by a significant proportion of amphipod-restricted proteins whose functions are still not characterized. Our results show that co-expression networks are efficient and adapted tools to identify new potential mode of actions from environmental sentinel species, such as G. fossarum, using a proteogenomic approach, even without an annotated genome.

Proteomic Interrogation in Cancer Biomarker

Biomarkers factor into the diagnosis and treatment of almost every patient with cancer. The innovation in proteomics follows improvement of mass spectrometry techniques and data processing strategy. Recently, proteomics and typical biological studies have been the answer for clinical applications. The clinical proteomics techniques are now actively adapted to protein identification in large patient cohort, biomarker development for more sensitive and specific screening based on quantitative data. And, it is important for clinical, translational researchers to be acutely aware of the issues surrounding appropriate biomarker development, in order to facilitate entry of clinically useful biomarkers into the clinic. Here, we discuss in detail include the case research for clinical proteomics. Furthermore, we give an overview on the current developments and novel findings in proteomics-based cancer biomarker research.

Proteomics reveals sex-specific heat shock response of Baikal amphipod Eulimnogammarus cyaneus

The ancient Lake Baikal is the largest source of liquid freshwater on Earth and home to a unique fauna. Several hundred mostly cold-adapted endemic amphipod species inhabit Baikal, an ecosystem that is already being influenced by global change. In this study, we characterized the core proteome and heat stress-induced changes in a temperature-tolerant endemic amphipod, Eulimnogammarus cyaneus, using a proteogenomic approach (PRIDE dataset PXD013237) to unravel the molecular mechanisms of the observed adverse effects. As males were previously found to be much more tolerant to thermal stress, we placed special emphasis on differences between the sexes. For both sexes, we observed adaption of energy metabolism, cytoskeleton, lipid, and carbohydrate metabolism upon heat stress. In contrast, significant differences were determined in the molecular chaperone response. Females from the control conditions possessed significantly higher levels of heat shock proteins (HSP70, HSPb1, Hsc70-3), which, in contrast to males, were not further increased in response to heat stress. The inability of females to further increase heat shock protein synthesis in response to temperature stress may be due to sex-specific processes, such as egg production, requiring a large proportion of the available energy. As ovigerous females synthesize generally higher amounts of protein, they also need higher levels of molecular chaperones for the folding of these new proteins. Thus, the higher sensitivity of females to heat shock may be due to the lack of molecular chaperone molecules to counteract the heat-induced protein denaturation.

Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome

BACKGROUND

The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities.

RESULTS

We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx.

CONCLUSIONS

We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract.

Aggresomes predict poor outcomes and implicate proteostasis in the pathogenesis of pediatric choroid plexus tumors

Purpose

Protein misfolding and aggregation result in proteotoxic stress and underlie the pathogenesis of many diseases. To overcome proteotoxicity, cells compartmentalize misfolded and aggregated proteins in different inclusion bodies. The aggresome is a paranuclear inclusion body that functions as a storage compartment for misfolded proteins. Choroid plexus tumors (CPTs) are rare neoplasms comprised of three pathological subgroups. The underlying mechanisms of their pathogenesis remain unclear. This study aims to elucidate the prognostic role and the biological effects of aggresomes in pediatric CPTs.

Methods

We examined the presence of aggresomes in 42 patient-derived tumor tissues by immunohistochemistry and we identified their impact on patients’ outcomes. We then investigated the proteogenomics signature associated with aggresomes using whole-genome DNA methylation and proteomic analysis to define their role in the pathogenesis of pediatric CPTs.

Results

Aggresomes were detected in 64.2% of samples and were distributed among different pathological and molecular subgroups. The presence of aggresomes with different percentages was correlated with patients’ outcomes. The ≥ 25% cutoff had the most significant impact on overall and event-free survival (p-value < 0.001) compared to the pathological and the molecular stratifications.

Conclusions

These results support the role of aggresome as a novel prognostic molecular marker for pediatric CPTs that was comparable to the molecular classification in segregating samples into two distinct subgroups, and to the pathological stratification in the prediction of patients’ outcomes. Moreover, the proteogenomic signature of CPTs displayed altered protein homeostasis, manifested by enrichment in processes related to protein quality control.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11060-020-03694-3.

A Primer and Guidelines for Shotgun Proteomic Analysis in Non-model Organisms

During the last decade, we have witnessed outstanding advances in proteomics led mostly by great technological improvements in mass spectrometry field allowing high-throughput production of high-quality data used for massive protein identification and quantification. From a practical viewpoint, these advances have been mainly exploited in research projects involving model organisms with abundant genomic and proteomic information available in public databases. However, there is a growing number of organisms of high interest in different disciplines, such as ecological, biotechnological, and evolutionary research, yet poorly represented in these databases. Important advances in massive parallel sequencing technology and easy accessibility of this technology to many research laboratories have made nowadays possible to produce customized genomic and proteomic databases of any organism. Along this line, the use of proteogenomic approaches by combining in the same analysis the data obtained from different omic levels has emerged as a very useful and powerful strategy to run shotgun proteomic experiments specially focused on non-model organisms. In this chapter, we provide detailed procedures to undertake shotgun quantitative proteomic experiments following either a label-free or an isobaric labeling approach in non-model organisms, emphasizing also a few key aspects related to experimental design and data analysis.

Proteogenomics for pediatric brain cancer

Pediatric central nervous system tumors are the most common tumors in children, it constitute 15%-20% of all malignancies in children and are the leading cause of cancer related deaths in children. Proteogenomics is an emerging field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of biomarkers for diagnosis and therapeutic purposes. Integrative proteogenomics analysis of pediatric tumors identified underlying biological processes and potential treatments as well as the functional effects of somatic mutations and copy number variation driving tumorigenesis.

Identification of novel protein-coding sequences in Eucalyptus grandis plants by high-resolution mass spectrometry

Eucalyptus species are widely used in the forestry industry, and a significant increase in the number of sequences available in database repositories has been observed for these species. In proteomics, a protein is identified by correlating the theoretical fragmentation spectrum derived from genomic/transcriptomic data against the experimental fragmentation mass spectrum acquired from large-scale analysis of protein mixtures. Proteogenomics is an alternative approach that can identify novel proteins encoded by regions previously considered as non-coding. This study aimed to confidently identify and confirm the existence of previously unknown protein-coding sequences in the Eucalyptus grandis genome. To this end, we used a modified spectral correlation strategy and a dedicated de novo peptide sequencing pipeline. Upon the strategy used here, we confidently identified 41 novel peptide forms and six peptides containing at least one single amino acid substitution. The most representative genomic class of novel peptides was identified as originating from alternative reading frames. In contrast, no clear single amino acid substitution pattern was identified. Validation of the identifications was carried out using a parallel reaction monitoring approach that provided further mass spectrometry support for the existence of the novel peptide sequences. Data are available via ProteomeXchange with identifier PXD022110.

Conus venom fractions inhibit the adhesion of Plasmodium falciparum erythrocyte membrane protein 1 domains to the host vascular receptors

Using high-throughput BioPlex assays, we determined that six fractions from the venom of Conus nux inhibit the adhesion of various recombinant PfEMP-1 protein domains (PF08_0106 CIDR1α3.1, PF11_0521 DBL2β3, and PFL0030c DBL3X and DBL5e) to their corresponding receptors (CD36, ICAM-1, and CSA, respectively). The protein domain-receptor interactions permit P. falciparum-infected erythrocytes (IE) to evade elimination in the spleen by adhering to the microvasculature in various organs including the placenta. The sequences for the main components of the fractions, determined by tandem mass spectrometry, yielded four T-superfamily conotoxins, one (CC-Loop-CC) with I-IV, II-III connectivity and three (CC-Loop-CX_aaC) with a I-III, II-IV connectivity. The 3D structure for one of the latter, NuxVA = GCCPAPLTCHCVIY, revealed a novel scaffold defined by double turns forming a hairpin-like structure stabilized by the two disulfide bonds. Two other main fraction components were a miniM conotoxin, and a O2-superfamily conotoxin with cysteine framework VI/VII. This study is the first one of its kind suggesting the use of conotoxins for developing pharmacological tools for anti-adhesion adjunct therapy against malaria. Similarly, mitigation of emerging diseases like AIDS and COVID-19, can also benefit from conotoxins as inhibitors of protein-protein interactions as treatment. BIOLOGICAL SIGNIFICANCE: Among the 850+ species of cone snail species there are hundreds of thousands of diverse venom exopeptides that have been selected throughout several million years of evolution to capture prey and deter predators. They do so by targeting several surface proteins present in target excitable cells. This immense biomolecular library of conopeptides can be explored for potential use as therapeutic leads against persistent and emerging diseases affecting non-excitable systems. We aim to expand the pharmacological reach of conotoxins/conopeptides by revealing their in vitro capacity to disrupt protein-protein and protein-polysaccharide interactions that directly contribute to pathology of Plasmodium falciparum malaria. This is significant for severe forms of malaria, which might be deadly even after treated with current parasite-killing drugs because of persistent cytoadhesion of P. falciparum infected erythrocytes even when parasites within red blood cells are dead. Anti-adhesion adjunct drugs would de-sequester or prevent additional sequestration of infected erythrocytes and may significantly improve survival of malaria patients. These results provide a lead for further investigations into conotoxins and other venom peptides as potential candidates for anti-adhesion or blockade-therapies. This study is the first of its kind and it suggests that conotoxins can be developed as pharmacological tools for anti-adhesion adjunct therapy against malaria. Similarly, mitigation of emerging diseases like AIDS and COVID-19, can also benefit from conotoxins as potential inhibitors of protein-protein interactions as treatment.

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

Comparison of different variant sequence types coupled with decoy generation methods used in concatenated target-decoy database searches for proteogenomic research

Concatenated target-decoy database searches are commonly used in proteogenomic research for variant peptide identification. Currently, protein-based and peptide-based sequence databases are applied to store variant sequences for database searches. The protein-based database records a full-length wild-type protein sequence but using the given variant events to replace the original amino acids, whereas the peptide-based database retains only the in silico digested peptides containing the variants. However, the performance of applying various decoy generation methods on the peptide-based variant sequence database is still unclear, compared to the protein-based database. In this paper, we conduct a thorough comparison on target-decoy databases constructed by the above two types of databases coupled with various decoy generation methods for proteogenomic analyses. The results show that for the protein-based variant sequence database, using the reverse or the pseudo reverse method achieves similar performance for variant peptide identification. Furthermore, for the peptide-based database, the pseudo reverse method is more suitable than the widely used reverse method, as shown by identifying 6% more variant PSMs in a HEK293 cell line data set. SIGNIFICANCE: In our survey of publications on proteogenomic studies, 57% of the studies adopt the peptide-based variant sequence database coupled with the reverse method for decoy generation to construct a target-decoy database for searches. However, our results show that when using the peptide-based variant sequence database, it is better to adopt the pseudo reverse method for generating decoy sequences, to avoid leading to fewer variant peptides being identified.

The tip of the iceberg for diagnostic dilemmas: Performance of current diagnostics and future complementary screening approaches

Genetic testing is currently the leading edge of clinical care when it comes to diagnostics. However, many questions remain unanswered even when employing next-generation sequencing techniques due to our inability to decode genetic variations and our limited repertoire of available diagnoses. Accordingly, diagnostic yields for current genomic screenings are <50% and fail to provide the whole picture, leaving the remaining patients without a definitive diagnosis. Human phenotypic/disease expression is explained by alterations not only at the genome, but also at the transcriptome, proteome and metabolome levels. These "higher" complexity levels represent at wealth of information, and diagnostic screenings tests at these levels have been shown to significantly improve diagnostic yields in specific populations compared to conventional diagnostic workup or gold standards in use (7-30% increase in diagnostic yields, depending on the population, approach and gold standard being compared against). However, these are not yet routinely available to clinicians. Due to their dynamic and modifiable nature, tapping into data from different omics will improve our understanding of the pathophysiological bases underlying (many yet to characterize) human disorders. We herein review how alterations at these levels (e.g. post-transcriptional and post-translational) may be pathogenic, how such tests may be implemented and in which situations they are of significant utility.

Modelling of pathogen-host systems using deeper ORF annotations and transcriptomics to inform proteomics analyses

The Zika virus is a flavivirus that can cause fulminant outbreaks and lead to Guillain-Barré syndrome, microcephaly and fetal demise. Like other flaviviruses, the Zika virus is transmitted by mosquitoes and provokes neurological disorders. Despite its risk to public health, no antiviral nor vaccine are currently available. In the recent years, several studies have set to identify human host proteins interacting with Zika viral proteins to better understand its pathogenicity. Yet these studies used standard human protein sequence databases. Such databases rely on genome annotations, which enforce a minimal open reading frame (ORF) length criterion. An ever-increasing number of studies have demonstrated the shortcomings of such annotation, which overlooks thousands of functional ORFs. Here we show that the use of a customized database including currently non-annotated proteins led to the identification of 4 alternative proteins as interactors of the viral capsid and NS4A proteins. Furthermore, 12 alternative proteins were identified in the proteome profiling of Zika infected monocytes, one of which was significantly up-regulated. This study presents a computational framework for the re-analysis of proteomics datasets to better investigate the viral-host protein interplays upon infection with the Zika virus.

Proteogenomic analysis of melanoma brain metastases from distinct anatomical sites identifies pathways of metastatic progression

Melanoma brain metastases (MBM) portend a grim prognosis and can occur in up to 40% of melanoma patients. Genomic characterization of brain metastases has been previously carried out to identify potential mutational drivers. However, to date a comprehensive multi-omics approach has yet to be used to analyze brain metastases. In this case report, we present an unbiased proteogenomics analyses of a patient's primary skin cancer and three brain metastases from distinct anatomic locations. We performed molecular profiling comprised of a targeted DNA panel and full transcriptome as well as proteomics using mass spectrometry. Phylogeny demonstrated that all MBMs shared a SMARCA4 mutation and deletion of 12q. Proteogenomics identified multiple pathways upregulated in the MBMs compared to the primary tumor. The protein, PIK3CG, was present in many of these pathways and had increased gene expression in metastatic melanoma tissue from the cancer genome atlas data. Proteomics demonstrated PIK3CG levels were significantly increased in all 3 MBMs and this finding was further validated by immunohistochemistry. In summary, this case report highlights the potential role of proteogenomics in identifying pathways involved in metastatic tumor progression. Furthermore, our multi-omics approach can be considered to aid in precision oncology efforts and provide avenues for therapeutic innovation.

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

Background

Cancer that arises from epithelial cells of the esophagus is called esophagus squamous cell carcinoma (ESCC) and is mostly observed in developing nations. Evaluation of cancer genomes and its regulation into proteins plays a predominant role in understanding the cancer progressions. Mass-spectrometry-based proteomics is a consequential tool to estimate proteomic variation and posttranslational modifications (PTMs) from standard protein databases. Post-translational modifications play a crucial role in protein folding and PTMs can be accounted for as a biological signal to interpret the structural changes and transition order of proteins. Functional validation of cancer-related mutations can explain the effects of mutations on genes and the identification of Oncogenes and tumor suppressor genes. Therefore, we present a study on protein variations to interpret the structural changes and transition order of proteins in ESCC carcinogenesis.

Methodology

We are using a bottom-up proteomics approach with Galaxy-P framework and RNA sequence data analysis to generate the sample-specific databases containing details of RNA splicing and variant peptides. Once the database generated with information on variable modification, only the curated PTMs at specific positions are considered to perform spectral matching. Proteogenomics mapping was performed to identify protein variations in ESCC.

Results

RNA-sequence proteogenomics with G-PTM (Global Post-Translational Modification) searching strategy has revealed proteomic events including several peptides that contain single amino acid variations, novel splice junction peptides and posttranslationally modified peptides. Proteogenomic mapping exhibited the splice junction peptides mapped predominantly for Malic enzyme exon type (ME-3) and MCM7 protein-coding genes that promote cancer progression, found to be exhibited in ESCC samples. Approximately 25 ± types of PTM modifications were recorded, and Protein Phosphorylation was largely noted.

Conclusion

ESCC cancer prognosis at the molecular level enables a better understanding of cancer carcinogenesis and protein modifications can be used as potential biomarkers.

The status of proteomics as we enter the 2020s: Towards personalised/precision medicine

The last decade has seen many major advances in proteomics, with over 70,000 publications in the field since 2010. A comprehensive omics toolbox has been developed facilitating rapid in depth analysis of the human proteome. Such studies are advancing our understanding of the biology of both health and disease. The combination of proteomics with other omics platforms (the omics pipeline), in particular proteogenomics, is giving important insights to the molecular changes leading to disease, covering the spectrum from genotype to phenotype and identifying potential biomarkers for disease detection, surveillance and monitoring, and revealing potential new drug targets. Discovery-based finding are now being translated to clinical application, supporting the rollout of precision/personalised medicine. This perspective has focused on twelve areas of importance that have fuelled the field. Recent exemplars are given to illustrate this and show how, together with some emerging technologies, they are anticipated to lead to further advances in the field. However, hurdles still remain to be overcome, especially in the area of Big Data analysis.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Interactive proteogenomic exploration of response to Fusarium head blight in oat varieties with different resistance

Fusarium species are cereal pathogens that cause the Fusarium Head Blight (FHB) disease. FHB can reduce yield, cause mycotoxin accumulation in the grain and reduce germination efficiency of the harvested seeds. Understanding the biochemical interactions between the host plants and the pathogen is crucial for controlling the disease and for the development of cultivars with improved tolerance to FHB. Here, we studied morphological and proteomic differences between the susceptible oat variety Belinda and the more resistant variety Argamak using variety-specific transcriptome assemblies as references. Measurements of deoxynivalenol toxin levels confirmed the partial resistance in Argamak and the susceptibility in Belinda. To jointly investigate the proteomics- and sequence data, we developed an RShiny-based interface for interactive exploration of the dataset using univariate and multivariate statistics. When applying this interface to the dataset, quantitative protein differences between Belinda and Argamak were detected, and eighteen peptides were found uniquely in Argamak during infection, among them several lipoxygenases. Such proteins can be developed as markers for Fusarium resistance breeding. In conclusion, this study provides the first proteogenomic insight on molecular Fusarium-oat interactions at both morphological and molecular levels and the data are openly available through an interactive interface for further inspection. SIGNIFICANCE: Fusarium head blight causes widespread damage to crops, and chronic and acute toxicity to human and livestock due to the accumulation of toxins during infection. In the present study, two oat varieties with differing resistance were challenged with Fusarium to understand the disease better, and studied both at morphological and molecular levels, identifying proteins which could play a role in the defense mechanism. Furthermore, a proteogenomics approach allows joint profiling of expression and sequence level differences to identify potentially functionally differing mutations. Here such analysis is made openly available through an interactive interface which allows other scientists to draw further findings from the data. This study may both serve as a basis for understanding oat disease response and developing breeding markers for Fusarium resistant oat and future proteogenomic studies using the interactive approach described.