Proteogenomic Approach Research Articles

A Proteogenomic Approach for the Identification of Virulence Factors in Leishmania Parasites.

Identifying new genes involved in virulence and drug resistance may hold the key to a better understanding of parasitic diseases. The proteogenomic profiling of various Leishmania species, the causative agents of leishmaniasis, has identified several novel genes, N- and C-terminal extensions of proteins, and corrections of existing gene models. Various virulence factors (VFs) responsible for leishmaniasis have been previously annotated through a proteogenomic approach, including the C-terminal extension of heat shock protein 70 (HSP70). Furthermore, the diversity of VFs across Leishmania donovani, L. infantum, L. major, and L. mexicana was determined using phylogenetic analysis. Moreover, protein-protein interaction networks (PPINs) of VFs with HSPs aid in making significant biological interpretations. Overall, an integrated omics approach involving proteogenomics was used to identify and study the relationship among VFs with other interacting proteins, including HSPs. This chapter provides a step-by-step guide to the identification of new genes in Leishmania using a proteogenomic approach and their functional assignment using a bioinformatics-based approach.

Introduction to Integrated Proteogenomic Pipeline for Dealing with Pathogenic Missense SNPs.

Proteogenomics is a multi-omics setup combining mass spectrometry and next-generation sequencing (NGS) technologies (using genomics and/or transcriptomics) with main aims of improving genome annotation and facilitating characterization of proteo-isoforms. However, working with proteogenomic approach is a very challenging task as it is generating multi-omics data and integrating these data for interpretation of results for biological or clinical implications. There is an urgent need for the development of protocols for integrated proteogenomics approaches. Genome resequencing yields massive data for missense single-nucleotide polymorphisms (SNP), and SNPs are yet not fully covered for their pathogenic nature using proteogenomic approaches. In this chapter, we present such a protocol for dealing with pathogenic missense SNPs using an integrated proteogenomics pipeline combining several steps: DNA-Seq, RNA-Seq, mass spectroscopy (MS), making customized databases of produced datasets, and screening and filtering for useful MS spectrums. This protocol also provides users with tricks and tips for the modifications, based on the requirements of the projects.

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics.

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach.

Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery.

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

Identification and phenotypic characterization of neoantigen-specific cytotoxic CD4+ T cells in endometrial cancer.

Tumor-reactive CD4+ T cells often accumulate in the tumor microenvironment (TME) in human cancer, but their functions and roles in antitumor responses remain elusive. Here, we investigated the immunopeptidome of HLA class II-positive (HLA-II+) endometrial cancer with an inflamed TME using a proteogenomic approach. We identified HLA-II neoantigens, one of which induced polyclonal CD4+ tumor-infiltrating lymphocyte (TIL) responses. We then experimentally demonstrated that neoantigen-specific CD4+ TILs lyse target cells in an HLA-II-dependent manner. Single cell transcriptomic analysis of the TME coupled with T-cell receptor (TCR) sequencing revealed the presence of CD4+ T-cell clusters characterized by CXCL13 expression. The CXCL13+ clusters contained two subclusters with distinct cytotoxic gene expression patterns. The identified neoantigen-specific CD4+ T cells were found exclusively in one of the CXCL13+ subclusters characterized by granzyme B and CCL5 expression. These results demonstrate the involvement of tumor-reactive CD4+ T cells with cytotoxic function in immune surveillance of endometrial cancer and reveal their transcriptomic signature.

Molecular Profiling of A549 Cell-Derived Exosomes: Proteomic, miRNA, and Interactome Analysis for Identifying Potential Key Regulators in Lung Cancer.

Exosomes, nano-sized extracellular vesicles released by all cells, play a key role in intercellular communication and carry tumorigenic properties that impact surrounding or distant cells. The complexity of the exosomal molecular interactome and its effects on recipient cells still remain unclear. This study aims to decipher the molecular profile and interactome of lung adenocarcinoma A549 cell-derived exosomes using multi-omics and bioinformatics approaches. We performed comprehensive morphological and physicochemical characterization of exosomes isolated from cell culture supernatant of A549 cells in vitro, using DLS, cryo-TEM, Western blot, and flow cytometry. Proteomic and miRNA high-throughput profiling, coupled with bioinformatics network analysis, were applied to elucidate the exosome molecular cargo. A comparative miRNA analysis was also conducted with exosomes derived from normal lung fibroblast MRC-5 cells. Exosomes exhibited an average size of ~40 nm and disk-shaped lipid bilayer structures, with tetraspanins CD9 and CD63 validated as exosomal markers. Proteomic analysis identified 68 proteins, primarily linked to the extracellular matrix organization and metabolic processes. miRNA sequencing revealed 72 miRNAs, notably hsa-miR-619-5p, hsa-miR-122-5p, hsa-miR-9901, hsa-miR-7704, and hsa-miR-151a-3p, which are involved in regulating metabolic processes, gene expression, and tumorigenic pathways. Th integration of proteomic and miRNA data through a proteogenomics approach identified dually affected genes including ERBB2, CD44, and APOE, impacted by both exosomal miRNA targeting and protein interactions through synergistic or antagonistic interactions. Differential analysis revealed a distinct miRNA profile in A549 exosomes, associated with cancer-related biological processes, compared to MRC-5 exosomes; notably, hsa-miR-619-5p emerged as a promising candidate for future clinical biomarker studies. The network analysis also revealed genes targeted by multiple upregulated tumor-associated miRNAs in potential exosome-recipient cells. This integrative study provides insights into the molecular interactome of lung adenocarcinoma A549 cell-derived exosomes, providing a foundation for future research on exosomal cargo and its role in tumor cell communication, growth, and progression.

SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire.

During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM/SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire in mice. SAP deficiency resulted in both a significant loss of an immature Gzma+Blk+Etv5+Tox2+ γδT17 precursor population and a significant increase in Cd4+Cd8+Rorc+Ptcra+Rag1+ thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data support a model in which SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.

Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies.

Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.

SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire

During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM/SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire in mice. SAP deficiency resulted in both a significant loss of an immature Gzma+Blk+Etv5+Tox2+ γδT17 precursor population and a significant increase in Cd4+Cd8+Rorc+Ptcra+Rag1+ thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data support a model in which SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.

A Proteogenomic Approach to Identify Potential Drivers of Hyperdiploid Multiple Myeloma

A Proteogenomic Approach to Identify Potential Drivers of Hyperdiploid Multiple Myeloma

Proteogenomic characterization of highly enriched viable leukemic blasts in acute myeloid leukemia: A SWOG report.

Acute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation. Given the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next-generation sequencing and mass spectrometry-based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis. We identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N=27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein-RNA correlation. Together, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.

A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma

Cancer immunotherapies produce remarkable results in B cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor and normal tissues to identify biologically relevant cell surface immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer. Proteogenomic analyses reveal sixty high-confidence candidate immunotherapeutic targets, and we prioritize delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlates with a super-enhancer. Immunofluorescence, flow cytometry, and immunohistochemistry show robust cell surface expression of DLK1. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells results in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Since high DLK1 expression is found in several adult and pediatric cancers, our study demonstrates the utility of a proteogenomic approach and credentials DLK1 as an immunotherapeutic target.

Proteogenomic Approaches for Diseasome Studies.

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Comparative Proteogenomic Approaches for Mapping the Global Proteome of the Unsequenced Leishmania Vector Phlebotomus papatasi.

The rapid improvements in next-generation sequencing technologies have made it possible to quickly access in-depth genome sequence data. This has resulted in a flurry of genome sequences of various organisms being published and made publicly available in the last two decades. However, not all organisms have genome sequence data available. Various factors play a role, such as the importance of the organism, either medically or economically, and the genome complexity of the organisms. Phlebotomus papatasi is the sandfly vector for the Leishmania parasite, which is the causative agent for leishmaniasis. P. papatasi is a hematophagous vector, and the female flies feed on human blood to complete their reproductive cycle. The P. papatasi genome is currently being sequenced as part of a multicentric consortium, and the genome sequence is not published to date. Hence, efforts to map its global proteome are hindered in P. papatasi. In such cases, comparative proteogenomic approaches can help map the global proteome of an unsequenced organism using homology-based methods.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities′ expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

HLA class II neoantigen presentation for CD4+ T cell surveillance in HLA class II-negative colorectal cancer

ABSTRACT Neoantigen-reactive CD4+ T cells play a key role in the anti-tumor immune response. However, the majority of epithelial tumors are negative for HLA class II (HLA-II) surface expression, and less is known about the processing of HLA-II antigens. Here, we directly identified naturally presented HLA-II neoantigens in HLA-II negative colorectal cancer (CRC) tissue using a proteogenomic approach. The neoantigens were immunogenic and induced patient CD4+ T cells with a Th1-like memory phenotype that produced IFN-γ, IL2 and TNF-α. Multiplex immunohistochemistry (IHC) demonstrated an interaction between Th cells and HLA-II-positive antigen-presenting cells (APCs) at the invasive margin and within the tertiary lymphoid structures (TLS). In our CRC cohort, the density of stromal APCs was associated with HLA-II antigen presentation in the tumor microenvironment (TME), and the number of TLS was positively correlated with the number of somatic mutations in the tumors. These results demonstrate the presence of neoantigen-specific CD4+ surveillance in HLA-II-negative CRC and suggest a potential role for macrophages and dendritic cells (DCs) at the invasive margin and in TLS for antigen presentation. Stromal APCs in the TME can potentially be used as a source for HLA-II neoantigen identification.

Microproteins unveiling new dimensions in cancer.

In the complex landscape of cancer biology, the discovery of microproteins has triggered a paradigm shift, thereby, challenging the conventional conceptions of gene regulation. Though overlooked for years, these entities encoded by the small open reading frames (100-150 codons), have a significant impact on various cellular processes. As precision medicine pioneers delve deeper into the genome and proteome, microproteins have come into the limelight. Typically characterized by a single protein domain that directly binds to the target protein complex and regulates their assembly, these microproteins have been shown to play a key role in fundamental biological processes such as RNA processing, DNA repair, and metabolism regulation. Techniques for identification and characterization, such as ribosome profiling and proteogenomic approaches, have unraveled unique mechanisms by which these microproteins regulate cell signaling or pathological processes in most diseases including cancer. However, the functional relevance of these microproteins in cancer remains unclear. In this context, the current review aims to "rethink the essence of these genes" and explore "how these hidden players-microproteins orchestrate the signaling cascades of cancer, both as accelerators and brakes.".

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection.

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire.

During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM-SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire. SAP deficiency resulted in both a significant loss of an immature Gzma + Blk + Etv5 + Tox2 + γδT17 precursor population, and a significant increase in Cd4 + Cd8+ Rorc + Ptcra + Rag1 + thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data suggest that SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.

Understanding bacterial pathogen diversity: A proteogenomic analysis and use of an array of genome assemblies to identify novel virulence factors of the honey bee bacterial pathogen Paenibacillus larvae.

Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.