Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Non-apoptotic FAS signaling controls mTOR activation and extrafollicular maturation in human B cells

Defective FAS (CD95/Apo-1/TNFRSF6) signaling causes autoimmune lymphoproliferative syndrome (ALPS). Hypergammaglobulinemia is a common feature in ALPS with FAS mutations (ALPS-FAS), but paradoxically, fewer conventional memory cells differentiate from FAS-expressing germinal center (GC) B cells. Resistance to FAS-induced apoptosis does not explain this phenotype. We tested the hypothesis that defective non-apoptotic FAS signaling may contribute to impaired B cell differentiation in ALPS. We analyzed secondary lymphoid organs of patients with ALPS-FAS and found low numbers of memory B cells, fewer GC B cells, and an expanded extrafollicular (EF) B cell response. Enhanced mTOR activity has been shown to favor EF versus GC fate decision, and we found enhanced PI3K/mTOR and BCR signaling in ALPS-FAS splenic B cells. Modeling initial T-dependent B cell activation with CD40L in vitro, we showed that FAS competent cells with transient FAS ligation showed specifically decreased mTOR axis activation without apoptosis. Mechanistically, transient FAS engagement with involvement of caspase-8 induced nuclear exclusion of PTEN, leading to mTOR inhibition. In addition, FASL-dependent PTEN nuclear exclusion and mTOR modulation were defective in patients with ALPS-FAS. In the early phase of activation, FAS stimulation promoted expression of genes related to GC initiation at the expense of processes related to the EF response. Hence, our data suggest that non-apoptotic FAS signaling acts as molecular switch between EF versus GC fate decisions via regulation of the mTOR axis and transcription. The defect of this modulatory circuit may explain the observed hypergammaglobulinemia and low memory B cell numbers in ALPS.

Interruption of p53-MDM2 Interaction by Nutlin-3a in Human Lymphoma Cell Models Initiates a Cell-Dependent Global Effect on Transcriptome and Proteome Level

In most lymphomas, p53 signaling pathway is inactivated by various mechanisms independent to p53 gene mutations or deletions. In many cases, p53 function is largely regulated by alterations in the protein abundance levels by the action of E3 ubiquitin-protein ligase MDM2, targeting p53 to proteasome-mediated degradation. In the present study, an integrating transcriptomics and proteomics analysis was employed to investigate the effect of p53 activation by a small-molecule MDM2-antagonist, nutlin-3a, on three lymphoma cell models following p53 activation. Our analysis revealed a system-wide nutlin-3a-associated effect in all examined lymphoma types, identifying in total of 4037 differentially affected proteins involved in a plethora of pathways, with significant heterogeneity among lymphomas. Our findings include known p53-targets and novel p53 activation effects, involving transcription, translation, or degradation of protein components of pathways, such as a decrease in key members of PI3K/mTOR pathway, heat-shock response, and glycolysis, and an increase in key members of oxidative phoshosphorylation, autophagy and mitochondrial translation. Combined inhibition of HSP90 or PI3K/mTOR pathway with nutlin-3a-mediated p53-activation enhanced the apoptotic effects suggesting a promising strategy against human lymphomas. Integrated omic profiling after p53 activation offered novel insights on the regulatory role specific proteins and pathways may have in lymphomagenesis.

Recurrent NOMO1 Gene Deletion Is a Potential Clinical Marker in Early-Onset Colorectal Cancer and Is Involved in the Regulation of Cell Migration

Simple Summary

The incidence of EOCRC (age < 50 years at diagnosis) with unknown causes is rising worldwide, necessitating the mechanistical analysis of its molecular basis. The NOMO1 gene is deleted in a high number of EOCRC tumors compared to LOCRC. In this work, we aimed to test the NOMO1 gene mutational profile in EOCRC tumors and to characterize the effect of NOMO1 loss in different CRISPR/cas9-edited cell lines, as well as in murine models. Here, we show that the NOMO1 gene can be inactivated not only by deletion but also by pathogenic mutations in EOCRC. Our results indicate that NOMO1 loss could be a passenger mutation in the development of EOCRC, although it contributes significantly to colon cancer cell migration.

Abstract

The incidence of early-onset colorectal cancer (EOCRC; age younger than 50 years) has been progressively increasing over the last decades globally, with causes unexplained. A distinct molecular feature of EOCRC is that compared with cases of late-onset colorectal cancer, in EOCRC cases, there is a higher incidence of Nodal Modulator 1 (NOMO1) somatic deletions. However, the mechanisms of NOMO1 in early-onset colorectal carcinogenesis are currently unknown. In this study, we show that in 30% of EOCRCs with heterozygous deletion of NOMO1, there were pathogenic mutations in this gene, suggesting that NOMO1 can be inactivated by deletion or mutation in EOCRC. To study the role of NOMO1 in EOCRC, CRISPR/cas9 technology was employed to generate NOMO1 knockout HCT-116 (EOCRC) and HS-5 (bone marrow) cell lines. NOMO1 loss in these cell lines did not perturb Nodal pathway signaling nor cell proliferation. Expression microarrays, RNA sequencing, and protein expression analysis by LC–IMS/MS showed that NOMO1 inactivation deregulates other signaling pathways independent of the Nodal pathway, such as epithelial–mesenchymal transition and cell migration. Significantly, NOMO1 loss increased the migration capacity of CRC cells. Additionally, a gut-specific conditional NOMO1 KO mouse model revealed no subsequent tumor development in mice. Overall, these findings suggest that NOMO1 could play a secondary role in early-onset colorectal carcinogenesis because its loss increases the migration capacity of CRC cells. Therefore, further study is warranted to explore other signalling pathways deregulated by NOMO1 loss that may play a significant role in the pathogenesis of the disease.

Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model

Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.

Dynamic Intracellular Metabolic Cell Signaling Profiles During Ag-Dependent B-Cell Differentiation

Human B-cell differentiation has been extensively investigated on genomic and transcriptomic grounds; however, no studies have accomplished so far detailed analysis of antigen-dependent maturation-associated human B-cell populations from a proteomic perspective. Here, we investigate for the first time the quantitative proteomic profiles of B-cells undergoing antigen-dependent maturation using a label-free LC-MS/MS approach applied on 5 purified B-cell subpopulations (naive, centroblasts, centrocytes, memory and plasma B-cells) from human tonsils (data are available via ProteomeXchange with identifier PXD006191). Our results revealed that the actual differences among these B-cell subpopulations are a combination of expression of a few maturation stage-specific proteins within each B-cell subset and maturation-associated changes in relative protein expression levels, which are related with metabolic regulation. The considerable overlap of the proteome of the 5 studied B-cell subsets strengthens the key role of the regulation of the stoichiometry of molecules associated with metabolic regulation and programming, among other signaling cascades (such as antigen recognition and presentation and cell survival) crucial for the transition between each B-cell maturation stage.

Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma

Meningioma are the most frequent primary intracranial tumour. Management of aggressive meningioma is complex, and development of effective biomarkers or pharmacological interventions is hampered by an incomplete knowledge of molecular landscape. Here, we present an integrated analysis of two complementary omics studies to investigate alterations in the "transcriptome-proteome" profile of high-grade (III) compared to low-grade (I) meningiomas. We identified 3598 common transcripts/proteins and revealed concordant up- and downregulation in grade III vs. grade I meningiomas. Concordantly upregulated genes included FABP7, a fatty acid binding protein and the monoamine oxidase MAOB, the latter of which we validated at the protein level and established an association with Food and Drug Administration (FDA)-approved drugs. Notably, we derived a plasma signature of 21 discordantly expressed genes showing positive changes in protein but negative in transcript levels of high-grade meningiomas, including the validated genes CST3, LAMP2, PACS1 and HTRA1, suggesting the acquisition of these proteins by tumour from plasma. Aggressive meningiomas were enriched in processes such as oxidative phosphorylation and RNA metabolism, whilst concordantly downregulated genes were related to reduced cellular adhesion. Overall, our study provides the first transcriptome-proteome characterisation of meningioma, identifying several novel and previously described transcripts/proteins with potential grade III biomarker and therapeutic significance.

Proteogenomics for the Comprehensive Analysis of Human Cellular and Serum Antibody Repertoires

The vast repertoire of immunoglobulins produced by the immune system is a consequence of the huge amount of antigens to which we are exposed every day. The diversity of these immunoglobulins is due to different mechanisms (including VDJ recombination, somatic hypermutation, and antigen selection). Understanding how the immune system is capable of generating this diversity and which are the molecular bases of the composition of immunoglobulins are key challenges in the immunological field. During the last decades, several techniques have emerged as promising strategies to achieve these goals, but it is their combination which appears to be the fruitful solution for increasing the knowledge about human cellular and serum antibody repertoires.In this chapter, we address the diverse strategies focused on the analysis of immunoglobulin repertoires as well as the characterization of the genomic and peptide sequences. Moreover, the advantages of combining various -omics approaches are discussed through review different published studies, showing the benefits in clinical areas.

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .

PepPSy: a web server to prioritize gene products in experimental and biocuration workflows

Among the 20 000 human gene products predicted from genome annotation, about 3000 still lack validation at protein level. We developed PepPSy, a user-friendly gene expression-based prioritization system, to help investigators to determine in which human tissues they should look for an unseen protein. PepPSy can also be used by biocurators to revisit the annotation of specific categories of proteins based on the ‘omics’ data housed by the system. In this study, it was used to prioritize 21 dubious protein-coding genes among the 616 annotated in neXtProt for reannotation. PepPSy is freely available at http://peppsy.genouest.org.

Database URL:http://peppsy.genouest.org.

A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro

Omics technologies, such as proteomics or metabolomics, have to date been applied in the field of nanomaterial safety assessment to a limited extent. To address this dearth, we developed an integrated approach combining the two techniques to study the effects of two sizes, 5 and 30 nm, of gold nanoparticles (AuNPs) in Caco-2 cells. We observed differences in cells exposed for 72 h to each size of AuNPs: 61 responsive (up/down-regulated) proteins were identified and 35 metabolites in the cell extract were tentatively annotated. Several altered biological pathways were highlighted by integrating the obtained multi-omics data with bioinformatic tools. This provided a unique set of molecular information on the effects of nanomaterials at cellular level. This information was supported by complementary data obtained by immunochemistry, microscopic analysis, and multiplexed assays. A part from increasing our knowledge on how the cellular processes and pathways are affected by nanomaterials (NMs), these findings could be used to identify specific biomarkers of toxicity or to support the safe-by-design concept in the development of new nanomedicines.