Analysis of asymptomatic Drosophila models for ALS and SMA reveals convergent impact on functional protein complexes linked to neuro-muscular degeneration.

BACKGROUND: Spinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS) share phenotypic and molecular commonalities, including the fact that they can be caused by mutations in ubiquitous proteins involved in RNA metabolism, namely SMN, TDP-43 and FUS. Although this suggests the existence of common disease mechanisms, there is currently no model to explain the resulting motor neuron dysfunction. In this work we generated a parallel set of Drosophila models for adult-onset RNAi and tagged neuronal expression of the fly orthologues of the three human proteins, named Smn, TBPH and Caz, respectively. We profiled nuclear and cytoplasmic bound mRNAs using a RIP-seq approach and characterized the transcriptome of the RNAi models by RNA-seq. To unravel the mechanisms underlying the common functional impact of these proteins on neuronal cells, we devised a computational approach based on the construction of a tissue-specific library of protein functional modules, selected by an overall impact score measuring the estimated extent of perturbation caused by each gene knockdown. RESULTS: Transcriptome analysis revealed that the three proteins do not bind to the same RNA molecules and that only a limited set of functionally unrelated transcripts is commonly affected by their knock-down. However, through our integrative approach we were able to identify a concerted effect on protein functional modules, albeit acting through distinct targets. Most strikingly, functional annotation revealed that these modules are involved in critical cellular pathways for motor neurons, including neuromuscular junction function. Furthermore, selected modules were found to be significantly enriched in orthologues of human neuronal disease genes. CONCLUSIONS: The results presented here show that SMA and ALS disease-associated genes linked to RNA metabolism functionally converge on neuronal protein complexes, providing a new hypothesis to explain the common motor neuron phenotype. The functional modules identified represent promising biomarkers and therapeutic targets, namely given their alteration in asymptomatic settings.

Comprehensive and systematic characterization of multi-functionalized cisplatin nano-conjugate: from the chemistry and proteomic biocompatibility to the animal model.

BACKGROUND: Nowadays, nanoparticles (NPs) have evolved as multifunctional systems combining different custom anchorages which opens a wide range of applications in biomedical research. Thus, their pharmacological involvements require more comprehensive analysis and novel nanodrugs should be characterized by both chemically and biological point of view. Within the wide variety of biocompatible nanosystems, iron oxide nanoparticles (IONPs) present mostly of the required features which make them suitable for multifunctional NPs with many biopharmaceutical applications. RESULTS: Cisplatin-IONPs and different functionalization stages have been broadly evaluated. The potential application of these nanodrugs in onco-therapies has been assessed by studying in vitro biocompatibility (interactions with environment) by proteomics characterization the determination of protein corona in different proximal fluids (human plasma, rabbit plasma and fetal bovine serum),. Moreover, protein labeling and LC-MS/MS analysis provided more than 4000 proteins de novo synthetized as consequence of the nanodrugs presence defending cell signaling in different tumor cell types (data available via ProteomeXchanges with identified PXD026615). Further in vivo studies have provided a more integrative view of the biopharmaceutical perspectives of IONPs. CONCLUSIONS: Pharmacological proteomic profile different behavior between species and different affinity of protein coating layers (soft and hard corona). Also, intracellular signaling exposed differences between tumor cell lines studied. First approaches in animal model reveal the potential of theses NPs as drug delivery vehicles and confirm cisplatin compounds as strengthened antitumoral agents.

Single-Cell Proteomics: The Critical Role of Nanotechnology.

In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.

Enhancement of Tumor Cell Immunogenicity and Antitumor Properties Derived from Platinum-Conjugated Iron Nanoparticles.

From chemistry design to clinical application, several approaches have been developed to overcome platinum drawbacks in antitumoral therapies. An in-depth understanding of intracellular signaling may hold the key to the relationship of both conventional drugs and nanoparticles. Within these strategies, first, nanotechnology has become an essential tool in oncotherapy, improving biopharmaceutical properties and providing new immunomodulatory profiles to conventional drugs mediated by activation of endoplasmic reticulum (ER) stress. Secondly, functional proteomics techniques based on microarrays have proven to be a successful method for high throughput screening of proteins and profiling of biomolecule mechanisms of action. Here, we conducted a systematic characterization of the antitumor profile of a platinum compound conjugated with iron oxide nanoparticles (IONPs). As a result of the nano-conjugation, cytotoxic and proteomics profiles revealed a significant improvement in the antitumor properties of the starting material, providing selectivity in certain tumor cell lines tested. Moreover, cell death patterns associated with immunogenic cell death (ICD) response have also been identified when ER signaling pathways have been triggered. The evaluation in several tumor cell lines and the analysis by functional proteomics techniques have shown novel perspectives on the design of new cisplatin-derived conjugates, the high value of IONPs as drug delivery systems and ICD as a rewarding approach for targeted oncotherapy and onco-immunotherapies.

Systematic Evaluation of Antigenic Stimulation in Chronic Lymphocytic Leukemia: Humoral Immunity as Biomarkers for Disease Evolution.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the Western world. Studies of CLL antibody reactivity have shown differential targets to autoantigens and antimicrobial molecular motifs that support the current hypothesis of CLL pathogenesis. METHODS: In this study, we conducted a quantitative serum analysis of 7 immunoglobulins in CLL and monoclonal B-cell lymphocytosis (MBL) patients (bead-suspension protein arrays) and a serological profile (IgG and IgM) study of autoantibodies and antimicrobial antigens (protein microarrays). RESULTS: Significant differences in the IgA levels were observed according to disease progression and evolution as well as significant alterations in IgG1 according to IGHV mutational status. More representative IgG autoantibodies in the cohort were against nonmutagenic proteins and IgM autoantibodies were against vesicle proteins. Antimicrobial IgG and IgM were detected against microbes associated with respiratory tract infections. CONCLUSIONS: Quantitative differences in immunoglobulin serum levels could be potential biomarkers for disease progression. In the top 5 tumoral antigens, we detected autoantibodies (IgM and IgG) against proteins related to cell homeostasis and metabolism in the studied cohort. The top 5 microbial antigens were associated with respiratory and gastrointestinal infections; moreover, the subsets with better prognostics were characterized by a reactivation of Cytomegalovirus. The viral humoral response could be a potential prognosis biomarker for disease progression.

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

SARS-CoV-2 Infection Triggers Auto-Immune Response in ARDS.

Acute respiratory distress syndrome (ARDS) is a severe pulmonary disease, which is one of the major complications in COVID-19 patients. Dysregulation of the immune system and imbalances in cytokine release and immune cell activation are involved in SARS-CoV-2 infection. Here, the inflammatory, antigen, and auto-immune profile of patients presenting COVID-19-associated severe ARDS has been analyzed using functional proteomics approaches. Both, innate and humoral responses have been characterized through acute-phase protein network and auto-antibody signature. Severity and sepsis by SARS-CoV-2 emerged to be correlated with auto-immune profiles of patients and define their clinical progression, which could provide novel perspectives in therapeutics development and biomarkers of COVID-19 patients. Humoral response in COVID-19 patients' profile separates with significant differences patients with or without ARDS. Furthermore, we found that this profile can be correlated with COVID-19 severity and results more common in elderly patients.

Systematic evaluation of plasma signaling cascades by functional proteomics approaches: SARS-CoV-2 infection as model.

PURPOSE: Acute phase reactants (APRs) play a critical role in inflammation. The difference in their physiological functions or the different dynamic ranges of these proteins in plasma makes it difficult to detect them simultaneously and to use several of these proteins as a tool in clinical practice. EXPERIMENTAL DESIGN: A novel multiplex assay has been designed and optimized to carry out a high-throughput and simultaneous screening of APRs, allowing the detection of each of them at the same time and in their corresponding dynamic range. RESULTS: Using Sars-CoV-2 infection as a model, it has been possible to profile different patterns of acute phase proteins that vary significantly between healthy and infected patients. In addition, severity profiles (acute respiratory distress syndrome and sepsis) have been established. CONCLUSIONS AND CLINICAL RELEVANCE: Differential profiles in acute phase proteins can serve as a diagnostic and prognostic tool, among patient stratification. The design of this new platform for their simultaneous detection paves the way for them to be more extensive use in clinical practice.

Unravelling soluble immune checkpoints in chronic lymphocytic leukemia: Physiological immunomodulators or immune dysfunction.

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape.

Deciphering Human Leukocyte Antigen Susceptibility Maps From Immunopeptidomics Characterization in Oncology and Infections.

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen [HLA] A, B, and C) may affect susceptibility to many diseases such as cancer, auto-immune or infectious diseases. Individual genetic variation may help to explain different immune responses to microorganisms across a population. HLA typing can be fast and inexpensive; however, deciphering peptides loaded on MHC-I and II which are presented to T cells, require the design and development of high-sensitivity methodological approaches and subsequently databases. Hence, these novel strategies and databases could help in the generation of vaccines using these potential immunogenic peptides and in identifying high-risk HLA types to be prioritized for vaccination programs. Herein, the recent developments and approaches, in this field, focusing on the identification of immunogenic peptides have been reviewed and the next steps to promote their translation into biomedical and clinical practice are discussed.

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis.

Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B .