RESUMEN

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Asunto(s)

RESUMEN

Uncontrolled use of pesticides has caused a dramatic reduction in the number of pollinators, including bees. Studies on the effects of pesticides on bees have reported effects on both metabolic and neurological levels under chronic exposure. In this study, variations in the differential expression of head and thorax-abdomen proteins in Africanized A. mellifera bees treated acutely with sublethal doses of glyphosate and imidacloprid were studied using a proteomic approach. A total of 92 proteins were detected, 49 of which were differentially expressed compared to those in the control group (47 downregulated and 2 upregulated). Protein interaction networks with differential protein expression ratios suggested that acute exposure of A. mellifera to sublethal doses of glyphosate could cause head damage, which is mainly associated with behavior and metabolism. Simultaneously, imidacloprid can cause damage associated with metabolism as well as, neuronal damage, cellular stress, and impairment of the detoxification system. Regarding the thorax-abdomen fractions, glyphosate could lead to cytoskeleton reorganization and a reduction in defense mechanisms, whereas imidacloprid could affect the coordination and impairment of the oxidative stress response.

Asunto(s)

RESUMEN

Background and Aims: Lung cancer is ranked as the second most prevalent form of cancer worldwide. Nonsmall cell lung cancer (NSCLC) represents the predominant histological subtype. Research suggests that one-third of lung cancer patients also experiencing depression. Antidepressants play an indispensable role in the management of NSCLC. Despite significant advancements in treatment, lung cancer patients still face a high mortality rate. Major depressive disorder (MDD) and related antidepressants involved in treatment efficacy and prognosis of NSCLC. However, there has been a lack of screening and analysis regarding genes and networks associated with both NSCLC and MDD. Methods: To investigate the correlation between MDD and NSCLC, our discovery and validation analysis included four datasets from the Gene Expression Omnibus database from NSCLC or MDD. Differential gene expression (DEGs) analysis, GO and KEGG Pathway, and protein-protein interaction network analyzes to identify hub genes, networks, and associated observations link between MDD and NSCLC. Results: The analysis of two datasets yielded a total of 84 downregulated and 52 upregulated DEGs. Pathway enrichment analyzes indicated that co-upregulated genes were enriched in the regulation of positive regulation of cellular development, collagen-containing extracellular matrix (ECM), cytokine binding, and axon guidance. We identified 20 key genes, which were further analyzed using the MCODE plugin to identify two core subnetworks. The integration of functionally similar genes provided valuable insights into the potential involvement of these hub genes in diverse biological processes including angiogenesis humoral immune response regulation inflammatory response organization ECM network. Conclusion: We have identified a total of 136 DEGs that participate in multiple biological signaling pathways. A total of 20 hub genes have demonstrated robust associations, potentially indicating novel diagnostic and therapeutic targets for both diseases.

RESUMEN

The hepatopancreas is the biggest digestive organ in Amphioctopus fangsiao (A. fangsiao), but also undertakes critical functions like detoxification and immune defense. Generally, pathogenic bacteria or endotoxin from the gut microbiota would be arrested and detoxified in the hepatopancreas, which could be accompanied by the inevitable immune responses. In recent years, studies related to cephalopods immune have been increasing, but the molecular mechanisms associated with the hepatopancreatic immunity are still unclear. In this study, lipopolysaccharide (LPS), a major component of the cell wall of Gram-negative bacteria, was used for imitating bacteria infection to stimulate the hepatopancreas of A. fangsiao. To investigate the immune process happened in A. fangsiao hepatopancreas, we performed transcriptome analysis of hepatopancreas tissue after LPS injection, and identified 2615 and 1943 differentially expressed genes (DEGs) at 6 and 24 h post-injection, respectively. GO and KEGG enrichment analysis showed that these DEGs were mainly involved in immune-related biological processes and signaling pathways, including ECM-receptor interaction signaling pathway, Phagosome signaling pathway, Lysosome signaling pathway, and JAK-STAT signaling pathways. The function relationships between these DEGs were further analyzed through protein-protein interaction (PPI) networks. It was found that Mtor, Mapk14 and Atm were the three top interacting DEGs under LPS stimulation. Finally, 15 hub genes involving multiple KEGG signaling pathways and PPI relationships were selected for qRT-PCR validation. In this study, for the first time we explored the molecular mechanisms associated with hepatopancreatic immunity in A. fangsiao using a PPI networks approach, and provided new insights for understanding hepatopancreatic immunity in A. fangsiao.

Asunto(s)

RESUMEN

Over the years, oysters have faced recurring mass mortality issues during the summer breeding season, with Vibrio infection emerging as a significant contributing factor. Tubules of gill filaments were confirmed to be in the hematopoietic position in Crassostrea gigas, which produce hemocytes with immune defense capabilities. Additionally, the epithelial cells of oyster gills produce immune effectors to defend against pathogens. In light of this, we performed a transcriptome analysis of gill tissues obtained from C. gigas infected with Vibrio alginolyticus for 12 h and 48 h. Through this analysis, we identified 1024 differentially expressed genes (DEGs) at 12 h post-injection and 1079 DEGs at 48 h post-injection. Enrichment analysis of these DEGs revealed a significant association with immune-related Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. To further investigate the immune response, we constructed a protein-protein interaction (PPI) network using the DEGs enriched in immune-associated KEGG pathways. This network provided insights into the interactions and relationships among these genes, shedding light on the underlying mechanisms of the innate immune defense mechanism in oyster gills. To ensure the accuracy of our findings, we validated 16 key genes using quantitative RT-PCR. Overall, this study represents the first exploration of the innate immune defense mechanism in oyster gills using a PPI network approach. The findings provide valuable insights for future research on oyster pathogen control and the development of oysters with enhanced antimicrobial resistance.

RESUMEN

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

Asunto(s)

RESUMEN

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

RESUMEN

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses. Methods: Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes. Results: The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified. Conclusion: This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Asunto(s)

RESUMEN

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

RESUMEN

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

Asunto(s)

RESUMEN

Colorectal cancer (CRC) is characterized by its heterogeneity and complex metastatic mechanisms, presenting significant challenges in treatment and prognosis. This study aimed to unravel the intricate interplay between the gut microbiota and metabolic alterations associated with CRC metastasis. By employing high-throughput sequencing and advanced metabolomic techniques, we identified distinct patterns in the gut microbiome and fecal metabolites across different CRC metastatic sites. The differential gene analysis highlighted significant enrichment in biological processes related to immune response and extracellular matrix organization, with key genes playing roles in the complement and clotting cascades, and staphylococcus aureus infections. Protein-protein interaction networks further elucidated the potential mechanisms driving CRC spread, emphasizing the importance of extracellular vesicles and the PPAR signaling pathway in tumor metastasis. Our comprehensive microbiota analysis revealed a relatively stable alpha diversity across groups but identified specific bacterial genera associated with metastatic stages. Metabolomic profiling using OPLS-DA models unveiled distinct metabolic signatures, with differential metabolites enriched in pathways crucial for cancer metabolism and immune modulation. Integrative analysis of the gut microbiota and metabolic profiles highlighted significant correlations, suggesting a complex interplay that may influence CRC progression and metastasis. These findings offer novel insights into the microbial and metabolic underpinnings of CRC metastasis, paving the way for innovative diagnostic and therapeutic strategies targeting the gut microbiome and metabolic pathways.

RESUMEN

INTRODUCTION: Prostate cancer patients with visceral metastases often exhibited poor prognoses. Few researches had compared the prognostic impact and gene expression profiles among distinct visceral metastatic sites. Therefore, we conducted a comprehensive study utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database and the Gene Expression Omnibus database. PATIENTS AND METHODS: We analyzed the prostate cancer-specific mortality (PCSM) risk for 8,170 patients diagnosed with metastatic prostate cancer (mPCa) between 2000 and 2019, utilizing data from the SEER 17 registry database. Patients with metastatic disease in nonregional lymph nodes, bones, brains, livers, and lungs were identified. Competing risks regression was employed to evaluate the effect of visceral metastatic disease sites on PCSM. Differentially expressed genes (DEGs) between visceral metastases were assessed using data from the GSE6752 dataset. A relative protein-protein interaction (PPI) network was constructed based on STRING analysis. Furthermore, we explored the distribution of DEGs in various normal tissues and tumor tissues using the Human Protein Atlas and GEPIA. RESULTS: Competing risks regression analysis revealed that liver and lung metastases had a substantial impact on PCSM (hazard ratio 2.24, 95% confidence interval 1.70-2.95, P < 0.001; hazard ratio 1.30, 95% confidence interval 1.06-1.59, P = 0.012, respectively). Seven significant DEGs were identified from samples of liver and lung metastases (HERV-FRD, NUDT12, FAM63A, SCGB3A1, CEACAM6, LOC440416, SFTPB) and were associated with respiratory gaseous exchange, pulmonary surfactant metabolism, and fibronectin matrix formation in PPI network analysis. Notably, the expression levels of the three DEGs significantly upregulated in lung metastases were also found to be higher in normal lung tissues compared to normal liver tissues. CONCLUSION: Patients diagnosed with mPCa and presenting with liver and/or lung metastases exhibit poorer prognoses. SCGB3A1, identified as a tumor suppressor gene, may contribute to the better survival prognosis observed in patients with prostate cancer lung metastases compared to those with liver metastases. The gene expression profiles in these two specific metastatic sites reveal a combination of both heterogeneity and homogeneity.

Asunto(s)

RESUMEN

Host cell proteins (HCPs) are process-related impurities of therapeutic proteins produced in for example, Chinese hamster ovary (CHO) cells. Protein A affinity chromatography is the initial capture step to purify monoclonal antibodies or Fc-based proteins and is most effective for HCP removal. Previously proposed mechanisms that contribute to co-purification of HCPs with the therapeutic protein are either HCP-drug association or leaching from chromatin heteroaggregates. In this study, we analyzed protein A eluates of 23 Fc-based proteins by LC-MS/MS to determine their HCP content. The analysis revealed a high degree of heterogeneity in the number of HCPs identified in the different protein A eluates. Among all identified HCPs, the majority co-eluted with less than three Fc-based proteins indicating a drug-specific co-purification for most HCPs. Only ten HCPs co-purified with over 50% of the 23 Fc-based proteins. A correlation analysis of HCPs identified across multiple protein A eluates revealed their co-elution as HCP groups. Functional annotation and protein interaction analysis confirmed that some HCP groups are associated with protein-protein interaction networks. Here, we propose an additional mechanism for HCP co-elution involving protein-protein interactions within functional networks. Our findings may help to guide cell line development and to refine downstream purification strategies.

Asunto(s)

RESUMEN

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Asunto(s)

RESUMEN

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Asunto(s)

RESUMEN

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A ß-lactamases.

Asunto(s)

RESUMEN

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

Asunto(s)

RESUMEN

MOTIVATION: Protein-protein interactions serve as the cornerstone for various biochemical processes within biological organisms. Existing research methodologies predominantly employ link prediction techniques to analyze these interaction networks. However, traditional approaches often fall short in delivering satisfactory predictive performance when applied to multi-species datasets. Current computational methods largely focus on analyzing the network topology, resulting in a somewhat monolithic feature set. The integration of diverse features in the model could potentially yield superior performance and broader applicability. To this end, we propose an autoencoder model built on graph neural networks, designed to enhance both predictive performance and generalizability by leveraging the integration of gene ontology. RESULTS: In this research, we developed AGraphSAGE, a model specifically designed for analyzing protein-protein interaction network data. By seamlessly integrating gene ontology into the graph structure, we employed a dual-channel graph sampling and aggregation network that capitalizes on topological information to process high-dimensional features. Feature fusion is achieved through the implementation of graph attention mechanisms, and we adopted a link prediction framework as the experimental training model. Performance was evaluated on real-world datasets using key metrics, such as Area Under the Curve (AUC). A hyperparameter search space was established, and a Bayesian optimization strategy was applied to iteratively fine-tune the model, assessing the impact of various parameters on predictive efficacy. The experimental results validate that our proposed model is capable of effectively predicting protein-protein interactions across diverse biological species.

Asunto(s)

RESUMEN

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.

RESUMEN

Background: Correlations between posttranslational modifications and atrial fibrillation (AF) have been demonstrated in recent studies. However, it is still unclear whether and how ubiquitylated proteins relate to AF in the left atrial appendage of patients with AF and valvular heart disease. Methods: Through LC-MS/MS analyses, we performed a study on tissues from eighteen subjects (9 with sinus rhythm and 9 with AF) who underwent cardiac valvular surgery. Specifically, we explored the ubiquitination profiles of left atrial appendage samples. Results: In summary, after the quantification ratios for the upregulated and downregulated ubiquitination cutoff values were set at >1.5 and <1:1.5, respectively, a total of 271 sites in 162 proteins exhibiting upregulated ubiquitination and 467 sites in 156 proteins exhibiting downregulated ubiquitination were identified. The ubiquitylated proteins in the AF samples were enriched in proteins associated with ribosomes, hypertrophic cardiomyopathy (HCM), glycolysis, and endocytosis. Conclusions: Our findings can be used to clarify differences in the ubiquitination levels of ribosome-related and HCM-related proteins, especially titin (TTN) and myosin heavy chain 6 (MYH6), in patients with AF, and therefore, regulating ubiquitination may be a feasible strategy for AF.