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.

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.
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.
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.
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.

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.

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.

BACKGROUND: 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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SIGNIFICANCE: As the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis\'s molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.