Distinct negative-sense RNA viruses induce a common set of transcripts encoding proteins forming an extensive network.

The large group of negative-strand RNA viruses (NSVs) comprises many important pathogens. To identify conserved patterns in host responses, we systematically compared changes in the cellular RNA levels after infection of human hepatoma cells with nine different NSVs of different virulence degrees. RNA sequencing experiments indicated that the amount of viral RNA in host cells correlates with the number of differentially expressed host cell transcripts. Time-resolved differential gene expression analysis revealed a common set of 178 RNAs that are regulated by all NSVs analyzed. A newly developed open access web application allows downloads and visualizations of all gene expression comparisons for individual viruses over time or between several viruses. Most of the genes included in the core set of commonly differentially expressed genes (DEGs) encode proteins that serve as membrane receptors, signaling proteins and regulators of transcription. They mainly function in signal transduction and control immunity, metabolism, and cell survival. One hundred sixty-five of the DEGs encode host proteins from which 47 have already been linked to the regulation of viral infections in previous studies and 89 proteins form a complex interaction network that may function as a core hub to control NSV infections.IMPORTANCEThe infection of cells with negative-strand RNA viruses leads to the differential expression of many host cell RNAs. The differential spectrum of virus-regulated RNAs reflects a large variety of events including anti-viral responses, cell remodeling, and cell damage. Here, these virus-specific differences and similarities in the regulated RNAs were measured in a highly standardized model. A newly developed app allows interested scientists a wide range of comparisons and visualizations.

Use of Integral Forage Palm Flour as an Innovative Ingredient in New Fettuccine-Type Pasta: Thermomechanical and Technological Properties, and Sensory Acceptance.

Dehydrated integral forage palm cladode flour (FPF) presents a promising nutritional and functional approach to enriching fettuccine-type pasta. This study investigated the use of microwave-dehydrated FPF (at 810 W) as a partial wheat flour substitute (0, 5, 10, 15, and 20% w/w) in fresh and dry fettuccine-type pasta. The thermomechanical properties of flour blends and the technological and sensory attributes of the resulting pasta were evaluated. FPF displayed a high protein (15.80%), mineral (15.13%), dietary fiber (67.35%), and total soluble phenolic compound (251 mg EAG·100 g-1) content. While water absorption (~58%) and dough stability remained consistent across formulations, a decrease in maximum torque during heating was observed (p < 0.05). Fettuccine-type pasta containing 10% FPF exhibited an acceptable optimal cooking time, solid loss, weight gain, and textural properties for both fresh and dry pasta. Sensory evaluation revealed acceptability above 63% for pasta with 10% FPF, with a slight preference for the fresh version. Fresh pasta flavored with garlic and extra virgin olive oil (garlic and oil pasta) achieved a sensory acceptance rate of 79.67%. These findings demonstrate the potential of FPF for fettuccine-type pasta production, contributing desirable technological characteristics and achieving acceptable sensory profiles.

Unraveling the roles of IFIT3 gene and immune-metabolic pathways in psoriasis: a bioinformatics exploration for diagnostic markers and therapeutic targets.

Background: The functions and related signal pathways of the IFIT3 gene in the skin lesions of patients with psoriasis were explored through bioinformatics methods to determine the potential specific molecular markers of psoriasis. Methods: The "limma" R package was used to analyze three datasets from the Gene Expression Omnibus database (GSE13355, GSE30999 and GSE106992), and the differential genes were screened. The STRING database was used for gene ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, and protein-protein interaction network integration. Then, the IFIT3 subnetwork was extracted and analyzed by gene set enrichment analysis (GSEA) using the Metascape database to verify the effectiveness of gene differentiation and disease tissue identification. Results: In this study, 426 differential genes were obtained, of which 322 were significantly upregulated and 104 were significantly downregulated. GO enrichment analysis showed that the differential genes were mainly involved in immunity and metabolism; the KEGG pathway enrichment analysis mainly included the chemokine signal pathway, PPAR signal pathway, and IL-17 signal pathway, among others. Based on the IFIT3 subnetwork analysis, it was found that IFIT3 was mainly involved in the biological processes of viruses, bacteria, and other microorganisms. The pathways obtained by GSEA were mainly related to immunity, metabolism, and antiviral activities. IFIT3 was highly expressed in psoriatic lesions and may thus be helpful in the diagnosis of psoriasis. Conclusion: The differential genes, biological processes, and signal pathways of psoriasis, especially information related to and diagnostic efficiency of the IFIT3 gene, were obtained by bioinformatics analysis. These results are expected to provide the theoretical basis and new directions for exploring the pathogenesis of psoriasis, in addition to helping with finding diagnostic markers and developing drug treatment targets.

Common protein networks for various drug regimens of major depression are associated with complement and immunity.

BACKGROUND: Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD. AIM: This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20). METHODS: Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied. RESULTS: Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity. CONCLUSIONS: Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Analysis of the anti-Alzheimer potential of bioactive compounds from Citrus hystrix DC. peel, leaf, and essential oil by network pharmacology.

Alzheimer's disease (AD) is the most known neurodegenerative disease, and its prevalence is predicted to increase significantly. Discovering novel drugs and treatments for AD is urgently needed. Drugs from natural products have been preferred lately due to their high potential and low toxicity. Citrus hystrix DC. (kaffir lime; KL) is one such herbal plant that is found abundantly in Southeast Asia with many biological activities. In this study, the potential of bioactive compounds from KL peel, leaf, and essential oil as anti-AD agents was explored using network pharmacology. First, the compounds were identified with KNApSAcK database and related literature. Subsequently, the targets of each corresponding compound were determined with SEA Search Server and Swiss Target Prediction, while the proteins associated with AD were identified using OMIM, GenCLiP3, and DisGeNET. Furthermore, a protein-protein interaction network and a compound-target interaction network were constructed to identify the most crucial proteins and compounds in the network by employing Cytoscape v3.9.1. The study continued with pathway enrichment analysis using STRING v1.7.1, molecular docking with PyRx and SwissDock, and molecular dynamics simulation with YASARA for further confirmation. Our results showed that almost all the secondary metabolites of KL targeted AD-associated genes, with oxypeucedanin and citrusoside A showing the highest anti-AD potential and targeting essential genes, EGFR and MAPK14, respectively. These targets were associated with inflammatory and oxidative stress pathways, indicating the potential mechanism of KL in attenuating AD clinical manifestation.

Virulence and proteomic responses of Metarhizium anisopliae against Aedes albopictus larvae.

The tropical climate in Malaysia provides an ideal environment for the rapid proliferation of Aedes mosquitoes, notably Aedes aegypti and Aedes albopictus, prominent vectors of dengue fever. Alarmingly, these species are increasingly developing resistance to conventional pesticides. This study aimed to evaluate the efficacy of Metarhizium anisopliae isolate HSAH5 spores, specifically on conidia (CO) and blastospores (BL), against Ae. albopictus larvae. The study centered on evaluating their pathogenic effects and the resultant changes in protein expression. Spore suspensions with varying concentrations were prepared for larvicidal bioassays, and protein expressions were analysed using liquid chromatography-mass spectrometry. Subsequently, protein annotation and network analysis were conducted to elucidate infection mechanisms and the proteomic response. Based on the lethal concentrations and time frames, CO exhibited faster larval mortality than BL at lower concentrations. Despite this, both spore types demonstrated comparable overall pathogenic effects. Results from the proteomic profiling revealed 150 proteins with varied expressions following exposure to Ae. albopictus extract, shedding light on distinct infection strategies between the spores. Gene Ontology enrichment and network analysis illustrated the diverse metabolic adaptations of M. anisopliae and interactions with mosquito larvae. This highlighted the complexity of host-pathogen dynamics and the significance of biosynthetic processes, energy storage, and cellular interaction pathways in disease progression. The BL network, consisting 80 proteins and 74 connections, demonstrates the intricate fungal mechanisms triggered by host stimuli. Conversely, the CO network, though smaller, displayed notable interconnectivity and concentrated involvement at the cell periphery, suggesting a deliberate strategy for initial host contact. This study offers valuable insights into proteome dynamics of M. anisopliae's BL and CO for managing mosquito populations and combating disease transmission, thereby significantly advancing public health and environmental conservation efforts.

Network-Based Drug Repurposing and Genomic Analysis to Unveil Potential Therapeutics for Monkeypox Virus.

The emergence of the human monkeypox virus (MPXV) and the lack of effective medications have necessitated the exploration of various strategies to combat its infection. This study employs a network-based approach to drug discovery, utilizing the BLASTn and phylogenetic analysis to compare the MPXV genome with those of 18 related orthopoxviruses, revealing over 75 % genomic similarity. Through a literature review, 160 human-host proteins linked to MPXV and its relatives were identified, leading to the construction of a human-host protein interactome. Analysis of this interactome highlighted 39 central hub proteins, which were then examined for potential drug targets. The process successfully revealed 15 targets already approved for use with medications. Additionally, the functional enrichment analysis provided insights into potential pathways and disorders connected with these targets. Four medications, namely Baricitinib, Infliximab, Adalimumab, and Etanercept, have been identified as potential candidates for repurposing to combat MPXV. In addition, the pharmacophore-based screening identified a molecule that is comparable to Baricitinib and has the potential to be effective against MPXV. The findings of the study suggest that ZINC22060520 is a promising medication for treating MPXV infection and proposes these medications as potential options for additional experimental and clinical assessment in the battle against MPXV.

Deciphering the pH-dependent oligomerization of aspartate semialdehyde dehydrogenase from Wolbachia endosymbiont of Brugia malayi: An in vitro and in silico approaches.

The enzyme aspartate semialdehyde dehydrogenase (ASDH) plays a pivotal role in the amino acid biosynthesis pathway, making it an attractive target for the development of new antimicrobial drugs due to its absence in humans. This study aims to investigate the presence of ASDH in the filarial parasite Wolbachia endosymbiont of Brugia malayi (WBm) using both in vitro and in silico approaches. The size exclusion chromatography (SEC) and Native-PAGE analysis demonstrate that WBm-ASDH undergoes pH-dependent oligomerization and dimerization. To gain a deeper understanding of this phenomenon, the modelled monomer and dimer structures were subjected to pH-dependent dynamics simulations in various conditions. The results reveal that residues Val240, Gln161, Thr159, Tyr160, and Trp316 form strong hydrogen bond contacts in the intersurface area to maintain the structure in the dimeric form. Furthermore, the binding of NADP+ induces conformational changes, leading to an open or closed conformation in the structure. Importantly, the binding of NADP+ does not disturb either the dimerization or oligomerization of the protein, a finding confirmed through both in vitro and in silico analysis. These findings shed light on the structural characteristics of WBm-ASDH and offer valuable insights for the development of new inhibitors specific to WBm, thereby contributing to the development of potential therapies for filarial parasitic infections.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

Wheat gluten structure and (non-)covalent network formation during deep-fat frying.

The aim of this work was to investigate wheat gluten protein network structure throughout the deep-frying process and evaluate its contribution to frying-induced micro- and macrostructure development. Gluten polymerization, gluten-water interactions, and molecular mobility were assessed as a function of the deep-frying time (0 - 180 s) for gluten-water model systems of differing hydration levels (40 - 60 % moisture content). Results showed that gluten protein extractability decreased considerably upon deep frying (5 s) mainly due to glutenin polymerization by disulfide covalent cross-linking. Stronger gliadin and glutenin protein-protein interactions were attributed to the formation of covalent linkages and evaporation of water interacting with protein chains. Longer deep-frying (> 60 s) resulted in progressively lower protein extractabilities, mainly due to the loss in gliadin protein extractability, which was associated with gliadin co-polymerization with glutenin by thiol-disulfide exchange reactions. The mobility of gluten polymers was substantially reduced during deep-frying (based on the lower T2 relaxation time of the proton fraction representing the non-exchanging protons of gluten) and gluten proteins gradually transitioned from the rubbery to the glassy state (based on the increased area of said protons). The sample volume during deep-frying was strongly correlated to the reduced protein extractability (r = -0.792, p < 0.001) and T2 relaxation time of non-exchanging protons of gluten proteins (r = -0.866, p < 0.001) thus demonstrating that the extent of gluten structural expansion as a result of deep-frying is dictated both by the polymerization of proteins and the reduction in their molecular mobility.

Ribosomal-processing cysteine protease homolog modulates Streptococcus mutans glucan production and interkingdom interactions.

Glucan-dependent biofilm formation is a crucial process in the establishment of Streptococcus mutans as a cariogenic oral microbe. The process of glucan formation has been investigated in great detail, with glycosyltransferases GtfB, GtfC, and GtfD shown to be indispensable for the synthesis of glucans from sucrose. Glucan production can be visualized during biofilm formation through fluorescent labeling, and its abundance, as well as the effect of glucans on general biofilm architecture, is a common phenotype to study S. mutans virulence regulation. Here, we describe an entirely new phenotype associated with glucan production, caused by a mutation in the open reading frame SMU_848, which is located in an operon encoding ribosome-associated proteins. This mutation led to the excess production and accumulation of glucan-containing droplets on the surface of biofilms formed on agar plates after prolonged incubation. While not characterized in S. mutans, SMU_848 shows homology to the phage-related ribosomal protease Prp, essential in cleaving off the N-terminal extension of ribosomal protein L27 for functional ribosome assembly in Staphylococcus aureus. We present a further characterization of SMU_848/Prp, demonstrating that the deletion of this gene leads to significant changes in S. mutans gtfBC expression. Surprisingly, it also profoundly impacts the interkingdom interaction between S. mutans and Candida albicans, a relevant dual-species interaction implicated in severe early childhood caries. The presented data support a potential broader role for SMU_848/Prp, possibly extending its functionality beyond the ribosomal network to influence important ecological processes. IMPORTANCE: Streptococcus mutans is an important member of the oral biofilm and is implicated in the initiation of caries. One of the main virulence mechanisms is the glucan-dependent formation of biofilms. We identified a new player in the regulation of glucan production, SMU_848, which is part of an operon that also encodes for ribosomal proteins L27 and L21. A mutation in SMU_848, which encodes a phage-related ribosomal protease Prp, leads to a significant accumulation of glucan-containing droplets on S. mutans biofilms, a previously unknown phenotype. Further investigations expanded our knowledge about the role of SMU_848 beyond its role in glucan production, including significant involvement in interkingdom interactions, thus potentially playing a global role in the virulence regulation of S. mutans.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Comprehensive Bioinformatic Investigation of TP53 Dysregulation in Diverse Cancer Landscapes.

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues.

Proteomics appending a complementary dimension to precision oncotherapy.

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases.

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein-protein and drug-protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases.

Combining association with linkage mapping to dissect the phenolamides metabolism of the maize kernel.

Phenolamides are important secondary metabolites in plant species. They play important roles in plant defense responses against pathogens and insect herbivores, protection against UV irradiation and floral induction and development. However, the accumulation and variation in phenolamides content in diverse maize lines and the genes responsible for their biosynthesis remain largely unknown. Here, we combined genetic mapping, protein regulatory network and bioinformatics analysis to further enhance the understanding of maize phenolamides biosynthesis. Sixteen phenolamides were identified in multiple populations, and they were all significantly correlated with one or several of 19 phenotypic traits. By linkage mapping, 58, 58, 39 and 67 QTLs, with an average of 3.9, 3.6, 3.6 and 4.2 QTLs for each trait were mapped in BBE1, BBE2, ZYE1 and ZYE2, explaining 9.47%, 10.78%, 9.51% and 11.40% phenotypic variation for each QTL on average, respectively. By GWAS, 39 and 36 significant loci were detected in two different environments, 3.3 and 2.8 loci for each trait, explaining 10.00% and 9.97% phenotypic variation for each locus on average, respectively. Totally, 58 unique candidate genes were identified, 31% of them encoding enzymes involved in amine and derivative metabolic processes. Gene Ontology term analysis of the 358 protein-protein interrelated genes revealed significant enrichment in terms relating to cellular nitrogen metabolism, amine metabolism. GRMZM2G066142, GRMZM2G066049, GRMZM2G165390 and GRMZM2G159587 were further validated involvement in phenolamides biosynthesis. Our results provide insights into the genetic basis of phenolamides biosynthesis in maize kernels, understanding phenolamides biosynthesis and its nutritional content and ability to withstand biotic and abiotic stress.

Uncovering the health implications of abandoned mines through protein profiling of local residents.

Residents in areas with abandoned mines risk significant exposure to abundant heavy metals in the environment. However, current clinical indicators cannot fully reflect the health changes associated with abandoned mine exposure. The aim of this study was to identify biological changes in the residents of abandoned mine areas via proteomic analysis of their blood. Blood samples were collected from abandoned mine and control areas, and mass spectrometry was used for protein profiling. A total of 138 unique or common proteins that were differentially expressed in low-exposure abandoned mine area (LoAMA) or high-exposure abandoned mine area (HiAMA) compared to non-exposure control area (NEA) were analyzed, and identified 4 clusters based on functional similarity. Among the 10 proteins that showed specific change in LoAMA, 4 proteins(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) were cluded in cluster 1(plasma lipoprotein remodeling), and linked to proteins that showed specific change in protein expression in HiAMA. Therefore, it is suggested that 4 proteins are changed at low exposure to an abandoned mine (or initial exposure), and then at high exposure, changes in various proteins involved in linked plasma lipoprotein remodeling are induced, which might triggered by the 4 proteins. Interestingly, in addition to plasma lipoprotein remodeling, proteins involved in other functional networks were changed in the high exposure group. These were all directly or indirectly linked to the 4 biomarkers(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) that changed during low exposure. This suggests their potential utility in identifying areas impacted by abandoned mines. Especially, proteins involved in lipid metabolism and renal function-related diseases in individuals exposed to heavy metals in abandoned mine areas were correlated. Chronic kidney disease is predominantly instigated by cardiovascular disease and is commonly accompanied by dyslipidemia.

Occupational second-hand smoke exposure: A comparative shotgun proteomics study on nasal epithelia from healthy restaurant workers.

Non-smokers exposed to second-hand smoke (SHS) present risk of developing tobacco smoke-associated pathologies. To investigate the airway molecular response to SHS exposure that could be used in health risk assessment, comparative shotgun proteomics was performed on nasal epithelium from a group of healthy restaurant workers, non-smokers (never and former) exposed and not exposed to SHS in the workplace. HIF1α-glycolytic targets (GAPDH, TPI) and proteins related to xenobiotic metabolism, cell proliferation and differentiation leading to cancer (ADH1C, TUBB4B, EEF2) showed significant modulation in non-smokers exposed. In never smokers exposed, enrichment of glutathione metabolism pathway and EEF2-regulating protein synthesis in genotoxic response were increased, while in former smokers exposed, proteins (LYZ, ATP1A1, SERPINB3) associated with tissue damage/regeneration, apoptosis inhibition and inflammation that may lead to asthma, COPD or cancer, were upregulated. The identified proteins are potential response and susceptibility/risk biomarkers for SHS exposure.

Impact of Corn Starch Molecular Structures on Texture, Water Dynamics, Microstructure, and Protein Structure in Silver Carp (Hypophthalmichthys molitrix) Surimi Gel.

This study systematically investigates the impact of corn starch molecular structures on the quality attributes of surimi gel products. Employing molecular analyses to characterize corn starch, three amylopectin fractions (A, B1, and B2), categorized by the degree of polymerization ranges (6 < X ≤ 12, 12 < X ≤ 24, and 24 < X ≤ 36, respectively) were specifically focused on. The surimi gel quality was comprehensively assessed through texture profile analysis, nuclear magnetic resonance, scanning electron microscopy, stained section analysis, and Fourier transform infrared spectroscopy. Results indicated the substantial volume expansion of corn amylopectin upon water absorption, effectively occupying the surimi gel matrix and fostering the development of a more densely packed protein network. Starch gels with higher proportions of A, B1, and B2 exhibited improved hardness, chewiness, and bound water content in the resultant surimi gels. The weight-average molecular weight and peak molecular weight of corn starch showed a strong positive correlation with surimi gel hardness and chewiness. Notably, the secondary structure of proteins within the surimi gel was found to be independent of corn starch's molecular structure. This study provides valuable insights for optimizing formulations in surimi gel products, emphasizing the significance of elevated A, B1, and B2 content in corn starch as an optimal choice for crafting dense, chewy, water-retaining surimi gels.

Identification of therapeutic targets and prognostic biomarkers of the ephrin receptor subfamily in pancreatic adenocarcinoma.

OBJECTIVES: We aimed to examine the significance of ephrin receptor A2 (EphA2) expression in pancreatic adenocarcinoma (PAAD) and its associated mechanism. METHODS: EphA2 mRNA expression patterns were compared in pancreatic cancer and normal tissues using GEPIA. Kaplan-Meier analysis was used to examine the correlation between EphA2 expression and PAAD patient prognosis. EphA2 gene methylation and associations with tumor immune cell infiltration were analyzed with UALCAN and TIMER, respectively. EphA2-interacting proteins were investigated with GeneMANIA, while STRING helped predict potentially relevant signaling pathways. EphA2 protein expression was examined with immunohistochemistry (IHC) in PAAD patient tissues. RESULTS: EphA2 was highly expressed in pancreatic cancer tissues and associated with pathological stage. PAAD patients with high EphA2 expression had shorter overall survival and disease-free survival times. EphA2 expression levels were significantly and positively associated with CD4+ T cell infiltration. EphA2 can interact with ENFNA1, ACP1, and CDC42. High EphA2 mRNA expression was enriched for regulation of cell size and cell proliferation. IHC assays suggested that pancreatic cancer tissues had higher EphA2 protein levels than normal pancreatic tissues. CONCLUSIONS: EphA2 is highly expressed in PAAD and closely related to poor patient prognosis, and is therefore a potential biomarker and target for PAAD diagnosis and treatment.