Protein Co-expression Network Research Articles

Identification of key proteins in early-onset Alzheimer’s disease based on WGCNA

IntroductionEarly-onset Alzheimer’s disease (EOAD) is sporadic, highly heterogeneous, and its underlying pathogenic mechanisms remain largely elusive. Proteomics research aims to uncover the biological processes and key proteins involved in disease progression. However, no proteomic studies to date have specifically focused on EOAD brain tissue.MethodWe integrated proteomic data from brain tissues of two Alzheimer’s disease (AD) cohorts and constructed a protein co-expression network using weighted gene co-expression network analysis (WGCNA). We identified modules associated with EOAD, conducted functional enrichment analysis to understand the biological processes involved in EOAD, and pinpointed potential key proteins within the core modules most closely linked to AD pathology.ResultsIn this study, we identified a total of 2,749 proteins associated with EOAD. Through protein co-expression network analysis, we discovered 41 distinct co-expression modules. Notably, the proteins within the core module most closely linked to AD pathology were significantly enriched in neutrophil degranulation. Additionally, we identified two potential key proteins within this core module that have not been previously reported in AD and validated their expression levels in 5xFAD mice.ConclusionIn summary, through a protein co-expression network analysis, we identified EOAD-related biological processes and molecular pathways, and screened and validated two key proteins, ERBB2IP and LSP1. These proteins may play an important role in the progression of EOAD, suggesting they could serve as potential therapeutic targets for the disease.

Heparin-enriched plasma proteome is significantly altered in Alzheimer’s disease

IntroductionHeparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer’s disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches.MethodsWe employed heparin-affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to enrich HBPs from plasma obtained from AD (n = 62) and control (n = 47) samples. These profiles were then correlated to Aβ, tau and phosphorylated tau (pTau) CSF biomarkers and plasma pTau181 from the same individuals, as well as a consensus brain proteome network to assess the overlap with AD brain pathophysiology.ResultsHeparin enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundant proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude in abundance, were measured across 109 samples. Compared to the consensus AD brain protein co-expression network, we observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, highlighting the complex interplay between the two compartments. Elevated proteins in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate with Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and the APOE4 proteoform. Additionally, heparin-enriched proteins in plasma demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau181. A panel of five plasma proteins classified AD from control individuals with an area under the curve (AUC) of 0.85. When combined with plasma pTau181, the panel significantly improved the classification performance of pTau181 alone, increasing the AUC from 0.93 to 0.98. This suggests that the heparin-enriched plasma proteome captures additional variance in cognitive dementia beyond what is explained by pTau181.ConclusionThese findings support the utility of a heparin-affinity approach coupled with TMT-MS for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Nicotinamide riboside modulates the reactive species interactome, bioenergetic status and proteomic landscape in a brain-region-specific manner

Nicotinamide riboside (NR), a precursor of nicotinamide adenine dinucleotide (NAD+), has robust cognitive benefits and alleviates neuroinflammation in Alzheimer's Disease (AD) mouse models without decreasing beta-amyloid plaque pathology. Such effects may be mediated by the reactive species interactome (RSI), at the metabolome level. In this study, we employed in vitro and in vivo models of oxidative stress, aging and AD to profile the effects of NR on neuronal survival, RSI, and the whole proteome characterization of cortex and hippocampus. RSI analysis yielded a complex modulation upon NR treatment. We constructed protein co-expression networks and correlated them to NR treatment and all measured reactive species. We observed brain-area specific effects of NR on co-expressed protein modules of oxidative phosphorylation, fatty acid oxidation, and neurotransmitter regulation pathways, which correlated with RSI components. The current study contributes to the understanding of modulation of the metabolome, specifically after NR treatment in AD and how it may play disease-modifying roles.

DNA damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease.

Cardiovascular disease (CVD) is associated with both genetic variants and environmental factors. One unifying consequence of the molecular risk factors in CVD is DNA damage, which must be repaired by DNA damage response proteins. However, the impact of DNA damage on global cardiomyocyte protein abundance, and its relationship to CVD risk remains unclear. We therefore treated induced pluripotent stem cell-derived cardiomyocytes with the DNA-damaging agent Doxorubicin (DOX) and a vehicle control, and identified 4,178 proteins that contribute to a network comprising 12 co-expressed modules and 403 hub proteins with high intramodular connectivity. Five modules correlate with DOX and represent distinct biological processes including RNA processing, chromatin regulation and metabolism. DOX-correlated hub proteins are depleted for proteins that vary in expression across individuals due to genetic variation but are enriched for proteins encoded by loss-of-function intolerant genes. While proteins associated with genetic risk for CVD, such as arrhythmia are enriched in specific DOX-correlated modules, DOX-correlated hub proteins are not enriched for known CVD risk proteins. Instead, they are enriched among proteins that physically interact with CVD risk proteins. Our data demonstrate that DNA damage in cardiomyocytes induces diverse effects on biological processes through protein co-expression modules that are relevant for CVD, and that the level of protein connectivity in DNA damage-associated modules influences the tolerance to genetic variation.

ASPG and DAD1 are potential placental-derived biomarkers for ASD-like symptom severity levels in male/female offspring

IntroductionAn early evaluating system for autism spectrum disorder (ASD) severity is crucial. Questionnaire survey is challenging for accurately assessing the severity levels for ASD in children. MethodsOffspring with ASD-like phenotypes were induced by treating pregnant mice with Poly (I:C) at GD12.5 and the placentae corresponding to the offspring were obtained by caesarean. The autism severity composite score (ASCS) for offspring was calculated through behavioral tests. HE staining and immunohistochemistry were used to observe the morphology of placenta. Candidate biomarkers were identified by weighted protein co-expression network analysis (WPCNA) combined with machine learning and further validated by ELISA. Sperman's was used to analyze the correlation between biomarkers and metabolome. ResultsThe placental weight and mean vascular area of male offspring with ASD-like phenotypes were significantly decreased compared with typical mice. According to the WPCNA, four modules were identified and significantly correlated with ASCS of offspring. Two biomarkers (ASPG and DAD1) with high correlation with ASCS in offspring were identified. DiscussionVEGF pathway may contribute to sexual dimorphism in placental morphology within mice with ASD-like phenotypes in term. The placental ASPG and DAD1 levels could reflect ASD-like symptom severity levels in male/female mice offspring.

Effect of Nutrient Solution Flow on Lettuce Root Morphology in Hydroponics: A Multi-Omics Analysis of Hormone Synthesis and Signal Transduction.

This study examined how the nutrient flow environment affects lettuce root morphology in hydroponics using multi-omics analysis. The results indicate that increasing the nutrient flow rate initially increased indicators such as fresh root weight, root length, surface area, volume, and average diameter before declining, which mirrors the trend observed for shoot fresh weight. Furthermore, a high-flow environment significantly increased root tissue density. Further analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and Weighted Protein Co-expression Network Analysis (WPCNA) identified modules that were highly correlated with phenotypes and hormones. The analysis revealed a significant enrichment of hormone signal transduction pathways. Differences in the expression of genes and proteins related to hormone synthesis and transduction pathways were observed among the different flow conditions. These findings suggest that nutrient flow may regulate hormone levels and signal transmission by modulating the genes and proteins associated with hormone biosynthesis and signaling pathways, thereby influencing root morphology. These findings should support the development of effective methods for regulating the flow of nutrients in hydroponic contexts.

Proteomic changes in the human cerebrovasculature in Alzheimer's disease and related tauopathies linked to peripheral biomarkers in plasma and cerebrospinal fluid.

Cerebrovascular dysfunction is a pathological hallmark of Alzheimer's disease (AD). Nevertheless, detecting cerebrovascular changes within bulk tissues has limited our ability to characterize proteomic alterations from less abundant cell types. We conducted quantitative proteomics on bulk brain tissues and isolated cerebrovasculature from the same individuals, encompassing control (N=28), progressive supranuclear palsy (PSP) (N=18), and AD (N=21) cases. Protein co-expression network analysis identified unique cerebrovascular modules significantly correlated with amyloid plaques, cerebrovascular amyloid angiopathy (CAA), and/or tau pathology. The protein products within AD genetic risk loci were concentrated within cerebrovascular modules. The overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with cerebrovascular network highlighted a significant increase of matrisome proteins, SMOC1 and SMOC2, in CSF, plasma, and brain. These findings enhance our understanding of cerebrovascular deficits in AD, shedding light on potential biomarkers associated with CAA and vascular dysfunction in neurodegenerative diseases.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Integrated Proteomics and Protein Co-expression Network Analysis Identifies Novel Epileptogenic Mechanism in Mesial Temporal Lobe Epilepsy.

Over 50 million people worldwide are affected by epilepsy, a common neurological disorder that has a high rate of drug resistance and diverse comorbidities such as progressive cognitive and behavioural disorders, and increased mortality from direct or indirect effects of seizures and therapies. Despite extensive research with animal models and human studies, limited insights have been gained into the mechanisms underlying seizures and epileptogenesis, which has not translated into significant reductions in drug resistance, morbidities, or mortality. To better understand the molecular signaling networks associated with seizures in MTLE patients, we analyzed the proteome of brain samples from MTLE and control cases using an integrated approach that combines mass spectrometry-based quantitative proteomics, differential expression analysis, and co-expression network analysis. Our analyses of 20 human brain tissues from MTLE patients and 20 controls showed the organization of the brain proteome into a network of 9 biologically meaningful modules of co-expressed proteins. Of these, 6 modules are positively or negatively correlated to MTLE phenotypes with hub proteins that are altered in MTLE patients. Our study is the first to employ an integrated approach of proteomics and protein co-expression network analysis to study patients with MTLE. Our findings reveal a molecular blueprint of altered protein networks in MTLE brain and highlight dysregulated pathways and processes including altered cargo transport, neurotransmitter release from synaptic vesicles, synaptic plasticity, proteostasis, RNA homeostasis, ion transport and transmembrane transport, cytoskeleton disorganization, metabolic and mitochondrial dysfunction, blood micro-particle function, extracellular matrix organization, immune response, neuroinflammation, and cell signaling. These insights into MTLE pathogenesis suggest potential new candidates for future diagnostic and therapeutic development.

Cancer cell immunity-related protein co-expression networks are associated with early-stage solid-predominant lung adenocarcinoma.

Solid-predominant lung adenocarcinoma (SPA), which is one of the high-risk subtypes with poor prognosis and unsatisfactory response to chemotherapy and targeted therapy in lung adenocarcinoma, remains molecular profile unclarified. Weighted correlation network analysis (WGCNA) was used for data mining, especially for studying biological networks based on pairwise correlations between variables. This study aimed to identify disease-related protein co-expression networks associated with early-stage SPA. We assessed cancerous cells laser-microdissected from formalin-fixed paraffin-embedded (FFPE) tissues of a SPA group (n = 5), referencing a low-risk subtype, a lepidic predominant subtype group (LPA) (n = 4), and another high-risk subtype, micropapillary predominant subtype (MPA) group (n = 3) and performed mass spectrometry-based proteomic analysis. Disease-related co-expression networks associated with the SPA subtype were identified by WGCNA and their upstream regulators and causal networks were predicted by Ingenuity Pathway Analysis. Among the forty WGCNA network modules identified, two network modules were found to be associated significantly with the SPA subtype. Canonical enriched pathways were highly associated with cellular growth, proliferation, and immune response. Upregulated HLA class I molecules HLA-G and HLA-B implicated high mutation burden and T cell activation in the SPA subtype. Upstream analysis implicated the involvement of highly activated oncogenic regulators, MYC, MLXIPL, MYCN, the redox master regulator NFE2L2, and the highly inhibited LARP1, leading to oncogenic IRES-dependent translation, and also regulators of the adaptive immune response, including highly activated IFNG, TCRD, CD3-TCR, CD8A, CD8B, CD3, CD80/CD86, and highly inhibited LILRB2. Interestingly, the immune checkpoint molecule HLA-G, which is the counterpart of LILRB2, was highly expressed characteristically in the SPA subtype and might be associated with antitumor immunity. Our findings provide a disease molecular profile based on protein co-expression networks identified for the high-risk solid predominant adenocarcinoma, which will help develop future therapeutic strategies.

Comprehensive analysis of sesame LRR-RLKs: structure, evolution and dynamic expression profiles under Macrophomina phaseolina stress.

Leucine-rich repeat receptor-like kinases (LRR-RLKs) can participate in the regulation of plant growth and development, immunity and signal transduction. Sesamum indicum, one of the most important oil crops, has a significant role in promoting human health. In this study, 175 SiLRR-RLK genes were identified in S. indicum, and they were subdivided into 12 subfamilies by phylogenetic analysis. Gene duplication analysis showed that the expansion of the SiLRR-RLK family members in the sesame was mainly due to segmental duplication. Moreover, the gene expansion of subfamilies IV and III contributed to the perception of stimuli under M. phaseolina stress in the sesame. The collinearity analysis with other plant species revealed that the duplication of SiLRR-RLK genes occurred after the differentiation of dicotyledons and monocotyledons. The expression profile analysis and functional annotation of SiLRR-RLK genes indicated that they play a vital role in biotic stress. Furthermore, the protein-protein interaction and coexpression networks suggested that SiLRR-RLKs contributed to sesame resistance to Macrophomina phaseolina by acting alone or as a polymer with other SiLRR-RLKs. In conclusion, the comprehensive analysis of the SiLRR-RLK gene family provided a framework for further functional studies on SiLRR-RLK genes.

Unravelling the Functional Role of GthGAPC2 in Cotton's Defense Against Verticillium dahliae through Proteome

AbstractCotton (Gossypium spp.) is an economically important crop, but its productivity is often hindered by the soil‐borne pathogen Verticillium dahliae. This study aimed to investigate the response of cotton roots to V. dahliae infection by analysing the proteome of Gossypium thurberi (resistant) and Gossypium raimondii (susceptible) at 0 h, 24 h, and 48 h post‐infection. Through weighted protein coexpression network analysis, fifteen hub proteins crucial for defense against V. dahliae were identified. Expression analysis revealed the pivotal role of GthGAPC2, encoding GLYCERALDEHYDE‐3‐PHOSPHATE DEHYDROGENASE 2, in conferring resistance to V. dahliae in cotton. Virus‐induced gene Silencing (VIGS) of GthGAPC2 increased susceptibility to V. dahliae, which was supported by oxidant and antioxidant enzyme activities. Furthermore, GthGAPC2 silencing influenced lignin content, indicating its involvement in lignin biosynthesis regulation. Transient overexpression of GthGAPC2 in tobacco supported its role in cell death processes. Subcellular localization studies showed predominant nuclear localization of GthGAPC2. Overexpression of GthGAPC2 in Arabidopsis also confirms its significant role in V. dahliae resistance. These findings shed light on the molecular mechanisms of disease resistance in Gossypium thurberi. Identification of GthGAPC2, as a key protein involved in V. dahliae resistance, and its functional implications can aid breeding strategies for enhancing cotton's disease resistance.

Post-translational modifications linked to preclinical Alzheimer's disease-related pathological and cognitive changes.

In this study, we leverage proteomic techniques to identify communities of proteins underlying Alzheimer's disease (AD) risk among clinically unimpaired (CU) older adults. We constructed a protein co-expression network using 3869 cerebrospinal fluid (CSF) proteins quantified by SomaLogic, Inc., in a cohort of participants along the AD clinical spectrum. We then replicated this network in an independent cohort of CU older adults and related these modules to clinically-relevant outcomes. We discovered modules enriched for phosphorylation and ubiquitination that were associated with abnormal amyloid status, as well as p-tau181 (M4: β=2.44, p<0.001, M7: β=2.57, p<0.001) and executive function performance (M4: β=-2.00, p=0.005, M7: β=-2.39, p<0.001). In leveraging CSF proteomic data from individuals spanning the clinical spectrum of AD, we highlight the importance of post-translational modifications for early cognitive and pathological changes.

Discovery of characteristic proteins linked to textural changes in fermented golden pompano (Trachinotus ovatus) based on a label-free proteomics strategy

Fermentation causes changes in fish texture, nevertheless, the mechanism of protein involvement in this process of textural changes are unknown. We investigated changes in the tissue microstructure and texture, combined with a multidimensional fusion strategy to characterized the oxidative degradation properties of myofibrillar proteins, and analyzed the protein expression using label-free quantitative proteomics. Muscle protein hydrolysis, increased oxidation levels, and changes in spatial conformation were observed in the fish texture and microstructure during fermentation. We identified 664 proteins and screened 330 differentially proteins. The weighted protein co-expression network model screening yielded a total of 38 key potential proteins associated with textural changes. Characteristic proteins at important nodes, which were mainly structural proteins and metabolic enzymes, were identified using protein-protein interaction network analysis. These results reveal a relationship between proteins and textural characteristics and provide a theoretical basis for improving the quality control of fermented marine fish.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

Proteomics Landscape and Machine Learning Predictor of Response to Splenectomy in Immune Thrombocytopenia

Introduction Immune thrombocytopenia (ITP) is an auto-immune disease characterized by antibody-mediated platelet destruction. Splenectomy remains the effective curative treatment for patients with ITP who failure to respond to TPO-RAs and/or rituximab (Mageau et al, Am J Hematol. 2022). The effectiveness rate of splenectomy for ITP is reported to be roughly 80%, with a 5-year remission rate ranging from 50% to 75% (Chaturvedi et al, Blood. 2018). For subjects presenting with severely low platelet counts or active bleeding, splenectomy represents the primary approach for rapidly increasing platelet levels and potentially saving lives. To achieve personalized and precise treatment, the prediction efficacy on splenectomy remains an urgent problem. Here, we have characterized immune features of ITP patients with splenectomy, and developed a predictive model for assessing the response to splenectomy. Methods Bone marrow plasma samples before splenectomy were collected and analyzed by high-resolution liquid chromatography-tandem mass spectrometry. Weight protein co-expression network analysis (WPCNA) was applied to generate modules from all proteins and mapped them to clinical characteristics. We used least absolute shrinkage and selection operator (Lasso) regression, recursive feature elimination (RFE) for predictors selection. Next, multivariable logistic regression (LR), support vector machine (SVM), and decision tree algorithms were used to develop a predictive model. Results 60 patients with ITP undergoing splenectomy treatment were enrolled into our study, and divided into three groups according to the response [patients with CR (n=30), NR (n=7), and Relapse (n=23)]. A pathway enrichment analysis of WPCNA modules by ClueGO revealed that megakaryocyte differentiation, humoral immune response mediated by circulating immunoglobulin, Fc receptor signaling pathway, and cGMP-PKG signaling pathway were correspondingly enriched in MEtan, MEturquoise, MEblue, and MEpink modules, which were associated with treatments with TPO-RA, IVIG, rhTPO, and post-operation platelet level, respectively. We then performed KEGG pathway enrichment analyses on the different expression proteins (DEPs) to deciphering the immune landscape among each group. For the relapse group, DEPs were primarily enriched in ribosome, spliceosome, neutrophil extracellular trap formation, and necroptosis pathways, comparing with the CR group. For the NR group, most DEPs were enriched in natural killer cell mediated cytotoxicity, NF-kappa B signaling, Fc gamma R-mediated phagocytosis pathways, comparing with the CR group. The models were based on a multi-step predictor pipeline. Inside the pipeline, features were first filtered by lasso regression and RFE algorithm. We identified five proteomic features present in the bone marrow plasm that associated with relapsed to therapy, which included CD40, COL4A3, GALNT6, GSTM3, and UBE2A (Figure). Each ensemble consisted of three algorithms acting in parallel: LR, SVM and decision tree. The AUC values of LR, SVM, and decision tree were 0.962 (95%CI: 0.915-1), 0.957 (95%CI: 0.897-1), and 0.875 (95%CI: 0.782-0.967), respectively. The three algorithms were then tested for performance. The sensitivity, specificity, positive predictive value, and negative predictive value analysis revealed that LR model reached the best performance. Targeting CD40 represents a promising therapeutic approach for enhancing clinical outcomes in patients with ITP who have shown inadequate response following splenectomy. Conclusion Our analysis revealed that ITP patients with splenectomy showed distinct immune characteristics when achieving different response. Protein features extracted from bone marrow plasma can be selected as predictors of response to splenectomy.

Virus-host protein co-expression networks reveal temporal organization and strategies of viral infection

Virus-host protein co-expression networks reveal temporal organization and strategies of viral infection

A protein-based prognostic model for pancreatic ductal adenocarcinoma: Construction and validation

BackgroundProbing relevant proteomic biomarkers may facilitate effective pancreatic adenocarcinoma (PDAC) diagnosis, treatment and prevention. Here, we developed a protein-based prognostic model for PDAC by using relevant proteomic biomarkers data from The Cancer Genome Atlas (TCGA). MethodsWe obtained PDAC's proteomic and clinical data from TCGA and used various analytical tools to identify differentially expressed proteins between normal and cancer tissues. We constructed our protein-based prognostic model and confirmed its accuracy using receiver operating characteristic curve and Kaplan-Meier survival analyses. We elucidated clinical factor-signature protein correlations by clinical correlation assessments and protein coexpression networks. We also used immunohistochemistry (protein expression assessment), Gene Set Enrichment Analysis (protein role identification) and CIBERSORT (infiltrating immune cell distribution assessment). ResultsCIITA, BRAF_pS445, AR, YTHDF2, IGFBP2 and CDK1_pT14 were identified as PDAC-associated prognostic proteins. All risk scores calculated using our model provided 1-, 3-, 5-year survival probability at 70 % accuracy. The reliability of our model was validated by the GEO as well. In high- and low-risk groups, age, sex, T- and N- stage disparities were significant, and prognostic and coexpressed proteins correlated. PDAC tissues demonstrated significant CDK1_pT14 overexpression but significant BRAF_pS445, YTHDF2, and IGFBP2 underexpression. Downstream proteins of BRAF were validated by IHC. Low-risk tissues demonstrated more naïve B cells, eosinophils, activated NK cells and regulatory T cells, whereas high-risk tissues demonstrated more activated memory T cells, monocytes, neutrophils, dendritic cells and resting NK cells. ConclusionsOur protein-based prognostic model for PDAC, along with six signature proteins, might aid in predicting PDAC prognosis and therapeutic targets.

Genetic background influences the 5XFAD Alzheimer's disease mouse model brain proteome.

There is an urgent need to improve the translational validity of Alzheimer's disease (AD) mouse models. Introducing genetic background diversity in AD mouse models has been proposed as a way to increase validity and enable the discovery of previously uncharacterized genetic contributions to AD susceptibility or resilience. However, the extent to which genetic background influences the mouse brain proteome and its perturbation in AD mouse models is unknown. In this study, we crossed the 5XFAD AD mouse model on a C57BL/6J (B6) inbred background with the DBA/2J (D2) inbred background and analyzed the effects of genetic background variation on the brain proteome in F1 progeny. Both genetic background and 5XFAD transgene insertion strongly affected protein variance in the hippocampus and cortex (n = 3,368 proteins). Protein co-expression network analysis identified 16 modules of highly co-expressed proteins common across the hippocampus and cortex in 5XFAD and non-transgenic mice. Among the modules strongly influenced by genetic background were those related to small molecule metabolism and ion transport. Modules strongly influenced by the 5XFAD transgene were related to lysosome/stress responses and neuronal synapse/signaling. The modules with the strongest relationship to human disease-neuronal synapse/signaling and lysosome/stress response-were not significantly influenced by genetic background. However, other modules in 5XFAD that were related to human disease, such as GABA synaptic signaling and mitochondrial membrane modules, were influenced by genetic background. Most disease-related modules were more strongly correlated with AD genotype in the hippocampus compared with the cortex. Our findings suggest that the genetic diversity introduced by crossing B6 and D2 inbred backgrounds influences proteomic changes related to disease in the 5XFAD model, and that proteomic analysis of other genetic backgrounds in transgenic and knock-in AD mouse models is warranted to capture the full range of molecular heterogeneity in genetically diverse models of AD.

Diversification of plant SUPPRESSOR OF MAX2 1 (SMAX1)-like genes and genome-wide identification and characterization of cotton SMXL gene family

BackgroundStrigolactones (SLs) are a recently discovered class of plant hormones. SUPPRESSOR OF MAX2 1 (SMAX1)-like proteins, key component of the SL signaling pathway, have been studied extensively for their roles in regulating plant growth and development, such as plant branching. However, systematic identification and functional characterization of SMXL genes in cotton (Gossypium sp.), an important fiber and oil crop, has rarely been conducted.ResultsWe identified 210 SMXL genes from 21 plant genomes and examined their evolutionary relationships. The structural characteristics of the SMXL genes and their encoded proteins exhibited both consistency and diversity. All plant SMXL proteins possess a conserved Clp-N domain, P-loop NTPase, and EAR motif. We identified 63 SMXL genes in cotton and classified these into four evolutionary branches. Gene expression analysis revealed tissue-specific expression patterns of GhSMXL genes, with some upregulated in response to GR24 treatment. Protein co-expression network analysis showed that GhSMXL6, GhSMXL7-1, and GhSMXL7-2 mainly interact with proteins functioning in growth and development, while virus-induced gene silencing revealed that GhSMAX1-1 and GhSMAX1-2 suppress the growth and development of axillary buds.ConclusionsSMXL gene family members show evolutionary diversification through the green plant lineage. GhSMXL6/7–1/7–2 genes play critical roles in the SL signaling pathway, while GhSMXL1-1 and GhSMXL1-2 function redundantly in growth of axillary buds. Characterization of the cotton SMXL gene family provides new insights into their roles in responding to SL signals and in plant growth and development. Genes identified in this study could be used as the candidate genes for improvement of plant architecture and crop yield.