Salivary gland amyloidosis: Proteomic identification and clinicopathologic characterization of 57 cases.

Salivary gland amyloidosis is an uncommon diagnosis. Most studies have focused on minor salivary gland biopsies as a surrogate site for diagnosing systemic amyloidosis, while only few studies have investigated major salivary gland amyloidosis. We retrospectively identified 57 major and minor salivary gland amyloidosis cases typed using a proteomics-based method between 2010 and 2022. Frequency of amyloid types, clinicopathologic features, and distribution patterns of amyloid deposits were assessed. The indication for salivary gland biopsy/resection (known in 34 cases) included suspected amyloidosis (N = 14; 41.2%), lesion/mass (N = 12; 35.3%), swelling/enlargement (N = 5; 14.7%), and rule out Sjogren syndrome (N = 3; 8.8%). Concurrent pathology was reported in 16 cases, and included chronic sialadenitis (N = 11), extranodal marginal zone lymphoma (N = 3), plasma cell neoplasm (N = 1), and pleomorphic adenoma (N = 1). We identified 3 types of amyloidosis: immunoglobulin light chain/AL (N = 47; 82.5%); immunoglobulin heavy chain/AH (N = 1; 1.8%), and transthyretin/ATTR (N = 9; 15.8%). The patterns of amyloid deposits (assessed in 35 cases) included: 1) Perivascular and/or periductal distribution (N = 18; 51.4%); 2) Mass formation (N = 9; 25.7%); 3) Stromal micronodule formation (N = 7; 20.0%); and 4) Diffuse interstitial involvement (N = 1; 2.9%). We also identified one case of AL amyloidosis localized to the major salivary gland, where only 6 other cases with adequate staging workup to exclude systemic amyloidosis were previously reported. In conclusion, salivary gland amyloidosis is an uncommon diagnosis but may be underrecognized due to low index of suspicion. Most cases of salivary gland amyloidosis are AL type, but a minority are ATTR. Therefore, proteomics-based typing remains essential for treatment and prognosis.

Potential Protein Biomarkers in Saliva for Detection of Frailty Syndrome by targeted proteomics.

Frailty is a physiological geriatric syndrome, caused by immunosenescence, inflammation and alterations at the protein level leading to metabolic and microbiota changes. Currently, this syndrome is evaluated clinically with the Frailty-VIG index. The aim of the study was therefore to investigate the potential suitability of saliva as a non-invasive proximal biological fluid for the characterisation and identification of possible protein-level biomarkers in frailty syndrome. This cross-sectional study was conducted in a rural population of older Spanish adults using the SMR proteomics technique. A differential protein profile of eight potential and surrogate proteins (CYTC, CYTD, CYTS, CYTB, MIF, ALBU, CD44 and B2MG) was detected in saliva, all of which correlated with factors characterising frailty syndrome, such as vascular ageing (arterial stiffness and cardiovascular disease), obesity, mood problems, global cognitive impairment, changes in gait and hand pressure strength. The proteins CYTD (r = 0.415, p = 0.013) and CYTC (r = 0.280, p = 0.026), which were detected differentially in the protein profile, were associated with the Frailty-VIG index. All analysed proteins are associated not only with the clinical symptoms of frailty syndrome, but also with an acute inflammatory response, endothelial cell proliferation and the complement system, among others.

A cloud-based learning module for biomarker discovery.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Investigating the impact of environmental enrichment on proteome and neurotransmitter-related profiles in an animal model of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with behavioral and cognitive impairments. Unfortunately, the drugs the Food and Drug Administration currently approved for AD have shown low effectiveness in delaying the progression of the disease. The focus has shifted to non-pharmacological interventions (NPIs) because of the challenges associated with pharmacological treatments for AD. One such intervention is environmental enrichment (EE), which has been reported to restore cognitive decline associated with AD effectively. However, the therapeutic mechanisms by which EE improves symptoms associated with AD remain unclear. Therefore, this study aimed to reveal the mechanisms underlying the alleviating effects of EE on AD symptoms using histological, proteomic, and neurotransmitter-related analyses. Wild-type (WT) and 5XFAD mice were maintained in standard housing or EE conditions for 4 weeks. First, we confirmed the mitigating effects of EE on cognitive impairment in an AD animal model. Then, histological analysis revealed that EE reduced Aß accumulation, neuroinflammation, neuronal death, and synaptic loss in the AD brain. Moreover, proteomic analysis by liquid chromatography-tandem mass spectrometry showed that EE enhanced synapse- and neurotransmitter-related networks and upregulated synapse- and neurotransmitter-related proteins in the AD brain. Furthermore, neurotransmitter-related analyses showed an increase in acetylcholine and serotonin concentrations as well as a decrease in polyamine concentration in the frontal cortex and hippocampus of 5XFAD mice raised under EE conditions. Our findings demonstrate that EE restores cognitive impairment by alleviating AD pathology and regulating synapse-related proteins and neurotransmitters. Our study provided neurological evidence for the application of NPIs in treating AD.

Clinical serum lipidomic profiling revealed potential lipid biomarkers for early diabetic retinopathy.

Diabetic retinopathy (DR) is a serious complication of diabetes featuring abnormal lipid metabolism. However, the specific lipid molecules associated with onset and progression remain unclear. We used a broad-targeted lipidomics approach to assess the lipid changes that occur before the proliferative retinopathy stage and to identify novel lipid biomarkers to distinguish between patients without DR (NDR) and with non-proliferative DR (NPDR). Targeted lipomics analysis was carried out on serum samples from patients with type I diabetes, including 20 NDRs and 20 NPDRs. The results showed that compared with the NDR group, 102 lipids in the NPDR group showed specific expressions. Four lipid metabolites including TAG58:2-FA18:1 were obtained using the Least Absolute Shrink And Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. The four-lipid combination diagnostic models showed good predictive ability in both the discovery and validation sets, and were able to distinguish between NDR patients and NPDR patients. The identified lipid markers significantly improved diagnostic accuracy within the NPDR group. Our findings help to better understand the complexity and individual differences of DR lipid metabolism.

Mitonuclear compatibility is maintained despite relaxed selection on male mitochondrial DNA in bivalves with doubly uniparental inheritance.

Mitonuclear coevolution is common in eukaryotes, but bivalve lineages that have doubly uniparental inheritance (DUI) of mitochondria may be an interesting example. In this system, females transmit mtDNA (F mtDNA) to all offspring, while males transmit a different mtDNA (M mtDNA) solely to their sons. Molecular evolution and functional data suggest oxidative phosphorylation (OXPHOS) genes encoded in M mtDNA evolve under relaxed selection due to their function being limited to sperm only (vs. all other tissues for F mtDNA). This has led to the hypothesis that mitonuclear coevolution is less important for M mtDNA. Here, we use comparative phylogenetics, transcriptomics, and proteomics to understand mitonuclear interactions in DUI bivalves. We found nuclear OXPHOS proteins coevolve and maintain compatibility similarly with both F and M mtDNA OXPHOS proteins. Mitochondrial recombination did not influence mitonuclear compatibility and nuclear-encoded OXPHOS genes were not upregulated in tissues with M mtDNA to offset dysfunction. Our results support that selection maintains mitonuclear compatibility with F and M mtDNA despite relaxed selection on M mtDNA. Strict sperm transmission, lower effective population size, and higher mutation rates may explain the evolution of M mtDNA. Our study highlights that mitonuclear coevolution and compatibility may be broad features of eukaryotes.

Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients.

With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue (n = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (TH) cells and cytotoxic (TC) cells of early (CD57-) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. TC and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ TC-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL.

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.

Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with m/z 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.

TMEM176B Promotes EMT via FGFR/JNK Signalling in Development and Tumourigenesis of Lung Adenocarcinoma.

Lung cancer, the leading cause of cancer-related incidence and mortality worldwide, is characterised by high invasiveness and poor prognosis. Novel therapeutic targets are required, especially for patients with inoperable metastatic disease requiring systemic therapies to improve patients' welfare. Recently, studies indicated that TMEM176B is a positive regulator in breast and gastric cancers, and it could be a potential target for treatment. In this study, we used single-cell sequencing, proteomics, Co-IP, and in vivo and in vitro experimental models to investigate the role of TMEM176B in lung adenocarcinoma development. Our study indicated that TMEM176B expression was enhanced in lung adenocarcinoma tissues, and it was associated with shorter overall survival (OS). TMEM176B promoted cellular functions, including cell proliferation, invasion, migration and adhesion in vitro and tumour growth in vivo. Moreover, the tube formation ability of endothelial cells was enhanced by treating with the tumour cell-conditioned medium. We have also demonstrated that TMEM176B regulated EMT via the FGFR1/JNK/Vimentin/Snail signalling cascade. Overall, our study suggests TMEM176B could be a potential therapeutic target in lung adenocarcinoma.

Integrative Analyses of Circulating Proteins and Metabolites Reveal Sex Differences in the Associations with Cardiac Function among DCM Patients.

Dilated cardiomyopathy (DCM) is characterized by reduced left ventricular ejection fraction (LVEF) and left or biventricular dilatation. We evaluated sex-specific associations of circulating proteins and metabolites with structural and functional heart parameters in DCM. Plasma samples (297 men, 71 women) were analyzed for proteins using Olink assays (targeted analysis) or LC-MS/MS (untargeted analysis), and for metabolites using LC MS/MS (Biocrates AbsoluteIDQ p180 Kit). Associations of proteins (n = 571) or metabolites (n = 163) with LVEF, measured left ventricular end diastolic diameter (LVEDDmeasured), and the dilation percentage of LVEDD from the norm (LVEDDacc. to HENRY) were examined in combined and sex-specific regression models. To disclose protein-metabolite relations, correlation analyses were performed. Associations between proteins, metabolites and LVEF were restricted to men, while associations with LVEDD were absent in both sexes. Significant metabolites were validated in a second independent DCM cohort (93 men). Integrative analyses demonstrated close relations between altered proteins and metabolites involved in lipid metabolism, inflammation, and endothelial dysfunction with declining LVEF, with kynurenine as the most prominent finding. In DCM, the loss of cardiac function was reflected by circulating proteins and metabolites with sex-specific differences. Our integrative approach demonstrated that concurrently assessing specific proteins and metabolites might help us to gain insights into the alterations associated with DCM.

Investigation of the human-gut-kidney axis by fecal proteomics, highlights molecular mechanisms affected in CKD.

Objective: The interplay of gut microbiota with the kidney system in chronic kidney disease (CKD), is characterized by increased concentrations of uric acid in the gut, which in turn, may increase bacterial uricase activity and may lead to the generation of uremic toxins. Nevertheless, knowledge on these underlying bidirectional molecular mechanisms is still limited. Methods: In this exploratory study, proteomic analysis was performed on fecal samples, targeting to investigate this largely unexplored biological material as a source of information reflecting the gut-kidney axis. Specifically, fecal suspension samples from patients with CKD1 (n = 12) and CKD4 (n = 17) were analysed by LC-MS/MS, using both the Human and Bacterial UniProt RefSeq reviewed databases. Results: This fecal proteomic analysis collectively identified 701 human and 1011 bacterial proteins of high confidence. Differential expression analysis (CKD4/CKD1) revealed significant changes in human proteins (n = 8, including proteins such as galectin-3-binding protein and prolactin-inducible protein), that were found to be associated with inflammation and CKD. The differential protein expression of pancreatic alpha-amylase further suggested plausible reduced saccharolytic fermentation in CKD4/CKD1. Significant changes in bacterial proteins (n = 9, such as glyceraldehyde-3-phosphate dehydrogenase and enolase), participating in various carbohydrate and metabolic pathways important for the synthesis of butyrate, in turn suggested differential butyrate synthesis in CKD4/CKD1. Further, targeted quantification of fecal pancreatic alpha-amylase and butyrate in the same fecal suspension samples, supported these hypotheses. Conclusion: Collectively, this exploratory fecal proteomic analysis highlighted changes in human and bacterial proteins reflecting inflammation and reduced saccharolytic fermentation in CKD4/CKD1, plausibly affecting the butyrate synthesis pathways in advanced stage kidney disease. Integrative multi-omics validation is planned.

Changes in protein fluxes and gene expression in non-injured muscle tissue distant from an acute myotoxic injury in male mice.

The response to acute myotoxic injury requires stimulation of local repair mechanisms in the damaged tissue. However, satellite cells in muscle distant from acute injury have been reported to enter a functional state between quiescence and active proliferation. Here, we asked whether protein flux rates are altered in muscle distant from acute local myotoxic injury and how they compare to changes in gene expression from the same tissue. Broad and significant alterations in protein turnover were observed across the proteome in the limb contralateral to injury during the first 10 days after. Interestingly, mRNA changes had almost no correlation with directly measured protein turnover rates. In summary, we show consistent and striking changes in protein flux rates in muscle tissue contralateral to myotoxic injury, with no correlation between changes in mRNA levels and protein synthesis rates. This work motivates further investigation of the mechanisms, including potential neurological factors, responsible for this distant effect. KEY POINTS: Previous literature demonstrates that stem cells of uninjured muscle respond to local necrotic muscle tissue damage and regeneration. We show that muscle tissue that was distant from a model of local necrotic damage had functional changes at both the gene expression and the protein turnover level. However, these changes in distant tissue were more pronounced during the earlier stages of tissue regeneration and did not correlate well with each other. The results suggest communication between directly injured tissue and non-affected tissues that are distant from injury, which warrants further investigation into the potential of this mechanism as a proactive measure for tissue regeneration from damage.

[Exploring the mechanism of IgA vasculitis pathogenesis through the interaction of thrombin and inflammatory factors using urinary proteomics]. / è¿ç”¨å°¿è›‹ç™½ç»„å­¦æŠ€æœ¯æŽ¢ç´¢å‡è¡€é ¶ä¸Žç‚Žç—‡å› å­ç›¸äº’ä½œç”¨å‚ä¸ŽIgAè¡€ç®¡ç‚Žå‘ç— çš„æœºåˆ¶.

OBJECTIVES: To explore the evidence, urinary biomarkers, and partial mechanisms of hypercoagulability in the pathogenesis of IgA vasculitis (IgAV). METHODS: Differential expression of proteins in the urine of 10 healthy children and 10 children with IgAV was screened using high-performance liquid chromatography-tandem mass spectrometry, followed by Reactome pathway analysis. Protein-protein interaction (PPI) network analysis was conducted using STRING and Cytoscape software. In the validation cohort, 15 healthy children and 25 children with IgAV were included, and the expression levels of differential urinary proteins were verified using enzyme-linked immunosorbent assay. RESULTS: A total of 772 differential proteins were identified between the IgAV group and the control group, with 768 upregulated and 4 downregulated. Reactome pathway enrichment results showed that neutrophil degranulation, platelet activation, and hemostasis pathways were involved in the pathogenesis of IgAV. Among the differential proteins, macrophage migration inhibitory factor (MIF) played a significant role in neutrophil degranulation and hemostasis, while thrombin was a key protein in platelet activation and hemostasis pathways. PPI analysis indicated that thrombin directly interacted with several proteins involved in inflammatory responses, and these interactions involved MIF. Validation results showed that compared to healthy children, children with IgAV had significantly higher urine thrombin/creatinine and urine MIF/creatinine levels (P<0.05). CONCLUSIONS: Thrombin contributes to the pathogenesis of IgAV through interactions with inflammatory factors. Urinary thrombin and MIF can serve as biomarkers reflecting the hypercoagulable and inflammatory states in children with IgAV.

Corrigendum: Targeted proteomics-determined multi-biomarker profiles developed classifier for prognosis and immunotherapy responses of advanced cervical cancer.

[This corrects the article DOI: 10.3389/fimmu.2024.1391524.].

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Quantitative mass spectrometry analysis of the injured proximal and distal human digital nerve ends.

Introduction: Proteomic analysis of injured human peripheral nerves, particularly focusing on events occurring in the proximal and distal nerve ends, remains relatively underexplored. This study aimed to investigate the molecular patterns underlying a digital nerve injury, focusing on differences in protein expression between the proximal and distal nerve ends. Methods: A total of 26 human injured digital nerve samples (24 men; 2 women; median age 47 [30-66] years), harvested during primary nerve repair within 48 h post-injury from proximal and distal nerve ends, were analyzed using mass spectrometry. Results: A total of 3,914 proteins were identified, with 127 proteins showing significant differences in abundance between the proximal and the distal nerve ends. The downregulation of proteins in the distal nerve end was associated with synaptic transmission, autophagy, neurotransmitter regulation, cell adhesion and migration. Conversely, proteins upregulated in the distal nerve end were implicated in cellular stress response, neuromuscular junction stability and muscle contraction, neuronal excitability and neurotransmitter release, synaptic vesicle recycling and axon guidance and angiogenesis. Discussion: Investigation of proteins, with functional annotations analysis, in proximal and the distal ends of human injured digital nerves, revealed dynamic cellular responses aimed at promoting tissue degeneration and restoration, while suppressing non-essential processes.

Proteomic analysis of serum in a population-based cohort did not reveal a biomarker for Modic changes.

Introduction: Modic changes (MC) are bone marrow lesions of vertebral bones, which can be detected with magnetic resonance imaging (MRI) adjacent to degenerated intervertebral discs. Defined by their appearance on T1 and T2 weighted images, there are three interconvertible types: MC1, MC2, and MC3. The inter-observer variability of the MRI diagnosis is high, therefore a diagnostic serum biomarker complementing the MRI to facilitate diagnosis and follow-up would be of great value. Methods: We used a highly sensitive and reproducible proteomics approach: DIA/SWATH-MS to find serum biomarkers in a subset of the Northern Finland Birth Cohort 1966. Separately, we measured a panel of factors involved in inflammation and angiogenesis to confirm some potential biomarkers published before with an ELISA-based method called V-Plex. Results: We found neither an association between the serum concentrations of the proteins detected with DIA/SWATH-MS with the presence of MC, nor a correlation with the size of the MC lesions. We did not find any association between the factors measured with the V-Plex and the presence of MC or their size. Conclusion: Altogether, our study suggests that a robust and generally usable biomarker to facilitate the diagnosis of MC cannot readily be found in serum.

Transcriptomic and proteomic investigations identify PI3K-akt pathway targets for hyperthyroidism management in rats via polar iridoids from radix Scrophularia.

High-polarity iridoids from Radix Scrophulariae (R. Scrophulariae) offer a range of benefits, including anti-inflammatory, antioxidant, antitumour, antibacterial, antiviral, and antiallergic effects. Although previous studies have indicated the potential of R. Scrophulariae for hyperthyroidism prevention and treatment, the specific active compounds involved and their mechanisms of action are not fully understood. This study explored the effects of high-polarity iridoid glycosides from R. Scrophulariae on hyperthyroidism induced in rats by levothyroxine sodium. The experimental design included a control group, a hyperthyroidism model group, and a group treated with iridoid glycosides. Serum triiodothyronine (T3) and thyroxine (T4) levels were quantified using an enzyme-linked immunosorbent assay (ELISA). Transcriptomic and proteomic analyses were applied to liver samples to identify differentially expressed genes and proteins. These analyses were complemented by trend analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The effectiveness of key factors was further examined through molecular biology techniques. ELISA results indicated a notable increase in T3 and T4 in the hyperthyroid rats, which was significantly mitigated by treatment with iridoid glycosides. Transcriptomic analysis revealed 6 upregulated and 6 downregulated genes in the model group, showing marked improvement following treatment. Proteomic analysis revealed changes in 30 upregulated and 50 downregulated proteins, with improvements observed upon treatment. The PI3K-Akt signalling pathway was investigated through KEGG enrichment analysis. Molecular biology methods verified the upregulation of Spp1, Thbs1, PI3K, and Akt in the model group, which was reversed in the treatment group. This study revealed that highly polar iridoids from R. Scrophulariae can modulate the Spp1 gene and Thbs1 protein via the PI3K-Akt signalling pathway, suggesting a therapeutic benefit for hyperthyroidism and providing a basis for drug development targeting this condition.

The quantification of zebrafish ocular-associated proteins provides hints for sex-biased visual impairments and perception.

Biochemical differences between sexes can also be seen in non-sexual organs and may affect organ functions and susceptibility to diseases. It has been shown that there are sex-biased visual perceptions and impairments. Abundance differences of eye proteins could provide explanations for some of these. Exploration of the ocular proteome was performed to find sex-based protein abundance differences in zebrafish Danio rerio. A label-free protein quantification workflow using high-resolution mass spectrometry was employed to find proteins with significant differences between the sexes. In total, 3740 unique master proteins were identified and quantified, and 49 proteins showed significant abundance differences between the eyes of male and female zebrafish. Those proteins belong to lipoproteins, immune system, blood coagulation, antioxidants, iron and heme-binding proteins, ion channels, pumps and exchangers, neuronal and photoreceptor proteins, and the cytoskeleton. An extensive literature review provided clues for the possible links between the sex-biased level of proteins and visual perception and impairments. In conclusion, sexual dimorphism at the protein level was discovered for the first time in the eye of zebrafish and should be accounted for in ophthalmological studies. Data are available via ProteomeXchange with identifier PXD033338.