Proteomic Features Research Articles

Mitochondrial Proteome Defined Molecular Pathological Characteristics of Oncocytic Thyroid Tumors.

Oncocytic thyroid tumors are characterized by an elevated mitochondrial density within the cells, distinguishing them from other thyroid tumors, exhibit distinct clinical behaviors, including increased invasiveness and iodine therapy resistance. However, the proteomic alterations in oncocytic thyroid tumors remain inadequately characterized. In this study, we analyzed 156 Asian patients with oncocytic thyroid adenomas (OA) and carcinomas (OCA) to explore their clinical, genetic, and proteomic features. Genetic testing of 73 samples revealed frequent mutations in TERT, NRAS, EIF1AX, EZH1, and HRAS, with TERT promoter mutations being exclusive to OCAs. Proteomic analysis identified 66 mitochondrial-specific proteins significantly highly expressed in oncocytic tumors than in non-oncocytic tumors. This led to the development of a thyroid oncocytic score (TOS) to quantify oncocytic characteristics. Among these proteins, isocitrate dehydrogenase 2 (IDH2) was substantially overexpressed in oncocytic tumors and further confirmed by immunohistochemistry in oncocytic tumor slides (n = 41) and non-oncocytic tumor slides (n = 40). Moreover, IDH2 is significantly overexpressed in OCA compared to OA highlighting its potential as a biomarker for differential diagnosis of oncocytic tumors and malignancy. These findings improve the understanding of oncocytic thyroid tumors molecular pathology and suggest IDH2 as a valuable marker for clinical management.

Lipoprotein(a) metabolomic and proteomic features and atherosclerotic cardiovascular disease in young, asymptomatic adults

Abstract Background Lipoprotein (a) [Lp(a)] is associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD) events. Although Lp(a) is a powerful genetic risk factor for ASCVD, the pathways that mediate this risk and the associated metabolomic and proteomic features that capture this risk are unknown. Methods In young, asymptomatic Coronary Artery Risk Development in Young Adults (CARDIA) participants, we defined the relationships between Lp(a) and metabolomic (n=563)/proteomic (n=184) features, derived quantitative Lp(a)-omic scores from these features, and related these quantitative scores to ASCVD phenotypes. Results CARDIA subjects had a mean age of 32 years and a median follow-up of 27 years. In the overall cohort (n=3920), Lp(a) levels at year 7 (Y7) were significantly associated with ASCVD endpoints, including hs-CRP, coronary artery calcification (CAC), death, and incident CHD (Figure 1A). In the subcohort (n=2290) that had measurements of Lp(a), proteomics, and metabolomics, Y7 Lp(a) levels were associated with distinct proteomic and metabolomic features indicative of immune responses, lipoprotein metabolism, atherogenesis, and arginine/steroid biosynthesis. Using machine learning approaches, Lp(a) metabolomic, proteomic, and transomic quantitative scores were derived. After adjustment, the Lp(a) transomic score was more strongly related to incident CAC, degree of CAC, CRP, and incident CHD than Lp(a) itself (Figure 1B). Conclusions To our knowledge, this is the first study to examine the relationship between Lp(a), metabolomic/proteomic features, and ASCVD phenotypes in young, asymptomatic adults. The multi-omics approaches employed here provide insight into the pathobiology of Lp(a)-driven ASCVD and enable more nuanced mechanistic risk assessment compared with Lp(a) alone.

Proteomic Signatures of Right Ventricular Outcomes in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a disease of progressive right ventricular (RV) failure with high morbidity and mortality. Our goal is to investigate proteomic features and pathways associated with RV-focused outcomes including mortality, RV dilation, and NT-proBNP (N-terminal pro-B-type natriuretic peptide) in PAH. Participants in a single-institution cohort with 3 years of follow-up underwent proteomic profiling of their plasma using 7288 aptamers (targeting 6467 unique human proteins). Partial least squares discriminant analysis was performed to assess global protein variation associated with mortality, RV dilation, and NT-proBNP levels. Differentially abundant proteins and enriched pathways associated with outcomes were identified following baseline adjustments. RV vulnerability models estimated associations for individuals with similar afterload following adjustment for pulmonary vascular resistance. A total of 117 participants with PAH were included. Partial least squares discriminant analysis of the proteome showed clear separation between survivors and nonsurvivors, participants with dilated versus nondilated RVs, and across NT-proBNP levels. Proteins and pathways involving the ECM (extracellular matrix) were upregulated in participants who died during follow-up, those with severe RV dilation, and those with higher levels of NT-proBNP. Pulmonary vascular resistance adjustment reinforced the importance of ECM proteins in the association with RV vulnerability, independent of afterload. These findings were confirmed in independent PAH cohorts with available plasma proteomics and RV tissue gene and protein expression. Distinct plasma proteomic profiles are associated with mortality, RV dilation, and NT-proBNP in PAH. Proteins and pathways governing tissue remodeling are strongly associated with poor outcomes, may mediate RV vulnerability to right heart failure, and represent promising candidates as biomarkers and potential therapeutic targets.

Proposal of a Theoretically Feasible Method to Perform Perilymph Sampling in Clinical Settings

The ability to analyze perilymph could allow inner ear pathologies to be studied. However, today, perilymph sampling is only performed for research purposes because of the risk of negative outcomes such as hearing loss or balance disorders. This paper aims to analyze the current literature on perilymph sampling and propose a method to collect perilymph in clinical settings. The published literature on perilymph sampling and its analyses was screened, and the results were analyzed and discussed in this mini review. Also, articles that discussed microneedle technology were reviewed and included in the analysis of the data. Based on the results of this review, we would like to propose a feasible technique to perform perilymph sampling in clinical settings. A total of eight studies analyzing perilymph were identified; data on proteomic, metabolomic and miRNA features present within human perilymph were collected and described. Two articles describing the use and auditory outcomes post microneedle drug injection into the inner ear were identified. Based on the methods for perilymph sampling described in humans and the recent innovations introduced by the use of microneedles, we suggested a feasible method to collect perilymph in the outpatient setting. The analysis of perilymph undoubtedly represents a valid instrument to fully understand inner ear diseases. A combination of traditional and innovative techniques, such as gaining access to the round window through the transcanalar approach using micro-endoscopes and microneedles to perform sampling, might simplify the sampling procedure and make it practicable in a clinical setting.

Characterization of the Angiogenic and Proteomic Features of Circulating Exosomes in a Canine Mandibular Model of Distraction Osteogenesis.

Distraction osteogenesis (DO) represents a highly effective method for addressing significant bone defects; however, it necessitates a long treatment period. Exosomes are key mediators of intercellular communication. To investigate their role in the angiogenesis and osteogenesis of DO, we established a canine mandibular DO model with a bone defect (BD) group as the control. Higher levels of angiogenesis were observed in the regenerating tissue from the DO group compared to those from the BD group, accompanied by earlier osteogenesis. Proteomic analysis was performed on circulating exosomes at different phases of the DO using a data-independent acquisition method. Data are available via ProteomeXchange with the identifier PXD050531. The results indicated specific alterations in circulating exosome proteins at different phases of DO, reflecting the regenerative activities in the corresponding tissues. Notably, fibronectin 1 (FN1), thrombospondin 1 (THBS1), and transferrin receptor (TFRC) emerged as potential candidate proteins related to the angiogenic response in DO. Further cellular experiments validated the potential of DO-associated circulating exosomes to promote angiogenesis in endothelial cells. Collectively, these data reveal previously unknown mechanisms that may underlie the efficacy of DO and suggest that exosome-derived proteins may be useful as therapeutic targets for strategies designed to improve DO-related angiogenesis and bone regeneration.

Effects of Cryptorchidism on the Semen Quality of Giant Pandas from the Perspective of Seminal Plasma Proteomics

Giant pandas are an endangered species with low reproductive rates. Cryptorchidism, which can negatively affect reproduction, is also often found in pandas. Seminal plasma plays a crucial role in sperm–environment interactions, and its properties are closely linked to conception potential in both natural and assisted reproduction. The research sought to identify seminal fluid protein content variations between normal and cryptorchid giant pandas. Methods: Using a label-free MS-based method, the semen proteomes of one panda with cryptorchidism and three normal pandas were studied, and the identified proteins were compared and functionally analyzed. Results: Mass spectrometry identified 2059 seminal plasma proteins, with 361 differentially expressed proteins (DEPs). Gene ontology (GO) analysis revealed that these DEPs are mainly involved in the phosphate-containing compound metabolic, hydrolase activity, and kinase activity areas (p ≤ 0.05). The KEGG functional enrichment analysis revealed that the top 20 pathways were notably concentrated in the adipocyte lipolysis and insulin metabolism pathway, with a significance level of p ≤ 0.05. Further analysis through a protein–protein interaction (PPI) network identified nine key proteins that may play crucial roles, including D2GXH8 (hexokinase Fragment), D2HSQ6 (protein tyrosine phosphatase), and G1LHZ6 (Calmodulin 2). Conclusions: We suspect that the high abundance of D2HSQ6 in cryptorchid individuals is associated with metabolic pathways, especially the insulin signal pathway, as a typical proteomic feature related to its pathological features. These findings offer insight into the ex situ breeding conditions of this threatened species.

A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The ‘MVME’ model is assessed using the O’Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC).

Are we closer to achieving precision medicine for migraine treatment? A narrative review.

The term 'precision medicine' encompasses strategies to optimize diagnosis and outcome prediction and to tailor treatment for individual patients, in consideration of their unique characteristics. The greater availability of multifaceted datasets and strategies to model such data have made precision medicine increasingly possible in recent years. Precision medicine is especially needed in the migraine field since the response to migraine treatments is not universal amongst all individuals with migraine. To provide a narrative review describing contributions to achieving precision medicine for migraine treatment. A search of PubMed for English language articles of human participants published from 2005 to January 2024 was conducted to identify articles that reported research contributing to precision medicine for migraine treatment. The published literature was categorized and summarized according to the type of data that were included: clinical phenotypes, genomics, proteomics, physiologic measures, and brain imaging. Published studies have investigated characteristics associated with acute and preventive treatment responses, such as nonsteroidal anti-inflammatory drugs, triptans, onabotulinumtoxinA, and anti-calcitonin gene-related peptide monoclonal antibodies, in patients with episodic or chronic migraine. There is evidence that clinical, genetic, epigenetic, proteomic, physiologic, and brain imaging features might associate with migraine treatment outcomes, although inconsistencies for such findings clearly exist. The published literature suggests that there are clinical and biological features which associate with, and might be useful for predicting, migraine treatment responses. To achieve precision medicine for migraine treatment, further research is needed that validates and expands on existing findings and tests the accuracy and value of migraine treatment prediction models in clinical settings.

Integrating Vascular Phenotypic and Proteomic Analysis in an Open Microfluidic Platform.

This research introduces a vascular phenotypic and proteomic analysis (VPT) platform designed to perform high-throughput experiments on vascular development. The VPT platform utilizes an open-channel configuration that facilitates angiogenesis by precise alignment of endothelial cells, allowing for a 3D morphological examination and protein analysis. We study the effects of antiangiogenic agents─bevacizumab, ramucirumab, cabozantinib, regorafenib, wortmannin, chloroquine, and paclitaxel─on cytoskeletal integrity and angiogenic sprouting, observing an approximately 50% reduction in sprouting at higher drug concentrations. Precise LC-MS/MS analyses reveal global protein expression changes in response to four of these drugs, providing insights into the signaling pathways related to the cell cycle, cytoskeleton, cellular senescence, and angiogenesis. Our findings emphasize the intricate relationship between cytoskeletal alterations and angiogenic responses, underlining the significance of integrating morphological and proteomic data for a comprehensive understanding of angiogenesis. The VPT platform not only advances our understanding of drug impacts on vascular biology but also offers a versatile tool for analyzing proteome and morphological features across various models beyond blood vessels.

Evaluation of single-cell sorting accuracy using antibody-derived tag-based qPCR.

Single-cell sorting (index sorting) is a widely used method to isolate one cell at a time using fluorescence-activated cell sorting (FACS) for downstream applications such as single-cell sequencing or single-cell expansion. Despite widespread use, few assays are available to evaluate the proteomic features of the sorted single cell and further confirm the accuracy of single-cell sorting. With this caveat, we developed a novel assay to confirm the protein expression of sorted single cells by co-staining cells with the same marker using both antibody-derived tags (ADTs) and fluorescent antibodies. After single-cell sorting, we amplified the oligo of the ADT reagent as a surrogate signal for the protein expression using multiplex TaqMan™ qPCR on sorted cells. This assay is not only useful for confirming the identity of a sorted single cell but also an efficient method to profile proteomic features at the single-cell level. Finally, we appliedthis assay to characterize protein expression on whole cell lysate. Because of the sensitivity of the TaqMan™ qPCR, we can detect protein expression from a small number of cells. In summary, the ADT-based qPCR assay developed here can be utilized to confirm single-cell sorting accuracy and characterizing protein expression on both single cells and whole cell lysate.

Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population

AimAmong multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications. Methods1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications. ResultsAmong 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline. ConclusionsEach T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.

Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level

Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically heterogenous. The application of omics technologies—mainly studying nucleic acids—has significantly improved MB classification and stratification, but treatment options are still unsatisfactory. The proteome and their N-glycans hold the potential to discover clinically relevant phenotypes and targetable pathways. We compile a harmonized proteome dataset of 167 MBs and integrate findings with DNA methylome, transcriptome and N-glycome data. We show six proteome MB subtypes, that can be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pG3myc), and synapses/immunological processes (pSHHs, pG3 and pG4). Multiomic analysis reveals different conservation levels of proteome features across MB subtypes at the DNA methylome level. Aggressive pGroup3myc MBs and favorable pWNT MBs are most similar in cluster hierarchies concerning overall proteome patterns but show different protein abundances of the vincristine resistance-associated multiprotein complex TriC/CCT and of N-glycan turnover-associated factors. The N-glycome reflects proteome subtypes and complex-bisecting N-glycans characterize pGroup3myc tumors. Our results shed light on targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.

Blood protein assessment of leading incident diseases and mortality in the UK Biobank.

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.

DIPG-06. FUNCTIONAL MULTI-OMIC PROFILING OF THE LANDSCAPE OF DIFFUSE MIDLINE GLIOMAS TO IDENTIFY THERAPEUTIC VULNERABILITIES

Abstract BACKGROUND Diffuse midline glioma (DMG) is a devastating pediatric high-grade glioma (pHGG) located along the midline structures of the brain and spine. Palliative radiotherapy is the only approved treatment outside of clinical trials, with patients given 9–11-months survival post-diagnosis. Over the last 10-years, it has become common for patients to be prescribed precision therapies targeting genetic alterations, however largely, DMG harbor genetic alterations that are untargetable. METHODS To improve DMG patient outcomes, we are performing multi-omic analysis on 210 DMG, normal controls and other pHGG samples to functionally characterize the genomic landscape of these tumors and understand the protein-controlled functional consequences of recurring somatic alterations. RESULTS Genomic sequencing has been performed on 40/210 pHGG samples, identifying 290 unique mutations and several recurrent chromosomal alterations. Additionally, proteomic/phosphoproteomic profiling identified both unique and similar features of post-translational landscapes across genotypes. Examining H3.3K27M DMG cell-lines, SU-DIPG-XIII and UON-VIBE5, genomic sequencing identified 11 and 6 mutations respectively, however, only UON-VIBE5 possessed a genomically-predicted target, a PDGFRA-D842V mutation, potentially targetable by Avapritinib. Conversely, investigation of proteomes/phosphoproteomes revealed several potential therapeutic vulnerabilities. SU-DIPG-XIII possessed high enrichment of HDAC/BRD and CDK pathways and UON-VIBE5, ATM-centric pathways. In vitro proliferation assays of Avapritinib treated UON-VIBE5 failed to elicit a response at clinically relevant doses, while all proteomically-predicted therapies (HDAC – Fimepinostat, BRD – JQ1, CDK – Ribociclib and ATM – WSD-0628) successfully influenced tumor cell growth whilst leaving microglia controls cells unaffected. Avapritinib and WSD-0628 (CNS-active ATM inhibitor) drug predictions were further validated in the RA055 DMG model, which possesses similar genomic and proteomic features to UON-VIBE5. Avapritinib alone failed to influence tumor growth, while WSD-0628 notably reduced cell proliferation. The combination of these drugs, however, synergistically inhibited RA055 proliferation. CONCLUSIONS These findings thus provide preliminary evidence supporting the need to take a multi-omic approach in treating DMGs.

Developmental and nuclear proteomic signatures characterize the temporal regulation of fibroin synthesis during the last molting-feeding transition of silkworm

Silkworm fibroins are natural proteinaceous macromolecules and provide core mechanical properties to silk fibers. The synthesis process of fibroins is posterior silk gland (PSG)-exclusive and appears active at the feeding stage and inactive at the molting stage. However, the molecular mechanisms controlling it remain elusive. Here, the silk gland's physiological and nuclear proteomic features were used to characterize changes in its structure and development from molting to feeding stages. The temporal expression profile and immunofluorescence analyses revealed a synchronous transcriptional on-off mode of fibroin genes. Next, the comparative nuclear proteome of the PSG during the last molting-feeding transition identified 798 differentially abundant proteins (DAPs), including 42 transcription factors and 15 epigenetic factors. Protein-protein interaction network analysis showed a “CTCF-FOX-HOX-SOX” association with activated expressions at the molting stage, suggesting a relatively complex and multifactorial regulation of the PSG at the molting stage. In addition, FAIRE-seq verification indicated “closed” and “open” conformations of fibroin gene promoters at the molting and feeding stages, respectively. Such proteome combined with chromatin accessibility analysis revealed the detailed signature of protein factors involved in the temporal regulation of fibroin synthesis and provided insights into silk gland development as well as silk production in silkworms.

Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer.

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

Machine learning-based health environmental-clinical risk scores in European children

BackgroundEarly life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.MethodsIn this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions.ResultsECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.ConclusionsBesides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

CISD3/MiNT is required for complex I function, mitochondrial integrity, and skeletal muscle maintenance

Mitochondria play a central role in muscle metabolism and function. A unique family of iron-sulfur proteins, termed CDGSH Iron Sulfur Domain-containing (CISD/NEET) proteins, support mitochondrial function in skeletal muscles. The abundance of these proteins declines during aging leading to muscle degeneration. Although the function of the outer mitochondrial CISD/NEET proteins, CISD1/mitoNEET and CISD2/NAF-1, has been defined in skeletal muscle cells, the role of the inner mitochondrial CISD protein, CISD3/MiNT, is currently unknown. Here, we show that CISD3 deficiency in mice results in muscle atrophy that shares proteomic features with Duchenne muscular dystrophy. We further reveal that CISD3 deficiency impairs the function and structure of skeletal muscles, as well as their mitochondria, and that CISD3 interacts with, and donates its [2Fe-2S] clusters to, complex I respiratory chain subunit NADH Ubiquinone Oxidoreductase Core Subunit V2 (NDUFV2). Using coevolutionary and structural computational tools, we model a CISD3-NDUFV2 complex with proximal coevolving residue interactions conducive of [2Fe-2S] cluster transfer reactions, placing the clusters of the two proteins 10 to 16 Å apart. Taken together, our findings reveal that CISD3/MiNT is important for supporting the biogenesis and function of complex I, essential for muscle maintenance and function. Interventions that target CISD3 could therefore impact different muscle degeneration syndromes, aging, and related conditions.

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort.

The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction. Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE. SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease. Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.

Proteomic features of soft tissue tumours in adolescents and young adults

BackgroundAdolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.MethodsTo define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).ResultsBiological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.ConclusionsOur study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.