Palaeoproteomic identification of the original binder and modern contaminants in distemper paints from Uvdal stave church, Norway.

Two distemper paint samples taken from decorative boards in Uvdal stave church, Norway, were analysed using palaeoproteomics, with an aim of identifying their binder and possible contaminants. The results point at the use of calfskin to produce hide glue as the original paint binder, and are consistent with the instructions of binder production and resource allocation in the historical records of Norway. Although we did not observe any evidence of prior restoration treatments using protein-based materials, we found abundant traces of human saliva proteins, as well as a few oats and barley peptides, likely deposited together on the boards during their discovery in the 1970s. This work illustrates the need to fully consider contamination sources in palaeoproteomics and to inform those working with such objects about the potential for their contamination.

In silico predicted compound targeting the IQGAP1-GRD domain selectively inhibits growth of human acute myeloid leukemia.

Acute myeloid leukemia (AML) is fatal in the majority of adults. Identification of new therapeutic targets and their pharmacologic modulators are needed to improve outcomes. Previous studies had shown that immunization of rabbits with normal peripheral WBCs that had been incubated with fluorodinitrobenzene elicited high titer antibodies that bound to a spectrum of human leukemias. We report that proteomic analyses of immunoaffinity-purified lysates of primary AML cells showed enrichment of scaffolding protein IQGAP1. Immunohistochemistry and gene-expression analyses confirmed IQGAP1 mRNA overexpression in various cytogenetic subtypes of primary human AML compared to normal hematopoietic cells. shRNA knockdown of IQGAP1 blocked proliferation and clonogenicity of human leukemia cell-lines. To develop small molecules targeting IQGAP1 we performed in-silico screening of 212,966 compounds, selected 4 hits targeting the IQGAP1-GRD domain, and conducted SAR of the 'fittest hit' to identify UR778Br, a prototypical agent targeting IQGAP1. UR778Br inhibited proliferation, induced apoptosis, resulted in G2/M arrest, and inhibited colony formation by leukemia cell-lines and primary-AML while sparing normal marrow cells. UR778Br exhibited favorable ADME/T profiles and drug-likeness to treat AML. In summary, AML shows response to IQGAP1 inhibition, and UR778Br, identified through in-silico studies, selectively targeted AML cells while sparing normal marrow.

Selection of texture-associated biomarkers in chilled and iced grass carp (Ctenopharyngodon idella) fillets via DIA-based proteomics.

Consumers care about the texture of fresh fish flesh, but a rapid quantitative analytical method for this has not been properly established. In this study, texture-associated biomarkers were selected by DIA-based proteomics for possible future application. Results indicated a significant decline in texture and moisture characteristics with extended storage under chilled and iced conditions, and flesh quality was categorized into three intervals. A total of 8 texture-associated biomarkers were identified in the chilled storage group, and 3 distinct ones in the iced storage group. Biomarkers were further refined based on their expression levels. Isobutyryl-CoA dehydrogenase, mitochondrial and [Phosphatase 2A protein]-leucine-carboxy methyltransferase were identified as effective texture-associated biomarkers for chilled fish, and Staphylococcal nuclease domain-containing protein 1 for iced fish. This study provided suitable proteins as indicators of fresh fish flesh texture, which could help establish a rapid and convenient texture testing method in future studies.

Protease S of entomopathogenic bacterium Photorhabdus laumondii: expression, purification and effect on greater wax moth Galleria mellonella.

BACKGROUND: Protease S (PrtS) from Photorhabdus laumondii belongs to the group of protealysin-like proteases (PLPs), which are understudied factors thought to play a role in the interaction of bacteria with other organisms. Since P. laumondii is an insect pathogen and a nematode symbiont, the analysis of the biological functions of PLPs using the PrtS model provides novel data on diverse types of interactions between bacteria and hosts. METHODS AND RESULTS: Recombinant PrtS was produced in Escherichia coli. Efficient inhibition of PrtS activity by photorin, a recently discovered emfourin-like protein inhibitor from P. laumondii, was demonstrated. The Galleria mellonella was utilized to examine the insect toxicity of PrtS and the impact of PrtS on hemolymph proteins in vitro. The insect toxicity of PrtS is reduced compared to protease homologues from non-pathogenic bacteria and is likely not essential for the infection process. However, using proteomic analysis, potential PrtS targets have been identified in the hemolymph. CONCLUSIONS: The spectrum of identified proteins indicates that the function of PrtS is to modulate the insect immune response. Further studies of PLPs' biological role in the PrtS and P. laumondii model must clarify the details of PrtS interaction with the insect immune system during bacterial infection.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response.

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Extracellular vesicle proteins as breast cancer biomarkers: Mass spectrometry-based analysis.

Extracellular vesicles (EVs) are membrane-surrounded vesicles released by various cell types into the extracellular microenvironment. Although EVs vary in size, biological function, and components, their importance in cancer progression and the potential use of EV molecular species to serve as novel cancer biomarkers have become increasingly evident. Cancer cells actively release EVs into surrounding tissues, which play vital roles in cancer progression and metastasis, including invasion and immune modulation. EVs released by cancer cells are usually chosen as a gateway in the search for biomarkers for cancer. In this review, we mainly focused on molecular profiling of EV protein constituents from breast cancer, emphasizing mass spectrometry (MS)-based proteomic approaches. To further investigate the potential use of EVs as a source of breast cancer biomarkers, we have discussed the use of these proteins as predictive marker candidates. Besides, we have also summarized the key characteristics of EVs as potential therapeutic targets in breast cancer and provided significant information on their implications in breast cancer development and progression. Information provided in this review may help understand the recent progress in understanding EV biology and their potential role as new noninvasive biomarkers as well as emerging therapeutic opportunities and associated challenges.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Bioinformatics for Dentistry: A secondary database for the genetics of tooth development.

Genes strictly regulate the development of teeth and their surrounding oral structures. Alteration of gene regulation leads to tooth disorders and developmental anomalies in tooth, oral, and facial regions. With the advancement of gene sequencing technology, genomic data is rapidly increasing. However, the large sets of genomic and proteomic data related to tooth development and dental disorders are currently dispersed in many primary databases and literature, making it difficult for users to navigate, extract, study, or analyze. We have curated the scattered genetic data on tooth development and created a knowledgebase called 'Bioinformatics for Dentistry' (https://dentalbioinformatics.com/). This database compiles genomic and proteomic data on human tooth development and developmental anomalies and organizes them according to their roles in different stages of tooth development. The database is built by systemically curating relevant data from the National Library of Medicine (NCBI) GenBank, OMIM: Online Mendelian Inheritance in Man, AlphaFold Protein Structure Database, Reactome pathway knowledgebase, Wiki Pathways, and PubMed. The accuracy of the included data was verified from supporting primary literature. Upon data curation and validation, a simple, easy-to-navigate browser interface was created on WordPress version 6.3.2, with PHP version 8.0. The website is hosted in a cloud hosting service to provide fast and reliable data transfer rate. Plugins are used to ensure the browser's compatibility across different devices. Bioinformatics for Dentistry contains four embedded filters for complex and specific searches and free-text search options for quick and simple searching through the datasets. Bioinformatics for Dentistry is made freely available worldwide, with the hope that this knowledgebase will improve our understanding of the complex genetic regulation of tooth development and will open doors to research initiatives and discoveries. This database will be expanded in the future by incorporating resources and built-in sequence analysis tools, and it will be maintained and updated annually.

CYP2E1 deficit mediates cholic acid-induced malignant growth in hepatocellular carcinoma cells.

BACKGROUND: Increased level of serum cholic acid (CA) is often accompanied with decreased CYP2E1 expression in hepatocellular carcinoma (HCC) patients. However, the roles of CA and CYP2E1 in hepatocarcinogenesis have not been elucidated. This study aimed to investigate the roles and the underlying mechanisms of CYP2E1 and CA in HCC cell growth. METHODS: The proteomic analysis of liver tumors from DEN-induced male SD rats with CA administration was used to reveal the changes of protein expression in the CA treated group. The growth of CA-treated HCC cells was examined by colony formation assays. Autophagic flux was assessed with immunofluorescence and confocal microscopy. Western blot analysis was used to examine the expression of CYP2E1, mTOR, AKT, p62, and LC3II/I. A xenograft tumor model in nude mice was used to examine the role of CYP2E1 in CA-induced hepatocellular carcinogenesis. The samples from HCC patients were used to evaluate the clinical value of CYP2E1 expression. RESULTS: CA treatment significantly increased the growth of HCC cells and promoted xenograft tumors accompanied by a decrease of CYP2E1 expression. Further studies revealed that both in vitro and in vivo, upregulated CYP2E1 expression inhibited the growth of HCC cells, blocked autophagic flux, decreased AKT phosphorylation, and increased mTOR phosphorylation. CYP2E1 was involved in CA-activated autophagy through the AKT/mTOR signaling. Finally, decreased CYP2E1 expression was observed in the tumor tissues of HCC patients and its expression level in tumors was negatively correlated with the serum level of total bile acids (TBA) and gamma-glutamyltransferase (GGT). CONCLUSIONS: CYP2E1 downregulation contributes to CA-induced HCC development presumably through autophagy regulation. Thus, CYP2E1 may serve as a potential target for HCC drug development.

Potential prognostic value of CSF-targeted proteomics across the Alzheimer's disease continuum.

BACKGROUND: Core biomarkers for Alzheimer's disease (AD), such as Aß42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis. METHODS: A cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships. RESULTS: During the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aß42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3-53.8% of the association between the three peptides and ADAS-Cog 13 scores. CONCLUSIONS: These findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.

[Preparation of magnetic carbon nitride composite toward phosphopeptide enrichment].

Protein phosphorylation plays an important role in cellular signaling and disease development. Advances in mass spectrometry-based proteomics have enabled qualitative and quantitative phosphorylation studies as well as in-depth biological explorations for biomarker discovery and signaling pathway analysis. However, the dynamic changes that occur during phosphorylation and the low abundance of target analytes render direct analysis difficult because mass spectral detection offers no selectivity, unlike immunoassays such as Western blot and enzyme-linked immunosorbent assay (ELISA). The present study aimed to solve one of the key problems in the specific and efficient isolation of phosphorylated peptides. A method based on a magnetic carbon nitride composite coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was developed for the enrichment and analysis of phosphopeptides with low abundance in complex samples. Magnetic carbon nitride composite was synthesized and characterized by electron microscopy, infrared spectroscopy, and X-ray diffractometry. The composite showed a well-distributed two-dimensional layered structure and functional groups with excellent paramagnetic performance. Two classical phosphoproteins, namely, α- and ß-caseins, were selected as model phosphorylated samples to assess the performance of the proposed enrichment technique. The magnetic carbon nitride composite exhibited high selectivity and sensitivity for phosphopeptide enrichment. The limit of detection was determined by MALDI-TOF-MS analysis to be 0.1 fmol. The selectivity of the method was investigated using the digest mixtures of α-casein, ß-casein, and bovine serum albumin (BSA) with different mass ratios (1∶1∶1000, 1∶1∶2000, and 1∶1∶5000). Direct analysis of the samples revealed the dominance of spectral signals from the abundant peptides in BSA. After enrichment with the magnetic carbon nitride composite, the high concentration of background proteins was washed away and only the signals of the phosphopeptides were captured. The signals from the casein proteins were clearly observed with little background noise, indicating the high selectivity of the composite material. The robustness of the method was tested by assessing the reusability of the same batch of magnetic carbon nitride materials over 20 cycles of enrichment. The composite showed nearly the same enrichment ability even after several cycles of reuse, demonstrating its potential applicability for a large number of clinical samples. Finally, the method was applied to the analysis of phosphopeptides from several commonly used phosphoprotein-containing samples, including skimmed milk digest, human serum, and human saliva; these samples are significant in the analysis of food quality, disease biomarkers, and liquid biopsies for cancer. Without enrichment, no phosphopeptide was detected because of the high abundance of nonphosphopeptide materials dominating the spectral signals obtained. After pretreatment with the developed magnetic carbon nitride composite, most of the phosphosites were identified with high selectivity and sensitivity via MALDI-TOF-MS. These results revealed the practicality of the developed approach for clinical applications. In addition, our method may potentially be employed for phosphoproteomics with real complex biological samples.

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

Background: Cervical cancer (CC) poses a global health challenge, with a particularly poor prognosis in cases of recurrence, metastasis, or advanced stages. A single biomarker is inadequate to predict CC prognosis or identify CC patients likely to benefit from immunotherapy, presumably owing to tumor complexity and heterogeneity. Methods: Using advanced Olink proteomics, we analyzed 92 oncology-related proteins in plasma from CC patients receiving immunotherapy, based upon the comparison of protein expression levels of pre-therapy with those of therapy-Cycle 6 in the partial response (PR) group and progressive disease (PD) group, respectively. Results: 55 proteins were identified to exhibit differential expression trends across pre-therapy and post-therapy in both PR and PD groups. Enriched GO terms and KEGG pathways were associated with vital oncological and immunological processes. A logistic regression model, using 5 proteins (ITGB5, TGF-α, TLR3, WIF-1, and ERBB3) with highest AUC values, demonstrated good predictive performance for prognosis of CC patients undergoing immunotherapy and showed potential across different cancer types. The effectiveness of these proteins in prognosis prediction was further validated using TCGA-CESC datasets. A negative correlation and previously unidentified roles of WIF-1 in CC immunotherapy was also first determined. Conclusion: Our findings reveal multi-biomarker profiles effectively predicting CC prognosis and identifying patients benefitting most from immunotherapy, especially for those with limited treatment options and traditionally poor prognosis, paving the way for personalized immunotherapeutic treatments and improved clinical strategies.

Urine Proteomic Signatures of Mild Hypothermia Treatment in Cerebral Ischemia-Reperfusion Injury in Rats.

Mild hypothermia (MH) is an effective measure to alleviate cerebral ischemia-reperfusion (I/R) injury. However, the underlying biological mechanisms remain unclear. This study set out to investigate dynamic changes in urinary proteome due to MH in rats with cerebral I/R injury and explore the neuroprotective mechanisms of MH. A Pulsinelli's four-vessel occlusion (4-VO) rat model was used to mimic global cerebral I/R injury. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed to profile the urinary proteome of rats with/without MH (32 °C) treatment after I/R injury. Representative differentially expressed proteins (DEPs) associated with MH were validated by western blotting in hippocampus. A total of 597 urinary proteins were identified, among which 119 demonstrated significant changes associated with MH. Gene Ontology (GO) annotation of the DEPs revealed that MH significantly enriched in endopeptidase activity, inflammatory response, aging, response to oxidative stress and reactive oxygen species, blood coagulation, and cell adhesion. Notably, changes in 12 DEPs were significantly reversed by MH treatment. Among them, 8 differential urinary proteins were previously reported to be closely associated with brain disease, including NP, FZD1, B2M, EPCR, ATRN, MB, CA1and VPS4A. Two representative proteins (FZD1, B2M) were further validated by western blotting in the hippocampus and the results were shown to be consistent with urinary proteomic analysis. Overall, this study strengthens the idea that urinary proteome can sensitively reflect pathophysiological changes in the brain, and appears to be the first study to explore the neuroprotective effects of MH by urinary proteomic analysis. FZD1 and B2M may be involved in the most fundamental molecular biological mechanisms of MH neuroprotection.

A reanalysis and integration of transcriptomics and proteomics datasets unveil novel drug targets for Mekong schistosomiasis.

Schistosomiasis, caused by Schistosoma trematodes, is a significant global health concern, particularly affecting millions in Africa and Southeast Asia. Despite efforts to combat it, the rise of praziquantel (PZQ) resistance underscores the need for new treatment options. Protein kinases (PKs) are vital in cellular signaling and offer potential as drug targets. This study focused on focal adhesion kinase (FAK) as a candidate for anti-schistosomal therapy. Transcriptomic and proteomic analyses of adult S. mekongi worms identified FAK as a promising target due to its upregulation and essential role in cellular processes. Molecular docking simulations assessed the binding energy of FAK inhibitors to Schistosoma FAK versus human FAK. FAK inhibitor 14 and PF-03814735 exhibited strong binding to Schistosoma FAK with minimal binding for human FAK. In vitro assays confirmed significant anti-parasitic activity against S. mekongi, S. mansoni, and S. japonicum, comparable to PZQ, with low toxicity in human cells, indicating potential safety. These findings highlight FAK as a promising target for novel anti-schistosomal therapies. However, further research, including in vivo studies, is necessary to validate efficacy and safety before clinical use. This study offers a hopeful strategy to combat schistosomiasis and reduce its global impact.

Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

Identification of cytokines in benign and malignant thymus tumors: based on Mendelian randomization and proteomics.

Objective: The aim of this study was to identify potential causal cytokines in thymic malignancies and benign tumors from the FinnGen database using Mendelian randomization (MR). Methods: In this study, data from genome-wide association studies (GWAS) of 91 cytokines were used as exposure factors, and those of thymic malignant tumors and thymic benign tumors were the outcome variables. Two methods were used to determine the causal relationship between exposure factors and outcome variables: inverse variance weighting (IVW) and MR-Egger regression. Sensitivity analysis was performed using three methods, namely, the heterogeneity test, the pleiotropy test, and the leave-one-out test. Results: There was a causal relationship between the expression of fibroblast growth factor 5, which is a risk factor for thymic malignant tumors, and thymic malignant tumors. C-C motif chemokine 19 expression, T-cell surface glycoprotein CD5 levels, and interleukin-12 subunit beta levels were causally related to thymic malignant tumors and were protective. Adenosine deaminase levels, interleukin-10 receptor subunit beta expression, tumor necrosis factor (TNF)-related apoptosis-inducing ligand levels, and TNF-related activation-induced cytokine levels showed a causal relationship with thymic benign tumors, which are its risk factors. Caspase 8 levels, C-C motif chemokine 28 levels, interleukin-12 subunit beta levels, latency-associated peptide transforming growth factor beta 1 levels, and programmed cell death 1 ligand 1 expression showed a causal relationship with thymic benign tumors, which are protective factors. Sensitivity analysis showed no heterogeneity. Conclusion: Cytokines showed a causal relationship with benign and malignant thymic tumors. Interleukin-12 subunit beta is a common cytokine that affects malignant and benign thymic tumors.

Molecular distinctions of bronchoalveolar and alveolar organoids under differentiation conditions.

The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex-vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre-differentiated and post-differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre- and post-differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting-edge AI-driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Association between plasma proteome and pulmonary heart disease: A two-stage Mendelian randomization analysis.

Pulmonary heart disease (PHD) involves altered structure and function of the right ventricle caused by an abnormal respiratory system that causes pulmonary hypertension. However, the association between changes in plasma proteomics and PHD remains unclear. Hence, we aimed to identify causal associations between genetically predicted plasma protein levels and PHD. Mendelian randomization was performed to test the target proteins associated with PHD. Summary statistics for the human plasma proteome and pulmonary heart disease were acquired from the UK Biobank (6038 cases and 426 977 controls) and the FinnGen study (6753 cases and 302 401 controls). Publicly available pQTLs datasets for human plasma proteins were obtained from a largescale genome-wide association study in the INTERVAL study. The results were validated using a case-control cohort. We first enrolled 3622 plasma proteins with conditionally independent genetic variants; three proteins (histo-blood group ABO system transferase, activating signal cointegration 1 complex subunit 1, and calcium/calmodulin-dependent protein kinase I [CAMK1]) were significantly associated with the risk of pulmonary heart disease in the UK Biobank cohort. Only CAMK1 was successfully replicated (odds ratio: 1.1056, 95% confidence interval: 1.019-1.095, p = 0.0029) in the FinnGen population. In addition, the level of CAMK1 in 40 patients with PHD was significantly higher (p = 0.023) than that in the control group. This work proposes that CAMK1 is associated with PHD, underscoring the importance of the calcium signaling pathway in the pathophysiology to improve therapies for PHD.