Predicted Protein Research Articles

LC-MS/MS analysis reveals plasma protein signatures associated with lymph node metastasis in colorectal cancer.

Colorectal cancer (CRC) is a major global health concern, ranking as the third most common cancer and the fourth leading cause of cancer-related deaths worldwide. Currently, the diagnostic accuracy of Lymph node metastasis (LNM) is currently unsatisfactory. Therefore, there is an urgent need to develop a reliable tool that can accurately predict lymph node metastasis (LNM) in patients diagnosed with CRC. We conducted an extensive proteomics investigation aimed at examining lymph node metastasis (LNM) in individuals diagnosed with colorectal cancer (CRC). In the discovery stage, employing a mass spectrometry-based proteomic approach, we analyzed a cohort of 60 colorectal cancer patients (NM=30, LNM=30), identifying distinct molecular profiles that differentiate patients with and without lymph node metastasis (LNM). Subsequently, we validated the protein classifier associated with lymph node metastasis. We elucidated a combinatorial predictive protein biomarker that can distinguish patients with and without lymph node metastasis by LC-MS/MS. The classifier achieved an area under the curve (AUC) of 0.892 (95% CI, 0.842-0.941), while in the testing cohort, it attained an AUC of 0.929 (95% CI, 0.824-1.000). Furthermore, the four protein markers demonstrated an AUC of 0.84 (95% CI, 0.783-0.890) in the validation cohort. Additionally, we categorized patients into three types based on immunophenotyping. Type 1 primarily consisted of patients with negative lymph node metastasis (NM), characterized by immune cells such as NK cells, CD4 T effector memory cells, and memory B cells. Type 2 mainly included patients with positive lymph node metastasis (LNM), characterized by immune cells such as mesangial cells, epithelial cells, and mononuclear cells. In Type 1, a prominent upregulation observed in immune inflammation, as well as in glucose and lipid metabolism. In Type 2, significant upregulation was evident in pathways such as pyrimidine metabolism and cell cycle regulation. The findings of this study suggest that immune mechanisms may exert a pivotal role in the process of lymph node metastasis in CRC. Here, we present plasma protein signatures associated with lymph node metastasis in colorectal cancer (CRC). However, further validation across multiple centers is necessary to generalize these findings.

82 Clinical Performance of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase L1 for Prediction of Intracranial Injuries on Head Computed Tomography in Mild Traumatic Brain Injury

82 Clinical Performance of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase L1 for Prediction of Intracranial Injuries on Head Computed Tomography in Mild Traumatic Brain Injury

Serum Glial Fibrillary Acidic Protein and S100B Profiles in Severity and Outcome Assessment of Moderate and Severe Head Injury Patients in India

Abstract Background: Traumatic brain injury (TBI) is a significant global health issue, with India witnessing approximately 150,000 deaths and 50,000 TBI-related fatalities annually. Severity is classified as mild, moderate, or severe using the Glasgow Coma Scale (GCS). Imaging and blood biomarkers such as serum glial fibrillary acidic protein (GFAP) and S100B aid in diagnosis and outcome prediction, yet imaging facilities are scarce in India. This highlights the necessity for dependable biomarkers. GFAP indicates astroglial injury, while S100B suggests neuronal injury, both in TBI patients’ blood. However, their associations and utility in the Indian population require further exploration. Our study addresses this gap by examining serum GFAP and S100B levels in moderate and severe TBI patients, correlating them with radiological findings and clinical outcomes. Methodology: This prospective observational study was conducted in a tertiary care hospital on 212 patients (106 each for moderate and severely injured groups). Initial disease severity was assessed by GCS score. Outcome assessments were required of surgical intervention and mortality within 5 days. Results: S100B superseded GFAP in assessing disease severity. Receiver operating characteristic analysis showed that S100B was successful with 66% sensitivity and specificity for a cutoff value of 78.77 pg/ml. For early mortality prediction, sensitivity (S100B = 81%–87%, GFAP = 64.1%–78.6%), specificity (S100B = 60.2%–99.2%, GFAP = 54.5%–74.6%), and area under the curve-wise (S100B = 0.721–0.909, GFAP = 0.614–0.763) S100B model performed better than the GFAP model for all corresponding cutoffs (ranged S100B = 86.53–118.56 pg/ml, GFAP = 30.87–34.5 ng/ml). Conclusion: Taken together, our study provides strong evidence that S100B is a better marker of severity and outcome assessment than GFAP.

The Potential Diagnostic Utility of SMAD4 and ACCS in the Context of Inflammation in Type 2 Diabetes Mellitus Patients.

The search for new parameters for the prediction of type 2 diabetes mellitus (T2DM) or its harmful consequences remains an important field of study. Depending on the low-grade inflammatory nature of diabetes, we investigated three proteins in T2DM patients: 1-aminocyclopropane-1-carboxylate synthase (ACCS), granulocyte-colony-stimulating factor (G-CSF), and Sma Mothers Against Decapentaplegic homolog-4 (SMAD4). In brief, sixty T2DM and thirty healthy controls had their serum levels of ACCS, G-CSF, SMAD4, and insulin tested using the ELISA method. The insulin resistance (IR) parameter (HOMA2IR), beta-cell function percentage (HOMA2%B), and insulin sensitivity (HOMA2%S) were all determined by the Homeostasis Model Assessment-2 (HOMA2) calculator. The predictability of these protein levels was investigated by neural network (NN) analysis and was associated with measures of IR. Based on the results, ACCS, G-CSF, and SMAD4 increased significantly in the T2DM group compared with the controls. Their levels depend on IR status and inflammation. The multivariate GLM indicated the independence of the levels of these proteins on the covariates or drugs taken. The receiver operating characteristic area under the curve (AUC) for the prediction of T2DM using NN analysis is 0.902, with a sensitivity of 71.4% and a specificity of 93.8%. The network predicts T2DM well with predicted pseudoprobabilities over 0.5. The model's predictive capability (normalized importance) revealed that ACCS is the best model (100%) for the prediction of T2DM, followed by G-CSF (75.5%) and SMAD4 (69.6%). It can be concluded that ACCS, G-CSF, and SMAD4 are important proteins in T2DM prediction, and their increase is associated with the presence of inflammation.

An extensive in silico analysis of missense mutations of the human AIMP2 gene

HLD17 (Hypomyelinating Leukodystrophy 17) is an inherited white matter disorder characterized by insufficient myelin production due to biallelic loss of function mutations in the aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) gene. In silico analysis of SNVs (single nucleotide variants) in the AIMP2 gene is an efficient and cost-effective method for analyzing and predicting the impact of mutations on protein function and disease pathophysiology.The study used dbSNP and Ensembl databases to obtain data on 343 nonsynonymous single nucleotide variants (nsSNVs) in the human AIMP2 gene. Six prediction algorithm tools were used to assess the effects of these nsSNVs on AIMP2's functions and structures. Results showed that 18 nsSNVs were located within functional domains, while 10 nsSNVs led to decreased protein stability. The structural and functional properties of the AIMP2 protein were investigated using databases such as Predict Protein, Mutpred2, and HOPE. ConSurf analysis provided information about conserved nsSNVs. GeneMANIA and STRING software tools were used to predict interactions between gene-gene and protein-protein, respectively. Phyre2 and I-TASSER web servers were used to predict the 3D structures of wild-type and mutant proteins.In addition, having the challenge of probable post-translational modification sites in the AIMP2, we made predictions using various bioinformatics tools. Consecuently, three minor mutations (L138Q, V161E, and I188N) and five major mutations (C23S, D121G, I122S, P128S, and W268S) were found to affect the AIMP2 protein's structure or function. Anyway, These mutations need to be further studied and confirmed through experimental investigation and GWAS studies.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

The MarR family regulator RmaH mediates acid tolerance of Lactococcus lactis through regulating peptidoglycan modification genes

Lactococcus lactis, widely used in the food fermentation industry, has developed various ways to regulate acid adaptation in the process of evolution. The investigation into how peptidoglycan (PG) senses and responds to acid stress is an expanding field. Here, we addressed the regulation of murT-gatD genes which are responsible for the amidation of PG D-Glu. We found that lactic acid stress reduced murT-gatD expression, and overexpressing these genes notably decreased acid tolerance of L. lactis NZ9000, possibly due to a reduction in PG's negative charge, facilitating the influx of extracellular protons into the cell. Subsequently, using a combination of DNA pull-down assay and electrophoretic mobility shift assay (EMSA), we identified a novel MarR family regulator, RmaH, as an activator of murT-gatD transcription. Further MEME motif prediction, EMSA verification and fluorescent protein reporter assay showed that RmaH directly bound to the DNA motif 5'-KGVAWWTTTTGCT-3' located in the upstream region of murT-gatD. Beyond the mechanistic investigation of RmaH activation of murT-gatD, this study provides new insight into how peptidoglycan modification is regulated and responds to lactic acid stress.

Streamlining heterologous expression of top carbonic anhydrases in Escherichia coli: bioinformatic and experimental approaches

BackgroundCarbonic anhydrase (CA) enzymes facilitate the reversible hydration of CO2 to bicarbonate ions and protons. Identifying efficient and robust CAs and expressing them in model host cells, such as Escherichia coli, enables more efficient engineering of these enzymes for industrial CO2 capture. However, expression of CAs in E. coli is challenging due to the possible formation of insoluble protein aggregates, or inclusion bodies. This makes the production of soluble and active CA protein a prerequisite for downstream applications.ResultsIn this study, we streamlined the process of CA expression by selecting seven top CA candidates and used two bioinformatic tools to predict their solubility for expression in E. coli. The prediction results place these enzymes in two categories: low and high solubility. Our expression of high solubility score CAs (namely CA5-SspCA, CA6-SazCAtrunc, CA7-PabCA and CA8-PhoCA) led to significantly higher protein yields (5 to 75 mg purified protein per liter) in flask cultures, indicating a strong correlation between the solubility prediction score and protein expression yields. Furthermore, phylogenetic tree analysis demonstrated CA class-specific clustering patterns for protein solubility and production yields. Unexpectedly, we also found that the unique N-terminal, 11-amino acid segment found after the signal sequence (not present in its homologs), was essential for CA6-SazCA activity.ConclusionsOverall, this work demonstrated that protein solubility prediction, phylogenetic tree analysis, and experimental validation are potent tools for identifying top CA candidates and then producing soluble, active forms of these enzymes in E. coli. The comprehensive approaches we report here should be extendable to the expression of other heterogeneous proteins in E. coli.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively reviewcomputational approaches for end-to-end prediction ofprotein complex structuresbased on artificial intelligence (AI) technologyand describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Evaluation of Procalcitonin and Presepsin in prediction for early onset neonatal sepsis.

Objective: To determine the presepsin and procalcitonin significance in cord blood and compare with lactate and C-Reactive protein for early prediction of neonatal sepsis. Study Design: Case Control Study. Setting: Military Hospital Rawalpindi. Period: Sep 2018 to July 2019. Methods: Mothers, having deliveries with early or prolonged rupture of membrane, preterm, dai handled, meconium and failure of induction have been included. Out of 60 neonates, nineteen were cases with a clearly documented suspicion of sepsis and confirmed by neonatologists, remaining were control. Mean and Standard Deviation were calculated. The difference in all biochemical markers levels among case and control groups were assessed by independent t-test. Sensitivity, specificity, accuracy, and predictive value of both markers were calculated by medcalc diagnostic calculator. Regression analysis to access the strength. Receiver Operating Characteristics curve for most accurate cut off values and Area Under the Curve was calculated. Results: Independent sample t test revealed the strong association of procalcitonin and presepsin with neonatal sepsis. Presepsin has higher positive predictive value 83.33% and negative predictive value 90.48% with 88.33% accuracy while procalcitonin has positive predictive value 62.50%, negative predictive value 88.89% and accuracy 78.33%. Stepwise regression analysis showed better in combination than single in predication of neonatal sepsis. The cutoff value for procalcitonin was 0.4ng/ml (AUC of 84.5%.) and for presepsin was 305pg/ml (AUC of 86.5%). Conclusion: In comparison to lactate and CRP, a prediction model that incorporates two biochemical indicators, procalcitonin and presepsin, can reduce infant mortality and morbidity by spotting neonatal sepsis early.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS)

BackgroundLong QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes.MethodsThe LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan.ResultsFive hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles.ConclusionThe expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6–1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4–4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

512P Predictive biomarkers and protein specific mismatch repair (MMR) loss in gastrointestinal (GI) tumours on immune checkpoint inhibitors (ICI): Correlation with survival

512P Predictive biomarkers and protein specific mismatch repair (MMR) loss in gastrointestinal (GI) tumours on immune checkpoint inhibitors (ICI): Correlation with survival

Investigation of a potential protein biomarker signature that may predict clinical benefit of NT-I7 and pembrolizumab in patients with cold gastrointestinal tumors.

2563 Background: Microsatellite stable colorectal (MSS-CRC) and pancreatic cancer (PDAC) are immunologically cold tumors due to low immunogenicity and lack of genomic diversity. NT-I7, a long-acting IL-7, and pembrolizumab (pembro) show efficacy in these hard-to-treat indications. While a limited set of patients (pts) achieve objective response, frequency and duration of the disease control rate point to a larger subset obtaining clinical benefit. To identify novel predictive biomarkers, we analyzed baseline peripheral and biopsy samples from pts based on treatment duration. Methods: Open-label Phase 2a study in pts with relapsed/refractory checkpoint inhibitor-naïve MSS-CRC and PDAC; NT-I7 1200 µg/kg IM every 6 weeks (Q6W), pembro 200 mg IV Q3W. Subjects were grouped by treatment duration, measured in NT-I7 doses administered before treatment discontinuation for any cause: 1 dose was short (ST), 2-3 doses medium (MT) and ≥4 doses long (LT). Correlative studies included peripheral (proteomics, T cell receptor sequencing [TCRseq], single cell RNA sequencing [scRNAseq]), and biopsy (genomics, transcriptomics, TCRseq). Results: As of 02OCT2023, 53 evaluable pts completed or discontinued treatment; 5 are still on follow up. ST group included 21 pts, MT 22 and LT 10 (including all 5 partial responders). Tumor biopsies were confirmed MSS with low tumor mutational burden (TMB). LT pts had similar age (59.0 [53.0-71.5] vs 66.0 [47.3-73.5]; ST vs LT) and lower baseline tumor burden (81.0 mm vs 58.0 mm; ST vs LT, p = 0.022). Biopsy analysis showed LT pts had, at baseline, upregulated pathways related to immune activity despite confirmed cold tumor status. Baseline scRNAseq in peripheral blood showed that stem-like CD8 T cells (precursors of exhausted [TPEX] and stem-cell memory [TSCM]) had a differential activation pattern in LT pts; those pathways were enriched in memory effector subsets in ST pts. Preserved antigen-specific stemness may be needed for NT-I7 + pembro efficacy. Baseline concentrations of 3 proteins that can be produced by growing tumors were significantly increased in ST pts. Pts were classified based on elevated levels of these potential biomarkers: POSITIVE (≥2 biomarkers; 20 pts) and NEGATIVE (≤1 biomarker; 33 pts). Pts with NEGATIVE signature at baseline had significantly higher overall survival, regardless of ST, MT or LT status (13.2 vs 8.9 months, p = 0.030). Conclusions: Preserved tumor-specific TPEX activity may be required for NT-I7 + pembro activity based on its presence in LT pts, who remained on treatment the longest. According to this analysis, there are 3 potentially predictive protein biomarkers that may help identify a pt subset more likely to experience clinical benefits from the combination treatment of NT-I7 + pembro. Further verification of the predictive nature of this signature in independent cohorts is ongoing. Clinical trial information: NCT04332653 .

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

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. 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. 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. 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.

Very short sleep duration reveals a proteomic fingerprint that is selectively associated with incident diabetes mellitus but not with incident coronary heart disease: a cohort study

BackgroundThe molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease.MethodsWe assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46–68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates.ResultsSixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06–1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD.ConclusionsWe here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.

Genome-wide identification and expression pattern analysis of the Aux/IAA (auxin/indole-3-acetic acid) gene family in alfalfa (Medicago sativa) and the potential functions under drought stress

BackgroundAuxin/induced-3-acetic acid (Aux/IAA) is an important plant hormone that affects plant growth and resistance to abiotic stresses. Drought stress is a vital factor in reducing plant biomass yield and production quality. Alfalfa (Medicago sativa L.) is the most widely planted leguminous forage and one of the most economically valuable crops in the world. Aux/IAA is one of the early responsive gene families of auxin, playing a crucial role in response to drought stress. However, the characteristics of the Aux/IAA gene family in alfalfa and its potential function in response to drought stress are still unknown.ResultA total of 41 Aux/IAA gene members were identified in alfalfa genome. The physicochemical, peptide structure, secondary and tertiary structure analysis of proteins encoded by these genes revealed functional diversity of the MsIAA gene. A phylogenetic analysis classified the MsIAA genes into I-X classes in two subgroups. And according to the gene domain structure, these genes were classified into typical MsIAA and atypical MsIAA. Gene structure analysis showed that the MsIAA genes contained 1–4 related motifs, and except for the third chromosome without MsIAAs, they were all located on 7 chromosomes. The gene duplication analysis revealed that segmental duplication and tandem duplication greatly affected the amplification of the MsIAA genes. Analysis of the Ka/Ks ratio of duplicated MsAux/IAA genes suggested purification selection pressure was high and functional differences were limited. In addition, identification and classification of promoter cis-elements elucidated that MsIAA genes contained numerous elements associated to phytohormone response and abiotic stress response. The prediction protein–protein interaction network showed that there was a complex interaction between the MsAux/IAA genes. Gene expression profiles were tissue-specific, and MsAux/IAA had a broad response to both common abiotic stress (ABA, salt, drought and cold) and heavy metal stress (Al and Pb). Furthermore, the expression patterns analysis of 41 Aux/IAA genes by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) showed that Aux/IAA genes can act as positive or negative factors to regulate the drought resistance in alfalfa.ConclusionThis study provides useful information for the alfalfa auxin signaling gene families and candidate evidence for further investigation on the role of Aux/IAA under drought stress. Future studies could further elucidate the functional mechanism of the MsIAA genes response to drought stress.

Role of C-reactive protein, IL-6, and D-dimers in prediction of severity of coronavirus disease 2019: A pilot study.

The global outbreak of coronavirus disease 2019 (COVID-19) presents numerous obstacles for healthcare professionals. The present study aimed to evaluate and compare the role of serum biomarkers like- C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimers in the severity of COVID-19 infection. A cross-sectional, observational retrospective pilot study was conducted in Udaipur, Rajasthan, wherein data was collected from 250 subjects, out of which, data of 100 subjects were included as per the inclusion criteria. The data was recorded retrospectively among the health professionals via Google Forms in Udaipur, Rajasthan. There were 1 (1%), 3 (3%), 31 (31%) and 65 (65%) participants with minor elevation (0.3-1.0), moderate elevation (1-10), marked elevation (10-50) and severe elevation (>50) of CRP respectively. The difference between the groups was statistically highly significant with a significantly higher number of study participants with a severe elevation of CRP levels (χ2 = 107.84, P < 0.001). The results showed that there was a significant difference between the groups with IL6 in 0-7 range while 96 (96%) study participants had >7 IL6, and the difference was statistically highly significant (2 = 84.640, P 0.001). In conclusion, the existing body of research indicates a discernible correlation between COVID-19 infection and the fluctuation of biomarker levels. This supplement has the potential to be utilised in clinical practice as a means of informing treatment decisions and determining the necessity of admission to the intensive care unit (ICU).

An extended-gate field-effect transistor (EG-FET) signal transducing combined with epitope molecular imprinting for selective chemosensing of chosen idiopathic pulmonary fibrosis (IPF) biomarkers

We propose a relatively rapid and straightforward procedure for the determination of chosen idiopathic pulmonary fibrosis (IPF) predictive protein biomarkers, vis., matrix metalloproteinase-1 (MMP-1) and surfactant-associated protein-A (SP-A), suitable for their determination in bodily fluids. IPF is a lethal lung disease, annually causing nearly as many deaths as breast cancer. Moreover, the coronavirus recently triggered acute exacerbation of patients suffering from IPF. Here, epitopes specific to the chosen IPF biomarkers were molecularly imprinted in a polymer as an alternative to imprinting fragile whole biomarkers’ molecules. A thin molecularly imprinted polymer (MIP) film-based recognition units were integrated by electropolymerization with gates of extended-gate field-effect transistors (EG-FETs) to fabricate biomarker-selective chemical sensors suitable as point-of-care testing (POCT) diagnostic devices. Either AQDDIDGIQAI or FKGNKYWAVQGQNV single-epitope imprinting allowed determining the MMP-1 biomarker in the 10 to 250 nM linear dynamic concentration range with a LOD of 7.5 or 2 nM, respectively. Simultaneous imprinting of three MMP-1 epitopes, vis., MIAHDFPGIGHK, HGYPKDIYSS, and FKGNKYWAVQGQNV, decreased this LOD to 0.1 nM, allowing for determining this biomarker in serum. Moreover, by FSSNGQSIT or YSDGTPVNYTNWYR single-epitope imprinting, chemosensors for SP-A were fabricated to show the versatility of epitope imprinting. These chemosensors selectively determined the SP-A biomarker with the LOD of 1.9 or 2.9 nM, allowing its determination in bodily fluids.