Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers.

Recent advances in "omics" technologies have enabled the identification of new beef quality biomarkers and have also allowed for the early detection of quality defects such as dark-cutting beef, also known as DFD (dark, firm, and dry) beef. However, most of the studies conducted were carried out on a small number of animals and mostly applied gel-based proteomics. The present study proposes for the first time a Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) proteomics approach to characterize and comprehensively quantify the post-mortem muscle proteome of DFD (pH24 ≥ 6.2) and CONTROL (5.4 ≤ pH24 ≤ 5.6) beef samples within the largest database of DFD/CONTROL beef samples to date (26 pairs of the Longissimus thoracis muscle samples of young bulls from Asturiana de los Valles breed, n = 52). The pairwise comparison yielded 35 proteins that significantly differed in their abundances between the DFD and CONTROL samples. Chemometrics methods using both PLS-DA and OPLS-DA revealed 31 and 36 proteins with VIP > 2.0, respectively. The combination of different statistical methods these being Volcano plot, PLS-DA and OPLS-DA allowed us to propose 16 proteins as good candidate biomarkers of DFD beef. These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). These results evidenced the potential of SWATH-MS and chemometrics to accurately identify novel biomarkers for meat quality defects, providing a deeper understanding of the molecular mechanisms underlying dark-cutting beef condition.

Association of major candidate protein biomarkers and long-term diabetic kidney disease progression among Asians with young-onset type 2 diabetes mellitus.

AIMS: We aim to determine the association of seven major candidate protein biomarkers and diabetic kidney disease (DKD) progression among Asians with young-onset type 2 diabetes mellitus (T2DM). METHODS: 824 T2DM patients (onset ≤ 40 years old) were classified as DKD progressors based on yearly estimated glomerular filtration rate (eGFR) decline of >3 ml/min/1.73 m2 or >40 % from baseline. Plasma leucine-rich α-2-glycoprotein 1 (pLRG1), tumor necrosis factor-receptor 1 (pTNF-R1), pigment epithelium-derived factor (pPEDF), urinary α-1-microglobulin (uA1M), kidney injury molecular 1 (uKIM-1), haptoglobin (uHP) and uromodulin (uUMOD) were measured using enzyme-linked immunoassays. RESULTS: Over 5.7 years of follow-up, 25.2 % of patients were DKD progressors. Elevated levels of pLRG1, pTNF-R1, pPEDF, uA1M, uKIM-1 and uHP were associated with DKD progression. The association between pTNF-R1 levels and DKD progression persisted after adjusting for clinical covariates (OR 1.84, 95 %CI 1.44-2.34, p < 0.001). The effects of pTNF-R1 were partially mediated through hyperglycemia (8 %) and albuminuria (10 %). Inclusion of pTNF-R1 in a clinical variable-based model improved the area under the receiver operating characteristics curve for predicting DKD progression by 0.02, from 0.72 (95 %CI 0.68-0.76) to 0.74 (95 %CI 0.70-0.78), p = 0.099. CONCLUSIONS: Among seven major candidate proteins, pTNF-R1 partially mediated through hyperglycemia and albuminuria, robustly predicted DKD progression among Asians with young-onset T2DM.

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status.

BACKGROUND: Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematological screening. METHOD: We have systematically searched the Human Protein Atlas database and the relevant literature for blood protein-coding genes. The CRC dataset from TCGA was used to compare the acquired genes and identify differentially expressed molecules (DEMs). Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules of co-expressed molecules and key molecules within the DEMs. Signature molecules of CRC were then identified from the key molecules using machine learning. These findings were further validated in clinical samples. Finally, Logistic regression was used to create a predictive model that calculated the likelihood of CRC in both healthy individuals and CRC patients. We evaluated the model's sensitivity and specificity using the ROC curve. RESULT: By utilizing the CRC dataset, WGCNA analysis, and machine learning, we successfully identified seven signature molecules associated with CRC from 1478 blood protein-coding genes. These markers include S100A11, INHBA, QSOX2, MET, TGFBI, VEGFA and CD44. Analyzing the CRC dataset showed its potential to effectively discriminate between CRC and normal individuals. The up-regulated expression of these markers suggests the existence of an immune evasion mechanism in CRC patients and is strongly correlated with poor prognosis. CONCLUSION: The combined detection of the seven signature molecules in CRC can significantly enhance diagnostic efficiency and serve as a novel index for hematological screening of CRC.

Unbiased plasma profiling using pre-selected RNA aptamer pools predicts mortality in COVID-19 and identifies protein risk factors.

The impact of the COVID-19 pandemic demands effective prognostic tools for precise risk evaluation and timely intervention. This study utilized the APTASHAPE technology to profile plasma proteins in COVID-19 patient samples. Employing a highly diverse 2'-fluoro-protected RNA aptamer pool enriched toward proteins in the plasma samples from COVID-19 patients, we performed a single round of parallel selection on the derivation cohort and identified 93 discriminatory aptamers capable of distinguishing COVID-19 and healthy plasma samples. A subset of these aptamers was then used to predict 30-day mortality with high sensitivity and specificity in a validation cohort of 165 patients. We predicted 30-day mortality with areas under the curve (AUCs) of 0.91 in females and 0.68 in males. Affinity purification coupled with mass spectrometry analysis of the aptamer-targeted proteins identified potential biomarkers associated with disease severity, including complement system components. The study demonstrates the APTASHAPE technology as an unbiased approach that not only aids in predicting disease outcomes but also offers insights into gender-specific differences, shedding light on the nuanced aspects of COVID-19 pathophysiology. In conclusion, the findings highlight the promise of APTASHAPE as a valuable tool for estimating risk factors in COVID-19 patients and enabling stratification for personalized treatment management.

Evaluation of maternal serum protein biomarkers in the prenatal evaluation of placenta accreta spectrum: A systematic scoping review.

INTRODUCTION: Placenta accreta spectrum (PAS) is an increasingly commonly reported condition due to the continuous increase in the rate of cesarean deliveries (CD) worldwide; however, the prenatal screening for pregnant patients at risk of PAS at birth remains limited, in particular when imaging expertise is not available. MATERIAL AND METHODS: Two major electronic databases (MEDLINE and Embase) were searched electronically for articles published in English between October 1992 and January 2023 using combinations of the relevant medical subject heading terms and keywords. Two independent reviewers selected observational studies that provided data on one or more measurement of maternal blood-specific biomarker(s) during pregnancies with PAS at birth. PRISMA Extension for Scoping Review (PRISMA-ScR) was used to extract data and report results. RESULTS: Of the 441 reviewed articles, 29 met the inclusion criteria reporting on 34 different biomarkers. 14 studies were retrospective and 15 prospective overall including 18 251 participants. Six studies had a cohort design and the remaining a case-control design. Wide clinical heterogeneity was found in the included studies. In eight studies, the samples were obtained in the first trimester; in five, the samples were collected on hospital admission for delivery; and in the rest, the samples were collected during the second and/or third trimester. CONCLUSIONS: Measurements of serum biomarkers, some of which have been or are still used in screening for other pregnancy complications, could contribute to the prenatal evaluation of patients at risk of PAS at delivery; however, important evidence gaps were identified for suitable cutoffs for most biomarkers, variability of gestational age at sampling and the potential overlap of the marker values with other placental-related complications of pregnancy.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-ß2 levels in metastatic colorectal cancer patients (Alliance).

BACKGROUND: Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC). The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings. RESULTS: The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-ß2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73. CONCLUSIONS: This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-ß2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion.

To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000，with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values. SIGNIFICANCE: We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4-12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.

Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence.

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Biomarkers associated with cardiovascular disease in women with spontaneous preterm birth: A case-control study.

INTRODUCTION: Women with spontaneous preterm birth have an increased risk of cardiovascular disease later in life. Studies suggest potential pathophysiological mechanisms in common, but whether these could be identified by measurement of soluble circulating protein biomarkers in women with spontaneous preterm birth is unknown. The aim of this study was to determine if protein biomarkers associated with cardiovascular disease distinguish women with spontaneous preterm birth from healthy controls, both at pregnancy and at follow up. MATERIAL AND METHODS: Study participants were identified in the population-based Uppsala biobank of pregnant women in Sweden, where plasma samples were collected in mid-pregnancy. In a first screening phase, we identified participants who subsequently experienced spontaneous preterm birth (<37 weeks) in the index pregnancy (N = 13) and controls (N = 6). In these samples, differences in protein expression were examined by comparative mass spectrometry. In a second validation phase, we invited 100 cases with previous spontaneous preterm birth in the index pregnancy and 100 controls (matched for age, body mass index, and year of delivery) from the same source population, to a follow-up visit 4-15 years after pregnancy. At follow up, we collected plasma samples and data on cardiovascular risk factors. We measured concentrations of selected biomarkers identified in the screening phase, as well as lipid profiles in samples both from pregnancy (biobank) and follow up. CLINICALTRIALS: gov registration NCT05693285. RESULTS: In the screening phase, fibrinogen, cadherin-5, complement C5, factor XII, plasma kallikrein, apolipoprotein M, and vitamin D-binding protein differed significantly at pregnancy. In the validation phase, 65 women agreed to participate (35 cases and 30 controls), with a median follow-up time of 11.8 years since pregnancy. The concentration of fibrinogen (p = 0.02) and triglycerides (p = 0.03) were slightly higher in cases compared with matched controls at follow up. CONCLUSIONS: Compared with women without preterm birth, those with spontaneous preterm birth had slightly higher concentrations of fibrinogen, both at mid-pregnancy and a decade after pregnancy. Additionally, we found slightly higher concentration of triglycerides at follow up in women with previous spontaneous preterm birth. The relevance of this finding is uncertain but might indicate potential pathophysiological mechanisms in common between spontaneous preterm birth and cardiovascular disease.

Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis.

OBJECTIVES: To identify molecular pathways and prognostic- and diagnostic plasma-protein biomarkers for diabetic retinopathy at various stages. METHODS: This exploratory, cross-sectional proteomics study involved plasma from 68 adults, including 15 healthy controls and 53 diabetes patients for various stages of diabetic retinopathy: non-diabetic retinopathy, non-proliferative diabetic retinopathy, proliferative diabetic retinopathy and diabetic macular edema. Plasma was incubated with peptide library beads and eluted proteins were tryptic digested, analyzed by liquid chromatography-tandem mass-spectrometry followed by bioinformatics. RESULTS: In the 68 samples, 248 of the 731 identified plasma-proteins were present in all samples. Analysis of variance showed differential expression of 58 proteins across the five disease subgroups. Protein-Protein Interaction network (STRING) showed enrichment of various pathways during the diabetic stages. In addition, stage-specific driver proteins were detected for early and advanced diabetic retinopathy. Hierarchical clustering showed distinct protein profiles according to disease severity and disease type. CONCLUSIONS: Molecular pathways in the cholesterol metabolism, complement system, and coagulation cascade were enriched in patients at various stages of diabetic retinopathy. The peroxisome proliferator-activated receptor signaling pathway and systemic lupus erythematosus pathways were enriched in early diabetic retinopathy. Stage-specific proteins for early - and advanced diabetic retinopathy as determined herein could be 'key' players in driving disease development and potential 'target' proteins for future therapies. For type 1 and 2 diabetes mellitus, the proteomic profiles were especially distinct during the early disease stage. Validation studies should aim to clarify the role of the detected molecular pathways, potential biomarkers, and potential 'target' proteins for future therapies in diabetic retinopathy.

Novel subtypes of severe COVID-19 respiratory failure based on biological heterogeneity: a secondary analysis of a randomized controlled trial.

BACKGROUND: Despite evidence associating inflammatory biomarkers with worse outcomes in hospitalized adults with COVID-19, trials of immunomodulatory therapies have met with mixed results, likely due in part to biological heterogeneity of participants. Latent class analysis (LCA) of clinical and protein biomarker data has identified two subtypes of non-COVID acute respiratory distress syndrome (ARDS) with different clinical outcomes and treatment responses. We studied biological heterogeneity and clinical outcomes in a multi-institutional platform randomized controlled trial of adults with severe COVID-19 hypoxemic respiratory failure (I-SPY COVID). METHODS: Clinical and plasma protein biomarker data were analyzed from 400 trial participants enrolled from September 2020 until October 2021 with severe COVID-19 requiring ≥ 6 L/min supplemental oxygen. Seventeen hypothesis-directed protein biomarkers were measured at enrollment using multiplex Luminex panels or single analyte enzyme linked immunoassay methods (ELISA). Biomarkers and clinical variables were used to test for latent subtypes and longitudinal biomarker changes by subtype were explored. A validated parsimonious model using interleukin-8, bicarbonate, and protein C was used for comparison with non-COVID hyper- and hypo-inflammatory ARDS subtypes. RESULTS: Average participant age was 60 ± 14 years; 67% were male, and 28-day mortality was 25%. At trial enrollment, 85% of participants required high flow oxygen or non-invasive ventilation, and 97% were receiving dexamethasone. Several biomarkers of inflammation (IL-6, IL-8, IL-10, sTNFR-1, TREM-1), epithelial injury (sRAGE), and endothelial injury (Ang-1, thrombomodulin) were associated with 28- and 60-day mortality. Two latent subtypes were identified. Subtype 2 (27% of participants) was characterized by persistent derangements in biomarkers of inflammation, endothelial and epithelial injury, and disordered coagulation and had twice the mortality rate compared with Subtype 1. Only one person was classified as hyper-inflammatory using the previously validated non-COVID ARDS model. CONCLUSIONS: We discovered evidence of two novel biological subtypes of severe COVID-19 with significantly different clinical outcomes. These subtypes differed from previously established hyper- and hypo-inflammatory non-COVID subtypes of ARDS. Biological heterogeneity may explain inconsistent findings from trials of hospitalized patients with COVID-19 and guide treatment approaches.

LC-MS bioanalysis of protein biomarkers and protein therapeutics in formalin-fixed paraffin-embedded tissue specimens.

Formalin-fixed paraffin-embedded (FFPE) is a form of preservation and preparation for biopsy specimens. FFPE tissue specimens are readily available as part of oncology studies because they are often collected for disease diagnosis or confirmation. FFPE tissue specimens could be extremely useful for retrospective studies on protein biomarkers because the samples preserved in FFPE blocks could be stable for decades. However, LC-MS bioanalysis of FFPE tissues poses significant challenges. In this Perspective, we review the benefits and recent developments in LC-MS approach for targeted protein biomarker and protein therapeutic analysis using FFPE tissues and their clinical and translational applications. We believe that LC-MS bioanalysis of protein biomarkers in FFPE tissue specimens represents a great potential for its clinical applications.

Microneedle patch-based enzyme-linked immunosorbent assay to quantify protein biomarkers of tuberculosis.

There is a clinical need for differential diagnosis of the latent versus active stages of tuberculosis (TB) disease by a simple-to-administer test. Alpha-crystallin (Acr) and early secretory antigenic target-6 (ESAT-6) are protein biomarkers associated with the latent and active stages of TB, respectively, and could be used for differential diagnosis. We therefore developed a microneedle patch (MNP) designed for application to the skin to quantify Acr and ESAT-6 in dermal interstitial fluid by enzyme-linked immunosorbent assay (ELISA). We fabricated mechanically strong microneedles made of polystyrene and coated them with capture antibodies against Acr and ESAT-6. We then optimized assay sensitivity to achieve a limit of detection of 750 pg/ml and 3,020 pg/ml for Acr and ESAT-6, respectively. This study demonstrates the feasibility of an MNP-based ELISA for differential diagnosis of latent TB disease.

Clinical diagnosis of viral hepatitis: Current status and future strategies.

Viral hepatitis (VH) is a significant public health issue with tremendous potential to aggravate into chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. Recent decade has witnessed remarkable uprising in the drug development and effective treatment of VH. An upsurge is seen in identification of antiviral therapies with low rates of viral resistance, the improvement of Hepatitis B Virus (HBV) vaccination and the development of direct-acting antivirals for Hepatitis C Virus (HCV). But unfortunately, the "2030 worldwide eradication" objective of World Health Organization (WHO) is still unmet. It can be largely attributed to the deficit faced by the healthcare system concerning screening and diagnosis. A timely, accurate and comprehensive screening; encompassing maximum population coverage is essential to combat this disease. However, advancements in VH diagnostics remain inadequate and with a marginal use in routine practice. This paper deliberates upon the lacunae in traditional and prevailing diagnostic methodology of viral hepatitis, especially their inadequacy in meeting the unique situations prevailing low- and middle-income countries (LMIC).

Association between adverse pregnancy outcome and placental biomarkers in the first trimester: A prospective cohort study.

OBJECTIVE: To determine the inter-relationships between five first-trimester biomarkers (pregnancy associated plasma protein A [PAPP-A], alpha-fetoprotein [AFP], beta human chorionic gonadotrophin [beta-hCG], placenta growth factor [PlGF] and soluble fms-like tyrosine kinase receptor-1 [sFlt-1]) and a range of adverse pregnancy outcomes (APOs). DESIGN: Prospective cohort study of nulliparous singleton pregnancy. SETTING: Cambridge, UK. POPULATION OR SAMPLE: 4056 pregnancy outcome prediction study participants. METHODS: The biomarker concentrations were measured in maternal serum at ~12 weeks of gestation. Univariable analysis of APOs was performed using logistic regression. Multivariable analysis used best subsets logistic regression with cross-validation. MAIN OUTCOME MEASURES: Pre-eclampsia (PE), small for gestational age (SGA), including severe SGA (birthweight <3rd), fetal growth restriction (FGR), preterm birth (PTB, both induced and spontaneous [iPTB and sPTB, respectively]), pre-viable loss and stillbirth, plus combinations of outcomes. RESULTS: Lower values of PAPP-A, PlGF and sFlt-1 and higher values of AFP were associated with FGR (OR for 1 SD higher value 0.59 [95% CI 0.48-0.74], OR 0.56 [95% CI 0.44-0.70], OR 0.68 [95% CI 0.54-0.87] and OR 1.53 [95% CI 1.25-1.88]), severe SGA (OR 0.59 [95% CI 0.49-0.72], OR 0.71 [95% CI 0.57-0.87], OR 0.74 [95% CI 0.60-0.91] and OR 1.41 [95% CI 1.17-1.71]), sPTB (OR 0.61 [95% CI 0.50-0.73], OR 0.79 [95% CI 0.66-0.96], OR 0.57 [95% CI 0.47-0.70] and OR 1.41 [95% CI 1.18-1.67]) and iPTB (OR 0.72 [95% CI 0.57-0.91], OR 0.62 [95% CI 0.49-0.78], OR 0.71 [95% CI 0.56-0.90] and OR 1.44 [95% CI 1.16-1.78]), respectively. When combinations of biomarkers were assessed, PAPP-A and AFP were independently associated with severe SGA; PAPP-A alone with PE + PTB; PlGF alone with severe PE; PlGF, beta-hCG, AFP and PAPP-A with the combination of PE and SGA; AFP and sFlt-1 with sPTB; and AFP and PlGF with iPTB. CONCLUSIONS: Combinations of first-trimester placental biomarkers are associated with APOs. However, the patterns vary for different types of APO, indicating heterogeneity in the underlying pathophysiological pathways.

Ultrasensitive and High-Resolution Protein Spatially Decoding Framework for Tumor Extracellular Vesicles.

Proteins localized on the surface or within the lumen of tumor-derived extracellular vesicles (EVs) play distinct roles in cancer progression. However, quantifying both populations of proteins within EVs has been hampered due to the limited sensitivity of the existing protein detection methods and inefficient EV isolation techniques. In this study, the eSimoa framework, an innovative approach enabling spatial decoding of EV protein biomarkers with unmatched sensitivity and specificity is presented. Using the luminal eSimoa pipeline, the absolute concentration of luminal RAS or KRASG12D proteins is released and measured, uncovering their prevalence in pancreatic tumor-derived EVs. The pulldown eSimoa pipeline measured absolute protein concentrations from low-abundance EV subpopulations. The eSimoa assays detected EVs in both PBS and plasma samples, confirming their applicability across diverse clinical sample types. Overall, the eSimoa framework offers a valuable tool to (1) detect EVs at concentrations as low as 105 EV mL-1 in plasma, (2) quantify absolute EV protein concentrations as low as fM, and (3) decode the spatial distribution of EV proteins. This study highlights the potential of eSimoa in identifying disease-specific EV protein biomarkers in clinical samples with minimal pre-purification, thereby driving advancements in clinical translation.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-ß2 levels in metastatic colorectal cancer patients (Alliance).

Background: Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC).The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings. Results: The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-ß2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73. Conclusions: This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-ß2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

Proteome Profiling of Brain Vessels in a Mouse Model of Cerebrovascular Pathology.

Cerebrovascular pathology that involves altered protein levels (or signaling) of the transforming growth factor beta (TGFß) family has been associated with various forms of age-related dementias, including Alzheimer disease (AD) and vascular cognitive impairment and dementia (VCID). Transgenic mice overexpressing TGFß1 in the brain (TGF mice) recapitulate VCID-associated cerebrovascular pathology and develop cognitive deficits in old age or when submitted to comorbid cardiovascular risk factors for dementia. We characterized the cerebrovascular proteome of TGF mice using mass spectrometry (MS)-based quantitative proteomics. Cerebral arteries were surgically removed from 6-month-old-TGF and wild-type mice, and proteins were extracted and analyzed by gel-free nanoLC-MS/MS. We identified 3602 proteins in brain vessels, with 20 demonstrating significantly altered levels in TGF mice. For total and/or differentially expressed proteins (p ≤ 0.01, ≥ 2-fold change), using multiple databases, we (a) performed protein characterization, (b) demonstrated the presence of their RNA transcripts in both mouse and human cerebrovascular cells, and (c) demonstrated that several of these proteins were present in human extracellular vesicles (EVs) circulating in blood. Finally, using human plasma, we demonstrated the presence of several of these proteins in plasma and plasma EVs. Dysregulated proteins point to perturbed brain vessel vasomotricity, remodeling, and inflammation. Given that blood-isolated EVs are novel, attractive, and a minimally invasive biomarker discovery platform for age-related dementias, several proteins identified in this study can potentially serve as VCID markers in humans.