Search

Abstract Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case–control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini–Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case–control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80–0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology. [ABSTRACT FROM AUTHOR]

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed. [ABSTRACT FROM AUTHOR]

Abstract Background Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process. Methods Plasma proteins (n = 1303) from pathologically and/or genetically confirmed FTD patients (n = 56; FTLD-Tau n = 16; age = 58.2 ± 6.2; 44% female, FTLD-TDP n = 40; age = 59.8 ± 7.9; 45% female), AD patients (n = 57; age = 65.5 ± 8.0; 39% female), and non-demented controls (n = 148; 61.3 ± 7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, exploratory analysis in post-mortem frontal brain cortex of FTD (n = 10; FTLD-Tau n = 5; age = 56.2 ± 6.9, 60% female, and FTLD-TDP n = 5; age = 64.0 ± 7.7, 60% female) and non-demented controls (n = 4; age = 61.3 ± 8.1; 75% female) were also performed. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis. Results Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p = 0.005), but none of the plasma proteins was specifically associated to FTD. The overall tissue protein expression profile differed between FTD and controls (7-proteins; p = 0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified. Conclusions We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which was primarily associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts.

IntroductionNecrotizing enterocolitis (NEC) is a potentially fatal intestinal disease primarily affecting preterm infants. Early diagnosis of neonates with NEC is crucial to improving outcomes; however, traditional diagnostic tools remain inadequate. Biomarkers represent an opportunity to improve the speed and accuracy of diagnosis, but they are not routinely used in clinical practice.MethodsIn this study, we utilized an aptamer-based proteomic discovery assay to identify new serum biomarkers of NEC. We compared levels of serum proteins in neonates with and without NEC and identified ten differentially expressed serum proteins between these groups.ResultsWe detected two proteins, C-C motif chemokine ligand 16 (CCL16) and immunoglobulin heavy constant alpha 1 and 2 heterodimer (IGHA1 IGHA2), that were significantly increased during NEC and eight that were significantly decreased. Generation of receiver operating characteristic (ROC) curves revealed that alpha-fetoprotein (AUC = 0.926), glucagon (AUC = 0.860), and IGHA1 IGHA2 (AUC = 0.826) were the proteins that best differentiated patients with and without NEC.DiscussionThese findings indicate that further investigation into these serum proteins as a biomarker for NEC is warranted. In the future, laboratory tests incorporating these differentially expressed proteins may improve the ability of clinicians to diagnose infants with NEC rapidly and accurately.

Simple Summary: Oral cancer is a life-threatening disease and among the ten most common cancer types. Specific protein biomarkers in the blood of these patients may allow early tumor detection, earlier intervention and help to individualize therapy resulting in improved patient survival. This exploratory study analyzed the serum proteome with a highly reliable technology to define novel, non-invasive biomarkers of oral cancer. Our results showed important differences of the serum levels of 63 proteins in oral cancer patients vs. controls and of 121 proteins discriminating between patients before and after curative surgery. Our study proves the feasibility of this approach to establish novel serum biomarkers that help to improve the treatment of patients with oral cancer. Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a complex disease characterized by the interplay of genetic and environmental factors for which, despite decades of intense research, diagnosis remains rather delayed, and most therapeutic options fail. Therefore, unravelling other potential pathogenetic mechanisms and searching for reliable markers are high priorities. In the present study, we employ the SOMAscan assay, an aptamer-based proteomic technology, to determine the circulating proteomic profile of ALS patients. The expression levels of ~1300 proteins were assessed in plasma, and 42 proteins with statistically significant differential expression between ALS patients and healthy controls were identified. Among these, four were upregulated proteins, Thymus- and activation-regulated chemokine, metalloproteinase inhibitor 3 and nidogen 1 and 2 were selected and validated by enzyme-linked immunosorbent assays in an overlapping cohort of patients. Following statistical analyses, different expression patterns of these proteins were observed in the familial and sporadic ALS patients. The proteins identified in this study might provide insight into ALS pathogenesis and represent potential candidates to develop novel targeted therapies. [ABSTRACT FROM AUTHOR]

End stage renal disease (ESRD) is characterized by complex metabolic abnormalities, yet the clinical relevance of specific biomarkers remains unclear. The development of multiplex diagnostic platforms is creating opportunities to develop novel diagnostic and therapeutic approaches. SOMAscan is an innovative multiplex proteomic platform which can measure >1300 proteins. In the present study, we performed SOMAscan analysis of plasma samples and validated the measurements by comparison with selected biomarkers. We compared concentrations of SOMAscan-measured prostate specific antigen (PSA) between males and females, and validated SOMAscan concentrations of fibroblast growth factor 23 (FGF23), FGF receptor 1 (FGFR1), and FGFR4 using Enzyme-Linked immunosorbent assay (ELISA). The median (25th and 75th percentile) SOMAscan PSA level in males and females was 4304.7 (1815.4 to 7259.5) and 547.8 (521.8 to 993.4) relative fluorescence units (p = 0.002), respectively, suggesting biological plausibility. Pearson correlation between SOMAscan and ELISA was high for FGF23 (R = 0.95, p < 0.001) and FGFR4 (R = 0.69, p < 0.001), indicating significant positive correlation, while a weak correlation was found for FGFR1 (R = 0.13, p = 0.16). In conclusion, there is a good to near-perfect correlation between SOMAscan and standard immunoassays for FGF23 and FGFR4, but not for FGFR1. This technology may be useful to simultaneously measure a large number of plasma proteins in ESRD, and identify clinically important prognostic markers to predict outcomes.

Background: Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process. Methods: Plasma proteins (n = 1303) from pathologically and/or genetically confirmed FTD patients (n = 56; FTLD-Tau n = 16; age = 58.2 ± 6.2; 44% female, FTLD-TDP n = 40; age = 59.8 ± 7.9; 45% female), AD patients (n = 57; age = 65.5 ± 8.0; 39% female), and non-demented controls (n = 148; 61.3 ± 7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, exploratory analysis in post-mortem frontal brain cortex of FTD (n = 10; FTLD-Tau n = 5; age = 56.2 ± 6.9, 60% female, and FTLD-TDP n = 5; age = 64.0 ± 7.7, 60% female) and non-demented controls (n = 4; age = 61.3 ± 8.1; 75% female) were also performed. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis. Results: Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p = 0.005), but none of the plasma proteins was specifically associated to FTD. The overall tissue protein expression profile differed between FTD and controls (7-proteins; p = 0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified. Conclusions: We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which was primarily associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts. [ABSTRACT FROM AUTHOR]

Introduction: Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions. Methods: We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support. Results: 580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10–4). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex. Conclusions: Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection. Keypoints: COVID-19 is associated with changes in cytokines, interleukins, and other immune-related proteins. However, previous research has failed to account for the dynamic nature of these changes over the course of infection, leading to often contradictory results. We measured 147 immune-related protein in 580 individuals in three large academic centers to precisely map the evolution of these proteins during acute COVID-19. COVID-19 was associated with a clear change in 69 proteins. More importantly, 3 of them may also help explain sex-differences in COVID-19 outcomes. These results provide greater insight into the COVID-19 immune response, and how it leads to severe illness. [ABSTRACT FROM AUTHOR]

Summary: Background: Alpha-1 Antitrypsin (AAT) deficiency (AATD), the most common genetic cause of emphysema presents with unexplained phenotypic heterogeneity in affected subjects. Our objectives to identify unique and shared AATD plasma biomarkers with chronic obstructive pulmonary disease (COPD) may explain AATD phenotypic heterogeneity. Methods: The plasma or serum of 5,924 subjects from four AATD and COPD cohorts were analyzed on SomaScan V4.0 platform. Using multivariable linear regression, inverse variance random-effects meta-analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression we tested the association between 4,720 individual proteins or combined in a protein score with emphysema measured by 15th percentile lung density (PD15) or diffusion capacity (DLCO) in distinct AATD genotypes (Pi*ZZ, Pi*SZ, Pi*MZ) and non-AATD, PiMM COPD subjects. AAT SOMAmer accuracy for identifying AATD was tested using receiver operating characteristic curve analysis. Findings: In PiZZ AATD subjects, 2 unique proteins were associated with PD15 and 98 proteins with DLCO. Of those, 68 were also associated with DLCO in COPD also and enriched for three cellular component pathways: insulin-like growth factor, lipid droplet, and myosin complex. PiMZ AATD subjects shared similar proteins associated with DLCO as COPD subjects. Our emphysema protein score included 262 SOMAmers and predicted emphysema in AATD and COPD subjects. SOMAmer AAT level

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Childrens Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

BackgroundPostoperative delirium (POD) is an acute altered mental state commonly encountered after cardiac surgery. The pathophysiological mechanisms underlying POD remain unclear. We aimed to identify circulating proteins significantly altered after major cardiac surgery with cardiopulmonary bypass (CPB). We also aimed to enable inferences on associations with POD.MethodsSerum and whole blood samples were collected before CPB (n = 16 patients; n = 8 with POD) and again from the same patients on postoperative day 1. All patients were clinically evaluated for POD on postoperative days 1–3. An aptamer-based proteomics platform (SOMAscan) was used to quantify serum protein abundance in patients with POD compared with non-POD controls. We also performed a lipopolysaccharide (LPS)-based in vitro functional analysis (TruCulture) on whole blood samples from patients with POD and non-POD controls to approximate surgical stress. Cytokine levels were determined using a Luminex immunoassay.ResultsCardiac surgery with CPB resulted in a significant (padj < 0.01) change in 48.8% (637 out of 1,305) of proteins detected by SOMAscan. Gene set enrichment showed that the most impacted biological processes involved myeloid cell activation. Specifically, activation and degranulation of neutrophils were the top five highest-scoring processes. Pathway analyses with the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that metabolic enzymes, particularly those of glycolysis, were elevated in serum concentration after surgery. Several proteins were significantly increased postoperatively in patients diagnosed with POD relative to the non-POD controls, with interleukin-6 (IL-6) showing the greatest fold-change. LPS stimulation of whole blood samples confirmed these findings. Linear regression analysis showed a highly significant correlation between Confusion Assessment Method (CAM) scores and CPB-mediated changes in cGMP-inhibited 3′,5′-cyclic phosphodiesterase A (PDE3A).ConclusionsCardiac surgery with CPB resulted in inflammasome changes accompanied by unexpected increases in metabolic pathways. In exploratory analyses, we found that POD was associated with changes in the expression level of various proteins, most notably IL-6 and PDE3A. This study and ongoing protein biomarker studies will likely help quantify risk or confirm the diagnosis for POD and increase understanding of its pathophysiological mechanisms.

Background: Postoperative delirium (POD) is an acute altered mental state commonly encountered after cardiac surgery. The pathophysiological mechanisms underlying POD remain unclear. We aimed to identify circulating proteins significantly altered after major cardiac surgery with cardiopulmonary bypass (CPB). We also aimed to enable inferences on associations with POD. Methods: Serum and whole blood samples were collected before CPB (n = 16 patients; n = 8 with POD) and again from the same patients on postoperative day 1. All patients were clinically evaluated for POD on postoperative days 1–3. An aptamer-based proteomics platform (SOMAscan) was used to quantify serum protein abundance in patients with POD compared with non-POD controls. We also performed a lipopolysaccharide (LPS)-based in vitro functional analysis (TruCulture) on whole blood samples from patients with POD and non-POD controls to approximate surgical stress. Cytokine levels were determined using a Luminex immunoassay. Results: Cardiac surgery with CPB resulted in a significant (p adj < 0.01) change in 48.8% (637 out of 1,305) of proteins detected by SOMAscan. Gene set enrichment showed that the most impacted biological processes involved myeloid cell activation. Specifically, activation and degranulation of neutrophils were the top five highest-scoring processes. Pathway analyses with the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that metabolic enzymes, particularly those of glycolysis, were elevated in serum concentration after surgery. Several proteins were significantly increased postoperatively in patients diagnosed with POD relative to the non-POD controls, with interleukin-6 (IL-6) showing the greatest fold-change. LPS stimulation of whole blood samples confirmed these findings. Linear regression analysis showed a highly significant correlation between Confusion Assessment Method (CAM) scores and CPB-mediated changes in cGMP-inhibited 3′,5′-cyclic phosphodiesterase A (PDE3A). Conclusions: ardiac surgery with CPB resulted in inflammasome changes accompanied by unexpected increases in metabolic pathways. In exploratory analyses, we found that POD was associated with changes in the expression level of various proteins, most notably IL-6 and PDE3A. This study and ongoing protein biomarker studies will likely help quantify risk or confirm the diagnosis for POD and increase understanding of its pathophysiological mechanisms. [ABSTRACT FROM AUTHOR]

BackgroundNew drug regimens of greater efficacy and shorter duration are needed for tuberculosis (TB) treatment. The identification of accurate, quantitative, non-culture based markers of treatment response would improve the efficiency of Phase 2 TB drug testing.MethodsIn an unbiased biomarker discovery approach, we applied a highly multiplexed, aptamer-based, proteomic technology to analyze serum samples collected at baseline and after 8 weeks of treatment from 39 patients with pulmonary TB from Kampala, Uganda enrolled in a Centers for Disease Control and Prevention (CDC) TB Trials Consortium Phase 2B treatment trial.ResultsWe identified protein expression differences associated with 8-week culture status, including Coagulation Factor V, SAA, XPNPEP1, PSME1, IL-11 Rα, HSP70, Galectin-8, α2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ, BGN, LYNB, and IL-7. Markers noted to have differential changes between responders and slow-responders included nectin-like protein 2, EphA1 (Ephrin type-A receptor 1), gp130, CNDP1, TGF-b RIII, MRC2, ADAM9, and CDON. A logistic regression model combining markers associated with 8-week culture status revealed an ROC curve with AUC = 0.96, sensitivity = 0.95 and specificity = 0.90. Additional markers showed differential changes between responders and slow-responders (nectin-like protein), or correlated with time-to-culture-conversion (KLRK1).ConclusionsSerum proteins involved in the coagulation cascade, neutrophil activity, immunity, inflammation, and tissue remodeling were found to be associated with TB treatment response. A quantitative, non-culture based, five-marker signature predictive of 8-week culture status was identified in this pilot study.

Purpose: There is a need to identify new biomarkers of radiation exposure both for use in the development of biodosimetry blood diagnostics for radiation exposure and for clinical use as markers of radiation injury. In the current study, a novel high-throughput proteomics screening approach was used to identify proteomic markers of radiation exposure in the plasma of total body irradiated mice. A subset panel of significantly altered proteins was selected to build predictive models of radiation exposure and received radiation dose useful for population screening in a future radiological or nuclear event.Methods: Female C57BL6 Mice of 8–14 weeks of age received a single total body irradiation (TBI) dose of 2, 3.5, 8 Gy or sham radiation and plasma was collected by cardiac puncture at days 1, 3, and 7 post-exposure. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (p < 0.05) in expression following radiation exposure were used to build predictive models of radiation exposure and radiation dose.Results: Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay. For the Control vs. Radiation model, the top predictive proteins were immunoglobulin heavy constant mu (IGHM), mitogen-activated protein kinase 14 (MAPK14), ectodysplasin A2 receptor (EDA2R) and solute carrier family 25 member 18 (SLC25A18). For the Control vs. Dose model, the top predictive proteins were cyclin dependent kinase 2/cyclin A2 (CDK2. CCNA2), E-selectin (SELE), BCL2 associated agonist of cell death (BAD) and SLC25A18. Following model validation with a training set of samples, both models tested with a new sample cohort had overall predictive accuracies of 85% and 73% for the Control vs. Radiation and Control vs. Dose models respectively.Conclusion: The SOMAscan proteomics platform is a useful screening tool to evaluate changes in biomarker expression. In our study we were able to identify a novel panel of radiation responsive proteins useful for predicting whether an animal had received a radiation exposure and to what dose they had received. Such diagnostic tools are needed for future medical management of radiation exposures.

The discovery and identification of biomarkers promote the rational and fast development of medical diagnosis and therapeutics. Clinically, the application of ideal biomarkers still is limited due to the suboptimal technology in biomarker discovery. Aptamers are single-stranded deoxyribonucleic acid or ribonucleic acid molecules and can selectively bind to varied targets with high affinity and specificity. Compared with antibody, aptamers have desirable advantages, such as flexible design, easy synthesis and convenient modification with different functional groups. Currently, different aptamer-based technologies have been developed to facilitate biomarker discovery, especially CELL-SELEX and SOMAScan technology. CELL-SELEX technology is mainly used to identify cell membrane surface biomarkers of various cells. SOMAScan technology is an unbiased biomarker detection method that can analyze numerous and even thousands of proteins in complex biological samples at the same time. It has now become a large-scale multi-protein biomarker discovery platform. In this review, we introduce the aptamer-based biomarker discovery technologies, and summarize and highlight the discovered emerging biomarkers recently in several diseases.

Introduction: African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer's disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD., Methods: Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy., Results: A plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94)., Discussion: This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology., Competing Interests: Conflict of Interest Statement: LAK, CDH, BMO, and MMC report no disclosures.

Abstract Background Selective kinase and immune checkpoint inhibitors, and their combinations, have significantly improved the survival of patients with advanced metastatic melanoma. Not all patients will respond to treatment however, and some patients will present with significant toxicities. Hence, the identification of biomarkers is critical for the selection and management of patients receiving treatment. Biomarker discovery often involves proteomic techniques that simultaneously profile multiple proteins but few studies have compared these platforms. Methods In this study, we used the multiplex bead-based Eve Technologies Discovery assay and the aptamer-based SomaLogic SOMAscan assay to identify circulating proteins predictive of response to immunotherapy in melanoma patients treated with combination immune checkpoint inhibitors. Expression of four plasma proteins were further validated using the bead-based Millipore Milliplex assay. Results Both the Discovery and the SOMAscan assays detected circulating plasma proteins in immunotherapy-treated melanoma patients. However, these widely used assays showed limited correlation in relative protein quantification, due to differences in specificity and the dynamic range of protein detection. Protein data derived from the Discovery and Milliplex bead-based assays were highly correlated. Conclusions Our study highlights significant limitations imposed by inconsistent sensitivity and specificity due to differences in the detection antibodies or aptamers of these widespread biomarker discovery approaches. Our findings emphasize the need to improve these technologies for the accurate identification of biomarkers.

The first human Zika virus (ZIKV) outbreak was reported in Micronesia in 2007, followed by one in Brazil in 2015. Recent studies have reported cases in Europe, Oceania and Latin America. In 2016, ZIKV transmission was also reported in the US and the World Health Organization declared it a Public Health Emergency of International Concern. Because various neurological conditions are associated with ZIKV, such as microcephaly, Guillain-Barré syndrome, and other disorders of both the central and peripheral nervous systems, including encephalopathy, (meningo)encephalitis and myelitis, and because of the lack of reliable patient diagnosis, numerous ongoing studies seek to understand molecular mechanisms underlying ZIKV pathogenesis. Astrocytes are one of the most abundant cells in the CNS. They control axonal guidance, synaptic signaling, neurotransmitter trafficking and maintenance of neurons, and are targeted by ZIKV. In this study, we used a newly developed multiplexed aptamer-based technique (SOMAScan) to examine > 1300 human astrocyte cell proteins. We identified almost 300 astrocyte proteins significantly dysregulated by ZIKV infection that span diverse functions and signaling pathways, including protein translation, synaptic control, cell migration and differentiation.

Background: Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease, where patients often suffer from fatigue. Biological pathways underlying fatigue are unknown. In this study aptamer-based SOMAscan technology is used to identify potential biomarkers and treatment targets for fatigue in pSS.Methods: SOMAscan® Assay 1.3k was performed on serum samples of healthy controls (HCs) and pSS patients characterized for interferon upregulation and fatigue. Differentially expressed proteins (DEPs) between pSS patients and HC or fatigued and non-fatigued pSS patients were validated and discriminatory capacity of markers was tested using independent technology.Results: Serum concentrations of over 1,300 proteins were compared between 63 pSS patients and 20 HCs resulting in 58 upregulated and 46 downregulated proteins. Additionally, serum concentrations of 30 interferon positive (IFNpos) and 30 interferon negative (IFNneg) pSS patients were compared resulting in 25 upregulated and 13 downregulated proteins. ELISAs were performed for several DEPs between pSS patients and HCs or IFNpos and IFNneg all showing a good correlation between protein levels measured by ELISA and relative fluorescence units (RFU) measured by the SOMAscan. Comparing 22 fatigued and 23 non-fatigued pSS patients, 16 serum proteins were differentially expressed, of which 14 were upregulated and 2 were downregulated. Top upregulated DEPs included neuroactive synaptosomal-associated protein 25 (SNAP-25), alpha-enolase (ENO1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1). Furthermore, the proinflammatory mediator IL36a and several complement factors were upregulated in fatigued compared to non-fatigued pSS patients. ROC analysis indicated that DEPs showed good capacity to discriminate fatigued and non-fatigued pSS patients.Conclusion: In this study we validated the use of aptamer-based proteomics and identified a novel set of proteins which were able to distinguish fatigued from non-fatigued pSS patients and identified a so-called “fatigue signature.”

The first human Zika virus (ZIKV) outbreak was reported in Micronesia in 2007, followed by one in Brazil in 2015. Recent studies have reported cases in Europe, Oceania and Latin America. In 2016, ZIKV transmission was also reported in the US and the World Health Organization declared it a Public Health Emergency of International Concern. Because various neurological conditions are associated with ZIKV, such as microcephaly, Guillain-Barré syndrome, and other disorders of both the central and peripheral nervous systems, including encephalopathy, (meningo)encephalitis and myelitis, and because of the lack of reliable patient diagnosis, numerous ongoing studies seek to understand molecular mechanisms underlying ZIKV pathogenesis. Astrocytes are one of the most abundant cells in the CNS. They control axonal guidance, synaptic signaling, neurotransmitter trafficking and maintenance of neurons, and are targeted by ZIKV. In this study, we used a newly developed multiplexed aptamer-based technique (SOMAScan) to examine > 1300 human astrocyte cell proteins. We identified almost 300 astrocyte proteins significantly dysregulated by ZIKV infection that span diverse functions and signaling pathways, including protein translation, synaptic control, cell migration and differentiation. [ABSTRACT FROM AUTHOR]

Background: Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease, where patients often suffer from fatigue. Biological pathways underlying fatigue are unknown. In this study aptamer-based SOMAscan technology is used to identify potential biomarkers and treatment targets for fatigue in pSS. Methods: SOMAscan® Assay 1.3k was performed on serum samples of healthy controls (HCs) and pSS patients characterized for interferon upregulation and fatigue. Differentially expressed proteins (DEPs) between pSS patients and HC or fatigued and non-fatigued pSS patients were validated and discriminatory capacity of markers was tested using independent technology. Results: Serum concentrations of over 1,300 proteins were compared between 63 pSS patients and 20 HCs resulting in 58 upregulated and 46 downregulated proteins. Additionally, serum concentrations of 30 interferon positive (IFNpos) and 30 interferon negative (IFNneg) pSS patients were compared resulting in 25 upregulated and 13 downregulated proteins. ELISAs were performed for several DEPs between pSS patients and HCs or IFNpos and IFNneg all showing a good correlation between protein levels measured by ELISA and relative fluorescence units (RFU) measured by the SOMAscan. Comparing 22 fatigued and 23 non-fatigued pSS patients, 16 serum proteins were differentially expressed, of which 14 were upregulated and 2 were downregulated. Top upregulated DEPs included neuroactive synaptosomal-associated protein 25 (SNAP-25), alpha-enolase (ENO1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1). Furthermore, the proinflammatory mediator IL36a and several complement factors were upregulated in fatigued compared to non-fatigued pSS patients. ROC analysis indicated that DEPs showed good capacity to discriminate fatigued and non-fatigued pSS patients. Conclusion: In this study we validated the use of aptamer-based proteomics and identified a novel set of proteins which were able to distinguish fatigued from non-fatigued pSS patients and identified a so-called "fatigue signature." [ABSTRACT FROM AUTHOR]

Background: Alternatives to phlebotomy in clinical trials increase options for patients and clinicians by simplifying and increasing accessibility to clinical trials. The authors investigated the technical and logistical considerations of one technology compared with phlebotomy. Methodology: Paired samples were collected from 16 donors via a second-generation serum gel microsampling device and conventional phlebotomy. Microsamples were subject to alternative sample handling conditions and were evaluated for quality, clinical testing and proteome profiling. Results: Timely centrifugation of blood serum microsamples largely preserved analyte stability. Conclusion: Centrifugation timing of serum microsamples impacts the quality of specific clinical chemistry and protein biomarkers. Microsampling devices with remote centrifugation and refrigerated shipping can decrease patient burden, expand clinical trial populations and aid clinical decisions.

Tuberculosis is a major global health problem caused by the bacterium Mycobacterium tuberculosis ( Mtb ) complex. According to WHO reports, 53 million TB patients died from 2000 to 2016. Therefore, early diagnosis of the disease is of great importance for global health care programs. The restrictions of traditional methods have encouraged the development of innovative methods for rapid, reliable, and cost-effective diagnosis of tuberculosis. In recent years, aptamer-based biosensors or aptasensors have drawn great attention to sensitive and accessible detection of tuberculosis. Aptamers are small short single-stranded molecules of DNA or RNA that fold to a unique form and bind to targets. Once combined with nanomaterials, nano-scale aptasensors provide powerful analytical platforms for diagnosing of tuberculosis. Various groups designed and studied aptamers specific for the whole cells of M. tuberculosis , mycobacterial proteins and IFN-γ for early diagnosis of TB. Advantages such as high specificity and strong affinity, potential for binding to a larger variety of targets, increased stability, lower costs of synthesis and storage requirements, and lower probability of contamination make aptasensors pivotal alternatives for future TB diagnostics. In recent years, the concept of SOMAmer has opened new horizons in high precision detection of tuberculosis biomarkers. This review article provides a description of the research progresses of aptamer-based and SOMAmer-based biosensors and nanobiosensors for the detection of tuberculosis. [ABSTRACT FROM AUTHOR]

SOMAscan™ is a complex proteomic platform created by SomaLogic. Experimental data resulting from the assay is provided by SomaLogic in a proprietary text-based format called ADAT. This manuscript describes a user-friendly point and click open source, platform-independent software tool designed to be used for navigating and plotting data from an ADAT file. This tool was used either alone or in conjunction with other tools as a first pass analysis of the data on several different on-going research projects. We have seen a need from our experience for a web interface to the ADAT file so that users can navigate, generate plots, perform QC and conduct statistical analysis on their own data in a point and click manner. After several rounds of interacting with biologists and their requirements with respect to data analysis, we present an online interactive Shiny Web Tool for Navigating and Plotting data contained within the ADAT file. Extensive video tutorials, example data, the tool and the source code are available online.

Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders.

End stage renal disease (ESRD) is characterized by complex metabolic abnormalities, yet the clinical relevance of specific biomarkers remains unclear. The development of multiplex diagnostic platforms is creating opportunities to develop novel diagnostic and therapeutic approaches. SOMAscan is an innovative multiplex proteomic platform which can measure >1300 proteins. In the present study, we performed SOMAscan analysis of plasma samples and validated the measurements by comparison with selected biomarkers. We compared concentrations of SOMAscan-measured prostate specific antigen (PSA) between males and females, and validated SOMAscan concentrations of fibroblast growth factor 23 (FGF23), FGF receptor 1 (FGFR1), and FGFR4 using Enzyme-Linked immunosorbent assay (ELISA). The median (25th and 75th percentile) SOMAscan PSA level in males and females was 4304.7 (1815.4 to 7259.5) and 547.8 (521.8 to 993.4) relative fluorescence units (p = 0.002), respectively, suggesting biological plausibility. Pearson correlation between SOMAscan and ELISA was high for FGF23 (R = 0.95, p < 0.001) and FGFR4 (R = 0.69, p < 0.001), indicating significant positive correlation, while a weak correlation was found for FGFR1 (R = 0.13, p = 0.16). In conclusion, there is a good to near-perfect correlation between SOMAscan and standard immunoassays for FGF23 and FGFR4, but not for FGFR1. This technology may be useful to simultaneously measure a large number of plasma proteins in ESRD, and identify clinically important prognostic markers to predict outcomes.

Assessments of disease progression and response to therapies in Duchenne muscular dystrophy (DMD) patients remain challenging. Current DMD patient assessments include complex physical tests and invasive procedures such as muscle biopsies, which are not suitable for young children. Deining alternative, less invasive and objective outcome measures to assess disease progression and response to therapy will aid drug development and clinical trials in DMD. In this review we highlight advances in development of non-invasive blood circulating biomarkers as a means to assess disease progression and response to therapies in DMD. [ABSTRACT FROM AUTHOR]

Aging associates with an increased susceptibility for disease and decreased quality of life. To date, processes underlying aging are still not well understood, leading to limited interventions with unknown mechanisms to promote healthy aging. Previous research suggests that changes in the blood proteome are reflective of age-associated phenotypes such as frailty. Moreover, experimentally induced changes in the blood proteome composition can accelerate or decelerate underlying aging processes. The aim of this study is to identify a set of proteins in the human plasma associated with aging by integration of the data of four independent, large-scaled datasets using the aptamer-based SomaScan platform on the human aging plasma proteome. Using this approach, we identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions. We validated the age-associated effects in an independent study using a centenarian population, showing highly concordant effects. Our results suggest that APs are more associated to diseases than other plasma proteins. Plasma levels of APs can predict chronological age, and a reduced selection of 15 APs can still predict individuals' age accurately, highlighting their potential as biomarkers of aging processes. Furthermore, we show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in the understanding of the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging., Competing Interests: Author BL was employed by the Alkahest Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Coenen, Lehallier, de Vries and Middeldorp.)

The yet-unrealized potential for more "personalized" Direct-to-Consumer (DTC) tests to fundamentally alter the practice and economics of healthcare is undeniable. However, there are also many challenges to be met, including the herculean task of ensuring that the information provided by such tests is scientifically sound and, ideally, medically actionable. We consider recent events in DTC testing and suggest a "thought experiment" of an approach that could ultimately meet the needs of patients, providers and regulatory authorities. [ABSTRACT FROM AUTHOR]

Purpose: Neurodegenerative disorders are a global health burden with costly and invasive diagnoses relying on brain imaging technology or CSF-based biomarkers. Therefore, considerable efforts to identify blood-biomarkers for Alzheimer's (AD) and Parkinson's diseases (PD) are ongoing. Objectives: This review evaluates the blood biomarkers for AD and PD for their diagnostic value. Methods: This study systematically reviewed articles published between July 1984 and February 2021. Among 1266 papers, we selected 42 studies for a systematic review and 23 studies for meta-analysis. Results & conclusion: Our analysis highlights P-tau181, T-tau and Nfl as promising blood biomarkers for AD diagnosis. Nfl levels were consistently raised in 16 AD and three PD cohorts. P-tau181 and T-tau were also significantly increased in 12 and eight AD cohorts, respectively.

Intermittent fasting and fasting mimetic diets ameliorate inflammation. Similarly, serum extracted from fasted healthy and asthmatic subjects' blunt inflammation in vitro, implicating serum components in this immunomodulation. To identify the proteins orchestrating these effects, SOMAScan technology was employed to evaluate serum protein levels in healthy subjects following an overnight, 24-h fast and 3 h after refeeding. Partial least square discriminant analysis identified several serum proteins as potential candidates to confer feeding status immunomodulation. The characterization of recombinant IGFBP1 (elevated following 24 h of fasting) and PYY (elevated following refeeding) in primary human CD4+ T cells found that they blunted and induced immune activation, respectively. Furthermore, integrated univariate serum protein analysis compared to RNA-seq analysis from peripheral blood mononuclear cells identified the induction of IL1RL1 and MFGE8 levels in refeeding compared to the 24-h fasting in the same study. Subsequent quantitation of these candidate proteins in lean versus obese individuals identified an inverse regulation of serum levels in the fasted subjects compared to the obese subjects. In parallel, IL1RL1 and MFGE8 supplementation promoted increased CD4+ T responsiveness to T cell receptor activation. Together, these data show that caloric load-linked conditions evoke serological protein changes, which in turn confer biological effects on circulating CD4+ T cell immune responsiveness. [ABSTRACT FROM AUTHOR]

Purpose: There is a need to identify new biomarkers of radiation exposure both for use in the development of biodosimetry blood diagnostics for radiation exposure and for clinical use as markers of radiation injury. In the current study, a novel high-throughput proteomics screening approach was used to identify proteomic markers of radiation exposure in the plasma of total body irradiated mice. A subset panel of significantly altered proteins was selected to build predictive models of radiation exposure and received radiation dose useful for population screening in a future radiological or nuclear event. Methods: Female C57BL6 Mice of 8–14 weeks of age received a single total body irradiation (TBI) dose of 2, 3.5, 8 Gy or sham radiation and plasma was collected by cardiac puncture at days 1, 3, and 7 post-exposure. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (p < 0.05) in expression following radiation exposure were used to build predictive models of radiation exposure and radiation dose. Results: Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay. For the Control vs. Radiation model, the top predictive proteins were immunoglobulin heavy constant mu (IGHM), mitogen-activated protein kinase 14 (MAPK14), ectodysplasin A2 receptor (EDA2R) and solute carrier family 25 member 18 (SLC25A18). For the Control vs. Dose model, the top predictive proteins were cyclin dependent kinase 2/cyclin A2 (CDK2. CCNA2), E-selectin (SELE), BCL2 associated agonist of cell death (BAD) and SLC25A18. Following model validation with a training set of samples, both models tested with a new sample cohort had overall predictive accuracies of 85% and 73% for the Control vs. Radiation and Control vs. Dose models respectively. Conclusion: The SOMAscan proteomics platform is a useful screening tool to evaluate changes in biomarker expression. In our study we were able to identify a novel panel of radiation responsive proteins useful for predicting whether an animal had received a radiation exposure and to what dose they had received. Such diagnostic tools are needed for future medical management of radiation exposures. [ABSTRACT FROM AUTHOR]