Nucleic Acid Ligands With Protein-like Side Chains: Modified Aptamers and Their Use as Diagnostic and Therapeutic Agents

The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes：A Mendelian randomization study

Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.

AFM-fishing technology for protein detection in solutions

The review considers the possibility of using atomic force microscopy (AFM) as a basic method for protein detection in solutions with low protein concentrations. The demand for new bioanalytical approaches is determined by the problem of insufficient sensitivity of systems used in routine practice for protein detection. Special attention is paid to demonstration of the use in bioanalysis of a combination of AFM and fishing methods as an approach of concentrating biomolecules from a large volume of the analyzed solution on a small surface area.

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Importance

There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia.

Objective

To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP.

Design, Setting, and Participants

This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates.

Exposures

Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models.

Main Outcomes and Measures

Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC).

Results

This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates. The predictive performance of the resulting model was modest, with a training set AUC of 0.64 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs.

Conclusions and Relevance

In this case-control study with detailed clinical data and stored plasma samples available in the first trimester, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above clinical and demographic factors that are routinely available.

Mineralocorticoid Receptor Antagonism Prevents Aortic Plaque Progression and Reduces Left Ventricular Mass and Fibrosis in Patients With Type 2 Diabetes and Chronic Kidney Disease: The MAGMA Trial

BACKGROUND

Persistent mineralocorticoid receptor activation is a pathologic response in type 2 diabetes and chronic kidney disease. Whereas mineralocorticoid receptor antagonists are beneficial in reducing cardiovascular complications, direct mechanistic pathways for these effects in humans are lacking.

METHODS

The MAGMA trial (Mineralocorticoid Receptor Antagonism Clinical Evaluation in Atherosclerosis) was a randomized, double-blind, placebo-controlled trial in patients with high-risk type 2 diabetes with chronic kidney disease (not receiving dialysis) on maximum tolerated renin-angiotensin system blockade. The primary end point was change in thoracic aortic wall volume, expressed as absolute or percent value (ΔTWV or ΔPWV), using 3T magnetic resonance imaging at 12 months. Secondary end points were changes in left ventricle (LV) mass; LV fibrosis, measured as a change in myocardial native T1; and 24-hour ambulatory and central aortic blood pressures. Tertiary end points included plasma proteomic changes in 7596 plasma proteins using an aptamer-based assay.

RESULTS

A total of 79 patients were randomized to placebo (n=42) or 25 mg of spironolactone daily (n=37). After a modified intent-to-treat, including available baseline data of study end points, patients who completed the trial protocol were included in the final analyses. At the 12-month follow-up, the average change in PWV was 7.1±10.7% in the placebo group and 0.87±10.0% in the spironolactone group (P=0.028), and ΔTWV was 1.2±1.7 cm3 in the placebo group and 0.037±1.9 cm3 in the spironolactone group (P=0.022). Change in LV mass was 3.1±8.4 g in the placebo group and -5.8±8.4 g in the spironolactone group (P=0.001). Changes in LV T1 values were significantly different between the placebo and spironolactone groups (26.0±41.9 ms in the placebo group versus a decrease of -10.1±36.3 ms in the spironolactone group; P=6.33×10-4). Mediation analysis revealed that the spironolactone effect on thoracic aortic wall volume and myocardial mass remained significant after adjustment for ambulatory and central blood pressures. Proteomic analysis revealed a dominant effect of spironolactone on pathways involving oxidative stress, inflammation, and leukocyte activation.

CONCLUSIONS

Among patients with diabetes with moderate to severe chronic kidney disease at elevated cardiovascular risk, treatment with spironolactone prevented progression of aortic wall volume and resulted in regression of LV mass and favorable alterations in native T1, suggesting amelioration of left-ventricular fibrosis.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02169089.

Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia

BACKGROUND

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited.

METHODS

In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry.

RESULTS

We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1, SH2B3, SERPINE2, RGS18, PZP, NOTUM, METAP1, MANEA, jun-D, GDF15 [growth/differentiation factor 15], FGL1, FGF5, FES, APOBR, ANP, ALDH-E2, ADAMTS13, and 3MG), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation.

CONCLUSIONS

This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study

BACKGROUND

Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.

METHODS

Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist).

RESULTS

We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes.

CONCLUSIONS

Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Aptamer Proteomics for Biomarker Discovery in Heart Failure With Preserved Ejection Fraction: The PARAGON-HF Proteomic Substudy

BACKGROUND

Prognostic markers and biological pathways linked to detrimental clinical outcomes in heart failure with preserved ejection fraction (HFpEF) remain incompletely defined.

METHODS AND RESULTS

We measured serum levels of 4123 unique proteins in 1117 patients with HFpEF enrolled in the PARAGON-HF (Efficacy and Safety of LCZ696 Compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction) trial using a modified aptamer proteomic assay. Baseline circulating protein concentrations significantly associated with the primary end point and the timing and occurrence of total heart failure hospitalization and cardiovascular death were identified by recurrent events regression, accounting for multiple testing, adjusted for age, sex, treatment, and anticoagulant use, and compared with published analyses in 2515 patients with heart failure with reduced ejection fraction from the PARADIGM-HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) and ATMOSPHERE (Efficacy and Safety of Aliskiren and Aliskiren/Enalapril Combination on Morbidity-Mortality in Patients With Chronic Heart Failure) clinical trials. We identified 288 proteins that were robustly associated with the risk of heart failure hospitalization and cardiovascular death in patients with HFpEF. The baseline proteins most strongly related to outcomes included B2M (β-2 microglobulin), TIMP1 (tissue inhibitor of matrix metalloproteinase 1), SERPINA4 (serpin family A member 4), and SVEP1 (sushi, von Willebrand factor type A, EGF, and pentraxin domain containing 1). Overall, the protein-outcome associations in patients with HFpEF did not markedly differ as compared with patients with heart failure with reduced ejection fraction. A proteomic risk score derived in patients with HFpEF was not superior to a previous proteomic score derived in heart failure with reduced ejection fraction nor to clinical risk factors, NT-proBNP (N-terminal pro-B-type natriuretic peptide), or high-sensitivity cardiac troponin.

CONCLUSIONS

Numerous serum proteins linked to metabolic, coagulation, and extracellular matrix regulatory pathways were associated with worse HFpEF prognosis in the PARAGON-HF proteomic substudy. Our results demonstrate substantial similarities among serum proteomic risk markers for heart failure hospitalization and cardiovascular death when comparing clinical trial participants with heart failure across the ejection fraction spectrum.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique Identifiers: NCT01920711, NCT01035255, NCT00853658.

Circulating biomarkers of airflow limitation across the life span

BACKGROUND

Airflow limitation is a hallmark of chronic obstructive pulmonary disease, which can develop through different lung function trajectories across the life span. There is a need for longitudinal studies aimed at identifying circulating biomarkers of airflow limitation across different stages of life.

OBJECTIVES

This study sought to identify a signature of serum proteins associated with airflow limitation and evaluate their relation to lung function longitudinally in adults and children.

METHODS

This study used data from 3 adult cohorts (TESAOD [Tucson Epidemiological Study of Airway Obstructive Disease], SAPALDIA [Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults], LSC [Lovelace Smoker Cohort]) and 1 birth cohort (TCRS [Tucson Children's Respiratory Study]) (N = 1940). In TESAOD, among 46 circulating proteins, we identified those associated with FEV1/forced vital capacity (FVC) percent (%) predicted levels and generated a score based on the sum of their z-scores. Cross-sectional analyses were used to test the score for association with concomitant lung function. Longitudinal analyses were used to test the score for association with subsequent lung function growth in childhood and decline in adult life.

RESULTS

After false discovery rate adjustment, serum levels of 5 proteins (HP, carcinoembryonic antigen, ICAM1, CRP, TIMP1) were associated with percent predicted levels of FEV1/FVC and FEV1 in TESAOD. In cross-sectional multivariate analyses the 5-biomarker score was associated with FEV1 % predicted in all adult cohorts (meta-analyzed FEV1 decrease for 1-SD score increase: -2.9%; 95% CI: -3.9%, -1.9%; P = 2.4 × 10-16). In multivariate longitudinal analyses, the biomarker score at 6 years of age was inversely associated with FEV1 and FEV1/FVC levels attained by young adult life (P = .02 and .005, respectively). In adults, persistently high levels of the biomarker score were associated with subsequent accelerated decline of FEV1 and FEV1/FVC (P = .01 and .001).

CONCLUSIONS

A signature of 5 circulating biomarkers of airflow limitation was associated with both impaired lung function growth in childhood and accelerated lung function decline in adult life, indicating that these proteins may be involved in multiple lung function trajectories leading to chronic obstructive pulmonary disease.

Comparison of Novel Proteomic Expression Profiles for Radiation Exposure in Male and Female C57BL6 Mice

There is a need for point-of-care diagnostics for future mass casualty events involving radiation exposure. The development of radiation exposure and dose prediction algorithms for biodosimetry is needed for screening of large populations during these scenarios, and exploration of the potential effects which sex, age, genetic heterogeneity, and physiological comorbidities may have on the utility of biodosimetry diagnostics is needed. In the current study, proteomic profiling was used to examine sex-specific differences in age-matched C57BL6 mice on the blood proteome after radiation exposure, and the usefulness of development and application of biodosimetry algorithms using both male and female samples. Male and female mice between 9-11 weeks of age received a dose of total-body irradiation (TBI) of either 2, 4 or 8 Gy and plasma was collected at days 1, 3 and 7 postirradiation. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrated significant (FDR < 0.05 and |logFC| > 0.2) changes in expression after radiation exposure. All proteins were used for feature selection to build predictive models of radiation exposure using different sample and sex-specific cohorts. Both binary (prediction of any radiation exposure) and multidose (prediction of specific radiation dose) model series were developed using either female and male samples combined or only female or only male samples. The binary series (models 1, 2 and 3) and multidose series (models 4, 5 and 6) included female/male combined, female only and male only respectively. Detectable values were obtained for all ∼7,000 proteins included in the SomaScan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies in the binary model series was ∼100% at the model training level, and when tested with fresh samples, 97.9% for model 1 (female and male) and 100% for model 2 (female only) and model 3 (male only). When sex-specific models 2 and 3 were tested with the opposite sex, the overall predictive accuracy rate dropped to 62.5% for model 2 and remained 100% for model 3. The overall predictive accuracy rate in the multidose model series was 100% for all models at the model training level and, when tested with fresh samples, 83.3%, 75% and 83.3% for Multidose models 4-6, respectively. When sex-specific model 5 (female only) and model 6 (male only) were tested with the opposite sex, the overall predictive accuracy rate dropped to 52.1% and 68.8%, respectively. These models represent novel predictive panels of radiation-responsive proteomic biomarkers and illustrate the utility and necessity of considering sex-specific differences in development of radiation biodosimetry prediction algorithms. As sex-specific differences were observed in this study, and as use of point-of-care radiation diagnostics in future mass casualty settings will necessarily include persons of both sexes, consideration of sex-specific variation is essential to ensure these diagnostic tools have practical utility in the field.

Targeting PCSK9 as a key player in lipid metabolism: exploiting the therapeutic and biosensing potential of aptamers

The degradation of low-density lipoprotein receptor (LDLR) is induced by proprotein convertase subtilisin/kexin type 9 (PCSK9), resulting in elevated plasma concentrations of LDL cholesterol. Therefore, inhibiting the interactions between PCSK9 and LDLR is a desirable therapeutic goal for managing hypercholesterolemia. Aptamers, which are RNA or single-stranded DNA sequences, can recognize their targets based on their secondary structure. Aptamers exhibit high selectivity and affinity for binding to target molecules. The systematic evolution of ligands by exponential enrichment (SELEX), a combination of biological approaches, is used to screen most aptamers in vitro. Due to their unique advantages, aptamers have garnered significant interest since their discovery and have found extensive applications in various fields. Aptamers have been increasingly utilized in the development of biosensors for sensitive detection of pathogens, analytes, toxins, drug residues, and malignant cells. Furthermore, similar to monoclonal antibodies, aptamers can serve as therapeutic tools. Unlike certain protein therapeutics, aptamers do not elicit antibody responses, and their modified sugars at the 2'-positions generally prevent toll-like receptor-mediated innate immune responses. The focus of this review is on aptamer-based targeting of PCSK9 and the application of aptamers both as biosensors and therapeutic agents.

The dynamic impact of location and resection on the glioma CSF proteome

While serial sampling of glioma tissue is rarely performed prior to recurrence, cerebrospinal fluid (CSF) is an underutilized longitudinal source of candidate glioma biomarkers for understanding therapeutic impacts. However, the impact of key variables to consider in longitudinal CSF samples, including anatomical location and post-surgical changes, remains unknown. To that end, pre- versus post-resection intracranial CSF samples were obtained at early (1-16 days; n=20) or delayed (86-153 days; n=11) timepoints for patients with glioma. Paired lumbar-versus-intracranial glioma CSF samples were also obtained (n=14). Using aptamer-based proteomics, we identify significant differences in the CSF proteome between lumbar, subarachnoid, and ventricular CSF. Our analysis of serial intracranial CSF samples suggests the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies. Importantly, we found that resection had a significant, evolving longitudinal impact on the CSF proteome. Proteomic data are provided with individual clinical annotations as a resource for the field.

One Sentence Summary

Glioma cerebrospinal fluid (CSF) accessed intra-operatively and longitudinally via devices can reveal impacts of treatment and anatomical location.

An integrated multi-omics analysis reveals osteokines involved in global regulation

Bone secretory proteins, termed osteokines, regulate bone metabolism and whole-body homeostasis. However, fundamental questions as to what the bona fide osteokines and their cellular sources are and how they are regulated remain unclear. In this study, we analyzed bone and extraskeletal tissues, osteoblast (OB) conditioned media, bone marrow supernatant (BMS), and serum, for basal osteokines and those responsive to aging and mechanical loading/unloading. We identified 375 candidate osteokines and their changes in response to aging and mechanical dynamics by integrating data from RNA-seq, scRNA-seq, and proteomic approaches. Furthermore, we analyzed their cellular sources in the bone and inter-organ communication facilitated by them (bone-brain, liver, and aorta). Notably, we discovered that senescent OBs secrete fatty-acid-binding protein 3 to propagate senescence toward vascular smooth muscle cells (VSMCs). Taken together, we identified previously unknown candidate osteokines and established a dynamic regulatory network among them, thus providing valuable resources to further investigate their systemic roles.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

The causal effect of mTORC1-dependent circulating protein levels on nonalcoholic fatty liver disease: A Mendelian randomization study

BACKGROUND

The mechanistic target of rapamycin (mTOR) signal pathway plays a crucial role in the development of nonalcoholic fatty liver disease (NAFLD). However, the causal effect of mTOR downstream proteins on NAFLD remains unknown.

AIMS

We conducted a two-sample Mendelian randomization (MR) study to investigate whether the mTOR-dependent circulating proteins, including Eukaryotic Initiation Factor 4E Binding Proteins (eIF4EBPs), Ribosomal Protein S6K kinase 1 (RP-S6K), Eukaryotic Initiation Factor 4E (eIF4E), Eukaryotic Initiation Factor 4A (eIF4A) and Eukaryotic Initiation Factor 4 G (eIF4G), have causal effects on the risk of NAFLD.

METHODS

The causal estimate was evaluated with the inverse-variance weighted (IVW) method in discovery stage and validation stage. The single-nucleotide polymorphisms (SNPs) were selected to genetically predict exposures from Genome-Wide Association Studies (GWAS). Exposures with statistically significant effects in the discovery dataset would be further validated in the validation dataset.

RESULTS

MR study revealed that eIF4E had a causal effect on NAFLD in both discovery stage (OR = 1.339, P = 0.037) and validation stage (OR = 1.0007, P = 0.022). Sensitivity analyses confirmed robustness of the results.

CONCLUSION

The genetically predicted higher level of mTOR-dependent eIF4E in plasma might have a causal effect on the occurrence of NAFLD.

Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration

The pathophysiological mechanisms driving disease progression of frontotemporal lobar degeneration (FTLD) and corresponding biomarkers are not fully understood. We leveraged aptamer-based proteomics (> 4,000 proteins) to identify dysregulated communities of co-expressed cerebrospinal fluid proteins in 116 adults carrying autosomal dominant FTLD mutations (C9orf72, GRN, MAPT) compared to 39 noncarrier controls. Network analysis identified 31 protein co-expression modules. Proteomic signatures of genetic FTLD clinical severity included increased abundance of RNA splicing (particularly in C9orf72 and GRN) and extracellular matrix (particularly in MAPT) modules, as well as decreased abundance of synaptic/neuronal and autophagy modules. The generalizability of genetic FTLD proteomic signatures was tested and confirmed in independent cohorts of 1) sporadic progressive supranuclear palsy-Richardson syndrome and 2) frontotemporal dementia spectrum syndromes. Network-based proteomics hold promise for identifying replicable molecular pathways in adults living with FTLD. 'Hub' proteins driving co-expression of affected modules warrant further attention as candidate biomarkers and therapeutic targets.

Exercise-Dependent Modulation of Immunological Response Pathways in Endurance Athletes With and Without Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a common arrhythmia characterized by uncoordinated atrial electrical activity. Lone AF occurs in the absence of traditional risk factors and is frequently observed in male endurance athletes, who face a 2- to 5-fold higher risk of AF compared with healthy, moderately active males. Our understanding of how endurance exercise contributes to the pathophysiology of lone AF remains limited. This study aimed to characterize the circulating protein fluctuations during high-intensity exercise as well as explore potential biomarkers of exercise-associated AF.

METHODS AND RESULTS

A prospective cohort of 12 male endurance cyclists between the ages of 40 and 65 years, 6 of whom had a history of exercise-associated AF, were recruited to participate using a convenience sampling method. The circulating proteome was subsequently analyzed using multiplex immunoassays and aptamer-based proteomics before, during, and after an acute high-intensity endurance exercise bout to assess temporality and identify potential markers of AF. The endurance exercise bout resulted in significant alterations to proteins involved in immune modulation (eg, growth/differentiation factor 15), skeletal muscle metabolism (eg, α-actinin-2), cell death (eg, histones), and inflammation (eg, interleukin-6). Subjects with AF differed from those without, displaying modulation of proteins previously known to have associations with incident AF (eg, C-reactive protein, insulin-like growth factor-1, and angiopoietin-2), and also with proteins having no previous association (eg, tapasin-related protein and α2-Heremans-Schmid glycoprotein).

CONCLUSIONS

These findings provide insights into the proteomic response to acute intense exercise, provide mechanistic insights into the pathophysiology behind AF in athletes, and identify targets for future study and validation.

Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Lipodystrophy in methylmalonic acidemia associated with elevated FGF21 and abnormal methylmalonylation

A distinct adipose tissue distribution pattern was observed in patients with methylmalonyl-CoA mutase deficiency, an inborn error of branched-chain amino acid (BCAA) metabolism, characterized by centripetal obesity with proximal upper and lower extremity fat deposition and paucity of visceral fat, that resembles familial multiple lipomatosis syndrome. To explore brown and white fat physiology in methylmalonic acidemia (MMA), body composition, adipokines, and inflammatory markers were assessed in 46 patients with MMA and 99 matched controls. Fibroblast growth factor 21 levels were associated with acyl-CoA accretion, aberrant methylmalonylation in adipose tissue, and an attenuated inflammatory cytokine profile. In parallel, brown and white fat were examined in a liver-specific transgenic MMA mouse model (Mmut-/- TgINS-Alb-Mmut). The MMA mice exhibited abnormal nonshivering thermogenesis with whitened brown fat and had an ineffective transcriptional response to cold stress. Treatment of the MMA mice with bezafibrates led to clinical improvement with beiging of subcutaneous fat depots, which resembled the distribution seen in the patients. These studies defined what we believe to be a novel lipodystrophy phenotype in patients with defects in the terminal steps of BCAA oxidation and demonstrated that beiging of subcutaneous adipose tissue in MMA could readily be induced with small molecules.

Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome

Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management

Despite advances in care, cardiovascular diseases remain the leading cause of death worldwide. As a result, identifying suitable biomarkers for early diagnosis and improving therapeutic and diagnostic strategies is crucial. Because of their significant advantages over other therapeutic approaches, nucleic-based therapies, particularly aptamers, are gaining increased attention. Aptamers are innovative synthetic polymers or oligomers of single-stranded DNA (ssDNA) or RNA molecules that can form 3-dimensional structures and thus interact with their targets with high specificity and affinity. Furthermore, they outperform classical protein-based antibodies in terms of in vitro selection, production, ease of modification and conjugation, high stability, low immunogenicity, and suitability for nanoparticle functionalization for targeted drug delivery. This work aims to review the advances made in the aptamers' field in biomarker detection, diagnosis, imaging, and targeted therapy, which highlight their huge potential in the management of cardiovascular diseases.

Plasma KL-6 as a Potential Biomarker for Bronchopulmonary Dysplasia in Preterm Infants

Background

KL-6 is a biomarker of interstitial lung injury and increases during repair.

Aim

Our aim was to determine the predictive value of plasma KL-6 for the development of bronchopulmonary dysplasia (BPD) in preterm infants.

Methods

Ninety-five extremely preterm infants (EPIs), born at <28 gestational age (GA), were divided into two main BPD groups as follows: the moderate/severe and the no/mild group. KL-6 was analyzed on days 7 and 14. Binary logistic regression analyses and ROC curve analyses were performed.

Results

Infants <26 + 0 weeks' GA have higher mean KL-6 than infants >25 + 6 weeks' GA on 7 and 14 days (335 vs. 286 U/ml and 378 vs. 260 U/ml; p = 0.005 and 0.018, respectively). In the binary regression model at KL-6 day 7, three of the prognostic factors remained significant-mechanical ventilation OR: 10.38 (95% CI: 3.57-30.14), PDA OR: 6.39 (95% CI: 0.87-46.74), and KL-6 OR: 4.98 (95% CI: 1.54-16.08). The AUC was 0.86 with a sensitivity and specificity of 79% at a cutoff value ≥0.34. In the binary regression model at KL-6 day 14, six of the prognostic factors were significant-PDA OR: 23.34 (95% CI: 2.14-254.24), KL-6 OR: 13.59 (95% CI: 3.19-57.96), GA OR: 4.58 (95% CI: 1.16-18.06), mechanical ventilation OR: 4.45 (95% CI: 1.23-16.16), antenatal steroids OR: 0.19 (95% CI: 0.04-0.95), and gender (female OR: 0.30 (95% CI 0.08-1.12)). The AUC was 0.91, and the sensitivity and accuracy for a cutoff ≥0.37 were 89% and 85%, respectively.

Conclusion

KL-6 could be a useful screening biomarker for early detection of infants at increased risk for developing BPD.

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization. Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets. Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses. Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.

An Aptamer-Based Proteomic Analysis of Plasma from Cats (Felis catus) with Clinical Feline Infectious Peritonitis

Feline infectious peritonitis (FIP) is a systemic disease manifestation of feline coronavirus (FCoV) and is the most important cause of infectious disease-related deaths in domestic cats. FIP has a variable clinical manifestation but is most often characterized by widespread vasculitis with visceral involvement and/or neurological disease that is typically fatal in the absence of antiviral therapy. Using an aptamer-based proteomics assay, we analyzed the plasma protein profiles of cats who were naturally infected with FIP (n = 19) in comparison to the plasma protein profiles of cats who were clinically healthy and negative for FCoV (n = 17) and cats who were positive for the enteric form of FCoV (n = 9). We identified 442 proteins that were significantly differentiable; in total, 219 increased and 223 decreased in FIP plasma versus clinically healthy cat plasma. Pathway enrichment and associated analyses showed that differentiable proteins were related to immune system processes, including the innate immune response, cytokine signaling, and antigen presentation, as well as apoptosis and vascular integrity. The relevance of these findings is discussed in the context of previous studies. While these results have the potential to inform diagnostic, therapeutic, and preventative investigations, they represent only a first step, and will require further validation.

N-terminal titin fragment: a non-invasive, pharmacodynamic biomarker for microdystrophin efficacy

BACKGROUND

Multiple clinical trials to assess the efficacy of AAV-directed gene transfer in participants with Duchenne muscular dystrophy (DMD) are ongoing. The success of these trials currently relies on standard functional outcome measures that may exhibit variability within and between participants, rendering their use as sole measures of drug efficacy challenging. Given this, supportive objective biomarkers may be useful in enhancing observed clinical results. Creatine kinase (CK) is traditionally used as a diagnostic biomarker of DMD, but its potential as a robust pharmacodynamic (PD) biomarker is difficult due to the wide variability seen within the same participant over time. Thus, there is a need for the discovery and validation of novel PD biomarkers to further support and bolster traditional outcome measures of efficacy in DMD.

METHOD

Potential PD biomarkers in DMD participant urine were examined using a proteomic approach on the Somalogic platform. Findings were confirmed in both mdx mice and Golden Retriever muscular dystrophy (GRMD) dog plasma samples.

RESULTS

Changes in the N-terminal fragment of titin, a well-known, previously characterized biomarker of DMD, were correlated with the expression of microdystrophin protein in mice, dogs, and humans. Further, titin levels were sensitive to lower levels of expressed microdystrophin when compared to CK.

CONCLUSION

The measurement of objective PD biomarkers such as titin may provide additional confidence in the assessment of the mechanism of action and efficacy in gene therapy clinical trials of DMD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03368742.

Molecular modeling and simulation studies of SELEX-derived high-affinity DNA aptamers to the Ebola virus nucleoprotein

Ebola viral disease (EVD) is a highly infectious and potentially fatal illness with a case fatality rate ranging from 25% to 90%. To effectively control its spread, there is a need for rapid, reliable and lowcost point-of-care (P OC) diagnostic tests. While various EVD diagnostic tests exist, few are P OC tests, and many are not cost-effective. The use of antibodies in these tests has limitations, prompting the exploration of aptamers as potential alternatives. Various proteins from the Ebola virus (EBOV) proteome, including EBOV nucleoprotein (NP), are considered viable targets for diagnostic assays. A previous study identified three aptamers (Apt1. Apt2 and Apt3) with high affinity for EBOV NP using systemic evolution of ligands by exponential enrichment (SELEX). This study aimed to employ in silico methods, such as Phyre2, RNAfold, RNAComposer, HADDOCK and GROMACS, to model the structures of EBOV NP and the aptamers, and to investigate their binding. The in silico analysis revealed successful binding of all the three aptamers to EBOV NP, with a suggested ranking of Apt1 > Apt2 > Apt3 based on binding affinity. Microscale thermophoresis (MST) analysis confirmed the binding, providing dissociation constants of 25 ± 2.84, 56 ± 2.76 and 140 ±3.69 nM for Apt1, Apt2 and Apt3, respectively. The study shows that the findings of the in silico analysis was in agreement with the MST analysis. Inclusion of these in silico approaches in diagnostic assay development can expedite the selection of candidate aptamers, potentially overcoming challenges associated with aptamer application in diagnostics.Communicated by Ramaswamy H. Sarma.

Increasing the functional density of threose nucleic acid

Chemical strategies that augment genetic polymers with amino acid residues that are overrepresented on the paratope surface of an antibody offer a promising route for enhancing the binding properties of nucleic acid aptamers. Here, we describe the chemical synthesis of α-l-threofuranosyl cytidine nucleoside triphosphate (tCTP) carrying either a benzyl or phenylpropyl side chain at the pyrimidine C-5 position. Polymerase recognition studies indicate that both substrates are readily incorporated into a full-length α-l-threofuranosyl nucleic acid (TNA) product by extension of a DNA primer-template duplex with an engineered TNA polymerase. Similar primer extension reactions performed using nucleoside triphosphate mixtures containing both C-5 modified tCTP and C-5 modified tUTP substrates enable the production of doubly modified TNA strands for a panel of 20 chemotype combinations. Kinetic measurements reveal faster on-rates (kon) and tighter binding affinity constants (Kd) for engineered versions of TNA aptamers carrying chemotypes at both pyrimidine positions as compared to their singly modified counterparts. These findings expand the chemical space of evolvable non-natural genetic polymers by offering a path for improving the quality of biologically stable TNA aptamers for future clinical applications.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Evaluation of a large-scale aptamer proteomics platform among patients with kidney failure on dialysis

BACKGROUND

Patients with kidney failure suffer high mortality, and we currently lack markers for risk stratification for these patients. We carried out a quality control study of a modified aptamer assay (SomaScan v.4.0) that measures ~ 5000 proteins, in preparation for a larger study using this platform in cohorts with kidney failure.

METHODS

Forty participants from the Cardiac, Endothelial Function and Arterial Stiffness in End-Stage Renal Disease (CERES study) were selected to analyze technical and short-term biological variability, orthogonal correlations and differential protein expression in plasma from patients who died during 2.5 year follow-up. Long-term (one year) variability was studied in 421 participants in the Chronic Renal Insufficiency Cohort. We evaluated 4849 aptamers (4607 unique proteins) using data formats including raw data and data formatted using Adaptive Normalization by Maximum Likelihood (ANML), an algorithm developed for SomaScan data in individuals with normal kidney function.

RESULTS

In ANML format, median[IQR] intra-assay coefficient of variation (CV) was 2.38%[1.76, 3.40] and inter-assay CV was 7.38%[4.61, 13.12]. Short-term within-subject CV was 5.76% [3.35, 9.72]; long-term CV was 8.71%[5.91, 13.37]. Spearman correlations between aptamer and traditional assays for PTH, NT-proBNP, FGF-23 and CRP were all > 0.7. Fold-change (FC) in protein levels among non-survivors, significant after Bonferroni correction, included SVEP1 (FC[95% CI] 2.14 [1.62, 2.82]), keratocan (1.74 [1.40, 2.15]) and LanC-like protein 1 (0.56 [0.45, 0.70]). Compared to raw aptamer data, technical and short-term biological variability in paired samples was lower in ANML-formatted data. ANML formatting had minimal impact on orthogonal correlations with traditional assays or the associations of proteins with the phenotype of mortality.

CONCLUSIONS

SomaScan had excellent technical variability and low within-subject short-term variability. ANML formatting could facilitate comparison of biomarker results with other studies that utilize this format. We expect SomaScan to provide novel and reproducible information in patients with kidney failure on dialysis.

Granulocyte Colony-Stimulating Factor is a Determinant of Severe Bronchopulmonary Dysplasia and Coincident Retinopathy

Bronchopulmonary dysplasia (BPD), also called chronic lung disease of immaturity, afflicts approximately one third of all extremely premature infants, causing lifelong lung damage. There is no effective treatment other than supportive care. Retinopathy of prematurity (ROP), which impairs vision irreversibly, is common in BPD, suggesting a related pathogenesis. However, specific mechanisms of BPD and ROP are not known. Herein, a neonatal mouse hyperoxic model of coincident BPD and retinopathy was used to screen for candidate mediators, which revealed that granulocyte colony-stimulating factor (G-CSF), also known as colony-stimulating factor 3, was up-regulated significantly in mouse lung lavage fluid and plasma at postnatal day 14 in response to hyperoxia. Preterm infants with more severe BPD had increased plasma G-CSF. G-CSF-deficient neonatal pups showed significantly reduced alveolar simplification, normalized alveolar and airway resistance, and normalized weight gain compared with wild-type pups after hyperoxic lung injury. This was associated with a marked reduction in the intensity, and activation state, of neutrophilic and monocytic inflammation and its attendant oxidative stress response, and protection of lung endothelial cells. G-CSF deficiency also provided partial protection against ROP. The findings in this study implicate G-CSF as a pathogenic mediator of BPD and ROP, and suggest the therapeutic utility of targeting G-CSF biology to treat these conditions.

Evaluation of a New Aptamer-Based Array for Soluble Suppressor of Tumorgenicity (ST2) and N-terminal Pro-B-Type Natriuretic Peptide (NTproBNP) in Heart Failure Patients

BACKGROUND

Recent advances in multi-marker platforms offer faster data generation, but the fidelity of these methods compared to the ELISA is not established. We tested the correlation and predictive performance of SOMAscan vs. ELISA methods for NTproBNP and ST2.

METHODS

Patients ≥ 18 years with heart failure and ejection fraction < 50% were enrolled. We tested the correlation between SOMA and ELISA for each biomarker and their association with outcomes.

RESULTS

There was good correlation of SOMA vs. ELISA for ST2 (ρ = 0.71) and excellent correlation for NTproBNP (ρ = 0.94). The two versions of both markers were not significantly different regarding survival association. The two ST2 assays and NTproBNP assays were similarly associated with all-cause mortality and cardiovascular mortality. These associations remained statistically significant when adjusted for MAGGIC risk score (all p < 0.05).

CONCLUSION

SOMAscan quantifications of ST2 and NTproBNP correlate to ELISA versions and carry similar prognosis.

Nanopore sequencing of DNA-barcoded probes for highly multiplexed detection of microRNA, proteins and small biomarkers

There is an unmet need to develop low-cost, rapid and highly multiplexed diagnostic technology platforms for quantitatively detecting blood biomarkers to advance clinical diagnostics beyond the single biomarker model. Here we perform nanopore sequencing of DNA-barcoded molecular probes engineered to recognize a panel of analytes. This allows for highly multiplexed and simultaneous quantitative detection of at least 40 targets, such as microRNAs, proteins and neurotransmitters, on the basis of the translocation dynamics of each probe as it passes through a nanopore. Our workflow is built around a commercially available MinION sequencing device, offering a one-hour turnaround time from sample preparation to results. We also demonstrate that the strategy can directly detect cardiovascular disease-associated microRNA from human serum without extraction or amplification. Due to the modularity of barcoded probes, the number and type of targets detected can be significantly expanded.

Single-molecule analysis of DNA base-stacking energetics using patterned DNA nanostructures

The DNA double helix structure is stabilized by base-pairing and base-stacking interactions. However, a comprehensive understanding of dinucleotide base-stacking energetics is lacking. Here we combined multiplexed DNA-based point accumulation in nanoscale topography (DNA-PAINT) imaging with designer DNA nanostructures and measured the free energy of dinucleotide base stacking at the single-molecule level. Multiplexed imaging enabled us to extract the binding kinetics of an imager strand with and without additional dinucleotide stacking interactions. The DNA-PAINT data showed that a single additional dinucleotide base stacking results in up to 250-fold stabilization for the DNA duplex nanostructure. We found that the dinucleotide base-stacking energies vary from -0.95 ± 0.12 kcal mol-1 to -3.22 ± 0.04 kcal mol-1 for C|T and A|C base-stackings, respectively. We demonstrate the application of base-stacking energetics in designing DNA-PAINT probes for multiplexed super-resolution imaging, and efficient assembly of higher-order DNA nanostructures. Our results will aid in designing functional DNA nanostructures, and DNA and RNA aptamers, and facilitate better predictions of the local DNA structure.

Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome

Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.

Functionally Enhanced XNA Aptamers Discovered by Parallelized Library Screening

In vitro evolution strategies have been used for >30 years to generate nucleic acid aptamers against therapeutic targets of interest, including disease-associated proteins. However, this process requires many iterative cycles of selection and amplification, which severely restricts the number of target and library design combinations that can be explored in parallel. Here, we describe a single-round screening approach to aptamer discovery that relies on function-enhancing chemotypes to increase the distribution of high-affinity sequences in a random-sequence library. We demonstrate the success of de novo discovery by affinity selection of threomers against the receptor binding domain of the S1 protein from SARS-CoV-2. Detailed biochemical characterization of the enriched population identified threomers with binding affinity values that are comparable to aptamers produced by conventional SELEX. This work establishes a highly parallelizable path for querying diverse chemical repertoires and may offer a viable route for accelerating the discovery of therapeutic aptamers.

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.

Plasma proteomics show altered inflammatory and mitochondrial proteins in patients with neurologic symptoms of post-acute sequelae of SARS-CoV-2 infection

Persistent symptoms of COVID-19 survivors constitute long COVID syndrome, also called post-acute sequelae of SARS-CoV-2 infection (PASC). Neurologic manifestations of PASC (Neuro-PASC) are particularly debilitating, long lasting, and poorly understood. To gain insight into the pathogenesis of PASC, we leveraged a well-characterized group of Neuro-PASC (NP) patients seen at our Neuro-COVID-19 clinic who had mild acute COVID-19 and never required hospitalization to investigate their plasma proteome. Using the SomaLogic platform, SomaScan, the plasma concentration of >7000 proteins was measured from 92 unvaccinated individuals, including 48 NP patients, 20 COVID-19 convalescents (CC) without lingering symptoms, and 24 unexposed healthy controls (HC) to interrogate underlying pathobiology and potential biomarkers of PASC. We analyzed the plasma proteome based on post-COVID-19 status, neurologic and non-neurologic symptoms, as well as subjective and objective standardized tests for changes in quality-of-life (QoL) and cognition associated with Neuro-PASC. The plasma proteome of NP patients differed from CC and HC subjects more substantially than post-COVID-19 groups (NP and CC combined) differed from HC. Proteomic differences in NP patients 3-9 months following acute COVID-19 showed alterations in inflammatory proteins and pathways relative to CC and HC subjects. Proteomic associations with Neuro-PASC symptoms of brain fog and fatigue included changes in markers of DNA repair, oxidative stress, and neutrophil degranulation. Furthermore, we discovered a correlation between NP patients lower subjective impression of recovery to pre-COVID-19 baseline with an increase in the concentration of the oxidative phosphorylation protein COX7A1, which was also associated with neurologic symptoms and fatigue, as well as impairment in QoL and cognitive dysfunction. Finally, we identified other oxidative phosphorylation-associated proteins correlating with central nervous system symptoms. Our results suggest ongoing inflammatory changes and mitochondrial involvement in Neuro-PASC and pave the way for biomarker validation for use in monitoring and development of therapeutic intervention for this debilitating condition.

Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission

OBJECTIVE

To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials.

RESEARCH DESIGN AND METHODS

We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants.

RESULTS

Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months.

CONCLUSIONS

Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.

Arylethynyl- or Alkynyl-Linked Pyrimidine and 7-Deazapurine 2'-Deoxyribonucleoside 3'-Phosphoramidites for Chemical Synthesis of Hypermodified Hydrophobic Oligonucleotides

We designed and synthesized a set of 2'-deoxyribonucleoside 3'-phosphoramidites derived from 5-phenylethynyluracil, 5-(pentyn-1-yl)cytosine, 7-(indol-3-yl)ethynyl-7-deazaadenine, and 7-isopropylethynyl-7-deazaguanine. These nucleoside phosphoramidites were successfully used for automated solid-phase synthesis of oligonucleotides containing one or several modifications, including fully modified sequences where every nucleobase was displaying a modification, and their hybridization was studied. The phosphoramidite building blocks have potential for synthesis of hypermodified aptamers and other functional nucleic acid-based polymers, which sequence-specifically display amino acid-like hydrophobic substituents.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

Proteomics of CKD progression in the chronic renal insufficiency cohort

Progression of chronic kidney disease (CKD) portends myriad complications, including kidney failure. In this study, we analyze associations of 4638 plasma proteins among 3235 participants of the Chronic Renal Insufficiency Cohort Study with the primary outcome of 50% decline in estimated glomerular filtration rate or kidney failure over 10 years. We validate key findings in the Atherosclerosis Risk in the Communities study. We identify 100 circulating proteins that are associated with the primary outcome after multivariable adjustment, using a Bonferroni statistical threshold of significance. Individual protein associations and biological pathway analyses highlight the roles of bone morphogenetic proteins, ephrin signaling, and prothrombin activation. A 65-protein risk model for the primary outcome has excellent discrimination (C-statistic[95%CI] 0.862 [0.835, 0.889]), and 14/65 proteins are druggable targets. Potentially causal associations for five proteins, to our knowledge not previously reported, are supported by Mendelian randomization: EGFL9, LRP-11, MXRA7, IL-1 sRII and ILT-2. Modifiable protein risk markers can guide therapeutic drug development aimed at slowing CKD progression.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Large-scale plasma proteomics comparisons through genetics and disease associations

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

Strict Interactions of Fifth Letters, Hydrophobic Unnatural Bases, in XenoAptamers with Target Proteins

XenoAptamers are DNA fragments containing additional letters (unnatural bases, UBs) that bind specifically to their target proteins with high affinities (sub-nanomolar KD values). One of the UBs is the highly hydrophobic 7-(2-thienyl)imidazo[4,5-b]pyridine (Ds), which significantly increases XenoAptamers' affinities to targets. Originally, Ds was developed as a third base pair with a complementary UB, 2-nitro-4-propynylpyrrole (Px), for replication, and thus it can be used for aptamer generation by an evolutional engineering method involving PCR amplification. However, it is unclear whether the Ds base is the best component as the hydrophobic fifth-letter ligand for interactions with target proteins. To optimize the ligand structure of the fifth letter, we prepared 13 Ds variants and examined the affinities of XenoAptamers containing these variants to target proteins. The results obtained using four XenoAptamers prepared by the replacement of Ds bases with variants indicated that subtle changes in the chemical structure of Ds significantly affect the XenoAptamer affinities. Among the variants, placing either 4-(2-thienyl)pyrrolo[2,3-b]pyridine (Ys) or 4-(2-thienyl)benzimidazole (Bs) at specific Ds positions in each original XenoAptamer greatly improved their affinities to targets. The Ys and Bs bases are variants derived by replacing only one nitrogen with a carbon in the Ds base. These results demonstrate the strict intramolecular interactions, which are not simple hydrophobic contacts between UBs and targets, thus providing a method to mature XenoAptamers' affinities to targets.

Terminal Alkyne-Modified DNA Aptamers with Enhanced Protein Binding Affinities

Nucleic acid-based receptors, known as aptamers, are relatively fast to discover and manufacture but lack the diverse functional groups of protein receptors (e.g., antibodies). The binding properties of DNA aptamers can be enhanced by attaching abiotic functional groups; for example, aromatic groups such as naphthalene slow dissociation from proteins. Although the terminal alkyne is a π-electron-rich functional group that has been used in small molecule drugs to enhance binding to proteins through noncovalent interactions, it remains unexplored for enhancing DNA aptamer binding affinity. Here, we demonstrate the utility of the terminal alkyne for improving the binding of DNA to proteins. We prepared a library of 256 terminal-alkyne-bearing variants of HD22, a DNA aptamer that binds the protein thrombin with nanomolar affinity. After a one-step thrombin-binding selection, a high-affinity aptamer containing two alkynes was discovered, exhibiting 3.2-fold tighter thrombin binding than the corresponding unmodified sequence. The tighter binding was attributable to a slower rate of dissociation from thrombin (5.2-fold slower than HD22). Molecular dynamics simulations with enhanced sampling by Replica Exchange with Solute Tempering (REST2) suggest that the π-electron-rich alkyne interacts with an asparagine side chain N-H group on thrombin, forming a noncovalent interaction that stabilizes the aptamer-protein interface. Overall, this work represents the first case of terminal alkynes enhancing the binding properties of an aptamer and underscores the utility of the terminal alkyne as an atom economical π-electron-rich functional group to enhance binding affinity with minimal steric perturbation.

Selection of optimised ligands by fluorescence-activated bead sorting

The chemistry of aptamers is largely limited to natural nucleotides, and although modifications of nucleic acids can enhance target aptamer affinity, there has not yet been a technology for selecting the right modifications in the right locations out of the vast number of possibilities, because enzymatic amplification does not transmit sequence-specific modification information. Here we show the first method for the selection of specific nucleoside modifications that increase aptamer binding efficacy, using the oncoprotein EGFR as a model target. Using fluorescence-activated bead sorting (FABS), we have successfully selected optimized aptamers from a library of >65 000 variations. Hits were identified by tandem mass spectrometry and validated by using an EGFR binding assay and computational docking studies. Our results provide proof of concept for this novel strategy for the selection of chemically optimised aptamers and offer a new method for rapidly synthesising and screening large aptamer libraries to accelerate diagnostic and drug discovery.

Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events

Importance

Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain.

Objective

To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations.

Design, Setting, and Participants

The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals.

Exposures

Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling.

Main Outcomes and Measures

Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death.

Results

In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population.

Conclusions and Relevance

A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.