Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Codetection of Proteins and RNAs on Extracellular Vesicles for Pancreatic Cancer Early Diagnosis.

Tumor-derived extracellular vesicles (EVs) carry tumor-specific proteins and RNAs, thus becoming prevalent targets for early cancer diagnosis. However, low expression of EV cargos and insufficient diagnostic power of individual biomarkers hindered EVs application in clinical practice. Herein, we propose a multiplex Codetection platform of proteins and RNAs (Co-PAR) for EVs. Co-PAR adopted a pair of antibody-DNA probes to recognize the same target protein, which in turn formed a double-stranded DNA. Thus, the target protein could be quantified by detecting the double-stranded DNA via qPCR. Meanwhile, qRT-PCR simultaneously quantified the target RNAs. Thus, with a regular qPCR instrument, Co-PAR enabled the codetection of multiplex proteins and RNAs, with the sensitivity of 102 EVs/µL (targeting CD63) and 1 EV/µL (targeting snRNA U6). We analyzed the coexpressions of three protein markers (CD63, GPC-1, HER2) and three RNA markers (snRNA U6, GPC-1 mRNA, miR-10b) on EVs from three pancreatic cell lines and 30 human plasma samples using Co-PAR. The diagnostic accuracy of the 6-biomarker combination reached 92.9%, which was at least 6.2% higher than that of 3-biomarker combinations and at least 13.5% higher than that of 6 single biomarkers. Co-PAR, as a multiparameter detection platform for EVs, has great potential in early disease diagnosis.

Single-Cell Study Unveils Lead Lifespan in Blood Cell Populations Follows a Universal Lognormal Distribution with Individual Skewness.

Lead is a widespread environmental hazard that can adversely affect multiple biological functions. Blood cells are the initial targets that face lead exposure. However, a systematic assessment of lead dynamics in blood cells at single-cell resolution is still absent. Herein, C57BL/6 mice were fed with lead-contaminated food. Peripheral blood was harvested at different days. Extracted red blood cells and leukocytes were stained with 19 metal-conjugated antibodies and analyzed by mass cytometry. We quantified the time-lapse lead levels in 12 major blood cell subpopulations and established the distribution of lead heterogeneity. Our results show that the lead levels in all major blood cell subtypes follow lognormal distributions but with distinctively individual skewness. The lognormal distribution suggests a multiplicative accumulation of lead with stochastic turnover of cells, which allows us to estimate the lead lifespan of different blood cell populations by calculating the distribution skewness. These findings suggest that lead accumulation by single blood cells follows a stochastic multiplicative process.

Quantitative Proteomics and Metabolomics of Culture Medium from Single Human Embryo Reveal Embryo Quality-Related Multiomics Biomarkers.

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

Recent Advances of Engineered Cell Membrane-Based Nanotherapeutics to Combat Inflammatory Diseases.

An immune reaction known as inflammation serves as a shield from external danger signals, but an overactive immune system may additionally lead to tissue damage and even a variety of inflammatory disorders. By inheriting biological functionalities and serving as both a therapeutic medication and a drug carrier, cell membrane-based nanotherapeutics offer the potential to treat inflammatory disorders. To further strengthen the anti-inflammatory benefits of natural cell membranes, researchers alter and optimize the membranes using engineering methods. This review focuses on engineered cell membrane-based nanotherapeutics (ECMNs) and their application in treating inflammation-related diseases. Specifically, this article discusses the methods of engineering cell membranes for inflammatory diseases and examines the progress of ECMNs in inflammation-targeted therapy, inflammation-neutralizing therapy, and inflammation-immunomodulatory therapy. Additionally, the article looks into the perspectives and challenges of ECMNs in inflammatory treatment and offers suggestions as well as guidance to encourage further investigations and implementations in this area.

Association of maternal SARS-CoV-2 infection at the time of admission for delivery with labor process and outcomes of vaginal birth: A cohort study.

INTRODUCTION: This study aimed to investigate the impact of maternal SARS-CoV-2 infection at the time of admission for delivery on labor process and outcomes of vaginal birth. MATERIAL AND METHODS: A cohort study was carried out at the Obstetrics Department of Anhui Provincial Hospital, China, where universal reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 infection was introduced for all women admitted for labor and delivery from December 1-31, 2022. Women were divided into positive and negative groups based on the test result. All women having a singleton vaginal birth were included in final analysis. The effect of SARS-CoV-2 positivity on labor process and outcomes of vaginal birth was estimated by regression analyses. RESULTS: Among a total of 360 women included, 87 had a positive SARS-CoV-2 test and 273 a negative test. Women in the positive group had an increased likelihood of having longer labor (median 9.3 vs 8.3 hours; sB [log-transformed] 0.19; 95% confidence interval [CI] 0.09-0.28), episiotomy (39.1% vs 23.8%; adjusted odds ratio [aOR] 2.31; 95% CI 1.27-4.21), grade III meconium-stained amniotic fluid (19.5% vs 7.0%; aOR 2.52; 95% CI 1.15-5.54) and postpartum hospital stay exceeding 37 hours (58.6% vs 46.5%; aOR 1.71; 95% CI 1.00-2.91). They had reduced rates exclusive breastfeeding (26.7% vs 39%; aOR 0.21; 95% CI 0.09-0.46) as well as mixed feeding (46.5% vs 52.2%; aOR 0.28; 95% CI 0.13-0.60) at 1 week postpartum. No significant differences were observed in other aspects of labor process and birth outcomes, including the uptake of labor analgesia, postpartum hemorrhage (>500 mL) or neonatal outcomes. CONCLUSIONS: A positive maternal SARS-CoV-2 test in labor among women having vaginal birth was associated with a slightly longer duration of labor, increased likelihood of episiotomy, increased incidence of grade III meconium-stained amniotic fluid, a longer postpartum hospital stay and a lower rate of breastfeeding 1 week postpartum. However, it did not have an adverse impact on other birth outcomes.

Neuroinflammation in Alzheimer's disease: insights from peripheral immune cells.

Alzheimer's disease (AD) is a serious brain disorder characterized by the presence of beta-amyloid plaques, tau pathology, inflammation, neurodegeneration, and cerebrovascular dysfunction. The presence of chronic neuroinflammation, breaches in the blood-brain barrier (BBB), and increased levels of inflammatory mediators are central to the pathogenesis of AD. These factors promote the penetration of immune cells into the brain, potentially exacerbating clinical symptoms and neuronal death in AD patients. While microglia, the resident immune cells of the central nervous system (CNS), play a crucial role in AD, recent evidence suggests the infiltration of cerebral vessels and parenchyma by peripheral immune cells, including neutrophils, T lymphocytes, B lymphocytes, NK cells, and monocytes in AD. These cells participate in the regulation of immunity and inflammation, which is expected to play a huge role in future immunotherapy. Given the crucial role of peripheral immune cells in AD, this article seeks to offer a comprehensive overview of their contributions to neuroinflammation in the disease. Understanding the role of these cells in the neuroinflammatory response is vital for developing new diagnostic markers and therapeutic targets to enhance the diagnosis and treatment of AD patients.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

BACKGROUND: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. METHODS: Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. RESULTS: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. CONCLUSION: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data.

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

Enzymes in Synergy: Bacteria Specific Molecular Probe for Locoregional Imaging of Urinary Tract Infection in vivo.

Uropathogenic Escherichia coli (UPECs) is a leading cause for urinary tract infections (UTI), accounting for 70-90% of community or hospital-acquired bacterial infections owing to high recurrence, imprecision in diagnosis and management, and increasing prevalence of antibiotic resistance. Current methods for clinical UPECs detection still rely on labor-intensive urine cultures that impede rapid and accurate diagnosis for timely UTI therapeutic management. Herein, we developed a first-in-class near-infrared (NIR) UPECs fluorescent probe (NO-AH) capable of specifically targeting UPECs through its collaborative response to bacterial enzymes, enabling locoregional imaging of UTIs both in vitro and in vivo. Our NO-AH probe incorporates a dual protease activatable moiety, which first reacts with OmpT, an endopeptidase abundantly present on outer membrane of UPECs, releasing an intermediate amino acid residue conjugated with a NIR hemicyanine fluorophore. Such liberated fragment would be subsequently recognized by aminopeptidase (APN) within periplasm of UPECs, activating localized fluorescence for precise imaging of UTIs in complex living environments. The peculiar specificity and selectivity of NO-AH, facilitated by the collaborative action of bacterial enzymes, features a timely and accurate identification of UPECs-infected UTIs, which could overcome misdiagnosis in conventional urine tests, thus opening new avenues towards reliable UTI diagnosis and personalized antimicrobial therapy management.

A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states.

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

Multiplex Protein Profiling by Low-Signal-Loss Single-Cell Western Blotting with Fluorescent-Quenching Aptamers.

Single-cell western blotting (scWB) is a prevalent technique for high-resolution protein analysis on low-abundance cell samples. However, the extensive signal loss during repeated antibody stripping precludes multiplex protein detection. Herein, we introduce Fluorescent-quenching Aptamer-based Single-cell Western Blotting (FAS-WB) for multiplex protein detection at single-cell resolution. The minimal size of aptamer probes allows rapid in-gel penetration, diffusion, and elution. Meanwhile, the fluorophore-tagged aptamers, coordinated with complementary quenching strands, avoid the massive signal loss conventionally caused by antibody stripping during repeated staining. Such a strategy also facilitates multiplex protein analysis with a limited number of fluorescent tags. We demonstrated FAS-WB for co-imaging four biomarker proteins (EpCAM, PTK7, HER2, CA125) at single-cell resolution with lower signal loss and enhanced signal-to-noise ratio compared to conventional antibody-based scWB. Being more time-saving (less than 25 min per cycle) and economical (1/1000 cost of conventional antibody probes), FAS-WB offers a highly efficient platform for profiling multiplex proteins at single-cell resolution.

PepDRED: De Novo Peptide Design with Strong Binding Affinity for Target Protein.

De novo design of peptides that bind specifically to functional proteins is beneficial for diagnostics and therapeutics. However, complex permutations and combinations of amino acids pose significant challenges to the rational design of peptides with desirable stability and affinity. Herein, we develop a computational-based evolution method, namely, peptidomimetics-driven recognition elements design (PepDRED), to derive hemoglobin-inspired peptidomimetics. PepDRED mimics the natural evolutionism pipeline to generate stable apovariant (AVs) structures for wild-type counterparts via automated point mutations and validates their efficiency through free binding energy analysis and per residue energy decomposition analysis. For application demonstration, we applied PepDRED to design de novo peptides to bind FhuA, a typical TonB-dependent transporter (TBDT). TBDTs are Gram-negative bacterial outer membrane proteins responsible for iron transport and vital for bacterial resistance. PepDRED generated a pool of AVs and proceeded to reach an optimized peptide, AV440, with a remarkable binding affinity of -21 kcal/mol. AV440 is ∼2.5-fold stronger than the existing FhuA inhibitor Microcin J25. Network energy analysis further unveils that incorporating methionine (M42) in the N-terminal region significantly enhances inter-residue contacts and binding affinity. PepDRED offers a prompt and efficient in silico approach to develop potent peptide candidates for target proteins.

Platinum-Chimeric Carrier Cells for Ultratrace Cell Analysis in Mass Cytometry.

Mass cytometry by time-of-flight (CyTOF), a high-dimensional single-cell analysis platform, detects up to 50 biomarkers at single-cell resolution. However, CyTOF analysis of biological samples with a minimal number of available cells or rare cell subsets remains a major technical challenge due to the extensive loss of cells during cell recovery, staining, and acquisition. Here, we introduce a platinum-chimeric carrier cell strategy for mass cytometry profiling of ultratrace cell samples. Cisplatin can rapidly enter broken plasma membranes of dead cells and form a chimeric interaction with cellular proteins, peptides, and amino acids. Thus, 198Pt-cisplatin is adopted to tag carrier cells in the pretreatment stage. We investigated 8 cell lines that are commonly accessible in laboratories for their potential as carrier cells to preserve rare target cells for CyTOF analysis. We designed a panel of 35 protein biomarkers to evaluate the comprehensive single-cell subtype classification capability with or without the carrier cell strategy. We further demonstrated the detection and analysis of as few as 1 × 104 immune cells using our method. The proposed method thus allows CyTOF analysis on precious clinical samples with less abundant cells.

Pairwise synthetic cytotoxicity between Paxlovid and 100 frequently prescribed FDA-approved small molecule drugs on liver cells.

Paxlovid is a recent FDA approved specific drug for COVID-19. Extensive prescription of Paxlovid could induce potential synthetic cytotoxicity with drugs. Herein, we aimed to examine pairwise synthetic cytotoxicity between Paxlovid and 100 frequently FDA approved small molecule drugs. Liver cell line HL-7702 or L02 was adopted to evaluate synthetic cytotoxicity between Paxlovid and the 100 small molecule drugs. Inhibitory concentration IC-10 and IC-50 doses for all the 100 small molecule drugs and Paxlovid were experimentally acquired. Then, pairwise synthetic cytotoxicity was examined with the fixed dose IC-10 for each drug. The most 4 significant interactive pairs (2 positively interactive and 2 negatively interactive) were further subjected to molecular docking simulation to reveal the structural modulation with Caspase-8, a key mediator for cell apoptosis.

lncRNA PRR34-AS1 knockdown represses neuroinflammation and neuronal death in traumatic brain injury by inhibiting microRNA-498 expression.

OBJECTIVE: Traumatic brain injury (TBI) can result in motor and cognitive dysfunction and is a possible risk factor for the subsequent development of dementia. However, the pathogenesis of TBI remains largely unclear. This study investigated the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in inflammation and neuronal apoptosis following TBI. METHODS: The lncRNA expression profiles in the cerebral cortices of TBI model mice and sham-operated mice were analyzed using microarray. We focused on an upregulated lncRNA, PRR34-AS1, because of its known modulatory role in apoptosis and inflammation. RESULTS: Our findings indicated that the knockdown of PRR34-AS1 inhibited inflammation and neuronal apoptosis and improved long-term neurological function. Using an in vitro, cell-based model of etoposide-induced primary cortical neuronal injury, we demonstrated that PRR34-AS1 levels were higher in injured model cells than in untreated control cells. Silencing of PRR34-AS1 suppressed etoposide-induced apoptosis and the production of inflammatory mediators in primary cortical neurons. PRR34-AS1 directly targets microRNA-498 (miR-498) in primary cortical neurons. Importantly, the inhibition of miR-498 expression counteracted the effects of PRR34-AS1 silencing on neuronal apoptosis and inflammation. CONCLUSIONS: These findings indicate that PRR34-AS1 may be a useful therapeutic target for TBI.

Superparamagnetic Composite Nanobeads Anchored with Molecular Glues for Ultrasensitive Label-free Proteomics.

Mass spectrometry has emerged as a mainstream technique for label-free proteomics. However, proteomic coverage for trace samples is constrained by adsorption loss during repeated elution at sample pretreatment. Here, we demonstrated superparamagnetic composite nanoparticles functionalized with molecular glues (MGs) to enrich proteins in trace human biofluid. We showed high protein binding (>95 %) and recovery (≈90 %) rates by anchor-nanoparticles. We further proposed a Streamlined Workflow based on Anchor-nanoparticles for Proteomics (SWAP) method that enabled unbiased protein capture, protein digestion and pure peptides elution in one single tube. We demonstrated SWAP to quantify over 2500 protein groups with 100 HEK 293T cells. We adopted SWAP to profile proteomics with trace aqueous humor samples from cataract (n=15) and wet age-related macular degeneration (n=8) patients, and quantified ≈1400 proteins from 5 µL aqueous humor. SWAP simplifies sample preparation steps, minimizes adsorption loss and improves protein coverage for label-free proteomics with previous trace samples.

An effector index to predict target genes at GWAS loci.

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.