Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study.

INTRODUCTION: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication. METHODS: Baseline urine was collected prospectively from healthy participants (n=100), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods. RESULTS: A total of 90 metabolites were identified, with 40 significantly altered (p<0.05, false discovery rate <0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10-20), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings. CONCLUSIONS: This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

The Dynamic Change of Gene-Regulated Networks in Cashmere Goat Skin with Seasonal Variation.

The Cashmere goat (Capra hircus) is renowned for its high-quality fiber production trait. The hair cycle in Cashmere goat has an annual rhythm. To deepen the understanding of the molecular foundation of annual rhythm in the skin of Cashmere goat, we did a comparative analysis of the Cashmere goat skin transcriptome all year round. 4002 Differentially expressed genes (DEGs) were identified with seasonal variations. 12 months transcriptome were divided into four developmental stages: Jan-Mar, Apr-Jul, Aug-Oct, and Nov-Dec based on gene expression patterns. 13 modules of highly correlated genes in skin were identified using WGCNA. Ten of these modules were consistent with the development stages. The gene function of those genes in each module was analyzed by functional enrichment. The results indicated that Wnt and Hedgehog signaling pathways were inhibited from January to March and activated from April to July. The cutaneous immune system of Cashmere goats has high activity from August to October. Fatty acid metabolism dominates goat skin from November to December. This study provides new information related to the annual skin development cycle, which could provide molecular biological significance for understanding the seasonal development and response to the annual rhythm of skin.

Incidence of extrauterine growth retardation and its risk factors in very preterm infants during hospitalization: a multicenter prospective study. / ä½é™¢æžæ—©äº§å„¿å®«å¤–ç”Ÿé•¿è¿Ÿç¼“åŠç›¸å ³å› ç´ çš„å¤šä¸­å¿ƒå‰çž»æ€§ç ”ç©¶.

OBJECTIVES: To investigate the incidence of extrauterine growth retardation (EUGR) and its risk factors in very preterm infants (VPIs) during hospitalization in China. METHODS: A prospective multicenter study was performed on the medical data of 2 514 VPIs who were hospitalized in the department of neonatology in 28 hospitals from 7 areas of China between September 2019 and December 2020. According to the presence or absence of EUGR based on the evaluation of body weight at the corrected gestational age of 36 weeks or at discharge, the VPIs were classified to two groups: EUGR group (n=1 189) and non-EUGR (n=1 325). The clinical features were compared between the two groups, and the incidence of EUGR and risk factors for EUGR were examined. RESULTS: The incidence of EUGR was 47.30% (1 189/2 514) evaluated by weight. The multivariate logistic regression analysis showed that higher weight growth velocity after regaining birth weight and higher cumulative calorie intake during the first week of hospitalization were protective factors against EUGR (P<0.05), while small-for-gestational-age birth, prolonged time to the initiation of total enteral feeding, prolonged cumulative fasting time, lower breast milk intake before starting human milk fortifiers, prolonged time to the initiation of full fortified feeding, and moderate-to-severe bronchopulmonary dysplasia were risk factors for EUGR (P<0.05). CONCLUSIONS: It is crucial to reduce the incidence of EUGR by achieving total enteral feeding as early as possible, strengthening breastfeeding, increasing calorie intake in the first week after birth, improving the velocity of weight gain, and preventing moderate-severe bronchopulmonary dysplasia in VPIs.

Ensembles of change-point detectors: implications for real-time BMI applications.

Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.

Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity.

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

Variation and signatures of selection on the human face.

There has been much debate about why humans throughout the world differ in facial form. Previous studies of human skull morphology found levels of among-population differentiation that were comparable to those of neutral genetic markers, suggesting that genetic drift (neutral processes) played an important role in influencing facial differentiation. However, variation in soft-tissue morphology has not been studied in detail. In this study, we analyzed high-resolution 3D images of soft-tissue facial form in four Eurasian populations: Han Chinese, Tibetans, Uyghur and Europeans. A novel method was used to establish a high-density alignment across all of the faces, allowing facial diversity to be examined at an unprecedented resolution. These data exhibit signatures of population structure and history. However, among-population differentiation was higher for soft-tissue facial form than for genome-wide genetic loci, and high-resolution analyses reveal that the nose, brow area and cheekbones exhibit particularly strong signals of differentiation (Qst estimates: 0.3-0.8) between Europeans and Han Chinese. Our results suggest that local adaptation and/or sexual selection have been important in shaping human soft-tissue facial morphology.

Detecting genetic association of common human facial morphological variation using high density 3D image registration.

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.

Temperature Dependence of Charge Transport Properties of Quasi-2D Chiral Perovskite Thin-Film Field-Effect Transistors.

Chiral halide perovskite materials promise both superior light response and the capability to distinguish circularly polarized emissions, which are especially common in the fluorescence spectra of organic chiral materials. Herein, thin-film field-effect transistors (FETs) based on chiral quasi-two-dimensional perovskites are explored, and the temperature dependence of the charge carrier transport mechanism over the broad temperature range (80-300 K) is revealed. A typical p-type charge transport behavior is observed for both left-handed (S-C6H5(CN2)2NH3)2(CH3NH3)n-1PbnI3n+1 and right-handed (R-C6H5(CN2)2NH3)2(CH3NH3)n-1PbnI3n+1 chiral perovskites, with maximum carrier mobilities of 1.7 × 10-5 cm2 V-1 s-1 and 2.5 × 10-5 cm2 V-1 s-1 at around 280 K, respectively. The shallow traps with smaller activation energy (0.03 eV) hinder the carrier transport over the lower temperature regime (80-180 K), while deep traps with 1 order of magnitude larger activation energy than the shallow traps moderate the charge carrier transport in the temperature range of 180-300 K. From the charge carrier mechanism point of view, impurity scattering is established as the dominant factor from 80 K until around 280 K, while phonon scattering becomes predominant up to room temperature. Responsivities of 0.15 A W-1 and 0.14 A W-1 for left-handed and right-handed chiral perovskite FET devices are obtained.

Expression Regulation of Gluconeogenesis Related Genes in Ovine Skeletal Muscle Cells.

BACKGROUND: Under fasting conditions, the pathway converting gluconeogenesis precursors into muscle glycogen becomes crucial due to reduced glycogen reserves. However, there is limited research on skeletal muscle gluconeogenesis and the impact of fasting on gluconeogenic gene expression. METHODS: Sheep fetal skeletal muscle cells cultured in vitro were used to study the effects of varying lactic acid concentrations (0 to 30 mM) and 2.5 mM glucose on the expression of gluconeogenesis-related genes after 6 h of fasting. The effects on mRNA and protein expression of key genes involved in skeletal muscle gluconeogenesis were measured by quantitative real time polymerase chain reaction (qRT-PCR), immunofluorescence, and western blotting at 48 h. RESULTS: Fasting increased the expression of key gluconeogenic genes, fructose-1,6-bisphosphatase 2 (FBP2), glucose-6-phosphatase 3 (G6PC3), pyruvate kinase M (PKM), monocarboxylate transporter1 (MCTS1), glucose transporter type 4 (GLUT4), pyruvate carboxylase (PC), and lactate dehydrogenase A (LDHA). The mRNA levels of FBP2, G6PC3, and MCTS1 significantly decreased with glucose addition. Additionally, 10 mM lactic acid significantly promoted the expression of FBP2, PC, MCTS1, LDHA, GLUT4, and PKM while inhibiting phosphoenolpyruvate carboxykinase (PEPCK) expression. At the protein level, 10 mM lactic acid significantly increased FBP2 and PKM protein expression. CONCLUSIONS: This study shows that fasting regulates key gluconeogenic gene expression in sheep skeletal muscle cells and highlights the role of lactic acid in inducing these gene expressions.

Coregulatory effects of multiple histone modifications in key ferroptosis-related genes for lung adenocarcinoma.

Aim: The present study was designed to investigate the coregulatory effects of multiple histone modifications (HMs) on gene expression in lung adenocarcinoma (LUAD). Materials & methods: Ten histones for LUAD were analyzed using ChIP-seq and RNA-seq data. An innovative computational method is proposed to quantify the coregulatory effects of multiple HMs on gene expression to identify strong coregulatory genes and regions. This method was applied to explore the coregulatory mechanisms of key ferroptosis-related genes in LUAD. Results: Nine strong coregulatory regions were identified for six ferroptosis-related genes with diverse coregulatory patterns (CA9, PGD, CDKN2A, PML, OTUB1 and NFE2L2). Conclusion: This quantitative method could be used to identify important HM coregulatory genes and regions that may be epigenetic regulatory targets in cancers.

Greenhouse gases emissions and carbon budget estimation in horizontal subsurface flow constructed wetlands with different plant species.

Constructed wetlands (CWs) are pivotal for wastewater treatment due to their high efficiency and numerous advantages. The impact of plant species and diversity on greenhouse gas (GHG) emissions from CWs requires a more comprehensive evaluation. Moreover, controversial perspectives persist about whether CWs function as carbon sinks or sources. In this study, horizontal subsurface flow (HSSF) CWs vegetated with Cyperus alternifolius, Typhae latifolia, Acorus calamus, and the mixture of these three species were constructed to evaluate pollutant removal efficiencies and GHG emissions, and estimate carbon budgets. Polyculture CWs can stably remove COD (86.79 %), NH4+-N (97.41 %), NO3--N (98.55 %), and TP (98.48 %). They also mitigated global warming potential (GWP) by suppressing N2O emissions compared with monoculture CWs. The highest abundance of the Pseudogulbenkiania genus, crucial for denitrification, was observed in polyculture CWs, indicating that denitrification dominated in nitrogen removal. While the highest nosZ copy numbers were observed in CWs vegetated with Cyperus alternifolius, suggesting its facilitation of denitrification-related microbes. Selecting Cyperus alternifolius to increase species diversity is proposed for simultaneously maintaining the water purification capacity and reducing GHG emissions. Carbon budget estimations revealed that all four types of HSSF CWs were carbon sinks after six months of operation, with carbon accumulation capacity of 4.90 ± 1.50 (Cyperus alternifolius), 3.31 ± 2.01 (Typhae latifola), 1.78 ± 1.30 (Acorus calamus), and 2.12 ± 0.88 (polyculture) kg C/m2/yr. This study implies that under these operation conditions, CWs function as carbon sinks rather than sources, aligning with carbon peak and neutrality objectives and presenting significant potential for carbon reduction efforts.

Inferring the history of population size change from genome-wide SNP data.

Dense, genome-wide single-nucleotide polymorphism (SNP) data can be used to reconstruct the demographic history of human populations. However, demographic inferences from such data are complicated by recombination and ascertainment bias. We introduce two new statistics, allele frequency-identity by descent (AF-IBD) and allele frequency-identity by state (AF-IBS), that make use of linkage disequilibrium information and show defined relationships to the time of coalescence. These statistics, when conditioned on the derived allele frequency, are able to infer complex population size changes. Moreover, the AF-IBS statistic, which is based on genome-wide SNP data, is robust to varying ascertainment conditions. We constructed an efficient approximate Bayesian computation (ABC) pipeline based on AF-IBD and AF-IBS that can accurately estimate demographic parameters, even for fairly complex models. Finally, we applied this ABC approach to genome-wide SNP data and inferred the demographic histories of two human populations, Yoruba and French. Our results suggest a rather stable ancestral population size with a mild recent expansion for Yoruba, whereas the French seemingly experienced a long-lasting severe bottleneck followed by a drastic population growth. This approach should prove useful for new insights into populations, especially those with complex demographic histories.

Metal Halide Perovskite Photo-Field-Effect Transistors with Chiral Selectivity.

Organic-inorganic (hybrid) metal halide perovskites (MHPs) incorporating chiral organic ligand molecules are naturally sensitive to left- and right-handed circular polarized light, potentially enabling selective circular polarized photodetection. Here, the photoresponses in chiral MHP polycrystalline thin films made of ((S)-(-)-α-methyl benzylamine)2PbI4 and ((R)-(+)-α-methyl benzylamine)2PbI4, denoted as (S-MBA)2 PbI4 and (R-MBA)2PbI4, respectively, are investigated by employing a thin-film field-effect transistor (FET) configuration. The left-hand-sensitive films made of (S-MBA)2PbI4 perovskite show higher photocurrent under left-handed circularly polarized (LCP) light than under right-handed circularly polarized (RCP) illumination under otherwise identical conditions. Conversely, the right-hand-sensitive films made of (R-MBA)2PbI4 are more sensitive to RCP than LCP illumination over a wide temperature range of 77-300 K. Furthermore, based on FET measurements, we found evidence of two different carrier transport mechanisms with two distinct activation energies in the 77-260 and 280-300 K temperature ranges, respectively. In the former temperature range, shallow traps are dominant in the perovskite film, which are filled by thermally activated carriers with increasing temperature; in the latter temperature range, deep traps with one order of magnitude larger activation energy dominate. Both types of chiral MHPs show intrinsic p-type carrier transport behavior regardless of the handedness (S or R) of these materials. The optimal carrier mobility for both handedness of material is around (2.7 ± 0.2) × 10-7 cm2 V-1 s-1 at 270-280 K, which is two magnitudes larger than those reported in nonchiral perovskite MAPbI3 polycrystalline thin films. These findings suggest that chiral MHPs can be an excellent candidate for selective circular polarized photodetection applications, without additional polarizing optical components, enabling simplified construction of detection systems.

Molecular Evolution and Protein Structure Variation of Dkk Family.

Dkks have inhibitory effects on the Wnt signaling pathway, which is involved in the development of skin and its appendages and the regulation of hair growth. The nucleotide sequences were compared and analyzed to further investigate the relationship between the structure and function of the Dkk gene family and vertebrate epidermal hair. The analysis of the molecular evolution of the Dkk family revealed that the evolution rate of the genes changed significantly after speciation, with the Aves and Reptilia branches showing accelerated evolution. Additionally, positive selection was observed at specific sites. The tertiary structure of the protein was also predicted. The analysis of the functional divergence of the Dkk family revealed that the functional divergence coefficient of each gene was greater than 0, with most of the functional divergence sites were located in the Cys-2 domain and a few in the Cys-1 domain. This suggests that the amino acid and functional divergence sites may play a role in regulating the binding of the Dkk family to LRP5/6, and thus affect the inhibition of Wnt signaling, leading to different functions of Dkk1, Dkk2, and Dkk4 in the development of skin hair follicles. In addition, the Dkk families of Aves and Reptilia may have undergone adaptive evolution and functional divergence.

Factors Influencing Gaseous Emissions in Constructed Wetlands: A Meta-Analysis and Systematic Review.

Constructed wetlands (CWs) are an eco-technology for wastewater treatment and are applied worldwide. Due to the regular influx of pollutants, CWs can release considerable quantities of greenhouse gases (GHGs), ammonia (NH3), and other atmospheric pollutants, such as volatile organic compounds (VOCs) and hydrogen sulfide (H2S), etc., which will aggravate global warming, degrade air quality and even threaten human health. However, there is a lack of systematic understanding of factors affecting the emission of these gases in CWs. In this study, we applied meta-analysis to quantitatively review the main influencing factors of GHG emission from CWs; meanwhile, the emissions of NH3, VOCs, and H2S were qualitatively assessed. Meta-analysis indicates that horizontal subsurface flow (HSSF) CWs emit less CH4 and N2O than free water surface flow (FWS) CWs. The addition of biochar can mitigate N2O emission compared to gravel-based CWs but has the risk of increasing CH4 emission. Polyculture CWs stimulate CH4 emission but pose no influence on N2O emission compared to monoculture CWs. The influent wastewater characteristics (e.g., C/N ratio, salinity) and environmental conditions (e.g., temperature) can also impact GHG emission. The NH3 volatilization from CWs is positively related to the influent nitrogen concentration and pH value. High plant species richness tends to reduce NH3 volatilization and plant composition showed greater effects than species richness. Though VOCs and H2S emissions from CWs do not always occur, it should be a concern when using CWs to treat wastewater containing hydrocarbon and acid. This study provides solid references for simultaneously achieving pollutant removal and reducing gaseous emission from CWs, which avoids the transformation of water pollution into air contamination.

Obviating Ligand Exchange Preserves the Intact Surface of HgTe Colloidal Quantum Dots and Enhances Performance of Short Wavelength Infrared Photodetectors.

A large volume, scalable synthesis procedure of HgTe quantum dots (QDs) capped initially with short-chain conductive ligands ensures ligand exchange-free and simple device fabrication. An effective n- or p-type self-doping of HgTe QDs is achieved by varying cation-anion ratio, as well as shifting the Fermi level position by introducing single- or double-cyclic thiol ligands, that is, 2-furanmethanethiol (FMT) or 2,5-dimercapto-3,4-thiadiasole (DMTD) in the synthesis. This allows for preserving the intact surface of the HgTe QDs, thus ensuring a one order of magnitude reduced surface trap density compared with HgTe subjected to solid-state ligand exchange. The charge carrier diffusion length can be extended from 50 to 90 nm when the device active area consists of a bi-layer of cation-rich HgTe QDs capped with DMTD and FMT, respectively. As a result, the responsivity under 1340 nm illumination is boosted to 1 AW-1 at zero bias and up to 40 AW-1 under -1 V bias at room temperature. Due to high noise current density, the specific detectivity of these photodetectors reaches up to 1010 Jones at room temperature and under an inert atmosphere. Meanwhile, high photoconductive gain ensures a rise in the external quantum efficiency of up to 1000% under reverse bias.

A prototype closed-loop brain-machine interface for the study and treatment of pain.

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top-down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity. METHODS: We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD. RESULTS: We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity. CONCLUSIONS: This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.