PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Cereblon influences the timing of muscle differentiation in Ciona tadpoles.

Thalidomide has a dark history as a teratogen, but in recent years, its derivates have been shown to function as potent chemotherapeutic agents. These drugs bind cereblon (CRBN), the substrate receptor of an E3 ubiquitin ligase complex, and modify its degradation targets. Despite these insights, remarkably little is known about the normal function of cereblon in development. Here, we employ Ciona, a simple invertebrate chordate, to identify endogenous Crbn targets. In Ciona, Crbn is specifically expressed in developing muscles during tail elongation before they acquire contractile activity. Crbn expression is activated by Mrf, the ortholog of MYOD1, a transcription factor important for muscle differentiation. CRISPR/Cas9-mediated mutations of Crbn lead to precocious onset of muscle contractions. By contrast, overexpression of Crbn delays contractions and is associated with decreased expression of contractile protein genes such as troponin. This reduction is possibly due to reduced Mrf protein levels without altering Mrf mRNA levels. Our findings suggest that Mrf and Crbn form a negative feedback loop to control the precision of muscle differentiation during tail elongation.

cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

Regulatory cocktail for dopaminergic neurons in a protovertebrate identified by whole-embryo single-cell transcriptomics.

The CNS of the protovertebrate Ciona intestinalis contains a single cluster of dopaminergic (DA) neurons, the coronet cells, which have been likened to the hypothalamus of vertebrates. Whole-embryo single-cell RNA sequencing (RNA-seq) assays identified Ptf1a as the most strongly expressed cell-specific transcription factor (TF) in DA/coronet cells. Knockdown of Ptf1a activity results in their loss, while misexpression results in the appearance of supernumerary DA/coronet cells. Photoreceptor cells and ependymal cells are the most susceptible to transformation, and both cell types express high levels of Meis Coexpression of both Ptf1a and Meis caused the wholesale transformation of the entire CNS into DA/coronet cells. We therefore suggest that the reiterative use of functional manipulations and single-cell RNA-seq assays is an effective means for the identification of regulatory cocktails underlying the specification of specific cell identities.

Two different viral proteins suppress NUCLEAR FACTOR-YC-mediated antiviral immunity during infection in rice.

Plant viruses have multiple strategies to counter and evade the host's antiviral immune response. However, limited research has been conducted on the antiviral defense mechanisms commonly targeted by distinct types of plant viruses. In this study, we discovered that NUCLEAR FACTOR-YC (NF-YC) and NUCLEAR FACTOR-YA (NF-YA), 2 essential components of the NF-Y complex, were commonly targeted by viral proteins encoded by 2 different rice (Oryza sativa L.) viruses, rice stripe virus (RSV, Tenuivirus) and southern rice black streaked dwarf virus (SRBSDV, Fijivirus). In vitro and in vivo experiments showed that OsNF-YCs associate with OsNF-YAs and inhibit their transcriptional activation activity, resulting in the suppression of OsNF-YA-mediated plant susceptibility to rice viruses. Different viral proteins RSV P2 and SRBSDV SP8 directly disrupted the association of OsNF-YCs with OsNF-YAs, thereby suppressing the antiviral defense mediated by OsNF-YCs. These findings suggest an approach for conferring broad-spectrum disease resistance in rice and reveal a common mechanism employed by viral proteins to evade the host's antiviral defense by hindering the antiviral capabilities of OsNF-YCs.

Histologically resolved multiomics enables precise molecular profiling of human intratumor heterogeneity.

Both the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multiomics in unfixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser microdissection of regions of interests. These microtissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human oral squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multiomics assessments.

Comprehensive single-cell transcriptome lineages of a proto-vertebrate.

Ascidian embryos highlight the importance of cell lineages in animal development. As simple proto-vertebrates, they also provide insights into the evolutionary origins of cell types such as cranial placodes and neural crest cells. Here we have determined single-cell transcriptomes for more than 90,000 cells that span the entirety of development-from the onset of gastrulation to swimming tadpoles-in Ciona intestinalis. Owing to the small numbers of cells in ascidian embryos, this represents an average of over 12-fold coverage for every cell at every stage of development. We used single-cell transcriptome trajectories to construct virtual cell-lineage maps and provisional gene networks for 41 neural subtypes that comprise the larval nervous system. We summarize several applications of these datasets, including annotating the synaptome of swimming tadpoles and tracing the evolutionary origin of cell types such as the vertebrate telencephalon.

Tissue-resident memory T cells exhibit phenotypically and functionally heterogeneous in human physiological and pathological nasal mucosa.

Pathogens commonly enter mucosal barrier tissues and tissue-resident memory T cells (TRM) are essential for preventing mucosal lesions. However, the immunological properties of TRM cells in nasal mucosa are poorly known. In comparison with control tissues, decreasing CD103+ TRM cells were observed in Chronic rhinosinusitis with nasal polyps (CRSwNPs) and sinonasal inverted papilloma (SNIP), which presented high capability to produce effector cytokines. In CRSwNPs, we found that CD103+ TRM cells with higher cytokine and Granzyme B coexpressed high PD-1, CD103- TRM cells expressed higher IL-10. Homogenates isolated from CRSwNPs induced CD103 expression on peripheral T cells which could be inhibited by blocking TGF-ß. The frequencies of CD103+ TRM cells in CRSwNPs were extremely negatively correlated with neutrophil infiltration. CD103+ TRM cells from Staphylococcus aureus positive CRSwNPs had a stronger response to SEB. Taken together, two phenotypically and functionally distinct subsets of TRM cells exist in nasal tissues and play critical roles in the progress of CRSwNPs and SNIPs.

Botanical drugs: a new strategy for structure-based target prediction.

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

Characterization of sub-nanosecond pulse compression based on frequency-detuning SBS.

High-frequency, high-power picosecond lasers have important and wide-ranging applications in laser ranging, optoelectronic countermeasures, and ultrafine industrial processing. Pulse compression based on stimulated Brillouin scattering (SBS) can achieve a highly efficient picosecond laser output, while improving the peak power and beam quality of the laser. In this paper, a generator-amplifier two-cell structure with frequency-detuning was proposed to achieve a pulse output that combines high compression ratio and high energy reflectivity. The experiment proved that under a pump pulse width of 15 ns and repetition frequency of 10 Hz, when the generator cell and amplifier cell media were selected as HT-230, the highest energy reflectivity of 46% and narrowest compression pulse width of 1.1 ns were achieved, and the pulse compression ratio was 13.6. When the amplifier cell was selected as FC-770 and the generator cell was selected as HT-230, an energy reflectivity of 52% and a compression pulse width of 840 ps could be achieved simultaneously, and the pulse compression ratio was 18.

Shared evolutionary origin of vertebrate neural crest and cranial placodes.

Placodes and neural crests represent defining features of vertebrates, yet their relationship remains unclear despite extensive investigation1-3. Here we use a combination of lineage tracing, gene disruption and single-cell RNA-sequencing assays to explore the properties of the lateral plate ectoderm of the proto-vertebrate, Ciona intestinalis. There are notable parallels between the patterning of the lateral plate in Ciona and the compartmentalization of the neural plate ectoderm in vertebrates4. Both systems exhibit sequential patterns of Six1/2, Pax3/7 and Msxb expression that depend on a network of interlocking regulatory interactions4. In Ciona, this compartmentalization network produces distinct but related types of sensory cells that share similarities with derivatives of both cranial placodes and the neural crest in vertebrates. Simple genetic disruptions result in the conversion of one sensory cell type into another. We focused on bipolar tail neurons, because they arise from the tail regions of the lateral plate and possess properties of the dorsal root ganglia, a derivative of the neural crest in vertebrates5. Notably, bipolar tail neurons were readily transformed into palp sensory cells, a proto-placodal sensory cell type that arises from the anterior-most regions of the lateral plate in the Ciona tadpole6. Proof of transformation was confirmed by whole-embryo single-cell RNA-sequencing assays. These findings suggest that compartmentalization of the lateral plate ectoderm preceded the advent of vertebrates, and served as a common source for the evolution of both cranial placodes and neural crest3,4.

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study.

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

Power analysis of transcriptome-wide association study: Implications for practical protocol choice.

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism.

Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source.

High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody's back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

Achieving High Selectivity in Photocatalytic Oxidation of Toluene on Amorphous BiOCl Nanosheets Coupled with TiO2.

The inert C(sp3)-H bond and easy overoxidation of toluene make the selective oxidation of toluene to benzaldehyde a great challenge. Herein, we present that a photocatalyst, constructed with a small amount (1 mol %) of amorphous BiOCl nanosheets assembled on TiO2 (denoted as 0.01BOC/TiO2), shows excellent performance in toluene oxidation to benzaldehyde, with 85% selectivity at 10% conversion, and the benzaldehyde formation rate is up to 1.7 mmol g-1 h-1, which is 5.6 and 3.7 times that of bare TiO2 and BOC, respectively. In addition to the charge separation function of the BOC/TiO2 heterojunction, we found that the amorphous structure of BOC endows its abundant surface oxygen vacancies (Ov), which can further promote the charge separation. Most importantly, the surface Ov of amorphous BOC can efficiently adsorb and activate O2, and amorphous BOC makes the product, benzaldehyde, easily desorb from the catalyst surface, which alleviates the further oxidation of benzaldehyde, and results in the high selectivity. This work highlights the importance of the microstructure based on heterojunctions, which is conducive to the rational design of photocatalysts with high performance in organic synthesis.

kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes.

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: huzheng1998@163.com, liangjiuxing@m.scnu.edu.cn, lizheyzy@163.com.

webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data integration.

SUMMARY: Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats. In this study, we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type. AVAILABILITY AND IMPLEMENTATION: webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST.