GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics.

The shared genetic basis offers very valuable insights into the etiology, diagnosis and therapy of complex traits. However, a comprehensive resource providing shared genetic basis using the accessible summary statistics is currently lacking. It is challenging to analyze the shared genetic basis due to the difficulty in selecting parameters and the complexity of pipeline implementation. To address these issues, we introduce GWAShug, a platform featuring a standardized best-practice pipeline with four trait level methods and three molecular level methods. Based on stringent quality control, the GWAShug resource module includes 539 high-quality GWAS summary statistics for European and East Asian populations, covering 54 945 pairs between a measurement-based and a disease-based trait and 43 902 pairs between two disease-based traits. Users can easily search for shared genetic basis information by trait name, MeSH term and category, and access detailed gene information across different trait pairs. The platform facilitates interactive visualization and analysis of shared genetic basic results, allowing users to explore data dynamically. Results can be conveniently downloaded via FTP links. Additionally, we offer an online analysis module that allows users to analyze their own summary statistics, providing comprehensive tables, figures and interactive visualization and analysis. GWAShug is freely accessible at http://www.gwashug.com.

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.

Photodimerization-Triggered Photopolymerization of Triene Coordination Polymers Enables Macroscopic Photomechanical Movements.

Controlling the packing of olefinic molecules in crystals is essential for triggering solid-state [2 + 2] photocycloaddition reactions and the synthesis of photocontrolled smart materials. Herein, we report the stepwise photodimerization-triggered photopolymerization of two triene coordination polymers (CPs), {[Zn(2-BBA)2(tpeb)]·0.5CH3CN}n (1, 2-HBBA = 2-bromobenzoic acid, tpeb = 1,3,5-tri-4-pyridyl-1,2-ethenylbenzene) and {[Zn(3-BBA)2(tpeb)]·CH3CN)}n (2, 3-HBBA = 3-bromobenzoic acid). Upon irradiation with 420 nm light, each pair of closely packed and parallel olefinic bonds in 1 undergoes a [2 + 2] cycloaddition reaction, which connects two adjacent Z-shaped chains into a ladder-like coordination chain [Zn(2-BBA)2(bpbdpvpcb)0.5]n (1a, bpbdpvpcb = 1,3-bis(4-pyridyl)-2,4-bis(3,5-di(2-(4-pyridyl)vinyl)phenyl]cyclobutene) through single-crystal to single-crystal (SCSC) transformation. After photodimerization from 1 to 1a has occurred, the olefinic bonds that were initially distant are brought in close enough proximity to meet the requirements for a subsequent [2 + 2] cycloaddition reaction. Upon further light irradiation, the neighboring bpbdpvpcb ligands in 1a experience a SCSC photopolymerization based on [2 + 2] photocycloaddition and transform into poly-3b,4,5,5a,8b,9,10a-octahydro-4,5,9,10-tetrapyridyl-2,7-di(2-(4-pyridyl)vinyl)dicyclobuta[e,l]-pyren (poly-otpdpvdcbp). 2 showed similar structural changes under UV light illumination. Under light exposure, single crystals of 1 and 2 with different morphologies exhibit bending, cracking, and jumping photomechanical motions. The composite film (1-PVA) engineered by dispersing crystalline particles of 1 in poly(vinyl alcohol) (PVA) displays interesting light-wavelength-dependent photomechanical motions and can perform photodriven swimming on a liquid surface. This work provides a useful and promising approach to enable photodimerization of those photoinactive olefin pairs embedded in CPs and opens a new route to synthesize organic polymers by using olefinic CP platforms.

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.

Ultra-Microporous Fe-MOF with Prolonged NO Delivery in Biological Media for Therapeutic Application.

Nitric oxide (NO), a key element in the regulation of essential biological mechanisms, presents huge potential as therapeutic agent in the treatment and prevention of chronic diseases. Metal-organic frameworks (MOFs) with open metal sites are promising carriers for NO therapies but delivering it over an extended period in biological media remains a great challenge due to i) a fast degradation of the material in body fluids and/or ii) a rapid replacement of NO by water molecules onto the Lewis acid sites. Here, a new ultra-narrow pores Fe bisphosphonate MOF, denoted MIP-210(Fe) or Fe(H2O)(Hmbpa) (H4mbpa = p-xylenediphosphonic acid) is described that adsorbs NO due to an unprecedented sorption mechanism: coordination of NO through the Fe(III) sites is unusually preferred, replacing bound water, and creating a stable interaction with the free H2O and P-OH groups delimiting the ultra-narrow pores. This, associated with the high chemical stability of the MOF in body fluids, enables an unprecedented slow replacement of NO by water molecules in biological media, achieving an extraordinarily extended NO delivery time over at least 70 h, exceeding by far the NO kinetics release reported with others porous materials, paving the way for the development of safe and successful gas therapies.

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.

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model's superior predictive capability and robustness.

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.

End-to-End Implicit Object Pose Estimation.

To accurately estimate the 6D pose of objects, most methods employ a two-stage algorithm. While such two-stage algorithms achieve high accuracy, they are often slow. Additionally, many approaches utilize encoding-decoding to obtain the 6D pose, with many employing bilinear sampling for decoding. However, bilinear sampling tends to sacrifice the accuracy of precise features. In our research, we propose a novel solution that utilizes implicit representation as a bridge between discrete feature maps and continuous feature maps. We represent the feature map as a coordinate field, where each coordinate pair corresponds to a feature value. These feature values are then used to estimate feature maps of arbitrary scales, replacing upsampling for decoding. We apply the proposed implicit module to a bidirectional fusion feature pyramid network. Based on this implicit module, we propose three network branches: a class estimation branch, a bounding box estimation branch, and the final pose estimation branch. For this pose estimation branch, we propose a miniature dual-stream network, which estimates object surface features and complements the relationship between 2D and 3D. We represent the rotation component using the SVD (Singular Value Decomposition) representation method, resulting in a more accurate object pose. We achieved satisfactory experimental results on the widely used 6D pose estimation benchmark dataset Linemod. This innovative approach provides a more convenient solution for 6D object pose estimation.