Ciclopirox inhibits SARS-CoV-2 replication by promoting the degradation of the nucleocapsid protein.

The nucleocapsid protein (NP) plays a crucial role in SARS-CoV-2 replication and is the most abundant structural protein with a long half-life. Despite its vital role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assembly and host inflammatory response, it remains an unexplored target for drug development. In this study, we identified a small-molecule compound (ciclopirox) that promotes NP degradation using an FDA-approved library and a drug-screening cell model. Ciclopirox significantly inhibited SARS-CoV-2 replication both in vitro and in vivo by inducing NP degradation. Ciclopirox induced abnormal NP aggregation through indirect interaction, leading to the formation of condensates with higher viscosity and lower mobility. These condensates were subsequently degraded via the autophagy-lysosomal pathway, ultimately resulting in a shortened NP half-life and reduced NP expression. Our results suggest that NP is a potential drug target, and that ciclopirox holds substantial promise for further development to combat SARS-CoV-2 replication.

Novel Uncertainty Quantification through Perturbation-Assisted Sample Synthesis.

This paper introduces a novel Perturbation-Assisted Inference (PAI) framework utilizing synthetic data generated by the Perturbation-Assisted Sample Synthesis (PASS) method. The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models. On one hand, PASS employs a generative model to create synthetic data that closely mirrors raw data while preserving its rank properties through data perturbation, thereby enhancing data diversity and bolstering privacy. By incorporating knowledge transfer from large pre-trained generative models, PASS enhances estimation accuracy, yielding refined distributional estimates of various statistics via Monte Carlo experiments. On the other hand, PAI boasts its statistically guaranteed validity. In pivotal inference, it enables precise conclusions even without prior knowledge of the pivotal's distribution. In non-pivotal situations, we enhance the reliability of synthetic data generation by training it with an independent holdout sample. We demonstrate the effectiveness of PAI in advancing uncertainty quantification in complex, data-driven tasks by applying it to diverse areas such as image synthesis, sentiment word analysis, multimodal inference, and the construction of prediction intervals.

TRIM6 facilitates SARS-CoV-2 proliferation by catalyzing the K29-typed ubiquitination of NP to enhance the ability to bind viral genomes.

The Nucleocapsid Protein (NP) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not only the core structural protein required for viral packaging, but also participates in the regulation of viral replication, and its post-translational modifications such as phosphorylation have been shown to be an important strategy for regulating virus proliferation. Our previous work identified NP could be ubiquitinated, as confirmed by two independent studies. But the function of NP ubiquitination is currently unknown. In this study, we first pinpointed TRIM6 as the E3 ubiquitin ligase responsible for NP ubiquitination, binding to NP's CTD via its RING and B-box-CCD domains. TRIM6 promotes the K29-typed polyubiquitination of NP at K102, K347, and K361 residues, increasing its binding to viral genomic RNA. Consistently, functional experiments such as the use of the reverse genetic tool trVLP model and gene knockout of TRIM6 further confirmed that blocking the ubiquitination of NP by TRIM6 significantly inhibited the proliferation of SARS-CoV-2. Notably, the NP of coronavirus is relatively conserved, and the NP of SARS-CoV can also be ubiquitinated by TRIM6, indicating that NP could be a broad-spectrum anti-coronavirus target. These findings shed light on the intricate interaction between SARS-CoV-2 and the host, potentially opening new opportunities for COVID-19 therapeutic development.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.

Investigating the Therapeutic Effects of Ferroptosis on Myocardial Ischemia-Reperfusion Injury Using a Dual-Locking Mitochondrial Targeting Strategy.

Research on ferroptosis in myocardial ischemia/reperfusion injury (MIRI) using mitochondrial viscosity as a nexus holds great promise for MIRI therapy. However, high-precision visualisation of mitochondrial viscosity remains a formidable task owing to the debilitating electrostatic interactions caused by damaged mitochondrial membrane potential. Herein, we propose a dual-locking mitochondria-targeting strategy that incorporates electrostatic forces and probe-protein molecular docking. Even in damaged mitochondria, stable and precise visualisation of mitochondrial viscosity in triggered and medicated MIRI was achieved owing to the sustained driving forces (e.g., pi-cation, pi-alkyl interactions, etc.) between the developed probe, CBS, and the mitochondrial membrane protein. Moreover, complemented by a western blot, we confirmed that ferrostatin-1 exerts its therapeutic effect on MIRI by improving the system xc-/GSH/GPX4 antioxidant system, confirming the therapeutic value of ferroptosis in MIRI. This study presents a novel strategy for developing robust mitochondrial probes, thereby advancing MIRI treatment.

The E3 ligase TRIM22 restricts SARS-CoV-2 replication by promoting proteasomal degradation of NSP8.

Replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome is mediated by a complex of non-structural proteins (NSPs), of which NSP7 and NSP8 serve as subunits and play a key role in promoting the activity of RNA-dependent RNA polymerase (RdRp) of NSP12. However, the stability of subunits of the RdRp complex has rarely been reported. Here, we found that NSP8 was degraded by the proteasome in host cells, and identified tripartite motif containing 22 (TRIM22) as its E3 ligase. The interferon (IFN) signaling pathway was activated upon viral invasion into host cells, and TRIM22 expression increased. TRIM22 interacted with NSP8 and ubiquitinated it at Lys97 via K48-type ubiquitination. TRIM22 overexpression significantly reduced viral RNA and protein levels. Knockdown of TRIM22 enhanced viral replication. This study provides a new explanation for treating patients suffering from SARS-CoV-2 with IFNs and new possibilities for drug development targeting the interaction between NSP8 and TRIM22.IMPORTANCENon-structural proteins (NSPs) play a crucial role in the replication of severe acute respiratory syndrome coronavirus 2, facilitating virus amplification and propagation. In this study, we conducted a comprehensive investigation into the stability of all subunits comprising the RNA-dependent RNA polymerase complex. Notably, our results reveal for the first time that NSP8 is a relatively unstable protein, which is found to be readily recognized and degraded by the proteasome. This degradation process is mediated by the host E3 ligase tripartite motif containing 22 (TRIM22), which is also a member of the interferon stimulated gene (ISG) family. Our study elucidates a novel mechanism of antiviral effect of TRIM22, which utilizes its own E3 ubiquitin ligase activity to hinder viral replication by inducing ubiquitination and subsequent degradation of NSP8. These findings provide new ideas for the development of novel therapeutic strategies. In addition, the conserved property of NSP8 raises the possibility of developing broad antiviral drugs targeting the TRIM22-NSP8 interaction.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.

Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.

Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for low-density lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, e.g. due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data.

Inference for a Large Directed Acyclic Graph with Unspecified Interventions.

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires to identify the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. The R implementation is available at https://github.com/chunlinli/intdag.

Distribution-Invariant Differential Privacy.

Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in a wide range of simulation studies and real-world benchmarks.

Pyroptosis burden is associated with anti-TNF treatment outcome in inflammatory bowel disease: new insights from bioinformatics analysis.

Biological agents known as anti-tumor necrosis factor (TNF) drugs are frequently utilized in the treatment of inflammatory bowel disease (IBD). In this study, we analyzed the shared processes of pyroptosis in Ulcerative colitis (UC) and Crohn's disease (CD), as well as explored the correlation between the burden of pyroptosis and the results of anti-TNF treatment based on bioinformatics analyses. We identified CAPS1, CASP5, GSDMD, AIM2, and NLRP3 as the hub genes, with AIM2 being the most effective indicator for predicting the response to anti-TNF therapy. We also noticed that non-responders received anti-TNF therapy exhibited elevated AIM2 protein expression. Subsequently, we conducted a cluster analysis based on AIM2-inflammasome-related genes and discovered that patients with a higher burden of AIM2 inflammasome displayed stronger immune function and a poor response to anti-TNF therapy. Overall, our study elucidates the pathway of pyroptosis in IBD and reveals AIM2 expression level as a potential biomarker for predicting the effectiveness of anti-TNF therapy.

Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.

We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.

Pyridine N-Oxide-Promoted Cobalt-Catalyzed Dioxygen-Mediated Methane Oxidation.

The partial oxidation of methane with O2 is significant due to its potential of providing abundant chemical feedstock. Only a few examples realized this type of reaction in homogeneous solvent systems, most of which are in low efficiency. Herein, we present a pyridine N-oxide-promoted cobalt-catalyzed O2-mediated methane oxidation to produce methylene bis(trifluoroacetate) with productivity over 500 molester molmetal-1 h-1.

Deep causal feature extraction and inference with neuroimaging genetic data.

Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three-dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual-level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR-CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR-CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual-level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.

Using GWAS summary data to impute traits for genotyped individuals.

Genome-wide association study (GWAS) summary data have become extremely useful in daily routine data analysis, largely facilitating new methods development and new applications. However, a severe limitation with the current use of GWAS summary data is its exclusive restriction to only linear single nucleotide polymorphism (SNP)-trait association analyses. To further expand the use of GWAS summary data, along with a large sample of individual-level genotypes, we propose a nonparametric method for large-scale imputation of the genetic component of the trait for the given genotypes. The imputed individual-level trait values, along with the individual-level genotypes, make it possible to conduct any analysis as with individual-level GWAS data, including nonlinear SNP-trait associations and predictions. We use the UK Biobank data to highlight the usefulness and effectiveness of the proposed method in three applications that currently cannot be done with only GWAS summary data (for SNP-trait associations): marginal SNP-trait association analysis under non-additive genetic models, detection of SNP-SNP interactions, and genetic prediction of a trait using a nonlinear model of SNPs.

A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (Differential Regulation Analysis by Bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

Computation-Assisted Design of ssDNA Framework Nanorobots for Cancer Logical Recognition, Toehold Disintegration, Visual Dual-Diagnosis, and Synergistic Therapy.

Single-stranded DNA (ssDNA) allows flexible and directional modifications for multiple biological applications, while being greatly limited by their poor stability, increased folding errors, and complicated sequence optimizations. This greatly challenges the design and optimization of ssDNA sequences to fold stable 3D structures for diversified bioapplications. Herein, the stable pentahedral ssDNA framework nanorobots (ssDNA nanorobots) were intelligently designed, assisted by examining dynamic folding of ssDNA in self-assemblies via all-atom molecular dynamics simulations. Assisted by two functional siRNAs (S1 and S2), two ssDNA strands were successfully assembled into ssDNA nanorobots, which include five functional modules (skeleton fixation, logical dual recognition of tumor cell membrane proteins, enzyme loading, dual-miRNA detection and synergy siRNA loading) for multiple applications. By both theoretical calculations and experiments, ssDNA nanorobots were demonstrated to be stable, flexible, highly utilized with low folding errors. Thereafter, ssDNA nanorobots were successfully applied to logical dual-recognition targeting, efficient and cancer-selective internalization, visual dual-detection of miRNAs, selective siRNA delivery and synergistic gene silencing. This work has provided a computational pathway for constructing flexible and multifunctional ssDNA frameworks, enlarging biological application of nucleic acid nanostructures.

Coupled generation.

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest.

Biomineralization of DNA Nanoframeworks for Intracellular Delivery, On-Demand Diagnosis, and Synergistic Cancer Treatments.

DNA nanoframeworks, with great biological information and controlled framework structures, exhibit great potentials in biological applications. Their applications are normally limited by unstable structures susceptible to hydrolysis, depurination, depyrimidination, oxidation, alkylation, or nuclease degradations. Herein, to ensure the mechanical and chemical stabilities of DNA nanoframeworks for intracellular applications, biomineralization of multifunctional DNA nanoframeworks with a tetrahedral skeleton is employed. Via silicification, the S-S bond is simultaneously introduced to obtain the silica-armored DNA nanoframeworks (Si-DNA nanoframeworks), mechanically and chemically stabilized for efficient intracellular deliveries. This successfully prevents degradations and leakages of reagents loaded on Si-DNA nanoframeworks, including biomolecular siRNA and small DOX drugs. Furthermore, the nucleic acid strands of the nanoframeworks are labeled with FAM and the quencher, facilitating miRNA detection upon "turn-on" signals from hybridizations. Therefore, the nanoframeworks collapse via double responses of the silica coating (silica acidic dissolution and S-S reduction by GSH) in cancer cells, realizing on-demand reagent release for miRNA detection and synergistic treatments (by siRNA and DOX). Demonstrated by both in vivo and in vitro experiments, the biomineralization has stabilized DNA nanomaterials for biological applications.