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.

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.

Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects.

With the increasing availability of large-scale GWAS summary data on various complex traits and diseases, there have been tremendous interests in applications of Mendelian randomization (MR) to investigate causal relationships between pairs of traits using SNPs as instrumental variables (IVs) based on observational data. In spite of the potential significance of such applications, the validity of their causal conclusions critically depends on some strong modeling assumptions required by MR, which may be violated due to the widespread (horizontal) pleiotropy. Although many MR methods have been proposed recently to relax the assumptions by mainly dealing with uncorrelated pleiotropy, only a few can handle correlated pleiotropy, in which some SNPs/IVs may be associated with hidden confounders, such as some heritable factors shared by both traits. Here we propose a simple and effective approach based on constrained maximum likelihood and model averaging, called cML-MA, applicable to GWAS summary data. To deal with more challenging situations with many invalid IVs with only weak pleiotropic effects, we modify and improve it with data perturbation. Extensive simulations demonstrated that the proposed methods could control the type I error rate better while achieving higher power than other competitors. Applications to 48 risk factor-disease pairs based on large-scale GWAS summary data of 3 cardio-metabolic diseases (coronary artery disease, stroke, and type 2 diabetes), asthma, and 12 risk factors confirmed its superior performance.

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.

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.

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.

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.

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.

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.

Deep reinforcement learning for personalized treatment recommendation.

In precision medicine, the ultimate goal is to recommend the most effective treatment to an individual patient based on patient-specific molecular and clinical profiles, possibly high-dimensional. To advance cancer treatment, large-scale screenings of cancer cell lines against chemical compounds have been performed to help better understand the relationship between genomic features and drug response; existing machine learning approaches use exclusively supervised learning, including penalized regression and recommender systems. However, it would be more efficient to apply reinforcement learning to sequentially learn as data accrue, including selecting the most promising therapy for a patient given individual molecular and clinical features and then collecting and learning from the corresponding data. In this article, we propose a novel personalized ranking system called Proximal Policy Optimization Ranking (PPORank), which ranks the drugs based on their predicted effects per cell line (or patient) in the framework of deep reinforcement learning (DRL). Modeled as a Markov decision process, the proposed method learns to recommend the most suitable drugs sequentially and continuously over time. As a proof-of-concept, we conduct experiments on two large-scale cancer cell line data sets in addition to simulated data. The results demonstrate that the proposed DRL-based PPORank outperforms the state-of-the-art competitors based on supervised learning. Taken together, we conclude that novel methods in the framework of DRL have great potential for precision medicine and should be further studied.

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environmental perturbations. With a large number of images generated by high-content microscopy while manual labeling is both labor-intensive and error-prone, machine learning offers a viable alternative for automatic labeling of subcellular localizations. Contrarily, in recent years applications of deep learning methods to large datasets in natural images and other domains have become quite successful. An appeal of deep learning methods is that they can learn salient features from complicated data with little data preprocessing. For such purposes, we applied several representative types of deep convolutional neural networks (CNNs) and two popular ensemble methods, random forests and gradient boosting, to predict protein subcellular localization with a moderately large cell image data set. We show a consistently better predictive performance of CNNs over the two ensemble methods. We also demonstrate the use of CNNs for feature extraction. In the end, we share our computer code and pretrained models to facilitate CNN's applications in genetics and computational biology.

Multi-Dimensionally Extended Functionalization Innovates to an Entropy-Driven Detection of Multi-miRNAs for One-Step Cancer Screening and Diagnosis in Living Cells.

Compared with tedious multi-step detections, multi-functional nanoprobes are effective for one-step screening and diagnosis of cancers by multi-detection of microRNAs (miRNAs). However, limited probe density, spatial mutual interference, and low target-triggered hybridization efficiency of nanoprobes will hinder intracellular applications. Here, for obtaining high loading density but low spatial mutual interference between functional biomolecules on nanoprobes, an extended biofunctionalization in three dimensions (the two-dimensional surface and a special "height" direction) is designed. Therefore, a multi-functional probe is constructed for one-step detection of multi-miRNAs for cancer screening and diagnosis. With linker-bridged multiple single-stranded DNAs swung out rigidly, multi-dimensionally extended upconversion nanorods (ME-UCNRs) covered by chitosan are constructed to load and deliver multiple biomolecules into living cells. Escaping from endolysosomes, ME-UCNRs maintain good biological activities of functionalized DNAs for effective detection of multi-miRNAs in living cells. Thereby, with multiple targets of miRNAs, toehold-mediated entropy-driven strand displacements are employed to give respectively changed fluorescent signals via fluorescence resonance energy transfer. Thus, a universal cancer biomarker of miR-21 and two specific liver-cancer biomarkers (miR-199a and miR-224) are efficiently detected through a one-step detection. By discriminating cancer cells from normal ones and determining liver-cancer cells simultaneously, this work innovates an efficient and definite one-step strategy for fast screening and early cancer diagnosis.

A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data.

MOTIVATION: Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance. RESULTS: First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance. AVAILABILITY AND IMPLEMENTATION: Computer code is available at https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

S6K1/S6 axis-regulated lymphocyte activation is important for adaptive immune response of Nile tilapia.

Ribosomal protein S6 kinase beta-1 (S6K1) is a serine/threonine kinase downstream of the mechanistic target of rapamycin (mTOR) pathway, and plays crucial roles in immune regulation. Although remarkable progress has been achieved with a mouse model, how S6K1 regulates adaptive immunity is largely unknown in early vertebrates. In this study, we identified an S6K1 from Nile tilapia Oreochromis niloticus (OnS6K1), and further investigated its potential regulatory role on the adaptive immunity of this fish species. Both sequence and structure of OnS6K1 were highly conserved with its homologs from other vertebrates and invertebrates. OnS6K1 was widely expressed in immune tissues, and with a relative higher expression level in the liver, spleen and head kidney. At the adaptive immune stage of Nile tilapia that infected with Aeromonas hydrophila, mRNA expression of OnS6K1 and its downstream effector S6 was significantly up-regulated in spleen lymphocytes. Meanwhile, their phosphorylation level was also enhanced during this process, suggesting that S6K1/S6 axis participated in the primary response of anti-bacterial adaptive immunity in Nile tilapia. Furthermore, after spleen lymphocytes were activated by the T cell-specific mitogen PHA or lymphocytes agonist PMA in vitro, mRNA and phosphorylation levels of S6K1 were elevated, and phosphorylation of S6 was also enhanced. Once S6K1 activity was blocked by a specific inhibitor, both mRNA and phosphorylation levels of S6 were severely impaired. More importantly, blockade of S6K1/S6 axis reduced the expression of T cell activation marker IFN-γ and CD122 in PHA-activated spleen lymphocytes, indicating the essential role of S6K1/S6 axis in regulating T cell activation of Nile tilapia. Together, our study suggests that S6K1 and its effector S6 regulate lymphocyte activation of Nile tilapia, and in turn promote lymphocyte-mediated adaptive immunity. This study enriched the mechanism of adaptive immune response in teleost and provided useful clues to understand the evolution of adaptive immune system.

Intensive removal of PAHs in constructed wetland filled with copper biochar.

In this study, biochar-loading copper ions (Cu-BC), a novel composite for removing phenanthrene very efficiently from water, was prepared using the impregnation method. The performance of constructed wetlands (CWs) with these modified and original biochar as substrates was analyzed. CW with Cu-BC removed a large amount of phenanthrene (94.09 ± 3.02%). According to the surface characteristics analysis, Cu-BC can promote the removal of pollutants via complex absorption, hydrophobic adsorption, increasing the Lewis Pair and electrostatic attraction. Furthermore the higher nitrate removal rate in the treated system (91.11 ± 1.17%) was observed to have higher levels of bacterial metabolic diversity and denitrifier types. The phenanthrene accumulated in plants with this treatment system was enhanced by the role of copper in photosynthesis. It is able to boost the plant extraction of organic matter.

A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion.

For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, in which an outcome is derived from one of a finite number of linear regression models, is a natural tool in this setting. In this article, we first propose a new penalized regression approach. Then, we demonstrate how the proposed approach better identifies subpopulations and their corresponding models than a semiparametric FMR method does. Our new method fits models for each person via grouping pursuit, utilizing a new group-truncated L 1 penalty that shrinks the differences between estimated parameter vectors. The methodology causes the individuals' models to cluster into a few common models, in turn revealing previously unknown subpopulations. In fact, by varying the penalty strength, the new method can reveal a hierarchical structure among the subpopulations that can be useful in exploratory analyses. Simulations using FMR models and a real-data analysis show that the method performs promisingly well.

LARGE MULTIPLE GRAPHICAL MODEL INFERENCE VIA BOOTSTRAP.

Large economic and financial networks may experience stage-wise changes as a result of external shocks. To detect and infer a structural change, we consider an inference problem within a framework of multiple Gaussian Graphical Models when the number of graphs and the dimension of graphs increase with the sample size. In this setting, two major challenges emerge as a result of the bias and uncertainty inherent in the regularization required to treat such overparameterized models. To deal with these challenges, the bootstrap method is utilized to approximate the sampling distribution of a likelihood ratio test statistic. We show theoretically that the proposed method leads to a correct asymptotic inference in a high-dimensional setting, regardless of the distribution of the test statistic. Simulations show that the proposed method compares favorably to its competitors such as the Likelihood Ratio Test. Finally, our statistical analysis of a network of 200 stocks reveals that the interacting units in the financial network become more connected as a result of the financial crisis between 2007 and 2009. More importantly, certain units respond more strongly than others. Furthermore, after the crisis, some changes weaken, while others strengthen.

[Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network].

To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately ( P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features ( P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.