Orthogonal multimodality integration and clustering in single-cell data.

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors.

BACKGROUND: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. METHODS: We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. RESULTS: By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70-87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. CONCLUSION: Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.

Transcriptional regulation of autophagy by an FXR-CREB axis.

Lysosomal degradation of cytoplasmic components by autophagy is essential for cellular survival and homeostasis under nutrient-deprived conditions. Acute regulation of autophagy by nutrient-sensing kinases is well defined, but longer-term transcriptional regulation is relatively unknown. Here we show that the fed-state sensing nuclear receptor farnesoid X receptor (FXR) and the fasting transcriptional activator cAMP response element-binding protein (CREB) coordinately regulate the hepatic autophagy gene network. Pharmacological activation of FXR repressed many autophagy genes and inhibited autophagy even in fasted mice, and feeding-mediated inhibition of macroautophagy was attenuated in FXR-knockout mice. From mouse liver chromatin immunoprecipitation and high-throughput sequencing data, FXR and CREB binding peaks were detected at 178 and 112 genes, respectively, out of 230 autophagy-related genes, and 78 genes showed shared binding, mostly in their promoter regions. CREB promoted autophagic degradation of lipids, or lipophagy, under nutrient-deprived conditions, and FXR inhibited this response. Mechanistically, CREB upregulated autophagy genes, including Atg7, Ulk1 and Tfeb, by recruiting the coactivator CRTC2. After feeding or pharmacological activation, FXR trans-repressed these genes by disrupting the functional CREB-CRTC2 complex. This study identifies the new FXR-CREB axis as a key physiological switch regulating autophagy, resulting in sustained nutrient regulation of autophagy during feeding/fasting cycles.

Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.

BACKGROUND: Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available methods treat gene expressions at different time points as replicates and test the significance of the mean expression difference between treatments or conditions irrespective of time. They thus fail to identify many DE genes with different profiles across time. In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes in time course RNA-Seq data. In the NBMM, mean gene expression is characterized by a fixed effect, and time dependency is described by random effects. The NBMM is very flexible and can be fitted to both unreplicated and replicated time course RNA-Seq data via a penalized likelihood method. By comparing gene expression profiles over time, we further classify the DE genes into two subtypes to enhance the understanding of expression dynamics. A significance test for detecting DE genes is derived using a Kullback-Leibler distance ratio. Additionally, a significance test for gene sets is developed using a gene set score. RESULTS: Simulation analysis shows that the NBMM outperforms currently available methods for detecting DE genes and gene sets. Moreover, our real data analysis of fruit fly developmental time course RNA-Seq data demonstrates the NBMM identifies biologically relevant genes which are well justified by gene ontology analysis. CONCLUSIONS: The proposed method is powerful and efficient to detect biologically relevant DE genes and gene sets in time course RNA-Seq data.

Expression profiling of the RPE in zebrafish smarca4 mutant revealed altered signals that potentially affect RPE and retinal differentiation.

PURPOSE: The purpose of this study was to develop a framework for analyzing retinal pigment epithelium (RPE) expression profiles from zebrafish eye mutants. METHODS: The fish model we used was SWI/SNF-related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 (smarca4), a retinal dystrophic mutant with a previously described retinal phenotype and expression profiles. Histological and Affymetrix GeneChip analyses were conducted to characterize the RPE defects and underlying differential expression, respectively. RESULTS: Histological analysis revealed that smarca4 RPE was formed, but its differentiation was abnormal. In particular, ultrastructural analysis of smarca4 RPE by transmission electron microscopy demonstrated several defects in melanogenesis. The nature of these defects also suggests that the cytoskeletal dynamics, which are tightly linked with melanogenesis, were impaired in smarca4 RPE. To compare the expression profile of normal wild-type (WT) and smarca4 RPE, the gene expression profiles of microdissected retinas and RPE-attached retinas were measured with Affymetrix GeneChip analysis. The RPE expression values were then estimated from these samples by subtracting the retinal expression values from the expression values of the RPE-attached retinas. A factorial analysis was conducted using the expression values of the RPE, retinal, and whole-embryo samples. Specific rules (contrasts) were built using the coefficients of the resulting fitted models to select for three groups of genes: 1) smarca4-regulated RPE genes, 2) smarca4-regulated retinal genes, and 3) smarca4-regulated RPE genes that are not differentially expressed in the retina. Interestingly, the third group consists of 39 genes that are highly related to cytoskeletal dynamics, melanogenesis, and paracrine and intracellular signal transduction. CONCLUSIONS: Our analytical framework provides an experimental approach to identify differentially-regulated genes in the retina and the RPE of zebrafish mutants in which both of these tissues are affected by the underlying mutation. Specifically, we have used the method to identify a group of 39 genes that can potentially explain the melanogenesis defect in the smarca4 RPE. In addition, several genes in this group are secreted signaling molecules. Thus, this observation further implicates that the smarca4 RPE might play a role in the retinal dystrophic phenotype in smarca4.

Identification of pathogenic fungi with an optoelectronic nose.

Human fungal infections have gained recent notoriety following contamination of pharmaceuticals in the compounding process. Such invasive infections are a more serious global problem, especially for immunocompromised patients. While superficial fungal infections are common and generally curable, invasive fungal infections are often life-threatening and much harder to diagnose and treat. Despite the increasing awareness of the situation's severity, currently available fungal diagnostic methods cannot always meet diagnostic needs, especially for invasive fungal infections. Volatile organic compounds produced by fungi provide an alternative diagnostic approach for identification of fungal strains. We report here an optoelectronic nose based on a disposable colorimetric sensor array capable of rapid differentiation and identification of pathogenic fungi based on their metabolic profiles of emitted volatiles. The sensor arrays were tested with 12 human pathogenic fungal strains grown on standard agar medium. Array responses were monitored with an ordinary flatbed scanner. All fungal strains gave unique composite responses within 3 hours and were correctly clustered using hierarchical cluster analysis. A standard jackknifed linear discriminant analysis gave a classification accuracy of 94% for 155 trials. Tensor discriminant analysis, which takes better advantage of the high dimensionality of the sensor array data, gave a classification accuracy of 98.1%. The sensor array is also able to observe metabolic changes in growth patterns upon the addition of fungicides, and this provides a facile screening tool for determining fungicide efficacy for various fungal strains in real time.

Volatilome and flavor analyses based on e-nose combined with HS-GC-MS provide new insights into ploidy germplasm diversity in Platostoma palustre.

Platostoma palustre (Mesona chinensis Benth or Hsian-tsao, also known as "Xiancao" in China), is an edible and medicinal plant native to India, Myanmar, and Indo-China. It is the main ingredient in the popular desserts Hsian-tsao tea, herbal jelly, and sweet herbal jelly soup. P. palustre is found abundantly in nutrient-rich substances and possesses unique aroma compounds. Variations in the contents of volatile compounds among different germplasms significantly affect the quality and flavor of P. palustre, causing contradiction in demand. This study investigates the variation in the volatile compound profiles of distinct ploidy germplasms of P. palustre by utilising headspace gas chromatography-mass spectrometry (HS-GC-MS) and an electronic nose (e-nose). The results showed significant differences in the aroma characteristics of stem and leaf samples in diverse P. palustre germplasms. A total of sixty-seven volatile compounds have been identified and divided into ten classes. Six volatile compounds (caryophyllene, α-bisabolol, benzaldehyde, ß-selinene, ß-elemene and acetic acid) were screened as potential marker volatile compounds to discriminate stems and leaves of P. palustre. In this study, leaves of P. palustre showed one odor pattern and stems showed two odor patterns under the influence of α-bisabolol, acetic acid, and butyrolactone. In addition, a correlation analysis was conducted on the main volatile compounds identified by HS-GC-MS and e-nose. This analysis provided additional insight into the variations among samples resulting from diverse germplasms. The present study provides a valuable volatilome, and flavor, and quality evaluation for P. palustre, as well as new insights and scientific basis for the development and use of P. palustre germplasm resources.

A Model-free Variable Screening Method Based on Leverage Score.

With rapid advances in information technology, massive datasets are collected in all fields of science, such as biology, chemistry, and social science. Useful or meaningful information is extracted from these data often through statistical learning or model fitting. In massive datasets, both sample size and number of predictors can be large, in which case conventional methods face computational challenges. Recently, an innovative and effective sampling scheme based on leverage scores via singular value decompositions has been proposed to select rows of a design matrix as a surrogate of the full data in linear regression. Analogously, variable screening can be viewed as selecting rows of the design matrix. However, effective variable selection along this line of thinking remains elusive. In this article, we bridge this gap to propose a weighted leverage variable screening method by utilizing both the left and right singular vectors of the design matrix. We show theoretically and empirically that the predictors selected using our method can consistently include true predictors not only for linear models but also for complicated general index models. Extensive simulation studies show that the weighted leverage screening method is highly computationally efficient and effective. We also demonstrate its success in identifying carcinoma related genes using spatial transcriptome data.

Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation.

Single cell RNA sequencing (scRNA-seq) technologies provide researchers with an unprecedented opportunity to exploit cell heterogeneity. For example, the sequenced cells belong to various cell lineages, which may have different cell fates in stem and progenitor cells. Those cells may differentiate into various mature cell types in a cell differentiation process. To trace the behavior of cell differentiation, researchers reconstruct cell lineages and predict cell fates by ordering cells chronologically into a trajectory with a pseudo-time. However, in scRNA-seq experiments, there are no cell-to-cell correspondences along with the time to reconstruct the cell lineages, which creates a significant challenge for cell lineage tracing and cell fate prediction. Therefore, methods that can accurately reconstruct the dynamic cell lineages and predict cell fates are highly desirable. In this article, we develop an innovative machine-learning framework called Cell Smoothing Transformation (CellST) to elucidate the dynamic cell fate paths and construct gene networks in cell differentiation processes. Unlike the existing methods that construct one single bulk cell trajectory, CellST builds cell trajectories and tracks behaviors for each individual cell. Additionally, CellST can predict cell fates even for less frequent cell types. Based on the individual cell fate trajectories, CellST can further construct dynamic gene networks to model gene-gene relationships along the cell differentiation process and discover critical genes that potentially regulate cells into various mature cell types.

[A nonlinear relationship between the hemoglobin level and prognosis of elderly patients with sepsis: an analysis based on MIMIC-IV].

OBJECTIVE: To investigate the correlation of hemoglobin (Hb) level with prognosis of elderly patients diagnosed as sepsis. METHODS: A retrospective cohort study was conducted. Information on the cases of elderly patients with sepsis in the Medical Information Mart for Intensive Care-IV (MIMIC-IV), including basic information, blood pressure, routine blood test results [the Hb level of a patient was defined as his/her maximum Hb level from 6 hours before admission to intensive care unit (ICU) and 24 hours after admission to ICU], blood biochemical indexes, coagulation function, vital signs, severity score and outcome indicators were extracted. The curves of Hb level vs. 28-day mortality risk were developed by using the restricted cubic spline model based on the Cox regression analysis. The patients were divided into four groups (Hb < 100 g/L, 100 g/L ≤ Hb < 130 g/L, 130 g/L ≤ Hb < 150 g/L, Hb ≥ 150 g/L groups) based on these curves. The outcome indicators of patients in each group were analyzed, and the 28-day Kaplan-Meier survival curve was drawn. Logistic regression model and Cox regression model were used to analyze the relationship between Hb level and 28-day mortality risk in different groups. RESULTS: A total of 7 473 elderly patients with sepsis were included. There was a "U" curve relationship between Hb levels within 24 hours after ICU admission and the risk of 28-day mortality in patients with sepsis. The patients with 100 g/L ≤ Hb < 130 g/L had a lower risk of 28-day mortality. When Hb level was less than 100 g/L, the risk of death decreased gradually with the increase of Hb level. When Hb level was ≥ 130 g/L, the risk of death gradually increased with the increase of Hb level. Multivariate Logistic regression analysis revealed that the mortality risks of patients with Hb < 100 g/L [odds ratio (OR) = 1.44, 95% confidence interval (95%CI) was 1.23-1.70, P < 0.001] and Hb ≥ 150 g/L (OR = 1.77, 95%CI was 1.26-2.49, P = 0.001) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (OR = 1.21, 95%CI was 0.99-1.48, P = 0.057). The multivariate Cox regression analysis suggested that the mortality risks of patients with Hb < 100 g/L [hazard ratio (HR) = 1.27, 95%CI was 1.12-1.44, P < 0.001] and Hb ≥ 150 g/L (HR = 1.49, 95%CI was 1.16-1.93, P = 0.002) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (HR = 1.17, 95%CI was 0.99-1.37, P = 0.053). Kaplan-Meier survival curve showed that the 28-day survival rate of elderly septic patients in 100 g/L ≤ Hb < 130 g/L group was significantly higher than that in Hb < 100 g/L, 130 g/L ≤ Hb < 150 g/L and Hb ≥ 150 g/L groups (85.26% vs. 77.33%, 79.81%, 74.33%; Log-Rank test: χ2 = 71.850, P < 0.001). CONCLUSIONS: Elderly patients with sepsis exhibited low mortality risk if their 100 g/L ≤ Hb < 130 g/L within 24 hours after admission to ICU, and both higher and lower Hb levels led to increased mortality risks.

Toward Enhanced Aerosol Particle Adsorption in Never-Bursting Bubble via Acoustic Levitation and Controlled Liquid Compensation.

Bubbles in air are ephemeral because of gravity-induced drainage and liquid evaporation, which severely limits their applications, especially as intriguing bio/chemical reactors. In this work, a new approach using acoustic levitation combined with controlled liquid compensation to stabilize bubbles is proposed. Due to the suppression of drainage by sound field and prevention of capillary waves by liquid compensation, the bubbles can remain stable and intact permanently. It has been found that the acoustically levitated bubble shows a significantly enhanced particle adsorption ability because of the oscillation of the bubble and the presence of internal acoustic streaming. The results shed light on the development of novel air-purification techniques without consuming any solid filters.

Smoothing splines approximation using Hilbert curve basis selection.

Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O(n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O(nq 2). These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly-distributed on a hyper-cube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O[n 2d/{(pr+1)(d +2)}] , where p ∈[1, 2] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors.

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks.

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. In this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors' inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for in-field deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

Cellular expression of Smarca4 (Brg1)-regulated genes in zebrafish retinas.

BACKGROUND: In a recent genomic study, Leung et al. used a factorial microarray analysis to identify Smarca4 (Brg1)-regulated genes in micro-dissected zebrafish retinas. Two hundred and fifty nine genes were grouped in three-way ANOVA models which carried the most specific retinal change. To validate the microarray results and to elucidate cellular expression patterns of the significant genes for further characterization, 32 known genes were randomly selected from this group. In situ hybridization of these genes was performed on the same types of samples (wild-type (WT) and smarca4a50/a50 (yng) mutant) at the same stages (36 and 52 hours post-fertilization (hpf)) as in the microarray study. RESULTS: Thirty out of 32 riboprobes showed a positive in situ staining signal. Twenty seven out of these 30 genes were originally further classified as Smarca4-regulated retinal genes, while the remaining three as retinal-specific expression independent of Smarca4 regulation. It was found that 90.32% of the significant microarray comparisons that were used to identify Smarca4-regulated retinal genes had a corresponding qualitative expression change in the in situ hybridization comparisons. This is highly concordant with the theoretical true discovery rate of 95%. Hierarchical clustering was used to investigate the similarity of the cellular expression patterns of 25 out of the 27 Smarca4-regulated retinal genes that had a sufficiently high expression signal for an unambiguous identification of retinal expression domains. Three broad groups of expression pattern were identified; including 1) photoreceptor layer/outer nuclear layer specific expression at 52 hpf, 2) ganglion cell layer (GCL) and/or inner nuclear layer (INL) specific expression at both 36 & 52 hpf, and 3) GCL and/or INL specific expression at 52 hpf only. Some of these genes have recently been demonstrated to play key roles in retinal cell-type specification, differentiation and lamination. For the remaining three retinal-specific genes that are independent of Smarca4 regulation, they all had a subtle expression difference between WT and smarca4a50/a50 retinas as detected by in situ hybridization. This subtle expression difference was also detected by the original microarray analysis. However, the difference was lower than the fold change cut-off used in that study and hence these genes were not inferred as Smarca4-regulated retinal genes. CONCLUSIONS: This study has successfully investigated the expression pattern of 32 genes identified from the original factorial microarray analysis. The results have demonstrated that the true discovery rate for identifying Smarca4-regulated retinal genes is 90.3%. Hence, the significant genes from the microarray study are good candidates for cell-type specific markers and will aid further investigation of retinal differentiation.

An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples.

Large samples are generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyse such large samples because of high computational costs. In particular, the daunting computational cost of selecting smoothing parameters renders smoothing spline ANOVA models impractical. In this article, we develop an asympirical, i.e., asymptotic and empirical, smoothing parameters selection method for smoothing spline ANOVA models in large samples. The idea of our approach is to use asymptotic analysis to show that the optimal smoothing parameter is a polynomial function of the sample size and an unknown constant. The unknown constant is then estimated through empirical subsample extrapolation. The proposed method significantly reduces the computational burden of selecting smoothing parameters in high-dimensional and large samples. We show that smoothing parameters chosen by the proposed method tend to the optimal smoothing parameters that minimize a specific risk function. In addition, the estimator based on the proposed smoothing parameters achieves the optimal convergence rate. Extensive simulation studies demonstrate the numerical advantage of the proposed method over competing methods in terms of relative efficacy and running time. In an application to molecular dynamics data containing nearly one million observations, the proposed method has the best prediction performance.

Details of Single-Molecule Force Spectroscopy Data Decoded by a Network-Based Automatic Clustering Algorithm.

Atomic force microscopy-single-molecule force spectroscopy (AFM-SMFS) is a powerful methodology to probe intermolecular and intramolecular interactions in biological systems because of its operability in physiological conditions, facile and rapid sample preparation, versatile molecular manipulation, and combined functionality with high-resolution imaging. Since a huge number of AFM-SMFS force-distance curves are collected to avoid human bias and errors and to save time, numerous algorithms have been developed to analyze the AFM-SMFS curves. Nevertheless, there is still a need to develop new algorithms for the analysis of AFM-SMFS data since the current algorithms cannot specify an unbinding force to a corresponding/each binding site due to the lack of networking functionality to model the relationship between the unbinding forces. To address this challenge, herein, we develop an unsupervised method, i.e., a network-based automatic clustering algorithm (NASA), to decode the details of specific molecules, e.g., the unbinding force of each binding site, given the input of AFM-SMFS curves. Using the interaction of heparan sulfate (HS)-antithrombin (AT) on different endothelial cell surfaces as a model system, we demonstrate that NASA is able to automatically detect the peak and calculate the unbinding force. More importantly, NASA successfully identifies three unbinding force clusters, which could belong to three different binding sites, for both Ext1f/f and Ndst1f/f cell lines. NASA has great potential to be applied either readily or slightly modified to other AFM-based SMFS measurements that result in "saw-tooth"-shaped force-distance curves showing jumps related to the force unbinding, such as antibody-antigen interaction and DNA-protein interaction.

Drug screening with zebrafish visual behavior identifies carvedilol as a potential treatment for an autosomal dominant form of retinitis pigmentosa.

Retinitis Pigmentosa (RP) is a mostly incurable inherited retinal degeneration affecting approximately 1 in 4000 individuals globally. The goal of this work was to identify drugs that can help patients suffering from the disease. To accomplish this, we screened drugs on a zebrafish autosomal dominant RP model. This model expresses a truncated human rhodopsin transgene (Q344X) causing significant rod degeneration by 7 days post-fertilization (dpf). Consequently, the larvae displayed a deficit in visual motor response (VMR) under scotopic condition. The diminished VMR was leveraged to screen an ENZO SCREEN-WELL REDOX library since oxidative stress is postulated to play a role in RP progression. Our screening identified a beta-blocker, carvedilol, that ameliorated the deficient VMR of the RP larvae and increased their rod number. Carvedilol may directly on rods as it affected the adrenergic pathway in the photoreceptor-like human Y79 cell line. Since carvedilol is an FDA-approved drug, our findings suggest that carvedilol can potentially be repurposed to treat autosomal dominant RP patients.

Dicer-like proteins influence Arabidopsis root microbiota independent of RNA-directed DNA methylation.

BACKGROUND: Plants are naturally associated with root microbiota, which are microbial communities influential to host fitness. Thus, it is important to understand how plants control root microbiota. Epigenetic factors regulate the readouts of genetic information and consequently many essential biological processes. However, it has been elusive whether RNA-directed DNA methylation (RdDM) affects root microbiota assembly. RESULTS: By applying 16S rRNA gene sequencing, we investigated root microbiota of Arabidopsis mutants defective in the canonical RdDM pathway, including dcl234 that harbors triple mutation in the Dicer-like proteins DCL3, DCL2, and DCL4, which produce small RNAs for RdDM. Alpha diversity analysis showed reductions in microbe richness from the soil to roots, reflecting the selectivity of plants on root-associated bacteria. The dcl234 triple mutation significantly decreases the levels of Aeromonadaceae and Pseudomonadaceae, while it increases the abundance of many other bacteria families in the root microbiota. However, mutants of the other examined key players in the canonical RdDM pathway showed similar microbiota as Col-0, indicating that the DCL proteins affect root microbiota in an RdDM-independent manner. Subsequently gene analysis by shotgun sequencing of root microbiome indicated a selective pressure on microbial resistance to plant defense in the dcl234 mutant. Consistent with the altered plant-microbe interactions, dcl234 displayed altered characters, including the mRNA and sRNA transcriptomes that jointly highlighted altered cell wall organization and up-regulated defense, the decreased cellulose and callose deposition in root xylem, and the restructured profile of root exudates that supported the alterations in gene expression and cell wall modifications. CONCLUSION: Our findings demonstrate an important role of the DCL proteins in influencing root microbiota through integrated regulation of plant defense, cell wall compositions, and root exudates. Our results also demonstrate that the canonical RdDM is dispensable for Arabidopsis root microbiota. These findings not only establish a connection between root microbiota and plant epigenetic factors but also highlight the complexity of plant regulation of root microbiota. Video abstract.

Colorimetric sensor array for determination and identification of toxic industrial chemicals.

A low-cost yet highly sensitive colorimetric sensor array for the detection and identification of toxic industrial chemicals (TICs) has been developed. The sensor consists of a disposable array of cross-responsive nanoporous pigments whose colors are changed by diverse chemical interactions with analytes. Clear differentiation among 20 different TICs has been easily achieved at both their IDLH (immediately dangerous to life or health) concentration within 2 min of exposure and PEL (permissible exposure limit) concentration within 5 min of exposure with no errors or misclassifications. Detection limits are generally well below the PEL (in most cases below 5% of PEL) and are typically in the low ppb range. The colorimetric sensor array is not responsive to changes in humidity or temperature over a substantial range. The printed arrays show excellent batch to batch reproducibility and long shelf life (greater than 3 months).