Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition.

Spatial transcripome (ST) profiling can reveal cells' structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge-the high-order structure hiding in whole transcriptome space over 2D/3D spatial coordinates requires modeling and detection of interpretable high-order elements and components for further functional analysis and interpretation. This paper presents a new method GraphTucker-graph-regularized Tucker tensor decomposition for learning high-order factorization in ST data. GraphTucker is based on a nonnegative Tucker decomposition algorithm regularized by a high-order graph that captures spatial relation among spots and functional relation among genes. In the experiments on several Visium and Stereo-seq datasets, the novelty and advantage of modeling multiway multilinear relationships among the components in Tucker decomposition are demonstrated as opposed to the Canonical Polyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial components of gene modules, clustering spatial coefficients for tissue segmentation and imputing complete spatial transcriptomes. The results of visualization show strong evidence that GraphTucker detect more interpretable spatial components in the context of the spatial domains in the tissues. AVAILABILITY AND IMPLEMENTATION: https://github.com/kuanglab/GraphTucker.

Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs.

MOTIVATION: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity. RESULTS: Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. AVAILABILITY AND IMPLEMENTATION: https://github.com/song0309/asGNN/.

MOF-Enhanced Chiral ECL Recognition System: Dual-Function in Phenylalanine Enantiomer Detection and Coreaction Acceleration.

The accurate discernment and separation of chiral isomers with high precision remain a significant challenge in various industries and biological fields. In this investigation, an electrochemiluminescent (ECL) chiral recognition platform was devised to ascertain the presence of phenylalanine (Phe). Notably, a homochiral [Ni2(l-asp)2(bipy)] (Ni-LAB) was established as a dual-function coreactant accelerator and chiral recognition substrate. Ni-LAB facilitates the reaction between the coreactant (K2S2O8) and the luminescent entity 3,4,9,10-perylenetetracar-boxylic-l-cysteine (PTCA-cys), thereby enhancing the ECL luminescence efficiency and improving the sensitivity of the chiral sensor. The chiral recognition potential of Ni-LAB was assessed to differentiate between Phe chiral isomers, and the underlying mechanism was comprehensively elucidated. This system exhibited remarkable proficiency in detecting Phe enantiomers and precisely differentiating a single Phe enantiomer within a mixture, showcasing exceptional levels of selectivity, stability, and reproducibility. This study paves the way for the development of advanced chiral recognition systems, potentially revolutionizing the field of chiral sensing and discrimination.

Chiral Discrimination of Penicillamine Enantiomers: The Role of Aggregation-Caused Quenching in Achieving High Selectivity.

The recognition and separation of chiral isomers are of great importance in both industrial and biological applications. In this study, a chiral recognition system based on electrochemiluminescence was established for the detection of penicillamine (PA) enantiomers. The system utilized a homochiral [Zn2(BDC)(d-lac)] (Zn-BL) platform for the uniform distribution of Ru(bpy)32+ nanoparticles, effectively mitigating aggregation-caused quenching. The chiral recognition ability of Zn-BL was tested to distinguish between PA enantiomers, and the results indicated a substantial increase in the chiral electrochemiluminescence (ECL) signal when l-PA was present, in contrast to d-PA. The mechanism underlying ECL chiral discrimination was investigated using water contact angle measurements, DFT calculations, and electrochemical characterization. The system exhibited high selectivity, stability, and reproducibility for PA enantiomer detection. Furthermore, the proposed method can accurately identify one enantiomer of PA in a mixture. This study provides a reliable and sensitive approach for achieving the highly selective detection of chiral molecules.

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this article, we present a convolutional neural network (CNN) regularized by the graph of protein-protein interaction (PPI) network to cluster spatially resolved gene expression. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization. RESULTS: In this study, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10× Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/kuanglab/CNN-PReg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Roles of Macrophages and Their Interactions with Schwann Cells After Peripheral Nerve Injury.

The adult peripheral nervous system has a significant ability for regeneration compared to the central nervous system. This is related to the unique neuroimmunomodulation after peripheral nerve injury (PNI). Unlike the repair of other tissues after injury, Schwann cells (SCs) respond immediately to the trauma and send out signals to precisely recruit macrophages to the injured site. Then, macrophages promote the degradation of the damaged myelin sheath by phagocytosis of local debris. At the same time, macrophages and SCs jointly secrete various cytokines to reconstruct a microenvironment suitable for nerve regeneration. This unique pathophysiological process associated with macrophages provides important targets for the repair and treatment of PNI, as well as an important reference for guiding the repair of other nerve injuries. To understand these processes more systematically, this paper describes the characteristics of macrophage activation and metabolism in PNI, discusses the underlying molecular mechanism of interaction between macrophages and SCs, and reviews the latest research progress of crosstalk regulation between macrophages and SCs. These concepts and therapeutic strategies are summarized to provide a reference for the more effective use of macrophages in the repair of PNI.

Machine learning and statistical methods for clustering single-cell RNA-sequencing data.

Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics. AVAILABILITY: All the source code and data are available at https://github.com/kuanglab/single-cell-review.

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion.

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

Platform-integrated mRNA isoform quantification.

MOTIVATION: Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. RESULTS: In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString's nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control.

U2 auxiliary factor 1 (U2AF1) functions in 3'-splice site selection during pre-mRNA processing. Alternative usage of duplicated tandem exons in U2AF1 produces two isoforms, U2AF1a and U2AF1b, but their functional differences are unappreciated due to their homology. Through integrative approaches of genome editing, customized-transcriptome profiling and crosslinking-mediated interactome analyses, we discovered that the expression of U2AF1 isoforms is controlled by mTOR and they exhibit a distinctive molecular profile for the splice site and protein interactomes. Mechanistic dissection of mutually exclusive alternative splicing events revealed that U2AF1 isoforms' inherent differential preferences of nucleotide sequences and their stoichiometry determine the 3'-splice site. Importantly, U2AF1a-driven transcriptomes feature alternative splicing events in the 5'-untranslated region (5'-UTR) that are favorable for translation. These findings unveil distinct roles of duplicated tandem exon-derived U2AF1 isoforms in the regulation of the transcriptome and suggest U2AF1a-driven 5'-UTR alternative splicing as a molecular mechanism of mTOR-regulated translational control.

A large-scale comparative study of isoform expressions measured on four platforms.

BACKGROUND: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. RESULTS: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. CONCLUSION: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response.

3'-untranslated regions (UTRs) can vary through the use of alternative polyadenylation sites during pre-mRNA processing. Multiple publically available pipelines combining high profiling technologies and bioinformatics tools have been developed to catalog changes in 3'-UTR lengths. In our recent RNA-seq experiments using cells with hyper-activated mammalian target of rapamycin (mTOR), we found that cellular mTOR activation leads to transcriptome-wide alternative polyadenylation (APA), resulting in the activation of multiple cellular pathways. Here, we developed a novel bioinformatics algorithm, IntMAP, which integrates RNA-Seq and PolyA Site (PAS)-Seq data for a comprehensive characterization of APA events. By applying IntMAP to the datasets from cells with hyper-activated mTOR, we identified novel APA events that could otherwise not be identified by either profiling method alone. Several transcription factors including Cebpg (CCAAT/enhancer binding protein gamma) were among the newly discovered APA transcripts, indicating that diverse transcriptional networks may be regulated by mTOR-coordinated APA. The prevention of APA in Cebpg using the CRISPR/cas9-mediated genome editing tool showed that mTOR-driven 3'-UTR shortening in Cebpg is critical in protecting cells from endoplasmic reticulum (ER) stress. Taken together, we present IntMAP as a new bioinformatics algorithm for APA analysis by which we expand our understanding of the physiological role of mTOR-coordinated APA events to ER stress response. IntMAP toolbox is available at http://compbio.cs.umn.edu/IntMAP/.

Obesity-induced protein carbonylation in murine adipose tissue regulates the DNA-binding domain of nuclear zinc finger proteins.

In obesity-linked insulin resistance, oxidative stress in adipocytes leads to lipid peroxidation and subsequent carbonylation of proteins by diffusible lipid electrophiles. Reduction in oxidative stress attenuates protein carbonylation and insulin resistance, suggesting that lipid modification of proteins may play a role in metabolic disease, but the mechanisms remain incompletely understood. Herein, we show that in vivo, diet-induced obesity in mice surprisingly results in preferential carbonylation of nuclear proteins by 4-hydroxy-trans-2,3-nonenal (4-HNE) or 4-hydroxy-trans-2,3-hexenal (4-HHE). Proteomic and structural analyses revealed that residues in or around the sites of zinc coordination of zinc finger proteins, such as those containing the C2H2 or MATRIN, RING, C3H1, or N4-type DNA-binding domains, are particularly susceptible to carbonylation by lipid aldehydes. These observations strongly suggest that carbonylation functionally disrupts protein secondary structure supported by metal coordination. Analysis of one such target, the nuclear protein estrogen-related receptor γ (ERR-γ), showed that ERR-γ is modified by 4-HHE in the obese state. In vitro carbonylation decreased the DNA-binding capacity of ERR-γ and correlated with the obesity-linked down-regulation of many key genes promoting mitochondrial bioenergetics. Taken together, these findings reveal a novel mechanistic connection between oxidative stress and metabolic dysfunction arising from carbonylation of nuclear zinc finger proteins, such as the transcriptional regulator ERR-γ.

Scalable remote homology detection and fold recognition in massive protein networks.

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC.

Hollow Polyhedral Arrays Composed of a Co3O4 Nanocrystal Ensemble on a Honeycomb-like Carbon Hybrid for Boosting Highly Active and Stable Evolution Oxygen.

We synthesize hollow polyhedral arrays composed of honeycomb-like nanosheets of Co3O4 nanocrystals imbedded on carbon quantum dots (CQDs)- and nitrogen-codoped carbon matrix via a facile in situ air oxidation pyrolysis for CQDs-incorporated metal-organic framework polyhedral arrays. The function of CQDs hollowing and forming porous nanosheet shells was found. The resulting hierarchical architecture displays excellent oxygen evolution reaction activity with a low overpotential of 301 mV to drive 100 mA cm-2 in 1.0 M KOH and long-playing durability in oxygen evolution. The high performance can be ascribed to its highly dispersed Co3O4 nanocrystals, CQDs and nitrogen codoping, internal cavities, and hierarchical pore system.

Global analysis of canola genes targeted by SHORT HYPOCOTYL UNDER BLUE 1 during endosperm and embryo development.

Seed development in dicots includes early endosperm proliferation followed by growth of the embryo to replace the endosperm. Endosperm proliferation in dicots not only provides nutrient supplies for subsequent embryo development but also enforces a space limitation, influencing final seed size. Overexpression of Arabidopsis SHORT HYPOCOTYL UNDER BLUE1::uidA (SHB1:uidA) in canola produces large seeds. We performed global analysis of the canola genes that were expressed and influenced by SHB1 during early endosperm proliferation at 8 days after pollination (DAP) and late embryo development at 13 DAP. Overexpression of SHB1 altered the expression of 973 genes at 8 DAP and 1035 genes at 13 DAP. We also surveyed the global SHB1 association sites, and merging of these sites with the RNA sequencing data identified a set of canola genes targeted by SHB1. The 8-DAP list includes positive and negative genes that influence endosperm proliferation and are homologous to Arabidopsis MINI3, IKU2, SHB1, AGL62, FIE and AP2. We revealed a major role for SHB1 in canola endosperm development based on the dynamics of SHB1-altered gene expression, the magnitude of SHB1 chromatin immunoprecipitation enrichment and the over-representation of eight regulatory genes for endosperm development. Our studies focus on an important agronomic trait in a major crop for global agriculture. The datasets on stage-specific and SHB1-induced gene expression and genes targeted by SHB1 also provide a useful resource in the field of endosperm development and seed size engineering. Our practices in an allotetraploid species will impact similar studies in other crop species.

Transfer learning across ontologies for phenome-genome association prediction.

Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms. Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions. Availability and Implementation: Source code is available at http://compbio.cs.umn.edu/onto phenome . Contact: kuang@cs.umn.com. Supplementary information: Supplementary data are available at Bioinformatics online.

HRB2 and BBX21 interaction modulates Arabidopsis ABI5 locus and stomatal aperture.

Blue light triggers the opening of stomata in the morning to allow CO2 uptake and water loss through transpiration. During the day, plants may experience periodic drought and accumulate abscisic acid (ABA). ABA antagonizes blue light signalling through phosphatidic acid and reduces stomatal aperture. This study reveals a molecular mechanism by which two light signalling proteins interact to repress ABA signalling in the control of stomatal aperture. A hypersensitive to red and blue 2 (hrb2) mutant has a defective ATP-dependent chromatin-remodelling factor, PKL, in the chromodomain/helicase/DNA binding family. HRB2 enhances the light-induced expression of a B-box transcription factor gene, BBX21. BBX21 binds a T/G box in the ABI5 promoter and recruits HRB2 to modulate the chromatin structure at the ABI5 locus. Mutation in either HRB2 or BBX21 led to reduced water loss and ABA hypersensitivity. This hypersensitivity to ABA was well explained by the enhanced expression of the ABA signalling gene ABI5 in both mutants. Indeed, stomatal aperture was significantly reduced by ABI5 overexpression in the absence or presence of ABA under monochromatic light conditions. Overall, we present a regulatory loop in which two light signalling proteins repress ABA signalling to sustain gas exchange when plants experience periodic drought.

TP53 mutations, tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer.

To determine early somatic changes in high-grade serous ovarian cancer (HGSOC), we performed whole genome sequencing on a rare collection of 16 low stage HGSOCs. The majority showed extensive structural alterations (one had an ultramutated profile), exhibited high levels of p53 immunoreactivity, and harboured a TP53 mutation, deletion or inactivation. BRCA1 and BRCA2 mutations were observed in two tumors, with nine showing evidence of a homologous recombination (HR) defect. Combined Analysis with The Cancer Genome Atlas (TCGA) indicated that low and late stage HGSOCs have similar mutation and copy number profiles. We also found evidence that deleterious TP53 mutations are the earliest events, followed by deletions or loss of heterozygosity (LOH) of chromosomes carrying TP53, BRCA1 or BRCA2. Inactivation of HR appears to be an early event, as 62.5% of tumours showed a LOH pattern suggestive of HR defects. Three tumours with the highest ploidy had little genome-wide LOH, yet one of these had a homozygous somatic frame-shift BRCA2 mutation, suggesting that some carcinomas begin as tetraploid then descend into diploidy accompanied by genome-wide LOH. Lastly, we found evidence that structural variants (SV) cluster in HGSOC, but are absent in one ultramutated tumor, providing insights into the pathogenesis of low stage HGSOC.