Elucidation and analyses of the regulatory networks of upland and lowland ecotypes of switchgrass in response to drought and salt stresses

Temporal regulation of cold transcriptional response in switchgrass

Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis-regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k-mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k-mers, referred to as putative cis-regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.

Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning

Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.

Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

MOTIVATION

Joint profiling of single-cell transcriptomics and epigenomics data enables us to characterize cell states and transcriptomics regulatory programs related to cellular heterogeneity. However, the highly different features on sparsity, heterogeneity, and dimensionality between multi-omics data have severely hindered its integrative analysis.

RESULTS

We proposed deep cross-omics cycle attention (DCCA) model, a computational tool for joint analysis of single-cell multi-omics data, by combining variational autoencoders (VAEs) and attention-transfer. Specifically, we show that DCCA can leverage one omics data to fine-tune the network trained for another omics data, given a dataset of parallel multi-omics data within the same cell. Studies on both simulated and real datasets from various platforms, DCCA demonstrates its superior capability: (i) dissecting cellular heterogeneity; (ii) denoising and aggregating data; and (iii) constructing the link between multi-omics data, which is used to infer new transcriptional regulatory relations. In our applications, DCCA was demonstrated to have a superior power to generate missing stages or omics in a biologically meaningful manner, which provides a new way to analyze and also understand complicated biological processes.

AVAILABILITY AND IMPLEMENTATION

DCCA source code is available at https://github.com/cmzuo11/DCCA, and has been deposited in archived format at https://doi.org/10.5281/zenodo.4762065.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomic and metabolomic analyses of Lycium ruthenicum and Lycium barbarum fruits during ripening

Red wolfberry (or goji berry, Lycium barbarum; LB) is an important agricultural product with a high content of pharmacologically important secondary metabolites such as phenylpropanoids. A close relative, black wolfberry (L. ruthenicum; LR), endemic to the salinized deserts of northwestern China, is used only locally. The two fruits exhibit many morphological and phytochemical differences, but genetic mechanisms underlying them remain poorly explored. In order to identify the genes of interest for further studies, we studied transcriptomic (Illumina HiSeq) and metabolomic (LC-MS) profiles of the two fruits during five developmental stages (young to ripe). As expected, we identified much higher numbers of significantly differentially regulated genes (DEGs) than metabolites. The highest numbers were identified in pairwise comparisons including the first stage for both species, but total numbers were consistently somewhat lower for the LR. The number of differentially regulated metabolites in pairwise comparisons of developmental stages varied from 66 (stages 3 vs 4) to 133 (stages 2 vs 5) in both species. We identified a number of genes (e.g. AAT1, metE, pip) and metabolites (e.g. rutin, raffinose, galactinol, trehalose, citrulline and DL-arginine) that may be of interest to future functional studies of stress adaptation in plants. As LB is also highly suitable for combating soil desertification and alleviating soil salinity/alkalinity/pollution, its potential for human use may be much wider than its current, highly localized, relevance.