Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type–specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Drought transcriptome analysis of finger millet (Eleusine coracana) by cDNA subtraction identified drought responsive genes that have a potential role in drought tolerance. Through virus-induced gene silencing (VIGS) in a related crop species, maize (Zea mays), several genes, including a G-BOX BINDING FACTOR 3 (GBF3) were identified as candidate drought stress response genes and the role of GBF3 in drought tolerance was studied in Arabidopsis thaliana. Overexpression of both EcGBF3 and AtGBF3 in A. thaliana resulted in improved tolerance to osmotic stress, salinity and drought stress in addition to conferring insensitivity to ABA. Conversely, loss of function of this gene increased the sensitivity of A. thaliana plants to drought stress. EcGBF3 transgenic A. thaliana results also suggest that drought tolerance of sensitive plants can be improved by transferring genes from far related crops like finger millet. Our results demonstrate the role of GBF3 in imparting drought tolerance in A. thaliana and indicate the conserved role of this gene in drought and other abiotic stress tolerance in several plant species.

The conversion of oleic acid (C18:1) to linoleic acid (C18:2) in the endoplasmic reticulum is critical to the accumulation of polyunsaturated fatty acids in seeds and other tissues, and this reaction is catalyzed by a Δ12-desaturase, FATTY ACID DESATURASE2 (FAD2). Here, we report that the tomato (Solanum lycopersicum) genome harbors two genes, SlFAD2-1 and SlFAD2-2, which encode proteins with in vitro Δ12-desaturase activity. In addition, tomato has seven divergent FAD2 members that lack Δ12-desaturase activity and differ from canonical FAD2 enzymes at multiple amino acid positions important to enzyme function. Whereas SlFAD2-1 and SlFAD2-2 are downregulated by biotic stress, the majority of divergent FAD2 genes in tomato are upregulated by one or more stresses. In particular, SlFAD2-7 is induced by the potato aphid (Macrosiphum euphorbiae) and has elevated constitutive expression levels in suppressor of prosystemin-mediated responses2 (spr2), a tomato mutant with enhanced aphid resistance and altered fatty acid profiles. Virus-induced gene silencing of SlFAD2-7 in spr2 results in significant increases in aphid population growth, indicating that a divergent FAD2 gene contributes to aphid resistance in this genotype. Thus, the FAD2 gene family in tomato is important both to primary fatty acid metabolism and to responses to biotic stress.

Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g. response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource – RECoN – that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice stress transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.