PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2.

In search for broad-spectrum antivirals, we discovered a small molecule inhibitor, RMC-113, that potently suppresses the replication of multiple RNA viruses including SARS-CoV-2 in human lung organoids. We demonstrated selective dual inhibition of the lipid kinases PIP4K2C and PIKfyve by RMC-113 and target engagement by its clickable analog. Advanced lipidomics revealed alteration of SARS-CoV-2-induced phosphoinositide signature by RMC-113 and linked its antiviral effect with functional PIP4K2C and PIKfyve inhibition. We discovered PIP4K2C's roles in SARS-CoV-2 entry, RNA replication, and assembly/egress, validating it as a druggable antiviral target. Integrating proteomics, single-cell transcriptomics, and functional assays revealed that PIP4K2C binds SARS-CoV-2 nonstructural protein 6 and regulates virus-induced impairment of autophagic flux. Reversing this autophagic flux impairment is a mechanism of antiviral action of RMC-113. These findings reveal virus-induced autophagy regulation via PIP4K2C, an understudied kinase, and propose dual inhibition of PIP4K2C and PIKfyve as a candidate strategy to combat emerging viruses.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach.

Severe dengue (SD) is a major cause of morbidity and mortality. To define dengue virus (DENV) target cells and immunological hallmarks of SD progression in children's blood, we integrated two single-cell approaches capturing cellular and viral elements: virus-inclusive single-cell RNA sequencing (viscRNA-Seq 2) and targeted proteomics with secretome analysis and functional assays. Beyond myeloid cells, in natural infection, B cells harbor replicating DENV capable of infecting permissive cells. Alterations in cell type abundance, gene and protein expression and secretion as well as cell-cell communications point towards increased immune cell migration and inflammation in SD progressors. Concurrently, antigen-presenting cells from SD progressors demonstrate intact uptake yet impaired interferon response and antigen processing and presentation signatures, which are partly modulated by DENV. Increased activation, regulation and exhaustion of effector responses and expansion of HLA-DR-expressing adaptive-like NK cells also characterize SD progressors. These findings reveal DENV target cells in human blood and provide insight into SD pathogenesis beyond antibody-mediated enhancement.

Long Non-coding RNA in Plants in the Era of Reference Sequences.

The discovery of non-coding RNAs (ncRNAs), and the subsequent elucidation of their functional roles, was largely delayed due to the misidentification of non-protein-coding parts of DNA as "junk DNA," which forced ncRNAs into the shadows of their protein-coding counterparts. However, over the past decade, insight into the important regulatory roles of ncRNAs has led to rapid progress in their identification and characterization. Of the different types of ncRNAs, long non-coding RNAs (lncRNAs), has attracted considerable attention due to their mRNA-like structures and gene regulatory functions in plant stress responses. While RNA sequencing has been commonly used for mining lncRNAs, a lack of widespread conservation at the sequence level in addition to relatively low and highly tissue-specific expression patterns challenges high-throughput in silico identification approaches. The complex folding characteristics of lncRNA molecules also complicate target predictions, as the knowledge about the interaction interfaces between lncRNAs and potential targets is insufficient. Progress in characterizing lncRNAs and their targets from different species may hold the key to efficient identification of this class of ncRNAs from transcriptomic and potentially genomic resources. In wheat and barley, two of the most important crops, the knowledge about lncRNAs is very limited. However, recently published high-quality genomes of these crops are considered as promising resources for the identification of not only lncRNAs, but any class of molecules. Considering the increasing demand for food, these resources should be used efficiently to discover molecular mechanisms lying behind development and a/biotic stress responses. As our understanding of lncRNAs expands, interactions among ncRNA classes, as well as interactions with the coding sequences, will likely define novel functional networks that may be modulated for crop improvement.

RNA Sequencing and Co-expressed Long Non-coding RNA in Modern and Wild Wheats.

There is an urgent need for the improvement of drought-tolerant bread and durum wheat. The huge and complex genome of bread wheat (BBAADD genome) stands as a vital obstruction for understanding the molecular mechanism underlying drought tolerance. However, tetraploid wheat (Triticum turgidum ssp., BBAA genome) is an ancestor of modern bread wheat and offers an important model for studying the drought response due to its less complex genome. Additionally, several wild relatives of tetraploid wheat have already shown a significant drought tolerance. We sequenced root transcriptome of three tetraploid wheat varieties with varying stress tolerance profiles, and built differential expression library of their transcripts under control and drought conditions. More than 5,000 differentially expressed transcripts were identified from each genotype. Functional characterization of transcripts specific to drought-tolerant genotype, revealed their association with osmolytes production and secondary metabolite pathways. Comparative analysis of differentially expressed genes and their non-coding RNA partners, long noncoding RNAs and microRNAs, provided valuable insight to gene expression regulation in response to drought stress. LncRNAs as well as coding transcripts share similar structural features in different tetraploid species; yet, lncRNAs slightly differ from coding transcripts. Several miRNA-lncRNA target pairs were detected as differentially expressed in drought stress. Overall, this study suggested an important pool of transcripts where their manipulations confer a better performance of wheat varieties under drought stress.

Leveling the Playing Field in Homozygosity Mapping Using Map Distances.

Studies of linkage disequilibrium (LD) and its variation in the genome are of central importance for understanding evolutionary history, population structure, and selective sweeps. Extreme forms of the latter may result in runs of homozygosity (ROH). In human gene mapping, long ROHs are the basis for homozygosity mapping (HM) with length measured in terms of Mb (106 base pairs physical distance). LD varies greatly over the human genome so that long ROHs tend to occur preferentially in regions of high LD and ROHs of the same length in different regions are not strictly comparable. Thus, in human gene mapping, LD appears as a confounder that needs to be taken into account in the interpretation of ROHs. The effect of varying LD can be mitigated by working on a scale of centimorgans (cM, genetic distance) instead of Mb. We demonstrate this effect for HapMap 3 data on chromosome 19 and show examples with different ROH lengths depending on whether physical or genetic lengths are used. These results suggest that HM should preferably be done on genetic rather than physical distances.