The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
The functions of RNA molecules are intimately linked to their ability to fold into complex secondary and tertiary structures. Thus, understanding how these molecules fold is essential to determining how they function. Current methods for investigating RNA structure often use small molecules, enzymes, or ions that cleave or modify the RNA in a solvent-accessible manner. While these methods have been invaluable to understanding RNA structure, they can be fairly labor intensive and often focus on short regions of single RNAs. Here we present a new method (Mod-seq) and data analysis pipeline (Mod-seeker) for assaying the structure of RNAs by high-throughput sequencing. This technique can be utilized both in vivo and in vitro, with any small molecule that modifies RNA and consequently impedes reverse transcriptase. As proof-of-principle, we used dimethyl sulfate (DMS) to probe the in vivo structure of total cellular RNAs in Saccharomyces cerevisiae. Mod-seq analysis simultaneously revealed secondary structural information for all four ribosomal RNAs and 32 additional noncoding RNAs. We further show that Mod-seq can be used to detect structural changes in 5.8S and 25S rRNAs in the absence of ribosomal protein L26, correctly identifying its binding site on the ribosome. While this method is applicable to RNAs of any length, its highthroughput nature makes Mod-seq ideal for studying long RNAs and complex RNA mixtures.
Paraspeckles are nuclear bodies that regulate multiple aspects of gene expression. The long non-coding RNA (lncRNA) NEAT1 is essential for paraspeckle formation. NEAT1 has a highly ordered spatial organization within the paraspeckle, such that its 5′ and 3′ ends localize on the periphery of paraspeckle, while central sequences of NEAT1 are found within the paraspeckle core. As such, the structure of NEAT1 RNA may be important as a scaffold for the paraspeckle. In this study, we used SHAPE probing and computational analyses to investigate the secondary structure of human and mouse NEAT1. We propose a secondary structural model of the shorter (3,735 nt) isoform hNEAT1_S, in which the RNA folds into four separate domains. The secondary structures of mouse and human NEAT1 are largely different, with the exception of several short regions that have high structural similarity. Long-range base-pairing interactions between the 5′ and 3′ ends of the long isoform NEAT1 (NEAT1_L) were predicted computationally and verified using an in vitro RNA–RNA interaction assay. These results suggest that the conserved role of NEAT1 as a paraspeckle scaffold does not require extensively conserved RNA secondary structure and that long-range interactions among NEAT1 transcripts may have an important architectural function in paraspeckle formation.
Translation regulation plays an important role in eukaryotic gene expression. Upstream open reading frames (uORFs) are potent regulatory elements located in 5′ mRNA transcript leaders. Translation of uORFs usually inhibit the translation of downstream main open reading frames, but some enhance expression. While a minority of uORFs encode conserved functional peptides, the coding regions of most uORFs are not conserved. Thus, the importance of uORF coding sequences on their regulatory functions remains largely unknown. We investigated the impact of an uORF coding region on gene regulation by assaying the functions of thousands of variants in the yeast YAP1 uORF. Varying uORF codons resulted in a wide range of functions, including repressing and enhancing expression of the downstream ORF. The presence of rare codons resulted in the most inhibitory YAP1 uORF variants. Inhibitory functions of such uORFs were abrogated by overexpression of complementary tRNA. Finally, regression analysis of our results indicated that both codon identity and position impact uORF function. Our results support a model in which a uORF coding sequence impacts its regulatory functions by altering the speed of uORF translation.
Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a non-parametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach.
BACKGROUND Insect chitinases play vital roles in postembryonic development, especially during the molting process, and are potential targets for the RNA interference (RNAi)‐based insecticidal strategy. Systematic functional analyses of chitinase genes have already been conducted on numerous insect pests, but similar analyses have not been carried out on Diaphorina citri. RESULTS Eleven chitinase/chitinase‐like genes and one endo‐β‐N‐acetylglucosaminidase (ENGase) gene were identified in the Diaphorina citri genome using various bioinformatic tools. Transcriptomes of the integument and midgut from fifth‐instar nymphs and freshly‐emerged adults of Diaphorina citri were generated and sequenced. Potential functions of 12 chitinase/chitinase‐like genes were examined during nymph–adult transitions. Four chitinase genes, including DcCht5, DcCht7, DcCht10‐1 and DcCht10‐2, were mainly expressed in the integument of fifth‐instar nymphs. These four genes were also up‐regulated significantly under 20‐hydroxyecdysone (20E) treatments. RNAi‐mediated knockdown of these four genes suggests that they are essential for nymph–adult transition. CONCLUSION Our results demonstrated essential roles of the chitinase/chitinase‐like genes during the nymph–adult transition in Diaphorina citri, which are potentially useful targets for controlling the Diaphorina citri pest. © 2022 Society of Chemical Industry
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