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RNA-protein interactions broadly regulate gene expression. To understand RNA regulation, it is critical to measure RNA-protein interactions in cells. Current approaches to measure RNA-protein interactions often rely on crosslinking and shortread RNA sequencing, which has considerably advanced the understanding of gene expression but also suffers from some limitations. We present REMORA (RNA Encoded Molecular Recording in Adenosines), a new strategy to measure RNA-binding events on single RNA molecules in cells. In REMORA, adenosine deamination serves as a molecular record of RNA-protein interactions that are identified by mutations by sequencing. We performed RNA-based directed evolution to identify an RNA deaminase variant with high activity on arbitrary adenosine residues in RNA. We show that this RNA deaminase has high activity, low local sequence or structure bias, low background, and is generally applicable to diverse RNA-binding proteins. By combining our improved A-to-I RNA deaminase with the C-to-U deaminase APOBEC1 and long-read RNA sequencing, our approach enables simultaneous recording of the locations two RNA binding proteins on single mRNA molecules. Orthogonal RNA molecular recording of two Pumilio family proteins, PUM1 and PUM2, reveals that PUM1 competes with PUM2 for some but not all Pumilio binding sites in cells, despite having the same in vitro binding preferences. Our work thus measures competition between RNA-binding proteins for RNA sites in cells, and our genetically encodable RNA deaminase enables single-molecule identification of RNA-protein interactions with cell type specificity.

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.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 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. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

Author(s): Gordon, David E; Jang, Gwendolyn M; Bouhaddou, Mehdi; Xu, Jiewei; Obernier, Kirsten; O'Meara, Matthew J; Guo, Jeffrey Z; Swaney, Danielle L; Tummino, Tia A; Huttenhain, Ruth; Kaake, Robyn M; Richards, Alicia L; Tutuncuoglu, Beril; Foussard, Helene; Batra, Jyoti; Haas, Kelsey; Modak, Maya; Kim, Minkyu; Haas, Paige; Polacco, Benjamin J; Braberg, Hannes; Fabius, Jacqueline M; Eckhardt, Manon; Soucheray, Margaret; Bennett, Melanie J; Cakir, Merve; McGregor, Michael J; Li, Qiongyu; Naing, Zun Zar Chi; Zhou, Yuan; Peng, Shiming; Kirby, Ilsa T; Melnyk, James E; Chorba, John S; Lou, Kevin; Dai, Shizhong A; Shen, Wenqi; Shi, Ying; Zhang, Ziyang; Barrio-Hernandez, Inigo; Memon, Danish; Hernandez-Armenta, Claudia; Mathy, Christopher JP; Perica, Tina; Pilla, Kala B; Ganesan, Sai J; Saltzberg, Daniel J; Ramachandran, Rakesh; Liu, Xi; Rosenthal, Sara B; Calviello, Lorenzo; Venkataramanan, Srivats; Lin, Yizhu; Wankowicz, Stephanie A; Bohn, Markus; Trenker, Raphael; Young, Janet M; Cavero, Devin; Hiatt, Joe; Roth, Theo; Rathore, Ujjwal; Subramanian, Advait; Noack, Julia; Hubert, Mathieu; Roesch, Ferdinand; Vallet, Thomas; Meyer, Bjorn; White, Kris M; Miorin, Lisa; Agard, David; Emerman, Michael; Ruggero, Davide; Garcia-Sastre, Adolfo; Jura, Natalia; von Zastrow, Mark; Taunton, Jack; Schwartz, Olivier; Vignuzzi, Marco; d'Enfert, Christophe; Mukherjee, Shaeri; Jacobson, Matt; Malik, Harmit S; Fujimori, Danica G; Ideker, Trey; Craik, Charles S | Abstract: An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity- purification mass spectrometry (AP-MS), which identified 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 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

In the past decade, long noncoding RNAs (lncRNAs) have been increasingly recognized as important regulators of gene expression at various levels (1). The human genome encodes thousands of lncRNAs (2), and an increasing number of these lncRNAs have been associated with humandiseases (3). lncRNA structures are expected to play essential roles in gene regulatory functions, but our current understanding of them remains limited. Traditional methods for RNA structure determination each has its limitations:biophysical approaches, such as NMR or crystallography, are not feasible for large RNAs which are relatively more flexible; traditional chemical probing methods often focus on small regions of single RNAs (4). To overcome theseconstraints, we developed a novel method for high-throughput probing of RNA structure using massively parallel sequencing (Mod-seq (5)). Compared to traditional RNA structure probing methods, Mod-seq provides substantialimprovements in throughput, allowing rapid and simultaneous probing of the whole transcriptome (5, 6). My thesis work focused on using both experimentalmethods and computational methods to study the structure of human lncRNAs. I first developed Mod-seeker, an automatic data analysis pipeline for Mod-seq(5, 6). I then focused on studying the structure of lncRNA NEAT1, an essential component of mammalian nuclear paraspeckles (7, 8). Structure probing and comparative analyses suggest lack of evidence of covariant base-pairs inNEAT1 across mammals. However, a conserved long-range interaction was observed that may contribute to NEAT1’s scaffolding function in paraspeckleformation. The experiments described in this thesis suggest that lncRNAs can have conserved cellular functions without maintaining conserved secondary structures, even when they function as structural scaffolds. This work is one ofthe first attempts to use both chemical probing and computational modelling to study the secondary structure of lncRNAs. The case study of NEAT1 lncRNAstructure helps us understand its function in paraspeckle formation and gives insights into the contributions of lncRNA structures towards their functions.

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.

Owing to the existence of high index gallium nitride (GaN) confinement layer, most of the light generated inside the active layer of GaN-based light emitting diode (LED) is converted to guided optical wave, propagating back and forth in the confinement layer, and fails to emit outwardly. This waveguide effect prevents light from being extracted from inside LED; therefore the extraction efficiency remains poor for such device. This study reports analysis of LED structure by waveguide theory and optimization of light extraction efficiency via patterned dielectric interface, of which the pattern was determined by satisfying momentum matching condition for coupling of bound mode to radiation mode. The proposal was validated by finite difference time domain (FDTD) simulation of top emitting GaN LED, in which the proposed pattern was respectively induced on GaN surface and sapphire substrate. The resultant structure exhibited significant extraction efficiency enhancement over conventional unpatterned LED. And two mechanisms for the enhancement – mode coupling and wave scattering – were compared. The results also revealed that the favorable location for the top emitting GaN LED to induce patterned dielectric interface is on the GaN surface.

It has become increasingly clear that large RNA molecules, especially long noncoding RNAs, function in almost all gene regulatory processes (CechSteitz, 2014). Many large RNAs appear to be structural scaffolds for assembly of important RNA/protein complexes. However, the structures of most large cellular RNA molecules are currently unknown (HennellySanbonmatsu, 2012). While chemical probing can reveal single-stranded regions of RNA, traditional approaches to identify sites of chemical modification are time consuming. Mod-seq is a high-throughput method used to map chemical modification sites on RNAs of any size, including complex mixtures of RNA. In this protocol, we describe preparation of Mod-seq high-throughput sequencing libraries from chemically modified RNA. We also describe a software package "Mod-seeker," which is a compilation of scripts written in Python, for the analysis of Mod-seq data. Mod-seeker returns statistically significant modification sites, which can then be used to aid in secondary structure prediction.

It has become increasingly clear that large RNA molecules, especially long noncoding RNAs, function in almost all gene regulatory processes ( Cech & Steitz, 2014 ). Many large RNAs appear to be structural scaffolds for assembly of important RNA/protein complexes. However, the structures of most large cellular RNA molecules are currently unknown ( Hennelly & Sanbonmatsu, 2012 ). While chemical probing can reveal single-stranded regions of RNA, traditional approaches to identify sites of chemical modification are time consuming. Mod-seq is a high-throughput method used to map chemical modification sites on RNAs of any size, including complex mixtures of RNA. In this protocol, we describe preparation of Mod-seq high-throughput sequencing libraries from chemically modified RNA. We also describe a software package “Mod-seeker,” which is a compilation of scripts written in Python, for the analysis of Mod-seq data. Mod-seeker returns statistically significant modification sites, which can then be used to aid in secondary structure prediction.

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 high-throughput nature makes Mod-seq ideal for studying long RNAs and complex RNA mixtures.