Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-strand RNA virus. The viral genome is capped at the 5′ end, followed by an untranslated region (UTR). There is a poly(A) tail at the 3′ end, preceded by a UTR. The self-interaction between the RNA regulatory elements present within the 5′ and 3′ UTRs and their interaction with host/virus-encoded proteins mediate the function of the 5′ and 3′ UTRs. Using an RNA-protein interaction detection (RaPID) assay coupled to liquid chromatography with tandem mass spectrometry, we identified host interaction partners of SARS-CoV-2 5′ and 3′ UTRs and generated an RNA-protein interaction network. By combining these data with the previously known protein-protein interaction data proposed to be involved in virus replication, we generated the RNA-protein-protein interaction (RPPI) network, likely to be essential for controlling SARS-CoV-2 replication. Notably, bioinformatics analysis of the RPPI network revealed the enrichment of factors involved in translation initiation and RNA metabolism. Lysosome-associated membrane protein-2a (Lamp2a), the receptor for chaperone-mediated autophagy, is one of the host proteins that interact with the 5′ UTR. Further studies showed that the Lamp2 level is upregulated in SARS-CoV-2-infected cells and that the absence of the Lamp2a isoform enhanced the viral RNA level whereas its overexpression significantly reduced the viral RNA level. Lamp2a and viral RNA colocalize in the infected cells, and there is an increased autophagic flux in infected cells, although there is no change in the formation of autophagolysosomes. In summary, our study provides a useful resource of SARS-CoV-2 5′ and 3′ UTR binding proteins and reveals the role of Lamp2a protein during SARS-CoV-2 infection.IMPORTANCE Replication of a positive-strand RNA virus involves an RNA-protein complex consisting of viral genomic RNA, host RNA(s), virus-encoded proteins, and host proteins. Dissecting out individual components of the replication complex will help decode the mechanism of viral replication. 5′ and 3′ UTRs in positive-strand RNA viruses play essential regulatory roles in virus replication. Here, we identified the host proteins that associate with the UTRs of SARS-CoV-2, combined those data with the previously known protein-protein interaction data (expected to be involved in virus replication), and generated the RNA-protein-protein interaction (RPPI) network. Analysis of the RPPI network revealed the enrichment of factors involved in translation initiation and RNA metabolism, which are important for virus replication. Analysis of one of the interaction partners of the 5′-UTR (Lamp2a) demonstrated its role in reducing the viral RNA level in SARS-CoV-2-infected cells. Collectively, our study provides a resource of SARS-CoV-2 UTR-binding proteins and identifies an important role for host Lamp2a protein during viral infection. Coronaviridae. Coronaviruses are known to be present in animals and humans for a long time, usually resulting in respiratory and intestinal dysfunction in the host  of the viral genome. These regulatory RNA elements also play significant roles in the life cycle and pathogenesis of the virus. Intraviral interactions between regulatory RNA elements of the virus and intraviral as well as virus-host RNA-protein interactions control the function of the 5′ and 3′ UTRs and internal cis-acting RNA elements of the virus . It contains a hypervariable region (HVR) which has been shown to be important for pathogenesis of mouse hepatitis virus (MHV). HVR contains a stem-loop II-like motif (S2M) that associates with host translation factors . The S2MInteraction of host proteins with the 5′ and 3′ UTRs of viral genomic RNA is important for the replication and pathogenesis of many RNA viruses. Interaction of polypyrimidine tract binding protein (PTB) with the 5′ UTR-TRS of MHV is known to control transcription of the viral RNA –16. Furt–Despite general acceptance of the importance of the 5′ and 3′ UTRs of RNA viruses in controlling their replication and pathogenesis, no systematic study has been undertaken to elucidate the molecular composition of the RNA-protein complex assembled at the 5′ and 3′ UTRs of SARS-CoV-2 and the function of 5′ and 3′ UTRs of SARS-CoV-2.In silico analyses of the RPPI network revealed enrichment of proteins involved in multiple processes, such as Cap-dependent translation and RNA metabolism. Further studies revealed an antiviral role of LAMP2a during SARS-CoV-2 infection. The functional significance of these findings in SARS-CoV-2 replication and pathogenesis is discussed.We employed an RNA-protein interaction detection (RaPID) assay coupled to liquid chromatography with tandem mass spectrometry (LC-MS/MS) to identify the repertoire of host proteins that interact with the SARS-CoV-2 5′ and 3′ UTRs . These din vivo biotin labeling of host proteins that interact with the RNA sequence of interest cloned between the BirA ligase-binding RNA motifs. Biotinylated proteins are captured using streptavidin agarose beads and identified by LC-MS/MS (Three hundred nucleotides from the 5′ end (designated the 5′-UTR RNA) and 203 nucleotides from the 3′ end (3′-UTR RNA) of the SARS-CoV-2 genomic RNA (Wuhan isolate), which includes the 5′ and 3′ UTRs , were cloned into the pRMB vector between the BirA ligase binding stem-loop (SL-A and SL-B) sequences  to C. 5′LC-MS/MS  (20). Th10.1128/mSystems.00643-21.1FIG S1FIG S1, PDF file, 0.10 MB.Schematic of predicted secondary structure of SARS-CoV-2 5′-UTR RNA. (A) Schematic of predicted secondary structure of SARS-CoV-2 5′-UTR RNA. An asterisk denotes an unannotated stem-loop in the 5′-UTR. (B) Schematic of predicted secondary structure of SARS-CoV-2 5′-UTR RNA + SL-A + SL-B hybrid RNA. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00643-21.2FIG S2FIG S2, PDF file, 0.07 MB.Schematic of predicted secondary structure of SARS-CoV-2 3′-UTR RNA. (A) Schematic of predicted secondary structure of SARS-CoV-2 3′-UTR RNA. (B) Schematic of predicted secondary structure of SARS-CoV-2 3′-UTR RNA + SL-A + SL-B hybrid RNA. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Expression of BirA ligase in HEK293T cells was confirmed by Western blotting using anti-hemagglutinin (anti-HA) antibody that recognizes the HA tag fused to the BirA ligase . The optHEK293T cells expressing the SL-A–5′ UTR–SL-B or SL-A–3′ UTR–SL-B or SL-A–SL-B (pRMB) were treated with biotin, followed by pulldown of biotinylated proteins and LC-MS/MS analysis. Biotin-untreated cells were processed in parallel as a control. Samples from three independent experiments were run in triplicate. Pearson correlation analysis of MS data demonstrated good correlation between replicates for individual biological samples  . Specifi10.1128/mSystems.00643-21.3FIG S3FIG S3, PDF file, 1.1 MB.Normalized distribution curve of LC-MS/MS identified peptides and RPPI network of SARS-CoV-2 5′-UTR RNA and 3′-UTR RNA. (A) PEP scorewise normal distribution of all peptides identified in LC-MS/MS in different biological replicates. Different samples are indicated in distinct colors as shown in the inset. pRMB is the control vector that expresses SL-A and SL-B RNA. R1 to R3, biological replicates of the same sample; B, biotin-treated sample. PEP score range considered for protein identification is shown in dashed-line box (15 to 110). (B) Schematic of RNA-protein interaction network of the 3′-UTR SARS-CoV-2 RNA. Black node, 3′-UTR; red node, host protein. Host proteins that interact with each other contain green color inside the node. (C) Schematic of RNA-protein interaction network of the 5′-UTR SARS-CoV-2 RNA. Black node, 5′-UTR; blue node, host protein. Host proteins that interact with each other contain yellow color inside the node. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00643-21.8TABLE S1Table S1, PDF file, 0.06 MB.List of host proteins that associate with SARS-CoV-2 5′-UTR and 3′-UTR RNA. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Hepeviridae family, and the 3′ end of HEV harbors a secondary structure composed of multiple stem-loops that have been predicted to be important for viral replication  of hepatitis E virus (HEV) genomic RNA . HEV is lication . In ordelication . Out of lication .10.1128/mSystems.00643-21.9TABLE S2Table S2, PDF file, 0.05 MB.Host proteins that interact with the 3′ end of the HEV genome, identified by RaPID assay. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00643-21.10TABLE S3Table S3, PDF file, 0.02 MB.Comparison of expression of prey proteins identified in RaPID assay in human lungs and intestine. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Recently, two independent studies identified the RNA-protein interactome of the SARS-CoV-2 genome by use of the RAP-MS (RNA antisense purification with mass spectrometry) and ChIRP-MS (comprehensive identification of RNA-binding proteins by mass spectrometry) techniques , 24. A cRecent studies have also identified a number of host factors that are important for SARS-CoV-2 infection –26. In o–Homo sapiens for our analysis). 5′-UTR and 5′-UTR + 3′-UTR interaction RPPI networks showed significant enrichment of interactions  ]. Note that based on the above network parameters, the 5′-UTR + 3′-UTR RPPI network is highly connected in comparison to the 5′-UTR network alone (5′-UTR and 3′-UTR RNA binding protein data sets were imported to Cytoscape to construct the RNA-protein-protein interaction (RPPI) network . Analysirk alone .Next, gene ontology (GO) and Reactome pathway analysis of the RPPI network was performed using the Gene Set Enrichment Analysis (GSEA) tool to determine the significantly enriched processes/pathways  and S5 , 29. Of 10.1128/mSystems.00643-21.4FIG S4FIG S4, PDF file, 1.6 MB.GO analysis of 5′-UTR and 3′-UTR SARS-CoV-2 RNA-interacting proteins. Proteins involved in the top 10 biological processes are indicated in blue. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00643-21.5FIG S5FIG S5, PDF file, 1.6 MB.Reactome pathway analysis of 5′-UTR and 3′-UTR SARS-CoV-2 RNA-interacting proteins. Proteins involved in the top 10 pathways are indicated in blue. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Analysis of the 57 proteins identified in RaPID assay using the CyTarget Linker plug-in in Cytoscape software  as well 32–Lamp2a (CD107b) was identified as an interaction partner of the 5′-UTR RNA in the RaPID assay. LAMp2a is the receptor for chaperone-mediated autophagy (CMA), and recent studies have proposed a role of autophagy in SARS-CoV-2 pathogenesis . Hence, A Vero E6 cell-based infection model of SARS-CoV-2 was established to investigate the role of Lamp2a in the SARS-CoV-2 life cycle. Productive infection of Vero E6 cells with SARS-CoV-2 was detected by immunofluorescence staining of the viral nucleocapsid (N) protein at 48 h postinfection using anti-N antibody . N stainin situ hybridization (FISH) was conducted to monitor the interaction between the 5′-UTRs of the viral genome and Lamp2 in SARS-CoV-2-infected Vero E6 cells. Lamp2 (green)- and 5′-UTR RNA-specific (red) probes colocalized (indicated by yellow), suggesting their interaction  cell-based infection model of SARS-CoV-2. Recent reports have shown the utility of Huh7 cells as a viable model for SARS-CoV-2 infection , 37. We 10.1128/mSystems.00643-21.6FIG S6P value was calculated using a two-tailed Student t test. (E) RT-qPCR measurement of intracellular level of SARS-CoV-2 RNA  in Huh7 cells transfected with nontargeting (NT) siRNA or pan-Lamp2 siRNA (Lamp2 siRNA) and infected with SARS-CoV-2 for 48 h. Data are the mean ± SEM of triplicate samples. P value was calculated using a two-tailed Student t test. (F) Western blot detection of nucleocapsid protein  and GAPDH (lower image) in nontargeting (NT) or pan-Lamp2 (Lamp2) siRNA-treated SARS-CoV-2-infected Huh7 cells, 48 h postinfection. (G) RT-qPCR measurement of SARS-CoV-2 RNA level  in culture medium of Huh7 cells transfected with the indicated plasmids and infected with SARS-CoV-2 for 48 h. Data are the mean ± SEM of triplicate samples. P value was calculated using a two-tailed Student t test. (H) RT-qPCR measurement of intracellular level of SARS-CoV-2 RNA  in Huh7 cells transfected with the indicated plasmids and infected with SARS-CoV-2 for 48 h. Data are the mean ± SEM of triplicate samples. P value was calculated using a two-tailed Student t test. (I) Western blot detection of Lamp2a protein level (using anti-HA antibody [upper panel] and anti-Lamp2 antibody [middle panel]) and GAPDH protein level (lower panel) in pCDNA3-transfected or pcDNA Lamp2a-transfected Huh7 cells. (J) Western blot detection of Lamp2b protein level (using anti-HA antibody [upper panel] and anti-Lamp2 antibody [middle panel]) and GAPDH protein level (lower panel) in pCDNA3-transfected or pcDNA Lamp2b-transfected Huh7 cells. (K) Western blot detection of Lamp2c protein level using anti-Lamp2 antibody (upper panel) and GAPDH protein level (lower panel) in pCDNA3-transfected or pcDNA Lamp2c-transfected Huh7 cells. Download FIG S6, PDF file, 0.2 MB.Lamp2 modulates viral RNA level in SARS-CoV-2-infected Huh7 cells. (A) Level of SARS-CoV-2 RNA  in the culture medium (secreted) and inside Huh7 cells  that are infected with the SARS-CoV-2 for the indicated periods. RT-qPCRs were performed using a SYBR green-based protocol. Data are the mean ± SEM of triplicate samples. (B) Western blot detection of SARS-CoV-2 nucleocapsid (upper panel) and GAPDH (lower panel) protein levels in SARS-CoV-2-infected Huh7 cells, 48 h postinfection. (C) Western blot detection of Lamp2  and GAPDH  protein levels in Huh7 cells transfected for 72 h with nontargeting (NT) siRNA or pan-Lamp2 siRNA (Lamp2 siRNA). (D) RT-qPCR measurement of SARS-CoV-2 RNA level  in the culture medium of Huh7 cells transfected with nontargeting (NT) siRNA or pan-Lamp2 siRNA (Lamp2 siRNA) and infected with SARS-CoV-2 for 48 h. Data are the mean ± SEM of triplicate samples. Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Lamp2 is present in three different forms in mammalian cells, including Lamp2a, Lamp2b, and Lamp2c, produced through alternative splicing . Lamp2a Next, we tested if a particular Lamp2 isoform is responsible for increased viral replication in Lamp2 siRNA-treated cells by designing siRNA against each isoform of Lamp2. Analysis of Lamp2 sequence in multiple siRNA designing platforms indicated that specific siRNAs could be designed only against the Lamp2a and Lamp2b isoforms. siRNAs against human Lamp2a and Lamp2b were synthesized , and their silencing efficacy was tested in Huh7 cells. Western blot analysis of Lamp2a, Lamp2b, and pan-Lamp2 siRNA-treated Huh7 cell whole-cell extract revealed that both Lamp2a and pan-Lamp2 siRNAs significantly reduced the total Lamp2 protein level at both 48 and 72 h of treatment. Lamp2b siRNA treatment reduced the total Lamp2 protein level to a much lesser extent . In ordeNext, the effect of overexpression of each Lamp2 isoform on SARS-CoV-2 replication was tested. Overexpression of Lamp2a and Lamp2b significantly reduced the viral RNA level, whereas Lamp2c overexpression did not alter viral the RNA level at 48 h postinfection, both in culture medium  and in iIn order to test if the observed reduction in viral RNA levels were specific to Lamp2, the effect of Lamp1 depletion on SARS-CoV-2 replication was evaluated. Western blot analysis using anti-Lamp1 antibody showed ∼90% reduction in Lamp1 protein level in siRNA-transfected Huh7 cells at 48-h and 72-h time points . RT-qPCRLamp2a is the receptor for chaperone-mediated autophagy (CMA), and silencing of Lamp2 expression promoted SARS-CoV-2 replication. GAPDH is known to be degraded by CMA, and a reduced GAPDH level is considered to be an indicator of CMA activation in a cell. An increase in the level of LAMP2 was observed; however, no significant change in GAPDH level was observed in SARS-CoV-2-infected Vero E6 or Huh7 cells, suggesting that CMA is not activated in these cells . We nextIn this study, we identified 57 host proteins that interact with the 5′ and 3′ UTRs of the SARS-CoV-2 genomic RNA, combined those data sets with the PPI data set proposed to be involved in viral replication, and constructed an RPPI network, which is likely to be assembled during the replication of SARS-CoV-2. Interactions of 5′ and 3′ regulatory elements with virus-encoded and host proteins mediate their function. Since 5′ and 3′ regulatory elements are known to play indispensable roles in the replication process of RNA viruses, identification of the components of the above-mentioned RNA-protein complex paves the way for designing novel antiviral therapeutic strategies against SARS-CoV-2.in vivo biotinylation of proteins that bind to the RNA of interest, which is expressed as a chimeric RNA in fusion with BirA ligase-recognizing RNA motifs. Under conditions of biotin availability, the BirA ligase biotinylates all proteins in its close proximity (∼10-nm range), including those associated with the RNA of interest. Thus, the RaPID assay detects both transient and stable interaction partners of an RNA under normal culture conditions. Since the RaPID protocol allows stringent washing of the pulldown samples, nonspecific binders are easily removed during the assay. Nonspecific proteins were further removed from the mass spectrometry data by using the following three controls: (i) subtraction of 5′- and 3′-UTR binding protein data sets from that of pRMB vector (which expresses the BirA-recognizing RNA motif), (ii) subtraction of 5′- and 3′-UTR binding protein data sets from that of pRMB HEV 3′ UTR (an unrelated RNA sequence derived from the 3′ UTR of HEV), and (iii) subtraction of 5′- and 3′-UTR binding protein data sets from that of a “no biotin treatment” control. However, it is noteworthy that the RaPID assay protocol used by us biotinylates the lysines exposed on the surface of proteins within close proximity and thus it has the inherent limitation that not all RNA binding proteins may be equally detected. Also, the specificity of the data may be further enhanced by using an additional control, which includes parallel processing of cells expressing pRMB 5′- and 3′-UTR RNAs in the absence of BirA ligase expression. Nevertheless, comparison of our RaPID data set with the data obtained by two other RNA-protein interaction detection techniques (RAP-MS and ChIRP-MS) identified 27 common proteins. Almost 50% overlap of our data set with those from other independent studies supports the specificity of our assay. Our study identified an additional 30 host proteins that associate with 5′ and 3′ UTRs of SARS-CoV-2 RNA. This may be attributed to the ability of the RaPID technique to detect both stable and transient as well as direct and indirect (as a protein complex) RNA-protein interactions. The specificity of the data is evident from the fact that only one protein (HIST1H3) is common between SARS-CoV-2 and HEV RPPI data sets. Note that like SARS-CoV-2, HEV is also a positive-strand RNA virus which contains a capped 5′ end, followed by a 5′ UTR and poly(A) tail preceded by a 3′ UTR  Schematic of RPPI network of the 5′-UTR + 3′-UTR SARS-CoV-2 RNAs + PPI involved in virus replication. Black nodes, 5′-UTR or 3′-UTR; blue or red nodes, host protein. Host proteins that interact with each other contain yellow or green color inside the node. Common host proteins that interact with both 5′-UTR and 3′-UTR RNA are represented by dual (red and blue) color. Viral protein nodes are labeled. Host proteins involved in PPI with viral proteins are indicated in sky blue. Interaction among viral proteins is represented by blue lines. (B) Schematic of PPI network involved in SARS-CoV-2 replication. Viral protein nodes are represented by purple diamonds. Host proteins involved in PPI with viral proteins are indicated in sky blue. Interaction among viral proteins is represented by blue lines. Download Copyright © 2021 Verma et al.2021Verma et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Gene ontology and Reactome pathway analysis of the SARS-CoV-2 5′- and 3′-UTR RPPI data sets show an enrichment of proteins involved in different processes, such as translation initiation, RNA metabolism, and infectious disease, which is expected. Therefore, the data obtained by the RaPID assay is a useful resource for future studies related to SARS-CoV-2. A comparison of SARS-CoV-2 5′-UTR and 3′-UTR-interacting proteins obtained in the RaPID assay with those shown in other positive-strand RNA viruses reveal a similar profile of many functional protein categories. Proteins belonging to the ribosome complex such as RPL7a, RPLP2, RPL13, and RPL4 and translation initiation factors such as eIF5B were found to interact with the SARS-CoV-2 5′ and 3′ UTR. Being a capped RNA, SARS-CoV-2 genomic RNA is supposed to utilize the canonical cap-dependent translation process for producing viral proteins, and the above-identified host factors might be used by the virus for efficiently translating its own RNA. The importance of host ribosomal proteins and translation regulators in the SARS-CoV-2 life cycle is supported by the finding that knockout of RPL7a, RPL13, and eIF5B significantly affected virus growth , 26. HeaATP-binding cassette subfamily E member 1 (ABCE1) is another important host protein that interacts with the 5′ UTR. ABCE1 (RNase L inhibitor) inhibits the activity of RNase L, which is activated by the host antiviral response mechanism in response to RNA virus infection or alpha/beta interferon (IFN-α/β) stimulation . Active The 5′ UTR was also found to interact with Lamp2a, which is another key protein involved in quality control processes of the host. Lamp2a is the cellular receptor for chaperone-mediated autophagy (CMA) . ActivatLamp2b and Lamp2c isoforms are known to directly interact with RNA and DNA, leading to RNautophagy/DNautophagy . Lamp2c No siRNA could be designed against the Lamp2c isoform. However, pan-Lamp2 siRNA used in our study targets all three isoforms, as it recognizes the 5′ end of the Lamp2 transcript . Note that Lamp2 isoforms differ in their C-terminal regions. Silencing of Lamp2a significantly reduced the level of total Lamp2 in Huh7 cells and increased the level of viral RNA, whereas its overexpression decreased the level of viral RNA in SARS-CoV-2-infected cells, suggesting its antiviral property. Silencing of Lamp2b moderately decreased the level of total Lamp2, but the level of viral RNA remained unaltered in SARS-CoV-2-infected cells. However, overexpression of Lamp2b reduced the level of viral RNA in infected cells. These differences might be attributed to the fact that Lamp2a, being the predominant isoform, maintains antiviral activity in the absence of Lamp2b; however, overexpression of Lamp2b might be affecting viral infection directly/indirectly by inducing RNautophagy/DNautophagy. Therefore, Lamp2b may not have a role in SARS-CoV-2 infection at its endogenous level. Although Lamp2c is expressed at a low level in Huh7 cells, its role in SARS-CoV-2 infection could not be confirmed. The Lamp2a–5′-UTR interaction may be involved in delivering viral RNA to the endosome, for recognition by TLR7, the sensor for single-stranded RNA (ssRNA). The Lamp2a–5′-UTR interaction may also be involved in sequestering Lamp2a away from inducing chaperone-mediated autophagy. At the same time, increased autophagic flux provides energy and prevents apoptosis of infected cells, which provides a favorable environment for the virus to complete its life cycle. Further studies are required to understand the actual process. Nevertheless, the current study identifies the repertoire of host proteins that associate with SARS-CoV-2 5′ and 3′ UTRs and characterizes the role of host Lamp2a protein during SARS-CoV-2 infection.NC_045512) were commercially synthesized and cloned into pUC57 vector . RNA motif plasmid cloning backbone (pRMB) , BASU RaPID plasmid , and RNA motif plasmid EDEN15  were gifted by Paul Khavari. pCDNA Lamp2b was a gift from Joshua Leonard , and pCDNA lamp2c was a gift from Janice Blum . pcDNA Lamp2a  was purchased from Sino Biologicals . Anti-Lamp2 , anti-Lamp1 , anti-LC3B , and anti-P62  antibodies were from Cell Signaling Technology . Anti-GAPDH antibody  was from Santa Cruz Biotechnology . Anti-Flag antibody  was from Bethyl Laboratories . Anti-CUGBP1 antibody  was from St John’s Laboratory . Goat anti-rabbit IgG–horseradish peroxidase (HRP)  and goat anti-mouse IgG–HRP  were from Southern Biotech . Goat anti-rabbit IgG–Alexa Fluor 488 , goat anti-mouse IgG–Alexa Fluor 647 (A-21235), and antifade gold with DAPI (P36931) were from Thermo Fisher Scientific . 3-MA  and anti-HA antibody  were from Sigma . Nontargeting siRNA , pan-Lamp1 siRNA , and pan-Lamp2 siRNA  were from Dharmacon . siRNAs against human Lamp2a and Lamp2b isoforms were designed using the GenScript siRNA target finder tool and synthesized at Eurogentec . The following sequences were selected: Lamp2a, 5′GGCAGGAGUACUUAUUCUAGU3′; Lamp2b, 5′GUGUUCGCUGGAUGAUGAC3′. A literature search revealed that similar sequences have been used earlier to specifically silence Lamp2a and Lamp2b isoforms in human cell lines  and 29676 to 29900 at the 3′ end (3′ UTR) of the SARS-CoV2 isolate Wuhan-Hu-1 genome  at a 1:1 ratio, in accordance with the manufacturer’s instruction. At 6 to 8 h posttransfection, culture medium was replaced with fresh DMEM plus 10% FBS.Vero E6 and HEK 293T cells were obtained from the ATCC . Huh7 cells have been described previously . Cells w2. The next day, 25 nmol siRNA was transfected into each well using 0.35 μl DharmaFECT transfection reagent, in accordance with the manufacturer’s instructions . At 18 h posttransfection, culture medium was replaced with DMEM plus 10% FBS and the cells were maintained at 37°C, 5% CO2, until further manipulation.For experiments involving siRNA-mediated gene silencing, Huh7 or Vero E6 cells were seeded at 70 to 80% confluence on 12-well tissue culture (TC) dishes and incubated overnight at 37°C, 5% COueous One Solution cell proliferation assay; Promega, Madison, USA) that employs a tetrazolium salt-based colorimetric assay. Details are as described previously  at the BsmBI restriction site using the following primers: 5′ UTR FP, Production of 5′-UTR and 3′-UTR RNA from the pRMB constructs was verified by transfection of pRMB 5′-UTR and pRMB 3′-UTR plasmids along with pRMB vector into HEK293T cells, followed by RT-PCR detection of the 5′-UTR and 3′-UTR RNA using the above-mentioned primer pairs. Expression of BirA ligase in HEK293T cells upon transfection of BASU plasmid was verified by Western blotting . Time dug for 45 min at 4°C. Free biotin was removed by a Macrosep advance spin filter . Aliquots of the samples were stored at −80°C for use as input in Western blotting. The protein concentration in each sample was determined using a bicinchoninic acid (BCA) protein assay kit , and equal amounts of proteins were incubated with MyOne streptavidin C1 magnetic beads  on a rotator at 4°C for 16 h. Samples were washed four times with washing buffer, and 2× Laemmli buffer was added to the beads and incubated at 95°C for 45 min. Both input and pulldown samples were resolved by SDS-PAGE, followed by Western blotting using anti-CUGBP1 and anti-GAPDH antibodies. Goat anti-rabbit IgG–HRP conjugated secondary antibody was used to detect the protein bands by chemiluminescence .For verifying the interaction between EDEN15 and CUGBP1 by the RaPID assay, HEK293T cells were transfected with pRMB EDEN15 and BASU plasmids (6:1 ratio) using Lipofectamine 2000 . At 40 h posttransfection, culture medium was replaced and 200 μM biotin was added. At 18 h later, cells were washed three times in phosphate-buffered saline (PBS) and lysed in prechilled radioimmunoprecipitation (RIPA) buffer  supplemented with protease and phosphatase inhibitor cocktail. The lysate was centrifuged at 14,000 × For mass spectrometry, clarified cell lysate was precipitated in acetone at −20°C for 10 min, followed by storage at −80°C for 20 min. The precipitates were solubilized in 8 M urea. The protein concentration was estimated by using the BCA protein assay kit .18 cartridge  and subsequently lyophilized in a SpeedVac.An equal amount of protein (10 mg) from each sample was treated with 10 mM dithiothreitol  and alkylated with 20 mM iodoacetamide (IAA) . Trypsin  was added to the samples at a 1:20 (wt/wt) ratio and incubated at pH 8, 37°C, for 24 h. Next, 1% formic acid was added to the samples, and peptides were desalted using a Sep-Pak C2HPO4, 150 mM NaCl, pH 7.2) before use.High-capacity streptavidin agarose resins  were used to pull down the biotinylated peptides. The beads were washed in binding buffer , and reconstituted with solvent A  for LC-MS/MS analysis.Lyophilized peptides were solubilized in 1 ml PBS and incubated with 150 μl washed streptavidin agarose beads for 2 h at room temperature. The beads were washed once in 1 ml PBS, once in 1 ml washing buffer (5% acetonitrile in PBS), and finally once in ultrapure water. Excess liquid was completely removed from the beads, and biotinylated peptides were eluted by adding 0.3 ml of a solution containing 0.1% formic acid and 80% acetonitrile in water by boiling at 95°C for 5 min. A total of 10 elutions were collected and dried together in a SpeedVac. Enriched peptides were desalted with C+ triple-time-of-flight (TOF) mass spectrometer coupled with a ChromXP reversed-phase 3-μm C18-CL trap column  and a nanoViper C18 separation column  in an Eksigent nanoLC (ultra 2D plus) system. The binary mobile solvent system used was as follows: solvent C, 2% (vol/vol) acetonitrile, 0.1% (vol/vol) formic acid in water; solvent B, 98% (vol/vol) acetonitrile, 0.1% (vol/vol) formic acid. The peptides were separated at a flow rate of 200 nl/min in a 60-min gradient with a total run time of 90 min. The MS data of each condition were acquired in IDA (information-dependent acquisition) with high sensitivity mode. Each cycle consisted of 250- and 100-ms acquisition times for MS1  and MS/MS  scans, respectively, with a total cycle time of ∼2.3 s. Each condition was run in triplicate.LC-MS/MS experiments were performed using a Sciex 5600Homo sapiens taxonomy filter). The search parameters for identification of biotinylated peptides were as follows: (i) trypsin as a proteolytic enzyme (with up to two missed cleavages); (ii) peptide mass error tolerance of 20 ppm; (iii) fragment mass error tolerance of 0.20 Da; and (iv) carbamido-methylation of cysteine (+57.02146 Da), oxidation of methionine (+15.99492 Da), deamination of NQ (+0.98416), and biotinylation of lysine (+226.07759 Da) as variable modifications. The quality of data between different samples and replicates was monitored by a Pearson correlation plot of peptide intensity against each run.All raw files (.wiff) were searched using ProteinPilot software  with the Mascot algorithm for protein identification and semiquantitation against the Swiss-Prot_57.15 database  to generate the Venn diagram to identify proteins that are unique interaction partners of 5′-UTR and 3′-UTR RNA. The background-subtracted data set was sorted on the basis of the parameters of a minimum of 2 unique peptides and a “prot score” of 40 or more to generate the final list of proteins, which were considered for further studies (Proteins with at least one corrected biotinylated peptide and a posterior error probability (PEP) score of ≥15 were considered to be identified successfully and extracted from the Gaussian smoothing curve (note that PEP score refers to the probability that the observed peptide spectrum matches are incorrect). Next, all data were analyzed using a Web-based tool, Bioinformatics & Evolutionary Genomics ( studies . Note thhttp://www.unafold.org/mfold/applications/rna-folding-form.php), based on a minimum free energy calculation at 25°C (https://www.gsea-msigdb.org/gsea/index.jsp)  , 29.50)/ml and added to a Vero E6/Huh7 cell monolayer (seeded in a 24-well plate) for 1 h at 37°C, supplemented with 5% CO2. At 1 h postincubation, the infection medium was removed, cells were washed twice with 500 μl of serum-free medium, and fresh DMEM plus 10% FBS was added. In the case of plasmid overexpression or siRNA transfection study, cells were transfected with the respective DNA or siRNA 24 h prior to infection with SARS-CoV-2. In the case of 3-MA treatment, 5 mM  3-MA was added to the culture medium during infection, again added to the complete medium after removal of the infection medium, and maintained for 48 h, followed by collection of culture medium and cells and subsequent experiments.SARS-CoV-2 was obtained from BEI Resources , amplified in Vero E6 cells in the biosafety level 3 (BSL3) facility of THSTI, India, titrated, and stored frozen in aliquots. For SARS-CoV-2 infection studies, 200 μl of stock virus was diluted in serum-free medium to 2,000 50% tissue culture infective doses , in accordance with the manufacturer’s instructions. Template DNA was removed by treatment with DNase I, followed by precipitation of RNA using LiCl. An aliquot of the probe was resolved by agarose gel electrophoresis to monitor its size and integrity.Sequence corresponding to the 5′ 300 bases of SARS-CoV-2 was PCR amplified from pUC57 vector and cloned into the pJet1.2 vector  under the control of the T7 promoter. pJet1.2_5′-300 plasmid was linearized by restriction digestion with XbaI. DIG-11-UTP -labeled RNA was in situ hybridization (FISH) was done as described previously, with minor modifications in which a Tyramide SuperBoost kit  was used to detect the fluorescence signal (2O2 solution (provided in the Tyramide SuperBoost kit) and incubating at room temperature for 1 h. Cells were then blocked in 4% bovine serum albumin (BSA) (in PBS) for 1 h at room temperature, followed by incubation with anti-LAMP2 (1:50 dilution) and biotin-conjugated anti-DIG antibody (1:200 dilution) prepared in 4% BSA in PBST (PBS plus 0.2% Tween 20) overnight at 4°C. The next day, the cells were washed three times in PBS (10 min each) and incubated with HRP-conjugated streptavidin (provided in the Tyramide SuperBoost kit) for 60 min at room temperature Next, the cells were washed three times in PBS and incubated with goat-anti rabbit IgG–Alexa Fluor 488  for 1 h at room temperature. Cells were washed three times in PBS. Next, freshly prepared tyramide working solution was added to the cells and incubated for 5 min at room temperature, followed by the addition of stop solution (provided with the Tyramide SuperBoost kit) and incubation for 3 min. Next, cells were washed three times in PBS and coverslips were mounted on a slide using antifade gold. Images were acquired using a 100× objective in a confocal microscope (Olympus FV3000) and analyzed by Image lab Fiji software.Fluorescence e signal . In summTGGACCCCAAAATCAGCGAA; SCoV2 QPCR RP, 5′-TCGTCTGGTAGCTCTTCGGT; RP II FP, 5′-GCACCACGTCCAATGACAT; RP II RP, 5′-GTCGGCTGCTTCCATAA; RP FP, 5′-AGATTTGGACCTGCGAGCG; RP RP, 5′-GAGCGGCTGTCTCCACAAGT; LAMP2A FP, 5′-TATGTGCAACAAAGAGCAGAC3; LAMP2A RP, 5′-AAGCCAGCAACACTAGAATAAG3; LAMP2B FP: 5′-TATGTGCAACAAAGAGCAGAC3, LAMP2B RP, 5′-TGCCAATTACGTAAGCAATCA. TaqMan-based RT-qPCR was done as described earlier, by following the protocol suggested by the CDC, USA  or RNase P .Intracellular RNA was isolated using TRI reagent , followed by reverse transcription (RT) using a FIREScript cDNA synthesis kit . RNA from culture medium was isolated using a Qiagen viral RNA minikit , followed by RT using a FIREScript cDNA synthesis kit . Random hexamers were used in cDNA synthesis. SYBR green-based quantitative real-time PCR (RT-qPCR) was done as described earlier . The folCDC, USA . The folP values were calculated using a two-tailed Student t test (paired two samples for means).Data are represented as the mean ± standard error of the mean (SEM) of three experiments. Confocal imaging was performed as described previously . In brieSamples were resolved by SDS-PAGE, transferred to a 0.4-μm polyvinylidene difluoride (PVDF) membrane. Membranes were blocked for 1 h at room temperature using 5% skimmed milk (in PBS). Next, membranes were incubated overnight with primary antibody in PBST (PBS plus 0.05% Tween 20) plus 5% skimmed milk at 4°C. Blots were washed 3 times in PBST, followed by incubation with HRP-tagged secondary antibody at room temperature for 1 h. Blots were washed 3 times in PBST, and protein bands were visualized by enhanced chemiluminescence using a commercially available kit .http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD026754.The mass spectrometry proteomics data are available in the MassIVE repository, which is a part of ProteomeXchange, under identifier number PXD026754. The data can be accessed directly at


However, recent studies have revealed that there is diversity between different nsNSVs with pneumovirus promoters directing the polymerase to initiate at positions 1 and 3 of the genome, and ebolavirus polymerases being able to initiate at position 2 on the template. Studies with other RNA viruses have shown that polymerases that engage in de novo initiation opposite position 1 typically have structural features to stabilize the initiation complex and ensure efficient and accurate initiation. This raised the question of whether different nsNSV polymerases have evolved fundamentally different structural properties to facilitate initiation at different sites on their promoters. Here we examined the functional properties of polymerases of respiratory syncytial virus (RSV), a pneumovirus, human parainfluenza virus type 3 (PIV-3), a paramyxovirus, and Marburg virus (MARV), a filovirus, both on their cognate promoters and on promoters of other viruses. We found that in contrast to the RSV polymerase, which initiated at positions 1 and 3 of its promoter, the PIV-3 and MARV polymerases initiated exclusively at position 1 on their cognate promoters. However, all three polymerases could recognize and initiate from heterologous promoters, with the promoter sequence playing a key role in determining initiation site selection. In addition to examining de novo initiation, we also compared the ability of the RSV and PIV-3 polymerases to engage in back-priming, an activity in which the promoter template is folded into a secondary structure and nucleotides are added to the template 3´ end. This analysis showed that whereas the RSV polymerase was promiscuous in back-priming activity, the PIV-3 polymerase generated barely detectable levels of back-primed product, irrespective of promoter template sequence. Overall, this study shows that the polymerases from these three nsNSV families are fundamentally similar in their initiation properties, but have differences in their abilities to engage in back-priming.It is generally thought that the promoters of non-segmented, negative strand RNA viruses (nsNSVs) direct the polymerase to initiate RNA synthesis exclusively opposite the 3´ terminal nucleotide of the genome RNA by a  The non-segmented negative strand RNA viruses are a large group of viruses that includes a number of significant human pathogens. Their viral genome is transcribed and replicated by a virally-encoded polymerase, which is considered to be a good target for intervention with antiviral drugs. Defining the similarities and differences between the polymerases of different viruses could be helpful for developing inhibitors with potency against a broad spectrum of related viruses. In this study, we examined the RNA synthesis initiation properties of polymerases of viruses from three different non-segmented negative strand RNA virus families and showed that they are fundamentally similar in the ways that they can initiate RNA synthesis. However, we also found that polymerases from two different virus families differed significantly in their ability to perform another activity, referred to as back-priming, which suggests that there might be structural differences between them. Thus, this study identifies similarities and differences between the polymerases in this group of viruses. Mononegavirales, otherwise known as the non-segmented, negative strand RNA viruses (nsNSVs), is a large and diverse group of viruses that are united by having a genome consisting of a single strand of RNA that is transcribed to produce a series of mRNAs, and replicated via a positive-sense antigenome intermediate to produce new RNA genomes -UTP included as a radioactive tracer  radiolabeled nucleotide if this incorporation site corresponded to the 5´ end of the template. Thus, the signal from the tr promoter was much weaker than from the le promoter. When le promoter reactions were performed with biased NTP concentrations of 1 mM ATP, 2 mM CTP and 50 μM GTP, to increase efficiency of initiation from position 1, the 14 nt band could be detected more readily than in the unbiased NTP conditions, although the 10–12 nt bands were still dominant . The first GTP incorporation site on both PIV-3 promoters is opposite position 10C , and several products between 10 and 15 nt in length migrated as doublets. This suggested that the RSV polymerase was capable of initiation at one or more internal sites, in addition to position 1, yielding a mixture of RNA products with slightly different migration properties  or primer extension analysis of viral RNA isolated from infected cells. While analysis of RNA isolated from virus particles or infected cells lends authenticity, these approaches leave open the possibility that small and/or highly unstable RNA products might not be detected, meaning that alternative initiation sites could be missed. The goal of this study was to use RNA oligonucleotide templates, representing the region of the RNA template exposed following N or NP dissociation for initiation complex formation, to characterize the initiation properties of the polymerases of PIV-3, a paramyxovirus, and MARV, a filovirus, using an le promoter region for both processes  CTP, 0.1 μM DNA annealed oligos and 1 unit/μL T7 RNA polymerase (New England Biolabs). Reactions were incubated at 37°C for 5 hours. Samples were treated with 1 μL DNase I (New England Biolabs) for 10 minutes at 37°C. Reactions were quenched by adding 1 μL of 0.5 M EDTA. Products were subjected to phenol chloroform extraction and ethanol precipitation prior to use as markers.RNA products ≥ 10 nt were analyzed alongside size markers generated by alkali hydrolysis of an end-labeled RNA oligonucleotide of the same sequence as the anticipated full length product, as described previously . 5΄ trip


An analysis of the hybrid choice model and further insights are presented in the article “How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.”We present stated preference data based on a national representative Swiss online panel survey related to preference of mixed renewable energy infrastructure in landscapes. Data were collected between November 2018 and March 2019 via an online questionnaire and yielded 1026 responses. The online questionnaire consisted of two main parts – (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure and questions regarding the “fit” of landscape/renewable energy infrastructure (REI) combinations and (2) a stated choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific REI, we also asked them to evaluate the fitting of seven different Swiss landscapes  with five different REI  combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two “energy system transformation” options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes  with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modeling and analysis was  They also provide a proof-of-concept for “landscape-technology fit” and contain information about predictors  of peoples’ preferences related to landscape developments. Also, the dataset highlights the interconnectedness of landscape and energy aspects in terms of the perceived landscape quality and its potential relevance for decision making processes.•The consideration of meanings for decision making processes and policy making  could be brought into all policy areas and technical decision-making tools, even those that are not landscape-oriented. During communication and planning residents of potential energy sites could be (1) informed early on and (2) invited to participatory workshops in which the meaning of landscape and REI is addressed in addition to usual visual scenarios and (3) discussing siting alternatives.•The dataset can be used to operationalize landscape-technology fit (LTF) concept which derived from place-technology fit (PTF). In particular, this dataset may be used as a base line for future LTF model improvements in alpine regions. They contain explicit information on meanings ascribed to alpine landscapes and to specific renewable energy infrastructures.1We conducted a representative online panel survey in Switzerland between November 2018 and March 2019 to elicit the preferences of Swiss residents for landscape oriented renewable energy infrastructure developments. The questionnaire was developed by WSL and operated by panel provider BILENDI GmbH. The survey is representative in language, age, gender, education and landscape.supplementary material of the present artice. Socio demographic items and respondent ID were provided by the panel provider (items 1 to 6). After starting the survey, respondents were first asked to select landscapes that most closely represent the landscape of their living, recreation and childhood environment (variables 160–162). In a next step, respondents were asked to evaluate (randomly presented) meanings ascribed to each of the seven landscapes presented. A generalized overview of the evaluation of landscape meaning items (variables 84 to 153) is provided in The questionnaire consisted of two major parts, where within the first part questions were related (1) to meanings ascribed to landscapes and renewable energy infrastructure, (2) to aspects of landscape-technology fit and (3) to exposure of people to landscapes and renewable energy infrastructures. Within the second part a stated choice model was presented. All respondents were designated to one of seven landscapes  according to the ZIP code of their origin. The landscape visualizations used in this study are illustrated in The second part of the online panel survey consisted of a discrete choice study in which respondents faced 15 consecutive choice tasks. Respondents were asked to choose among two landscape oriented renewable energy infrastructure alternatives and one opt-out option. Each of these alternatives (beside the opt-out option) had four attributes . Choice design, consecutive choice tasks and choice attributes are presented in https://doi.org/10.16904/envidat.206).For reasons of confidentiality we anonymized the data by removing all fields that would enable personal identification. The complete questionnaire, the dataset and data description are available on the Environmental Data Platform EnviDat of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL , whereas option 3 described an opt-out (no-action). Relevant attributes and credible attribute levels were developed based literature research, project meetings and workshops with the project steering group from different disciplines We identified four relevant attributes and the respective levels. The choice design was generated with Ngene software The participation in the survey was operated and organized by a panel provider. Respondent participation was voluntary and respondents were informed that the data will be analyzed anonymously. Data collection and handling were implemented in accordance with the social data gathering ethics regulations of the institution conducting this research.Salak B.: Resources, Methodology, Conceptualization, Formal analysis, Investigation, Data Curation, Visualization, Writing - original draft; Lindberg K.: Methodology, Formal analysis, Writing - review & editing, Software, Validation; Kienast F.: Funding acquisition, Conceptualization, Writing - review & editing, Validation; Hunziker M.: Funding acquisition, Project administration, Conceptualization, Writing - review & editing, Validation, Supervision.The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.


To assess the sexual and reproductive health (SRH) needs of women admitted to a psychiatric intensive care unit (PICU), and acceptability of delivering specialist SRH assessments/interventions in this setting. Secondary aims were to explore the barriers to access and the feasibility of providing SRH assessments and interventions in the PICU.A retrospective analysis of fifteen months’ activity data found that only 25 SRH referrals had been made across 205 PICU admissions. This low referral rate of 12% likely reflected pathway barriers and was unlikely to represent the actual clinical need in female PICU patients. A bi-monthly SRH in-reach clinic and a nurse led SRH referral pathway were implemented on the PICU over a seven-month period. Within a quality improvement framework, a staff training needs assessment was performed, training delivered, a protocol developed, staff attitudes explored, and patient and carer engagement sought.A quality improvement approach streamlined SRH assessments on the PICU and resulted in 42% of women being assessed and a 3.5-fold increase in uptake. At least 30% of the women in the PICU had unmet SRH needs identified and proceeded to a specialist appointment. This amounts to a minimum 2.5-fold increase in SRH unmet need detection.The most common SRH needs were complex gynaecological issues , STI advice/testing and contraception advice/options. 21% of women initiated SRH interventions, and 14% completed all the interventions required for their needs. The most common interventions were in the areas of contraception advice/family planning and STI advice/testing.Staff confidence on assessing SRH topics was identified as a barrier to access with a positive shift noted after bespoke SRH training was implemented and a protocol introduced: on a scale of 0-10 (with 10 being high), 81.3% of staff rated their confidence 8 or above in relation to discussing contraception/sexually transmitted infections (pre-training: 25.0%), and 93.8% in relation to discussing risky behaviours (pre-training: 18.8%). All 11 patient and carer participants felt it was important to have a forum to talk about SRH and 8 (72.7%) agreed it was important in the PICU.Results identify that SRH needs for PICU admissions are greater than previously realised. Staff highlighted the acceptability and importance of SRH care, if interventions are appropriately timed and the patient's individual risk profile considered. Providing a nurse led referral pathway for an SRH in-reach clinic is acceptable, feasible and beneficial for PICU patients.


Self-rated health (SRH), individuals’ overall perception of their health, is a key predictor of health events. To target disease prevention efforts, it is important to understand how SRH develops over time. The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping, a time series comparison technique initially developed for speech recognition. Revealing prototypic SRH trajectories can help direct disease prevention efforts towards trajectories that are more likely to result in adverse health events. Based on data from a Dutch representative sample of 2,154 individuals, our dynamic time warp analysis suggests that Dutch individuals do not typically show a steady growth or decline in SRH. Instead, we identified four relatively stable SRH trajectories that differed in average SRH. One of these trajectories is a path of consistent low SRH. Individuals who report lower levels of health care, among others, more likely to experience a stroke , get hosAlthough much is known about the predictive value of SRH, less is known about intra-individual changes in SRH . Given tactual similar SRH trajectories move at a different pace or start at a different time, it is probable that the trajectories are perceived as dissimilar. Thus, while investigating SRH trajectories, it is important to account for local accelerations and decelerations in the time axis.Previous studies investigating SRH trajectories compared changes in SRH as a function of age  and the The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping (DTW). This time series comparison technique accounts for local accelerations and decelerations in the time axis , making 22.1www.lissdata.nl . In this paper, the sample included 2,154 respondents (49% women) who, with the exception of 2014, rated their health on a yearly basis from 2009 to 2018. The age of the respondents ranged from 16 to 96 .In this paper we make use of data of the LISS  panel administered by CentERdata . The LISS panel is a representative sample of Dutch individuals . The dat3spontaneous assessment  of overall health status or an enduring self-concept   to 5 . Even though researchers have put forward two interpretations of SRH, as reflecting either a urement) , the sinurement) .4SciPy library’s squareform function , Cluster 2  consists of individuals with a (very) good SRH , Cluster 3  is made up of individuals with a good SRH , and Cluster 4  consists of individuals scoring consistently low on SRH . At the within-person level, individuals in Clusters 2 and 4 fluctuated the most in terms of SRH, mostly ranging mostly from good to very good  and around moderate SRH . Individuals in Clusters 1 and 3 varied within a narrower bandwidth, with SRH ranging mostly from very good to excellent  and around good .2η = 0.07. When then conducted ANOVAs to examine the univariate differences in the personal characteristics. These ANOVAs suggested univariate differences in all five personal characteristics (p < .05). The effect sizes were largest for medication use (2η = 0.16). To evaluate differences between the SRH trajectories, we then conducted post hoc pairwise comparisons using Bonferroni correction (α = 0.05). The pairwise comparisons revealed that the individuals in the different SRH trajectories differed especially in age, employment, and medication use. Individuals in clusters that scored higher on SRH often were younger, had a lower Body Mass Index (BMI), and used less often medicine. The proportion of women was significantly (p < .05) lower in the “Very Good – Excellent” SRH cluster than in the “Good” SRH cluster. Furthermore, the cluster of individuals from the “Very-good – Excellent” SRH cluster were significantly more often employed than individuals from the clusters “Good” (p < .01) and “Moderate” SRH (p < .05).6The aim of this short communication was to find prototypic SRH trajectories by applying DTW. The data used for this study clustered well into four different trajectories. All of these trajectories showed a relatively flat SRH pattern. The most common trajectory (64.3%) was of “good” SRH. A small group of individuals (7.0%) experience a trajectory of “very good” to “excellent” health, whereas the remaining individuals (14.3% and 14.4%) consistently scored their health as either “moderate” or ”very good”.By contributing to the understanding of SRH trajectories, the ultimate goal of this study was to identify opportunities to prevent adverse health events. Previous research suggests that sharp declines in SRH predicts adverse major health events . In the In contrast to previous studies e.g., , we did Brian M. Doornenbal: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Supervision. Renz Bakx: Writing - review & editing, Formal analysis.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Tumour hypoxia is associated with poor patient prognosis and therapy resistance. A unique transcriptional response is initiated by hypoxia which includes the rapid activation of numerous transcription factors in a background of reduced global transcription. Here, we show that the biological response to hypoxia includes the accumulation of R-loops and the induction of the RNA/DNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. Therefore, suggesting that, SETX plays a role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we propose that the mechanism of SETX induction in hypoxia is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to hypoxia, which includes both a replication stress-dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways. Hypoxia induces a change in transcriptional response in mammalian cells. Here the authors reveal a role for the RNA/DNA helicase Senataxin in protecting cells from DNA damage induced during transcription in hypoxia. The level of hypoxia associated with radiation resistance (<0.13% O2) is characterised by a rapid induction of replication stress, which is defined as the slowing or stalling of replication forks. We have attributed hypoxia-induced replication stress to decreased nucleotide availability4. Hypoxia-induced replication stress leads to the induction of an ATR- and ATM-dependent DNA damage response (DDR), although hypoxia-induced replication stress is insufficient to induce ATM-mediated signalling and increased levels of heterochromatic histone marks, notably H3K9me3 are also required7. Importantly, both hypoxia-induced ATR and ATM activity occur in the absence of detectable DNA damage8. Transcriptional stress, defined as global repression of RNA synthesis, has also been identified in the hypoxic conditions which lead to a DDR9.The microenvironment of the majority of solid tumours is characterised by regions of low oxygen (hypoxia), which result from abnormal vasculature and increased oxygen demand. Tumour hypoxia plays a pivotal role in tumorigenesis and has been associated with increased aggressiveness, metastasis, resistance to radiotherapy, and poor patient prognosis10. R-loops are comprised of an RNA/DNA hybrid and a displaced single-stranded DNA (ssDNA), for recent reviews see13. R-loops form within the genome at actively transcribed genes preferentially occurring at gene promoters and terminators. R-loop homoeostasis is regulated through several pathways including mRNA processing, RNA modifications, DNA topology, single-stranded DNA-binding proteins and by specialised R-loop processing enzymes such as RNase H, RNA/DNA hybrid helicases and DNA nucleases17. R-loops regulate gene expression but can also lead to genome instability, which is particularly apparent in cells with deficiencies in R-loop processing and binding factors, including for example BRCA122. Recently, a role for R-loops has been described in the regulation of RNA polymerase I (Pol I) activity by RNA polymerase II (Pol II)23. R-loops have not been studied in the context of hypoxia however, hypoxia initiates a significant change to cellular transcription suggesting there may be an impact on R-loop formation and resolution24. Despite a reduction in global levels of transcription, a number of transcriptional pathways are induced in hypoxia including the hypoxia-inducible factors (HIF1-3), p53 and those initiated by the unfolded protein response (UPR)27. The UPR is an ER stress-response pathway that leads to global translation inhibition while upregulating selective genes including those involved in protein folding, redox homoeostasis and protein degradation28. In contrast to the DDR, which is activated by ssDNA in hypoxia, the UPR is activated by misfolded proteins. Notably, the DNA repair pathways, including critical R-loop associated factors such as BRCA1 and Rad51, have been shown to be repressed through a variety of mechanisms in response to hypoxia31.R-loops are 3–stranded nucleic acid structures that are linked to transcription and can contribute to replication stress36. The yeast homologue of SETX, Sen1, was shown to be involved in transcription-coupled repair and protect cells from transcription-associated recombination39. In addition, Sen1 has been shown to act at the replication fork and facilitate replication at highly transcribed genes40. SETX has also been linked to R-loops at transcription–replication collisions and to participate in the antiviral response41. Mutations in SETX have been associated with two neurodegenerative diseases, ataxia with oculomotor apraxia type 2 (AOA2) and amyotrophic lateral sclerosis type 4 (ALS4)43.Senataxin (SETX) is a putative RNA/DNA helicase that can facilitate transcription termination and gene transcription of a subset of genesThe role of R-loops and factors involved in R-loop processing including RNA/DNA helicases have not been investigated in response to hypoxia. We show that in hypoxia, which includes transcriptional and replication stress, R-loops increase. The majority of the factors investigated associated with R-loops were found to be repressed with the notable exception of SETX, which was induced in hypoxia, prompting us to focus on the role of SETX in hypoxia. We found that the loss of SETX leads to an accumulation of R-loops, and that hypoxia-induced SETX protected cells from replication stress, transcription-dependent DNA damage and apoptosis. Most importantly, hypoxia-induced SETX was dependent on the PERK branch of the UPR, suggestive of a link between the UPR and replication stress in hypoxia.2) led to the rapid accumulation of RPA foci and pan-nuclear γH2AX in the absence of 53BP1 foci. Importantly, the cells that had pan-nuclear γH2AX also had RPA foci which indicate a replication stress-dependent DDR in the absence of detectable DNA damage , the transcript levels of a number of R-loop processing factors including RNase H1, RNase H2B, PIF1, DHX9, RTEL1, and AQR, were significantly repressed, in contrast, SETX was upregulated  in normoxia, and of these genes, 5 were previously identified as regulated in a SETX-dependent manner. In contrast, SETX depletion in hypoxia caused 341 genes to increase and 256 genes to decrease in expression  and H3K9me3 (3-fold) in hypoxia but had no significant impact on the levels of these marks in normoxic conditions  supporting that the biological response is affected by the accumulation of R-loops in hypoxia.To determine the impact of loss of SETX in hypoxia on R-loops, we used the S9.6 antibody, which specifically recognises RNA/DNA hybrids. The nuclear intensity of S9.6 increased in response to hypoxia and importantly, this was ablated by treatment with RNase H, an endonuclease that degrades the RNA component of R-loops Fig. . A slot xia Fig. 48,49. Toons Fig. . As expeons Fig. . It has xia Fig. . Togethed-ribofuranosylbenzimidazole (DRB), which inhibits Pol II-mediated transcription. DRB rescued the DNA damage observed in the SETX-depleted hypoxic cells suggesting that the damage observed in SETX-depleted hypoxic cells was transcription-dependent  shown to induce SETX, including HIF, p53 and those involved in the UPR    did not induce SETX  Fig. . ConsistETX Fig.  and we sETX Fig. . The datETX Fig. . Cells wETX Fig. . ImportaETX Fig. . Signallion Fig.  but thation Fig. .Fig. 5SEAs expected, depleting PERK using siRNA abrogated the hypoxia-mediated induction of SETX Fig. . In addiD210N to determine changes in R-loop levels with siRNA to PERK or PERKi. siRNA mediated PERK depletion and PERKi led to an increase in R-loop levels in hypoxia using both detection methods suggesting that, like SETX, PERK signalling negatively regulates R-loops in hypoxia  demonstrate high levels of replication stress due to reduced dNTP levels. However, it was apparent that the cells with reduced PERK activity had increased numbers of RPA foci per cell in hypoxia suggesting again that hypoxia cells with reduced PERK activity have increased levels of replication stress  of thapsigargin or DTT, PERK inhibits replication fork progression, and origin firing through a Claspin/Chk1 pathway63 and in support of this, SETX expression was found to be reduced in a number of cancers compared to normal tissue controls43. However, AOA2 patients do not present with increased cancer susceptibility suggesting a level of redundancy with other R-loop resolution mechanisms exists to protect genome stability. In this study, SETX depletion increased R-loops, replication stress, DNA damage and apoptosis in hypoxia suggesting that SETX may promote hypoxic tumour growth, and hence be a potential therapeutic target. Interestingly, SETX was recently identified as a gene whose knockout increases sensitivity to cisplatin in a set of genome-wide CRISPR-Cas9 screens64. It is possible that in the hypoxic microenvironment in which multiple pathways including DNA repair and potentially R-loop resolution factors are repressed, loss of SETX function would not be readily compensated by other mechanisms in a manner similar to its deficiency in the cerebellum. In addition, this study demonstrates that inhibiting PERK leads to increased R-loops, DNA damage and replication stress in hypoxia, and therefore PERK could also be a potential therapeutic target. Targeting PERK in cancer has been controversial as PERK has demonstrated both tumours promoting and suppressive activities depending on the type of cancer and intensity of stress65. However, in hypoxia, PERK supports tumour survival and growth, therefore suggesting PERK as a potential therapeutic target specifically for hypoxic tumours67. Since PERK inhibition also increased replication stress in hypoxic conditions, we postulate that using PERK inhibitors in conjunction with replication stress inducers such as ATR inhibitors may be beneficial in solid tumours.SETX has been identified as a potential tumour suppressor2. Cells were routinely tested for mycoplasma and found to be negative. For plasmid transfections, JetPrime (Polyplus transfection) was used. ppyCAG_RNaseH1_WT (#111906) and ppyCAG_RNaseH1_D210N (#111904) were supplied by Addgene. siRNA transfections were performed using Dharma-FECT-1 (ThermoFisher Scientific), sequences are available in the SI. siSETX-A siRNA was used for Figs. RKO, HCT116 and A549 (ATCC) were grown in DMEM supplemented with 10% FBS and cultured in humidified incubators at 37 °C and 5% CO2. An M35 hypoxia work station (Don Whitley Ltd) was used for 2% O2. All experiments at <0.1% O2 were plated in glass dishes, except for colony survival assays, which were plated in 6 well plates. All hypoxic treatments were harvested within the chamber using equilibrated buffers. Oxygen tensions were confirmed with the Oxylite probe (Oxford Optronix).Bactron II and BactronEZ anaerobic chambers (Shel Lab) were used for <0.1% OCells were harvested in UTB lysis buffer . Blots were visualised using the Odyssey Infrared imaging system. Antibodies used were SETX, KAP1 (Bethyl); GRP78, HIF1α, (ΒD Biosciences); ATM-S1981, RPA-S4/8, β-Tubulin (Abcam); ATM, RPA, KAP1-S824, p53-S15, H3K9me2, H3K4me2, H3K9Ac, H3-S10, H3, H3K36me3, Chk1-S317, PERK ; p53, β-actin, RNase H1, Chk1 (Santa Cruz); and H3K9me3, γH2AX, H2AX (Millipore).RNA was extracted using TRI reagent (Sigma), treated with DNase I (NEB) and cDNA prepared using the Verso cDNA synthesis kit (ThermoScientific). SYBR Green PCR master mix (Applied Biosystems) and 7500 FAST Real-Time PCR machine (Applied Biosystems) were used. The ΔΔCt method was used to determine relative mRNA fold change with 18S as the reference gene. Primer sequences can be found in Table 3 and 1% SDS, cross-linking reversed and treated with proteinase K. DNA was purified with Qiagen PCR purification kits and qPCR was carried out using the following primers: SETX For: CTCAGGTGTCTCAGCGGATG, SETX Rev: CGCATTGTTCGCAAGACCTA, CHOP For: AAGAGGCTCACGACCGACTA and CHOP Rev: ATGATGCAATGTTTGGCAAC. The amount of immunoprecipitated material was calculated as the fold enrichment over IgG control.Treated cells were fixed with 1% formaldehyde and quenched in 125 mM glycine. Cells were lysed with SDS lysis buffer . After sonication with Diagenode Bioruptor, samples were precleared with protein A agarose beads and subsequently incubated with ATF4 antibody  overnight. Antibody–antigen complexes were then pulled down with agarose beads and washed with the following buffers—low salt wash buffer , high salt wash , LiCl wash buffer , TE wash buffer . DNA was eluted with 0.1 M NaHCO14. Coverslips were treated with RNase A (Fermentas) for 1 h at 37 °C and where indicated with RNase H (NEB) for 24 h at 37 °C prior to blocking. Coverslips were then incubated with 1:100 diluted S9.6 antibody (Kerafast) for 36 h at 4 °C. Cells were mounted and the entire nucleus imaged using LSM780 or LSM710 confocal microscope , using a ×63/1.40 Oil DIC M27 Plan-ApoChromat objective. Detection of R-loops using RNase H1D210N-V5 was carried out as previously described68. In brief, cells transfected with RNase H1D210N-V5 were pre-extracted in ice-cold PBS/0.2% Triton-X 100 for 2 min, fixed in 4% (w/v) paraformaldehyde, permeabilised in 0.1% Triton-X 100, blocked with 1% (w/v) BSA in 1% (w/v) FBS in PBS, incubated with 1:500 diluted V5 antibody (ThermoFisher Scientific) for 1 h at room temperature before confocal imaging. DAPI was excited with laser line 405 nm and emission collected between 410 and 495 nm. Alexa-fluor 488 was excited with laser line 488 nm and emission collected between 495 and 596 nm. Alexa-fluor 594 was excited with laser line 594 nm and emission collected between 599 and 734 nm. Alexa-fluor 647 was excited with laser line 633 nm and emission collected between 638 and 755 nm. The 5′EU, S9.6 and V5 mean nuclear intensity signal (including the nucleolus) was determined using the ImageJ plugin/algorithm kindly provided by Dr Kienan Savage, Queen’s University Belfast69. A minimum of 100 cells was included per treatment.Cells were fixed in 4% (w/v) paraformaldehyde in PBS, permeabilised in 1% Triton-X 100 in PBS and blocked with 2% (w/v) BSA in 0.1% Tween-20 in PBS. Primary antibodies 53BP1 , RPA  and cleaved caspase 3  were used. For 5′EU staining, cells were incubated with 5′ethynyluridine (EU) (0.5 mM) (Jena Bioscience) and Click-iT Alexa Fluor 647 labelling kit (ThermoFisher) was used to measure nascent transcription in the entire nucleus. For EdU staining, cells were incubated with EdU (10 µM) and Click-iT Alexa Fluor 647 labelling kit (ThermoFisher) was used to measure replication. The S9.6 staining protocol used was as previously describedRNA was extracted using TRI reagent (Sigma) and treated with DNase I (NEB). The samples were purified using the RNeasy MinElute Clean-up kit (Qiagen) and purity confirmed using a Bioanalyzer. The mRNA fraction was selected for reverse transcription to cDNA. The cDNA generated was end-repaired, A-tailed and adaptor-ligated. cDNA libraries were size selected, multiplexed, tested for quality and subjected to paired-end sequencing in one lane of a flow cell. Accession for RNA-seq data is GSE157371.75.The analyses were run through an established RNA-seq pipeline by the Buffa lab. The technical adaptors in the pair-end raw reads were trimmed out by trimmomatic-0.32 and the clear reads were aligned to human genome version GRCh38 release 82 using tophat v2.0.13. The mapped reads from each gene were counted and normalised to fragments per kilobase of transcript per million (FPKM) using Cuffdiff-2.2.1. Fragments from PCR duplication were removed by Picard 1.124. The significance of fold changes of genes among samples was estimated by the nonparametric method Rank Product 3.3, a package in Rhttp://www.cbioportal.org/ on the 12th April 2017). To examine SETX expression against hypoxia, a validated hypoxia metagene signature was used44. To examine SETX expression against tumour-associated, hypoxia-induced p53 activity (referred to as hypoxic p53 targets in the figure), raw data for each sequenced gene were rescaled to set the median equal to 1, and hypoxic p53 targets were determined by quantifying the median expression of 6 p53 target genes associated with hypoxia-induced p53 activity 25.RNA-seq data (RNA Seq V2 RSEM) for 382 colorectal adenocarcinoma tumours, 501 lung squamous cell carcinomas and 517 lung adenocarcinomas were downloaded from the TCGA project  using default settings. Bedtools76 was used to merge RLFS with at least 1 base pair overlap and these merged regions were used for subsequent analysis.The quantitative model of R-loop forming sequences (qm-RLFS) was used to predict RLFS from fasta files of the human genome build GRCh38.p13  was sampled 100,000 times, drawing 20% of the number of genes in the SETX KD group with each iteration. The mean of each RLFS metric was calculated for each iteration and the values plotted as probability densities and compared using two-tailed Student’s t-tests.Gene coordinates were accessed via BiomaRt using the Ensembl hg38 build version 1002) treatments, cells were pulse-labelled with CldU (25 mM) for 20 min, washed once with fresh media, followed by the second label IdU (250 mM) for 20 min. For hypoxic (<0.1% O2) treatments, cells were incubated in hypoxia for 3 h before being pulse-labelled with CldU (25 mM) for 2 h, washed once with fresh media, then followed by IdU (250 mM) for 1 h78. Fibres were spread, stained and mounted (Invitrogen) and imaged using LSM780 confocal microscope (Carl Zeiss Microscopy Ltd) using a ×63/1.40 Oil DIC M27 Plan-ApoChromat objective. Alexa-fluor 488 was excited with laser line 488 nm and emission collected between 493 and 551 nm. Alexa-fluor 555 was excited with laser line 543 nm and emission collected between 559 and 659 nm. Replication rates were calculated as VldU (kb/min) = [(x ∗ 0.132 μm) ∗ 2.59 kb/μm]/t (min), where x = length of IdU.For normoxic  and include one-way ANOVA, two-way ANOVA, two-tailed Student’s Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (“Stanford OpenVaccine”) on Kaggle, involving single-nucleotide resolution measurements on 6043 102–130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. However, the chemical instability of RNA sets a fundamental limit on the stability of RNA-based therapeutics such as mRNA-based vaccines8. RNA hydrolysis is the limiting factor in lipid nanoparticle (LNP)-based formulations10. Hydrolysis in LNP formulations degrades the amount of mRNA remaining during shipping and storage, and hydrolysis in vivo after vaccine injection limits the amount of resulting protein produced over time9. Better methods to develop thermostable RNA therapeutics would allow for increasing the equitability of their distribution, reducing their cost, and possibly increasing their potency11. Initial results have demonstrated that more stable mRNAs can be designed via stochastic algorithms that identify mRNA sequences that code for the same protein12, but are predicted to be more resistant to hydrolysis14. Importantly, these mRNAs are demonstrated to produce equivalent amounts of protein to non-optimized mRNAs14. These design strategies are predicted to be able to produce mRNAs that do not activate double-stranded RNA immune sensors15, and have also demonstrated to be compatible with mRNAs synthesized from modified nucleotides including pseudouridine14, which are used in mRNA vaccine formulations16.Therapeutics based on messenger RNA (mRNA) have shown immense promise as a modular therapeutic platform, allowing potentially any protein to be delivered and translated13. Computational studies with this model suggested that at least a two-fold increase in stability could be achieved through sequence design, while maintaining a wide diversity of sequences and features related to translatability, immunogenicity, and global structure14. However, it is unlikely that degradation depends only on the probability of a nucleotide being unpaired: local sequence-and structure-specific contexts may vary widely, as evidenced by ribozyme RNAs found in nature, whose sequences adopt specific structures that undergo self-scission17.A key prediction task underpinning algorithms for designing mRNAs with increased stability is the model used to predict of RNA hydrolysis from sequence . Previou19, and the diversity of resulting structures on its platform have more recently contributed to advancing RNA secondary structure prediction20. We reasoned that crowdsourcing the problem of obtaining data on a wide diversity of sequences and structures would rapidly lead to a diverse dataset, and that crowdsourcing the second problem of obtaining a machine learning architecture would result in a model capable of expressing the resulting complexity of sequence- and structure-dependent degradation patterns. We hypothesized this “dual crowdsourcing” would lead to stringent and independent tests of the models developed, minimizing interplay between the individuals designing the constructs to test (Eterna participants) and the individuals building the models (Kaggle participants) and leading to better generalizability on independent data sets.We wished to understand the maximum predictive power achievable for RNA degradation on a short timescale for model development. To do this, we combined two crowdsourcing platforms: Eterna, an RNA design platform, and Kaggle, a platform for machine learning competitions. Eterna has previously been able to solve near-intractable problems in RNA design14 have been developed to characterize the overall degradation rates per mRNA molecule, which is the primary value of interest to minimize when designing stabilized RNA-based therapeutics. In principle, the overall degradation rate of a mRNA molecule of length N is equivalent to the sum of degradation rates at each dinucleotide linkage in the backbone21:i. The half-life of the mRNA is calculated asThe resulting models were subjected to two blind prediction challenges. The first was in the context of the Kaggle competition, where the RNA structure probing and degradation data that participants would be aiming to predict was not acquired until after the competition was announced. The experimental method used for these data, In-line-seq, allowed for measuring the degradation rate of individual dinucleotide linkages. However, this method relies on probing short RNA fragments and is unable to scale to make single-nucleotide degradation measurements of full-length mRNAs for protein targets of interest. Other experimental methods such as PERSIST-seqgreement . Using t14 3029 RNA designs of length 107 nt were collected in the first “Roll-Your-Own-Structure” round I (RYOS-I), which was opened March 26, 2020, and closed upon reaching 3029 constructs on June 19, 2020 23, a technique to characterize RNA secondary structure. The Kaggle competition was designed to create models that would have predictive power for three of these data types, given RNA sequence and secondary structure as input  RNA secondary structure from the ViennaRNA package25; and a six-character featurization of the MFE structure calculated using bpRNA26.In total, each independent construct of length N required predicting 3×N values for the 3 data types. In addition to these experimental data, Kaggle participants were also provided with features related to RNA secondary structure computed from available biophysical models to use if they wished. These features included 107×107 base pairing probability matrices from EternaFoldWe developed training and “public test” datasets from the RYOS-I dataset . The pub2+, pH 10, 1 day, 24 °C; and 10 mM Mg2+, pH 7.2, 1 day, 50 °C. Models were scored using the mean column RMSE (MCRMSE) across three data types, defined asTo ensure that the majority of the data used for the private test set was fully blind, we initiated a second “Roll-your-own-structure” challenge that was launched for Eterna design collection on August 18, 2020. Given that useful models for degradation should be agnostic to RNA length, we designed the constructs in RYOS-II to be 34 nucleotides longer (102 vs. 68 nts) than the constructs in RYOS-I to discourage modelling methods that would overfit to constructs of length 68. Design collection was closed on September 7th, three days before the launch of the Kaggle challenge on September 10th. The RYOS-II wet-lab experiments were conducted concurrently with the Kaggle challenge, enabling a completely blind test for the models developed on Kaggle. The Kaggle competition was closed on October 6th. The RYOS-II was similarly clustered and filtered to ensure that the test set used for scoring consisted primarily of singly- and double-clustered constructs. Three data types were used to score models: SHAPE; 10 mM MgNt is the number of scored data types, n is the number of nucleotides in the dataset, and t, yij is the measured data value, and Where 14 by 37 % in MCRMSE for the public test set and 25% for the private test set . Kaggle participants developed feature encodings beyond what was provided. One of the most widely-used community-developed featurizations was a graph-based distance embedding depicted in https://www.kaggle.com/code/mrkmakr/covid-ae-pretrain-gnn-attn-cnn/), which alone achieved a MCRMSE of 0.24860 on the Public and 0.36106 on the Private test set (https://github.com/DasLab/arnie) and iteratively scored based on these predictions for their model as well  and a Private MCRMSE of 0.3397 (compared to the best Private test set MCRMSE of 0.3420). In comparison, averaging the outputs of the top two models gave a result of 0.2244 Public, 0.33788 Private. Blending the top two solutions with the 3rd solution did not improve the result. An estimated bound of ensembling can be found by optimizing directly to the Private ensemble score. With this method, it was possible to achieve a Private ensemble score of 0.3382 . The improvement of 0.0038 over the leaderboard for this last approach is about the distance between the 1st place and 10th place teams, and the “correct” way gives an improvement that is the distance between the 1st and 5th place teams. All these experiments suggest that most of the signal has been captured by the top two models, and that the use of further ensembling provides, at best, modest improvements. The seemingly puzzling result that the simple ensemble of the top two models outperforms the genetic algorithm blend of the top 10 (on the private test set) suggests that the genetic algorithm did not find a global minimum for model weights.14. The most reactive RNA structure motifs were triloops, a previously unknown biological finding. Another unexpected finding from these data were that internal loops with symmetric lengths on either side recovered stability over internal loops with asymmetric lengths. The fact that the Nullrecurrent model was able to capture this trend indicates that using such models within a design algorithm would allow for an automated way to impart such biological attributes within a designed mRNA. Constructs with the highest RMSE demonstrate indicators that the provided structure features were incorrect. 18, and RMSE summed per construct for the private test constructs, suggesting that in general, quality of the input structure features was not a limitation in model training  in depth to better understand its performance. Across all nucleotides in the private test set, 41% of nucleotide-level predictions for SHAPE reactivity agreed with experimental measurements within an error that was lower than experimental uncertainty; for comparison, if experimental errors are distributed as normal distributions, a perfect predictor would agree with experimental values over 68% of data points. For Deg_Mg_pH10 and Deg_Mg_50C, 28% and 42% of predictions were within error, respectively. Per-construct RMSE between data types was moderately correlated, with the highest correlation being Spearman R=0.63 between deg_Mg_pH10 and deg_Mg_50C . The nuctraining .14. The lengths of these mRNAs ranged from 504 to 1588 with a median length of 928 , these mRNAs could not be probed at a per-nucleotide level akin to the datasets used in the Kaggle experiments. However, their overall degradation rates , the DegScore linear regression model  and the DegScore-XGBoost model . An ensemble of the Nullrecurrent and Kazuki2 models did not outperform the Kazuki2 model , again suggesting that the models themselves had reached their predictive potential. In comparison, resampling the measured degradation rates from within experimental error and calculating the correlation to the mean degradation rate, as a measure of the upper limit of experimental noise, resulted in a Spearman correlation of 0.88  and coding sequence (CDS) . Carryin of 0.88 .The OpenVaccine competition uniquely leveraged resources from two complementary crowd-sourcing platforms: Kaggle and Eterna. The participants in the Kaggle competition were tasked with predicting stability measurements of individual RNA nucleotides. The urgency of timely development of a stable COVID-19 mRNA vaccine necessitated that the competition be run on a relatively short timeframe of three weeks, as opposed to three months, which is more common with the Kaggle competitions.21 to minimize the predicted degradation. There is likely further opportunity to leverage advancements in natural language processing to use datasets such as the ones presented here to generate candidate mRNA designs using text generation approaches30. The degradation data used in this competition were from unmodified nucleotides, but mRNA vaccines are being formulated with modified nucleotides including pseudouridine or N-1-methyl-pseudouridine16. Modified nucleotides in general will have differing underlying thermodynamics12, and there is a need to develop datasets and predictive models to predict structures and resulting stabilization of mRNAs formulated with modified nucleotides. Short of developing complete new thermodynamic parameters for modified nucleotides, it may be possible to develop principled heuristics to adapt models to mRNAs synthesized with modified nucleotides. For instance, Leppek et al. modified the DegScore model for pseudouridine by setting all contributions to degradation from uracil to zero to mimic the stabilization effect, and saw moderate improvement in correlation14.The models presented here are immediately useful for mRNA design in that they could be called within a stochastic mRNA design algorithmKaggle competitions with relatively small datasets can be subject to serious overfitting to the public leaderboard, which often leads to a major “shake up” of the leaderboard when the results on the unseen test set are announced. In this competition the shakeup was minimal - most of the top teams were ranked close to the same position on the private leaderboard as they were on the public leaderboard. As the private leaderboard was determined on data that had not been collected at the time of the competition launch, this result suggests that the models that were developed are robust and generalizable. Furthermore, the models generalized to the task of predicting degradation for full-length mRNA molecules that were ten-fold longer than the constructs used for training. We speculate that the use of a separate, independently collected data set for the private leaderboard tests -- a true blind prediction challenge -- was important for ensuring generalizability. The winning solutions all used neural network architectures that are commonly used with modeling of 1D sequential data: recurrent NNs (LSTMs and GRUs) and 1D CNNs. The effectiveness of pseudo-labeling has two implications: more data will likely benefit any future modeling efforts, and the simple neural networks that were used have enough capacity to benefit from more data.20, and integrating into inference frameworks for RNA structure prediction31.An under-investigated aspect of the models presented here is the effect of training on multiple data types. We speculate that because SHAPE reactivity has higher signal-noise than the degradation data types cf. , models Finally, the models for predicting RNA hydrolysis developed in this work may prove useful in computationally identifying classes of natural RNAs which have evolved to be resistant to degradation. Such future bioinformatic analysis may suggest entirely new biologically-inspired approaches for designing hydrolysis-resistant RNA therapeutics.25, loop type assignments generated with bpRNA26  and the base pair probability matrix according to the EternaFold20 energy model. These features were generated using Arnie (https://github.com/DasLab/arnie).As a starting point for Kaggle teams, we supplied a collection of features for each RNA sequence, including the minimum free energy (MFE) structure according to the ViennaRNA 2 energy model14.The first experimental dataset used in this work, for the public training and test set, resulted from the “Roll-Your-Own-Structure” Round I lab on Eterna, and had been generated previously by Leppek et al14 In brief, DNA templates were ordered via custom oligonucleotide pool from Custom Array/Genscript, prepended by the T7 RNA polymerase promoter. Templates were amplified via PCR, transcribed to RNA via the TranscriptAid T7 High Yield Transcription Kit , and the purified RNA was subjected to degradation conditions: 1) 50 mM Na-CHES buffer (pH 10.0) at room temperature without added MgCl2; 2) 50 mM Na-CHES buffer (pH 10.0) at room temperature with 10 mM MgCl2; 3) phosphate buffered saline  at 50°C without added MgCl2; and 4) PBS (pH 7.2) at 50°C with 10 mM MgCl2. In parallel, purified RNA was subjected to SHAPE structure probing conditions, and one sample was subjected to the SHAPE protocol absent addition of the 1-methyl-7-nitroisatoic anhydride reagent.The second experimental dataset used in this work, for the private test set, was generated for this work specifically. To produce these data, and for precise consistency with the public training and test set, In-line-seq was carried out as described by Leppek et al.https://ribokit.github.io/MAPseeker) following the recommended steps for sequence assignment, peak fitting, background subtraction of the no-modification control, correction for signal attenuation, and reactivity profile normalization as described in Seetin et al.22.cDNA was prepared from the six RNA samples . We pooled 1.5 μL of each cDNA sample together, ligated with an Illumina adapter, washed, and resuspended the ligated product, which was quantified by qPCR, sequenced using an Illumina Miseq. Resulting reads were analyzed using MAPseeker  within either dataset (RYOS 1 or RYOS 2) (Where μ RYOS 2) . However RYOS 2) .We wished to ascertain if the measured reactivities and degradation from Round 2 needed to be rescaled to match Round 1. To assess this, we compared distributions of nucleotide reactivities from nucleotide types. We found that for each data type and nucleotide type, the median values for Round 2 were within the 50% interquartile range of Round 1 . We also32 and then clustered at a cophenetic distance of 0.5. That is, sequences within the same cluster have <50% sequence similarity. All sequences that were in clusters with 1, 2, or 3 members were included in the private test set, as well as one cluster member from other randomly-selected clusters to attain the desired number of test set constructs.The private test set was curated to avoid bias toward more highly-represented sequence motifs from the Eterna designs. Sequences that passed the above filters were clustered hierarchically using the `ward` method in scikit-learn14, which models degradation at a given nucleotide k as a linear function of nucleotides surrounding k:We compared Kaggle models to the “DegScore” linear modelβ are the learned coefficients and I is an indicator function corresponding to the identity of nucleotide k+i. I accounts for sequence identity n  and its loop type assignment s . w is the maximum window distance, set to be 12 in Leppek et al14. For a window size of w=12, or 25 positions, there are 251 parameters .Where rmdb.stanford.edu at the following accession numbers: SHAPE_RYOS_0620, RYOS1_NMD_0000, RYOS1_PH10_0000, RYOS1_MGPH_0000, RYOS1_50C_0000, RYOS1_MG50_0000, RYOS2_1M7_0000, RYOS2_MGPH_0000, RYOS2_MG50_0000. Kaggle-formatted train and test sets are downloadable from https://www.kaggle.com/c/stanford-covid-vaccine. Datasets, scripts, and models are also included at https://www.github.com/eternagame/KaggleOpenVaccine.All datasets are downloadable in raw RDAT format from www.github.com/eternagame/KaggleOpenVaccine. Code to use and reproduce the linear regression DegScore model is available at www.github.com/eternagame/DegScore.Code to run the Nullrecurrent model and the DegScore-XGBoost model is available at 1