key: cord-0894383-nztxv6uj
authors: McDonald, J. Tyson; Enguita, Francisco Javier; Taylor, Deanne; Griffin, Robert J.; Priebe, Waldemar; Emmett, Mark R.; Sajadi, Mohammad M.; Harris, Anthony D.; Clement, Jean; Dybas, Joseph M.; Aykin-Burns, Nukhet; Guarnieri, Joseph W.; Singh, Larry N.; Grabham, Peter; Baylin, Stephen B.; Yousey, Aliza; Pearson, Andrea N.; Corry, Peter M.; Saravia-Butler, Amanda; Aunins, Thomas R.; Sharma, Sadhana; Nagpal, Prashant; Meydan, Cem; Foox, Jonathan; Mozsary, Christopher; Cerqueira, Bianca; Zaksas, Viktorija; Singh, Urminder; Wurtele, Eve Syrkin; Costes, Sylvain V.; Davanzo, Gustavo Gastão; Galeano, Diego; Paccanaro, Alberto; Meinig, Suzanne L.; Hagan, Robert S.; Bowman, Natalie M.; Wolfgang, Matthew C.; Altinok, Selin; Sapoval, Nicolae; Treangen, Todd J.; Moraes-Vieira, Pedro M.; Vanderburg, Charles; Wallace, Douglas C.; Schisler, Jonathan; Mason, Christopher E.; Chatterjee, Anushree; Meller, Robert; Beheshti, Afshin
title: Role of miR-2392 in Driving SARS-CoV-2 Infection
date: 2021-09-30
journal: Cell Rep
DOI: 10.1016/j.celrep.2021.109839
sha: e29e2fe2456b180ff33e7ccd6fb1b925c1e48c8d
doc_id: 894383
cord_uid: nztxv6uj

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provides an exciting avenue towards antiviral therapeutics. From patient transcriptomic data, we determined a circulating miRNA, miR-2392, is directly involved with SARS-CoV-2 machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia as well as promoting many symptoms associated with COVID-19 infection. We demonstrate miR-2392 is present in the blood and urine of patients positive for COVID-19, but not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters and may potentially inhibit a COVID-19 disease state in humans.

To determine a more comprehensive impact of miR-2392 affected pathways in patients, gene targets were predicted by seed-region base pairing in the miRmap database 218 (Vejnar and Zdobnov, 2012) . This list was refined by overlap in several miRNA databases 219 including miRmap, miRwalk (Dweep and Gretz, 2015) , miRDB (Chen and Wang, 2020), miRnet 220 (Chang et al., 2020) , and ClueGo (Bindea et al., 2009) . We added RNA-seq analysis of 39 221 autopsy tissue samples from the heart, lung, kidney, liver, and lymph node of COVID-19-222 positive patients with high or low viral loads (Park et al., 2021) . MiR-2392 gene targets (375 223 genes) were visualized using volcano plots ( Fig. 3A-F) . 224 To further ascertain the systemic impact on miR-2392 gene targets in COVID-19, we (Fig. 3H) . This revealed 14 genes harboring miR-2392 seed sequences that were 239 significantly dysregulated in the nasal and heart samples. In nasal samples, SLC25A28, a 240 mitoferrin which mediates mitochondrial iron transport, was strongly upregulated along with 241 IBA57, which is involved in iron sulfur assembly. The mitochondrial outer membrane protein 242 import complex subunit TOMM20, cytochrome c oxidase (complex IV) subunit COX6B1, and 243 mitochondrial transcription factor COT-2 (NR2F2) were strongly downregulated. In the heart, 244 the folate enzyme MTHFD2L (methylenetetrahydrofolate dehydrogenase) was upregulated while 245 all of the other nuclear-coded mitochondrial genes were downregulated. Downregulated heart 246 mitochondrial genes included NDUFS5 (complex I subunit), COX6B1 and COX10 (complex IV 247 structural and assembly subunits), CKMT1A (mitochondrial creatine kinase), MRPL34

(mitochondrial ribosome small subunit), COT-2 (NR2F2), AK4 and MSRB3 (adenylate kinase 4 249 and methionine-R-sulfoxide reductase which mitigate oxidative stress), MRS2 (magnesium 250 transporter) and CLIC4 (chloride channel). The kidney showed mild upregulation of complex I 251 and single methyl group metabolism, but down regulation of complex IV (COX10), regulatory 252 factor (COT-2), and iron sulfur center protein (IBA57). Hence, SARS-CoV-2 seems to 253 downregulate nuclear mitochondrial gene transcription in the more oxidative organs, heart and 254 kidney, as well as in nasal tissues.

Since inflammation is a key component of COVID-19, we overlaid known inflammatory 256 genes determined from Loza et al. (Loza et al., 2007) with miR-2392 targets (Fig. 3I) . From for miR-2392 targets from miRmap, ClueGO, miRwalk, miRnet, and miRDB ( Fig. 3J and 3K ).

Several miR-2392 targets in tissue show a significant transcriptional increase in positive samples with small to no changes on the proteomics level: PLK1, CD38, PYCR1, 267 RNASE1, BIRC5, RRM2, SIGLEC1 (Fig. 3J) . Interestingly, all these genes were also positively 268 regulated for the majority of tissues when considering only miR-2392 gene targets with miRmap 269 (Figs. S1 and S2). The miR-2392 targets CXCL10, STAT1, IFIT3, and C1QC were positively 270 regulated at both the protein and gene levels for the blood and other tissues. The correlation 271 between RNA and protein expression was very close for miR-2392 targets and was slightly 272 stronger in COVID-19 negative (cor=0.209, p=4e-10) versus positive (cor=0.205, p=8e-10) 273 samples (Fig. 3K) . Further investigation is needed to understand if increased levels of miR-2392 274 could potentially bind genes' mRNAs at a higher rate and therefore prevent translation to protein 275 or if there are other mechanisms preventing mRNA translation to protein.

Overexpression of miR-2392 simulates a phenotype similar to To determine if miR-2392 upregulation would elicit effects similar to a COVID-19 infection, 279 cells were treated with a miR-2392 mimic. Using RNA-seq, 649 genes had a fold-change greater 280 than ±1.2 and a p-value less than 0.05 (Fig. 4A ) and many genes were miR-2392 predicted 281 targets (Fig. 4B) . These genes were then compared to whole cell proteome data from a human- altered proteins in normalized ubiquitin abundance that were also dysregulated genes by miR-288 2392 overexpression. We also found miR-2392 overexpression impacted genes involved with 289 mitochondria and inflammation ( Fig. 4D-4F) . 290 To determine if there was a direct correlation between miR-2392 overexpression and SARS- Fig. 4I-J) . There was a positive correlation to lung and lymph node tissues with 301 miR-2392 expression. Interestingly, there was a significant and positive correlation to liver tissue 302 when comparing gene fold-change values ( Fig. 4I) but not C2 curated biological genesets (Fig. 303 4J). In contrast, a negative correlation to heart tissue was observed.

Statistically significant pathways that were enriched due to miR-2392 treatment were 305 examined using fGSEA (Fig. 4K-O) . MiR-2392 treatment induced a pathway response that was 306 significantly related to SARS-CoV-2 pathways. One obvious relationship shows that the 307 Reactome SARS-CoV-2 pathways were significantly activated for the miR-2392 treated cells 308 compared to the controls ( Fig. 4K and 4L ). Significant Hallmark pathways (Fig. 4N) show CoV-2 reference strain (i.e. strain from Wuhan) and also P1 variant (Fig. 5D) . We saw an 340 increase of miR-2392 with the reference SARS-CoV-2 infected cells and a significant increase in 341 the P1 variant infected cells. Previously, it was shown that these monocytes infected with SARS-inhibited by glycolysis inhibitors (Codo et al., 2020) . Since we hypothesize that miR-2392 is a 344 primary initiator for systemic impact of the infection, this might indicate that miR-2392 does not 345 strongly appear until the virus has established its presence in the body. 346 We also quantified miR-1-3p (Fig. S3) , miR-155-5p (Fig. S4) , and miR-124-3p (Fig. S5 ) 347 which were predicted to be inhibited by COVID-19 infection (Fig. 1A) . MiR-1-3p and miR-155-348 5p were significantly suppressed in the serum with no differences in the urine or nasopharyngeal 349 samples ( Fig. S3 and S4A-C) . MiR-1-3p is known to be beneficial for cardiovascular functions, 350 with its inhibition leading to heart failure and disease (Condorelli et al., 2010) . Interestingly, 351 when quantifying miR-1-3p in the SARS-CoV-2 infected monocytes we observe a significant 352 decrease with the reference strain, while no different with the P1 variant (Fig. S3D) . For miR-353 124-3p, we observed very low amounts (on average < 2 copies/5 ng RNA), for all conditions, 354 which indicates that miR-124-3p is not circulating for any of the patients for any the conditions 355 observed (Fig. S4D) . MiR-124-3p provides an ideal miRNA negative control candidate for The anti-miR-2392 Nanoligomers was then evaluated in a Syrian hamster infection model 372 (Fig. 6H-6J) . Six hamsters were treated with Nanoligomers for 72 hours without infection and Loss of body weight over the course of the experiment was <10% in all groups and 381 significantly different for the IN treatment one day before viral inoculation (compared to the 382 control) but not in other groups (Fig. 6H) . Virus titers from oropharyngeal swabs for IN 383 treatment were significantly lower (p = 0.018) than those from hamsters receiving Nanoligomers 384 IP or PBS on day 1 post-challenge, but there were no differences among groups on days 2 and 3 385 post-challenge (Fig. 6I) . Although not statistically different than the control, the data indicates a 386 J o u r n a l P r e -p r o o f downward trend with Nanoligomers treatment (Fig. 6J ) and the total histopathological score for 387 the IN was lower than the controls.

The impact of miR-2392 on diseases, relationship to and predicted FDA 390 drugs to target miR-2392 391 To predict if miR-2392 has a direct relationship to COVID-19 symptoms, we determined the 392 pathway and disease relevance using miRnet. Among the diseases predicted to be associated with 393 miR-2392 were a surprising number of clinical observations present in individuals with COVID-394 19 infection (Fig. 7A) . These include heart or cardiovascular disease and failure, both known to 

Using the tool Kaplan-Meier Plotter (Győrffy, 2021) to associate miR-2392 expression with pan-405 cancer patient survival (Fig. S5) , a high expression of miR-2392 was generally related to poor 406 prognosis with the majority of cancer types (p<0.05). Intriguingly, one miR-2392 predicted 407 consequence was decreased antibody levels in the blood; this might account for the reported loss COVID-19 viral loads compared to those without disease as predicted from RNA-seq data. The 435 expression of seven miRNAs was decreased (miR-10, miR-1, miR-34a-5p, miR-30c-5p, miR-436 29b-3p, miR-124-3p, and miR-155-5p) while a single miRNA, miR-2392, was significantly 437 increased (Fig. 1) . This key miRNA signature was involved in major cellular and molecular 438 mechanisms that drives the viral-host response.

Several studies have measured differential expression of miRNAs in COVID-19 patients and 440 proposed their use as biomarkers or therapeutics. Lung biopsies from 9 COVID-19 patients 441 showed miR-26a, miR-29b, and miR-34a correlated to endothelial dysfunction and inflammatory 442 biomarkers (Centa et al., 2020) . Sequencing in the blood from patients with moderate or severe 443 COVID-19 identified miR-146a, miR-21, miR-142, and miR-15b as potential biomarkers as well 444 as contributors to disease pathogenesis (Tang et al., 2020) . While these studies are limited to a 445 specific tissue, our data that correlates miRNA signatures from multiple tissues (Fig. 3) suggests variant. Comparisons with three other miRNAs were made with miR-1-3p (Fig. S3) , miR-155-5p 649 ( Fig. S4A-C) , and miR-124-3p (Fig. S4D) . tissue sections were fixed in 10% neutral buffered formalin for 48 hours before processing and sectioning. These cases had a post-mortem interval of less than 48 hours. For bulk RNA-seq tissues, post-mortem intervals ranged from less than 24 hours to 72 hours (with 2 exceptions -

Analysis Combining Autopsy and Nasopharyngeal Swab RNA-seq data 956 To combine the results from the autopsy and nasopharyngeal swab RNA-seq data, we utilized 957 the t-score values from the DESeq2 analysis. Heatmaps were displayed using pheatmap (Kolde, 958 2015).

Gene Set Enrichment Analysis (GSEA) 961 For pathway analysis on the miR-2392 targets (Fig. 3) we utilized ShinyGO (Ge et al., 2020) 962 to determine the significantly regulated pathways for each main cluster in the heatmap. The 971 For the analysis of the miR-2392 targets in the blood tissue, we downloaded whole blood 972 transcriptome data and plasma proteome data from The COVIDome Explorer Researcher Portal 973 (Sullivan et al., 2021) . For Transcriptome data we used the following filters: Category "Effect of 974 COVID-19 status", Platform "Blood", Statistical test "Student's t-test", Adjustment method 975 "none", Sex "male" and "female", Age Group "All". For Proteome data we used the following 976 filters: Category "Effect of COVID-19 status", Platform "SOMAscan", Statistical test "Student's 977 t-test", Adjustment method "none", Sex "male" and "female", Age Group "All". We created the 978 list of the intersecting genes from both datasets. We analyzed the list using RStudio Desktop of 583459. 

Multilevel proteomics reveals host perturbations by 1321 SARS-CoV-2 and SARS-CoV. bioRxiv