Liu, W.-C. | Search bioRxiv

bioRxiv2026

N6-methyladenosine regulates Influenza A virus mRNA stability yet is rarely found on genomic RNA

Previous studies have found widespread N6-methyladenosine (m6A methylation) on all forms of Influenza A virus (IAV) RNA, with m6A found critical for viral replication, pathogenicity as well as viral RNA packaging. Here we applied the latest quantitative technologies to revisit the methylation landscape on the anti-sense genomic RNA of IAV. Unexpectedly, upon Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) analysis of IAV virion -extracted genomic RNA, we detected very little m6A regardless of production from human cells or chicken eggs. Concordantly, Nanopore direct RNA sequencing also detected an overall low occurrence and stoichiometry (generally <5%) of m6A across all viral genomic RNA segments, compared with abundant m6A sites on viral mRNAs at ~20-30% m6A. Cross validation with glyoxal- and nitrite-mediated deamination of unmethylated adenosines (GLORI) confirmed multiple m6A sites on viral mRNA yet very few m6A on the genomic RNA. This paucity of m6A on genomic RNA makes it unlikely that m6A contributes to viral RNA packaging. Knockdown or pharmacological inhibition of the m6A methyltransferase METTL3 as well as the reader protein YTHDF2 both reduced viral mRNA levels and infectious viral particle production, with YTHDF2 promoting viral mRNA stability. Thus, the presence of m6A on IAV transcripts is indeed proviral, yet it is the mRNAs instead of genomic RNAs that are methylated at functionally relevant levels. Lastly, we provide proof of concept that a METTL3 small molecule inhibitor can be antiviral, and propose that m6A-targeted antivirals would mainly impact the intracellular gene expression phase of IAV replication.

microbiology↗

bioRxiv2017

Response to Qian et al (2017): Daily and seasonal climate variations are both critical in the evolution of species’ elevational range size

In their recent critique, Qian et al. (2017) claimed that the results of structural equation modeling analysis (SEM) in Chan et al. (2016) were flawed. Here, we show that the source of the difference in their re-analysis is that Qian et al. did not follow the standard, iterative process of SEM, which allows researchers to evaluate which model offers the best account of the data in both absolute and relative senses. Here, we provide step-by-step instructions to reproduce our published results. All of Qian et al.s concerns regarding SEM can be put to rest. Moreover, in our original paper we used three distinct statistical methods--hierarchical partitioning, SEM, and stationary bootstrap--to show that different temporal scales of environmental variability can differentially impact the elevational range size (ERS) of species. It is time to move on to probing the pressing issue of how and why climatic variability impacts ERS.

ecology↗