Adamantanes (amantadine and rimantadine) have been used to prevent and treat influenza A virus infections for many years; however, resistance to these drugs has been widely reported in the world. To investigate the frequency and distribution of M2 gene mutations in adamantane-resistant influenza variants circulated in the world between 1902 and 2013, 31251 available M2 protein sequences from different HA-subtype influenza A viruses (H1–H17) were analyzed and adamantane resistance-associated mutations were compared (L26F, V27A, A30T, A30V, S31N, G34E, and L38F). We find that 45.2% (n = 14132) of influenza A (H1–H17) viruses circulating globally were resistant to adamantanes, and the vast majority of resistant viruses (95%) bear S31N mutations. Whereas, only about 1% have V27A mutations and other mutations (L26F, A30T, G34E, and L38F) were extremely rare (their prevalence appeared to be < 0.2%). Our results confirm that H1, H3, H5, H7, H9, and H17 subtype influenza A viruses exhibit high-level resistance to adamantanes. In contrast, the appearance of adamantane-resistant mutants in H2, H4, H6, H10, and H11 subtypes was rare. However, no adamantane resistance viruses were identified among other HA subtypes (H8, H12–H16). Our findings indicate that the frequency and distribution of adamantane-resistant influenza variants varied among different HA subtypes, host species, years of isolation, and geographical areas. This comprehensive study raises concerns about the increasing prevalence of adamantane-resistant influenza A viruses and highlights the importance of monitoring the emergence and worldwide spread of adamantane-resistant variants.

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To understand the evolution and molecular characteristics of Jiangxi H9N2 viruses, we isolated 17 viruses in 2011 and analyzed their characteristics. Phylogenetic analyses revealed that their hemagglutinin genes originate from JS/1/00‐like sublineage, neuraminidase genes originate from BJ/94‐like sublineage, PB1, PA, NP, and NS genes all come from SH/F/98‐like sublineage, PB2 genes originate from ST/163/04‐like sublineage, while M genes come from G1‐like sublineage. Genotype analysis showed that our isolates were classified as genotype 57. Molecular analyses indicated that our strains contained specific sites characteristic of low‐pathogenic viruses. The current study once again highlights the necessity for continued surveillance of novel H9N2 viruses.

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Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (OsMADS5) and LOC_Os07g47330 (FZP). Further differential expression analysis and functional enrichment analysis identified 30 candidate genes. Of these candidate genes, 27 genes had high expression in specific tissues, and 19 of these 27 genes were homologous to known genes in Arabidopsis. Haplotype difference analysis revealed that LOC_Os04g53210 and LOC_Os07g42440 are possibly associated with yield, and LOC_Os04g53210 may be useful around a QEI for flowering time. These results provide insights for future breeding for high quality and yield in rice.

IntroductionQuantitative trait nucleotide (QTN)-by-environment interactions (QEIs) play an increasingly essential role in the genetic dissection of complex traits in crops as global climate change accelerates. The abiotic stresses, such as drought and heat, are the major constraints on maize yields. Multi-environment joint analysis can improve statistical power in QTN and QEI detection, and further help us to understand the genetic basis and provide implications for maize improvement.MethodsIn this study, 3VmrMLM was applied to identify QTNs and QEIs for three yield-related traits (grain yield, anthesis date, and anthesis-silking interval) of 300 tropical and subtropical maize inbred lines with 332,641 SNPs under well-watered and drought and heat stresses.ResultsAmong the total 321 genes around 76 QTNs and 73 QEIs identified in this study, 34 known genes were reported in previous maize studies to be truly associated with these traits, such as ereb53 (GRMZM2G141638) and thx12 (GRMZM2G016649) associated with drought stress tolerance, and hsftf27 (GRMZM2G025685) and myb60 (GRMZM2G312419) associated with heat stress. In addition, among 127 homologs in Arabidopsis out of 287 unreported genes, 46 and 47 were found to be significantly and differentially expressed under drought vs well-watered treatments, and high vs. normal temperature treatments, respectively. Using functional enrichment analysis, 37 of these differentially expressed genes were involved in various biological processes. Tissue-specific expression and haplotype difference analysis further revealed 24 candidate genes with significantly phenotypic differences across gene haplotypes under different environments, of which the candidate genes GRMZM2G064159, GRMZM2G146192, and GRMZM2G114789 around QEIs may have gene-by-environment interactions for maize yield.DiscussionAll these findings may provide new insights for breeding in maize for yield-related traits adapted to abiotic stresses.