<b>Abstract</b><br/><p>In recent decades, <em>Dothistroma</em> needle blight (DNB), a pine tree disease caused by the fungal pathogen <em>Dothistroma septosporum, </em>has severely damaged lodgepole pine (<em>Pinus contorta</em> Dougl. ex. Loud.) in British Columbia, Canada, and raised health concerns for jack pine (<em>Pinus banksiana </em>Lamb.). The pathogen has already shown signs of host shift eastward to the hybrid populations between lodgepole pine and jack pine (<em>Pinus contorta</em> ´ <em>P. </em><em>banksiana</em>), and possibly into pure jack pine. However, we have little knowledge about mechanisms of resistance to <em>D</em>.<em> septosporum</em>, especially the underlying genetic basis of variation in pines. In this study, we conducted controlled inoculations to induce infection by <em>D. </em><em>septosporum</em> and performed a genome-wide case-control association study with pooled sequencing (pool-seq) data to dissect the genetic architecture underlying response in lodgepole pine, jack pine, and their hybrids. We identified candidate genes associated with <em>D. septosporum</em> response in lodgepole pine and in<em> </em>hybrid samples. We also assessed genetic structure in hybrid populations and inferred how introgression may affect the distribution of genetic variation involved in <em>D. </em><em>septosporum</em> response in the studied samples. These results can be used to develop genomic tools to evaluate DNB risk, guide forest management strategies, and potentially select for resistant genotypes.</p>

<b>Abstract</b><br/><p class="MsoNormal"><span lang="EN-CA">Understanding the genetic architecture of tolerance and resistance to pathogens is important to monitor and maintain resilient tree populations. Here we investigate the genetic basis of tolerance and resistance to needle cast disease in Douglas-fir (<em>Pseudotsuga menziesii</em>) caused by two fungal pathogens: Swiss needle cast (SNC) caused by <em>Nothophaeocryptopus gaeumannii</em>, and Rhabdocline needle cast (RNC) caused by <em>Rhabdocline pseudotsugae</em>). We performed a case-control genome-wide association analysis (GWA) and found these traits to be polygenic and under selection.</span> <span lang="EN-CA">We showed that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identified a key upstream transcription factor of plant defence, ERF1, as the main candidate for RNC resistance. Our findings contribute to the understanding of the highly polygenic architectures underlying disease resistance and tolerance in Douglas-fir and have important implications for forestry and conservation as the climate changes.</span></p>

No description available

<b>Abstract</b><br/><p>Despite their suitability for studying evolution, many conifer species have large and repetitive giga-genomes (16-31Gbp) that create hurdles to producing high coverage SNP datasets that capture diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within <i>Pinaceae</i>, false diversity from the misalignment of paralog copies creates further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost-saving benefits of pool-seq and exome-capture to discover SNPs in two conifer species, Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>menziesii </i>(Mirb.) Franco, <i>Pinaceae</i>) and jack pine (<i>Pinus banksiana</i> Lamb., <i>Pinaceae</i>). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong positive correlation with those estimated by sequencing the same population as individuals (r &gt; 0.948), on par with such comparisons made in model organisms. Further, we highlight the utility of haploid megagametophyte tissue for identifying sites that are likely due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r &gt; 0.973). Our work addresses bioinformatic challenges in non-model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralog sites, and suggests the combination of pool-seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.</p>

Boletus longipes was described in 1909 by George Massee from a specimen collected in Singapore. The species was not mentioned again until E.J.H. Corner’s book in 1972 on Malaysian boletes. Corner had collected it many times during his tenure in Singapore, and he synonymized Boletus tristis with B. longipes described nine years after B. longipes by Patouillard and Baker from the same site. Among the distinguishing characters of B. longipes were deep vinous red spore deposit, red oxidation reaction of the hymenophore when bruised, and spores that displayed a strong violet color reaction in contact with KOH. C.B. Wolfe ultimately moved both species to a new genus Austroboletus. During recent efforts to circumscribe Austroboletus in Australasia using morphological and molecular phylogenetic inferences, it became clear that B. longipes was neither in harmony with Boletus, Porphyrellus, nor Austroboletus. Rather, it is a new genus, which we describe here as Ionosporus, allied to Borofutus, Spongiforma, and Rhodactina of subfamily Leccinoideae. In addition, recent collections from Queensland and New South Wales, Australia, that are morphologically similar to I. longipes, are inferred to be a separate new species, I. australis.

Information about qPCR primers used in this study. (XLSX 12 kb)

No description available

Information about qPCR primers used in this study. (XLSX 12 kb)

Statistical results and raw gene expression data. (XLSX 31 kb)

Statistical results and raw gene expression data. (XLSX 31 kb)