In highly-repetitive, densely-methylated conifer genomes, DNA methylation may play an important role in the response to pathogens, as varying levels of resistance and high levels of phenotypic plasticity are observed in natural populations of conifer species. Conifers possess some distinctive features in the RdDM pathway, such as low frequency of 24nt sRNAs, low numbers of Dicer-like 3 (DCL3) proteins, MIR loci composed by many long-terminal repeat-retrotransposons (LTR-RTs), and a high frequency of 21nt sRNAs. However, huge and complex draft genomes, limited genome-wide studies, outcrossing populations, and long-generation times have complicated the study of transgenerational inheritance. This project will investigate the White Pine Blister Rust (WPBR) - sugar pine pathosystem to understand the role of DNA methylation in regulating trans-generational immune responses. White pine blister rust (WPBR) is a devastating fungal disease causing great economic and ecological loss in five-needle pines such as sugar pine (Pinus lambertiana) in North America. In our previous studies in the pathosystem, we genotyped a large number of individuals for the presence or absence of resistance (major gene resistance, MGR and quantitative). These individuals were cloned and grown in contrasting environments for the last 20-30 years. To further examine resistance in sugar pine, we will construct a high-resolution map of genome-wide DNA methylation and examine whole-genome DNA methylation and expression in the progeny of resistant and susceptible trees after initial pathogen infection. The complex and fragmented genome assembly will be enhanced through long-read technologies and Hi-C. The resulting improvements in contiguity and accuracy will be paired with transcriptomic technologies, PacBio Iso-Seq. This will result in an enhanced understanding of transgenerational epigenetics, which could be used to reduce the very long time (~20 years) required by traditional breeding to generate resistant individuals. In specific, we aim to 1) Assess whether the RdDM pathway activity is reduced during maturity due to a decrease in the frequency of 24nt sRNAs, resulting in increased TE activity; 2) Identify the parental’ environmental factors (biotic and abiotic) that lead to hypomethylation resulting in heritable increased expression of NBS-LRR resistance genes in the offspring; 3) Evaluate the changes in DNA methylation and expression of NBS-LRR and other MGR resistance-related genes during different infection stages.
Team:
Susan McEvoy
Collaboration with: Amanda De La Torre (NAU)
With the recent invasion of emerald ash borer (EAB) in North America, the health and quantity of American ash (genus: Fraxinus) trees has never been more at risk. Targeting every member of the Fraxinus genus, including Fraxinus americana (white ash), Fraxinus pennsylvanica (green ash), and Fraxinus nigra (black ash), EAB is proving to be detrimental to the American ash population as a whole. It was introduced to the United States from Asia through international trade, and now the invasive pest targets both healthy and unhealthy Fraxinus ash trees, feeds on their nutrient channels, and ultimately causes death in less than 3 years with a near 100% kill rate. In just five years after its initial discovery in 2002, EAB had destroyed more than 53 million native ash trees, and has been rapidly spreading throughout New England and as far west as Colorado. With the vast economic and ecological impacts the total destruction of this natural resource could have, researchers are under immense pressure to discover methods of protecting these species. We will study the impact of EAB through informed sampling of impacted populations and perform a genome-wide association study (ddRADSeq). The variants will be investigated with phenotypes assessed by our colleagues (metabolomics, growth traits, and disease resistance) While it is unlikely we can identify full resistance to this high mortality pest, we hope to uncover potential genes in lingering ash that may reduce susceptibility.
Team: Jeremy Bennett, Ava Fritz
Sugar maple, a long-lived deciduous hardwood native to northeastern North America, is a dominant species in temperate forests. This shade-tolerant tree is present at low to mid elevations and has a continuous distribution from southern Quebec to the southeastern United States. Sugar maples promote Nitrogen mineralization, reduce nitrate load in groundwater, and generate beneficial nutrients for the soil. They are also an iconic species associated with vibrant Autumn hues. From an economic perspective, sugar maple may be the most valuable member of northeastern temperate forests as the primary source of maple syrup as well as hardwood timber. During the past decades, sugar maple decline increased across the natural range, with notable reductions in northeastern forests. Declining populations are characterized by a loss of crown vigor, dieback of fine branches, reduced radial growth, and associated low regeneration. Inconsistent regional patterns in both managed and unmanaged forests made the source of sugar maple decline elusive despite substantial research. Adaptive genetic variation in relevant genes and phenotypic plasticity are essential for survival in future conditions. Predominantly outcrossing, sugar maple populations are long lived and sessile, making them ideal for associating genetic diversity with environmental metrics. Despite this advantage, genomic resources for hardwood species have been slow to develop. Their evolutionary history contributes to divergent chromosomal architectures and multiple whole-genome duplications and rearrangements. These qualities introduce complexities in the assembly and annotation of reference sequences. Reduced cost and increased availability of long read technologies has allowed for the recent efforts to sequence, assemble, and annotate three Acer species to conduct comparative genomics analysis and further understand their adaptive potential.
Team:
Susan McEvoy
Collaboration with: Nathan Swenson (UMD), Alex Touern-Trend (UConn), Uzay Sezen (UConn), Paul Schaberg (USDA)
Eastern larch (or tamarack) is one species among a handful of deciduous conifers. There are only 19 gymnosperms that undergo Autumn leaf senescence, and most belong to the Larix (larch) genus. While the majority of conifers shed their needles at varying intervals over their lifespan (4-5 years), tamaracks behave like many deciduous angiosperms, displaying a deep yellow hue in early to mid Autumn and fully losing all of their needles by winter. Leaf (or needle) senescence is considered the final stage in leaf development, and is associated with cellular death. This process is highly coordinated as there are parallel changes in metabolism and cell structure. A better understanding of Autumn leaf senescence in gymnosperms can provide further insight on their evolutionary history. The abscission zone (tissue providing the attachment of the needle to the stem) was sampled and sequenced for a replicated timecourse study to identify gene expression changes through seasonal senescence. We are currently investigating the pathways and genes that are well conserved with broad leaf angiosperms and those that are unique to conifers.
Lead: Kristiana Stover
Understanding the population genetic structure, and gene expression patterns as it relates to different soil conditions can predict future trajectories of forest composition. No genetic studies have been carried out on the trees in the long-term ecological monitoring site, Hubbard Brook Experimental Forest (HBEF) in New Hampshire. Monitoring of growth performance in the field has revealed that sugar maple is on the decline. On the other hand, American beech is performing well in exacerbated cation depleted soils. Controlled field experiments have examined the effects of Ca and Al treatments when applied through the soil. Dominant sugar maple trees remained unaffected but non-dominant trees responded positively to Ca amendment. Transcriptomics of the plant tissues and metagenomics of the associated soil microbial/fungal communities are underway to build a more complete picture of forest response.
Lab Team: Alex Trouern-Trend
Collaboration with: Uzay Sezen and Paul Schaberg (US Forest Service)
This NSF-funded project examines how species adapt and persist across rapidly changing Arctic landscapes by integrating genomic, ecological, and environmental data across a connected meta-ecosystem. We focus on Salix (willow), Arctic grayling (Thymallus arcticus), and key aquatic invertebrates (mayflies, beetles) sampled along longitudinal gradients that span major shifts in climate, hydrology, and nutrient flow. Using whole-genome and reduced-representation sequencing, coupled with trait and environmental data, the project explores dispersal, gene flow, local adaptation, and eco-evolutionary feedbacks under climate change.
Lab Team: Brandon Lind, Airianna McGuire, Samira Obbu, Mary RutterThis project develops a Tsuga pangenome to identify the genomic, structural, and regulatory variation associated with host defense to Hemlock Woolly Adelgid (HWA). By integrating long-read sequencing, structural variant detection, comparative genomics, and transcriptomics, we aim to pinpoint candidate resistance loci and evaluate their conservation across Tsuga species. This work supports breeding, conservation, and restoration strategies for this foundation forest genus under severe biotic threat.
Lab Team: Meghan MylesBristlecone pines (Pinus longaeva) can live for more than 5,000 years, making them the longest-lived non-clonal organisms on Earth. This project leverages long-read genome sequencing, annotation, and comparative genomics across conifers to investigate the evolutionary basis of exceptional lifespan, cellular resilience, stress response, and maintenance pathways. Insights into genome stability, transposable element dynamics, and protective biochemical mechanisms aim to illuminate how perennial plants evade senescence over millennia.
Lab Team: Ruiwen Lin, Meghan MylesThis applied genomics initiative provides foundational resources for Abies, a genus of ecological and economic importance facing increasing climate- and pest-related pressures. We are assembling reference data, generating SNP-based genotyping tools, and building workflows to support association studies, breeding programs, and conservation planning. Outcomes will guide trait improvement, resilience assessment, and informed management decisions for fir species across North America.
Lab Team: Vidya VuruputoorThis work integrates methylome profiling, gene expression, and genomic variation to understand the role of epigenetic regulation in stress response and adaptation in white pines (Pinus spp.). Leveraging nanopore-based methylation data, the project explores how heritable and inducible epigenetic modifications shape local adaptation and may contribute to resilience in changing environments.
Lab Team: Emily LesinkskiAmerican beech (Fagus grandifolia) is threatened by two major diseases: Beech Bark Disease, a scale insect–fungal pathogen complex that has transformed eastern forests for over a century, and the emerging Beech Leaf Disease caused by Litylenchus crenatae ssp. mccannii. This project is developing comprehensive genomic resources including reference-quality datasets, transcriptomes and population resequencing to investigate host response, resistance mechanisms and adaptive potential. Fine-scale mapping and association studies are resolving a major QTL linked to BBD resistance, while complementary efforts are characterizing early defense signaling and resilience to BLD. These resources support marker-assisted resistance screening, inform deployment strategies and aim to safeguard the future of this keystone species through science-guided restoration.
Lab Team: Michelle Neitzey, Airianna McGuire, Cynthia Webster
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EnTAP is designed to improve the accuracy, speed, and flexibility of functional gene annotation. EnTAP integrates taxonomic scope to optimize the selection of the most appropriate descriptors, as well as filter for contaminants common in transcriptomes and genomes. EnTAP addresses the challenges associated with de novo transcriptome assembly that lead to inflated and inaccurate transcripts through target transcript coverage (RSEM expression estimates) and the prediction of viable open reading frames (TransDecoder). Following filters applied through assessment of true expression and frame selection, translated proteins are compared to up to three protein databases, and independently assigned to gene families via EggNOG. Sequence similarity is implemented through Diamond for rapid assessment, and Gene Ontology terms are assigned from a combination of high quality UniProt alignments, when available, and EggNOG. EnTAP can process header information from both EBI and NCBI databases to aid in the selection of a single alignment that results from a combination of weighted metrics describing similarity search score, taxonomic relationship, and informativeness.
EASEL is an open source genome annotation tool that leverages machine learning, RNA folding, and functional annotations to enhance gene prediction accuracy. It aligns high throughput short read data (RNA-Seq) and assembles putative transcripts via StringTie2 and PsiCLASS. Complete open reading frames are subsequently predicted through TransDecoder using a gene family database (EggNOG) and coding region hints are generated. Gene models are independently used to train AUGUSTUS, and the resulting predictions are combined into a single gene set using AGAT. Implicated gene structures are filtered by primary and secondary features (RNA folding structure, free energy, primary sequences, EggNOG protein homology, OrthoDB alignments, RNA expression, exon number, length and prediction overlap) with a random forest algorithm and clade-specific training set. Transcripts that have a predicted translation initiation site and which score above a certain F1 threshold are retained and functionally annotated with EnTAP.
Published genome annotations are filled with erroneous gene models that represent issues associated with frame, start side identification, splice sites, and related structural features. The source of these inconsistencies can often be traced to translated text file formats designed to describe long read alignments and predicted gene structures. The majority of gene prediction frameworks do not provide downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. In addition, these frameworks lack consideration for functional attributes, such as the presence or absence of protein domains which can be used for gene model validation. gFACs operates across a wide range of alignment, analysis, and gene prediction software inputs with a flexible framework for defining gene models with reliable structural and functional attributes. gFACs supports common downstream applications, including genome browsers and generates extensive details on the filtering process, including distributions that can be visualized to further assess the proposed gene space.
Argonaut streamlines these steps in a single NextFlow workflow, including: short and long-read read quality control, genome size estimation, genome assembly, polishing, redundancy reduction, assembly-to-assembly scaffolding, and assembly quality assessment. Given the variability of sequence inputs, depth, and genome complexity, Argonaut is designed with flexibility in mind - including the ability to modify read input types and assembly tools. It is compatible with both long (ONT and PacBio HiFi) and short (Illumina) reads, and supports hybrid assembly. Argonaut uniquely provides users with significant adjustability within the framework of the current best practices for genome assembly. The workflow produces well documented products at each stage including decontaminated reads, genome size estimates, coverage estimates, up to six de novo assemblies (Flye, Canu, Hifiasm, Masurca for short and hybrid assembly, and Redundans) and assembly quality statistics (BUSCO, Quast, Merqury).
