Next Article in Journal
A Novel Bacteriophage Targeting mcr-9 Enterobacter kobei with Potential Application in Fresh Leafy Greens
Next Article in Special Issue
Consuming a High-Pectin Smoothie Has Different Effects on the Uric Acid Levels and Gut Microbiota of Healthy Women
Previous Article in Journal
Impact of Crude Oil and Dispersant Use on the Baltic Sea Blue Mussel Mytilus trossulus Microbiome
Previous Article in Special Issue
Capacity of the Fungi Trichoderma Koningiopsis and Talaromyces Verruculosus for Hg Leaching, Immobilization and Absorption During the Dissolution of Cinnabar
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Microbiology
Volume 5
Issue 1
10.3390/applmicrobiol5010024
applmicrobiol-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia)

by
Savanah Senn
1,*,
Ray A. Enke
2,
Steven J. Carrell
3,
Bradley Nations
1,
Meika Best
1,
Mathew Kostoglou
1,
Karu Smith
1,
Jieyao Yan
1,
Jillian M. Ford
1,
Les Vion
1 and
Gerald Presley
4
1
Plant Science Program, Department of Agriculture Sciences, Los Angeles Pierce College, Woodland Hills, CA 91367, USA
2
Department of Biology, James Madison University, Harrisonburg, VA 22807, USA
3
Center for Quantitative Life Sciences, Oregon State University, Corvallis, OR 97331, USA
4
Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA
*
Author to whom correspondence should be addressed.
Appl. Microbiol. 2025, 5(1), 24; https://doi.org/10.3390/applmicrobiol5010024
Submission received: 31 December 2024 / Revised: 6 February 2025 / Accepted: 11 February 2025 / Published: 22 February 2025
(This article belongs to the Special Issue Current Trends in the Applications of Probiotics and Other Beneficial Microbes)
Browse Figures
Versions Notes

Abstract

:
Coast Live Oak (Quercus agrifolia) is a native keystone hardwood species of the California coastal and semi-arid forest environment. Q. agrifolia is threatened by pathogens such as the oomycete Phytophthora ramorum, which is known to cause Sudden Oak Death in environments from Southern California to Oregon. This study considers oaks and their rootzone microbes recovering from moderate and low-intensity fires in rapid succession, compared to high- and low-intensity fires with a large time gap between them. cDNA libraries from nine oak leaf tissue samples were sequenced on DNBseq. Soil samples were sent out for shotgun metagenomics and for 16S community profiling. The de novo Q. agrifolia assembly yielded 521,817 transcripts with an average length of 805.2 bp. Among identified DEGs (differentially expressed genes) between the trail areas, several candidate genes were identified including shikimate dehydrogenase and myrcene synthase. The MegaBLAST results showed a high degree of similarity to WGS sequences from Q. agrifolia that had been previously annotated in other closely related Quercus species. There was a differential abundance of microbial genera associated with the different burn areas, including Pedobacter, Filimonas, Cohnella, and Sorangium. The data embody the first Q. agrifolia transcriptome that with further development could be used to screen oak seedlings for resistance; beneficial microbial populations have been identified that are associated with fire recovery under varied conditions.

1. Introduction

Coast Live Oak (Quercus agrifolia) is a native keystone hardwood species of the California coastal and semi-arid forest environment. It provides native plant and animal species with direct and indirect biological support within its ecosystems [1] and oak individuals may live for centuries. It is recognized in Southern California for its role in soil carbon sequestration and erosion control. Q. agrifolia also provides shelter and food resources to multiple species of fauna, mycorrhizal fungi, and bacteria. Q. agrifolia is a source of quality timber and has a long cultural history of use among indigenous peoples as a source of food through its acorns.
Q. agrifolia is threatened by pathogens such as the oomycete Phytophthora ramorum, which is known to cause Sudden Oak Death in environments from Southern California to Oregon. This phenomenon results in sudden deterioration of plant health and death and is a threat to whole communities of Q. agrifolia. Whereas fire decreases the abundance of fungal pathogens in the soil, even with a full burn, there are low levels of the pathogen that tend to persist in streams and soil nearby; this indication was supported by our previous results [2,3]. This threat, combined with the ongoing challenges of climate change, has made Q. agrifolia vulnerable to population decline over the past decade [4,5,6]. Wildfires endemic to Southern California also pose an additional threat to Q. agrifolia and other critical native tree species. Multiple oak species have evolved to withstand fire disturbances [7]. With future climate models forecasting increased fire frequency in Southern California [7], it is critical to study these biotic and abiotic stressors to Q. agrifolia growth and regeneration.
Our study focuses on samples obtained from the Gold Creek Field Station of the Angeles National Forest, an area with a unique and diverse fire history. This study plot contains distinct subregions with documented low-, moderate-, and high-intensity wildfire histories, respectively [2]. It has been suggested that studying plants and rootzone microbes using a combination of soil metagenomics and plant RNAseq will yield valuable insights into secondary metabolite production [8]. Our samples were subjected to multi-omics analysis to better understand how these unique histories affect molecular mechanisms within Q. agrifolia as well as the microbiomes of the soil in which these trees grow. Our study is the first of its scope aimed at characterizing Q. agrifolia and will provide a greater understanding of this critical tree’s physiology and resilience to biotic and abiotic stressors and provide insight into restoration strategies in Southern California.

2. Materials and Methods

The study employed a multi-omics approach. The main methods used were transcriptome sequencing of plant tissue, 16S community profiling of soil, and soil metagenomics. Together, these methods provide a comprehensive view of the plant–microbe interactions shaping post-fire recovery of the oak populations studied.

2.1. Field Sampling and Logistics

Snap-frozen plant tissue samples were obtained in May 2022 from three trail areas of Gold Creek Preserve, Angeles National Forest with histories of wildfire at low, moderate, and high fire intensities, here referred to, respectively, as Blue, Green, and Red Trails. The Red Trail is a high-fire-intensity history area that was affected by the Station Fire in 2009 and the Creek Fire in 2018. The Green Trail is a moderate-intensity fire history area that was affected by the Sand Fire in 2017 and the Creek Fire in 2018; the fires on the Green Trail occurred in rapid succession. The fires on the Red Trail had a long time gap between the occurrences. The Blue Trail was affected by the low-intensity Creek Fire in 2018.
Three trees were sampled from each of the three trail areas, for a total of nine samples. Samples were immediately snap-frozen in liquid nitrogen using a portable dewar on May 12, 2022, and sent to the Beijing Genomics Institute Americas (BGIA) logistics center in San Jose, CA. Plant tissue samples were shipped to BGI Americas on dry ice for RNA extraction, library construction, and sequencing on the DNBSeq platform. Frozen soil samples were sent overnight with ice packs to BGIA for soil DNA shotgun metagenomics and James Madison University for 16S bacterial community profiling. For metagenomics, only Red and Green Trail soil sample DNAs were sequenced.

2.2. RNA Extraction and Sequencing

RNA extraction from plant leaf tissue was performed at BGIA. Library construction at BGI Americas used the approach of Oligo(dT) magnetic beads for mRNA enrichment, followed by mRNA fragmentation, cDNA synthesis, size selection, and PCR [9]. cDNA libraries were sequenced on the DNBseq platform to obtain the raw whole transcriptome shotgun sequences.

2.3. De Novo Assembly, Assessment, and Quantification of mRNA Transcripts

Quality control with MultiQC [10] and SOAPNuke [11] was performed. Trinity was used for de novo assembly with the default parameters and using sequence data from tissues originating from all 9 Q. agrifolia individuals [12]. The transcriptome assembly was evaluated with BUSCO [13]. The BUSCO evaluation implements a search in the OrthoDB reference database for a highly conserved set of genes from eudicots, which allows for an assessment of transcriptome completeness. Quantification was performed with Salmon [14] using default settings, and transcripts were annotated with Trinotate [15] using the best SwissPROT blastx hits [16]. Differential expression analysis was performed in DESeq2. The DESeq2 algorithm is a statistical method used for identifying differentially expressed genes (DEGs) in RNA-seq data as previously described [17]. Briefly, the method is based on the negative binomial distribution, which accounts for the overdispersion commonly observed in RNA-seq count data. Contrasts were performed and DE genes of interest related to secondary metabolite production and disease resistance were queried with the blastn MegaBLAST module [18] using restricted search parameters for “Quercus sp.” taxa in the core nucleotide collection. The search was repeated in the whole genome shotgun collection restricting the search to the taxon “Quercus agrifolia”.

2.4. Soil DNA Extraction

The Qiagen DNeasy PowerSoil Pro kit was used for total genomic DNA purification following the manufacturer’s protocol. Plant tissue samples were added to PowerBead Pro Tubes for lysis and removal of metals and were given multiple buffer solutions to remove proteins and impurities followed by multiple centrifugation and alcohol wash steps in filter columns, to precipitate the DNA. Concentration measurements were recorded in ng/uL using the Nanodrop instrument.

2.5. 16S Microbial Community Diversity Analysis

We performed 16S metagenome amplicon sequencing as previously described [2]. Briefly, the V4 region of the bacterial 16s rRNA gene was amplified and barcoded for each of the nine leaf samples as well as two microbial community standard samples using primers developed by [19] at the James Madison University Center for Genome and Metagenome Studies (JMU CGEMS). Samples were screened for successful amplification on an agarose gel and pooled. A double-sided bead cleanup was carried out to remove primer dimers and a low amount of off-target larger PCR products. The quality and concentration of the pooled library were checked using a Bioanalyzer (Agilent) and NEB’s Library Quant Kit for Illumina. The library was then sequenced on an Illumina MiSeq using a version 2, 500-cycle reagent cartridge (2 × 250). Before loading, the library was combined with Illumina’s PhiX control (50:50 16s:PhiX) to ensure a high-quality run despite the low diversity of the 16S library.
Sequences were demultiplexed, adaptors were trimmed using Illumina’s Generate FASTQ Analysis Module, and processed raw reads were fed into the QIIME 2 [20] software suite housed within the DNA Subway Purple Line GUI [21]. High-quality raw sequence data was determined by FastQC and MultiQC analysis. DADA2 [22] was used for the identification of microbial taxa from amplicon reads and alpha rarefaction analysis was used to determine sufficient sample depth. Beta diversities between each pair of samples were calculated using the Bray–Curtis distance metric and visualized in Emperor [23] with a principal coordinate analysis (PCoA) plot. A feature table, taxonomy assignments, and metadata for each sample were used to create stacked bar plots showing the relative abundance of taxa in each sample.

2.6. Soil Shotgun Metagenomics

After soil DNA extraction at BGIA, WGS metagenomics sequencing libraries were prepared using a PCR-based method and libraries were sequenced on the DNBseq platform. Raw reads were assembled using metaSPades [24], taxonomic and pathway profiles were quantified using the Nephele WGS2A workflow [25]; functional profiles were visualized in Krona [26]. WGS metagenomics analyses and data visualizations were created using the web-based platforms MicrobiomeDB and MetaGENassist [27].

3. Results

In addition to genomic insights from the Q. agrifolia assembly and differential gene expression analysis, our results reveal shifts in microbial community composition across burn areas, highlighting potential interactions between plant genetic responses and rootzone microbial dynamics.

3.1. Plant Transcriptome Analysis

To study how Coast Live Oaks in semi-arid environments react to biotic and abiotic stress, we first set out to characterize the transcriptome of Q. agrifolia specimens found in the Gold Creek Field Station of the Angeles National Forest from different fire history areas. Tree leaves from three trail areas with low (Blue Trail), moderate (Green Trail), and high (Red Trail) fire intensity histories were sampled. Three trees from each trail were sampled for a total of nine specimens. Total RNA was used for RNA-seq transcriptome analysis. Due to the lack of a Q. agrifolia chromosome-level genome assembly, the Trinity de novo transcriptome assembly pipeline was used generating 521,817 assembled transcripts with an average length of 805.2 bp (Table 1). The Benchmarking Universal Single-Copy Orthologs (BUSCO) tool was used to determine that the Q. agrifolia draft transcriptome achieved >95% completeness (Figure 1).
Principal component analysis (PCA) of transcriptomes assembled from different specimens demonstrated clear clustering based on the trail in which each Q. agrifolia tree resides. Specimens collected from Red Trail and Green Trail, respectively, reflected the most distinct cluster groups (Figure 2). These two groups were used for differential expression analysis using the DEseq2 pipeline. Our study focuses on the contrast between the Red and Green Trails; limited information about the remaining contrast is available in Supplementary Materials.
DEseq2 analysis with a 5-fold change and 99% confidence interval cutoff yielded 145 genes upregulated in the Red Trail Q. agrifolia leaf tissue and 176 genes upregulated in the Green Trail Q. agrifolia leaf tissue (Figure 3). Among the identified differentially expressed genes, several interesting candidate genes were identified for further characterization including shikimate dehydrogenase and myrcene synthase (Table 2). Furthermore, the MegaBLAST results showed that the transcripts of interest had a high degree of similarity to WGS sequences from Q. agrifolia [28] that had been previously annotated in other closely related Quercus species (Table 3).

3.2. Microbial Community Profiling

To better understand microbial variation in soils Coast Live Oaks grow in, soils were sampled from different regions of the Gold Creek Field Station and used in 16S metabarcoding analysis to characterize soil microbial taxa. Total DNA purified from soil was used as input for PCR amplification of the bacterial 16S locus using indexed Illumina primers. Subsequent sequencing yielded 425,782 high-quality 2 × 250 bp paired end reads corresponding to microbial taxa spanning the nine soil samples and two microbial community standard samples (Table 4). Reads were input into the DNA Subway Purple Line QIIME 2 analysis pipeline identifying a total of 244,348 features. Alpha rarefaction analysis—a measure of species richness and diversity—indicated that analysis of 6000 reads per sample was sufficient for detecting adequate sample diversity as well as retaining all nine samples in the study (Figure S1).
Principal coordinate analysis (PCoA) Beta diversity was used to create a graphic representation of differences in microbial community composition between soil samples (Figure 4). Highly complex differences between each pair of samples are decomposed into principal coordinates (PCos) in a dimension reduction process, which constitute the x-, y-, and z-axes. Similar samples with similar microbial community compositions cluster together, while dissimilar samples spread apart. Beta diversity between each of the nine samples was calculated using the Bray–Curtis distance metric and indicates clear clustering of Q. agrifolia soils growing on different trails (Figure 4). The first three axes accounted for 67.61% of the variation in the dataset. Red Trail samples tended to be higher on Axis 2 and Green Trail samples tended to be higher on Axis 3 (Figure 4).
To visualize specific microbial taxa associated with each soil sample, the feature table taxonomy assignments and metadata from each sample were used to create stacked bar plots (Figure 5). A visual inspection of the 16S metabarcoding taxonomic barplots for the Q. agrifolia rhizosphere indicated that there was a higher abundance of Sphingobacteriaceae on the Red Trail. Meanwhile, there was a higher abundance on the Blue and Green Trails of Steroidobacteraceae and Cyclobacteraceae family taxa (Figure 5).
Similar to our previous results [2], the rootzone microbes on the different trails demonstrated functional redundancy. Although there were no significant differences in functional profiles, according to the MetaGENassist differential abundance analysis results, there were several genera of differentially abundant taxa. There were four genera from each trail that were significant in the results of the T-test applying the FDR cutoff of 0.01 and log2FC values > 1.33. These included genera such as Pedobacter on the Red Trail and Cohnella on the Green Trail (Table 5). Interestingly, there was also a higher relative abundance of Bradyrhizobium sp. reads associated with Green Trail soil samples, although not significant after multiple testing corrections, according to the results of the MetaGENassist analysis (Figure 6).

4. Discussion

Recent research underscores the necessity of assessing the influence of fire on the soil to understand oak regeneration [29]. Understanding the interplay between soil microbial communities and plant gene expression is crucial for informed ecological management and predicting the impacts of fire intensity and frequency on oak ecosystems. For example, a previous study found that Miombo forest ecosystem soil microbes were more likely to have mutualistic functions when arising from low-frequency fire-affected soils rather than high-frequency fire-affected soils [30]. Other forest ecology studies have called for the integration of plant community response data with response data from soil microflora [31] and have found that vegetation interacts with fire and influences fire frequency. An obvious example of this would be the interaction of oak traits for fungal infection resistance with protective soil microbial communities that contribute to plant defense. Since the sudden decline in plant health caused by Phytophthora infection contributes to hazards such as increased rates of crown ignition of the trees under medium-intensity fire conditions [3], it is evident that trees, fire, and soil microbes influence one another in the ecosystem. The results of the transcriptome and microbiome analysis can be considered together for a panoramic picture of the above- and below-ground responses to the environment.

4.1. Transcriptome Analysis

Disease resistance protein RPS4 was differentially expressed on the Green Trail compared to the Red Trail. In Arabidopsis, this gene has been shown to encode a resistance protein specific to Pseudomonas syringae [32]. Disease resistance protein Roq1 was upregulated on the Green Trail. Differential expression of several disease resistance proteins in oak leaf tissue from the Green Trail is of high importance to understanding resistance to fungal pathogens.
Geraniol-8-hydroxylase was upregulated in Green Trail samples compared to Red Trail samples (Table 2). Geraniol 10-hydroxylase [33] was previously characterized in Catharanthus as an enzyme leading to hydroxygeraniol, a precursor of terpenoid indole alkaloids. It is upregulated by methyl jasmonate treatment [34] and to a lesser extent, fungal elicitors [33]. Geraniol-8-oxidase from C. roseus was the top SwissProt blastx hit. Interestingly, the top core nucleotide collection BLAST hit was for Q. robur iridoid oxidase (Table 3). Iridoids are monoterpenes that are synthesized from geraniol in plants [35].
The 11-betahydroxysteroid dehydrogenase was differentially expressed on the Green Trail. The betahydroxysteroid dehydrogenase enzyme was originally characterized as a sulfotransferase that inactivated brassinosteroids in sesame [36]. Later, this gene was characterized in Arabidopsis [37] and had the effect of increasing growth and seed set.
Of particular interest is the Class V chitinase candidate gene (Table 3) that was differentially expressed on the Green Trail; this gene was differentially expressed in Q. suber when infected with Phytophthora cinnamomi in a previous study [38]. Although oomycetes have a cell wall composed of cellulose and glucan, significantly higher chitinase activity has also been observed in black pepper under P. capsici infection [39]. Although chitinase does not directly degrade glucan–cellulose structures, and glucanase alone did not inhibit the growth of oomycetes, it was speculated that elevated chitinase activity when combined with glucanase activity contributed to general plant resistance (Ibid). This gene has also been shown to be crucial to the symbiosis between Lotus and Rhizobium sp. [40] and may play a role in mutualism with rootzone microbes.
Green Trail trees also showed elevated expression of shikimate and quinate dehydrogenase (Table 2), pointing to additional antioxidant production and further oxidative stress resistance. The shikimate pathway leads to phenolic compounds such as cinnamic acid, coumarins, stilbenes, chalcones, flavonoids, lignans, and polyketides in plants [41]. These compounds have antioxidant and anticancer properties [42]. The production of phenolic compounds is elicited by biotic and abiotic stress. Lignin is also synthesized from phenylalanine resulting from the shikimate pathway. Trees can produce reaction wood that is higher in lignin content in response to injury. Shikimate and quinate dehydrogenase catalyze the reaction from 3-dehydroquinate leading to 3-dehydroshikhimate [43]. More specifically, there was a high degree of similarity between the sequence of the DEG (differentially expressed gene) in our study with Q. lobata secoisolariciresinol dehydrogenase according to core nucleotide MegaBLAST results. This enzyme is involved in lignan biosynthesis [44].
Chloroplastic myrcene synthase was differentially expressed on the Red Trail. Pinene and myrcene are monoterpenes that form the scaffolding for the production of a variety of other terpenoids. Terpenes are part of the natural defense system of plants, including protection for plants from photooxidative stress, thermotolerance, and tolerance to microbes and insects [45]. Variations in isoprene and monoterpene production in other Quercus species had previously been linked to drought conditions [46]. For example, in a previous study of Q. ilex, monoterpene emissions were reduced during drought [47]. However, Q. suber populations have been shown to intrinsically release isoprenoids, and levels could be explained by gene flow between species [48].
There were two variants of putative disease resistance RPP13-like protein 1 that were differentially expressed on the Red Trail. RPPs are ribosomal protective proteins [49]. In Arabidopsis, this gene is at a single locus and the RPP13-Nd allele confers resistance to Peronospora parasitica powdery mildew [50]. The transcript that was originally identified as putative disease resistance protein At3g14460 mapped to the Q. robur RPP13-like protein. The two RPP13-like variants from our Red Trail DEGs both mapped to Q. agrifolia WGS Scaffold 3 (Table 3). Roq1 is a disease-resistance gene that works as a pathogen detector and sends a signal to increase response to pathogens. This gene had the highest expression on the Red Trail. Furthermore, it indicates that the specific environmental conditions of this area have influenced the prevalence of expression of this gene among the Red Trail oaks population.
The pleiotropic drug resistance protein was differentially expressed on the Red Trail. The pleiotropic drug resistance genes are typically upregulated by the presence of pathogens, such as Botrytis cinerea, Phytophthora infestans, or treatment with methyl jasmonate, P. syringae derived flagellin, or yeast extract [51]. This gene is also important for plant heavy metal tolerance. Phosphoenolpyruvate carboxylase 4 had a higher expression on the Red Trail when compared with the Green Trail. In Q. lobata, this enzyme is involved in glycolysis in the dark cycle or gluconeogenesis during the light cycle of carbon fixation [52].
Expression of 1-aminocyclopropane-1-carboxylate oxidase homolog 4 was higher on the Red Trail when compared to the Green Trail. This gene is involved in ethylene production in plants and has been used to study microbial interactions. There was low-level differential expression of this gene in a previous study of Q. robur when inoculated with ectomycorrhizae [53].
Differential expression in the isoprenoid pathway among the samples points to the varying ability of the trees to resist oxidative stress and may also inhibit the growth of harmful bacteria, fungi, or viruses [54]. Differences in trail conditions related to fire history, sunlight intensity, elevation, and other environmental factors may be the deciding factors in these gene expressions. The products of these genes suggest the potential for robust antimicrobial and antifungal responses in sampled trees.

4.2. Bacterial Community Profiling

The high abundance of Bradyrhizobium sp. in the oak rootzones on the Green Trail, coupled with the differential abundance of Cohnella sp. nitrogen fixers and Planococcus sp., who are nitrate reducers, highlight the mutualistic exchanges and nitrogen cycling interactions between plant and soil on the Green Trail. Sorangium sp. is known to be a rich source of secondary metabolites such as antifungal polyketides [55] and isocoumarins [56]. Kibdelosporangium are endophytic Actinomycetes; Kibdelosporangium aridum, for instance, has an ammonia assimilation pathway leading to L-glutamine [57].
The differential abundance of Pedobacter sp. in the rootzone highlights the importance of antioxidants in defending the plant hosts on the Red Trail. Pedobacter, a member of Sphingobacteriaceae, has been shown to significantly increase the flavonoid and antioxidant content of strawberries [58]. Flavonoids have also been shown to have plant-protective antimicrobial properties against pathogenic fungi [59]. Furthermore, the production of flavonoids in the rootzone is expected to recruit mycorrhizal inoculants [60]. Microlunatus sp. are endophytic actinomycetes. Members of Roseomonas have been shown to metabolize amino acids at a high rate [61,62]. In the realm of carbon metabolism, Filimonas sp. such as Filimonas lacunae thrive in high -CO2 environments, degrade carbohydrates [63], and colonize fire-altered organic matter [64]. The organic matter in our study was not pyrolyzed, since it was in the context of wildfire and not involved in the industrial production of liquid hydrocarbons or biochar in the absence of oxygen [65]; however, the carbon was pyrogenic and had been altered by fire [66]. Mineralization of charred organic matter is difficult for microorganisms [67].

5. Conclusions

This study is part of an exciting and emerging field examining the relationships between rootzone microbes and their plant hosts using plant tissue RNAseq analysis and soil metagenomics at the same time. Furthermore, the study presents a novel ecological study of the effects on oaks and their rootzone microbes recovering from moderate to low-intensity fires in rapid succession on the Green Trail, compared to high- and low-intensity fires with a large time gap between them on the Red Trail. Finally, the data embody the first published Q. agrifolia transcriptome. The data are useful for monitoring and make SNP panels possible from the well-annotated ESTs (Expressed Sequence Tags) to develop QTLs (Quantitative Trait Loci) that would allow nurseries to screen seedlings for Phytophthora resistance.
We initially expected to see higher biotic stress indications in general on the Red Trail due to greater fire damage. However, our findings indicate that both populations are responding to significant biotic stress, based on the number of DE genes for disease resistance proteins. The Green Trail samples exemplified their resiliency by expressing a variety of resistance genes and strategies to increase growth. The strategies on the Red Trail were related to increased photosynthesis, terpenes, and antioxidants. Multiple plant DEGs were related to potential pathogen defense against Phytophthora, powdery mildew, and Pseudomonas, based on information about the candidate genes from previous studies. In the soil metagenomes of the clusters of trees, there was functional redundancy. However, there were distinct microbial communities associated with each cluster of trees. On the high-fire-intensity history Red Trail area, insight into functions from the soil microbial communities focused on carbon cycling and flavonoid production in the top taxa. With regard to the medium fire intensity history area, soil microbial communities, nitrogen cycling and polyketide production characterized the most abundant microbes on the Green Trail. Both analyzed geographies had endophytic Actinomycetes.
Together, these findings highlight the complex interplay between oaks and their rootzone microbial communities in post-fire recovery, demonstrating how both plant genetic responses and microbial functional shifts contribute to resilience. To further establish Q. agrifolia’s responses to biotic and abiotic stresses, additional analysis is required to determine secondary metabolite constituents, especially regarding different developmental stages. Root RNA sequences as well as metagenomic leaf analysis from Q. agrifolia will provide further information vital to understanding the biological processes of Q. agrifolia. This will allow us to better understand the interactions between soil microbes, pathogens, and plants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/applmicrobiol5010024/s1, Figure S1: FastQC and MultiQC Analysis of Illumina 16S Metagenomics Sequencing Reads; Table S1: The Numbver of Differentially Expressed Genes in Each of the Contrasts; Figure S2; The Volcano Plot Depicting Differentially Expressed Genes Between Blue and Red Trail Oaks; Figure S3: The Volcano Plot Depicting Differentially Expressed Genes Between Green and Blue Trail Oaks.

Author Contributions

Conceptualization, S.S.; methodology, S.J.C. and S.S.; software, S.J.C. and S.S.; formal analysis, S.S., S.J.C. and R.A.E.; investigation, S.S., K.S., M.K., L.V., M.B., J.M.F., B.N. and J.Y.; resources, G.P. and R.A.E.; data curation, S.S. and R.A.E.; writing—original draft preparation, S.S., B.N., M.K. and J.Y.; writing—review and editing, J.M.F. and R.A.E.; visualization, S.S.; funding acquisition, R.A.E. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by training and resources from the National Science Foundation IUSE grant, NSF DUE-1821657 awarded to Ray A. Enke in part to support Community College faculty in microbial metabarcoding analysis.

Data Availability Statement

The NCBI SRA for plant transcriptomic data is available under BioProject PRJNA1046570. The NCBI SRA for soil metagenomics data is available at BioProject PRJNA1064914. Metabarcoding data is available on dnasubway.cyverse.org (1 December 2024) under Purple Line public project #11913.

Acknowledgments

The authors thank Karen Barnard-Kubow for technical assistance with metabarcoding sequencing. The authors would like to acknowledge Bruce Nash for mentorship and resources. The authors thank Renato Aguila for technical assistance with data interpretation. Thank you to Kelly Pangell and Adrianna Bowerman for technical assistance in the field and laboratory support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Calscape. Coast Live Oak. California Native Plant Society. 2024. Available online: https://calscape.org/Quercus-agrifolia-(Coast-Live-Oak) (accessed on 14 October 2024).
  2. Senn, S.; Bhattacharyya, S.; Presley, G.; Taylor, A.E.; Nash, B.; Enke, R.A.; Barnard-Kubow, K.B.; Ford, J.; Jasinski, B.; Badalova, Y. The Functional Biogeography of eDNA Metacommunities in the Post-Fire Landscape of the Angeles National Forest. Microorganisms 2022, 10, 1218. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  3. Chris, L. Sudden Oak Death and Fire in California. 2009. Available online: https://www.suddenoakdeath.org/wp-content/uploads/pdf/summary%20of%20fire%20and%20p%20ramorum%20issues%20v5.3.pdf (accessed on 18 October 2024).
  4. McPherson, B.A.; Mori, S.R.; Wood, D.L.; Storer, A.J.; Svihra, P.; Kelly, N.M.; Standiford, R.B. Sudden oak death in California: Disease progression in oaks and tanoaks. For. Ecol. Manag. 2005, 213, 71–89. [Google Scholar] [CrossRef]
  5. McPherson, B.A.; Mori, S.R.; Wood, D.L.; Kelly, M.; Storer, A.J.; Svihra, P.; Standiford, R.B. Responses of oaks and tanoaks to the sudden oak death pathogen after 8y of monitoring in two coastal California forests. For. Ecol. Manag. 2010, 259, 2248–2255. [Google Scholar] [CrossRef]
  6. Parke, J.L.; Rizzo, D.M. Phytophthora ramorum. For. Phytophthoras 2011, 1, 10. [Google Scholar] [CrossRef]
  7. CEC-500-2012-026; Fire and Climate Change in California. California Energy Commission: Sacramento, CA, USA, 2012.
  8. Pang, Z.; Chen, J.; Wang, T.; Gao, C.; Li, Z.; Guo, L.; Xu, J.; Cheng, Y. Linking plant secondary metabolites and plant microbiomes: A review. Front. Plant Sci. 2021, 12, 621276. [Google Scholar] [CrossRef] [PubMed]
  9. Peng, Z.; Wang, Z. State-of-the-Art RNA Sequencing for Drug Discovery. White Paper, BGI Global Genomics Services. 31 May 2018. Available online: www.bgi.com/wp-content/uploads/sites/2/2018/06/BGI-RNA-Transcriptome-Public-white-paper.pdf (accessed on 8 November 2024).
  10. Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047–3048. [Google Scholar] [CrossRef] [PubMed]
  11. Chen, Y.; Chen, Y.; Shi, C.; Huang, Z.; Zhang, Y.; Li, S.; Li, Y.; Ye, J.; Yu, C.; Li, Z.; et al. SOAPnuke: A MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. GigaScience 2018, 7, gix120. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  12. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.D.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  13. Manni, M.; Berkeley, M.R.; Seppey, M.; Simão, F.A.; Zdobnov, E.M. BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol. Biol. Evol. 2021, 38, 4647–4654. [Google Scholar] [CrossRef] [PubMed]
  14. Patro, R.; Duggal, G.; Love, M.; Irizarry, R.A.; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 2017, 14, 417–419. [Google Scholar] [CrossRef] [PubMed]
  15. Bryant, D.M.; Johnson, K.; DiTommaso, T.; Tickle, T.; Couger, M.B.; Payzin-Dogru, D.; Lee, T.J.; Leigh, N.D.; Kuo, T.-H.; Davis, F.G.; et al. A Tissue-Mapped Axolotl De Novo Transcriptome Enables Identification of Limb Regeneration Factors. Cell Rep. 2017, 18, 762–776. [Google Scholar] [CrossRef] [PubMed]
  16. Bairoch, A.; Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000, 28, 45–48. [Google Scholar] [CrossRef]
  17. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  18. Morgulis, A.; Coulouris, G.; Raytselis, Y.; Madden, T.L.; Agarwala, R.; Schäffer, A.A. Database indexing for production MegaBLAST searches. Bioinformatics 2008, 24, 1757–1764, Erratum in Bioinformatics 2008, 24, 2942. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  19. Kozich, J.J.; Westcott, S.L.; Baxter, N.T.; Highlander, S.K.; Schloss, P.D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 2013, 79, 5112–5120. [Google Scholar] [CrossRef]
  20. Hall, M.; Beiko, R.G. 16S rRNA gene analysis with QIIME2. In Microbiome Analysis: Methods and Protocols; Springer: Berlin/Heidelberg, Germany, 2018; pp. 113–129. [Google Scholar]
  21. Hilgert, U.; McKay, S.; Khalfan, M.; Williams, J.; Ghiban, C.; Micklos, D. DNA Subway: Making Genome Analysis Egalitarian. In Proceedings of the 2014 Annual Conference Extreme Science and Engineering Discovery Environment (XSEDE ’14), Association for Computing Machinery, Atlanta, GA, USA, 13–18 July 2014; Article 70. pp. 1–3. [Google Scholar]
  22. Callahan, B.J.; Mcmurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [PubMed]
  23. Vázquez-Baeza, Y.; Pirrung, M.; Gonzalez, A.; Knight, R. EMPeror: A tool for visualizing high throughput microbial community data. GigaScience 2013, 2, 16. [Google Scholar] [CrossRef] [PubMed]
  24. Nurk, S.; Meleshko, D.; Korobeynikov, A.; Pevzner, P.A. metaSPAdes: A new versatile metagenomic assembler. Genome Res. 2017, 27, 824–834. [Google Scholar] [CrossRef] [PubMed]
  25. Angelova, A.G.; Doan, D.; Subramanian, P.; Quiñones, M.; Dolan, M.; Hurt, D.E. WGSA2 Workflow—A Tutorial. 2023. Available online: https://www.protocols.io/view/wgsa2-workflow-a-tutorial-n92ldm98xl5b/v1 (accessed on 23 October 2024).
  26. Ondov, B.D.; Bergman, N.H.; Phillippy, A.M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 2011, 12, 385. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  27. Arndt, D.; Xia, J.; Liu, Y.; Zhou, Y.; Guo, A.C.; Cruz, J.A.; Sinelnikov, I.; Budwill, K.; Nesbø, C.L.; Wishart, D.S. METAGENassist: A comprehensive web server for comparative metagenomics. Nucleic Acids Res. 2012, 40, W88–W95. [Google Scholar] [CrossRef] [PubMed]
  28. Dodd, R.; Marimuthu, M.P.A.; Nguyen, O.; Fairbairn, C.W.; Seligmann, W.E.; Escalona, M.; Shaffer, H.B. Whole Genome Shotgun Sequence of Q. agrifolia. Available online: https://www.ncbi.nlm.nih.gov/nucleotide/JARQAE010000006.1 (accessed on 14 November 2024).
  29. Beals, K.K.; Scearce, A.E.; Swystun, A.T.; Schweitzer, J.A. Belowground mechanisms for oak regeneration: Interactions among fire, soil microbes, and plant community alter oak seedling growth. For. Ecol. Manag. 2022, 503, 119774. [Google Scholar] [CrossRef]
  30. António, C.B.S.; Obieze, C.; Jacinto, J.; Maquia, I.S.A.; Massad, T.; Ramalho, J.C.; Ribeiro, N.S.; Máguas, C.; Marques, I.; Ribeiro-Barros, A.I. Linking Bacterial Rhizosphere Communities of Two Pioneer Species, Brachystegia boehmii and B. spiciformis, to the Ecological Processes of Miombo Woodlands. Forests 2022, 13, 1840. [Google Scholar] [CrossRef]
  31. Hart, S.C.; DeLuca, T.H.; Newman, G.S.; MacKenzie, M.D.; Boyle, S.I. Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils. For. Ecol. Manag. 2005, 220, 166–184. [Google Scholar] [CrossRef]
  32. Zhang, X.-C.; Gassmann, W. Alternative Splicing and mRNA Levels of the Disease Resistance Gene RPS4 Are Induced during Defense Responses. Plant Physiol. 2007, 145, 1577–1587. [Google Scholar] [CrossRef] [PubMed]
  33. Suttipanta, N.; Pattanaik, S.; Gunjan, S.; Xie, C.H.; Littleton, J.; Yuan, L. Promoter analysis of the Catharanthus roseus geraniol 10-hydroxylase gene involved in terpenoid indole alkaloid biosynthesis. Biochim. Biophys. Acta-Gene Struct. Expr. 2007, 1769, 139–148. [Google Scholar] [CrossRef] [PubMed]
  34. Collu, G.; Unver, N.; Peltenburg-Looman, A.M.; van der Heijden, R.; Verpoorte, R.; Memelink, J. Geraniol 10-hydroxylase1, a cytochrome P450 enzyme involved in terpenoid indole alkaloid biosynthesis. FEBS Lett. 2001, 508, 215–220. [Google Scholar] [CrossRef] [PubMed]
  35. Frezza, C.; de Vita, D.; Toniolo, C.; Ventrone, A.; Tomassini, L.; Foddai, S.; Nicoletti, M.; Guiso, M.; Bianco, A.; Serafini, M. Harpagide: Occurrence in plants and biological activities—A review. Fitoterapia 2020, 147, 104764. [Google Scholar] [CrossRef] [PubMed]
  36. Lange, B.M.; Ghassemian, M. Genome organization in Arabidopsis thaliana: A survey for genes involved in isoprenoid and chlorophyll metabolism. Plant Mol. Biol. 2003, 51, 925–948. [Google Scholar] [CrossRef] [PubMed]
  37. Li, F.; Asami, T.; Wu, X.; Tsang, E.W.; Cutler, A.J. A putative hydroxysteroid dehydrogenase involved in regulating plant growth and development. Plant Physiol. 2007, 145, 87–97. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  38. Ebadzad, G.; Cravador, A. Quantitative RT-PCR analysis of differentially expressed genes in Quercus suber in response to Phytophthora cinnamomi infection. SpringerPlus 2014, 3, 613. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  39. Varma, R.S.; George, K.J.; Balaji, S.; Parthasarathy, V. Differential induction of chitinase in Piper colubrinum in response to inoculation with Phytophthora capsici, the cause of foot rot in black pepper. Saudi J. Biol. Sci. 2009, 16, 11–16. [Google Scholar] [CrossRef]
  40. Malolepszy, A.; Kelly, S.; Sørensen, K.K.; James, E.K.; Kalisch, C.; Bozsoki, Z.; Panting, M.; Andersen, S.U.; Sato, S.; Tao, K.; et al. A plant chitinase controls cortical infection thread progression and nitrogen-fixing symbiosis. eLife 2018, 7, e38874. [Google Scholar] [CrossRef] [PubMed]
  41. Santos-Sánchez, N.F.; Salas-Coronado, R.; Hernández-Carlos, B.; Villanueva-Cañongo, C. Shikimic acid pathway in biosynthesis of phenolic compounds. In Plant Physiological Aspects of Phenolic Compounds; IntechOpen: Rijeka, Croatia, 2019; Volume 1, pp. 1–15. [Google Scholar]
  42. Albuquerque, T.G.; Nunes, M.A.; Bessada, S.M.; Costa, H.S.; Oliveira, M.B.P. Biologically active and health promoting food components of nuts, oilseeds, fruits, vegetables, cereals, and legumes. In Chemical Analysis of Food; Academic Press: Cambridge, MA, USA, 2020; pp. 609–656. [Google Scholar]
  43. Muir, R.M. Analysis of Gallic Acid Production by the Bi-Functional Enzyme Shikimate-5-Dehydrogenase in Higher Plants and Bacteria; University of California: Davis, CA, USA, 2005. [Google Scholar]
  44. Youn, B.; Moinuddin, S.G.; Davin, L.B.; Lewis, N.G.; Kang, C. Crystal Structures of Apo-form and Binary/Ternary Complexes of Podophyllum secoisolariciresinol dehydrogenase, an Enzyme Involved in Formation of Health-protecting and Plant Defense Lignans. J. Biol. Chem. 2005, 280, 12917–12926. [Google Scholar] [CrossRef] [PubMed]
  45. Tholl, D. Terpene synthases and the regulation, diversity and biological roles of terpene metabolism. Curr. Opin. Plant Biol. 2006, 9, 297–304. [Google Scholar] [CrossRef] [PubMed]
  46. Geron, C.; Daly, R.; Harley, P.; Rasmussen, R.; Seco, R.; Guenther, A.; Karl, T.; Gu, L. Large drought-induced variations in oak leaf volatile organic compound emissions during PINOT NOIR 2012. Chemosphere 2016, 146, 8–21. [Google Scholar] [CrossRef]
  47. Lavoir, A.; Staudt, M.; Schnitzler, J.; Landais, D.; Massol, F.; Rocheteau, A.; Rodríguez, R.; Zimmer, I.; Rambal, S. Drought reduced monoterpene emissions from Quercus ilex trees: Results from a throughfall displacement experiment within a forest ecosystem. Biogeosci. Discuss. 2009, 6, 863–893. [Google Scholar]
  48. Staudt, M.; Mir, C.; Joffre, R.; Rambal, S.; Bonin, A.; Landais, D.; Lumaret, R. Isoprenoid emissions of Quercus spp. (Q. suber and Q. ilex) in mixed stands contrasting in interspecific genetic introgression. New Phytol. 2004, 163, 573–584. [Google Scholar] [CrossRef]
  49. Connell, S.R.; Tracz, D.M.; Nierhaus, K.H.; Taylor, D.E. Ribosomal protection proteins and their mechanism of tetracycline resistance. Antimicrob. Agents Chemother. 2003, 47, 3675–3681. [Google Scholar] [CrossRef]
  50. Bittner-Eddy, P.D.; Crute, I.R.; Holub, E.B.; Beynon, J.L. RPP13 is a simple locus in Arabidopsis thaliana for alleles that specify downy mildew resistance to different avirulence determinants in Peronospora parasitica. Plant J. 2000, 21, 177–188. [Google Scholar] [CrossRef] [PubMed]
  51. Crouzet, J.; Trombik, T.; Fraysse, Å.S.; Boutry, M. Organization and function of the plant pleiotropic drug resistance ABC transporter family. FEBS Lett. 2006, 580, 1123–1130. [Google Scholar] [CrossRef] [PubMed]
  52. Kegg Pathway qlo00710. Carbon Fixation by Calvin Cycle—Quercus Lobata (Valley Oak). Available online: https://www.kegg.jp/entry/pathway+qlo00710 (accessed on 10 November 2024).
  53. Tarkka, M.T.; Herrmann, S.; Wubet, T.; Feldhahn, L.; Recht, S.; Kurth, F.; Mailänder, S.; Bönn, M.; Neef, M.; Angay, O.; et al. OakContig DF 159.1, a reference library for studying differential gene expression in Quercus robur during controlled biotic interactions: Use for quantitative transcriptomic profiling of oak roots in ectomycorrhizal symbiosis. New Phytol. 2013, 199, 529–540. [Google Scholar] [CrossRef]
  54. Escandón, M.; Castillejo, M.Á.; Jorrín-Novo, J.V.; Rey, M.-D. Molecular Research on Stress Responses in Quercus spp.: From Classical Biochemistry to Systems Biology through Omics Analysis. Forests 2021, 12, 364. [Google Scholar] [CrossRef]
  55. Zirkle, R.; Ligon, J.M.; MolnáR, I. Cloning, sequence analysis and disruption of the mglA gene involved in swarming motility of Sorangium cellulosum So ce26, a producer of the antifungal polyketide antibiotic soraphen A. J. Biosci. Bioeng. 2004, 97, 267–274. [Google Scholar] [CrossRef] [PubMed]
  56. Saeed, A. Isocoumarins, miraculous natural products blessed with diverse pharmacological activities. Eur. J. Med. Chem. 2016, 116, 290–317. [Google Scholar] [CrossRef] [PubMed]
  57. Karp, P.D.; Billington, R.; Caspi, R.; Fulcher, C.A.; Latendresse, M.; Kothari, A.; Keseler, I.M.; Krummenacker, M.; Midford, P.E.; Ong, Q.; et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief. Bioinform. 2019, 20, 1085–1093. [Google Scholar] [CrossRef] [PubMed]
  58. Morais, M.C.; Mucha, Â.; Ferreira, H.; Gonçalves, B.; Bacelar, E.; Marques, G. Comparative study of plant growth-promoting bacteria on the physiology, growth and fruit quality of strawberry. J. Sci. Food Agric. 2019, 99, 5341–5349. [Google Scholar] [CrossRef] [PubMed]
  59. Hassan, S.; Mathesius, U. The role of flavonoids in root-rhizosphere signalling: Opportunities and challenges for improving plant-microbe interactions. J. Exp. Bot. 2012, 63, 3429–3444. [Google Scholar] [CrossRef]
  60. Shah, A.; Smith, D.L. Flavonoids in Agriculture: Chemistry and Roles in, Biotic and Abiotic Stress Responses, and Microbial Associations. Agronomy 2020, 10, 1209. [Google Scholar] [CrossRef]
  61. Grouzdev, D.S.; Babich, T.L.; Tourova, T.P.; Sokolova, D.S.; Abdullin, R.R.; Poltaraus, A.B.; Schevchenko, M.A.; Toshchakov, S.V.; Nazina, T.N. Draft Genome Sequence of Roseomonas aestuarii Strain JR1/69-1-13 Isolated from Nitrate- and Radionuclide-Contaminated Groundwater in Russia. Genome Announc. 2018, 6, e00583-18. [Google Scholar] [CrossRef] [PubMed]
  62. Yin, L.Z.; Li, J.L.; Fang, B.Z.; Liu, Z.T.; Wang, P.; Dong, L.; Duan, L.; Luo, X.Q.; Li, S.H.; Li, W.J. Roseomonas ponticola sp. nov., a novel bacterium isolated from Pearl River estuary. Int. J. Syst. Evol. Microbiol. 2021, 71, 004994. [Google Scholar] [CrossRef]
  63. Shiratori-Takano, H.; Takano, H.; Ueda, K. Whole-Genome Sequence of Filimonas lacunae, a Bacterium of the Family Chitinophagaceae Characterized by Marked Colony Growth under a High-CO2 Atmosphere. Genome Announc. 2016, 4, e00667-16. [Google Scholar] [CrossRef] [PubMed]
  64. Whitman, T.; Singh, B.P.; Zimmerman, A.R. Priming effects in biochar-amended soils: Implications of biochar-soil organic matter interactions for carbon storage. In Biochar for Environmental Management; Lehmann, L., Joseph, S., Eds.; Routledge: New York, NY, USA, 2015; pp. 455–487. [Google Scholar]
  65. United States Department of Agriculture, Agriculture Research Service, Eastern Regional Research Center. What Is Pyrolysis? Biomass Pyrolysis Research. 2025. Available online: www.ars.usda.gov/northeast-area/wyndmoor-pa/eastern-regional-research-center/docs/biomass-pyrolysis-research-1/what-is-pyrolysis/#:~:text=Pyrolysis%20is%20the%20heating%20of,strong%20bio-polymers%20mentioned%20above (accessed on 10 January 2025).
  66. Santín, C.; Doerr, S.H.; Preston, C.M.; González-Rodríguez, G. Pyrogenic organic matter production from wildfires: A missing sink in the global carbon cycle. Glob. Chang. Biol. 2015, 21, 1621–1633. [Google Scholar] [CrossRef] [PubMed]
  67. United States Department of Energy. Fungal Recyclers: Fungi Reuse Fire-Altered Organic Matter. In Biological and Environmental Research; United States Department of Energy: Washington, DC, USA, 2022. Available online: www.energy.gov/science/ber/articles/fungal-recyclers-fungi-reuse-fire-altered-organic-matter#:~:text=Wildfires%20can%20cause%20significant%20changes,organisms%20to%20use%20as%20food (accessed on 2 November 2024).
Applmicrobiol 05 00024 g001
Figure 1. The Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment shows that the Quercus agrifolia transcriptome has >95% completeness.
Figure 1. The Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment shows that the Quercus agrifolia transcriptome has >95% completeness.
Applmicrobiol 05 00024 g001
Applmicrobiol 05 00024 g002
Figure 2. The PCA of transcriptomic data showed there was some heterogeneity of expression in the Blue Trail transcripts. The Red and Green trails showed the best clustering.
Figure 2. The PCA of transcriptomic data showed there was some heterogeneity of expression in the Blue Trail transcripts. The Red and Green trails showed the best clustering.
Applmicrobiol 05 00024 g002
Applmicrobiol 05 00024 g003
Figure 3. The Volcano plot shows a visualization of the DE genes from the Red Trail vs. Green Trail analysis of plant tissue transcripts in DESeq2.
Figure 3. The Volcano plot shows a visualization of the DE genes from the Red Trail vs. Green Trail analysis of plant tissue transcripts in DESeq2.
Applmicrobiol 05 00024 g003
Applmicrobiol 05 00024 g004
Figure 4. The PCoA analysis using Bray–Curtis distance demonstrates clustering of Q. agrifolia soils sampled from different trails in the Gold Creek Field Station, in the 16S community metabarcoding data.
Figure 4. The PCoA analysis using Bray–Curtis distance demonstrates clustering of Q. agrifolia soils sampled from different trails in the Gold Creek Field Station, in the 16S community metabarcoding data.
Applmicrobiol 05 00024 g004
Applmicrobiol 05 00024 g005
Figure 5. The 16S metabarcoding taxonomic barplots for Coast Live Oak rootzone microbial communities sorted at the level of family reflected that Red Trail samples had increased Sphingobacteriaceae family taxa, and Blue and Green Trail samples had increased Cyclobacteraceae and Steroidobacteraceae family taxa. The right-hand panel of the figure highlights the differences between the groups.
Figure 5. The 16S metabarcoding taxonomic barplots for Coast Live Oak rootzone microbial communities sorted at the level of family reflected that Red Trail samples had increased Sphingobacteriaceae family taxa, and Blue and Green Trail samples had increased Cyclobacteraceae and Steroidobacteraceae family taxa. The right-hand panel of the figure highlights the differences between the groups.
Applmicrobiol 05 00024 g005
Applmicrobiol 05 00024 g006
Figure 6. The % abundance barplot from MetaGENassist shows that Bradyrhizobium sp. constituted 16.5% of reads in the Green Trail oak rootzone soil samples (A) and 6.8% of Red Trail oak soil samples (B).
Figure 6. The % abundance barplot from MetaGENassist shows that Bradyrhizobium sp. constituted 16.5% of reads in the Green Trail oak rootzone soil samples (A) and 6.8% of Red Trail oak soil samples (B).
Applmicrobiol 05 00024 g006
Table 1. Trinity de novo transcriptome assembly statistics are summarized.
Table 1. Trinity de novo transcriptome assembly statistics are summarized.
Number of SequencesTotal LengthAverage LengthMin LengthMax LengthL50N50N80
521,8174.2 × 108805.207317415,95887,2061294470
Table 2. Selected DE genes in oak leaf tissue from DEseq2 results. The negative log2FCvalues are for DE genes from the Green Trail. The positive log2FC values are for DE genes from the Red Trail oaks. The log2FC cutoff was 1.33 and the FDR cutoff was 0.01 for this table.
Table 2. Selected DE genes in oak leaf tissue from DEseq2 results. The negative log2FCvalues are for DE genes from the Green Trail. The positive log2FC values are for DE genes from the Red Trail oaks. The log2FC cutoff was 1.33 and the FDR cutoff was 0.01 for this table.
Gene_IDBase Meanlog2Fold ChangepadjSprot_Top_BLASTX_Hit
TRINITY_DN5071_c0_g128.23026136−8.8904131589.02324 × 10−6Putative disease resistance protein At1g50180
TRINITY_DN2917_c2_g113.61452133−6.6903306550.00184770211-beta-hydroxysteroid dehydrogenase-like 6
TRINITY_DN15594_c0_g131.84983276−6.085167352.9024 × 10−5Disease resistance protein Roq1
TRINITY_DN20992_c0_g130.84011525−5.3655490410.000863898Disease resistance protein RPS4
TRINITY_DN6739_c0_g2117.1948209−2.806311310.001647927Geraniol 8-hydroxylase
TRINITY_DN2023_c0_g1148.4293476−2.5802182297.44233 × 10−5Bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase
TRINITY_DN35565_c0_g176.15119378−2.4748403280.004799669Class V chitinase
TRINITY_DN2666_c0_g1108.25990351.4289337031.22801 × 10−6Putative disease resistance protein At3g14460
TRINITY_DN79417_c0_g185.515710692.0116133780.00986672Pleiotropic drug resistance protein 3
TRINITY_DN3071_c1_g1117.80217012.1161912690.003537541Phosphoenolpyruvate carboxylase 4
TRINITY_DN12296_c0_g132.404192964.3679235830.001988292Myrcene synthase, chloroplastic
TRINITY_DN798_c0_g186.318115037.0151114421.00934 × 10−81-aminocyclopropane-1-carboxylate oxidase homolog 4
TRINITY_DN1798_c0_g290.523796892.8027935280.000880927Putative disease resistance RPP13-like protein 1
Table 3. Differentially expressed gene mappings to the NCBI GenBank core nucleotide collection and to the Q. agrifolia whole genome sequence (WGS) were obtained to gain insight into the functions. The % identity and GenBank Accession are provided for the top hits.
Table 3. Differentially expressed gene mappings to the NCBI GenBank core nucleotide collection and to the Q. agrifolia whole genome sequence (WGS) were obtained to gain insight into the functions. The % identity and GenBank Accession are provided for the top hits.
Gene_IdQuercus sp. NT Hit%IDGenBank AccessionQ. agrifolia WGS Contig%IDGenBank Accession
TRINITY_DN5071_c0_g1Q. robur90.63%XM_050406478.1Scaffold699.62%JARQAE010000006.1
TRINITY_DN2917_c2_g1Q. robur98.22%XM_050400348.1Scaffold6100%JARQAE010000006.1
TRINITY_DN15594_c0_g1Q. lobata87.19%XM_031105504.1Scaffold586.77%JARQAD010000005.1
TRINITY_DN20992_c0_g1Q robur91.67%XR_007653105.1Scaffold494.54%JARQAE010000004.1
TRINITY_DN6739_c0_g2Q. robur97.41%XM_050387445.1Scaffold11100%JARQAE010000011.1
TRINITY_DN2023_c0_g1Q. lobata97.12%XM_031090969.1Scaffold1298.39%JARQAE010000012.1
TRINITY_DN35565_c0_g1Q. suber97.05%XM_024056073.2Scaffold199.92%JARQAE010000001.1
TRINITY_DN2666_c0_g1Q. robur95.26%XM_050423093.1Scaffold395.39%JARQAD010000003.1
TRINITY_DN79417_c0_g1Q. robur97.99%XM_050383676.1Scaffold999.78%JARQAE010000009.1
TRINITY_DN3071_c1_g1Q. lobata97.57%XM_031104334.1Scaffold599.69%JARQAD010000005.1
TRINITY_DN12296_c0_g1Q. lobata97.34%XR_004090747.1Scaffold8100%JARQAE010000008.1
TRINITY_DN798_c0_g1Q. robur96.03%XM_050419551.1Scaffold498.29%JARQAE010000004.1
TRINITY_DN1798_c0_g2Q. robur86.92%XM_050427734.1Scaffold385.70%JARQAD010000003.1
Table 4. Raw sequencing read and feature count metrics from the microbial 16S metabarcoding sequencing experiment.
Table 4. Raw sequencing read and feature count metrics from the microbial 16S metabarcoding sequencing experiment.
Sample IDInput ReadsFiltered Reads% Passed Filter ReadsFeature Count
Green Trail 141,97035,70885.124,252
Green Trail 252,13745,76687.831,251
Green Trail 336,58032,84889.823,318
Blue Trail 152,16144,65785.630,309
Blue Trail 238,03033,75688.823,720
Blue Trail 334,08829,89987.719,880
Red Trail 130,01726,46388.220,646
Red Trail 235,07631,72590.525,632
Red Trail 334,03830,55289.824,720
Community standard 144,89839,60588.228,068
Community standard 226,78721,84581.616,272
Total425,782372,82487.6244,348
Table 5. List of differentially abundant microbes in Green vs. Red Trail oak tree rootzone soil from the MetaGENassist pipeline. The FDR cutoff was 0.01.
Table 5. List of differentially abundant microbes in Green vs. Red Trail oak tree rootzone soil from the MetaGENassist pipeline. The FDR cutoff was 0.01.
GenusFCLog2(FC)p ValueHigher Abundance
Microlunatus0.10638−3.23280.003384Red Trail
Roseomonas0.25615−1.9650.000421Red Trail
Filimonas0.29897−1.74190.004025Red Trail
Pedobacter0.32759−1.61010.00882Red Trail
Sorangium3.02021.59470.003868Green Trail
Kibdelosporangium3.95141.98240.000904Green Trail
Cohnella6.07692.60330.000123Green Trail
Planococcus10.5713.40210.005461Green Trail
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Senn, S.; Enke, R.A.; Carrell, S.J.; Nations, B.; Best, M.; Kostoglou, M.; Smith, K.; Yan, J.; Ford, J.M.; Vion, L.; et al. De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia). Appl. Microbiol. 2025, 5, 24. https://doi.org/10.3390/applmicrobiol5010024

AMA Style

Senn S, Enke RA, Carrell SJ, Nations B, Best M, Kostoglou M, Smith K, Yan J, Ford JM, Vion L, et al. De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia). Applied Microbiology. 2025; 5(1):24. https://doi.org/10.3390/applmicrobiol5010024

Chicago/Turabian Style

Senn, Savanah, Ray A. Enke, Steven J. Carrell, Bradley Nations, Meika Best, Mathew Kostoglou, Karu Smith, Jieyao Yan, Jillian M. Ford, Les Vion, and et al. 2025. "De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia)" Applied Microbiology 5, no. 1: 24. https://doi.org/10.3390/applmicrobiol5010024

APA Style

Senn, S., Enke, R. A., Carrell, S. J., Nations, B., Best, M., Kostoglou, M., Smith, K., Yan, J., Ford, J. M., Vion, L., & Presley, G. (2025). De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia). Applied Microbiology, 5(1), 24. https://doi.org/10.3390/applmicrobiol5010024

Article Metrics

Back to TopTop