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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Microbiol.</journal-id>
<journal-title>Frontiers in Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">1664-302X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmicb.2023.1097787</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Microbiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Gene abundance linked to climate zone: Parallel evolution of gene content along elevation gradients in lichenized fungi</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Merges</surname>
<given-names>Dominik</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="aff3" ref-type="aff"><sup>3</sup></xref>
<xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2082099/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Dal Grande</surname>
<given-names>Francesco</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="aff4" ref-type="aff"><sup>4</sup></xref>
<xref rid="aff5" ref-type="aff"><sup>5</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1652769/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Valim</surname>
<given-names>Henrique</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2139564/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Singh</surname>
<given-names>Garima</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="aff4" ref-type="aff"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Schmitt</surname>
<given-names>Imke</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="aff6" ref-type="aff"><sup>6</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1919328/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Senckenberg Biodiversity and Climate Research Centre</institution>, <addr-line>Frankfurt am Main</addr-line>, <country>Germany</country></aff>
<aff id="aff2"><sup>2</sup><institution>LOEWE Centre for Translational Biodiversity Genomics (LOEWE-TBG)</institution>, <addr-line>Frankfurt am Main</addr-line>, <country>Germany</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences</institution>, <addr-line>Uppsala</addr-line>, <country>Sweden</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Biology, University of Padova</institution>, <addr-line>Padua</addr-line>, <country>Italy</country></aff>
<aff id="aff5"><sup>5</sup><institution>National Biodiversity Future Center (NBFC)</institution>, <addr-line>Palermo</addr-line>, <country>Italy</country></aff>
<aff id="aff6"><sup>6</sup><institution>Goethe University Frankfurt, Institute of Ecology, Evolution and Diversity</institution>, <addr-line>Frankfurt am Main</addr-line>, <country>Germany</country></aff>
<author-notes>
<fn id="fn0001" fn-type="edited-by"><p>Edited by: Jos&#x00E9; A. Siles, Center for Edaphology and Applied Biology of Segura (CSIC), Spain</p></fn>
<fn id="fn0002" fn-type="edited-by"><p>Reviewed by: Yuriy L. Orlov, I.M. Sechenov First Moscow State Medical University, Russia; Adriana Lucia Romero-Olivares, New Mexico State University, United States</p></fn>
<corresp id="c001">&#x002A;Correspondence: Dominik Merges, <email>mergesd01@gmail.com</email></corresp>
<fn id="fn0003" fn-type="other"><p>This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>22</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1097787</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>23</day>
<month>02</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Merges, Dal Grande, Valim, Singh and Schmitt.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Merges, Dal Grande, Valim, Singh and Schmitt</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Intraspecific genomic variability affects a species&#x2019; adaptive potential toward climatic conditions. Variation in gene content across populations and environments may point at genomic adaptations to specific environments. The lichen symbiosis, a stable association of fungal and photobiont partners, offers an excellent system to study environmentally driven gene content variation. Many of these species have remarkable environmental tolerances, and often form populations across different climate zones. Here, we combine comparative and population genomics to assess the presence and absence of genes in high and low elevation genomes of two lichenized fungi of the genus <italic>Umbilicaria</italic>.</p>
</sec>
<sec>
<title>Methods</title>
<p>The two species have non-overlapping ranges, but occupy similar climatic niches in North America (<italic>U. phaea</italic>) and Europe (<italic>U. pustulata</italic>): high elevation populations are located in the cold temperate zone and low elevation populations in the Mediterranean zone. We assessed gene content variation along replicated elevation gradients in each of the two species, based on a total of 2050 individuals across 26 populations. Specifically, we assessed shared orthologs across species within the same climate zone, and tracked, which genes increase or decrease in abundance within populations along elevation.</p>
</sec>
<sec>
<title>Results</title>
<p>In total, we found 16 orthogroups with shared orthologous genes in genomes at low elevation and 13 at high elevation. Coverage analysis revealed one ortholog that is exclusive to genomes at low elevation. Conserved domain search revealed domains common to the protein kinase superfamily. We traced the discovered ortholog in populations along five replicated elevation gradients on both continents and found that the number of this protein kinase gene linearly declined in abundance with increasing elevation, and was absent in the highest populations.</p>
</sec>
<sec>
<title>Discussion</title>
<p>We consider the parallel loss of an ortholog in two species and in two geographic settings a rare find, and a step forward in understanding the genomic underpinnings of climatic tolerances in lichenized fungi. In addition, the tracking of gene content variation provides a widely applicable framework for retrieving biogeographical determinants of gene presence/absence patterns. Our work provides insights into gene content variation of lichenized fungi in relation to climatic gradients, suggesting a new research direction with implications for understanding evolutionary trajectories of complex symbioses in relation to climatic change.</p>
</sec>
</abstract>
<kwd-group>
<kwd>environmental change</kwd>
<kwd>gene presences/absence</kwd>
<kwd>gene loss/gain</kwd>
<kwd>metagenomics</kwd>
<kwd>convergent evolution</kwd>
<kwd>population genomics</kwd>
<kwd>PacBio</kwd>
<kwd>protein kinase</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="63"/>
<page-count count="8"/>
<word-count count="6149"/>
</counts>
</article-meta>
</front>
<body>
<sec id="sec1" sec-type="intro">
<title>Introduction</title>
<p>Intraspecific genomic variability substantially affects a species&#x2019; adaptive potential to ecological interactions and climatic conditions (<xref ref-type="bibr" rid="ref24">Hirsch et al., 2014</xref>; <xref ref-type="bibr" rid="ref3">Badet et al., 2020</xref>; <xref ref-type="bibr" rid="ref51">Resl et al., 2022</xref>). Variation in genomic content at the gene-level, i.e., presence/absences of genes, within species are regularly found in bacteria, and are often associated with adaptations to specific environments and antibiotic resistances (<xref ref-type="bibr" rid="ref38">Masignani et al., 2005</xref>; <xref ref-type="bibr" rid="ref23">Heuer et al., 2011</xref>). Recent studies suggest that eukaryotic species, like bacterial ones, can have intraspecific variation in genomic content at the gene-level (<xref ref-type="bibr" rid="ref24">Hirsch et al., 2014</xref>; <xref ref-type="bibr" rid="ref3">Badet et al., 2020</xref>; <xref ref-type="bibr" rid="ref18">Gerdol et al., 2020</xref>). To this date, it is largely unknown how relevant such variation in gene content is for eukaryotes, and if it is ecologically important for adaptations to specific environments. Intraspecific variation in gene content contributes to the formation of genetically diverse populations, and therefore its characterization is vital to understand the mechanisms of adaptation to specific environments (<xref ref-type="bibr" rid="ref46">Plissonneau et al., 2018</xref>; <xref ref-type="bibr" rid="ref15">Drott et al., 2021</xref>; <xref ref-type="bibr" rid="ref42">Merges et al., 2022</xref>).</p>
<p>The lichen symbiosis, a stable association of mainly fungal and photobiont partners as well as an associated microbiome, lends itself to the study of environmentally-driven gene content, because many species have remarkable environmental tolerances and maintain populations in different climate zones (<xref ref-type="bibr" rid="ref31">Kappen, 2000</xref>; <xref ref-type="bibr" rid="ref62">Werth and Sork, 2014</xref>; <xref ref-type="bibr" rid="ref60">Singh et al., 2017</xref>; <xref ref-type="bibr" rid="ref19">Grimm et al., 2021</xref>; <xref ref-type="bibr" rid="ref30">Jung et al., 2021</xref>; <xref ref-type="bibr" rid="ref61">Tanunchai et al., 2022</xref>). Previous studies showed that environmental differentiation in lichens can be found at the level of single nucleotide polymorphisms (SNPs), which often significantly correlate with differences in geography and ecology and may thus be involved in environmental specialization (<xref ref-type="bibr" rid="ref44">Peksa and Skaloud, 2011</xref>; <xref ref-type="bibr" rid="ref9">Castillo et al., 2012</xref>; <xref ref-type="bibr" rid="ref25">Hodkinson et al., 2012</xref>). Population genomic analyses based on SNPs suggest the presence of genome-wide differentiation between populations in different climate zones (<xref ref-type="bibr" rid="ref12">Dal Grande et al., 2017</xref>; <xref ref-type="bibr" rid="ref54">Rolshausen et al., 2022</xref>). To date, the only study which assesses variation in gene content associated with environmental adaptation in lichens is limited to a single species: <xref ref-type="bibr" rid="ref59">Singh et al. (2021)</xref> show that some gene clusters associated with natural product biosynthesis in <italic>Umbilicaria pustulata</italic> have elevation-specific distributions. However, we currently lack information on the extent of parallel evolution in populations of different species that have independently evolved under similar environmental conditions. We do not know, e.g., (1) whether different species of lichen-forming fungi maintain homologous population-specific genes along comparable environmental gradients and (2) whether intraspecific variation in gene content (e.g., abundance patterns of genes) has independently converged on the same altitudinal patterns in different species, and can thus be linked to environmental preferences of lichens.</p>
<p>Modern evolutionary approaches leverage DNA sequencing to infer ecological and evolutionary processes that occur at the population level. Here we combine comparative genomics and population genomics to assess the presence/absence of genes in high elevation and low elevation genomes of two lichenized fungi of the genus <italic>Umbilicaria</italic> (<italic>Umbilicaria phaea</italic> and <italic>U. pustulata</italic>). The two species have evolved on different continents under similar climatic selective pressures: <italic>U. pustulata</italic> in Europe and <italic>U. phaea</italic> in North America each occupy the cold temperate as well as the Mediterranean climate zone. We tracked gene content variation along replicated elevation gradients in both species based on a total of 2050 individuals in 26 populations. Specifically, we addressed the following research questions: (a) Which genes are linked to environmental conditions at high elevation (cold temperate climate) and low elevation (Mediterranean climate) across species? To address this question, we assessed which genes are exclusive to climate zones. (b) Do abundances of genes specific to a climate zone co-vary with elevation in populations of <italic>U. phaea</italic> and <italic>U. pustulata</italic>? To address the second question, we assessed which genes increase or decline in abundance with increasing elevation.</p>
</sec>
<sec id="sec2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="sec3">
<title>Study site and sample collection</title>
<p>We sampled 15 <italic>U. pustulata</italic> populations along three elevational gradients in Spain and Italy and 11 <italic>U. phaea</italic> populations along two elevational gradients in California, United States (<xref ref-type="bibr" rid="ref41">Merges et al., 2021</xref>; <xref ref-type="bibr" rid="ref58">Singh et al., 2022</xref>). Our choice of gradients was based on the high abundance of <italic>U. pustulata</italic> and <italic>U. phaea</italic> in these respective areas, and because the gradients span two contrasting bioclimates, the Mediterranean and the cold temperate zones. A population of lichens is here defined as a group of individuals collected on rocks within an area of approximately 10&#x2009;&#x00D7;&#x2009;10&#x2009;m. Detailed sampling procedures are described in <xref ref-type="bibr" rid="ref12">Dal Grande et al. (2017)</xref> and <xref ref-type="bibr" rid="ref55">Rolshausen et al. (2020)</xref>. The altitudinal spacing of populations along the gradients can be seen in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>. The studied <italic>Umbilicaria</italic> species entail foliose, monophyllous lichens, which are attached to rock surfaces with a central holdfast. This growth form facilitates the recognition of individuals. Details of the sampling of European material are described in <xref ref-type="bibr" rid="ref58">Singh et al. (2022)</xref> and the details of sampling of the North American material in <xref ref-type="bibr" rid="ref12">Dal Grande et al. (2017)</xref> and <xref ref-type="bibr" rid="ref41">Merges et al. (2021)</xref>. Briefly, two of the European gradients are located in Central Spain, Sierra de Gredos (40.2028, &#x2212;5.2334 and 39.9946, &#x2212;4.8679) and one on the island of Sardinia (40.7577, 9.0794). We collected fragments of 100 individuals each, at Mount Limbara (Sardinia, Italy; 6 populations), Sierra de Gredos (Sistema Central, Spain; 6 populations) and Talavera-Puerto de Pico (Sistema Central, Spain; 3 populations), as described in <xref ref-type="bibr" rid="ref12">Dal Grande et al. (2017)</xref>. The Californian gradients are spatially separated by approx. 700&#x2009;km. We collected fragments of 50 individuals each, at four populations along the Sierra Nevada gradient (38.084, &#x2212;120.484) and at seven population along the Mt. Jacinto gradient (33.435, &#x2212;116.484). Schemes indicating the geographic location and sites of the sample collection are given in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>. We additionally collected four whole lichen thalli, one low-altitude individual from the Sierra Nevada population and one from the high-altitude population, as well as a low-altitude and a high-altitude individual from populations of the Sierra des Gredos gradient for the reconstruction of reference genomes using PacBio Sequel II data (<xref ref-type="bibr" rid="ref41">Merges et al., 2021</xref>; <xref ref-type="bibr" rid="ref58">Singh et al., 2022</xref>; <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S2</xref>).</p>
</sec>
<sec id="sec4">
<title>DNA extraction for population pooled sequencing</title>
<p>Genomic DNA was extracted separately from each fragment from all populations using a cetyltrimethylammonium bromide-based (CTAB) method (<xref ref-type="bibr" rid="ref01">Cubero and Crespo, 2002</xref>; <xref ref-type="bibr" rid="ref12">Dal Grande et al., 2017</xref>). Further, we created a pooled sample for each population containing equal amounts of DNA from each sample and Novogene Co., Ltd. (Cambridge, United Kingdom) performed the library preparation (200&#x2013;300&#x2009;bp insert size; <xref ref-type="bibr" rid="ref12">Dal Grande et al., 2017</xref>). Libraries were sequenced on an Illumina HiSeq2000 with 150&#x2009;bp paired-end chemistry at ~90&#x00D7; coverage per population (i.e., Pool-seq; <xref ref-type="bibr" rid="ref12">Dal Grande et al., 2017</xref>).</p>
</sec>
<sec id="sec5">
<title>DNA extraction for genomic sequencing</title>
<p>Genomic DNA for genome sequencing was extracted from dry thallus material of two samples of the same species (i.e., <italic>U. phaea</italic> or <italic>U. pustulata</italic>) collected in different climatic zones (i.e., low elevation/Mediterranean climate zone and high elevation/temperate climate zone). Lichen thalli were thoroughly washed with sterile water and checked under the stereomicroscope for the presence of possible contamination or other lichen thalli. DNA was extracted from all of the samples using CTAB-based method (<xref ref-type="bibr" rid="ref39">Mayjonade et al., 2016</xref>) as presented in <xref ref-type="bibr" rid="ref41">Merges et al. (2021)</xref>.</p>
</sec>
<sec id="sec6">
<title>PacBio library preparation and sequencing</title>
<p>For PacBio single-molecule real-time (SMRT) sequencing, SMRTbell libraries were constructed according to the manufacturer&#x2019;s instructions of the SMRTbell Express Prep kit v2.0 following the Low DNA Input Protocol (Pacific Biosciences, Menlo Park, CA, United States). Total input DNA was approximately 140 and 800&#x2009;ng, respectively. Ligation with T-overhang SMRTbell adapters was performed at 20&#x00B0;C overnight. Following ligation, the SMRTbell library was purified with an AMPure PB bead clean up step with 0.45X volume of AMPure PB beads. Subsequently a size-selection step with AMPure PB Beads was performed to remove short SMRTbell templates &#x003C;3&#x2009;kb. For this purpose, the AMPure PB beads stock solution was diluted with elution buffer (40% volume/volume) and then added to the DNA sample with 2.2X volume. The size and concentration of the final libraries were assessed using the TapeStation (Agilent Technologies) and the Qubit Fluorometer with Qubit dsDNA HS reagents Assay kit (Thermo Fisher Scientific, Waltham, MA, United States). Sequencing primer v4 and Sequel<sup>&#x00AE;</sup> II Polymerase 2.0 were annealed and bound, respectively, to each SMRTbell library. SMRT sequencing was performed on the Sequel System II with Sequel II Sequencing Kit 2.0 in &#x201C;continuous long read&#x201D; (i.e., CLR) mode, 30&#x2009;h movie time with no pre-extension and Software SMRTLINK 8.0 (<xref ref-type="bibr" rid="ref02">Pacific Biosciences of California, 2022</xref>). One SMRT Cell was run for each sample. A SMRT cell contains millions of wells called zero-mode waveguides (ZMWs; <xref ref-type="bibr" rid="ref02">Pacific Biosciences of California, 2022</xref>). Within each ZMWs single molecules of DNA are immobilized and as the polymerase incorporates each nucleotide, light is emitted, and nucleotide incorporation is measured in real time (<xref ref-type="bibr" rid="ref02">Pacific Biosciences of California, 2022</xref>).</p>
</sec>
<sec id="sec7">
<title><italic>De novo</italic> assembly of PacBio metagenomic sequence reads</title>
<p>We largely followed the pipeline described in <xref ref-type="bibr" rid="ref41">Merges et al. (2021)</xref>. In summary, we generated HiFi reads from the PacBio Sequel II run using the PacBio tool CCS v5.0.0 with default parameters, i.e., --min-passes 3, remove subreads with lengths &#x003C;50% or&#x2009;&#x003E;&#x2009;200% of the median subread length, &#x2212;-max-insertion-size to 30&#x2009;bp (<xref ref-type="bibr" rid="ref02">Pacific Biosciences of California, 2022</xref>).<xref rid="fn0004" ref-type="fn"><sup>1</sup></xref> Metagenomic sequence reads were assembled into contigs using the long-read based assembler metaFlye v2.7 (<xref ref-type="bibr" rid="ref32">Kolmogorov et al., 2019</xref>). The assembled contigs were scaffolded with LRScaf v1.1.12 (<xref ref-type="bibr" rid="ref49">Qin et al., 2019</xref>).<xref rid="fn0005" ref-type="fn"><sup>2</sup></xref> To retrieve the mycobiont genome, the received scaffolds were taxonomically binned <italic>via</italic> blastx using DIAMOND (&#x2212;-more-sensitive --frameshift 15 &#x2013;range-culling) on a custom database (<xref ref-type="bibr" rid="ref58">Singh et al., 2022</xref>) and the Metagenome Analyzer MEGAN6 Community Edition pipeline (<xref ref-type="bibr" rid="ref28">Huson et al., 2007</xref>; <xref ref-type="bibr" rid="ref8">Buchfink et al., 2014</xref>). The completeness of the genomes represented by the binned Ascomycota scaffolds was estimated using Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis in BUSCO v4 using the Ascomycota dataset (<xref ref-type="bibr" rid="ref57">Sim&#x00E3;o et al., 2015</xref>).</p>
</sec>
<sec id="sec8">
<title>Pool-seq data processing</title>
<p>We filtered the pool-seq data for reads shorter than 80&#x2009;bp, reads with N&#x2019;s, and reads with average base quality scores less than 26 along with their pairs, and discarded them. We mapped the trimmed paired-end reads of each pool to the database of the identified genes using bowtie2 v2.4.1 (<xref ref-type="bibr" rid="ref35">Langmead and Salzberg, 2012</xref>), using the flags: --very-sensitive-local, &#x2212;-no-mixed, &#x2212;-no-unal, &#x2212;-no-discordant.</p>
</sec>
<sec id="sec9">
<title>Gene prediction and genome annotation</title>
<p>Functional annotation of genomes, including genes and proteins (antiSMASH; antibiotics and SM Analysis Shell, v5.0) was performed with scripts implemented in the funannotate pipeline (<xref ref-type="bibr" rid="ref5">Blin et al., 2017</xref>; <xref ref-type="bibr" rid="ref43">Palmer and Stajich, 2019</xref>). First, the genomes were masked for repetitive elements, and then the gene prediction was performed using BUSCO2 to train Augustus and self-training GeneMark-ES (<xref ref-type="bibr" rid="ref7">Borodovsky and Lomsadze, 2011</xref>; <xref ref-type="bibr" rid="ref57">Sim&#x00E3;o et al., 2015</xref>). Functional annotation was done with InterProScan (<xref ref-type="bibr" rid="ref50">Quevillon et al., 2005</xref>), egg-NOG-mapper (<xref ref-type="bibr" rid="ref26">Huerta-Cepas et al., 2017</xref>, <xref ref-type="bibr" rid="ref27">2019</xref>), and evolutionarily-informed expectations of gene content of near-universal single-copy orthologs using BUSCO v 5.1.2 with included Ascomycota dataset (<xref ref-type="bibr" rid="ref57">Sim&#x00E3;o et al., 2015</xref>). Secreted proteins were predicted using SignalP (<xref ref-type="bibr" rid="ref1">Armenteros et al., 2019</xref>) as implemented in the funannotate &#x201C;annotate&#x201D; command. Proteins where further characterized by NCBI conserved domain search (<xref ref-type="bibr" rid="ref37">Lu et al., 2020</xref>).<xref rid="fn0006" ref-type="fn"><sup>3</sup></xref></p>
</sec>
<sec id="sec10">
<title>Assessing gene content variation in the assembled fungal genomes</title>
<p>To identify presence/absences patterns of genes, we identified orthologs using orthoFinder (<xref ref-type="bibr" rid="ref16">Emms and Kelly, 2015</xref>, <xref ref-type="bibr" rid="ref17">2019</xref>). OrthoFinder provides the most accurate ortholog inference method on the Quest for Orthologs benchmark test (<xref ref-type="bibr" rid="ref16">Emms and Kelly, 2015</xref>, <xref ref-type="bibr" rid="ref17">2019</xref>). In orthoFinder (v.2.5.4), we assigned all genes to orthogroups using protein homology and constructed a pangenome of all four complete genomes (<xref ref-type="bibr" rid="ref3">Badet et al., 2020</xref>). Shared orthologs (i.e., members of the some orthogroup) of low elevation (warm adapted) and high elevational (cold adapted) genomes were extracted using R v3.6.1 (<xref ref-type="bibr" rid="ref03">R Core Team, 2019</xref>).</p>
</sec>
<sec id="sec11">
<title>Validating presence/absence of genes at population level</title>
<p>To validate population-level gene presence or absence, we estimated the abundance of each ortholog in the low elevation (warm adapted) and high elevation (cold adapted) population based on the median coverage of pool-seq reads associated to each ortholog contig. Specifically, we used samtools (v1.15) depth to estimate the coverage of each basepair within the contig (<xref ref-type="bibr" rid="ref13">Danecek et al., 2021</xref>). We assessed and visualized the data in R v3.6.1 (<xref ref-type="bibr" rid="ref03">R Core Team, 2019</xref>).</p>
</sec>
<sec id="sec12">
<title>Gene distribution across <italic>Umbilicaria</italic> populations</title>
<p>Bowtie2 (v2.2.2) was used to map pool-seq reads to all ortholog contigs (using default settings). The number of mapped reads was counted per sample and normalized by dividing the number of mapped reads by the total read number of the respective sample to account for differences in sequencing depth. We modeled gene abundance (i.e., normalized read count) as a function of elevation using linear models. Linear models were fitted and plotted in R v3.6.1 (<xref ref-type="bibr" rid="ref03">R Core Team, 2019</xref>).</p>
</sec>
</sec>
<sec id="sec13" sec-type="results">
<title>Results</title>
<sec id="sec14">
<title>HiFi metagenomic sequencing reads of mycobiont</title>
<p>We reconstructed metagenomic sequences from a low-elevation and a high-elevation specimen of <italic>U. pustulata</italic> and <italic>U. phaea</italic>. Sequence output and quality for <italic>U. pustulata</italic> were summarized in <xref ref-type="bibr" rid="ref58">Singh et al. (2022)</xref>, for <italic>U. phaea</italic> in <xref ref-type="bibr" rid="ref41">Merges et al. (2021)</xref> and in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S2</xref>.</p>
</sec>
<sec id="sec15">
<title>Altitude-specific genes in the <italic>de novo</italic> assembled genomes</title>
<p>We screened the de-novo assembled genomes of the low-and high-altitude samples for altitude-specific genes (<xref rid="fig1" ref-type="fig">Figure 1</xref>). Orthofinder revealed 16 orthogroups with shared orthologous genes (0.2% of the total orthogroups) in low-elevation genomes and 13 in high-elevational genomes (0.1% of the total orthogroups).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Venn diagram displaying orthogroups of <italic>U. phaea</italic> and <italic>U. pustulata</italic>. Red box highlights orthologs of the warm adapted (low elevation) <italic>U. phaea</italic> and <italic>U</italic>. <italic>pustulata</italic> genomes and the blue box of the cold adapted (high elevation) genomes.</p>
</caption>
<graphic xlink:href="fmicb-14-1097787-g001.tif"/>
</fig>
</sec>
<sec id="sec16">
<title>Presence/absence of genes at population level</title>
<p>To verify the presence/absence of detected orthologs, the coverage of each ortholog was calculated for the respective population at low and high elevation. The coverage analysis revealed one ortholog present in the genomes at low elevation to be consistently absent in populations at high elevation (<xref rid="fig2" ref-type="fig">Figure 2</xref>). The amino acid sequences of the ortholog could not be functionally annotated using the funannotate pipeline and was classified as &#x201C;hypothetical protein.&#x201D; NCBI&#x2019;s conserved domain search revealed an alignment with the catalytic domain of protein kinase superfamily member PKc cd00180 (Position-specific scoring matrix (PSSM) accession cl214531, NCBI <italic>Conserved Domain Database</italic>) as well as seven Tetratricopeptide repeats (Tetratricopeptide-like helical domain superfamily, InterPro entry IPR011990), indicating putative protein binding surfaces (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S1</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Presence/absences of orthologs in four <italic>de novo</italic> sequenced genomes of <italic>U. phaea</italic> and <italic>U. pustulata</italic> were verified by assessing the scaffold coverage in the warm adapted (low elevation) and the cold adapted population (high elevation) respectively. <bold>(A)</bold> Coverage of ortholog in <italic>U. phaea</italic>: High coverage in warm adapted (low elevation) population and no coverage in high elevational population. <bold>(B)</bold> Coverage of ortholog in <italic>U. pustulata</italic>: High coverage in warm adapted (low elevation) population.</p>
</caption>
<graphic xlink:href="fmicb-14-1097787-g002.tif"/>
</fig>
</sec>
<sec id="sec17">
<title>Gene abundance distributions along gradients</title>
<p>The normalized read number of the identified orthologs, annotated as members of the Protein Kinases superfamily, showed a decline with increasing elevation across all populations in both species (value of <italic>p</italic>&#x2009;=&#x2009;0.00373, <xref rid="fig3" ref-type="fig">Figure 3</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Read abundance of identified ortholog decreases significantly with increasing elevation across <italic>U. phaea</italic> (brown circles, lower regression line) and <italic>U. pustulata</italic> (blue circles, upper regression line) populations.</p>
</caption>
<graphic xlink:href="fmicb-14-1097787-g003.tif"/>
</fig>
</sec>
</sec>
<sec id="sec18" sec-type="discussions">
<title>Discussion</title>
<p>Although adaptations to environmental gradients may lead to variation in gene content, assessments of gene presence/absence patterns across populations and species of lichenized fungi are still missing. Here we assess signatures of parallel evolution at the level of gene presence and absence in lichenized fungi of the genus <italic>Umbilicaria</italic>, and trace the discovered genes in lichen populations along five replicated elevation gradients across two continents. While our whole genome comparison based on four <italic>de novo</italic> sequenced specimen (two per species) suggested up to 29 elevation-specific orthogroups (i.e., 16 low elevation-specific and 13 high elevation-specific orthogroups, <xref rid="fig1" ref-type="fig">Figure 1</xref>), the population-level verification approach showed only one gene, putatively encoding a protein kinase (PK), which linearly declined in abundance with increasing elevation, and was truly absent in the highest population. This suggests either high strain-specificity of certain genes, or high false positive recovery of gene presence/absence patterns when relying on comparative genomics approaches based on only a few individuals. Thereby extrapolating the significance of gene content variation assessed only with comparative genomics approaches on a few individuals can be potentially misleading when interpreting the evolutionary significance of the variation at population level. Regarding the PK gene consistently absent in high elevation genomes and populations, we found that the discovered gene declines linearly across all populations, suggesting an evolutionary benefit only at lower altitudes. Alternatively, the loss of the gene at higher elevations might benefit individuals in cold climates. To our knowledge, we report for the first time parallel gene presence and absence patterns correlating with climatic niches in different species of lichenized fungi. However, it remains to be analyzed, if the identified molecular trait is associated to a particular phenotype that can be associated with an adaptive function.</p>
<p>In bacteria variation in gene content is assumed to be driven by selection for environmental conditions that are relatively rare across the entire range of a species (<xref ref-type="bibr" rid="ref48">Qi et al., 2017</xref>). Recent evidence suggests that specific populations of lichenized fungi may contain unique biosynthetic gene clusters (<xref ref-type="bibr" rid="ref59">Singh et al., 2021</xref>), and our current findings show that also other genes can be elevation-specific. The gradual gene loss across populations with increasing elevation may suggests a decline of selective benefit and may indicate that certain variations in gene content could be of functional importance for local adaptation and climatic tolerances in lichenized fungi. The conserved domain search revealed a catalytic domain of a PK, a common eukaryotic protein superfamily. PKs selectively modify other proteins by phosphorylation, changing their enzymatic activity, cellular location and association with other proteins (<xref ref-type="bibr" rid="ref10">Cheng et al., 2002</xref>; <xref ref-type="bibr" rid="ref20">Hanks, 2003</xref>; <xref ref-type="bibr" rid="ref2">Asano et al., 2005</xref>; <xref ref-type="bibr" rid="ref22">Heinisch and Rodicio, 2018</xref>). Within a genome, PKs are encoded by a large multigene family with genes being distributed among multiple chromosomes. Putatively, the high number of PK genes has arisen by genome segmental duplication events (<xref ref-type="bibr" rid="ref2">Asano et al., 2005</xref>; <xref ref-type="bibr" rid="ref22">Heinisch and Rodicio, 2018</xref>). In our study, the presence/absence of a single PK gene may suggest a climate-specific ancestral genome segmental duplication event. Across the tree of life, e.g., in bacteria (<xref ref-type="bibr" rid="ref11">Christ and Chin, 2008</xref>), fungi (<xref ref-type="bibr" rid="ref29">James et al., 2008</xref>) and plants (<xref ref-type="bibr" rid="ref14">DeBolt, 2010</xref>; <xref ref-type="bibr" rid="ref47">Prunier et al., 2019</xref>), organisms subjected to selection under high temperatures show higher probability for genome segmental duplications (<xref ref-type="bibr" rid="ref11">Christ and Chin, 2008</xref>; <xref ref-type="bibr" rid="ref29">James et al., 2008</xref>; <xref ref-type="bibr" rid="ref14">DeBolt, 2010</xref>; <xref ref-type="bibr" rid="ref33">Kondrashov, 2012</xref>). For example, adaptation to heat stress through gene duplication of stress-related genes has been shown in <italic>Escherichia coli</italic> (<xref ref-type="bibr" rid="ref52">Riehle et al., 2001</xref>; <xref ref-type="bibr" rid="ref33">Kondrashov, 2012</xref>), where an upregulation through gene duplication of genes may play a role in adaptation (<xref ref-type="bibr" rid="ref33">Kondrashov, 2012</xref>; <xref ref-type="bibr" rid="ref34">Kuzmin et al., 2022</xref>). However, due to the scarcity of functional annotation of non-model organisms and the resulting lack of in-depth functional annotation of the gene in question, the mechanisms generating such population level diversification and the underlying molecular mechanisms behind such adaptations are yet to be understood.</p>
<p>While environmental adaptations are commonly highly polygenic (<xref ref-type="bibr" rid="ref53">Rivas et al., 2018</xref>; <xref ref-type="bibr" rid="ref4">Barghi et al., 2019</xref>; <xref ref-type="bibr" rid="ref21">Hartke et al., 2021</xref>; <xref ref-type="bibr" rid="ref45">Pfenninger et al., 2021</xref>), there is increasing evidence of the effect of single gene content variation (<xref ref-type="bibr" rid="ref36">Liu et al., 2021</xref>). For example, as has been recently shown in agave, where a single gene encoding a phosphoenolpyruvate carboxylase enhances the plant&#x2019;s climate resilience (<xref ref-type="bibr" rid="ref36">Liu et al., 2021</xref>). Not only the gain of genes, but also the loss of genes has been associated with adaptive traits, such as the evolution of particular diets in bats (<xref ref-type="bibr" rid="ref6">Blumer et al., 2022</xref>). Therefore, we consider the parallel loss of a homologous gene in two species and two geographic settings a rare find, and a step forward in understanding the genomic underpinnings of climatic tolerances in lichenized fungi. Future research should address the functional importance of the gene present at low altitude in the Mediterranean climate zone, and specifically explore the effects of variation in gene abundances across populations. Additionally, future research should consider using heterologous expression approaches to reveal whether the gene presence could induce tolerances to warm conditions.</p>
</sec>
<sec id="sec19" sec-type="conclusions">
<title>Conclusion</title>
<p>Our study demonstrates how comparative genomics in combination with population genomic data can reveal patterns of gene content variation across climatic gradients. In addition, the tracking of gene content variation across populations provides a widely applicable framework for retrieving meaningful biogeographical determinants of gene presence/absence patterns. We contribute to understanding convergence and parallel evolution at the genomic level, by providing insights into gene content variation of lichenized fungi in relation to climatic gradients. This suggests a promising new research direction with implications for understanding evolutionary trajectories in relation to climatic change.</p>
</sec>
<sec id="sec20" sec-type="data-availability">
<title>Data availability statement</title>
<p>Raw sequence reads were deposited in the National Centre for Biotechnology Information (NCBI) Sequence Read Archive under the BioProject accession numbers PRJNA693984 and PRJNA820300.</p>
</sec>
<sec id="sec21">
<title>Author contributions</title>
<p>DM and IS conceived the ideas and wrote the manuscript. IS and FD collected the data. DM, GS, and HV performed genome assembly and annotations. DM analyzed the data. FD provided analytical guidance. All authors contributed to the various drafts and gave final approval for publication.</p>
</sec>
<sec id="sec22" sec-type="funding-information">
<title>Funding</title>
<p>This research has been partly funded by the Hesse&#x2019;s &#x201C;state initiative for the development of scientific and economic excellence&#x201D; (LOEWE initiative) through the LOEWE Center for Translational Biodiversity Genomics (TBG).</p>
</sec>
<sec id="conf1" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack>
<p>We thank Carola Greve and J&#x00FC;rgen Otte for the laboratory procedures, and Christoph Sinai, Tilman Schell, and Anjuli Calchera (all Frankfurt am Main) for support with bioinformatics. We thank Barbara Feldmeyer and Markus Pfenninger (Frankfurt am Main) for their valuable suggestions on analyses.</p>
</ack>
<sec id="sec24" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fmicb.2023.1097787/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fmicb.2023.1097787/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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