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Looking to the past to inform the future: What eDNA from herbarium specimens can tell us about plant–animal interactions 回顾过去,展望未来:植物标本中的eDNA可以告诉我们植物与动物之间的相互作用
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-02-05 DOI: 10.1002/aps3.11633
Christopher Waters, Carla Hurt, Shawn Krosnick

Premise

The importance of natural history collections in modern ecological and genetic research cannot be overstated. Herbarium specimens provide historical information that can be used to investigate community ecology, phenology, and population genetics. In this study, environmental DNA (eDNA) metabarcoding and next-generation sequencing were used to test the efficacy of detecting historical plant–animal interactions from herbarium specimen flowers.

Methods

A modified eDNA isolation method and standard Illumina sequencing protocols were used. Animal eDNA was amplified using both cytochrome c oxidase subunit I (COI) and 16S primers to increase detection probability. The relationship between specimen age (0–69 years) and target taxa read depth was also investigated.

Results

We generated and identified over 1.5 million sequences of animal taxa belonging to 29 clades (families or orders). These methods enabled the detection of taxa including birds, mammals, hymenopterans, lepidopterans, coleopterans, and taxa belonging to “intrafloral” communities. While herbarium specimens overall yielded less identifiable eDNA compared to fresh material, the age of the herbarium specimen negligibly affected the amount of target and/or non-target eDNA detected in flowers.

Discussion

With careful consideration of the types of data that may be obtained through sampling eDNA from herbarium specimens, these methods could prove valuable to future research on plant–animal interactions.

自然历史收藏在现代生态学和遗传学研究中的重要性怎么强调都不为过。植物标本室标本提供了可用于调查群落生态学、物候学和群体遗传学的历史信息。本研究利用环境DNA (eDNA)元条形码技术和新一代测序技术,对植物标本花中植物-动物相互作用的检测效果进行了验证。方法采用改良的eDNA分离方法和标准Illumina测序方案。利用细胞色素c氧化酶亚基I (COI)和16S引物扩增动物eDNA,提高检测概率。研究了标本年龄(0 ~ 69岁)与目标分类群阅读深度的关系。结果生成并鉴定了29个进化支(科或目)的150多万个动物分类群序列。这些方法可以检测到鸟类、哺乳动物、膜翅目、鳞翅目、鞘翅目以及属于“花内”群落的分类群。虽然与新鲜材料相比,植物标本馆标本总体上产生的可识别eDNA较少,但植物标本馆标本的年龄对花中检测到的目标和/或非目标eDNA的数量影响很小。仔细考虑从植物标本室标本中取样eDNA可能获得的数据类型,这些方法可能对未来植物与动物相互作用的研究有价值。
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引用次数: 0
Optogenetic control of transgene expression in Marchantia polymorpha 多形地豆转基因表达的光遗传调控
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-01-28 DOI: 10.1002/aps3.11632
Anya Lillemor Lindström Battle, Lee James Sweetlove

Premise

The model liverwort Marchantia polymorpha is an emerging testbed species for plant metabolic engineering but lacks well-characterized inducible promoters, which are necessary to minimize biochemical and physiological disruption when over-accumulating target products. Here, we demonstrate the functionality of the light-inducible plant-usable light-switch elements (PULSE) optogenetic system in Marchantia and exemplify its use through the light-inducible overproduction of the bioplastic poly-3-hydroxybutyrate (PHB).

Methods

The PULSE system was used to drive expression of luciferase as a reporter and characterize its induction in transgenic M. polymorpha. Additionally, PULSE was used to drive expression of the PHB biosynthetic pathway; the accumulation of PHB under light-inducible control was compared to constitutive overexpression.

Results

PULSE was fully functional and minimally leaky in M. polymorpha. The presence of the PULSE construct, even in the absence of induction, resulted in a developmental phenotype. Constitutive and inducible expression resulted in similar PHB accumulation levels.

Discussion

PHB biosynthesis in plants is known to adversely affect plant health, but placing its production under optogenetic control alleviated negative effects on biomass accumulation in some instances. The work presented here represents a significant expansion of the toolbox for the metabolic engineering of M. polymorpha.

模式地茅(Marchantia polymorpha)是一种新兴的植物代谢工程实验物种,但缺乏特征明确的诱导启动子,而这些启动子是减少过度积累目标产物时生化和生理破坏所必需的。在这里,我们展示了光诱导植物可用光开关元件(PULSE)光遗传系统的功能,并通过光诱导过量生产生物塑料聚3-羟基丁酸酯(PHB)举例说明了它的用途。方法利用PULSE系统作为报告基因驱动荧光素酶的表达,并对其在转基因多形霉中的诱导作用进行表征。此外,PULSE还用于驱动PHB生物合成途径的表达;将PHB在光诱导下的积累与组成型过表达进行比较。结果多形霉的脉冲功能齐全,漏损最小。即使在没有诱导的情况下,PULSE结构的存在也会导致发育表型。组成型表达和诱导型表达导致相似的PHB积累水平。众所周知,植物中的PHB生物合成会对植物健康产生不利影响,但在某些情况下,将其生产置于光遗传控制下可以减轻对生物量积累的负面影响。这里提出的工作代表了多形霉代谢工程工具箱的重大扩展。
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引用次数: 0
mvh: An R tool to assemble and organize virtual herbaria from openly available specimen images mvh:一个R工具,用于从公开可用的标本图像中组装和组织虚拟植物标本库
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-01-26 DOI: 10.1002/aps3.11631
Thais Vasconcelos, James D. Boyko

Premise

Recent advances in imaging herbarium specimens have enhanced their use in biodiversity studies. However, user-friendly tools that facilitate the assembly of customized sets of herbarium specimen images on personal devices are still lacking.

Methods and Results

Here we present the R package mvh (“my virtual herbarium”), which includes functions designed to search and download metadata and openly available images associated with herbarium specimens based on taxon or geography. We tested the functionalities of mvh by searching metadata associated with five sets of 10 vascular plant species and five sets of 10 terrestrial coordinates. The download function had a success rate of 99%, downloading 291 out of the 293 images found in the search. Possible reasons for download failure are discussed.

Conclusions

As long as an internet connection is available, mvh simplifies the assembly and organization of virtual herbaria, thereby facilitating the investigation of novel empirical questions as well as trends in digitization efforts.

植物标本馆标本成像技术的最新进展,增强了其在生物多样性研究中的应用。然而,便于在个人设备上组装定制的植物标本室标本图像集的用户友好工具仍然缺乏。方法和结果本文介绍了R包mvh(“我的虚拟植物标本馆”),它包括基于分类群或地理位置搜索和下载与植物标本馆标本相关的元数据和公开可用图像的功能。我们通过搜索与5组10种维管植物物种和5组10种陆地坐标相关的元数据来测试mvh的功能。下载功能的成功率为99%,下载了293张图片中的291张。讨论了下载失败的可能原因。只要有互联网连接,mvh就简化了虚拟植物标本馆的组装和组织,从而促进了对新的实证问题的研究以及数字化工作的趋势。
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引用次数: 0
Acknowledgment of Reviewers 审稿人致谢
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-01-20 DOI: 10.1002/aps3.11630
<p>The editors gratefully acknowledge our reviewers, who have generously given their time and expertise to review manuscripts submitted to <i>Applications in Plant Sciences</i>. The list includes those who reviewed manuscripts from December 31, 2023, to December 31, 2024. Thank you for helping <i>APPS</i> maintain a prompt and fair peer-review process.</p><p>Abrahams, R. Shawn</p><p>Adit, Arjun</p><p>Ajay, B. C.</p><p>Ameen, Safinatu (Safeenah)</p><p>Angeles, Guillermo</p><p>Arias, Tatiana</p><p>Armbruster, Scott</p><p>Arstingstall, Katherine</p><p>Attigala, Lakshmi</p><p>Avila-Lovera, Eleinis</p><p>Awana, Monika</p><p>Baker, Robert</p><p>Banerjee, Pritam</p><p>Barclay, Richard S.</p><p>Bird, Kevin</p><p>Blischak, Paul</p><p>Borokini, Israel</p><p>Borràs, Joshua</p><p>Boyko, James</p><p>Brown, Herrick</p><p>Brown, Matilda</p><p>Brun, Guillaume</p><p>Chauhan, Chetan</p><p>Clare, Shaun</p><p>Cobo-Simón, Irene</p><p>Colli-Silva, Matheus</p><p>Cort, John</p><p>Crawford, Daniel</p><p>Cruz, Rafael</p><p>Davis, Mark</p><p>Diaz Tapia, Pilar</p><p>Dikow, Rebecca</p><p>Emelianova, Katherine</p><p>Ezquer, Ignacio</p><p>Fernie, Alisdair</p><p>Giongo, Adriana</p><p>Gladish, Daniel</p><p>Godbout, Julie</p><p>Goldberg, Jay K.</p><p>Hanschen, Erik</p><p>Hendrickson, Brandon</p><p>Hernández-Castellano, Carlos</p><p>Heyduk, Karolina</p><p>Hodel, Richard</p><p>Hossain, Kabir</p><p>Hu, Guanjing</p><p>Iniesto, Miguel</p><p>Jangid, Vinod</p><p>Katabuchi, Masatoshi</p><p>Kattenborn, Teja</p><p>Kobrlová, Lucie</p><p>Kolter, Andreas</p><p>Krieg, Christopher</p><p>LaFountain, Amy</p><p>Le Guillarme, Nicolas</p><p>Leroy, Thibault</p><p>Long, Evan</p><p>Ma, Chuang</p><p>Mabry, Makenzie</p><p>Malik, Afsheen</p><p>Mannochio Russo, Helena</p><p>Mason, Chase</p><p>McAdam, Scott</p><p>Milleville, Kenzo</p><p>Mohn, Rebekah</p><p>Monroe, Grey</p><p>Moore, Abigail</p><p>Muangmai, Narongrit</p><p>Murch, Susan</p><p>Noman, Muhammad</p><p>Ochoa-Fernandez, Rocio</p><p>Odufuwa, Phebian</p><p>Opedal, Øystein</p><p>Osorio Zambrano, Mayra Andreina</p><p>Paredes Burneo, Diego F.</p><p>Petruzzellis, Francesco</p><p>Pluskal, Tomas</p><p>Pramanik, Dewi</p><p>Puppo, Pamela</p><p>Rey, Elodie</p><p>Rodríguez López, Carlos</p><p>Salzman, Shayla</p><p>Sanchez, Jose Maria</p><p>Santos, Wagner Luiz dos</p><p>Sarkar, Shagor</p><p>Saroja, Seethapathy G.</p><p>Schmidt-Lebuhn,Alexander</p><p>Schmitt, Sylvain</p><p>Seglias, Alexandra</p><p>Senthil-Kumar,Muthappa</p><p>Serra Marin, Pau Enric</p><p>Session, Adam</p><p>Sigel, Erin</p><p>Smith, Alison</p><p>Smith, Stephen</p><p>Soltis, Pamela</p><p>Sorojsrisom, Elissa</p><p>Steinecke, Christina</p><p>Storchova, Helena</p><p>Storey, Gary</p><p>Suissa, Jacob</p><p>Sutherland, Brittany</p><p>Svolacchia, Noemi</p><p>Takano, Atsuko</p><p>Talavera, Alicia</p><p>Tavares, Rachel</p><p>Thomas, Shawn</p><p>Trindade, Weverton</p><p>Urbina-Casanova, Rafael</p><p>Van Steenderen, Clarke</p><p>Vivek, A. T.</p><p>Vlcek, Jakub</p><p>Walker, Joseph</p><p>Wallace, Lisa</p><p>We
编辑们感谢我们的审稿人,他们慷慨地付出了他们的时间和专业知识来审查提交给《植物科学应用》的手稿。该名单包括从2023年12月31日至2024年12月31日审查手稿的人员。感谢您帮助APPS保持及时和公平的同行评审过程。亚伯拉罕斯,R. ShawnAdit, ArjunAjay, B. C.Ameen, Safinatu (Safeenah)Angeles, GuillermoArias, TatianaArmbruster, scottarstinstall, KatherineAttigala, LakshmiAvila-Lovera, EleinisAwana, MonikaBaker, RobertBanerjee, PritamBarclay, Richard S.Bird, KevinBlischak, PaulBorokini, IsraelBorràs, joshua aboyko, JamesBrown, HerrickBrown, MatildaBrun, guillaumecchauhan, ChetanClare, ShaunCobo-Simón, IreneColli-Silva, MatheusCort, JohnCrawford, DanielCruz, RafaelDavis, MarkDiaz Tapia, PilarDikow,RebeccaEmelianova, KatherineEzquer, ignacio ernie, AlisdairGiongo, AdrianaGladish, DanielGodbout, JulieGoldberg, Jay K.Hanschen, ErikHendrickson, BrandonHernández-Castellano, carlos sheyduk, KarolinaHodel, RichardHossain, KabirHu, GuanjingIniesto, MiguelJangid, VinodKatabuchi, MasatoshiKattenborn, tejakobrlov, LucieKolter, AndreasKrieg, ChristopherLaFountain, AmyLe Guillarme, NicolasLeroy, ThibaultLong, EvanMa, ChuangMabry, MakenzieMalik, AfsheenMannochio Russo, HelenaMason, ChaseMcAdam,ScottMilleville, KenzoMohn, RebekahMonroe, GreyMoore, AbigailMuangmai, NarongritMurch, SusanNoman, MuhammadOchoa-Fernandez, RocioOdufuwa, PhebianOpedal, ØysteinOsorio Zambrano, Mayra AndreinaParedes Burneo, Diego f.p truzzellis, FrancescoPluskal, TomasPramanik, DewiPuppo, PamelaRey, ElodieRodríguez López, CarlosSalzman, ShaylaSanchez, Jose MariaSantos, Wagner Luiz dosSarkar, ShagorSaroja, Seethapathy g.s mitt - lebuhn,AlexanderSchmitt, SylvainSeglias, AlexandraSenthil-Kumar,MuthappaSerra Marin,paul EnricSession, AdamSigel, ErinSmith, AlisonSmith, StephenSoltis, pamelasorojrisom, ElissaSteinecke, ChristinaStorchova, HelenaStorey, garysusissa, JacobSutherland, BrittanySvolacchia, NoemiTakano, AtsukoTalavera, AliciaTavares, RachelThomas, ShawnTrindade, WevertonUrbina-Casanova, RafaelVan Steenderen, ClarkeVivek, a.t.v lcek, JakubWalker, JosephWallace, LisaWeaver, WilliamWilde, BrendanWu, XingboWu,徐志强,yuguyoyamav, RyoZanini, silvizhang, Xiaoqing
{"title":"Acknowledgment of Reviewers","authors":"","doi":"10.1002/aps3.11630","DOIUrl":"10.1002/aps3.11630","url":null,"abstract":"&lt;p&gt;The editors gratefully acknowledge our reviewers, who have generously given their time and expertise to review manuscripts submitted to &lt;i&gt;Applications in Plant Sciences&lt;/i&gt;. The list includes those who reviewed manuscripts from December 31, 2023, to December 31, 2024. Thank you for helping &lt;i&gt;APPS&lt;/i&gt; maintain a prompt and fair peer-review process.&lt;/p&gt;&lt;p&gt;Abrahams, R. Shawn&lt;/p&gt;&lt;p&gt;Adit, Arjun&lt;/p&gt;&lt;p&gt;Ajay, B. C.&lt;/p&gt;&lt;p&gt;Ameen, Safinatu (Safeenah)&lt;/p&gt;&lt;p&gt;Angeles, Guillermo&lt;/p&gt;&lt;p&gt;Arias, Tatiana&lt;/p&gt;&lt;p&gt;Armbruster, Scott&lt;/p&gt;&lt;p&gt;Arstingstall, Katherine&lt;/p&gt;&lt;p&gt;Attigala, Lakshmi&lt;/p&gt;&lt;p&gt;Avila-Lovera, Eleinis&lt;/p&gt;&lt;p&gt;Awana, Monika&lt;/p&gt;&lt;p&gt;Baker, Robert&lt;/p&gt;&lt;p&gt;Banerjee, Pritam&lt;/p&gt;&lt;p&gt;Barclay, Richard S.&lt;/p&gt;&lt;p&gt;Bird, Kevin&lt;/p&gt;&lt;p&gt;Blischak, Paul&lt;/p&gt;&lt;p&gt;Borokini, Israel&lt;/p&gt;&lt;p&gt;Borràs, Joshua&lt;/p&gt;&lt;p&gt;Boyko, James&lt;/p&gt;&lt;p&gt;Brown, Herrick&lt;/p&gt;&lt;p&gt;Brown, Matilda&lt;/p&gt;&lt;p&gt;Brun, Guillaume&lt;/p&gt;&lt;p&gt;Chauhan, Chetan&lt;/p&gt;&lt;p&gt;Clare, Shaun&lt;/p&gt;&lt;p&gt;Cobo-Simón, Irene&lt;/p&gt;&lt;p&gt;Colli-Silva, Matheus&lt;/p&gt;&lt;p&gt;Cort, John&lt;/p&gt;&lt;p&gt;Crawford, Daniel&lt;/p&gt;&lt;p&gt;Cruz, Rafael&lt;/p&gt;&lt;p&gt;Davis, Mark&lt;/p&gt;&lt;p&gt;Diaz Tapia, Pilar&lt;/p&gt;&lt;p&gt;Dikow, Rebecca&lt;/p&gt;&lt;p&gt;Emelianova, Katherine&lt;/p&gt;&lt;p&gt;Ezquer, Ignacio&lt;/p&gt;&lt;p&gt;Fernie, Alisdair&lt;/p&gt;&lt;p&gt;Giongo, Adriana&lt;/p&gt;&lt;p&gt;Gladish, Daniel&lt;/p&gt;&lt;p&gt;Godbout, Julie&lt;/p&gt;&lt;p&gt;Goldberg, Jay K.&lt;/p&gt;&lt;p&gt;Hanschen, Erik&lt;/p&gt;&lt;p&gt;Hendrickson, Brandon&lt;/p&gt;&lt;p&gt;Hernández-Castellano, Carlos&lt;/p&gt;&lt;p&gt;Heyduk, Karolina&lt;/p&gt;&lt;p&gt;Hodel, Richard&lt;/p&gt;&lt;p&gt;Hossain, Kabir&lt;/p&gt;&lt;p&gt;Hu, Guanjing&lt;/p&gt;&lt;p&gt;Iniesto, Miguel&lt;/p&gt;&lt;p&gt;Jangid, Vinod&lt;/p&gt;&lt;p&gt;Katabuchi, Masatoshi&lt;/p&gt;&lt;p&gt;Kattenborn, Teja&lt;/p&gt;&lt;p&gt;Kobrlová, Lucie&lt;/p&gt;&lt;p&gt;Kolter, Andreas&lt;/p&gt;&lt;p&gt;Krieg, Christopher&lt;/p&gt;&lt;p&gt;LaFountain, Amy&lt;/p&gt;&lt;p&gt;Le Guillarme, Nicolas&lt;/p&gt;&lt;p&gt;Leroy, Thibault&lt;/p&gt;&lt;p&gt;Long, Evan&lt;/p&gt;&lt;p&gt;Ma, Chuang&lt;/p&gt;&lt;p&gt;Mabry, Makenzie&lt;/p&gt;&lt;p&gt;Malik, Afsheen&lt;/p&gt;&lt;p&gt;Mannochio Russo, Helena&lt;/p&gt;&lt;p&gt;Mason, Chase&lt;/p&gt;&lt;p&gt;McAdam, Scott&lt;/p&gt;&lt;p&gt;Milleville, Kenzo&lt;/p&gt;&lt;p&gt;Mohn, Rebekah&lt;/p&gt;&lt;p&gt;Monroe, Grey&lt;/p&gt;&lt;p&gt;Moore, Abigail&lt;/p&gt;&lt;p&gt;Muangmai, Narongrit&lt;/p&gt;&lt;p&gt;Murch, Susan&lt;/p&gt;&lt;p&gt;Noman, Muhammad&lt;/p&gt;&lt;p&gt;Ochoa-Fernandez, Rocio&lt;/p&gt;&lt;p&gt;Odufuwa, Phebian&lt;/p&gt;&lt;p&gt;Opedal, Øystein&lt;/p&gt;&lt;p&gt;Osorio Zambrano, Mayra Andreina&lt;/p&gt;&lt;p&gt;Paredes Burneo, Diego F.&lt;/p&gt;&lt;p&gt;Petruzzellis, Francesco&lt;/p&gt;&lt;p&gt;Pluskal, Tomas&lt;/p&gt;&lt;p&gt;Pramanik, Dewi&lt;/p&gt;&lt;p&gt;Puppo, Pamela&lt;/p&gt;&lt;p&gt;Rey, Elodie&lt;/p&gt;&lt;p&gt;Rodríguez López, Carlos&lt;/p&gt;&lt;p&gt;Salzman, Shayla&lt;/p&gt;&lt;p&gt;Sanchez, Jose Maria&lt;/p&gt;&lt;p&gt;Santos, Wagner Luiz dos&lt;/p&gt;&lt;p&gt;Sarkar, Shagor&lt;/p&gt;&lt;p&gt;Saroja, Seethapathy G.&lt;/p&gt;&lt;p&gt;Schmidt-Lebuhn,Alexander&lt;/p&gt;&lt;p&gt;Schmitt, Sylvain&lt;/p&gt;&lt;p&gt;Seglias, Alexandra&lt;/p&gt;&lt;p&gt;Senthil-Kumar,Muthappa&lt;/p&gt;&lt;p&gt;Serra Marin, Pau Enric&lt;/p&gt;&lt;p&gt;Session, Adam&lt;/p&gt;&lt;p&gt;Sigel, Erin&lt;/p&gt;&lt;p&gt;Smith, Alison&lt;/p&gt;&lt;p&gt;Smith, Stephen&lt;/p&gt;&lt;p&gt;Soltis, Pamela&lt;/p&gt;&lt;p&gt;Sorojsrisom, Elissa&lt;/p&gt;&lt;p&gt;Steinecke, Christina&lt;/p&gt;&lt;p&gt;Storchova, Helena&lt;/p&gt;&lt;p&gt;Storey, Gary&lt;/p&gt;&lt;p&gt;Suissa, Jacob&lt;/p&gt;&lt;p&gt;Sutherland, Brittany&lt;/p&gt;&lt;p&gt;Svolacchia, Noemi&lt;/p&gt;&lt;p&gt;Takano, Atsuko&lt;/p&gt;&lt;p&gt;Talavera, Alicia&lt;/p&gt;&lt;p&gt;Tavares, Rachel&lt;/p&gt;&lt;p&gt;Thomas, Shawn&lt;/p&gt;&lt;p&gt;Trindade, Weverton&lt;/p&gt;&lt;p&gt;Urbina-Casanova, Rafael&lt;/p&gt;&lt;p&gt;Van Steenderen, Clarke&lt;/p&gt;&lt;p&gt;Vivek, A. T.&lt;/p&gt;&lt;p&gt;Vlcek, Jakub&lt;/p&gt;&lt;p&gt;Walker, Joseph&lt;/p&gt;&lt;p&gt;Wallace, Lisa&lt;/p&gt;&lt;p&gt;We","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The sensitivity of reconstructed carbon dioxide concentrations to stomatal preparation methods using a leaf gas exchange model 利用叶片气体交换模型重建的二氧化碳浓度对气孔制备方法的敏感性
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-01-19 DOI: 10.1002/aps3.11629
Michael D. Machesky, Nathan D. Sheldon, Michael T. Hren, Selena Y. Smith

Premise

Mechanistic models using stomatal traits and leaf carbon isotope ratios to reconstruct atmospheric carbon dioxide (CO2) concentrations (ca) are important to understand the Phanerozoic paleoclimate. However, methods for preparing leaf cuticles to measure stomatal traits have not been standardized.

Methods

Three people measured the stomatal density and index, guard cell length, guard cell pair width, and pore length of leaves from the same Ginkgo biloba, Quercus alba, and Zingiber mioga leaves growing at known CO2 levels using four preparation methods: fluorescence on cleared leaves, nail polish, dental putty on fresh leaves, and dental putty on dried leaves.

Results

There are significant differences between trait measurements from each method. Modeled ca calculations are less sensitive to method than individual traits; however, the choice of assumed carbon isotope fractionation also impacted the accuracy of the results.

Discussion

We show that there is not a significant difference between ca estimates made using any of the four methods. Further study is needed on the fractionation due to carboxylation of ribulose bisphosphate (RuBP) in individual plant species before use as a paleo-CO2 barometer and to refine estimates based upon widely applied taxa (e.g., Ginkgo). Finally, we recommend that morphological measurements be made by multiple observers to reduce the effect of individual observational biases.

利用气孔特征和叶片碳同位素比值重建大气二氧化碳(CO2)浓度(ca)的机制模型对认识显生宙古气候具有重要意义。然而,制备叶片角质层以测量气孔性状的方法尚未标准化。方法3人分别对已知CO2浓度下生长的银杏、白栎和姜叶片,采用荧光处理、指甲油处理、鲜叶涂牙泥处理和干叶涂牙泥处理4种处理方法,测定叶片气孔密度、气孔指数、保护细胞长度、保护细胞对宽度和气孔长度。结果两种方法的性状测量值差异有统计学意义。模型ca计算对方法的敏感性低于个体特征;然而,假设碳同位素分馏的选择也影响了结果的准确性。我们表明,使用四种方法中的任何一种进行的ca估计之间没有显着差异。在将二磷酸核酮糖(RuBP)用作古二氧化碳晴雨表之前,需要进一步研究单个植物物种中由于羧化作用而产生的分馏,并根据广泛应用的分类群(例如银杏)来完善估算。最后,我们建议由多个观察者进行形态学测量,以减少个体观察偏差的影响。
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引用次数: 0
Microbial methods matter: Identifying discrepancies between microbiome denoising pipelines using a leaf biofilm taphonomic dataset 微生物方法很重要:利用叶片生物膜分类数据集识别微生物组去噪管道之间的差异
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2025-01-08 DOI: 10.1002/aps3.11628
Brianne Palmer, Sabina Karačić, Gabriele Bierbaum, Carole T. Gee

Premise

The occurrence of different microorganisms on aquatic macrophyte fossils suggests that biofilm microbes may facilitate leaf preservation. Understanding the impact of microorganisms on leaf preservation requires studies on living plants coupled with microbial amplicon sequencing. Choosing the most suitable bioinformatic pipeline is pivotal to accurate data interpretation, as it can lead to considerably different estimations of microbial community composition.

Methods

We analyze biofilms from floating and submerged leaves of Nymphaea alba and Nuphar lutea and mock communities using primers for the 16S ribosomal RNA (rRNA), 18S rRNA, and ITS amplicon regions and compare the microbial community compositions derived from three bioinformatic pipelines: DADA2, Deblur, and UNOISE.

Results

The choice of denoiser alters the total number of sequences identified and differs in the identified taxa. Results from all three denoising pipelines show that the leaf microbial communities differed between depths and that the effect of the environment varied depending on the amplicon region.

Discussion

Considering the performance of denoising algorithms and the identification of amplicon sequence variants (ASVs), we recommend DADA2 for analyzing 16S rRNA and 18S rRNA. For the ITS region, the choice is more nuanced, as Deblur identified the most ASVs and was compositionally similar to DADA2.

水生植物化石上不同微生物的出现表明生物膜微生物可能有助于叶片的保存。了解微生物对叶片保存的影响需要对活的植物进行研究,并结合微生物扩增子测序。选择最合适的生物信息管道对于准确的数据解释至关重要,因为它可以导致对微生物群落组成的相当不同的估计。方法利用16S核糖体RNA (rRNA)、18S rRNA和ITS扩增子区的引物,对白花莲叶和黄花莲叶的生物膜和模拟群落进行分析,并比较三个生物信息学管道(DADA2、Deblur和UNOISE)的微生物群落组成。结果去噪器的选择改变了识别序列的总数,并在识别的分类群中有所不同。三个去噪管道的结果表明,不同深度的叶片微生物群落不同,环境的影响取决于扩增区的不同。考虑到去噪算法的性能和扩增子序列变异(asv)的识别,我们推荐DADA2用于分析16S rRNA和18S rRNA。对于ITS区域,选择更加微妙,因为Deblur识别了最多的asv,并且在组成上与DADA2相似。
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引用次数: 0
Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species 不同的同源推断算法在芸苔科物种中产生相似的预测同源群
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2024-12-25 DOI: 10.1002/aps3.11627
Irene T. Liao, Karen E. Sears, Lena C. Hileman, Lachezar A. Nikolov

Premise

Orthology inference is crucial for comparative genomics, and multiple algorithms have been developed to identify putative orthologs for downstream analyses. Despite the abundance of proposed solutions, including publicly available benchmarks, it is difficult to assess which tool is most suitable for plant species, which commonly have complex genomic histories.

Methods

We explored the performance of four orthology inference algorithms—OrthoFinder, SonicParanoid, Broccoli, and OrthNet—on eight Brassicaceae genomes in two groups: one group comprising only diploids and another set comprising the diploids, two mesopolyploids, and one recent hexaploid genome.

Results

The composition of the orthogroups reflected the species' ploidy and genomic histories, with the diploid set having a higher proportion of identical orthogroups. While the diploid + higher ploidy set had a lower proportion of orthogroups with identical compositions, the average degree of similarity between the orthogroups was not different from the diploid set.

Discussion

Three algorithms—OrthoFinder, SonicParanoid, and Broccoli—are helpful for initial orthology predictions. Results produced using OrthNet were generally outliers but could still provide detailed information about gene colinearity. With our Brassicaceae dataset, slight discrepancies were found across the orthology inference algorithms, necessitating additional analyses such as tree inference to fine-tune results.

前提同源推断对于比较基因组学是至关重要的,并且已经开发了多种算法来确定下游分析的假定同源。尽管提出了大量的解决方案,包括公开可用的基准,但很难评估哪种工具最适合植物物种,因为植物物种通常具有复杂的基因组历史。方法研究了orthofinder、SonicParanoid、Broccoli和orthnet四种同源推断算法在两组油菜科8个基因组上的表现:一组只包含二倍体,另一组包含二倍体、两个中多倍体和一个最近的六倍体基因组。结果正极群的组成反映了物种的倍性和基因组历史,二倍体组中相同正极群的比例较高。二倍体+高倍性组合具有相同组成的正群比例较低,但正群之间的平均相似度与二倍体没有差异。三种算法- orthofinder, SonicParanoid和broccoli -有助于初始正畸预测。使用OrthNet产生的结果通常是异常值,但仍然可以提供有关基因共线性的详细信息。对于我们的芸苔科数据集,在不同的同源推断算法中发现了轻微的差异,需要额外的分析,如树推断来微调结果。
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引用次数: 0
An optimized CTAB method for genomic DNA extraction from green seaweeds (Ulvophyceae) 优化的CTAB法提取绿海藻基因组DNA
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2024-11-29 DOI: 10.1002/aps3.11625
Riyad Hossen, Myles Courtney, Alasdair Sim, Md Abdullah Al Kamran Khan, Heroen Verbruggen, Trevor Bringloe

Premise

Seaweeds are gaining substantial research interest, particularly for genomic applications, where high-quality DNA is a prerequisite. Extracting DNA from these organisms presents challenges due to high levels of biomacromolecules resulting from their diverse cell structures. Existing protocols often lack versatility, leading to inconsistent outcomes across various materials and taxa, which highlights the need for a universal method for use with a variety of green seaweed samples.

Methods and Results

We optimized the conventional cetyltrimethylammonium bromide (CTAB) protocol for green seaweed DNA extraction. Our method, involving an initial sample treatment, lysis buffer adjustment, and enzyme incubation alterations, outperformed the conventional CTAB and commercial kits in terms of DNA yield and purity. Notably, the protocol's effectiveness was demonstrated across various green algal materials and preservation methods, and was tested with downstream applications with satisfactory results.

Conclusions

Our optimized CTAB protocol offers a reliable solution for high-quality genomic DNA extraction from a wide variety of green seaweed samples.

海藻正在获得大量的研究兴趣,特别是在基因组应用中,高质量的DNA是先决条件。从这些生物中提取DNA面临着挑战,因为它们的细胞结构不同,生物大分子含量高。现有的方案往往缺乏通用性,导致不同材料和分类群的结果不一致,这突出了对各种绿海藻样品使用的通用方法的需求。方法与结果优化了传统十六烷基三甲基溴化铵(CTAB)提取绿海藻DNA的工艺。我们的方法包括初始样品处理、裂解缓冲液调整和酶孵育改变,在DNA产量和纯度方面优于传统的CTAB和商业试剂盒。值得注意的是,该协议的有效性在各种绿藻材料和保存方法中得到了证明,并在下游应用中进行了测试,结果令人满意。结论优化后的CTAB方案为高质量提取各种绿海藻样品基因组DNA提供了可靠的解决方案。
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引用次数: 0
From phylogenomics to breeding: Can universal target capture probes be used in the development of SNP markers for kinship analysis? 从系统基因组学到育种:通用目标捕获探针能否用于开发用于亲属关系分析的SNP标记?
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2024-11-12 DOI: 10.1002/aps3.11624
Kedra M. Ousmael, Ole K. Hansen

Premise

Leveraging DNA markers, particularly single-nucleotide polymorphisms (SNPs), in parentage analysis, sib-ship reconstruction, and genomic relatedness analysis can enhance plant breeding efficiency. However, the limited availability of genomic information, confined to the most commonly used species, hinders the broader application of SNPs in species of lower economic interest (e.g., most tree species). We explored the possibility of using universal target capture probes, namely Angiosperms353, to identify SNPs and assess their effectiveness in genomic relatedness analysis.

Methods

We tested the approach in 11 tree species, six of which had a half-sib family structure. Variants were called within species, and genomic relatedness analysis was conducted in species with two or more families. Scalability via amplicon sequencing was tested by designing primers and testing them in silico.

Results

Adequate SNPs for relatedness analysis were identified in all species. Relatedness values from Angiosperms353-based SNPs highly correlated with those from thousands of genome-wide DArTseq SNPs in Cordia africana, one of the species with a family structure. The in silico performance of designed primers demonstrated the potential for scaling up via amplicon sequencing.

Discussion

Utilizing universal target capture probes for SNP identification can help overcome the limitations of genomic information availability, thereby enhancing the application of genomic markers in breeding plant species with lower economic interest.

利用DNA标记,特别是单核苷酸多态性(snp)进行亲本分析、兄弟姐妹重建和基因组亲缘关系分析可以提高植物育种效率。然而,基因组信息的有限可用性,仅限于最常用的物种,阻碍了SNPs在经济利益较低的物种(例如,大多数树种)中的广泛应用。我们探索了使用通用靶捕获探针(Angiosperms353)识别snp并评估其在基因组相关性分析中的有效性的可能性。方法在11种具有半同胞家族结构的树种中进行了实验。在种内称为变异,并在具有两个或两个以上科的物种中进行基因组相关性分析。通过设计引物并在计算机上测试扩增子测序的可扩展性。结果所有物种均鉴定出足够的snp进行相关性分析。基于被子植物353的snp的亲缘性值与非洲考迪亚(Cordia africana)数千个全基因组DArTseq snp的亲缘性值高度相关,后者是具有家族结构的物种之一。设计的引物的硅性能证明了通过扩增子测序扩大规模的潜力。利用通用靶标捕获探针进行SNP鉴定有助于克服基因组信息可用性的限制,从而提高基因组标记在经济效益较低的植物物种育种中的应用。
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引用次数: 0
Ensemble automated approaches for producing high-quality herbarium digital records 用于生产高质量植物标本馆数字记录的集成自动化方法
IF 2.4 3区 生物学 Q2 PLANT SCIENCES Pub Date : 2024-11-05 DOI: 10.1002/aps3.11623
Robert P. Guralnick, Raphael LaFrance, Julie M. Allen, Michael W. Denslow

Premise

One of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.

Methods

We first showcase the development of a rule-based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule-based approaches often have high commission error rates.

Results

Our results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.

Discussion

This work shows that an ensemble approach has particular value for creating high-quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.

自然历史藏品数字化最慢的步骤之一是将与标本相关的标签转换为可用于藏品管理和研究的数字数据记录。在这里,我们将讨论如何通过提取标准化的达尔文核心字段将标本馆标本标签转换为数字数据记录。我们首先展示了基于规则的方法的发展,并将结果与基于大型语言模型的方法(特别是ChatGPT4)进行了比较。接下来,我们对两种方法使用光学字符识别(OCR)转录的一组标签的目标字段的遗漏率和委托错误率进行了量化。例如,我们发现ChatGPT4经常创建不符合Darwin Core的字段名,而基于规则的方法通常有很高的委托错误率。结果这些方法各有优势和局限性。因此,我们开发了一种集成方法,它利用了每个单独方法的优势,并记录了集成大大减少了总体信息提取错误。这项工作表明,集成方法对于创建高质量的数字数据记录具有特殊的价值,即使对于复杂的标签内容也是如此。虽然仍然需要人工验证以确保最佳质量,但自动化方法可以加速植物标本馆标本标签的数字化,并且可能广泛适用于所有自然历史收藏类型。
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引用次数: 0
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Applications in Plant Sciences
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