@article{ding_molecular_2024,
	title = {Molecular {Advances} of {Bud} {Dormancy} in {Trees}},
	issn = {0022-0957},
	url = {https://doi.org/10.1093/jxb/erae183},
	doi = {10.1093/jxb/erae183},
	abstract = {Seasonal bud dormancy in perennial woody plants is a crucial and intricate process that is vital for the survival and development of plants. Over the past few decades, significant advancements have been made in understanding many features of bud dormancy, particularly in model species, where certain molecular mechanisms underlying this process have been elucidated. In this review, we provide an overview of recent molecular progress in understanding bud dormancy in trees, with a specific emphasis on the integration of common signaling and molecular mechanisms identified across different tree species. Additionally, we address some challenges that have emerged in the in-depth understanding of bud dormancy and offer insights for future studies.},
	urldate = {2024-04-29},
	journal = {Journal of Experimental Botany},
	author = {Ding, Jihua and Wang, Kejing and Pandey, Shashank and Perales, Mariano and Allona, Isabel and Khan, Md Rezaul Islam and Busov, Victor B and Bhalerao, Rishikesh P},
	month = apr,
	year = {2024},
	pages = {erae183},
}

Seasonal bud dormancy in perennial woody plants is a crucial and intricate process that is vital for the survival and development of plants. Over the past few decades, significant advancements have been made in understanding many features of bud dormancy, particularly in model species, where certain molecular mechanisms underlying this process have been elucidated. In this review, we provide an overview of recent molecular progress in understanding bud dormancy in trees, with a specific emphasis on the integration of common signaling and molecular mechanisms identified across different tree species. Additionally, we address some challenges that have emerged in the in-depth understanding of bud dormancy and offer insights for future studies.

@article{nanda_chlorospec_2024,
	title = {{ChloroSpec}: {A} new in vivo chlorophyll fluorescence spectrometer for simultaneous wavelength- and time-resolved detection},
	volume = {176},
	copyright = {© 2024 The Authors. Physiologia Plantarum published by John Wiley \& Sons Ltd on behalf of Scandinavian Plant Physiology Society.},
	issn = {1399-3054},
	shorttitle = {{ChloroSpec}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ppl.14306},
	doi = {10.1111/ppl.14306},
	abstract = {Chlorophyll fluorescence is a ubiquitous tool in basic and applied plant science research. Various standard commercial instruments are available for characterization of photosynthetic material like leaves or microalgae, most of which integrate the overall fluorescence signals above a certain cut-off wavelength. However, wavelength-resolved (fluorescence signals appearing at different wavelengths having different time dependent decay) signals contain vast information required to decompose complex signals and processes into their underlying components that can untangle the photo-physiological process of photosynthesis. Hence, to address this we describe an advanced chlorophyll fluorescence spectrometer - ChloroSpec - allowing three-dimensional simultaneous detection of fluorescence intensities at different wavelengths in a time-resolved manner. We demonstrate for a variety of typical examples that most of the generally used fluorescence parameters are strongly wavelength dependent. This indicates a pronounced heterogeneity and a highly dynamic nature of the thylakoid and the photosynthetic apparatus under actinic illumination. Furthermore, we provide examples of advanced global analysis procedures integrating this three-dimensional signal and relevant information extracted from them that relate to the physiological properties of the organism. This conveniently obtained broad range of data can make ChloroSpec a new standard tool in photosynthesis research.},
	language = {en},
	number = {2},
	urldate = {2024-04-29},
	journal = {Physiologia Plantarum},
	author = {Nanda, Sanchali and Shutova, Tatyana and Cainzos, Maximiliano and Bag, Pushan and Jansson, Stefan and Holzwarth, Alfred R.},
	month = apr,
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ppl.14306},
	pages = {e14306},
}

Chlorophyll fluorescence is a ubiquitous tool in basic and applied plant science research. Various standard commercial instruments are available for characterization of photosynthetic material like leaves or microalgae, most of which integrate the overall fluorescence signals above a certain cut-off wavelength. However, wavelength-resolved (fluorescence signals appearing at different wavelengths having different time dependent decay) signals contain vast information required to decompose complex signals and processes into their underlying components that can untangle the photo-physiological process of photosynthesis. Hence, to address this we describe an advanced chlorophyll fluorescence spectrometer - ChloroSpec - allowing three-dimensional simultaneous detection of fluorescence intensities at different wavelengths in a time-resolved manner. We demonstrate for a variety of typical examples that most of the generally used fluorescence parameters are strongly wavelength dependent. This indicates a pronounced heterogeneity and a highly dynamic nature of the thylakoid and the photosynthetic apparatus under actinic illumination. Furthermore, we provide examples of advanced global analysis procedures integrating this three-dimensional signal and relevant information extracted from them that relate to the physiological properties of the organism. This conveniently obtained broad range of data can make ChloroSpec a new standard tool in photosynthesis research.

@article{tian_plant-lncpipe_2024,
	title = {Plant-{LncPipe}: a computational pipeline providing significant improvement in plant {lncRNA} identification},
	volume = {11},
	issn = {2662-6810},
	shorttitle = {Plant-{LncPipe}},
	url = {https://doi.org/10.1093/hr/uhae041},
	doi = {10.1093/hr/uhae041},
	abstract = {Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.},
	number = {4},
	urldate = {2024-04-29},
	journal = {Horticulture Research},
	author = {Tian, Xue-Chan and Chen, Zhao-Yang and Nie, Shuai and Shi, Tian-Le and Yan, Xue-Mei and Bao, Yu-Tao and Li, Zhi-Chao and Ma, Hai-Yao and Jia, Kai-Hua and Zhao, Wei and Mao, Jian-Feng},
	month = apr,
	year = {2024},
	pages = {uhae041},
}

Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.

@article{sandell_genomic_2024,
	title = {Genomic basis of seed colour in quinoa inferred from variant patterns using extreme gradient boosting},
	volume = {22},
	copyright = {© 2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley \& Sons Ltd.},
	issn = {1467-7652},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/pbi.14267},
	doi = {10.1111/pbi.14267},
	abstract = {Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome-wide association approach. We provide re-sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.},
	language = {en},
	number = {5},
	urldate = {2024-04-19},
	journal = {Plant Biotechnology Journal},
	author = {Sandell, Felix L. and Holzweber, Thomas and Street, Nathaniel R. and Dohm, Juliane C. and Himmelbauer, Heinz},
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.14267},
	keywords = {betalain synthesis pathway, genome sequencing, genotype-phenotype relationships, machine learning, quinoa, seed colour},
	pages = {1312--1324},
}

Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome-wide association approach. We provide re-sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.

@article{abdel-fattah_growth-regulated_2024,
	title = {Growth-regulated co-occupancy of {Mediator} and {Lsm3} at intronic ribosomal protein genes},
	issn = {0305-1048},
	url = {https://doi.org/10.1093/nar/gkae266},
	doi = {10.1093/nar/gkae266},
	abstract = {Mediator is a well-known transcriptional co-regulator and serves as an adaptor between gene-specific regulatory proteins and RNA polymerase II. Studies on the chromatin-bound form of Mediator revealed interactions with additional protein complexes involved in various transcription-related processes, such as the Lsm2–8 complex that is part of the spliceosomal U6 small nuclear ribonucleoprotein complex. Here, we employ Chromatin Immunoprecipitation sequencing (ChIP-seq) of chromatin associated with the Lsm3 protein and the Med1 or Med15 Mediator subunits. We identify 86 genes co-occupied by both Lsm3 and Mediator, of which 73 were intron-containing ribosomal protein genes. In logarithmically growing cells, Mediator primarily binds to their promoter regions but also shows a second, less pronounced occupancy at their 3′-exons. During the late exponential phase, we observe a near-complete transition of Mediator from these promoters to a position in their 3′-ends, overlapping the Lsm3 binding sites ∼250 bp downstream of their last intron–exon boundaries. Using an unbiased RNA sequencing approach, we show that transition of Mediator from promoters to the last exon of these genes correlates to reduction of both their messenger RNA levels and splicing ratios, indicating that the Mediator and Lsm complexes cooperate to control growth-regulated expression of intron-containing ribosomal protein genes at the levels of transcription and splicing.},
	urldate = {2024-04-19},
	journal = {Nucleic Acids Research},
	author = {Abdel-Fattah, Wael R and Carlsson, Mattias and Hu, Guo-Zhen and Singh, Ajeet and Vergara, Alexander and Aslam, Rameen and Ronne, Hans and Björklund, Stefan},
	month = apr,
	year = {2024},
	pages = {gkae266},
}

Mediator is a well-known transcriptional co-regulator and serves as an adaptor between gene-specific regulatory proteins and RNA polymerase II. Studies on the chromatin-bound form of Mediator revealed interactions with additional protein complexes involved in various transcription-related processes, such as the Lsm2–8 complex that is part of the spliceosomal U6 small nuclear ribonucleoprotein complex. Here, we employ Chromatin Immunoprecipitation sequencing (ChIP-seq) of chromatin associated with the Lsm3 protein and the Med1 or Med15 Mediator subunits. We identify 86 genes co-occupied by both Lsm3 and Mediator, of which 73 were intron-containing ribosomal protein genes. In logarithmically growing cells, Mediator primarily binds to their promoter regions but also shows a second, less pronounced occupancy at their 3′-exons. During the late exponential phase, we observe a near-complete transition of Mediator from these promoters to a position in their 3′-ends, overlapping the Lsm3 binding sites ∼250 bp downstream of their last intron–exon boundaries. Using an unbiased RNA sequencing approach, we show that transition of Mediator from promoters to the last exon of these genes correlates to reduction of both their messenger RNA levels and splicing ratios, indicating that the Mediator and Lsm complexes cooperate to control growth-regulated expression of intron-containing ribosomal protein genes at the levels of transcription and splicing.