@article{liu_photosynthesis-related_2026,
	title = {Photosynthesis-related genetic and transcriptomic variations contribute to adaptive trait diversity in global {Arabidopsis} thaliana populations},
	volume = {26},
	issn = {1471-2229},
	url = {https://doi.org/10.1186/s12870-026-08279-2},
	doi = {10.1186/s12870-026-08279-2},
	abstract = {Photosynthesis is the foundational process for carbon fixation in terrestrial ecosystems. Although allelic variations in photosynthesis-related genes have the potential to enhance carbon assimilation efficiency, their functional roles in local adaptation are still not well understood. In this study, we systematically examined the genetic and transcriptomic diversity among globally distributed natural accessions of Arabidopsis thaliana, focusing on 1,103 genes associated with photosynthetic pathways. By assembling chloroplast genomes from 28 representative accessions and integrating whole-genome and transcriptome sequencing data from over 1,000 accessions, we identified extensive allelic variation. Notably, 34.0\% of these genes exhibited regulatory variations through expression quantitative trait locus mapping, including key components such as Rubisco and Rubisco activase. Functional validation demonstrated that overexpression of these genes increased cotyledon size and root length. Additionally, genome-wide and transcriptome-wide association studies revealed that natural selection acting on these allelic variations significantly contributes to local environmental adaptation. Our findings elucidate the connection between genetic variation in photosynthetic pathways and their ecological significance, providing valuable insights for optimizing carbon fixation in dynamic environments.},
	language = {en},
	number = {1},
	urldate = {2026-04-10},
	journal = {BMC Plant Biology},
	author = {Liu, Wei and Hao, Ruili and Liu, Li and Hou, Jing and Lei, Mengyu and Han, Yu and Zhu, Mingjia and Liang, Lei and Yu, Le and Si, Huan and Liu, Jianquan and Zan, Yanjun and Ji, Yan},
	month = feb,
	year = {2026},
	keywords = {Arabidopsis thaliana, Local adaptation, Natural variation, Photosynthesis pathways},
	pages = {468},
}

Photosynthesis is the foundational process for carbon fixation in terrestrial ecosystems. Although allelic variations in photosynthesis-related genes have the potential to enhance carbon assimilation efficiency, their functional roles in local adaptation are still not well understood. In this study, we systematically examined the genetic and transcriptomic diversity among globally distributed natural accessions of Arabidopsis thaliana, focusing on 1,103 genes associated with photosynthetic pathways. By assembling chloroplast genomes from 28 representative accessions and integrating whole-genome and transcriptome sequencing data from over 1,000 accessions, we identified extensive allelic variation. Notably, 34.0% of these genes exhibited regulatory variations through expression quantitative trait locus mapping, including key components such as Rubisco and Rubisco activase. Functional validation demonstrated that overexpression of these genes increased cotyledon size and root length. Additionally, genome-wide and transcriptome-wide association studies revealed that natural selection acting on these allelic variations significantly contributes to local environmental adaptation. Our findings elucidate the connection between genetic variation in photosynthetic pathways and their ecological significance, providing valuable insights for optimizing carbon fixation in dynamic environments.

@article{moreno-sanguino_phloem_2026,
	title = {Phloem {Proteomics} to {Identify} {Small} {Open} {Reading} {Frame} ({sORF})-encoded {Peptides} {With} a {Putative} {Role} in the {Control} of {Flowering} {Time} in {Arabidopsis}},
	volume = {178},
	copyright = {© 2026 The Author(s). Physiologia Plantarum published by John Wiley \& Sons Ltd on behalf of Scandinavian Plant Physiology Society.},
	issn = {1399-3054},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ppl.70860},
	doi = {10.1111/ppl.70860},
	abstract = {Phloem sap proteomic studies have previously revealed that phloem sap composition varies during development and upon floral induction. Specific proteins, lipids, messenger RNAs (mRNAs), and peptides have been shown to accumulate at different developmental stages. Peptides are of special interest since they have the potential to act as regulatory molecules controlling plant responses to environmental changes, such as salinity and water stress, plant–microbe interactions, and developmental changes. In this context, we have characterized Arabidopsis thaliana phloem exudates to identify proteins and peptides with the potential to control flowering time, acting as signals fine-tuning plant development. In this work, we present the proteomic profiles of the phloem sap samples during floral transition along with the identification of proteins and peptides that showed changes in abundance during floral transition, suggesting that they could potentially have a role in the control of flowering. Among those, we have described the abundance pattern of the sORF1511 peptide in the phloem sap, which varies upon floral induction. We show that sORF1511 overexpression affects bolting time and alters the expression of several genes involved in the control of flowering time.},
	language = {en},
	number = {2},
	urldate = {2026-04-10},
	journal = {Physiologia Plantarum},
	author = {Moreno-Sanguino, Irene and Collás, Lucía Argente and Samuelsson, Göran and Wingsle, Gunnar and Benlloch, Reyes},
	year = {2026},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ppl.70860},
	keywords = {flowering time, peptides, phloem proteomics, small open reading frames},
	pages = {e70860},
}

Phloem sap proteomic studies have previously revealed that phloem sap composition varies during development and upon floral induction. Specific proteins, lipids, messenger RNAs (mRNAs), and peptides have been shown to accumulate at different developmental stages. Peptides are of special interest since they have the potential to act as regulatory molecules controlling plant responses to environmental changes, such as salinity and water stress, plant–microbe interactions, and developmental changes. In this context, we have characterized Arabidopsis thaliana phloem exudates to identify proteins and peptides with the potential to control flowering time, acting as signals fine-tuning plant development. In this work, we present the proteomic profiles of the phloem sap samples during floral transition along with the identification of proteins and peptides that showed changes in abundance during floral transition, suggesting that they could potentially have a role in the control of flowering. Among those, we have described the abundance pattern of the sORF1511 peptide in the phloem sap, which varies upon floral induction. We show that sORF1511 overexpression affects bolting time and alters the expression of several genes involved in the control of flowering time.

@article{pravdova_rapid_2026,
	title = {Rapid {Analysis} of {NAD} and {Other} {Phosphorylated} {Metabolites} in {Complex} {Biological} {Samples} by {Hydrophilic} {Interaction} {Liquid} {Chromatography} {Coupled} with {Tandem} {Mass} {Spectrometry}},
	issn = {0003-2700},
	url = {https://doi.org/10.1021/acs.analchem.6c00721},
	doi = {10.1021/acs.analchem.6c00721},
	abstract = {Nucleotides and coenzymes play critical roles in energy metabolism and cellular signaling and as building blocks of nucleic acids. This work addresses the challenges in the measurement of the phosphorylated metabolites using hydrophilic interaction liquid chromatography coupled with mass spectrometry, which facilitates the separation and detection of polar metabolites. Here, we present optimized HILIC-MS/MS methods for rapid analysis of polar metabolites including nucleotides and their derivatives in complex biological matrices, such as murine adipose, skeletal, and liver tissues, human plasma, and bacteria. The developed methodologies enable separation of key nucleotides and other phosphorylated metabolites within 6 min and cofactors such as NAD+, NADH, NADP+, and NADPH within 4 min. Validation of these methods demonstrated high accuracy, precision, and sensitivity and stresses the substantial impact of matrix effects. The applicability of the methods was also tested on 13C-labeling experiments with mouse pluripotent stem cells. Additionally, sample pretreatment techniques, such as liquid–liquid extraction and solid-phase extraction, were evaluated as a tool to decrease the negative impact of matrix effects in complex samples. This work enhances the analytical capabilities for nucleotide quantification in metabolomics, facilitating the study of metabolic pathways and disease markers.},
	urldate = {2026-04-10},
	journal = {Analytical Chemistry},
	publisher = {American Chemical Society},
	author = {Pravdova, Adela and Kleinert, Maximilian and Henderson, John and Kafkia, Eleni and Pladevall-Morera, David and Yonamine, Caio Y. and Treebak, Jonas T. and Brodiazhenko, Tetiana and Terenin, Ilya and Zylicz, Jan Jakub and Moritz, Thomas and Hodek, Ondrej},
	month = apr,
	year = {2026},
}

Nucleotides and coenzymes play critical roles in energy metabolism and cellular signaling and as building blocks of nucleic acids. This work addresses the challenges in the measurement of the phosphorylated metabolites using hydrophilic interaction liquid chromatography coupled with mass spectrometry, which facilitates the separation and detection of polar metabolites. Here, we present optimized HILIC-MS/MS methods for rapid analysis of polar metabolites including nucleotides and their derivatives in complex biological matrices, such as murine adipose, skeletal, and liver tissues, human plasma, and bacteria. The developed methodologies enable separation of key nucleotides and other phosphorylated metabolites within 6 min and cofactors such as NAD+, NADH, NADP+, and NADPH within 4 min. Validation of these methods demonstrated high accuracy, precision, and sensitivity and stresses the substantial impact of matrix effects. The applicability of the methods was also tested on 13C-labeling experiments with mouse pluripotent stem cells. Additionally, sample pretreatment techniques, such as liquid–liquid extraction and solid-phase extraction, were evaluated as a tool to decrease the negative impact of matrix effects in complex samples. This work enhances the analytical capabilities for nucleotide quantification in metabolomics, facilitating the study of metabolic pathways and disease markers.

@article{guo_genomic_2026,
	title = {Genomic selection for tar content in {Nicotiana} tabacum: genetic architecture analysis and model evaluation},
	volume = {17},
	issn = {1664-462X},
	shorttitle = {Genomic selection for tar content in {Nicotiana} tabacum},
	url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1721129/full},
	doi = {10.3389/fpls.2026.1721129},
	abstract = {IntroductionDespite its economic importance, reducing tobacco tar content remains challenging due to its complex genetic basis.MethodsHere, we evaluated 436 diverse tobacco accessions to characterize the genetic architecture of tar content and develop an optimized genomic selection strategy. Based on these findings, sixteen genomic prediction models were assessed using five-fold cross-validation.ResultsGenome-wide association analysis detected no major-effect loci, and regional heritability mapping revealed localized enrichment of small-effect variants, particularly on chromosome 17, indicating a predominantly polygenic architecture. rrBLUP achieved the highest prediction accuracy (0.84) with superior computational efficiency, followed closely by GBM (0.83). The robustness of rrBLUP was further confirmed in an independent panel of 36 accessions (Pearson r = 0.888).DiscussionTogether, our results demonstrate that tobacco tar content is governed by dispersed small-effect loci with regional aggregation and establish rrBLUP as a robust and practical model for genome-wide prediction, providing methodological guidance for low-tar tobacco breeding.},
	language = {English},
	urldate = {2026-04-10},
	journal = {Frontiers in Plant Science},
	publisher = {Frontiers},
	author = {Guo, Linjie and Kong, Bo and Chen, Hui and Ren, Min and Cheng, Lirui and Yang, Aiguo and Liang, Lei and Zan, Yanjun and Si, Huan and Cai, Changchun},
	month = mar,
	year = {2026},
	keywords = {genome-wide association analysis, genomic selection, model evaluation, tar content, tobacco},
}

IntroductionDespite its economic importance, reducing tobacco tar content remains challenging due to its complex genetic basis.MethodsHere, we evaluated 436 diverse tobacco accessions to characterize the genetic architecture of tar content and develop an optimized genomic selection strategy. Based on these findings, sixteen genomic prediction models were assessed using five-fold cross-validation.ResultsGenome-wide association analysis detected no major-effect loci, and regional heritability mapping revealed localized enrichment of small-effect variants, particularly on chromosome 17, indicating a predominantly polygenic architecture. rrBLUP achieved the highest prediction accuracy (0.84) with superior computational efficiency, followed closely by GBM (0.83). The robustness of rrBLUP was further confirmed in an independent panel of 36 accessions (Pearson r = 0.888).DiscussionTogether, our results demonstrate that tobacco tar content is governed by dispersed small-effect loci with regional aggregation and establish rrBLUP as a robust and practical model for genome-wide prediction, providing methodological guidance for low-tar tobacco breeding.

@article{yu_construction_2026,
	title = {Construction of genomic prediction models for leaf protein content in \textit{{Nicotiana} tabacum}},
	volume = {243},
	issn = {0926-6690},
	url = {https://www.sciencedirect.com/science/article/pii/S0926669026004772},
	doi = {10.1016/j.indcrop.2026.123090},
	abstract = {With its high soluble protein content, large biomass yield, and ease of cultivation, tobacco leaves show strong potential as a novel protein source for livestock. However, the genetic basis underlying leaf protein content remains poorly understood, necessitating the use of genomic prediction models to screen germplasm resources and accelerate the improvement of this trait in future breeding programs. To address this, we analyzed 2517 tobacco germplasm accessions from the Chinese National Tobacco Germplasm Resource Bank, which represent broad genetic diversity, to investigate the genetic architecture of leaf protein content and construct genomic prediction models. Tobacco leaf protein content exhibited a moderate heritability of 0.16, and association analysis identified a significant peak that explained approximately 1\% of the phenotypic variance. We further evaluated the performance of 16 mainstream genomic prediction models using five-fold cross-validation. Among these models, best linear unbiased prediction (rrBLUP) model achieved the highest prediction accuracy (0.87). In addition, rrBLUP required less computational time and resources compared with other models, highlighting its stability and efficiency. Field validation (Longshan County, Hunan Province, 111°37′45″E, 27°30′52″N) confirmed the robustness and accuracy of our genomic selection model. Overall, our results demonstrate that genomic prediction can enable rapid screening of tobacco germplasm resources and substantially enhance the efficiency of developing high-protein varieties.},
	urldate = {2026-04-10},
	journal = {Industrial Crops and Products},
	author = {Yu, Le and Guo, Linjie and Liu, Li and Ren, Min and Cheng, Lirui and Liang, Lei and Yang, Aiguo and Si, Huan and Cai, Changchun and Zan, Yanjun},
	month = apr,
	year = {2026},
	keywords = {Genome-wide association study, Genomic selection, Germplasm, Leaf protein content, Nicotiana tabacum},
	pages = {123090},
}

With its high soluble protein content, large biomass yield, and ease of cultivation, tobacco leaves show strong potential as a novel protein source for livestock. However, the genetic basis underlying leaf protein content remains poorly understood, necessitating the use of genomic prediction models to screen germplasm resources and accelerate the improvement of this trait in future breeding programs. To address this, we analyzed 2517 tobacco germplasm accessions from the Chinese National Tobacco Germplasm Resource Bank, which represent broad genetic diversity, to investigate the genetic architecture of leaf protein content and construct genomic prediction models. Tobacco leaf protein content exhibited a moderate heritability of 0.16, and association analysis identified a significant peak that explained approximately 1% of the phenotypic variance. We further evaluated the performance of 16 mainstream genomic prediction models using five-fold cross-validation. Among these models, best linear unbiased prediction (rrBLUP) model achieved the highest prediction accuracy (0.87). In addition, rrBLUP required less computational time and resources compared with other models, highlighting its stability and efficiency. Field validation (Longshan County, Hunan Province, 111°37′45″E, 27°30′52″N) confirmed the robustness and accuracy of our genomic selection model. Overall, our results demonstrate that genomic prediction can enable rapid screening of tobacco germplasm resources and substantially enhance the efficiency of developing high-protein varieties.