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For: Wang B, Zhang Y, Haque ME, Xu W, Li F, Liu A. Transcriptomic analyses reveal complex and interconnected sucrose signaling cascades in developing seeds of castor bean. J Plant Physiol 2018;221:1-10. [PMID: 29223877 DOI: 10.1016/j.jplph.2017.11.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 11/29/2017] [Accepted: 11/29/2017] [Indexed: 06/07/2023]

For:	Wang B, Zhang Y, Haque ME, Xu W, Li F, Liu A. Transcriptomic analyses reveal complex and interconnected sucrose signaling cascades in developing seeds of castor bean. J Plant Physiol 2018;221:1-10. [PMID: 29223877 DOI: 10.1016/j.jplph.2017.11.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 11/29/2017] [Accepted: 11/29/2017] [Indexed: 06/07/2023]

Number

Cited by Other Article(s)

Lee JH, Lee U, Yoo JH, Lee TS, Jung JH, Kim HS. AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings. PLANT METHODS 2024;20:44. [PMID: 38493119 PMCID: PMC10943777 DOI: 10.1186/s13007-024-01171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024]

Abstract

BACKGROUND

Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.

RESULTS

Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.

CONCLUSIONS

AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

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Patel R, Menon J, Kumar S, Nóbrega MB, Patel DA, Sakure AA, Vaja MB. Modern day breeding approaches for improvement of castor. Heliyon 2024;10:e27048. [PMID: 38463846 PMCID: PMC10920369 DOI: 10.1016/j.heliyon.2024.e27048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open

Li W, Huang L, Liu N, Pandey MK, Chen Y, Cheng L, Guo J, Yu B, Luo H, Zhou X, Huai D, Chen W, Yan L, Wang X, Lei Y, Varshney RK, Liao B, Jiang H. Key Regulators of Sucrose Metabolism Identified through Comprehensive Comparative Transcriptome Analysis in Peanuts. Int J Mol Sci 2021;22:ijms22147266. [PMID: 34298903 PMCID: PMC8306169 DOI: 10.3390/ijms22147266] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/02/2021] [Accepted: 07/03/2021] [Indexed: 12/02/2022] Open

Abstract

Sucrose content is a crucial indicator of quality and flavor in peanut seed, and there is a lack of clarity on the molecular basis of sucrose metabolism in peanut seed. In this context, we performed a comprehensive comparative transcriptome study on the samples collected at seven seed development stages between a high-sucrose content variety (ICG 12625) and a low-sucrose content variety (Zhonghua 10). The transcriptome analysis identified a total of 8334 genes exhibiting significantly different abundances between the high- and low-sucrose varieties. We identified 28 differentially expressed genes (DEGs) involved in sucrose metabolism in peanut and 12 of these encoded sugars will eventually be exported transporters (SWEETs). The remaining 16 genes encoded enzymes, such as cell wall invertase (CWIN), vacuolar invertase (VIN), cytoplasmic invertase (CIN), cytosolic fructose-bisphosphate aldolase (FBA), cytosolic fructose-1,6-bisphosphate phosphatase (FBP), sucrose synthase (SUS), cytosolic phosphoglucose isomerase (PGI), hexokinase (HK), and sucrose-phosphate phosphatase (SPP). The weighted gene co-expression network analysis (WGCNA) identified seven genes encoding key enzymes (CIN, FBA, FBP, HK, and SPP), three SWEET genes, and 90 transcription factors (TFs) showing a high correlation with sucrose content. Furthermore, upon validation, six of these genes were successfully verified as exhibiting higher expression in high-sucrose recombinant inbred lines (RILs). Our study suggested the key roles of the high expression of SWEETs and enzymes in sucrose synthesis making the genotype ICG 12625 sucrose-rich. This study also provided insights into the molecular basis of sucrose metabolism during seed development and facilitated exploring key candidate genes and molecular breeding for sucrose content in peanuts.

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Affiliation(s)

Weitao Li Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Li Huang Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Nian Liu Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Manish K. Pandey Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; (M.K.P.); (R.K.V.)
Yuning Chen Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Liangqiang Cheng Oil Research Institute of Guizhou Province, Guizhou Academy of Agricultural Science, Guiyang 550006, China;
Jianbin Guo Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Bolun Yu Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Huaiyong Luo Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Xiaojing Zhou Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Dongxin Huai Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Weigang Chen Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Liying Yan Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Xin Wang Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Yong Lei Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Rajeev K. Varshney Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; (M.K.P.); (R.K.V.) State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch 6150, Australia
Boshou Liao Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.)
Huifang Jiang Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (W.L.); (L.H.); (N.L.); (Y.C.); (J.G.); (B.Y.); (H.L.); (X.Z.); (D.H.); (W.C.); (L.Y.); (X.W.); (Y.L.); (B.L.) Correspondence: ; Tel.: +86-27-8671-1550; Fax: +86-27-8681-6451

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Srivastava RK, Shetti NP, Reddy KR, Kwon EE, Nadagouda MN, Aminabhavi TM. Biomass utilization and production of biofuels from carbon neutral materials. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021;276:116731. [PMID: 33607352 DOI: 10.1016/j.envpol.2021.116731] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/01/2021] [Accepted: 02/09/2021] [Indexed: 05/22/2023]

Abstract

The availability of organic matters in vast quantities from the agricultural/industrial practices has long been a significant environmental challenge. These wastes have created global issues in increasing the levels of BOD or COD in water as well as in soil or air segments. Such wastes can be converted into bioenergy using a specific conversion platform in conjunction with the appropriate utilization of the methods such as anaerobic digestion, secondary waste treatment, or efficient hydrolytic breakdown as these can promote bioenergy production to mitigate the environmental issues. By the proper utilization of waste organics and by adopting innovative approaches, one can develop bioenergy processes to meet the energy needs of the society. Waste organic matters from plant origins or other agro-sources, biopolymers, or complex organic matters (cellulose, hemicelluloses, non-consumable starches or proteins) can be used as cheap raw carbon resources to produce biofuels or biogases to fulfill the ever increasing energy demands. Attempts have been made for bioenergy production by biosynthesizing, methanol, n-butanol, ethanol, algal biodiesel, and biohydrogen using different types of organic matters via biotechnological/chemical routes to meet the world's energy need by producing least amount of toxic gases (reduction up to 20-70% in concentration) in order to promote sustainable green environmental growth. This review emphasizes on the nature of available wastes, different strategies for its breakdown or hydrolysis, efficient microbial systems. Some representative examples of biomasses source that are used for bioenergy production by providing critical information are discussed. Furthermore, bioenergy production from the plant-based organic matters and environmental issues are also discussed. Advanced biofuels from the organic matters are discussed with efficient microbial and chemical processes for the promotion of biofuel production from the utilization of plant biomasses.

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Identification and characterization of invertase family genes reveal their roles in vacuolar sucrose metabolism during Pyrus bretschneideri Rehd. fruit development. Genomics 2021;113:1087-1097. [PMID: 33705883 DOI: 10.1016/j.ygeno.2021.01.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/06/2020] [Accepted: 01/24/2021] [Indexed: 11/23/2022]

Kolachevskaya OO, Lomin SN, Arkhipov DV, Romanov GA. Auxins in potato: molecular aspects and emerging roles in tuber formation and stress resistance. PLANT CELL REPORTS 2019;38:681-698. [PMID: 30739137 DOI: 10.1007/s00299-019-02395-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 02/02/2019] [Indexed: 05/04/2023]