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Zhang H, He X, Munyaneza V, Zhang G, Ye X, Wang C, Shi L, Wang X, Ding G. Acid phosphatase involved in phosphate homeostasis in Brassica napus and the functional analysis of BnaPAP10s. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 208:108389. [PMID: 38377886 DOI: 10.1016/j.plaphy.2024.108389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/21/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024]
Abstract
Purple acid phosphatases (PAPs) are involved in activating the rhizosphere's organic phosphorus (P) and promoting P recycling during plant development, especially under the long-term P deficiency conditions in acid soil. However, the function of BnaPAPs in response to P deficiency stress in Brassica napus has rarely been explored. In this study, we found that the acid phosphatase activities (APA) of rapeseed shoot and root increased under P deficienct conditions. Genome-wide identification found that 82 PAP genes were unevenly distributed on 19 chromosomes in B. napus, which could be divided into eight subfamilies. The segmental duplication events were the main driving force for expansion during evolution, and the gene structures and conserved motifs of most members within the same subfamily were highly conservative. Moreover, the expression levels of 37 and 23 different expressed genes were induced by low P in leaf and root, respectively. BnaA09.PAP10a and BnaC09.PAP10a were identified as candidate genes via interaction networks. Significantly, both BnaPAP10a overexpression lines significantly increased root-related APA and total phosphate concentration under P deficiency and ATP supply conditions, thereby improving plant growth and root length. In summary, our results provided a valuable foundation for further study of BnaPAP functions.
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Affiliation(s)
- Hao Zhang
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Xuyou He
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Venuste Munyaneza
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Guangzeng Zhang
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Xiangsheng Ye
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Chuang Wang
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Lei Shi
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China
| | - Xu Wang
- Institute of Quality Standard and Monitoring Technology for Agro-products of Guangdong Academy of Agricultural Sciences, 510000, Guangdong, China
| | - Guangda Ding
- College of Resources and Environment/Microelement Research Center/Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, 430070, Wuhan, China.
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2
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Guo H, He X, Zhang H, Tan R, Yang J, Xu F, Wang S, Yang C, Ding G. Physiological Responses of Cigar Tobacco Crop to Nitrogen Deficiency and Genome-Wide Characterization of the NtNPF Family Genes. PLANTS (BASEL, SWITZERLAND) 2022; 11:3064. [PMID: 36432793 PMCID: PMC9697317 DOI: 10.3390/plants11223064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
Tobacco prefers nitrate as a nitrogen (N) source. However, little is known about the molecular components responsible for nitrate uptake and the physiological responses of cigar tobacco to N deficiency. In this study, a total of 117 nitrate transporter 1 (NRT1) and peptide transporter (PTR) family (NPF) genes were comprehensively identified and systematically characterized in the whole tobacco genome. The NtNPF members showed significant genetic diversity within and across subfamilies but showed conservation between subfamilies. The NtNPF genes are dispersed unevenly across the chromosomes. The phylogenetic analysis revealed that eight subfamilies of NtNPF genes are tightly grouped with their orthologues in Arabidopsis. The promoter regions of the NtNPF genes had extensive cis-regulatory elements. Twelve core NtNPF genes, which were strongly induced by N limitation, were identified based on the RNA-seq data. Furthermore, N deprivation severely impaired plant growth of two cigar tobaccos, and CX26 may be more sensitive to N deficiency than CX14. Moreover, 12 hub genes respond differently to N deficiency between the two cultivars, indicating the vital roles in regulating N uptake and transport in cigar tobacco. The findings here contribute towards a better knowledge of the NtNPF genes and lay the foundation for further functional analysis of cigar tobacco.
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Affiliation(s)
- Hao Guo
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Xuyou He
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Zhang
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Ronglei Tan
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinpeng Yang
- Tobacco Research Institute of Hubei Province, Wuhan 430030, China
| | - Fangsen Xu
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Sheliang Wang
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Chunlei Yang
- Tobacco Research Institute of Hubei Province, Wuhan 430030, China
| | - Guangda Ding
- Microelement Research Center, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
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3
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Nitrogen Assimilation Related Genes in Brassicanapus: Systematic Characterization and Expression Analysis Identified Hub Genes in Multiple Nutrient Stress Responses. PLANTS 2021; 10:plants10102160. [PMID: 34685969 PMCID: PMC8539475 DOI: 10.3390/plants10102160] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/30/2021] [Accepted: 10/02/2021] [Indexed: 11/17/2022]
Abstract
Nitrogen (N) is an essential macronutrient for plants. However, little is known about the molecular regulation of N assimilation in Brassica napus, one of the most important oil crops worldwide. Here, we carried out a comprehensive genome-wide analysis of the N assimilation related genes (NAGs) in B. napus. A total of 67 NAGs were identified encoding major enzymes involved in N assimilation, including asparagine synthetase (AS), glutamate dehydrogenase (GDH), glutamine oxoglutarate aminotransferase (GOGAT), glutamine synthetase (GS), nitrite reductase (NiR), nitrate reductase (NR). The syntenic analysis revealed that segmental duplication and whole-genome duplication were the main expansion pattern during gene evolution. Each NAG family showed different degrees of differentiation in characterization, gene structure, conserved motifs and cis-elements. Furthermore, diverse responses of NAG to multiple nutrient stresses were observed. Among them, more NAGs were regulated by N deficiency and ammonium toxicity than by phosphorus and potassium deprivations. Moreover, 12 hub genes responding to N starvation were identified, which may play vital roles in N utilization. Taken together, our results provide a basis for further functional research of NAGs in rapeseed N assimilation and also put forward new points in their responses to contrasting nutrient stresses.
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4
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Wei H. Construction of a hierarchical gene regulatory network centered around a transcription factor. Brief Bioinform 2020; 20:1021-1031. [PMID: 29186304 DOI: 10.1093/bib/bbx152] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 10/11/2017] [Indexed: 12/24/2022] Open
Abstract
We have modified a multitude of transcription factors (TFs) in numerous plant species and some animal species, and obtained transgenic lines that exhibit phenotypic alterations. Whenever we observe phenotypic changes in a TF's transgenic lines, we are always eager to identify its target genes, collaborative regulators and even upstream high hierarchical regulators. This issue can be addressed by establishing a multilayered hierarchical gene regulatory network (ML-hGRN) centered around a given TF. In this article, a practical approach for constructing an ML-hGRN centered on a TF using a combined approach of top-down and bottom-up network construction methods is described. Strategies for constructing ML-hGRNs are vitally important, as these networks provide key information to advance our understanding of how biological processes are regulated.
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Affiliation(s)
- Hairong Wei
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang, China.,School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA
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5
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Ge M, Wang Y, Liu Y, Jiang L, He B, Ning L, Du H, Lv Y, Zhou L, Lin F, Zhang T, Liang S, Lu H, Zhao H. The NIN-like protein 5 (ZmNLP5) transcription factor is involved in modulating the nitrogen response in maize. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:353-368. [PMID: 31793100 PMCID: PMC7217196 DOI: 10.1111/tpj.14628] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 11/01/2019] [Accepted: 11/11/2019] [Indexed: 05/12/2023]
Abstract
Maize exhibits marked growth and yield response to supplemental nitrogen (N). Here, we report the functional characterization of a maize NIN-like protein ZmNLP5 as a central hub in a molecular network associated with N metabolism. Predominantly expressed and accumulated in roots and vascular tissues, ZmNLP5 was shown to rapidly respond to nitrate treatment. Under limited N supply, compared with that of wild-type (WT) seedlings, the zmnlp5 mutant seedlings accumulated less nitrate and nitrite in the root tissues and ammonium in the shoot tissues. The zmnlp5 mutant plants accumulated less nitrogen than the WT plants in the ear leaves and seed kernels. Furthermore, the mutants carrying the transgenic ZmNLP5 cDNA fragment significantly increased the nitrate content in the root tissues compared with that of the zmnlp5 mutants. In the zmnlp5 mutant plants, loss of the ZmNLP5 function led to changes in expression for a significant number of genes involved in N signalling and metabolism. We further show that ZmNLP5 directly regulates the expression of nitrite reductase 1.1 (ZmNIR1.1) by binding to the nitrate-responsive cis-element at the 5' UTR of the gene. Interestingly, a natural loss-of-function allele of ZmNLP5 in Mo17 conferred less N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants. Our findings show that ZmNLP5 is involved in mediating the plant response to N in maize.
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Affiliation(s)
- Min Ge
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Yuancong Wang
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Yuhe Liu
- Department of Crop SciencesUniversity of IllinoisUrbana‐ChampaignILUSA
| | - Lu Jiang
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Bing He
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Lihua Ning
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Hongyang Du
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Yuanda Lv
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Ling Zhou
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Feng Lin
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Tifu Zhang
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Shuaiqiang Liang
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Haiyan Lu
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
| | - Han Zhao
- Institute of Crop Germplasm and BiotechnologyProvincial Key Laboratory of AgrobiologyJiangsu Academy of Agricultural SciencesNanjing210014China
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6
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Vijayakumar P, Bakyaraj S, Singaravadivelan A, Vasanthakumar T, Suresh R. Meta-analysis of mammary RNA seq datasets reveals the molecular understanding of bovine lactation biology. Genome 2019; 62:489-501. [PMID: 31071269 DOI: 10.1139/gen-2018-0144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A better understanding of the biology of lactation, both in terms of gene expression and the identification of candidate genes for the production of milk and its components, is made possible by recent advances in RNA seq technology. The purpose of this study was to understand the synthesis of milk components and the molecular pathways involved, as well as to identify candidate genes for milk production traits within whole mammary transcriptomic datasets. We performed a meta-analysis of publically available RNA seq transcriptome datasets of mammary tissue/milk somatic cells. In total, 11 562 genes were commonly identified from all RNA seq based mammary gland transcriptomes. Functional annotation of commonly expressed genes revealed the molecular processes that contribute to the synthesis of fats, proteins, and lactose in mammary secretory cells and the molecular pathways responsible for milk synthesis. In addition, we identified several candidate genes responsible for milk production traits and constructed a gene regulatory network for RNA seq data. In conclusion, this study provides a basic understanding of the lactation biology of cows at the gene expression level.
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Affiliation(s)
- Periyasamy Vijayakumar
- a Veterinary College and Research Institute, TANUVAS, Orathanadu-614 625, Thanjavur, Tamil Nadu, India
| | - Sanniyasi Bakyaraj
- b College of Poultry Production and Management, TANUVAS, Hosur-635 110, Krishnagiri, Tamil Nadu, India
| | | | - Thangavelu Vasanthakumar
- a Veterinary College and Research Institute, TANUVAS, Orathanadu-614 625, Thanjavur, Tamil Nadu, India
| | - Ramalingam Suresh
- a Veterinary College and Research Institute, TANUVAS, Orathanadu-614 625, Thanjavur, Tamil Nadu, India
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7
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Abstract
Nitrogen (N) fertilizer has a major influence on the yield and quality. Understanding and optimising the response of crop plants to nitrogen fertilizer usage is of central importance in enhancing food security and agricultural sustainability. In this study, the analysis of gene regulatory networks reveals multiple genes and biological processes in response to N. Two microarray studies have been used to infer components of the nitrogen-response network. Since they used different array technologies, a map linking the two probe sets to the maize B73 reference genome has been generated to allow comparison. Putative Arabidopsis homologues of maize genes were used to query the Biological General Repository for Interaction Datasets (BioGRID) network, which yielded the potential involvement of three transcription factors (TFs) (GLK5, MADS64 and bZIP108) and a Calcium-dependent protein kinase. An Artificial Neural Network was used to identify influential genes and retrieved bZIP108 and WRKY36 as significant TFs in both microarray studies, along with genes for Asparagine Synthetase, a dual-specific protein kinase and a protein phosphatase. The output from one study also suggested roles for microRNA (miRNA) 399b and Nin-like Protein 15 (NLP15). Co-expression-network analysis of TFs with closely related profiles to known Nitrate-responsive genes identified GLK5, GLK8 and NLP15 as candidate regulators of genes repressed under low Nitrogen conditions, while bZIP108 might play a role in gene activation.
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8
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Huang J, Vendramin S, Shi L, McGinnis KM. Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data. PLANT PHYSIOLOGY 2017; 175:568-583. [PMID: 28768814 PMCID: PMC5580776 DOI: 10.1104/pp.17.00825] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 07/31/2017] [Indexed: 05/22/2023]
Abstract
With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.
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Affiliation(s)
- Ji Huang
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Stefania Vendramin
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Lizhen Shi
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306
| | - Karen M McGinnis
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
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9
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Khan F, Chai HH, Ajmera I, Hodgman C, Mayes S, Lu C. A Transcriptomic Comparison of Two Bambara Groundnut Landraces under Dehydration Stress. Genes (Basel) 2017; 8:E121. [PMID: 28420201 PMCID: PMC5406868 DOI: 10.3390/genes8040121] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 04/06/2017] [Accepted: 04/10/2017] [Indexed: 11/18/2022] Open
Abstract
The ability to grow crops under low-water conditions is a significant advantage in relation to global food security. Bambara groundnut is an underutilised crop grown by subsistence farmers in Africa and is known to survive in regions of water deficit. This study focuses on the analysis of the transcriptomic changes in two bambara groundnut landraces in response to dehydration stress. A cross-species hybridisation approach based on the Soybean Affymetrix GeneChip array has been employed. The differential gene expression analysis of a water-limited treatment, however, showed that the two landraces responded with almost completely different sets of genes. Hence, both landraces with very similar genotypes (as assessed by the hybridisation of genomic DNA onto the Soybean Affymetrix GeneChip) showed contrasting transcriptional behaviour in response to dehydration stress. In addition, both genotypes showed a high expression of dehydration-associated genes, even under water-sufficient conditions. Several gene regulators were identified as potentially important. Some are already known, such as WRKY40, but others may also be considered, namely PRR7, ATAUX2-11, CONSTANS-like 1, MYB60, AGL-83, and a Zinc-finger protein. These data provide a basis for drought trait research in the bambara groundnut, which will facilitate functional genomics studies. An analysis of this dataset has identified that both genotypes appear to be in a dehydration-ready state, even in the absence of dehydration stress, and may have adapted in different ways to achieve drought resistance. This will help in understanding the mechanisms underlying the ability of crops to produce viable yields under drought conditions. In addition, cross-species hybridisation to the soybean microarray has been shown to be informative for investigating the bambara groundnut transcriptome.
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Affiliation(s)
- Faraz Khan
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nottingham LE12 5RD, UK.
| | - Hui Hui Chai
- Crops for the Future, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
| | - Ishan Ajmera
- Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham LE12 5RD, UK.
| | - Charlie Hodgman
- Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Nottingham LE12 5RD, UK.
| | - Sean Mayes
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nottingham LE12 5RD, UK.
- Crops for the Future, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
| | - Chungui Lu
- School of Animal Rural and Environmental Sciences, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK.
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10
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Kong FY, Zhu T, Li N, Cai YF, Zhou K, Wei X, Kou YB, You HJ, Zheng KY, Tang RX. Bioinformatics analysis of the proteins interacting with LASP-1 and their association with HBV-related hepatocellular carcinoma. Sci Rep 2017; 7:44017. [PMID: 28266596 PMCID: PMC5339786 DOI: 10.1038/srep44017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 02/02/2017] [Indexed: 12/11/2022] Open
Abstract
LIM and SH3 domain protein (LASP-1) is responsible for the development of several types of human cancers via the interaction with other proteins; however, the precise biological functions of proteins interacting with LASP-1 are not fully clarified. Although the role of LASP-1 in hepatocarcinogenesis has been reported, the implication of LASP-1 interactors in HBV-related hepatocellular carcinoma (HCC) is not clearly evaluated. We obtained information regarding LASP-1 interactors from public databases and published studies. Via bioinformatics analysis, we found that LASP-1 interactors were related to distinct molecular functions and associated with various biological processes. Through an integrated network analysis of the interaction and pathways of LASP-1 interactors, cross-talk between different proteins and associated pathways was found. In addition, LASP-1 and several its interactors are significantly altered in HBV-related HCC through microarray analysis and could form a complex co-expression network. In the disease, LASP-1 and its interactors were further predicted to be regulated by a complex interaction network composed of different transcription factors. Besides, numerous LASP-1 interactors were associated with various clinical factors and related to the survival and recurrence of HBV-related HCC. Taken together, these results could help enrich our understanding of LASP-1 interactors and their relationships with HBV-related HCC.
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Affiliation(s)
- Fan-Yun Kong
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Zhu
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Nan Li
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yun-Fei Cai
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kai Zhou
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiao Wei
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yan-Bo Kou
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Hong-Juan You
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kui-Yang Zheng
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ren-Xian Tang
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
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11
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Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues. Sci Rep 2016; 6:39655. [PMID: 28000796 PMCID: PMC5175133 DOI: 10.1038/srep39655] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 11/24/2016] [Indexed: 12/23/2022] Open
Abstract
The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.
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12
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Faria JP, Davis JJ, Edirisinghe JN, Taylor RC, Weisenhorn P, Olson RD, Stevens RL, Rocha M, Rocha I, Best AA, DeJongh M, Tintle NL, Parrello B, Overbeek R, Henry CS. Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation. Front Microbiol 2016; 7:1819. [PMID: 27933038 PMCID: PMC5121216 DOI: 10.3389/fmicb.2016.01819] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 10/28/2016] [Indexed: 01/13/2023] Open
Abstract
Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.
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Affiliation(s)
- José P Faria
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Centre of Biological Engineering, University of Minho, Campus de GualtarBraga, Portugal; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
| | - James J Davis
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA
| | - Janaka N Edirisinghe
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA
| | - Ronald C Taylor
- Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory (U.S. Dept. of Energy) Richland, WA, USA
| | - Pamela Weisenhorn
- Mathematics and Computer Science Division, Argonne National Laboratory Argonne, IL, USA
| | - Robert D Olson
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA
| | - Rick L Stevens
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Department of Computer Science, Ryerson Physical Laboratory, University of ChicagoChicago, IL, USA
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus de Gualtar Braga, Portugal
| | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Campus de Gualtar Braga, Portugal
| | - Aaron A Best
- Biology Department, Hope College Holland, MI, USA
| | | | - Nathan L Tintle
- Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA
| | - Bruce Parrello
- Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA
| | - Ross Overbeek
- Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Fellowship for Interpretation of GenomesBurr Ridge, IL, USA
| | - Christopher S Henry
- Computation Institute, University of ChicagoChicago, IL, USA; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA
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Li J, Zhao PX. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach. FRONTIERS IN PLANT SCIENCE 2016; 7:903. [PMID: 27446133 PMCID: PMC4916224 DOI: 10.3389/fpls.2016.00903] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 06/08/2016] [Indexed: 06/06/2023]
Abstract
Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.
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Riccadonna S, Jurman G, Visintainer R, Filosi M, Furlanello C. DTW-MIC Coexpression Networks from Time-Course Data. PLoS One 2016; 11:e0152648. [PMID: 27031641 PMCID: PMC4816347 DOI: 10.1371/journal.pone.0152648] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 03/17/2016] [Indexed: 01/01/2023] Open
Abstract
When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy.
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Affiliation(s)
| | - Giuseppe Jurman
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
| | - Roberto Visintainer
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
| | - Michele Filosi
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
| | - Cesare Furlanello
- Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
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15
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Gasser B, Steiger MG, Mattanovich D. Methanol regulated yeast promoters: production vehicles and toolbox for synthetic biology. Microb Cell Fact 2015; 14:196. [PMID: 26627685 PMCID: PMC4667464 DOI: 10.1186/s12934-015-0387-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 11/25/2015] [Indexed: 11/26/2022] Open
Abstract
Promoters are indispensable elements of a standardized parts collection for synthetic biology. Regulated promoters of a wide variety of well-defined induction ratios and expression strengths are highly interesting for many applications. Exemplarily, we discuss the application of published genome scale transcriptomics data for the primary selection of methanol inducible promoters of the yeast Pichia pastoris (Komagataella sp.). Such a promoter collection can serve as an excellent toolbox for cell and metabolic engineering, and for gene expression to produce heterologous proteins.
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Affiliation(s)
- Brigitte Gasser
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB GmbH), Muthgasse 11, Vienna, Austria.
| | - Matthias G Steiger
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB GmbH), Muthgasse 11, Vienna, Austria.
| | - Diethard Mattanovich
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB GmbH), Muthgasse 11, Vienna, Austria.
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16
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Li J, Dai X, Zhuang Z, Zhao PX. LegumeIP 2.0--a platform for the study of gene function and genome evolution in legumes. Nucleic Acids Res 2015; 44:D1189-94. [PMID: 26578557 PMCID: PMC4702875 DOI: 10.1093/nar/gkv1237] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 11/02/2015] [Indexed: 01/04/2023] Open
Abstract
The LegumeIP 2.0 database hosts large-scale genomics and transcriptomics data and provides integrative bioinformatics tools for the study of gene function and evolution in legumes. Our recent updates in LegumeIP 2.0 include gene and protein sequences, gene models and annotations, syntenic regions, protein families and phylogenetic trees for six legume species: Medicago truncatula, Glycine max (soybean), Lotus japonicus, Phaseolus vulgaris (common bean), Cicer arietinum (chickpea) and Cajanus cajan (pigeon pea) and two outgroup reference species: Arabidopsis thaliana and Poplar trichocarpa. Moreover, the LegumeIP 2.0 features the following new data resources and bioinformatics tools: (i) an integrative gene expression atlas for four model legumes that include 550 array hybridizations from M. truncatula, 962 gene expression profiles of G. max, 276 array hybridizations from L. japonicas and 56 RNA-Seq-based gene expression profiles for C. arietinum. These datasets were manually curated and hierarchically organized based on Experimental Ontology and Plant Ontology so that users can browse, search, and retrieve data for their selected experiments. (ii) New functions/analytical tools to query, mine and visualize large-scale gene sequences, annotations and transcriptome profiles. Users may select a subset of expression experiments and visualize and compare expression profiles for multiple genes. The LegumeIP 2.0 database is freely available to the public at http://plantgrn.noble.org/LegumeIP/.
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Affiliation(s)
- Jun Li
- Bioinformatics Lab, Plant Biology Division, Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA
| | - Xinbin Dai
- Bioinformatics Lab, Plant Biology Division, Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA
| | - Zhaohong Zhuang
- Bioinformatics Lab, Plant Biology Division, Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA
| | - Patrick X Zhao
- Bioinformatics Lab, Plant Biology Division, Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA
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17
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Sircar S, Parekh N. Functional characterization of drought-responsive modules and genes in Oryza sativa: a network-based approach. Front Genet 2015; 6:256. [PMID: 26284112 PMCID: PMC4519691 DOI: 10.3389/fgene.2015.00256] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2015] [Accepted: 07/16/2015] [Indexed: 01/18/2023] Open
Abstract
Drought is one of the major environmental stress conditions affecting the yield of rice across the globe. Unraveling the functional roles of the drought-responsive genes and their underlying molecular mechanisms will provide important leads to improve the yield of rice. Co-expression relationships derived from condition-dependent gene expression data is an effective way to identify the functional associations between genes that are part of the same biological process and may be under similar transcriptional control. For this purpose, vast amount of freely available transcriptomic data may be used. In this study, we consider gene expression data for different tissues and developmental stages in response to drought stress. We analyze the network of co-expressed genes to identify drought-responsive genes modules in a tissue and stage-specific manner based on differential expression and gene enrichment analysis. Taking cues from the systems-level behavior of these modules, we propose two approaches to identify clusters of tightly co-expressed/co-regulated genes. Using graph-centrality measures and differential gene expression, we identify biologically informative genes that lack any functional annotation. We show that using orthologous information from other plant species, the conserved co-expression patterns of the uncharacterized genes can be identified. Presence of a conserved neighborhood enables us to extrapolate functional annotation. Alternatively, we show that single 'guide-gene' approach can help in understanding tissue-specific transcriptional regulation of uncharacterized genes. Finally, we confirm the predicted roles of uncharacterized genes by the analysis of conserved cis-elements and explain the possible roles of these genes toward drought tolerance.
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Affiliation(s)
- Sanchari Sircar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad, India
| | - Nita Parekh
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad, India
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18
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Kennedy RB, Ovsyannikova IG, Lambert ND, Haralambieva IH, Poland GA. The personal touch: strategies toward personalized vaccines and predicting immune responses to them. Expert Rev Vaccines 2014; 13:657-69. [PMID: 24702429 DOI: 10.1586/14760584.2014.905744] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The impact of vaccines on public health and wellbeing has been profound. Smallpox has been eradicated, polio is nearing eradication, and multiple diseases have been eliminated from certain areas of the world. Unfortunately, we now face diseases such as hepatitis C, malaria or tuberculosis, as well as new and re-emerging pathogens for which we lack effective vaccines. Empirical approaches to vaccine development have been successful in the past, but may not be up to the current infectious disease challenges facing us. New, directed approaches to vaccine design, development, and testing need to be developed. Ideally these approaches will capitalize on cutting-edge technologies, advanced analytical and modeling strategies, and up-to-date knowledge of both pathogen and host. These approaches will pay particular attention to the causes of inter-individual variation in vaccine response in order to develop new vaccines tailored to the unique needs of individuals and communities within the population.
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