The work was supported by the University of Verona within the frame of Joint Project 2014 (Characterization of the ATL gene family in grapevine and of its involvement in resistance to Plasmopara viticola).

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The authors have nothing to disclose.

In the genomic era, many gene families have been deeply characterized in several plant species. This information is preliminary to functional studies and provide a frame to investigate further the role of different members in a family. In this context, there is also a need for a nomenclature system allowing to uniquely identify each member in a family, avoiding the redundancy and confusions that may arise when names are assigned independently to different genes by different research groups.
After thoughtful consideration, the grapevine scientific community agreed to name grapevine genes in a family based on similarities with Arabidopsis genes and established a series of rules that must be applied to describe new gene families in grapevine, basically starting from the phylogenetic comparison of nucleotide sequences between grapevine and Arabidopsis family members8. Therefore, only genes that are already annotated and named properly in Arabidopsis can be used in the grapevine nomenclature. The procedure applied for the identification of grapevine ATL orthologues in Arabidopsis described here was therefore carried out solely to fulfill the requirement of assigning the correct grapevine gene family nomenclature. Nevertheless, for other plant species, alternative approaches could be an option. For instance, orthology could be inferred using a bidirectional BLAST hits (BBH), where orthologues are defined as pairs of genes in two species that are more similar (i.e., with highest alignment score) to one another than to any other gene in the other species44. However, this method could miss many orthologues in the case of high rate of gene duplication, such as in plants and animals45. Moreover, in the case of ATL-encoding genes, BBH may retrieve genes lacking the precise ATL-type RING-H2 structure (including the proline residue) or genes that are not annotated and named as ATLs in Arabidopsis. Although from an evolutionary perspective this search may be relevant, the retrieval of orthologues that are not annotated would not have fulfilled the scope of grapevine ATL gene family annotation and nomenclature, and orthologues that are not annotated as ATLs cannot be used to name grapevine family members. Another possibility is to infer orthology based on amino acid instead of nucleotide sequences using InParanoid46, or the most recent Hieranoid 247, albeit such workflows are not expressly recommended by the scientific community.
Expression meta-analysis, which can be defined as a systematic approach to study and combine different publicly available dataset repositories of expression data, allows highlighting shared and different molecular mechanisms in a variety of conditions. Thus, the integration of gene expression information from multiple large-scale transcriptomic experiments can improve the characterization of a gene family, by defining the expression profiles of the family members across experiments, thus minimizing the impact of experiment-specific factors and supporting a more robust assumption of putative gene function in particular processes. However, the use of microarray data requires the integration of expression data obtained with different platforms, considering their own limitations. For instance, in the grapevine Nimblegen microarray platform, a significant proportion of probesets for corresponding genes represented on the array (~ 13,000 genes) have potentially cross-hybridization issues48. In the case of the grapevine ATL family, 15 genes may be affected by such phenomenon. Nevertheless, as discussed by Cramer et al.48, the cross-identification of highly similar gene family members by the same probe could provide interesting information regarding the expression, in specific conditions, not only of a single gene but of two to more genes sharing high sequence similarities, and thus potentially sharing targets and functions. Another potential issue related to microarray datasets is the expression detection limit of microarray platforms, which are not very sensitive. To solve both concerns, i.e., cross-hybridization and signal sensitivity, a possible solution could be to consider only RNAseq expression datasets. However, the meta-analysis of RNAseq data of very large datasets from many different studies can become highly time-consuming and may require many computational resources and high expertise.
Though the approach presented here aims to be exhaustive, it can be certainly further complemented with other analyses. First, to achieve further insights into the molecular evolution and phylogenetic relationship among gene family members in plants, the phylogenetic analysis could be extended building a phylogenetic tree using multiple sequence alignments of family members from several plant species. It is also possible to calculate the evolutionary time of family genes, an estimation of their synonymous and non-synonymous substitution rates during evolution, by determining the values Ks (number of synonymous substitutions per synonymous site in a given period of time) and Ka (number of nonsynonymous substitutions per non-synonymous site in the same period). The Ka/Ks ratio is used to infer the mechanisms of gene duplication events after divergence from their ancestors. A value of Ka/Ks = 1 suggests neutral selection, a Ka/Ks value of &#60; 1 suggests purifying selection, and a Ka/Ks value of &#62; 1 suggests positive selection49. Moreover, if gene structure analysis reveals the presence of introns, the gene family characterization can be further extended to the detection of alternative splicing variants. Indeed, based on a deep survey of RNA-seq data from different tissues, stress conditions and genotypes43, 21 (of the 96) ATLs are strong candidates for alternative splicing events, with potential number of isoforms ranging from 2 to 16 for these ATLs (see Supplementary Table 1). Alternative transcripts frequently produce protein isoforms that vary in amino acid sequences and these changes may alter the cellular properties of proteins and may cause alterations from subtle modulation to loss of function of the gene product. For that reason, alternative splicing events have been involved in important plant functions, including stress response, disease resistance, photosynthesis, and flowering50,51. Integration of ATL gene promoter information that contains putative cis-regulatory elements52 or finding molecules (e.g., microRNA and long non-coding RNA) potentially targeting ATLs53 can also be supplemented to reveal system insights into the complex molecular regulation and interaction of grapevine ATLs.
In conclusion, the choice of the analyses to be performed as well as the procedures to be applied to characterize a new gene family in a plant species are mainly driven by scientific community rules as well as by the scope of gene family identification. It is important to keep in mind the possible subsequent investigation steps, which will exploit the set of information, among which includes gene evolution among plant species, genome structure description, or reliable candidates for selection in functional studies.

During the last decade, much research has been carried out in grapevine genomics. Grapevine is a recognized economically relevant crop, which has become a model for research on fruit development and on the responses of woody plants to biotic and abiotic stresses. In this context, the release of the Vitis vinifera cv. PN40024 genome in 20071 and its updated version in 20112 led to a rapid accumulation of &#34;Omics&#34;-scale data and to a burst of high-throughput studies. Based on the published sequence data, the comprehensive analysis of a given gene family (generally composed of proteins sharing conserved motifs, structural and/or functional similarities and evolutionary relationships), can now be performed to uncover its molecular functions, evolution, and gene expression profiles. These analyses can contribute to understanding how gene families control physiological processes at a genome-wide level.
Many aspects of the plant life cycle are regulated by ubiquitin-mediated degradation of key proteins, which require a fine-tuned turnover to ensure regular cellular processes. Important components of the ubiquitin-mediated degradation process are the E3 ubiquitin ligases, which are responsible for system flexibility, thanks to the recruitment of specific targets3. Accordingly, these enzymes represent a huge gene family, with around 1,400 E3 ligase-encoding genes predicted in Arabidopsis thaliana genome4, each E3 ubiquitin ligase acting for the ubiquitination of specific target proteins. Despite the importance of substrate-specific ubiquitination in cellular regulation in plants, little is known about how the ubiquitination pathway is regulated and target proteins have been identified only in a few cases. The deciphering of such specificity and regulation mechanisms relies first on the identification and characterization of the different components of the system, in particular the E3 ligases. Among ubiquitin ligases, the ATL subfamily is characterized by 91 members identified in A. thaliana displaying a RING-H2 finger domain5,6, some of them playing a role in defense and hormone responses7.
The first crucial step to define the members of a new gene family is the precise definition of the family features, such as consensus motifs, key domains, and protein sequence characteristics. Indeed, the reliable retrieval of all gene family members based on BLAST analysis requires some mandatory sequence characteristics, in particular protein domains responsible for protein function/activity, serving as protein signature. This can be facilitated by previous characterization of the same gene family in other plant species or achieved by analyzing different genes putatively belonging to the same family in different plant species, to isolate common sequences. The family members can then be individually named following common rules settled by international consortia for a given plant species. In grapevine, for instance, such procedure is subjected to the recommendations of the Super-Nomenclature Committee for Grape Gene Annotation (sNCGGa), establishing the construction of a phylogenetic tree including V. vinifera and A. thaliana gene family members to allow gene annotation based on nucleotide sequences8.
Chromosome localization of family members and gene duplication survey allow highlighting the presence of whole-genome or tandem duplicated genes. Such information appears useful to unravel putative gene functions, since it might show functional redundancy or reveal different situations, i.e., non-functionalization, neo-functionalization, or sub-functionalization9. Both neo- and sub-functionalization are important events that create genetic novelty, providing new cellular components for plant adaptation to changing environments10. In particular, duplications of ancestral genes and production of new genes were very frequent during the evolution of the grapevine genome and newly formed genes originating from proximal and tandem duplications in grapevine were more likely to produce new functions11.
Another key factor in deciphering gene family function is the transcriptomic profile. The availability of public databases giving access to a huge amount of transcriptomic data can be thus exploited to assign putative functions to gene family members using large-scale in silico expression analyses. Indeed, the peculiar expression of some genes in specific plant organs or in response to certain stresses can give some hints regarding the putative roles of the corresponding proteins in defined conditions, and give support to hypotheses about possible sub-functionalization of duplicated genes to respond to different challenges. For that purpose, it is important to consider several datasets: these can be already available gene expression matrixes, such as the genome-wide transcriptomic atlas of grapevine organs and developmental stages12, or can be built ad hoc by retrieving transcriptomic datasets for the particular plant species subjected to defined stresses. Moreover, a simple approach using two matrices, one with pairwise similarity data and the other one with pairwise co-expression coefficients can be applied to evaluate the relationships between sequence similarity and expression patterns within a gene family.
The aim of this work is to provide a global approach, defining gene structure, conserved protein motifs, chromosomal location, gene duplications, and expression patterns, as well the prediction of protein localization and phosphorylation sites, to attain an exhaustive characterization of a gene family in plants. Such a comprehensive approach is applied here to the characterization of the ATL E3 ubiquitin ligase family in grapevine. According to the emerging role of ATL subfamily members in regulating key cellular processes7, this work can well assist the identification of strong candidates for functional studies, and eventually unravel the molecular mechanisms governing the adaptation of this important crop to its environment.

<table>
	<tbody>
		<tr>
			<td>Personal computer</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Basic Local Alignment Search Tool (BLAST)</td>
			<td></td>
			<td></td>
			<td>https://blast.ncbi.nlm.nih.gov/Blast.cgi</td>
		</tr>
		<tr>
			<td>Molecular Evolutionary Genetics Analysis (MEGA)</td>
			<td></td>
			<td></td>
			<td>http://www.megasoftware.net/</td>
		</tr>
		<tr>
			<td>Motif-based sequence analysis tools (MEME)</td>
			<td></td>
			<td></td>
			<td>http://meme-suite.org/</td>
		</tr>
		<tr>
			<td>Geneious</td>
			<td>Biomatters Limited</td>
			<td></td>
			<td>http://www.geneious.com/</td>
		</tr>
		<tr>
			<td>ProtParam Tool</td>
			<td></td>
			<td></td>
			<td>http://web.expasy.org/protparam/</td>
		</tr>
		<tr>
			<td>ngLOC</td>
			<td></td>
			<td></td>
			<td>http://genome.unmc.edu/ngLOC/index.html</td>
		</tr>
		<tr>
			<td>TargetP v1.1 Server</td>
			<td></td>
			<td></td>
			<td>http://www.cbs.dtu.dk/services/TargetP/</td>
		</tr>
		<tr>
			<td>Protein Prowler</td>
			<td></td>
			<td></td>
			<td>http://bioinf.scmb.uq.edu.au:8080/pprowler_webapp_1-2/</td>
		</tr>
		<tr>
			<td>MUsite</td>
			<td></td>
			<td></td>
			<td>http://musite.sourceforge.net/</td>
		</tr>
		<tr>
			<td>Pfam</td>
			<td></td>
			<td></td>
			<td>http://pfam.xfam.org/</td>
		</tr>
		<tr>
			<td>TMHMM Server v. 2.0</td>
			<td></td>
			<td></td>
			<td>http://www.cbs.dtu.dk/services/TMHMM/</td>
		</tr>
		<tr>
			<td>ProtScale</td>
			<td></td>
			<td></td>
			<td>http://web.expasy.org/protscale/</td>
		</tr>
		<tr>
			<td>Grape Genome Database (CRIBI)</td>
			<td></td>
			<td></td>
			<td>http://genomes.cribi.unipd.it/grape/</td>
		</tr>
		<tr>
			<td>PhenoGram</td>
			<td></td>
			<td></td>
			<td>http://visualization.ritchielab.psu.edu/phenograms/plot</td>
		</tr>
		<tr>
			<td>MCScanX</td>
			<td></td>
			<td></td>
			<td>http://chibba.pgml.uga.edu/mcscan2/</td>
		</tr>
		<tr>
			<td>Interactive Tree Of Life (iTOL)</td>
			<td></td>
			<td></td>
			<td>http://itol.embl.de/</td>
		</tr>
		<tr>
			<td>UniProt</td>
			<td></td>
			<td></td>
			<td>http://www.uniprot.org/</td>
		</tr>
		<tr>
			<td>Phylogeny.fr</td>
			<td></td>
			<td></td>
			<td>http://www.phylogeny.fr/index.cgi</td>
		</tr>
		<tr>
			<td>MUSCLE</td>
			<td></td>
			<td></td>
			<td>http://www.ebi.ac.uk/Tools/msa/muscle/</td>
		</tr>
		<tr>
			<td>Gblocks Server</td>
			<td></td>
			<td></td>
			<td>http://molevol.cmima.csic.es/castresana/Gblocks_server.html</td>
		</tr>
		<tr>
			<td>Vitis vinifera cv. Corvina gene expression Atlas datamatrix</td>
			<td></td>
			<td></td>
			<td>https://www.researchgate.net/publication/273383414_54sample_datamatrix_geneIDs_Fasoli2012</td>
		</tr>
		<tr>
			<td>Multi Experiment Viewer (MeV)</td>
			<td></td>
			<td></td>
			<td>http://mev.tm4.org/#/welcome</td>
		</tr>
		<tr>
			<td>Sequence Read Archive (SRA)</td>
			<td></td>
			<td></td>
			<td>https://www.ncbi.nlm.nih.gov/sra</td>
		</tr>
		<tr>
			<td>R</td>
			<td></td>
			<td></td>
			<td>https://www.r-project.org/</td>
		</tr>
		<tr>
			<td>EMBOSS Needle (EMBL-EBI)</td>
			<td></td>
			<td></td>
			<td>http://www.ebi.ac.uk/Tools/psa/emboss_needle/</td>
		</tr>
	</tbody>
</table>

comprehensive workflow for the genome-wide identification and expression meta-analysis of the atl e3 ubiquitin ligase gene family in grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

The VIT_05s0077g01970 gene, identified as the most similar to A. thaliana ATL2 (At3g16720) through a BLASTp search, was used as probe to survey the ATL family members in the grapevine genome (V. vinifera cv Pinot Noir PN40024). The PSI-BLAST analysis converged after a few cycles revealing a list of putative genes belonging to the grapevine ATL gene family (Figure 1A). The presence of the canonical RING-H2 domain for each candidate was evaluated by the visual inspection of the MUSCLE alignment of all the entries identified in the analysis (Figure 1B). Only those genes containing the correctly spaced conserved amino acids, the two histidine residues, as well as the proline residue before the third cysteine were considered as ATLs according to the original ATL definition in Arabidopsis5. A total of 96 grapevine genes fulfilled the requirements and were considered for further characterization. Each ATL family member was analyzed to define the specific characteristics of the gene and the corresponding encoded protein, i.e., the presence of other known domain(s) in addition to the RING-H2, transmembrane or hydrophobic rich regions, subcellular localization, and putative phosphorylation sites (Table 1 and Table 2).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56626/56626fig1.jpg" /> 
Figure 1: PSI-BLAST survey and alignment of putative grapevine ATLs. (A) Screenshot of the top 10 hits of the first PSI-BLAST iteration search using the protein sequence VIT_05s0077g01970 as bait. (B) Portion of the alignment of the 96 selected grapevine putative ATLs showing their RING-H2 domain and the corresponding LOGO obtained using a suite of molecular biology (see Table of Materials). Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/56626fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Name</td>
			<td colspan="2">Gene ID</td>
			<td>Gene length (bp)</td>
			<td>Intron number</td>
			<td>UniProt ID</td>
			<td>Protein length (aa)</td>
			<td>RING-H2 motif</td>
			<td>TM/H domain number</td>
			<td>Other domains</td>
		</tr>
		<tr>
			<td colspan="2">VviATL3</td>
			<td colspan="2">VIT_09s0002g00220</td>
			<td>1245</td>
			<td>0</td>
			<td>F6HXK6</td>
			<td>304</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL4[VviRHX1A]</td>
			<td colspan="2">VIT_15s0021g00890</td>
			<td>1827</td>
			<td>3</td>
			<td>D7SM36</td>
			<td>203</td>
			<td>PxC</td>
			<td>0</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL18</td>
			<td colspan="2">VIT_11s0118g00780</td>
			<td>1113</td>
			<td>2</td>
			<td>F6HCI8</td>
			<td>193</td>
			<td>PC</td>
			<td>0</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL23a</td>
			<td colspan="2">VIT_18s0001g01060</td>
			<td>935</td>
			<td>0</td>
			<td>F6H0E4</td>
			<td>114</td>
			<td>PxC</td>
			<td>0.5</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL23b</td>
			<td colspan="2">VIT_18s0001g01050</td>
			<td>399</td>
			<td>0</td>
			<td>E0CQX3</td>
			<td>132</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL24</td>
			<td colspan="2">VIT_17s0000g06460</td>
			<td>4466</td>
			<td>4</td>
			<td>D7SI89</td>
			<td>217</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL27</td>
			<td colspan="2">VIT_00s0264g00020</td>
			<td>2554</td>
			<td>4</td>
			<td>D7T1R5</td>
			<td>235</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL43</td>
			<td colspan="2">VIT_11s0052g00530</td>
			<td>1576</td>
			<td>2</td>
			<td>D7SQD9</td>
			<td>457</td>
			<td>PxC</td>
			<td>3</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL54a</td>
			<td colspan="2">VIT_18s0001g06640</td>
			<td>3221</td>
			<td>1</td>
			<td>F6H0Y5</td>
			<td>405</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL54b</td>
			<td colspan="2">VIT_03s0017g00670</td>
			<td>2774</td>
			<td>1</td>
			<td>F6HTI0</td>
			<td>427</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL55[VviRING1]</td>
			<td colspan="2">VIT_07s0191g00230</td>
			<td>1844</td>
			<td>0</td>
			<td>F6HRP9</td>
			<td>372</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL63</td>
			<td colspan="2">VIT_06s0004g06930</td>
			<td>804</td>
			<td>0</td>
			<td>D7SJU6</td>
			<td>267</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL65</td>
			<td colspan="2">VIT_03s0063g01890</td>
			<td>2068</td>
			<td>0</td>
			<td>F6HQI8</td>
			<td>396</td>
			<td>PxC</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL82</td>
			<td colspan="2">VIT_01s0026g02540</td>
			<td>820</td>
			<td>0</td>
			<td>F6HPQ9</td>
			<td>233</td>
			<td>PC</td>
			<td>0.5</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL83</td>
			<td colspan="2">VIT_17s0000g08400</td>
			<td>1887</td>
			<td>0</td>
			<td>F6GSQ4</td>
			<td>143</td>
			<td>PC</td>
			<td>0</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL84</td>
			<td colspan="2">VIT_06s0004g00120</td>
			<td>1853</td>
			<td>0</td>
			<td>F6GUP5</td>
			<td>368</td>
			<td>PC</td>
			<td>0.5</td>
			<td>zf-RING_3</td>
		</tr>
		<tr>
			<td colspan="2">VviATL85</td>
			<td colspan="2">VIT_12s0034g01400</td>
			<td>786</td>
			<td>0</td>
			<td>F6H965</td>
			<td>261</td>
			<td>PC</td>
			<td>0.5</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL86</td>
			<td colspan="2">VIT_12s0034g01390</td>
			<td>1434</td>
			<td>1</td>
			<td>D7T016</td>
			<td>451</td>
			<td>PC</td>
			<td>0.5</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">VviATL87</td>
			<td colspan="2">VIT_18s0001g03270</td>
			<td>1002</td>
			<td>0</td>
			<td>F6H0T2</td>
			<td>333</td>
			<td>PC</td>
			<td>0.5</td>
			<td>zf-RING_3</td>
		</tr>
		<tr>
			<td colspan="2">VviATL88</td>
			<td colspan="2">VIT_08s0040g00590</td>
			<td>1320</td>
			<td>0</td>
			<td>F6HQR2</td>
			<td>314</td>
			<td>PC</td>
			<td>0</td>
			<td>zf-RING_3</td>
		</tr>
	</tbody>
</table>
Table 1: First 20 VviATL genes and sequence characteristics of the corresponding proteins. TM: transmembrane; H: hydrophobic; 0.5 indicates the presence of one or more hydrophobic regions. Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42.
<img alt="Table 2" src="/files/ftp_upload/56626/56626table2.jpg" /> 
Table 2: Details on the first 20 VviATL gene position in V. vinifera genome, duplication state, and ATL protein physico-chemical characteristics and location. (a) Number of phosphorylation sites predicted by Musite; (b) similar predictions obtained with at least two software are highlighted in bold; ngLOC was used with default settings, whereas TargetP v1.1 and Protein Prowler Subcellular Localization were used with a cut-off of probability of 0.5. NUC, nucleus; MIT, mitochondria; CHL, chloroplast; PLA, plasma membrane; S, secretory pathway (presence of a signal peptide); M, mitochondria; C, chloroplast; O or -, other locations; nd, not determined (i.e., value below the threshold). Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/Table_2.xlsx">Please click here to download this file.</a>
A phylogenetic analysis including the nucleotide sequences of identified grapevine ATL-encoding genes together with the sequences of the reference A. thaliana ATL gene family was used for grapevine ATL nomenclature, according to the guidelines of the sNCGGa8. Ninety-six and 83 nucleotide sequences from V. vinifera and A. thaliana, respectively, were subjected to the Phylogeny.fr pipeline to obtain a reliable phylogenetic tree. The latter sequences were later used to annotate and name grapevine genes on the basis of solid relationships (Figure 2). Following this approach, 13 out of 96 grapevine ATLs received a specific identifier considering their one-to-one orthology with an A. thaliana ATL. The names of the other 83 genes were assigned based on the phylogenetic tree, with a progressive numbering from top to bottom, starting from an ATL gene number higher than the highest number used in A. thaliana.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56626/56626fig2.jpg" /> 
Figure 2: Phylogenetic tree of V. vinifera and A. thaliana ATL E3 ubiquitin ligase-encoding genes. The unrooted tree was generated with the Phylogeny.fr suite (V. vinifera (in green) and the 83 ATL genes of A. thaliana reported in the UniProt database (in yellow). Branch support values were obtained from 100 bootstrap replicates. The red stars indicate the presence of a BCA2 zinc finger (BZF) domain in the corresponding proteins. Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/56626fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Mapping ATL-encoding genes to the grapevine chromosomes showed a wide distribution throughout the genome, suggesting whole-genome duplication as the major evolutionary force in the expansion of ATL gene family in grapevine. Indeed, 31 ATLs were found in homologous chromosomal regions potentially originating from segmental or whole genome duplication events. Moreover, the same analysis highlighted 13 tandemly duplicated genes, one proximal duplicate, and 51 dispersed duplicates (Figure 3). Considering the very large number of duplicated genes in the ATL family, we performed an enrichment test (Fisher&#39;s exact test) to check the preferential retention of the duplicated genes during the genome fractionation. With a p-value &#60; 0.001, this test confirmed the hypothesis that duplicated ATL genes were retained more than randomly expected, suggesting a role for the ATL gene family during grapevine adaptation and evolution.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56626/56626fig3.jpg" /> 
Figure 3: Grapevine ATL-encoding gene distribution on V. vinifera chromosomes and duplication state. The 96 grapevine ATL genes with exact chromosomal information available in the database were mapped to the 19 V. vinifera chromosomes. The colors indicate the original duplication event. Vertical black lines and red lines identify pairs derived from tandem duplications and whole genome duplications, respectively. Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/56626fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To further investigate the putative biological functions of the ATLs in grapevine, a meta-analysis was carried out on the V. vinifera cv. Corvina global gene expression Atlas12. The dataset includes whole-genome expression values of 54 different grapevine organs and developmental stages and was used to perform a hierarchical bi-clustered analysis. Results not only confirmed that all the 96 ATLs were expressed in at least one of the 54 tissues/stages, but also pointed out the presence of five main clusters of expression profiles (Figure 4A). Briefly, clusters A and E showed opposite behaviors, in particular the first is characterized by a general downregulation of ATL genes in juvenile samples, including early berry stages, young leaf, tendrils, inflorescence, and most of the bud stages. On the other hand, in the same cluster A, mature samples such as berries at ripening and post-harvest withering stages, woody tissues, and late stages of seed development ATL genes showed a predominant upregulation. Genes in Cluster C were mainly downregulated in most of the samples, while ATL genes in cluster D were often upregulated at late stages of berry development. Finally, cluster B did not show any relevant variation in the expression profiles.
A similar approach was applied to study the expression of grapevine ATL family members in response to biotic and abiotic stresses, using specific datasets built for this purpose. A huge amount of expression data deriving from microarray and RNA-seq experiments are available from public access databases such as Gene Expression Omnibus (GEO) and ArrayExpress. Once collected and conveniently normalized, the information was exploited for further insights into the potential function of ATLs in plant response to stresses. Analyzing the expression profiles of grapevine ATLs in response to biotic stresses revealed that 62 out of 96 transcripts showed a significant modulation (log2 fold-change (FC) &#62;|0.5|) in at least two conditions, with a false discovery rate (FDR) &#60; 0.05 (Figure 4B). The number increases to 81 considering only the FDR threshold in a single condition. These results strongly suggested a direct involvement of the ATL gene family in the response to pathogens also in grapevine. In particular, a group of 12 genes (VviATL3-27-54b-55-90-97-123-144-148-149-156) were strongly upregulated in response to most pathogens, including biotrophic and necrotrophic fungi and herbivores, and thus, deserve attention for further functional analyses.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56626/56626fig4.jpg" /> 
Figure 4: Hierarchical clusteringof ATL gene expression in grapevine Atlas and in grapevine biotic stress-related dataset. (A) The log transformed expression values of grapevine ATL genes in the grapevine Atlas12 were used for hierarchical cluster analysis based on Pearson&#39;s distance metric. The color scale represents higher (red) or lower (green) expression levels with respect to the median transcript abundance of each gene across all samples. Letters A to E on the right side indicate the different clusters identified. AB: after burst; B: burst; bud-W: winter bud; F: flowering; FB: flowering begins; FS: fruit set; G: green; MR: mid-ripening; PFS: post-fruit set; PHWI-II-III: post-harvest withering 1, 2 and 3 months; R: ripening; S: senescent; stem-W: woody stem; V: veraison; WD: well developed; Y: young. (B) The color scale represents increased (red) or decreased (blue) fold changes of grapevine ATL gene expression in infected samples compared to controls for each condition. Asterisks indicate the significant differential expression (FDR &#60; 0.05) of each ATL under the corresponding conditions. Reproduced from Ariani et al. licensed under a Creative Commons Attribution 4.0 International License42. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/56626fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Table 1: ATL genes candidates for alternative splicing. (a) ATL gene ID according to the V1 grape gene prediction and annotation, (b) ATL gene ID according to the V2 grape gene prediction and annotation43, (c) number of putative ATL alternative splicing variants, (d) information on coding sequence of each putative ATL variant. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/Supplementary_Table_I.xlsx">Please click here to download this file.</a>
Supplementary Table 2: <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/Supplementary_Table_II.xlsx">Please click here to download this file.</a>
Supplementary File 1: <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56626/Supplementary_File_1.txt">Please click here to download this file.</a>

Watch this Scientific Journal Video about Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine at JoVE.com

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

comprehensive-workflow-for-genome-wide-identification-expression-meta

Nanyang Technological University

Research

JoVE Journal

Biology

10.4K Views.  Università degli Studi di Verona. This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis Tóxicos in Levadura (ATL) E3 ubiquitin ligases.

Video: Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

The Preparation of Primary Hematopoietic Cell Cultures From Murine Bone Marrow for Electroporation

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This procedure describes how to establish primary hematopoietic cell cultures from murine bone marrow and is followed by transfection using the Gene Pulser MXCell electroporation system.

It is becoming increasingly apparent that electroporation is the most effective way to introduce plasmid DNA or siRNA into primary cells. The Gene Pulser ...

Spinal Cord Electrophysiology

The neonatal mouse spinal cord is a model for studying the development of neural circuitries and locomotor movement. We demonstrate the spinal cord dissection and preparation of recording bath artificial cerebrospinal fluid used for locomotor studies. Once dissected, the spinal cord ventral nerve roots can be attached to a recording electrode to record the electrophysiologic signals of the central pattern generating circuitry within the lumbar cord.

A demonstration of the isolation of neonatal mouse spinal cord for electrophysiologic studies.

The neonatal mouse spinal cord is a model for studying the development of neural circuitries and locomotor movement. We demonstrate the spinal cord ...

Using the Gene Pulser MXcell Electroporation System to Transfect Primary Cells with High Efficiency

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This procedure shows how to use the Gene Pulser MXcell electroporation system to rapidly and easily identify the best electroporation conditions for mouse embryonic fibroblasts (MEFs) or other primary cells. Considerations for troubleshooting are also discussed in the associated video.

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

The use of siRNA mediated gene knockdown is continuing to be an important tool in studies of gene expression.  siRNA studies are being conducted not only to study the effects of downregulating single genes, but also to interrogate signaling pathways and other complex interaction networks.  These pathway analyses require both the use of relevant cellular models and methods that cause less perturbation to the cellular physiology.  Electroporation is increasingly being used as an effective way to introduce siRNA and other nucleic acids into difficult to transfect cell lines and primary cells without altering the signaling pathway under investigation. There are multiple critical steps to a successful siRNA experiment, and there are ways to simplify the work while improving the data quality at several experimental stages.  To help you get started with your siRNA mediated gene knockdown project, we will demonstrate how to perform a pathway study complete from collecting and counting the cells prior to electroporation through post transfection real-time PCR gene expression analysis.  The following study investigates the role of the transcriptional activator STAT6 in IL-4 dependent gene expression of CCL17 in a Burkitt lymphoma cell line (Namalwa).  The techniques demonstrated are useful for a wide range of siRNA-based experiments on both adherent and suspension cells.  We will also show how to streamline cell counting with the TC10 automated cell counter, how to electroporate multiple samples simultaneously using the MXcell electroporation system, and how to simultaneously assess RNA quality and quantity with the Experion automated electrophoresis system.

This procedure describes a quick and easy workflow to introduce siRNA into difficult to transfect cell lines and follow gene expression by real-time PCR. Use of an automated cell counter, multi-well electroporation plate, and automated electrophoresis station provide quick and reliable results without the need for expensive robotic handling.

The use of siRNA mediated gene knockdown is continuing to be an important tool in studies of gene expression.  siRNA studies are being conducted not only ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

In vitro Reconstitution of the Active T. castaneum Telomerase

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more than a decade. Here, we present methods for the isolation of the recombinant Tribolium castaneum TERT (TcTERT) and the reconstitution of the active T. castaneum telomerase ribonucleoprotein (RNP) complex in vitro.
Telomerase is a specialized reverse transcriptase1 that adds short DNA repeats, called telomeres, to the 3' end of linear chromosomes2 that serve to protect them from end-to-end fusion and degradation. Following DNA replication, a short segment is lost at the end of the chromosome3 and without telomerase, cells continue dividing until eventually reaching their Hayflick Limit4. Additionally, telomerase is dormant in most somatic cells5 in adults, but is active in cancer cells6 where it promotes cell immortality7.
The minimal telomerase enzyme consists of two core components: the protein subunit (TERT), which comprises the catalytic subunit of the enzyme and an integral RNA component (TER), which contains the template TERT uses to synthesize telomeres8,9. Prior to 2008, only structures for individual telomerase domains had been solved10,11. A major breakthrough in this field came from the determination of the crystal structure of the active12, catalytic subunit of T. castaneum telomerase, TcTERT1.
Here, we present methods for producing large quantities of the active, soluble TcTERT for structural and biochemical studies, and the reconstitution of the telomerase RNP complex in vitro for telomerase activity assays. An overview of the experimental methods used is shown in Figure 1.

In vitro Reconstitution of the Active T. castaneum Telomerase

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more than a decade. Here, we present methods for the isolation of the recombinant Tribolium castaneum TERT (TcTERT) and the reconstitution of the active T. castaneum telomerase ribonucleoprotein (RNP) complex in vitro.

Efforts to isolate the catalytic subunit of telomerase, TERT, in sufficient quantities for structural studies, have been met with limited success for more ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia

Hypoxia or lowering of the oxygen availability is involved in many physiological and pathological processes. At the molecular level, cells initiate a particular transcriptional program in order to mount an appropriate and coordinated cellular response. The cell possesses several oxygen sensor enzymes that require molecular oxygen as cofactor for their activity. These range from prolyl-hydroxylases to histone demethylases. The majority of studies analyzing cellular responses to hypoxia are based on cellular populations and average studies, and as such single cell analysis of hypoxic cells are seldom performed. Here we describe a method of analysis of global RNA synthesis at the single cell level in hypoxia by using Click-iT RNA imaging kits in an oxygen controlled workstation, followed by microscopy analysis and quantification.&nbsp; Using cancer cells exposed to hypoxia for different lengths of time, RNA is labeled and measured in each cell. This analysis allows the visualization of temporal and cell-to-cell changes in global RNA synthesis following hypoxic stress.

We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.

Hypoxia or lowering of the oxygen availability is involved in many physiological and pathological processes. At the molecular level, cells initiate a ...

siRNA Screening to Identify Ubiquitin and Ubiquitin-like System Regulators of Biological Pathways in Cultured Mammalian Cells

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that regulates diverse biological pathways including the hypoxia response, proteostasis, the DNA damage response and transcription.&#160; To better understand how UBLs regulate pathways relevant to human disease, we have compiled a human siRNA &#8220;ubiquitome&#8221; library consisting of 1,186 siRNA duplex pools targeting all known and predicted components of UBL system pathways. This library can be screened against a range of cell lines expressing reporters of diverse biological pathways to determine which UBL components act as positive or negative regulators of the pathway in question.&#160; Here, we describe a protocol utilizing this library to identify ubiquitome-regulators of the HIF1A-mediated cellular response to hypoxia using a transcription-based luciferase reporter.&#160; An initial assay development stage is performed to establish suitable screening parameters of the cell line before performing the screen in three stages: primary, secondary and tertiary/deconvolution screening. &#160;The use of targeted over whole genome siRNA libraries is becoming increasingly popular as it offers the advantage of reporting only on members of the pathway with which the investigators are most interested.&#160; Despite inherent limitations of siRNA screening, in particular false-positives caused by siRNA off-target effects, the identification of genuine novel regulators of the pathways in question outweigh these shortcomings, which can be overcome by performing a series of carefully undertaken control experiments.

Here, we describe a methodology to perform a targeted siRNA &#8220;ubiquitome&#8221; screen to identify novel ubiquitin and ubiquitin-like regulators of the HIF1A-mediated cellular response to hypoxia.&#160; This can be adapted to any biological pathway where a robust read out of reporter activity is available.

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that ...