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).

<ol>
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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>&lt;em&gt;Vitis vinifera&lt;/em&gt; cv. Corvina gene expression Atlas datamatrix</td><td></td><td></td><td>https://www.researchgate.net/publication/273383414_54sample_&lt;br /&gt;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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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

Universidad San Sebastian

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 Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to ...

Genetics

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect and quantify hundreds of thousands of metabolite features. However, the application of these techniques to a large number of samples is subject to more complex interactions, particularly for genome-wide association studies (GWAS). This protocol describes an optimized metabolic workflow, which combines an efficient and fast sample preparation with the analysis of a large number of samples for legume crop species. This slightly modified extraction method was initially developed for the analysis of plant and animal tissues and is based on extraction in methyl tert-butyl ether: methanol solvent to allow the capture of polar and lipid metabolites. In addition, we provide a step-by-step guide for reducing analytical variations, which are essential for the high-throughput evaluation of metabolic variance in GWAS.

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect ...

Biochemistry

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

Synapses are composed of a presynaptic active zone in the signaling cell and a postsynaptic terminal in the target cell. In the case of chemical synapses, messages are carried by neurotransmitters released from presynaptic terminals and received by receptors on postsynaptic cells. Our previous research in Caenorhabditis elegans has shown that VSM-1 negatively regulates exocytosis. Additionally, analysis of synapses in vsm-1 mutants showed that animals lacking a fully functional VSM-1 have increased synaptic connectivity. Based on these preliminary findings, we hypothesized that C. elegans VSM-1 may play a crucial role in synaptogenesis. To test this hypothesis, double-labeled microarray analysis was performed, and gene expression profiles were determined. First, total RNA was isolated, reversely transcribed to cDNA, and hybridized to the DNA microarrays. Then, in-silico analysis of fluorescent probe hybridization revealed significant induction of many genes coding for members of the major sperm protein family (MSP) in mutants with enhanced synaptogenesis. MSPs are the major component of sperm in C. elegans and appear to signal nematode oocyte maturation and ovulation . In fruit flies, Chai and colleagues 1 demonstrated that MSP-like molecules regulate presynaptic bouton number and size at the neuromuscular junction. Moreover, analysis performed by Tsuda and coworkers 2 suggested that MSPs may act as ligands for Eph receptors and trigger receptor tyrosine kinase signaling cascades. Lastly, real time PCR analysis corroborated that the gene coding for MSP-32 is induced in vsm-1(ok1468) mutants. Taken together, research performed by our laboratory has shown that vsm-1 mutants have a significant increase in synaptic density, which could be mediated by MSP-32 signaling.

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

Microarray analysis was conducted to determine genetic expression profiles in C. elegans, and real-time PCR was used to validate and quantify microarray data.

Synapses are composed of a presynaptic active zone in the signaling cell and a postsynaptic terminal in the target cell. In the case of chemical synapses, ...

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.

Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as ...

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the subcellular distribution of a specific tagged fluorescent marker in the secretory pathway. Arabidopsis is a powerful biological model for genetic studies because of its genome size, generation time, and conservation of molecular mechanisms among kingdoms. The array genotyping as an approach to map the mutation in alternative to the traditional method based on molecular markers is advantageous because it is relatively faster and may allow the mapping of several mutants in a really short time frame. This method allows the identification of proteins that can influence the integrity of any organelle in plants. Here, as an example, we propose a screen to map genes important for the integrity of the endoplasmic reticulum (ER). Our approach, however, can be easily extended to other plant cell organelles (for example see1,2), and thus represents an important step toward understanding the molecular basis governing other subcellular structures.

A fundamental quest in cell biology is to define the mechanisms that underlie the identity of the organelles that make eukaryotic cells. Here we propose a method to identify the genes responsible for the morphological and functional integrity of plant organelles using fluorescence microscopy and next-generation sequencing tools.

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the ...

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during development and in disease states. Single-cell RNA sequencing allows us to comprehensively profile gene expression changes observed in retinal development and disease at a cellular level, while single-cell ATAC-Seq allows analysis of chromatin accessibility and transcription factor binding to be profiled at similar resolution. Here the use of these techniques in the developing retina is described, and MULTI-Seq is demonstrated, where individual samples are labeled with a modified oligonucleotide-lipid complex, enabling researchers to both increase the scope of individual experiments and substantially reduce costs.

Here, the authors showcase the utility of MULTI-seq for phenotyping and subsequent paired scRNA-seq and scATAC-seq in characterizing the transcriptomic and chromatin accessibility profiles in retina.

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during ...

Neuroscience

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&amp;RUN Method and Data Analysis Workflow

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&amp;RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&amp;RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&amp;RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&amp;RUN data analysis.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

This protocol describes an experimental method and data analysis workflow for cleavage under targets and release using nuclease (CUT&amp;RUN) in the human fungal pathogen Candida albicans.

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations ...

Isolation and Transcriptome Analysis of Plant Cell Types

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, which is known as cellular heterogeneity. In recent years, single-cell and cell-type isolation combined with next-generation sequencing (NGS) techniques have become important tools for studying biological processes at single-cell resolution. However, isolating plant cells is relatively more difficult due to the presence of plant cell walls, which limits the application of single-cell approaches in plants. This protocol describes a robust procedure for fluorescence-activated cell sorting (FACS)-based single-cell and cell-type isolation with plant cells, which is suitable for downstream multi-omics analysis and other studies. Using Arabidopsis root fluorescent marker lines, we demonstrate how particular cell types, such as xylem-pole pericycle cells, lateral root initial cells, lateral root cap cells, cortex cells, and endodermal cells, are isolated. Furthermore, an effective downstream transcriptome analysis method using Smart-seq2 is also provided. The cell isolation method and transcriptome analysis techniques can be adapted to other cell types and plant species and have broad application potential in plant science.

The feasibility and effectiveness of high-throughput scRNA-seq methods herald a single-cell era in plant research. Presented here is a robust and complete procedure for isolating specific Arabidopsis thaliana root cell types and subsequent transcriptome library construction and analysis.

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, ...

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

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Chapters in this video

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Determining Genetic Expression Profiles in C. elegans Using Microarray and Real-time PCR

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

Isolation and Transcriptome Analysis of Plant Cell Types