Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

doi:10.1186/s12864-020-07287-2

. 2021 Jan 6;22(1):18.

doi: 10.1186/s12864-020-07287-2.

Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

Nancy Dong¹, Julia Bandura¹, Zhaolei Zhang², Yan Wang^{3

4}, Karine Labadie⁵, Benjamin Noel⁶, Angus Davison⁷, Joris M Koene⁸, Hong-Shuo Sun^{1

9}, Marie-Agnès Coutellec¹⁰, Zhong-Ping Feng¹¹

Affiliations

¹ Department of Physiology, University of Toronto, 3308 MSB, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
² Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada.
³ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S 3B2, Canada.
⁴ Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, M1C 1A4, Canada.
⁵ Genoscope, Institut de biologie François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, BP5706, 91057, Evry, France.
⁶ Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry, Université Paris-Saclay, 91057, Evry, France.
⁷ School of Life Sciences, University of Nottingham, University Park, Nottingham, UK, NG7 2RD, UK.
⁸ Department of Ecological Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands.
⁹ Department of Surgery, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
¹⁰ ESE, Ecology and Ecosystem Health, INRAE, Agrocampus Ouest, 35042, Rennes, France.
¹¹ Department of Physiology, University of Toronto, 3308 MSB, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. zp.feng@utoronto.ca.

PMID: 33407100
PMCID: PMC7789530
DOI: 10.1186/s12864-020-07287-2

Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

Nancy Dong et al. BMC Genomics. 2021.

. 2021 Jan 6;22(1):18.

doi: 10.1186/s12864-020-07287-2.

Authors

Affiliations

¹ Department of Physiology, University of Toronto, 3308 MSB, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
² Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada.
³ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S 3B2, Canada.
⁴ Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, M1C 1A4, Canada.
⁵ Genoscope, Institut de biologie François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, BP5706, 91057, Evry, France.
⁶ Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry, Université Paris-Saclay, 91057, Evry, France.
⁷ School of Life Sciences, University of Nottingham, University Park, Nottingham, UK, NG7 2RD, UK.
⁸ Department of Ecological Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands.
⁹ Department of Surgery, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
¹⁰ ESE, Ecology and Ecosystem Health, INRAE, Agrocampus Ouest, 35042, Rennes, France.
¹¹ Department of Physiology, University of Toronto, 3308 MSB, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. zp.feng@utoronto.ca.

PMID: 33407100
PMCID: PMC7789530
DOI: 10.1186/s12864-020-07287-2

Abstract

Background: The pond snail Lymnaea stagnalis (L. stagnalis) has been widely used as a model organism in neurobiology, ecotoxicology, and parasitology due to the relative simplicity of its central nervous system (CNS). However, its usefulness is restricted by a limited availability of transcriptome data. While sequence information for the L. stagnalis CNS transcripts has been obtained from EST libraries and a de novo RNA-seq assembly, the quality of these assemblies is limited by a combination of low coverage of EST libraries, the fragmented nature of de novo assemblies, and lack of reference genome.

Results: In this study, taking advantage of the recent availability of a preliminary L. stagnalis genome, we generated an RNA-seq library from the adult L. stagnalis CNS, using a combination of genome-guided and de novo assembly programs to identify 17,832 protein-coding L. stagnalis transcripts. We combined our library with existing resources to produce a transcript set with greater sequence length, completeness, and diversity than previously available ones. Using our assembly and functional domain analysis, we profiled L. stagnalis CNS transcripts encoding ion channels and ionotropic receptors, which are key proteins for CNS function, and compared their sequences to other vertebrate and invertebrate model organisms. Interestingly, L. stagnalis transcripts encoding numerous putative Ca²⁺ channels showed the most sequence similarity to those of Mus musculus, Danio rerio, Xenopus tropicalis, Drosophila melanogaster, and Caenorhabditis elegans, suggesting that many calcium channel-related signaling pathways may be evolutionarily conserved.

Conclusions: Our study provides the most thorough characterization to date of the L. stagnalis transcriptome and provides insights into differences between vertebrates and invertebrates in CNS transcript diversity, according to function and protein class. Furthermore, this study provides a complete characterization of the ion channels of Lymnaea stagnalis, opening new avenues for future research on fundamental neurobiological processes in this model system.

Keywords: CNS; Ion channels; Ionotropic receptors; Lymnaea stagnalis; Transcriptome; de novo assembly.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Workflow of quality control, assembly and prediction of protein-coding transcripts in the *L. stagnalis* CNS

**Fig. 2**
Comparison of predicted *L. stagnalis* protein-coding transcripts identified in the current assembly with those identified in the previously published EST library [49] and de novo assembly [50]. a Distribution of translated amino acid sequence lengths of predicted protein-coding transcripts as a percentage of the total number of transcripts in previous and current assemblies. The current assembly contains a greater percentage of longer transcripts than previous assemblies. b Overlapping and distinct Nr database hits found in predicted protein-coding sequences in previous and current assemblies. The current assembly defines a greater number of new and distinct hits than previous assemblies

**Fig. 3**
Comparison of KOG annotations of protein-coding transcripts expressed in the CNS of key vertebrate and invertebrate neuroscience model organisms (E-value <1E-5). Markedly, compared to invertebrates, vertebrate model organisms display increased percentage of transcripts involved in transcription, intracellular trafficking, and cytoskeleton. Meanwhile, compared to vertebrates, invertebrate model organisms display increased percentage of transcripts involved in energy production and conversion, amino acid transport and metabolism, carbohydrate transport and metabolism, and lipid transport and metabolism

**Fig. 4**
Membership of species in the orthogroups identified amongst protein-coding transcripts expressed in the CNS of *M. musculus*, *X. tropicalis,* *D. rerio*, *L. stagnalis*, *D. melanogaster*, and *C. elegans*. For clarity, only the 20 largest sets of shared orthogroups are shown. Dark and grey circles represent presence and absence, respectively, of the given species in each set of shared orthogroups. The number of orthogroups that each species is present in is shown in the inset bar graph. All species share the greatest number of orthogroups, followed by all vertebrates. Strikingly, of all three invertebrate species, *L. stagnalis* shares the greatest number of orthogroups with the three vertebrate species (1111)

**Fig. 5**
Enriched GO terms of genes encoded by transcripts in the 3729 orthogroups shared amongst *M. musculus*, *X. tropicalis,* *D. rerio*, *L. stagnalis*, *D. melanogaster*, and *C. elegans*. Using the REVIGO tool, GO terms in the “Biological process” (a), “Cellular components” (b) and “Molecular function” (c) classes were clustered in two-dimensional space via the SimRel semantic similarity measure to summarize their relationship to each other and coloured according to their positions on the semantic x-axis. Consequently, related GO terms are closer on the plot and more similar in colour. The arbitrary semantic space units on either axis have no intrinsic meaning. Orthogroups common amongst all six organisms commonly involved a intracellular signaling pathways (red/orange), b ionotropic glutamate receptor signaling (blue), and c ion transport (red/orange), among others

**Fig. 6**
Enriched GO terms of genes encoded by transcripts in the 2154 orthogroups shared amongst only *M. musculus*, *X. tropicalis,* and *D. rerio*. GO terms in the “Biological process” (a), “Cellular components” (b) and “Molecular function” (c) classes are clustered and coloured in the same manner as described in Fig. 5. Orthogroups unique to these vertebrates commonly involved a intercellular communication (blue/green), b plasma membrane structure (blue), and c G-protein coupled, peptide, and cytokine receptor activity (red/orange), among others

**Fig. 7**
Enriched GO terms of genes encoded by transcripts in the 88 orthogroups shared amongst only *L. stagnalis*, *D. melanogaster*, and *C. elegans*. GO terms in the “Biological process” (a), “Cellular components” (b) and “Molecular function” (c) classes are clustered and coloured in the same manner as described in Fig. 5. Orthogroups unique to these invertebrates commonly involved a voltage-gated calcium channel activity regulation (blue), b plasma membrane structure (red), and c G-protein coupled peptide and peptide receptor activity, among others

**Fig. 8**
Ionotropic neurotransmitter receptor families identified in the *L. stagnalis* CNS transcriptome assembly. The tree with the highest log likelihood (− 7343.03) is shown. The percentage of trees in which the associated taxa clustered together (bootstrap values) is shown next to the branches. The tree is rooted at the mid-point and is drawn to scale, with branch lengths measured in the number of substitutions per site (see scale in bottom left). This analysis involved 33 amino acid sequences. There were a total of 133 positions in the final dataset. Evolutionary analyses were conducted in MEGA X (ver. 10.1.8) and tree visualization and annotation was conducted using the ggtree package (ver. 2.0.4) for R (ver. 3.6.3). This *L. stagnalis* transcriptome assembly identifies a wide diversity of putative acetylcholine, GABA/glycine, and glutamate receptors. The accession numbers and BLAST information of transcripts represented in this tree are provided in Tables S20, S21 and S22

**Fig. 9**
K⁺ channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 36 amino acid sequences and yielded a total of 75 positions in the final dataset, resulting in a tree with highest log likelihood of − 4511.88. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S23. A wide diversity of putative *L. stagnalis* K⁺ channel subtypes and families are identified, including a potentially novel family of voltage-gated K⁺ channels (denoted by red label, #)

**Fig. 10**
Ca²⁺ channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 24 amino acid sequences and yielded a total of 4018 positions in the final dataset, resulting in a tree with highest log likelihood of − 47,232.52. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S24. A diverse set of putative *L. stagnalis* Ca²⁺ channel subtypes and families are identified, including subtypes not yet cloned from *L. stagnalis*, such as Orai-2

**Fig. 11**
Na⁺ channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 12 amino acid sequences and yielded a total of 345 positions in the final dataset, resulting in a tree with highest log likelihood of − 6272.64. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S25. A variety of putative *L. stagnalis* Na⁺ channel subtypes and families are identified

**Fig. 12**
Cl⁻ channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 18 amino acid sequences and yielded a total of 1233 positions in the final dataset, resulting in a tree with highest log likelihood of − 22,161.37. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S26. A variety of putative *L. stagnalis* Cl⁻ channel subtypes and families are identified, of which anoctamin, bestrophin, and intracellular chloride channel protein have not yet been cloned from *L. stagnalis*

**Fig. 13**
Cation channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 17 amino acid sequences and yielded a total of 8 positions in the final dataset, resulting in a tree with highest log likelihood of − 339.65. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S27. The majority of these diverse cation channels have not been cloned from *L. stagnalis*

**Fig. 14**
Transient receptor potential (TRP) channel subtypes and families identified in the *L. stagnalis* CNS transcriptome assembly. The tree was generated as described in Fig. 8, where the analysis involved 33 amino acid sequences and yielded a total of 133 positions in the final dataset, resulting in a tree with highest log likelihood of − 7343.03. The accession numbers and BLAST information of transcripts represented in this tree are provided in Table S28. This suggests a vertebrate-like diversity of TRP channels expressed by *L. stagnalis*

**Fig. 15**
Heat maps of protein sequence similarity as measured by BLASTP bitscore between putative *L. stagnalis* ion channel/ionotropic receptor transcripts and homologs in mouse, *X. tropicalis,* zebrafish, fruit fly and *C. elegans*. Each bitscore is standardized by the average and standard deviation of all bitscores in the matrix. Each row corresponds to a single *L. stagnalis* transcript and each column to a species, such that each cell represents a standardized bitscore. Colour of each cell is proportional to the bitscore, where a darker colour indicates higher bitscore and consequently sequence similarity. A, Transcripts are sorted by the ion channel/ionotropic receptor class and species are sorted in descending order by the absolute sum of bitscores of its homologs, i.e. species with the highest bitscores across all transcripts is on the left of the heatmap. Transcripts encoding Ca²⁺ and cation channels appear to share the greatest sequence similarity across all six species. B1 Transcripts are sorted in descending order by the absolute sum of bitscores, i.e. transcripts with the highest sequence similarities across species are at the top of the heatmap. B2 The top 20 transcripts thus ranked are labeled by transcript ID and annotation. The colour scale is adjusted as compared to in B1 to increase contrast between cells. Interactive Clustergrammer heat map can be accessed at http://amp.pharm.mssm.edu/clustergrammer/viz/5b6ba1ca7226c37ceda3505b/LS_channel_comparisons.txt. The majority of these top 20 transcripts encode calcium channels

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References

1. Spencer GE, Kazmi MH, Syed NI, Lukowiak K. Changes in the activity of a CpG neuron after the reinforcement of an operantly conditioned behavior in Lymnaea. J Neurophysiol. 2002;88(4):1915–1923. doi: 10.1152/jn.2002.88.4.1915. - DOI - PubMed
1. Scheibenstock A, Krygier D, Haque Z, Syed N, Lukowiak K. The soma of RPeD1 must be present for long-term memory formation of associative learning in Lymnaea. J Neurophysiol. 2002;88(4):1584–1591. doi: 10.1152/jn.2002.88.4.1584. - DOI - PubMed
1. Sunada H, Watanabe T, Hatakeyama D, Lee S, Forest J, Sakakibara M, Ito E, Lukowiak K. Pharmacological effects of cannabinoids on learning and memory in Lymnaea. J Exp Biol. 2017;220(17):3026–3038. doi: 10.1242/jeb.159038. - DOI - PubMed
1. Syed N, Bulloch A, Lukowiak K. In vitro reconstruction of the respiratory central pattern generator of the mollusk Lymnaea. Science. 1990;250(4978):282–285. doi: 10.1126/science.2218532. - DOI - PubMed
1. Elliott CJH, Vehovszky Á. Polycyclic neuromodulation of the feeding rhythm of the pond snail Lymnaea stagnalis by the intrinsic octopaminergic interneuron, OC. Brain Res. 2000;887(1):63–69. doi: 10.1016/S0006-8993(00)02968-1. - DOI - PubMed

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[1] Spencer GE, Kazmi MH, Syed NI, Lukowiak K. Changes in the activity of a CpG neuron after the reinforcement of an operantly conditioned behavior in Lymnaea. J Neurophysiol. 2002;88(4):1915–1923. doi: 10.1152/jn.2002.88.4.1915. - DOI - PubMed

[2] Spencer GE, Kazmi MH, Syed NI, Lukowiak K. Changes in the activity of a CpG neuron after the reinforcement of an operantly conditioned behavior in Lymnaea. J Neurophysiol. 2002;88(4):1915–1923. doi: 10.1152/jn.2002.88.4.1915. - DOI - PubMed

[3] Scheibenstock A, Krygier D, Haque Z, Syed N, Lukowiak K. The soma of RPeD1 must be present for long-term memory formation of associative learning in Lymnaea. J Neurophysiol. 2002;88(4):1584–1591. doi: 10.1152/jn.2002.88.4.1584. - DOI - PubMed

[4] Scheibenstock A, Krygier D, Haque Z, Syed N, Lukowiak K. The soma of RPeD1 must be present for long-term memory formation of associative learning in Lymnaea. J Neurophysiol. 2002;88(4):1584–1591. doi: 10.1152/jn.2002.88.4.1584. - DOI - PubMed

[5] Sunada H, Watanabe T, Hatakeyama D, Lee S, Forest J, Sakakibara M, Ito E, Lukowiak K. Pharmacological effects of cannabinoids on learning and memory in Lymnaea. J Exp Biol. 2017;220(17):3026–3038. doi: 10.1242/jeb.159038. - DOI - PubMed

[6] Sunada H, Watanabe T, Hatakeyama D, Lee S, Forest J, Sakakibara M, Ito E, Lukowiak K. Pharmacological effects of cannabinoids on learning and memory in Lymnaea. J Exp Biol. 2017;220(17):3026–3038. doi: 10.1242/jeb.159038. - DOI - PubMed

[7] Syed N, Bulloch A, Lukowiak K. In vitro reconstruction of the respiratory central pattern generator of the mollusk Lymnaea. Science. 1990;250(4978):282–285. doi: 10.1126/science.2218532. - DOI - PubMed

[8] Syed N, Bulloch A, Lukowiak K. In vitro reconstruction of the respiratory central pattern generator of the mollusk Lymnaea. Science. 1990;250(4978):282–285. doi: 10.1126/science.2218532. - DOI - PubMed

[9] Elliott CJH, Vehovszky Á. Polycyclic neuromodulation of the feeding rhythm of the pond snail Lymnaea stagnalis by the intrinsic octopaminergic interneuron, OC. Brain Res. 2000;887(1):63–69. doi: 10.1016/S0006-8993(00)02968-1. - DOI - PubMed

[10] Elliott CJH, Vehovszky Á. Polycyclic neuromodulation of the feeding rhythm of the pond snail Lymnaea stagnalis by the intrinsic octopaminergic interneuron, OC. Brain Res. 2000;887(1):63–69. doi: 10.1016/S0006-8993(00)02968-1. - DOI - PubMed

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Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

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Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

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