Probing the evolutionary dynamics of whole-body regeneration within planarian flatworms
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Abstract
Why some animals can regenerate while many others cannot remains a fascinating question. Even amongst planarian flatworms, well-known for their ability to regenerate complete animals from small body fragments, species exist that have restricted regeneration abilities or are entirely regeneration incompetent. Towards the goal of probing the evolutionary dynamics of regeneration, we have assembled a diverse live collection of planarian species from around the world. The combined quantification of species-specific head regeneration abilities and comprehensive transcriptome-based phylogeny reconstructions reveals multiple independent transitions between robust whole-body regeneration and restricted regeneration in the freshwater species. Our demonstration that the RNAi -mediated inhibition of canonical Wnt signalling can nevertheless bypass all experimentally tractable head regeneration defects in the current collection indicates that the pathway may represent a hot spot in the evolution of planarian regeneration defects. Combined with our finding that Wnt signalling has multiple roles in the reproductive system of the model species S. mediterranea , this raises the possibility of a trade-off between egg-laying and asexual reproduction by fission/regeneration as a driver of regenerative trait evolution. Although initial quantitative comparisons of Wnt signalling levels, reproductive investment, and regenerative abilities across the collection confirm some of the model’s predictions, they also highlight the diversification of molecular mechanisms amongst the divergent planarian lineages. Overall, our study establishes a framework for the mechanistic evolution of regenerative abilities and planarians as model taxon for comparative regeneration research.
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Benchmarking Universal Single-Copy Orthologs - v 5.2.2 - metazoa odb10 - parameters: - protein
Given that you are using transcriptome assemblies and presumaby amino acid sequences since you're using the "protein" method, it is important that some degree of protein redundancy reduction takes place prior to analysis with BUSCO (i.e. using CDHIT or retaining only the longest protein per assembled gene). Use of transdecoders '--single_best_orf' will still retain alternative isoforms which will lead the count of "duplicates" by busco to be inflated.
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The optimal parameters for PhyloPyPruner were chosen by comparing the outcome when adjusting for minimum sequence length, long branch trimming factor, minimum support value, minimum number of taxa, minimum OTU occupancy, tree pruning method, and minimum gene occupancy. The optimisation script, including the tested parameter values, can be found in the supplementary material.
The supplementary material hosted on biorxiv doesn not seem to include these scripts/parameters.
I'm also curious: on what basis were were parameter combinations judged to be more optimal than another? I like that you explored how these parameters impacted phylogenetic inference, but it's unclear what the actual optimality criteria were.
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Then the appropriate transition matrix for ASR was determined by fitting MK-models with equal transition rates (ER), with symmetric transition rates (SYM), and with all transition rates different (ARD) and then evaluating the model fit using the corrected Akaike information criterion (AIC).
Based on the supplement it seems you defined these models as a set of three ordered, discrete states (i.e. A <-> B <-> C). Did you consider/did you fit another set of models where there could be a two-state jump (i.e. A <-> C)? These might be worth fitting/considering, as it's not inconceivable that such transitions might occur - likewise, this additional type of transition may be a bit less sensitive under scenarios of incomplete sampling with respect to the focal trait (i.e. in cases where transitions did in fact occur from A -> B -> C, but the …
Then the appropriate transition matrix for ASR was determined by fitting MK-models with equal transition rates (ER), with symmetric transition rates (SYM), and with all transition rates different (ARD) and then evaluating the model fit using the corrected Akaike information criterion (AIC).
Based on the supplement it seems you defined these models as a set of three ordered, discrete states (i.e. A <-> B <-> C). Did you consider/did you fit another set of models where there could be a two-state jump (i.e. A <-> C)? These might be worth fitting/considering, as it's not inconceivable that such transitions might occur - likewise, this additional type of transition may be a bit less sensitive under scenarios of incomplete sampling with respect to the focal trait (i.e. in cases where transitions did in fact occur from A -> B -> C, but the intervening species in character state B were not sampled (or trait data is missing).
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Phylogenetic trees were constructed using IQ-TREE77 (version: 2.1.2, parameters: -m MFP -bb 1000 -bnni) or via ASTRAL78 (version 5.7.1), using standard parameter settings (S. Fig. 3a). The phylogeny combining triclads, mammals and nematodes was built following the same approach as for the planarian phylogeny.
Are these phylogenetic trees the species trees? or all gene family trees?
If the species tree, were these single-copy ortholog multiple sequence alignments for each orthogroup concatenated for inference with IQ-TREE, or using multiple partitions? Based on parameters, this would suggest the former (concatenation), but I would suggest being explicit in the methods.
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On the other hand, transitions between “restricted regeneration” and “robust regeneration” appear to be limited to the Continenticola, but likely occur frequently and in both directions (particularly in the Planariidae; Fig. 3e and S. Table 3).
So transitions between A & C were included in these Mk models? I suggest including these specifics in the methods.
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To comprehensively reconstruct the phylogeny of our planarian species collection, we used the pipeline cartooned in Fig. 3a to extract broadly conserved single-copy orthologues from our transcriptomes50–52.
Here again, it will have been quite important that protein redundancy (i.e. retaining only one protein per assembled gene) has been reduced, as even these methods to prune gene families down to single copy orthologs will be more prone to inclusion of paralogs when alternative isoforms persist in a transcriptome.
When running orthofinder (or any clustering-based ortholog identification method) on transcriptome datasets, performance will be contingent upon the filtering strategy used.
-
he consistently high completeness and low fragmentation of BUSCO gene copies in our transcriptomes indicated a high assembly quality of our data set (Fig. 3b).
I would suggest reporting all standard BUSCO estimates: Complete single copy, Complete duplicated, fragmented, and missing. Partitioning out into this resolution provides a more complete understanding of transcriptome/proteome completeness/quality.
As I describe in the methods, it is important to reduce protein redundancy (using CDHIT, retaining the longest protein per assembled gene) prior to running busco - if you don't the number of duplicated buscos will be artificially inflated.
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Branch length comparisons with selected mammalian and nematode transcriptomes quantitatively confirmed the extreme divergence of planarian lineages, which exceeds the observed sequence divergence between mammalian species and is on par with that of nematodes (S. Fig. 3b).
Was a second round of orthogroup inference conducted by including an additional set of mammalian transcriptomes?
The methods used to perform this analysis are not described in the methods section - these should be included/described. Without inclusion of these methods, it's impossible to meaningfully understand/interpret these results.
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Transcriptome assembly was carried out with our established pipeline46.
I strongly suggest providing specific details of what is involved in this pipeline. What method is used for assembly? Are any steps conducted for reducing the redundancy in these assemblies (e.g. using CD hit to remove transcripts that are highly similar in sequence)? As is, the reader needs to go to the article you cite here, and then follow a hyperlink: http://planmine.mpi-cbg.de/planmine/PlanMine_Help.html#assembly
This is purported to include a description of the assembly pipeline, but is in fact a dead hyperlink.
As is, there is no way to interpret the transcriptome assemblies produced here.
-
Branch length comparisons with selected mammalian and nematode transcriptomes quantitatively confirmed the extreme divergence of planarian lineages, which exceeds the observed sequence divergence between mammalian species and is on par with that of nematodes (S. Fig. 3b).
Was a second round of orthogroup inference conducted by including an additional set of mammalian transcriptomes?
The methods used to perform this analysis are not described in the methods section - these should be included/described. Without inclusion of these methods, it's impossible to meaningfully understand/interpret these results.
-
On the other hand, transitions between “restricted regeneration” and “robust regeneration” appear to be limited to the Continenticola, but likely occur frequently and in both directions (particularly in the Planariidae; Fig. 3e and S. Table 3).
So transitions between A & C were included in these Mk models? I suggest including these specifics in the methods.
-
To comprehensively reconstruct the phylogeny of our planarian species collection, we used the pipeline cartooned in Fig. 3a to extract broadly conserved single-copy orthologues from our transcriptomes50–52.
Here again, it will have been quite important that protein redundancy (i.e. retaining only one protein per assembled gene) has been reduced, as even these methods to prune gene families down to single copy orthologs will be more prone to inclusion of paralogs when alternative isoforms persist in a transcriptome.
When running orthofinder (or any clustering-based ortholog identification method) on transcriptome datasets, performance will be contingent upon the filtering strategy used.
-
he consistently high completeness and low fragmentation of BUSCO gene copies in our transcriptomes indicated a high assembly quality of our data set (Fig. 3b).
I would suggest reporting all standard BUSCO estimates: Complete single copy, Complete duplicated, fragmented, and missing. Partitioning out into this resolution provides a more complete understanding of transcriptome/proteome completeness/quality.
As I describe in the methods, it is important to reduce protein redundancy (using CDHIT, retaining the longest protein per assembled gene) prior to running busco - if you don't the number of duplicated buscos will be artificially inflated.
-
Then the appropriate transition matrix for ASR was determined by fitting MK-models with equal transition rates (ER), with symmetric transition rates (SYM), and with all transition rates different (ARD) and then evaluating the model fit using the corrected Akaike information criterion (AIC).
Based on the supplement it seems you defined these models as a set of three ordered, discrete states (i.e. A <-> B <-> C). Did you consider/did you fit another set of models where there could be a two-state jump (i.e. A <-> C)? These might be worth fitting/considering, as it's not inconceivable that such transitions might occur - likewise, this additional type of transition may be a bit less sensitive under scenarios of incomplete sampling with respect to the focal trait (i.e. in cases where transitions did in fact occur from A -> B -> C, but the …
Then the appropriate transition matrix for ASR was determined by fitting MK-models with equal transition rates (ER), with symmetric transition rates (SYM), and with all transition rates different (ARD) and then evaluating the model fit using the corrected Akaike information criterion (AIC).
Based on the supplement it seems you defined these models as a set of three ordered, discrete states (i.e. A <-> B <-> C). Did you consider/did you fit another set of models where there could be a two-state jump (i.e. A <-> C)? These might be worth fitting/considering, as it's not inconceivable that such transitions might occur - likewise, this additional type of transition may be a bit less sensitive under scenarios of incomplete sampling with respect to the focal trait (i.e. in cases where transitions did in fact occur from A -> B -> C, but the intervening species in character state B were not sampled (or trait data is missing).
-
Phylogenetic trees were constructed using IQ-TREE77 (version: 2.1.2, parameters: -m MFP -bb 1000 -bnni) or via ASTRAL78 (version 5.7.1), using standard parameter settings (S. Fig. 3a). The phylogeny combining triclads, mammals and nematodes was built following the same approach as for the planarian phylogeny.
Are these phylogenetic trees the species trees? or all gene family trees?
If the species tree, were these single-copy ortholog multiple sequence alignments for each orthogroup concatenated for inference with IQ-TREE, or using multiple partitions? Based on parameters, this would suggest the former (concatenation), but I would suggest being explicit in the methods.
-
The optimal parameters for PhyloPyPruner were chosen by comparing the outcome when adjusting for minimum sequence length, long branch trimming factor, minimum support value, minimum number of taxa, minimum OTU occupancy, tree pruning method, and minimum gene occupancy. The optimisation script, including the tested parameter values, can be found in the supplementary material.
The supplementary material hosted on biorxiv doesn not seem to include these scripts/parameters.
I'm also curious: on what basis were were parameter combinations judged to be more optimal than another? I like that you explored how these parameters impacted phylogenetic inference, but it's unclear what the actual optimality criteria were.
-
Benchmarking Universal Single-Copy Orthologs - v 5.2.2 - metazoa odb10 - parameters: - protein
Given that you are using transcriptome assemblies and presumaby amino acid sequences since you're using the "protein" method, it is important that some degree of protein redundancy reduction takes place prior to analysis with BUSCO (i.e. using CDHIT or retaining only the longest protein per assembled gene). Use of transdecoders '--single_best_orf' will still retain alternative isoforms which will lead the count of "duplicates" by busco to be inflated.
-
Transcriptome assembly was carried out with our established pipeline46.
I strongly suggest providing specific details of what is involved in this pipeline. What method is used for assembly? Are any steps conducted for reducing the redundancy in these assemblies (e.g. using CD hit to remove transcripts that are highly similar in sequence)? As is, the reader needs to go to the article you cite here, and then follow a hyperlink: http://planmine.mpi-cbg.de/planmine/PlanMine_Help.html#assembly
This is purported to include a description of the assembly pipeline, but is in fact a dead hyperlink.
As is, there is no way to interpret the transcriptome assemblies produced here.
-
