Dnmt1 mediates epigenetic restriction of invasive traits in clonal crayfish

Domain: neuroscience
Published: 2026-03-26
Editorial outcome: minor_revision

Abstract
Procambarus virginalis (marbled crayfish) is a parthenogenetically reproducing invasive crayfish species. Its global population is monoclonal, which raises questions about the mechanisms promoting their invasiveness. Here we show that environmental changes downregulate the highly conserved Dnmt1 DNA methyltransferase in marbled crayfish. When phenocopying this effect through a dsRNA-based in vivo knockdown, we observe enhanced invasiveness-related behavioral traits. Image cytometry and single-cell RNA sequencing reveal an expansion of mature granular immune cells and depletion of hemocyte-derived neuronal precursors, which support adult neurogenesis. Whole-genome bisulfite sequencing shows that these phenotypes coincide with a global loss of gene body DNA methylation and dysregulation of nervous and immune system genes. Additionally, we observe nucleosome destabilization to be associated with transcriptional changes after methylation loss. Taken together, our findings identify Dnmt1 as a potential canalizer of cellular and organismal phenotypes, thus providing a framework for how epigenetic mechanisms modulate invasiveness.

Introduction
Biological invasions are a globally underestimated problem, with pressing ecological and economic consequences, often leading to ecosystem disruption and significant financial costs1. The success of invasive species is frequently attributed to their ability to rapidly adapt to new environments, despite low genetic diversity2. This adaptability has been linked to epigenetic mechanisms, particularly DNA methylation, which can mediate phenotypic plasticity and enable organisms to thrive in diverse environments3,4. In fact, DNA methylation has already been implicated in a range of traits relevant to invasiveness, including stress, temperature, salinity or pollution response, albeit with little or no mechanistic insights5,6.

Non-indigenous crayfish species represent a well-studied paradigm for successful biological invasions7. Unlike other freshwater species, crayfish possess the ability to disperse overland and survive outside aquatic habitats for extended periods, which enhances their invasive potential8. Important examples for successful crayfish invasions include Pacifastacus leniusculus (signal crayfish), whose aggressiveness and adaptability has made it a successful invader in Europe, California and Japan, where it has displaced the autochthonous Cambaroides japonicus9,10. Similarly, Procambarus clarkii (red swamp crayfish) has been shown to feed on a wide variety of amphibian species, because it can avoid the natural defenses they have created and is a more effective predator than the indigenous species11.

Studies of invasive crayfish species have identified behavioral traits, such as increased activity, boldness, and exploratory drive, as critical factors for successful invasion12. Interestingly, freshwater crayfish exhibit neurogenesis throughout their lifespan, which is driven by neuronal precursors derived from a subset of hemocytes13. Crayfish hemocytes are multifunctional immune cells critical for survival in pathogen-rich environments. They are comprised by three major cell types: hyaline cells (HCs), semigranular cells (SGCs), and granular cells (GCs), each with distinct functions and degrees of maturation14–16. Hemocyte-derived neural precursors (HDNPs) migrate to the neural stem cell niche, where they differentiate into mature neurons17,18, thus linking the immune and nervous systems into a complex regulatory network with considerable relevance for invasive success.

Physiological adaptations that infer phenotypic flexibility have been discussed in the context of several ecological models of invasiveness. For example, the Evolution of Increased Competitive Ability (EICA) hypothesis proposes that invading species may reallocate energy and resources from immunity towards growth and reproduction to enhance competitiveness19. Furthermore, the General-Purpose Genotype (GPG) hypothesis suggests that clonal or genetically homogenous species can achieve ecological success through a phenotype that tolerates a wide range of ecological conditions20. Both frameworks emphasize physiological flexibility as a main driver of invasion success. In this context, DNA methylation has been proposed to regulate plasticity by stabilizing certain developmental trajectories and buffering environmental effects, a concept referred to as epigenetic canalization21.

Procambarus virginalis (marbled crayfish) is a novel parthenogenetic freshwater crayfish species22. It produces clonal, all-female offspring that exhibit remarkable phenotypic plasticity despite minimal genetic variation22,23. Since its emergence in the mid-1990s, the marbled crayfish has spread rapidly across global freshwater ecosystems, mainly because of anthropogenic introductions24–26. As the species thrived in diverse environments despite the lack of genetic variation, epigenetic regulation, such as DNA methylation, was suggested to play a central role in mediating its invasive potential4. We have shown previously that the marbled crayfish genome is methylated and that it encodes a conserved DNA methylation toolkit consisting of a Dnmt1, Dnmt3 and Tet homolog, respectively27. We have also provided evidence for alterations in DNA methylation patterns in response to environmental parameters28,29, suggesting that DNA methylation may play a role in the adaptivity of P. virginalis.

DNA methyltransferase 1 (Dnmt1) is an evolutionary conserved enzyme responsible for the maintenance of DNA methylation patterns. It is the main enzyme for catalyzing the covalent addition of methyl groups to cytosine bases, predominantly within CpG dinucleotides30. This reaction produces 5-methylcytosine (5mC), the most widely studied epigenetic mark31. The functional consequences of DNA methylation vary significantly depending on the genomic context. The methylation of gene regulatory elements, such as promoters and enhancers, has been linked to the regulation of gene expression by modulating the recruitment of transcription factors or other transcription modifiers32,33. The role of DNA methylation in other regions, such as gene bodies, is less well understood, although increasingly considered as an additional regulatory mechanism of eukaryotes34,35.

The functional analysis of Dnmt1 was pioneered through the generation of Dnmt1 knockout mice36, which revealed an important role of this enzyme in mammalian cell fate specification37,38. This includes the hematopoietic system, where Dnmt1 was shown to protect hematopoietic stem cells from lineage restriction39. In insects like Bemisia tabaci (whitefly) or Oncopeltus fasciatus (large milkweed bug) Dnmt1 has been suggested to play a role in gametogenesis40,41, although these observations lack a mechanistic understanding or are not clearly related to changes in DNA methylation42.

In a previous study, we have found that global DNA methylation was significantly lower in P. virginalis compared to its non-invasive parent species, Procambarus fallax27. This suggested that lower methylation levels may promote invasive traits. In this study, we first ask whether Dnmt1 expression responds to ecological changes (temperature and water quality), reflecting the new and variable conditions potentially encountered by an invading animal. After observing a pronounced downregulation of Dnmt1 under these conditions, we developed an RNAi-based in vivo knockdown approach in marbled crayfish to assess the functional consequences of Dnmt1 depletion. Our results show that the reduction of Dnmt1 promotes behavioral traits associated with successful invasions. This effect is accompanied by a shift in hemocytic cell differentiation, as knockdown of Dnmt1 depletes the numbers of immature HDNPs, while expanding those of the mature immune GCs. Further analysis shows a global reduction of gene body DNA methylation and transcriptional dysregulation of genes involved in nervous and immune system functions, potentially caused by a decrease in nucleosome positioning stability. Together, our findings highlight Dnmt1 as an important mediator of invasive behavior through its effects on cellular differentiation and gene expression.

Results
Environmental changes downregulate Dnmt1 expression
To test whether environmental conditions can modulate the expression of the DNA methylation machinery of marbled crayfish, we quantified the levels of Dnmt1, Dnmt3, and Tet mRNA in hemocytes by quantitative PCR (qPCR) after changing rearing conditions in laboratory culture. In a first experiment, we transferred the animals from room temperature (RT) to a cold environment (4 °C) for 24 h and then returned them to RT for a 24 h recovery period. Compared to animals maintained at RT throughout the whole experiment, cold-treated crayfish exhibited a clear (2.1-fold) and significant (p < 0.05, unpaired t test) downregulation of Dnmt1 in hemocytes which became even more pronounced (11.1-fold, p < 0.01, unpaired t test) during the recovery phase (Fig. 1a). In contrast, Dnmt3 remained largely unchanged under both conditions (Fig. 1b), while Tet expression displayed a transient upregulation during the cold challenge that returned to baseline levels during recovery (Fig. 1c). Given the pronounced downregulation of Dnmt1 following cold exposure, we next asked whether other types of environmental shifts could elicit similar effects. Therefore, we transferred animals that were reared in a biofloc environment39 to a clear-water aquarium environment. Two days post-transfer, Dnmt1 expression in hemocytes was strongly (4.3-fold) and significantly (p < 0.05, unpaired t test) downregulated compared to animals exclusively reared in the aquarium (Fig. 1d). Expression levels remained lower at seven- and fourteen-days post-transfer, although these differences were less pronounced and not statistically significant (Fig. 1d). These findings strongly suggest that changes in environmental conditions downregulate Dnmt1 mRNA expression.

Establishment of an in vivo Dnmt1 knockdown in marbled crayfish
To investigate the functional role of Dnmt1 in marbled crayfish adaptability and physiology, we established an in vivo knockdown (KD) of Dnmt1. We adapted an RNA interference (RNAi) approach from other species43 by generating ~500 bp double-stranded RNAs (dsRNAs) for both P. virginalis Dnmt1 and A. victoria GFP (as a control) via in vitro transcription. After processing, the resulting dsRNAs were then injected into the crayfish hemolymph through the arthrodial membrane between the cephalothorax and the abdomen44, which reduces the excessive bleeding of the more traditional approach through the sinus between the third and fourth walking leg45.

To assess the efficacy of the knockdown, hemocytes were collected 48 hours post injection (hpi) using a hypodermic syringe and processed immediately for RNA isolation. qPCR showed that the expression of Dnmt1 was robustly (63%) and significantly (p < 0.01, unpaired t test) reduced in hemocytes from KD animals relative to controls. This depletion increased with time, resulting in a more pronounced (79%) reduction at 14 days-post injection (dpi) and 74% at 28 dpi (Fig. 1e). Analysis of Dnmt1 mRNA levels in other tissues 14 dpi revealed different degrees of knockdown efficiency with the highest levels of reduction in heart (93%; Fig. 1f), abdominal muscle (85%; Fig. 1g) and hepatopancreas (83%; Fig. 1h). Other organs like brain, gills, ovary and the hematopoietic tissue (HPT), showed more moderate Dnmt1 depletion (52–14%, Fig. 1i–l). These findings demonstrate that our RNAi protocol produces a stable and systemic in vivo knockdown of Dnmt1 in marbled crayfish.

Dnmt1 knockdown enhances behavioral traits associated with invasiveness in marbled crayfish
To evaluate the role of Dnmt1 in marbled crayfish invasiveness, we conducted behavioral assays using a plus-shaped maze46. Following one week of isolation from other animals to remove any previous social memory47, three independent batches of KD and control animals with similar weight distribution (Supplementary Fig. S1a, b) were evaluated under identical conditions. The results revealed that depletion of Dnmt1 expression significantly reduced the total time animals remained immobile (47%, p < 0.05; unpaired t test; Fig. 2a) and the average duration of individual stops (54%, p = 0.06; unpaired t test; Fig. 2b), indicating increased activity levels in KD animals. Furthermore, KD animals exhibited a significant (p < 0.05; unpaired t test; Fig. 2c) two-fold increase in the average number of climbing attempts and a 46% increase in movement frequency between maze sections (Fig. 2d), suggesting enhanced boldness and exploratory drive, which have previously been linked to crayfish invasiveness12.

Interestingly, Dnmt1 KD animals also presented behavioral traits indicative of reduced anxiety. They spent 31% more time in the light zones of the maze compared to controls, despite crayfish natural aversion of well-lit areas (Fig. 2e). In addition, the KD group exhibited a 46% reduction in the number of retreats (Fig. 2f), defined as incomplete attempts to enter other sections, although these differences were not statistically significant. Principal component analysis (PCA) of all the parameters measured showed a statistically significant (p < 0.05; Monte Carlo Permutation test) separation of the treatment groups, consistent with a global difference in behavior. Collectively, these findings demonstrate the influence of Dnmt1 in modulating P. virginalis behavior and that reduced expression of Dnmt1 can result in a behavioral pattern typical for successful crayfish invaders48.

Knockdown of Dnmt1 affects cell fates in hemocytic cell populations
We also examined hemocyte populations to understand the cellular basis of the observed behavioral alterations. Hemocytes are the cellular component of crayfish immunity, crucial for pathogen defense, encapsulation and surveillance49. The three main immune cell types develop along a single developmental trajectory from HCs to SGCs and finally to GCs16. In addition to their immune functions, hemocytes also serve as a source of neuronal progenitors, here termed HDNPs, which migrate to the neural stem cell niche to produce new neurons13 (Supplementary Fig. S1c). Due to the absence of antibody-based techniques for labeling these cells, we developed an image cytometry-based protocol that enables high-resolution imaging of individual hemocytes from the three main immune cell types (Fig. 2g). This approach allowed us to classify and quantify hemocyte populations based on morphological characteristics, such as cell size and granularity (Supplementary Fig. S1d). We analyzed one thousand hemocytes per sample from six control and six Dnmt1 KD animals at 28 dpi. The quantification of these cell types showed that KD animals presented a significant (p < 0.05, unpaired t test) two-fold increase in GCs (Fig. 2j), with a concomitant reduction of SGCs from 80% to 55% (Fig. 2i), as well as a non-significant increase of HCs (Fig. 2h). These results demonstrate that the knockdown of Dnmt1 affects hemocyte differentiation, shifting the balance towards more differentiated GCs.

To analyze the effects of Dnmt1 knockdown on hemocyte cell types in more detail, we performed single-cell RNA sequencing (scRNA-seq) using the 10X Genomics platform on 3 control and 3 KD samples 28 dpi (Supplementary Table S1). Analysis of the control sample with the highest number of cells (n = 5318) identified four transcriptionally different clusters (Fig. 3a, b). Cluster annotation was inferred using sets of differentially expressed genes and homology-based comparisons from previously published crustacean datasets. Cluster 0 (80.5%), expressed markers consistent with SGCs, including a homolog of the crustacean hematopoietic factor50,51, Kazal-type serine protease inhibitors52, or Cathepsin-L, found in phagocytic cells53,54. Cluster 1 (10.2%) showed a strong expression of FAT1, a protocadherin homolog related to hemocyte-derived neuronal precursors in P. leniusculus55. The expression of other neuronal markers such as Netrin receptor UNC-5 (ref. 56), gap-junction component Innexin 2 (ref. 57) and genes related to lipid metabolism, which is essential for the correct functioning of neurons58, further supports this cluster as HDNPs. Cluster 2 (5.2%) was characterized by the expression of mature immune cells markers like crustins59,60, invertebrate-type lysozyme55, several serpin proteases61, and a C-type lectin62, identifying the cluster as GCs. Finally, cluster 3 (4.1%) exhibited high levels of ribosomal structural components63, Serp2 and CISD3, which are characteristic of proliferative and metabolically active cells and have previously been identified as markers of hematopoietic tissue cells in other crayfish species55. At the same time, this cluster also expressed glycine-rich antimicrobial peptides64, suggesting an intermediate phenotype associated with undifferentiated circulating prohemocytes (CPHs).

In subsequent steps, we integrated this control sample with the KD sample showing the highest number of cells (n = 2869). Comparative analysis revealed a pronounced (92%) depletion of HDNPs in KD animals, and a 2.9-fold increase of the GC population. CPHs also became markedly (90%) reduced in the KD sample, while SGCs remained largely unchanged (Fig. 3c–e). Integration of the complete dataset (n = 3 for each experimental group) revealed a significant upregulation of the GC signature in KD animals, both in the GC and in the SGC clusters (p < 2e-16, Wilcoxon Rank-sum test; Fig. 3h). Further analysis revealed that the HDNP signature was significantly downregulated in the SGC, GC, and HDNP clusters (p < 0.01, Wilcoxon Rank-sum test; Fig. 3i), while the SGC signature was significantly reduced in the SGC and GC clusters (p < 0.001, Wilcoxon Rank-sum test; Fig. 3g). The CPH signature was also downregulated in the SGCs (p < 2e-16, Wilcoxon Rank-sum test) and GCs (p < 2e-16, Wilcoxon Rank-sum test; Fig. 3j). Finally, trajectory analysis revealed a developmental relationship between the clusters, with CPHs (undifferentiated hemocytes) developing into SGCs and then committing to either a HDNP or GC cell fate (Fig. 3f). These results suggest that Dnmt1 controls the differentiation of crayfish hemocytes by influencing the decision making from SGCs to become HDNPs and not GCs.

Dnmt1 knockdown reduces gene body DNA methylation
To identify potential mechanisms driving the observed cellular phenotype, we investigated the effect of Dnmt1 knockdown on the DNA methylation level. Whole-genome bisulfite sequencing (WGBS) was performed on DNA isolated from hemocytes of two control and two KD animals (28 dpi), with an average sequencing depth of 18.2 X (Supplementary Table S2). Data analysis showed a highly significant reduction of the average CpG methylation levels from 13.8% in control animals to 8.3% in KD samples over sixteen million mapped CpGs (p < 2e-16, Wilcoxon rank sum test; Fig. 4a). Methylation in non-CpG contexts (CHG, CHH, CN or CNH) remained unchanged (Supplementary Fig. S2a).

As P. virginalis DNA methylation is known to be targeted to gene bodies27, we also determined average gene body methylation levels in both groups. The average results showed a pronounced and consistent reduction of gene body methylation from 27.1% in controls to 16.4% in KD samples (Fig. 4b). Further analysis also confirmed that most methylated genes were affected by methylation loss in Dnmt1 KD hemocytes in a consistent way across the replicates (Fig. 4c). These results were validated via targeted bisulfite sequencing on selected differentially methylated regions (DMRs) using hemocyte DNA from six additional animals (three KD and three control), at 28 dpi and 6-months post-injection. This confirmed the stability of the KD effects and also validated the WGBS results at a much higher sequencing depth (Supplementary Fig. S2b–d). These findings demonstrate that the knockdown of Dnmt1 results in a pronounced and genome-wide reduction of DNA methylation.

Loss of DNA methylation leads to changes in hemocyte gene expression
To explore the relationship between marbled crayfish gene body methylation and gene expression, bulk RNA sequencing (RNA-seq) was performed on the same four samples (two control and two KD) that were used for WGBS (Supplementary Table. S3). Differential expression analysis identified 147 significantly upregulated and 308 downregulated genes between KD and control hemocytes 28 dpi, suggesting a role of gene body methylation in gene expression regulation in crustaceans (Fig. 4d). Validation of these findings via qPCR for 18 randomly selected genes supported the RNA-seq results (Supplementary Fig. S2e).

Interestingly, among the validated genes, a group of key crustacean hematopoietic genes15, including prophenoloxidase (ProPO), mannose-binding protein (MBP), peroxinectin and superoxide dismutase (SOD), showed a general upregulation in KD samples (Fig. 4e). In addition, developmental genes like klumpfuss or Notch, which are essential for hematopoiesis in other arthropods like Drosophila melanogaster65, were also upregulated. Conversely, a key crustacean cytokine, astakine 1 (Ast1), which was previously connected to increased neuronal incorporation in the neurogenic niche13, was downregulated, supporting the above-mentioned results. For a more systematic analysis, we integrated the RNA-seq and WGBS datasets and identified 97 hypomethylated and downregulated genes along with 24 genes that were hypomethylated and upregulated (Fig. 4f). Functional annotation of hypomethylated and downregulated genes showed a remarkable enrichment for neuron-related processes in almost 40% of the genes, as well as roles in lipid metabolism (6.3%) and cell polarity (4.8%) which are crucial for neuronal function (Fig. 4g). A similar analysis of upregulated genes did not show a clear functional enrichment (Supplementary Fig. S2f). These results suggest that the reduction of gene body methylation in KD animals affects the expression of genes associated with neuronal functions, which could explain the observed organismal and cellular phenotypes.

Genetic features and nucleosome positioning correlate with transcriptional effects of gene body methylation
DNA methylation within gene bodies has been associated with transcriptional activation across many species66. We also observed a positive correlation between gene body methylation and expression for various gene categories (Supplementary Figs. S3, S4). However, while many genes were downregulated following Dnmt1 KD as expected, a subset of them was upregulated, suggesting more complex underlying mechanisms. To investigate why certain genes responded differently to methylation loss, we analyzed their structural features. Our analysis revealed that hypomethylated and differentially expressed genes contained significantly more but shorter exons than genes that were unaffected (Fig. 5a, b). Furthermore, genes that were hypomethylated and downregulated exhibited significantly shorter introns and lower CpG density than upregulated genes (Fig. 5c, d). These observations suggest that gene structure influences transcriptional dependency on gene body methylation, with intron length playing a role in how methylation loss affects expression (Fig. 5e).

Finally, given the established link between gene body DNA methylation and nucleosome positioning67, we examined how the loss of Dnmt1 affects chromatin organization in marbled crayfish. We therefore performed Micrococcal Nuclease sequencing (MNase-seq) on hemocytes from four control and six Dnmt1 KD animals (Supplementary Table S4). Global analysis of nucleosome positioning on hypomethylated genes revealed no significant differences in nucleosome occupancy (p > 0.05; Wilcoxon Rank Sum test; Fig. 5f) and minor changes in overal distribution (p < 0.001; Kolgomorov-Smirnov test). However, after integration of the transcriptomic data, we observed that genes which were both differentially methylated and differentially expressed upon Dnmt1 reduction (Fig. 5g and h) had a strongly different nucleosome distribution (p < 0.0001, Kolgomorov-Smirnov test), characterized by a less consistent spacing between the nucleosomes, and significantly reduced occupancy levels (p < 0.0001, Wilcoxon Rank sum test). These results suggest that loss of methylation alone is not sufficient to alter gene expression; instead, transcriptional changes seem to require a change in nucleosomal positioning.

To further quantify this effect, we calculated the percentage of gene body regions exhibiting a substantial change in nucleosome stability or fuzziness. Dnmt1 KD resulted in a significant reduction in the proportion of gene body regions with stably positioned nucleosomes in both differentially methylated and differentially expressed genes (p < 2e-16 in both comparisons, Wilcoxon Rank sum test; Fig. 5i, k). An increase in nucleosome fuzziness was specifically observed in differentially methylated and downregulated genes (p < 0.001, Wilcoxon Rank sum test; Fig. 5j). Together, these findings strongly suggest a link between gene body methylation, gene expression and nucleosome positioning, although the establishment of a direct causal relationship will require further experimentation.

Methods
Abbreviations used throughout the manuscript are listed in Supplementary Note 1.

Ethics approval statement
All laboratory experiments were conducted by approval of the institutional animal welfare committee, in compliance with regional standards guidelines, and regularly monitored by the authorities (Permits 35-9185.82/A-5/20 and 35-9185.82/A-7/25).

Animal husbandry
Laboratory animals were individually kept in 26x18x14cm plastic containers with size-appropriate shelters and environmental enrichment objects, and fed with aquarium feed three times a week. Animals were daily monitored throughout and after the experiments for indications of distress. Tap water at RT was used as the water source and replaced once a week. Animals were anesthetized in iced water for >5 min and euthanized by decapitation.

Environmental exposure experiments
To assess the impact of sudden cold stress on Dnmt1 expression, marbled crayfish were exposed to a temperature fluctuation experiment. Three to six individuals were transferred from their standard rearing conditions at RT (20–22 °C) to a cold environment (4 °C) for 24 h. Crayfish were then returned to RT for an additional 24 h period. Hemolymph samples were collected at three time points: immediately before cold exposure (t=0), after 24 h at 4 °C (t=1), and after the subsequent 24 h recovery at RT (t=2). Control animals remained at RT throughout the experiment and were sampled in the same way.

In a similar manner, to evaluate the effects of environmental changes associated with different aquaculture practices on Dnmt1 expression, three individual crayfish raised in a biofloc-based system were transferred to an aquarium system and maintained for 14 days. Hemolymph samples were collected at four time points: immediately before transfer (t=0), and at 2, 7, and 14 days post-transfer. Control animals were continuously housed in the aquarium system and sampled at the same time points.

Sample collection
Hemocytes were collected following available protocols85. Briefly, crayfish were placed on a paper tissue with their eyes covered and their claws and pereopods immobilized. Then, the abdomen was stretched and wiped with 70% ethanol and a 20G X 2” needle (0.9 X 50 mm) was introduced at the right side of one of the superior abdominal segments. Hemolymph was collected in a tube containing 0.4 ml of filtered anticoagulant solution [NaCl 0.14M, Glucose 0.1M, sodium citrate dihydrate 30mM, citric acid 26mM, EDTA 0.01M at pH 4.6], and mixed by inverting the tube. After centrifugation, the pellet was snap-frozen in liquid nitrogen before further use. For the collection of abdominal muscle, heart, gills, hepatopancreas, ovary, hematopoietic tissue (HPT) and brain, the animals were anesthetized in iced water for >5 min and euthanized by decapitation. Each tissue was rinsed with PBS, snap-frozen in liquid nitrogen and stored at −80 °C before further use. The numbers of replicates per treatment group in each experiment are available in Supplementary Table S5.

Gene expression analysis using qPCR
Hemocytic RNA for qPCR and RNA-seq was isolated using the RiboPure kit (Invitrogen, Cat. No. AM1924) according to the manufacturer's instructions. cDNA was produced using the Quantitect Reverse Transcriptase kit (Qiagen, Cat. No. 205311). qPCR analyses were performed on a LightCycler 480 Real-Time PCR System (Roche) using the MESA Green qPCR Mastermix Plus for SYBR Assay (Eurogentec, Cat. No. RT-SY2X-03+WOU). The expression levels of the different genes were determined by the average cycle threshold (Ct) value of three technical replicates using TATA-box-binding protein (TBP) as a reference gene for normalization by the 2-ΔΔCT method86. Primer sequences for qPCR were generated using Primer3 and are provided in Supplementary Table S6.

dsRNAi-based knockdown
Double-strand RNA was produced by in vitro transcription using the MEGAscript kit (Invitrogen, Cat. No. AM1334). Two oligonucleotides previously designed with eRNAi87 were used to amplify a ~0.5 kb long region of the target gene by PCR from a pool of marbled crayfish cDNA generated with the Quantitect Reverse Transcriptase Kit (Qiagen, Cat. No. 205311). Subsequently, the in vitro transcribed dsRNA was column-purified following the instructions of the MEGAclear kit. dsRNA targeting Aequorea victoria GFP was produced as a negative control from a commercial plasmid containing that gene (LentiCRISPRv2GFP, Addgene, Cat. No. 82416). Primer sequences were generated using Primer3 and are available in Supplementary Table S7.

Animals were injected in two consecutive days with 2 µg of dsRNA per g of body weight per injection using a 30G x 8 mm U-100 insulin syringe (BD Micro-Fine, Cat. No. 324825) through the arthrodial membrane between the cephalothorax and abdomen, while their head was covered to reduce stress43.

Behavioral experiments
Behavioral experiments were conducted in a subaquatic dark/light plus-shaped maze46, to measure the crayfish spontaneous exploratory behavior. Briefly, using a 50x50 cm arena comprising two dark (red-light intensity: 10 lux) and illuminated (white-light intensity: 70 lux) perpendicular sections with 15 cm in length and 12.5 cm in width, 15 min videos of each animal were blindly recorded using a high-definition video camera (model HC-380, Panasonic). Videos were analyzed using BORIS software (v.8.27.10), which allowed for the tracking of different parameters. Parameters measured included anxiety46 and invasiveness traits12, i.e., total time in light (TTL; seconds in light arms/total seconds of recording, retreat ratio (NR; number of retreats/total number of change of space), total immobile time (TTI; seconds the animal spends immobile / total seconds of recording), stop average duration (SAD; total time duration of stops/number of times the animal stops), change of spaces (TSC; number of times the animal moves from light to darkness or vice versa), and climbing attempts (CA; number of times the animal goes against a wall of the maze and incorporates its body 45°–90°). Animals were isolated in opaque tanks to remove any effect of previously established social memory one week before the recordings. Three independent batches of similar weight animals (Supplementary Fig. S1), three control and three KD animals per batch (total n=9 Ctrl and 9 KD animals per group) were recorded. One KD animal, which presented no locomotion, was excluded from the analysis, following established exclusion criteria46. Each parameter was normalized to the control biological replicates in each batch and Levene's statistical tests (from R package car) were performed to measure the equality of the variances. Whenever a parameter's variances were significantly unequal, a Welch's t test was used to compare control and knockdown performance. If Levene's test was not significant, a Student's t test was used. Monte Carlo Permutation tests (n=500 permutations) were performed to analyze significance in the PCA using the vegan package in R.

Image Cytometry
Total filtered hemolymph was pelleted and resuspended in 1% dilution of paraformaldehyde in water, where it was fixed for 20 min at RT. Cell-type proportions were assessed using an Image Cytometer ImageStreamX MKII from Merck Millipore. One thousand cell events were acquired from each sample using the INSPIRE software. Fluidics speed was adjusted to Low, and magnification at 40X. Channels used included the standard bright field configuration in channels 01 and 09, which allowed for cell visualization and side scatter detection in channel 06, (λ=745 – 785 nm) for granularity assessment. Gating between the different cell populations was done with the IDEAS software. Briefly, the initially recorded sample population was filtered to keep the focused signals (Gradient RMS M01 Ch01>30) and single cells (Area M01 vs AspectRatioIntensity M01 Ch01). Cells were then split into smooth and rough cells based on the (Intensity MC Ch06, >1.5e5 associated to rough cells). Using differential size and granularity (Intensity MC Ch06 vs Area Ch01) granular cells were identified as cells with an area >200 µm2 and a high scatter intensity (>1e5) and making use of the naturally observed separation between cell populations. All cells were visually inspected. Spot Count function which allows to count the number of spots, in our case, granules in the side scatter channel (Ch06) was used to identify the HCs as those containing 0 or 1 granules. Those cells that were not classified as either GCs or HCs were considered SGCs. Signals associated with non-cellular identities, i.e., debris, were discarded. Statistical analysis was performed using GraphPad Prism.

scRNA sequencing
Fresh hemocytes were extracted from three control and three Dnmt1 knockdown animals as stated above. Subsequently, about 10,000 single cells per sample were used to prepare sequencing libraries with the Chromium Single Cell Reagent kit (10X Genomics, v3.1 chemistry; Cat. No. 1000128), according to the manufacturer's protocol. Library concentration was determined with the Qubit dsDNA HS Assay kit (Life Technologies, Cat. No. Q32851), and cDNA integrity was measured with 2200 TapeStation (Agilent Technologies, Cat: No. 5067-5582). Finally, pair-end sequencing was performed with the NovaSeq 6000 device (Illumina).

scRNA-seq data analysis
Raw sequencing reads from all samples were processed with the CellRanger v7.0.0 software (10X Genomics) without forcing any cell number and including intronic counts (command cellranger count --include-introns true --transcriptome=[genome index] --fastqs=[FASTQ directory] --sample=[sample name]) and analyzed using the Seurat v4.3.0 and SeuratObject v4.1.3 R packages. Seurat objects were initialized with a minimum of 3 cells and 100 features per cell. If not specified otherwise, default parameters were used for all functions. Cells with more than 5% of mitochondrial counts as well as over 2000 or less than 20 unique feature counts were filtered out. For the integration of the six samples, down-sampling to 622 cells was performed. Data were then normalized with a scale factor of 10,000 and highly variable features were identified with the vst method, selecting the top 2000 features for downstream analysis. Data scaling was performed with 50 principal components, and clustering dimensions were adjusted according to dataset size: 9 dimensions for the single control sample with the highest number of cells, 15 for the integrated control versus KD dataset, and 11 for the full final integration. Downstream low resolutions (0.1, 0.09 and 0.1, respectively) were used throughout the analysis. Clustering analyses were repeated multiple times using identical parameters to assess robustness to stochastic variation, yielding consistent results across runs. No explicit random seed was set. The most DEGs between the detected clusters were identified using the FindAllMarkers function, considering genes present in a minimum fraction of 25% of the cells. These genes were then used for cluster association based on markers and homology to similarly annotated cell types from related crustaceans and arthropods, and the top 5 genes of each identified cluster were considered as the respective gene signature. Because validated antibody markers are not available for P. virginalis, annotations should be regarded as putative rather than definitive. Statistical analysis was performed using the Wilcoxon rank sum test on the six-sample integrated object.

Trajectory inference
Slingshot (v2.6.0) was used to perform pseudotime analysis to infer lineage relationships among the characterized cell clusters. The integrated Seurat object of the highest number of cells (Ctrl1 and KD3) containing the UMAP embeddings and correct cluster labels was first processed using the SingleCellExperiment function. Only unsupervised analysis and curve lines were used for this experiment.

DNA methylation analysis (WGBS and MiSeq)
Genomic DNA (gDNA) for WGBS and MiSeq was isolated from the respective tissue samples as described in other studies28. Briefly, after homogenizing the tissue, either by pipetting for hemocytes and the HPT, or by use of a TissueRuptor (Qiagen) for the rest. Samples were digested using proteinase K (Invitrogen, AM2546) and RNAse A (Sigma-Aldrich, R6513). gDNA was precipitated using isopropanol and its quality assessed on a 2200 TapeStation (Agilent). The EZ DNA Methylation-Gold kit (Zymo Research, Cat. No. D5006) was used for bisulfite conversion. Library amplification was performed using KAPA HIFI HotStart ReadyMix (Roche, Cat. No. 07958935001). Samples were sequenced on an Illumina HiSeq X platform using a 150 bp paired-reads protocol (PE150). Raw reads were trimmed using TrimGalore (v0.6.6) with default Phred33 and a minimum read length of 36 bp. 10 bases were clipped from the 5´ ends of both forward and reverse reads. Trimmed reads were then aligned to the P. virginalis reference genome using Bismark (v0.20.0) with Bowtie2 (v2.4.2) as the underlying aligner. Mapping was performed in paired-end mode with the following parameters: --score_min L,0, -0.4. Duplicate reads were then removed using the deduplicate_bismark function, and methylation calls were extracted using bismark_methylation_extractor with the options --paired-end, --bedGraph, --comprehensive and --scaffolds to obtain CpG methylation profiles in BEDGraph format. Data were processed adapting a previously published pipeline27 filtering out those cytosines with <10X combined coverage in all samples, and keeping annotated genes with at least 10 CpGs. Mapped cytosines were uniquely annotated to display methylation values using tabix and annotated to all transcript variants regardless of the overlapping using the intersect argument of bedtools (v2.24.0) for integration purposes. Genes were considered to have differential methylation whenever the difference between control and knockdown averages was more than |0.1| as performed in previous studies27. Plots such as box plots, violin plots, heatmaps and doughnut plots were created with the specific packages such as ggplot2 (v3.4.4), vioplot (v0.4.0) and heatmap.2. After integration with the RNA-seq data, functional annotation of the differentially methylated and differentially expressed genes was retrieved. For those genes with no annotation, a BLAST analysis of the unknown protein sequences was performed against the D. melanogaster proteome available in Uniprot as UP000000803 on the 28th of February of 2024. If genes had still no ID associated, they were not taken into consideration for further analysis. For the integration with the WGBS, after transcripts per million (TPM) calculation, genes with 0 TPM were excluded, correlation level and statistical significance were calculated using the test for association/correlation cor.test from R. Gene classification was done after BLASTp (e-value 10-10) of crayfish proteome to sequences of D. melanogaster available in FlyBase for TFs, meiosis and hematopoiesis genes; a list of human housekeeping genes88; a list of crayfish immune genes89; and a list of nervous system genes from Homarus americanus (American lobster)90.

For targeted bisulfite-sequencing, primers were designed to amplify the top differentially methylated regions (DMRs) identified using a sliding-window approach (1000 bp) and a Fisher's exact test performed between the four samples analyzed. Regions containing at least 15 CpGs were selected, and primer pairs were generated with the Free Bisulfite Primer Calculator (Zymo Research). All primer sequences are listed in Supplementary Table S8. Briefly, purified DNA was bisulfite converted using the EZ DNA Methylation-Gold kit and 250 ng of genomic DNA as input. Sequencing libraries were prepared and amplified using the Nextera XT Index kit v2 (Illumina, Cat. No. 15052163) and the KAPA HIFI HotStart ReadyMix. Libraries were purified using AMPure XP magnetic beads (Beckman Colter Cat. No. A63881), pooled and sequenced using the Illumina MiSeq V2 platform and paired-end 150 bp reads (PE 150 Nano). Results were analyzed using BisAMP91 setting parameters Ccut=301 and alpha=0. Unconversion ratios per DMR were calculated by averaging the ratio of each CpG, and significance was calculated using ANOVA. Box plots were generated using gplots (v3.1.3.1).

Bulk transcriptomics
RNA was processed using the RNA clean-up and concentrator kit (Zymo Research, Cat. No. R1013). Four samples (two control and two knockdown) were sequenced using the Illumina HiSeq 4k platform and 50 bp single reads (SR50). Raw data were mapped using Hisat2 (v2.2.1) with parameters -p 16 -q --rna-strandness R followed by sorting and indexing with Samtools (v1.9) after filtering out the second and third exon of the Dnmt1 gene, since its sequence overlapped with the injected dsRNA-Dnmt1 and was also sequenced. Counts were produced using featureCounts (v1.5.1) with the options -S 2 -a. Genes with low counts (<10 across samples) were filtered out, and differential expression analysis was performed using DESeq2. TPM values were calculated and used for comparison to qPCR data and integration analysis without filtering out genes with low counts. Integration analysis with WGBS to identify groups of genes differentially methylated and expressed, was done after removing Dnmt1 from the list of downregulated genes to avoid cofounding effects.

Nucleosome positioning analysis
Hemocyte samples from four Ctrl and six Dnmt1 KD animals 28 dpi were isolated and treated as described92. One million cells were fixed with 1% PFA for 10 min at RT, using 26 µl of 2.5 mM glycine to stop the reaction. Fixed cells were then washed with PBS and, after counting, resuspended in 300 µl swelling buffer [HEPES 25 mM, MgCl₂ 1 mM, KCl 10 mM, and 0.1% NP-40 at pH 7.8 and enriched with cOmplete protease inhibitor (Roche, Cat No. 11697498001)] and incubated for 10 min on ice. After centrifugation (5 min, 845 g), the pellet was resuspended in 90 µL MNase buffer (KCl 25 mM; MgCl₂ 4 mM; CaCl₂ 1 mM; Tris 50 mM at pH 7.4; and enriched with cOmplete protease inhibitor) and treated with 8 U of micrococcal nuclease (MNase) (New England Biolabs, Cat. No.: M0247S) for 15 min at 37 °C and stopped by adding 10 µL 10x sonication buffer (Tris 10 mM, EDTA 1 mM, 0.1% SDS at pH 8.0 and enriched with cOmplete protease inhibitor) followed by 10 min incubation on ice. Chromatin was then fragmented using a Covaris E220 sonicator (200 cycles, 75 pi pW for 15 min). After centrifugation, the supernatant was collected, and the DNA concentration measured. For each immunoprecipitation, 5 µg chromatin were incubated with 4 µg anti-H3 histone antibody (Abcam, Cat. No.: ab1791), for 2 h at 4 °C, followed by the addition of 25 µL magnetic Protein G beads (Promega, Cat. No. G747A), pre-equilibrated in sonication buffer, and overnight rotation at 4 °C. Beads were sequentially washed for 5 min each at RT with rotation in 500 µL of sonication buffer, High-salt buffer (HEPES 50 mM, NaCl 500 mM, EDTA 1 mM, Triton-X-100 1%, Na-deoxycholate 0.1%, SDS 0.1% and PMSF 0.5 mM at pH 7.9), Li buffer (Tris 20 mM, EDTA 1 mM, LiCl 250 mM, NP-40 0.5%, Na-deoxycholate 0.5%, PMSF 0.5 mM at pH 8.0], and twice with TE buffer (10 mM Tris and EDTA 1 mM at pH 8.0). DNA was eluted with 100 µL elution buffer (Tris 50 mM, 1 mM EDTA, SDS 1% SDS, NaCl 300 mM, NaHCO₃ 50 mM at pH 8.0) containing 2 µL Proteinase K (20 mg/mL) and incubated overnight at 65 °C to reverse crosslinking. DNA was finally purified the following day using 150 µl Ampure XP beads and eluted in 20 µL H₂O for quantification via 2200 TapeStation. Libraries were prepared using at least 25 ng of each sample via the Watchmaker kit (Watchmaker Genomics, Cat. No. 7K0102-024), with IDT xGen UDI-UMI Adapters (Integrated DNA Technologies, Cat. No. 10005925) and sequenced on an Illumina NovaSeq X+10 B platform using a Paired-end 100 reads (PE100; Supplementary Table S4). Raw reads were aligned using bwa (v0.7.15) with default parameters and 12 computational threads (-t 12). The resulting SAM output was converted to BAM format, sorted and indexed using Samtools (v1.5) and transformed into BED files using bedtools (v2.24.0). From here, data were analyzed using the NucTools package following the published pipeline93. First, reads were sorted by name and the occupancy of the scaffolds containing genes was calculated using a window of 10 bp. Occupancy was calculated per scaffold, in an analogous way as the per chromosome way established by the pipeline generating .occ files and normalized per library size and scaffold length. Normalized occupancy levels for the first 1500 bp were extracted per gene and averaged per sample to produce aggregated profiles. Kolgomorov-Smirnov statistical tests were used to assess differences in the nucleosome distribution, and Wilcoxon Rank sum tests to evaluate differential occupation. Stability and fuzziness were computed for all the replicates of each treatment group using a variation of the stable_nucs_replicates.pl script including a modification in line 277 to capture only fuzzy regions whose relative error was higher than a determined threshold. Thresholds for stability were 0.2, and for, fuzziness, 1.5. The percentage of stable/fuzzy regions of each scaffold were calculated and compared using a Wilcoxon-Rank Sum test in R.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The article is very interesting, clearly and fluently written. A wide variety of analyses have been performed to support the claims made. The methodology is well described, and the statistical analyses are appropriate. Overall, the entire article is very well developed, and only a few minor changes are required before final acceptance (see the attached PDF file for details on the minor corrections). One point I consider important is a final section listing the most frequently used abbreviations throughout the manuscript (those repeated more than twice). Otherwise, everything seems correct. Many congratulations to the authors for their excellent work.", "reviewer_2": "This manuscript examines how the Dnmt1 gene contributes to epigenetic regulation underlying invasiveness mechanisms in the marbled crayfish (Procambarus virginalis). This work also investigates the impact of environmental changes on Dnmt1 expression and their consequences for phenotypes.\nTo address this question, the authors combined complementary approaches, including RNAi-based knockdown, immune cell characterization by cytometry, single-cell RNA sequencing, and bisulfite whole-genome sequencing.\nThis is overall a very strong and well-executed study. The writing is clear and enjoyable to read. While the range of analytical approaches could appear overwhelming, the explanations are sufficiently detailed to ensure clarity. The introduction is particularly well structured, with a smooth flow that clearly highlights the underlying scientific problem.\nThe manuscript provides complete analytical information (sample sizes, statistical methods, significance criteria). Results are well substantiated, and the figures and tables (including supMat) are all correctly referenced in the text. The graphical presentation is clear and adequate, aside from a few minor typos that I detail below.\nLine 61: ‘)’ maybe a typo.\nLine 662 (figure 2 legend): “This assay was performed).” is a typo ? What the authors wanted to explain here ?\nLegend figure 3. Although group colors for plots a,c,d,e and f seems to be the same as f color legend, maybe it would be possible to indicate this information within the legend in order to avoid any misleading (even if I agree that it’s intuitive).\nData availability => Please add in which platform raw data will be positioned (ncbi ? Other ?).", "reviewer_3": "• What are the noteworthy results?\nDiaz-Larrosa et al. present a highly interesting and relevant study investigating how epigenetic regulation contributes to the invasiveness of marbled crayfish (Procambarus virginalis), a globally invasive, parthenogenetic species with a monoclonal genome. The study provides novel mechanistic insight by identifying Dnmt1-mediated DNA methylation as a critical regulator of phenotypic stability (“canalization”) in the marbled crayfish. Environmental downregulation or experimental knockdown of Dnmt1 leads to global DNA hypomethylation, altered chromatin organization, and transcriptional deregulation of genes linked to immune and neural functions. Reduced methylation enhances behavioral traits linked to invasion success —boldness, activity, and exploration—demonstrating a connection between epigenetic relaxation and invasive potential.\nThe findings show that Dnmt1 loss biases hemocyte differentiation toward granular immune cells while depleting neuronal precursors, resulting in a more robust immune system and altered neurogenesis. This bridges molecular, cellular, and behavioral layers of adaptation. The study supports the concept of the “General-Purpose Genotype”, proposing that epigenetic plasticity compensates for genetic uniformity in clonal species, enabling rapid responses to environmental challenges. By linking DNA methylation dynamics with ecological adaptability, the work provides a model for how epigenetic mechanisms facilitate biological invasions, offering a framework applicable to other clonal or low-diversity invaders.\n• Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nThe manuscript provides a significant contribution to the field of invasion biology, because it provides a mechanistic, epigenetic explanation for how clonal invasive species can achieve high adaptability and ecological success despite minimal genetic diversity. This research significantly advances invasion biology by shifting focus from purely genetic to epigenetic determinants of invasiveness, showing how DNA methylation plasticity drives behavioral, immunological, and ecological flexibility in a globally invasive species. However, the ecological components of this research could be outlined more in the introduction by mentioning the 2 central hypothesis addressed, namely the concept of the “General-Purpose Genotype” and the the “Evolution of Increased Competitive Ability” model. See more specific comments below.\n• Does the work support the conclusions and claims, or is additional evidence needed?\nThe manuscript presents a comprehensive and technically advanced study integrating behavioral assays with multi-omics (WGBS, RNA-seq, scRNA-seq, and MNase-seq) analyses to elucidate the epigenetic regulation of invasiveness in P. virginalis. The data analysis is methodologically sound, employing appropriate statistical frameworks and bioinformatic tools. However, several aspects of interpretation and conclusion require tempering. While the observed correlations between Dnmt1 knockdown, global hypomethylation, transcriptional dysregulation, and behavioral changes are compelling, the causal links among these levels remain inferred rather than experimentally demonstrated. Sample sizes in the omics datasets (n = 2/3 per group, depending on the method) limit statistical power, and some behavioral effects are modest and based on small cohorts. For the nucleosome analysis, the sample size is not stated and should be added. The extension of these results to general principles of invasiveness and immune advantage should be framed more cautiously, as ecological fitness was not directly assessed. Overall, the study is scientifically rigorous and provides a pioneering epigenetic framework for invasion biology, but its broader mechanistic and ecological conclusions should be presented as hypotheses for future validation rather than definitive claims. See more detailed comments below.\n• Are there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\nThe data analyses in the manuscript are largely robust, but not without a few weaknesses or overextensions (see below), requiring revisions, mainly in terms of rewording, prior publication. The data interpretation is mainly biologically coherent: environmental Dnmt1 downregulation → hypomethylation → chromatin destabilization → altered differentiation → behavioral plasticity. The research elegantly connects molecular and ecological scales — a rare achievement in invasion biology. However, there are in our opinion a few overinterpretations that should be downtoned. Below we give some reasons why/where we think that the presented results should be in parts interpreted more cautiosly:\n- Correlation vs. causation in methylation–expression relationships: The study reports that gene body hypomethylation correlates with both up- and downregulation of genes but interprets this as a causal regulatory effect of Dnmt1. However, correlation between methylation and transcription is not necessarily causative, especially when Dnmt1 knockdown may affect transcription through indirect chromatin remodeling or RNAi off-target effects. Without rescue experiments (e.g., Dnmt1 re-expression) or temporal sequencing of methylation vs. transcriptional changes, causality remains suggestive, not proven.\n- Sample sizes in multi-omics datasets: WGBS, RNA-seq, and MNase-seq each use only low numbers of biological replicates per group, which is below standard statistical power for genome-wide analysis. Although replication is high at the cellular and molecular level, the low n increases the risk of false positives in identifying differentially methylated or expressed genes. These number of replicates is also not very clearly reported, and we recommend to include an overview table of sample replicates per experiment/method.\n- scRNA-seq integration and cell-type annotation: Cell-type identities are inferred via homology to genes from other crustaceans (e.g., Pacifastacus leniusculus), but functional validation (e.g., FACS, immunostaining) is missing. This is acceptable for exploratory work but weakens the certainty that “hemocyte-derived neuronal precursors” (HDNPs) are truly neurogenic cells rather than transcriptionally similar subtypes.\n- Behavioral analysis statistics: Behavioral parameters are normalized and compared across relatively small groups (n = 8–9). Some parameters show only marginal p-values (e.g., p = 0.06 for “stop average duration”). Yet, these are still interpreted as meaningful behavioral enhancement. The study could have benefited from multivariate behavioral models or power analysis to substantiate these effects.\n- MNase-seq interpretation: The claim that “nucleosome destabilization underlies transcriptional changes” is plausible but not directly demonstrated — correlation of fuzziness with expression change is indirect evidence.\n- “Epigenetic canalization” claim: The concept that Dnmt1 maintains phenotypic canalization is intriguing but speculative. Demonstrating true canalization requires developmental trajectory experiments or quantitative measures of phenotypic variance, not just observation of altered differentiation after knockdown. Also, the introduction would benefit from mentioning such general hypothesis from the start.\n- Behavioral causality: Enhanced boldness and activity are attributed to reduced Dnmt1 and downstream neurogenic effects. However, no direct neurophysiological measurements (e.g., neuronal firing, neurotransmitter assays) support this link. The behavioral phenotype could result from systemic metabolic stress or immune activation rather than neurogenesis impairment.\n- Ecological generalization: The conclusion that “epigenetic relaxation promotes invasiveness” is reasonable but extrapolates from one clonal species to invasiveness in general. This generalization would need comparative or field-based validation (e.g., across multiple invasive crayfish or other clonal taxa).\n- EICA hypothesis alignment: The discussion links the immune findings to the “Evolution of Increased Competitive Ability” model. However, whether increased GC counts truly improve fitness or competitive ability was not tested. The immune enhancement is inferred, not demonstrated. Again, the introduction would benefit for the reader to have a mentioning of the EICA hypothesis.\n• Is the methodology sound? Does the work meet the expected standards in your field?\nThe methodology described is sound, rigorous, state-of-the-art and cutting-edge for non-model invertebrates. RNAi, adapted to crustaceans using optimized injection routes and validated via tissue-wide qPCR, represents a rare but solid functional genomics tool in non-model invertebrates. High-coverage, genome-wide methylation profiling at single-base resolution is the standard for methylome analysis. scRNA-seq using 10x Genomics platform is a cutting-edge method for cell-type resolution, enabling the identification of hemocyte subpopulations and differentiation trajectories. MNase-seq used to probe nucleosome positioning is an advanced approach that links chromatin architecture to transcriptional outcomes. Especially the integration of WGBS + RNA-seq + MNase-seq is convincingly combining epigenomic and transcriptomic layers, analytically sophisticated and current in systems epigenetics.\nAs minor caveats we want to mention, however, that the reference genome annotation for P. virginalis remains incomplete, which can limit functional interpretation of omics data. The authors mitigate this with cross-species BLAST annotation, a reasonable workaround but not ideal for pathway analysis.\n• Is there enough detail provided in the methods for the work to be reproduced?\nThe methods are detailed and include many essential reagents, instruments, kit names, parameter values, software versions and QC steps, but a few missing specifics should be added to make full, unambiguous reproduction straightforward.\n- Full bioinformatics parameter sets and scripts: please provide exact mapping parameters used for WGBS (e.g., aligner and arguments), bisulfite mapping tool/version, DMR calling method and thresholds (they mention |Δmethylation| > 0.1 but not statistical cutoffs), read filtering thresholds, and command-line options for Hisat2/CellRanger/NucTools. Provide the analysis scripts (R, python, shell) or a GitHub repository and mention random seeds used for any stochastic steps (important e.g., for PCA/UMAP/seurat integration).\n- Exact single-cell filtering thresholds & post-filter cell counts: the authors describe filters (mitochondrial >5%, >2000 or <20 features), but reproducibility needs the final per-sample cell counts after filtering, and the exact Seurat parameters used for normalization, variable feature selection (they gave top 2,000), dimensionality used, and clustering resolution(s).\n- MNase digestion and sonication specifics: the authors state “8 U MNase” and use Covaris E220 but do not give MNase digestion time/temperature, Covaris settings (duty cycle, cycles per burst, time), crosslinking conditions (concentration/time), or how samples were normalized between libraries. Please add exact experimental settings.\n- WGBS library prep and mapping QC thresholds: State bisulfite conversion QC metrics (unconversion rate threshold), minimal coverage cutoffs (the authors mention filtering out cytosines with <10X combined coverage — good, but confirm how combined across replicates was treated), adapter-trimming settings and mapping software for bisulfite reads (and versions).\n- Exact behavioral sample sizes and exclusion criteria: The authors note “animals with no locomotion were not analyzed” — state how many were excluded and final N per group per batch in a table. Also provide any blinding/randomization procedures used during behavioral scoring.\n- QC metrics for sequencing libraries (e.g., insert size distributions, average depth per sample, mapping rates) is usually included as Supplementary Tables.\n- Power analysis or rationale for chosen sample sizes for behavioral assays and omics replicates (acknowledging constraints for non-model organisms).\n- Ethical permit ID numbers (they say approved; please include approval reference).\nAdditional specific comments:\nL46-47: This sentence is not reflecting well the content of this paragraph. We suggest to use this sentence for starting a specific additional paragraph on behavioural traits in invasiveness, and include here also the EICA hypothesis and the General-Purpose Genotype hypothesis.\nL60: delete closing bracket „)“\nL64: replace „sibling species“ with „parent species“ to be consistent with the discussion wording.\nL79: For crayfish you stated first the scientific name, than the common name in brackets - stay consistent.\nL83ff: the introduction would clearly benefit from the statement of working hypotheses to be tested.\nL98: It is not clear from reading the introduction why the authors have done a temperature experiment. The ecological reasoning is missing from the intro and the discussion. It is only mentioned in the method section (LL396-403).\nL106: It is also not clear from the introduction why a biofloc environment was tested. What was the stressor here? Please clarify.\nL107: was the Dnmt1 expression in hemocytes done with qPCR? qPCR is mentioned in the next subsection as method, but not here, please clarify.\nL117: via „in“ vitro\nL127: replace knockdown with „KD“ as it is introduced.\nL177: no space between 55 and % (general comment, please check throughout the manuscript)\nL232: The other KD samples were taken 28 dpi. And here they were taken 1 and 6 months. This is not mentioned in the methods. What is the reason for the 6 months?\nLL245-247: In the methods is mentioned supplement material S6, where a lot of other genes are listed. Why discussing only these genes presented here, while the other results are only mentioned in supplement figure S2d?\nL258: There is no figure s2f.\nL329: replace knockdown with „KD“\nL349: replace knockdown with „KD“\nL351: do not start a sentence with abbreviated genus name.\nL364: please specify whether the non-invasive population was from the same species\nLL376-378: can you provide any hypothesis for the potential impact on adult neurogenesis?\nL379: this is a very blunt final statement. Please specify how this research could benefit strategies for invasion management, as this seems a bit far fetched.\nBest regards,", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers’ comments\nReviewer #1\nThe article is very interesting, clearly and fluently written. A wide variety of analyses have\nbeen performed to support the claims made. The methodology is well described, and the\nstatistical analyses are appropriate. Overall, the entire article is very well developed, and only\na few minor changes are required before final acceptance (see the attached PDF file for\ndetails on the minor corrections). One point I consider important is a final section listing the\nmost frequently used abbreviations throughout the manuscript (those repeated more than\ntwice). Otherwise, everything seems correct. Many congratulations to the authors for their\nexcellent work.\n>> We thank the reviewer for these very encouraging comments. We accepted the large\nmajority of the suggested edits into the manuscript. A comprehensive list of abbreviations is\nnow provided as an appendix at the end of the Supplementary Information document.\nReviewer #2\nThis manuscript examines how the Dnmt1 gene contributes to epigenetic regulation\nunderlying invasiveness mechanisms in the marbled crayfish (Procambarus virginalis). This\nwork also investigates the impact of environmental changes on Dnmt1 expression and their\nconsequences for phenotypes.\nTo address this question, the authors combined complementary approaches, including RNAi-\nbased knockdown, immune cell characterization by cytometry, single-cell RNA sequencing,\nand bisulfite whole-genome sequencing.\nThis is overall a very strong and well-executed study. The writing is clear and enjoyable to\nread. While the range of analytical approaches could appear overwhelming, the explanations\nare sufficiently detailed to ensure clarity. The introduction is particularly well structured, with a\nsmooth flow that clearly highlights the underlying scientific problem.\nThe manuscript provides complete analytical information (sample sizes, statistical methods,\nsignificance criteria). Results are well substantiated, and the figures and tables (including\nsupMat) are all correctly referenced in the text. The graphical presentation is clear and\nadequate, aside from a few minor typos that I detail below.\n>> We thank the reviewer for these very encouraging comments.\nLine 61: ‘)’ maybe a typo.\n>> The extra parenthesis has been removed.\nLine 662 (figure 2 legend): “This assay was performed).” is a typo? What the authors wanted\nto explain here?\n>> The incomplete phrase has been removed.\nLegend figure 3. Although group colors for plots a,c,d,e and f seems to be the same as f\ncolor legend, maybe it would be possible to indicate this information within the legend in\norder to avoid any misleading (even if I agree that it’s intuitive).\n>> Clarified as suggested.\nData availability => Please add in which platform raw data will be positioned (ncbi? Other?).\n>> Specified as requested in LL674-675: “scRNA-seq, WGBS, RNA-seq and MNase-seq\ndata are available from the NCBI Gene Expression Omnibus (GEO).”.\nReviewer #3\nWe thank the reviewer for her very encouraging general comments. A detailed point-by-point\nresponse to the specific comments is provided below:\n1. Correlation vs. causation in methylation–expression relationships: The study reports that\ngene body hypomethylation correlates with both up- and downregulation of genes but\ninterprets this as a causal regulatory effect of Dnmt1. However, correlation between\nmethylation and transcription is not necessarily causative, especially when Dnmt1\nknockdown may affect transcription through indirect chromatin remodeling or RNAi off-target\neffects. Without rescue experiments (e.g., Dnmt1 re-expression) or temporal sequencing of\nmethylation vs. transcriptional changes, causality remains suggestive, not proven.\n>> We have toned down the corresponding text to avoid implying direct cause-and-effect\nrelationships. In particular, we replaced “are underpinned by” with “coincide with” in the\nAbstract (L19) and clarified in the Discussion (LL383-386) that a causal relationship would\nrequire further experimental validation, such as time-resolved analyses or Dnmt1 rescue\nexperiments.\n2. Sample sizes in multi-omics datasets: WGBS, RNA-seq, and MNase-seq each use only\nlow numbers of biological replicates per group, which is below standard statistical power for\ngenome-wide analysis. Although replication is high at the cellular and molecular level, the low\nn increases the risk of false positives in identifying differentially methylated or expressed\ngenes. These number of replicates is also not very clearly reported, and we recommend to\ninclude an overview table of sample replicates per experiment/method.\n>> A table containing the number of biological replicates per experiment/method has now\nbeen included as suggested as Supplementary table S5. Additionally, the main text has been\nrevised, and sample sizes have been explicitly stated where appropriate: Results L234;\nLL254-255.\n3. scRNA-seq integration and cell-type annotation: Cell-type identities are inferred via\nhomology to genes from other crustaceans (e.g., Pacifastacus leniusculus), but functional\nvalidation (e.g., FACS, immunostaining) is missing. This is acceptable for exploratory work\nbut weakens the certainty that “hemocyte-derived neuronal precursors” (HDNPs) are truly\nneurogenic cells rather than transcriptionally similar subtypes.\n>> We have now clarified this point in the Results (LL198-199) and Methods (LL542-545)\nsections. Specifically, we state that cluster annotations were inferred using differentially\nexpressed gene sets and homology-based comparisons to published crustacean datasets. In\nthe discussion, we emphasize that these identities should be considered putative.\n4. Behavioral analysis statistics: Behavioral parameters are normalized and compared\nacross relatively small groups (n = 8–9). Some parameters show only marginal p-values\n(e.g., p = 0.06 for “stop average duration”). Yet, these are still interpreted as meaningful\nbehavioral enhancement. The study could have benefited from multivariate behavioral\nmodels or power analysis to substantiate these effects.\n>> We would like to clarify that a multivariate behavioral analysis was already performed in\nthe original version of the manuscript, as described by Fossat et al. (2014). The results\nrevealed a statistically significant separation between control and Dnmt1 KD groups (p<0.05;\nMonte-Carlo permutation test) supporting a global difference in behavior.\n5. MNase-seq interpretation: The claim that “nucleosome destabilization underlies\ntranscriptional changes” is plausible but not directly demonstrated — correlation of fuzziness\nwith expression change is indirect evidence.\n>> We have revised the text to describe nucleosome destabilization as correlating with\ntranscriptional changes in the Results section (LL310-312) and added a statement in the\nDiscussion (LL383-386) noting that direct mechanistic evidence would require further\nexperiments. Additionally, we replaced “key mechanism influencing transcriptional changes”\nwith “mechanism associated with transcriptional changes” in the Abstract (LL20-21).\n6. “Epigenetic canalization” claim: The concept that Dnmt1 maintains phenotypic canalization\nis intriguing but speculative. Demonstrating true canalization requires developmental\ntrajectory experiments or quantitative measures of phenotypic variance, not just observation\nof altered differentiation after knockdown. Also, the introduction would benefit from\nmentioning such general hypothesis from the start.\n>> We have now added a brief explanation of the canalization concept to the Introduction\n(LL60-62) and toned down the interpretation in the Discussion.\n7. Behavioral causality: Enhanced boldness and activity are attributed to reduced Dnmt1 and\ndownstream neurogenic effects. However, no direct neurophysiological measurements (e.g.,\nneuronal firing, neurotransmitter assays) support this link. The behavioral phenotype could\nresult from systemic metabolic stress or immune activation rather than neurogenesis\nimpairment.\n>> Several observations support that the behavioral phenotype is Dnmt1-dependent rather\nthan a nonspecific response. First, the dsGFP control accounts for handling and general\ndsRNA-related effects. Second, no differences were observed in survival, weight,\nreproduction, or general health, and transcriptomic data did not indicate broad metabolic\nchanges. Finally, systemic immune activation typically reduces locomotion and exploratory\nbehavior (Gruber et al., 2014)1, contrasting with the increased activity observed here.\nNevertheless, we have now used more cautious wording in the Discussion (L333;\n“underpinned by” replaced with “accompanied”).\n1. Gruber, C. et al. Variation in resistance to the invasive crayfish plague and immune\ndefence in the native noble crayfish. Ann. Zool. Fenn. 51, 371–389 (2014).\n8. Ecological generalization: The conclusion that “epigenetic relaxation promotes\ninvasiveness” is reasonable but extrapolates from one clonal species to invasiveness in\ngeneral. This generalization would need comparative or field-based validation (e.g., across\nmultiple invasive crayfish or other clonal taxa).\n>> We have now restricted the sentence to P. virginalis (L372).\n9. EICA hypothesis alignment: The discussion links the immune findings to the “Evolution of\nIncreased Competitive Ability” model. However, whether increased GC counts truly improve\nfitness or competitive ability was not tested. The immune enhancement is inferred, not\ndemonstrated. Again, the introduction would benefit for the reader to have a mentioning of\nthe EICA hypothesis.\n>> We agree that our data only allows inference of a potentially stronger immune system in\nDnmt1 KD animals. Accordingly, we now phrase this as a suggestion in the Discussion\n(LL360-362), saying the increase of GC counts and the upregulation of effector genes in our\nDnmt1 KD crayfish also suggest a potentially stronger immune system, which could be\nadvantageous in new environments. To improve clarity, we now also introduce the Evolution\nof Increased Competitive Ability (EICA) hypothesis earlier in the manuscript, in the context of\nphysiological adaptability (Introduction, LL53–56).\n10. As minor caveats we want to mention, however, that the reference genome annotation for\nP. virginalis remains incomplete, which can limit functional interpretation of omics data. The\nauthors mitigate this with cross-species BLAST annotation, a reasonable workaround but not\nideal for pathway analysis.\n>> We agree with the reasoning of the reviewer and have provided a detailed description of\nour approach in the Methods section.\n11. Full bioinformatics parameter sets and scripts: please provide exact mapping parameters\nused for WGBS (e.g., aligner and arguments), bisulfite mapping tool/version, DMR calling\nmethod and thresholds (they mention |Δmethylation| > 0.1 but not statistical cutoffs), read\nfiltering thresholds, and command-line options for Hisat2/CellRanger/NucTools. Provide the\nanalysis scripts (R, python, shell) or a GitHub repository and mention random seeds used for\nany stochastic steps (important e.g., for PCA/UMAP/seurat integration).\n>> The requested bioinformatic parameters have been included under each specific Methods\nsubsection: scRNA-seq (LL527-529); WGBS/MiSeq (LL565-572; 593-594); RNA-seq (LL609-\n610; NucTools (LL648-650). The different scripts from NucTools can be found in the\nmentioned reference or in https://homeveg.github.io/nuctools/. Pipeline and scripts were ran\nas established with the changes, thresholds and parameters mentioned in the Methods\nsection. No manual random seed was set, but all analyses were repeated to confirm result\nstability.\n12. Exact single-cell filtering thresholds & post-filter cell counts: the authors describe filters\n(mitochondrial >5%, >2000 or <20 features), but reproducibility needs the final per-sample\ncell counts after filtering, and the exact Seurat parameters used for normalization, variable\nfeature selection (they gave top 2,000), dimensionality used, and clustering resolution(s).\n>> Pre- and post-filter cell counts for each sample are reported in Supplementary Table S1.\nIn addition, we have expanded the Methods section (LL536-539) to include the exact Seurat\nparameters used for normalization, feature selection, and clustering. For the integration of\nthe 3 control and 3 KD samples, we also added additional information to the Methods (L533):\n“We downsampled each replicate to 622 cells, the number of cells of the least represented\nsample”.\n13. MNase digestion and sonication specifics: the authors state “8 U MNase” and use\nCovaris E220 but do not give MNase digestion time/temperature, Covaris settings (duty\ncycle, cycles per burst, time), crosslinking conditions (concentration/time), or how samples\nwere normalized between libraries. Please add exact experimental settings.\n>> Detailed experimental parameters have been added to the Methods (LL622-645), as\nrequested.\n14. WGBS library prep and mapping QC thresholds: State bisulfite conversion QC metrics\n(unconversion rate threshold), minimal coverage cutoffs (the authors mention filtering out\ncytosines with <10X combined coverage — good, but confirm how combined across\nreplicates was treated), adapter-trimming settings and mapping software for bisulfite reads\n(and versions).\n>> All relevant QC metrics, including raw read numbers, mapping efficiency, sequencing\ndepth, post-filtering coverage, and bisulfite conversion ratios, are provided in Supplementary\nTable S2. Bisulfite conversion efficiency exceeded 99.7 % for all samples. As stated in the\nMethods, cytosines with a coverage below 10X were excluded from further analysis, and only\nsites covered in all samples were kept. Adapter trimming settings and mapping software with\nversions have now been included in the Methods section (LL565-572).\n15. Exact behavioral sample sizes and exclusion criteria: The authors note “animals with no\nlocomotion were not analyzed” — state how many were excluded and final N per group per\nbatch in a table. Also provide any blinding/randomization procedures used during behavioral\nscoring.\n>> Three independent batches of animals with similar body weight were recorded and\nanalyzed (three control and three KD animals per batch; total n=9 per group). One KD animal\nexhibited no locomotion and was therefore excluded from analysis, following the same\nexclusion criterion as other studies (Fossat et al., 2014). Animal identities were revealed only\nafter completion of data analysis for treatment group assignment and comparison. This\ninformation has now been added to the Methods (LL476; LL487-489).\n16. QC metrics for sequencing libraries (e.g., insert size distributions, average depth per\nsample, mapping rates) is usually included as Supplementary Tables.\n>> Comprehensive QC metrics have been provided in the Supplementary Information.\nSpecifically, Supplementary Tables S1-S4 include QC statistics for the scRNA-seq, WGBS,\nRNA-seq, and MNase-seq libraries, respectively.\n17. Power analysis or rationale for chosen sample sizes for behavioral assays and omics\nreplicates (acknowledging constraints for non-model organisms).\n>> Sample sizes were determined by feasibility constraints inherent to behavioral and omics\nwork in non-model organisms. For behavioral assays, 18 animals (9 per group) were\nanalyzed across 3 independent rounds of recordings, limited by the availability of 6 isolation\ntanks. This design was sufficient to detect consistent behavioral trends. For omics datasets,\nwe used 3 control and 3 KD biological replicates for scRNA-seq, 4 control and 6 KD samples\nfor MNase-seq, and a matched set of 2 control and 2 KD samples for WGBS and RNA-seq,\nrespectively to enable direct per-sample comparisons. Differential methylation and gene\nexpression was extensively validated by amplicon sequencing and qPCR, respectively, in 3\nor more independent samples.\n18. Ethical permit ID numbers (they say approved; please include approval reference).\n>> Ethical permit numbers have now been provided (Methods; L410).\n19. L46-47: This sentence is not reflecting well the content of this paragraph. We suggest to\nuse this sentence for starting a specific additional paragraph on behavioural traits in\ninvasiveness, and include here also the EICA hypothesis and the General-Purpose Genotype\nhypothesis.\n>> We have now restructured the Introduction (LL53-62) to create a distinct paragraph\nfocusing on behavioural and physiological flexibility as key features of invasive success. We\nuse this to then introduce the Increased Competitive Ability (EICA) and General-Purpose\nGenotype (GPG) hypotheses providing a connection between phenotypic plasticity, ecology\nand DNA methylation as a mediator of advantageous processes in invasiveness.\n20. L60: delete closing bracket „)“\n>> Corrected as suggested.\n21. L64: replace „sibling species“ with „parent species“ to be consistent with the discussion\nwording.\n>> Corrected as suggested.\n22. L79: For crayfish you stated first the scientific name, than the common name in brackets\n- stay consistent.\n>> Corrected as suggested, throughout the manuscript.\n23. L83ff: the introduction would clearly benefit from the statement of working hypotheses to\nbe tested.\n>> A clear statement of the working hypothesis has been included at the end of the\nIntroduction (LL92-93).\n24. L98: It is not clear from reading the introduction why the authors have done a\ntemperature experiment. The ecological reasoning is missing from the intro and the\ndiscussion. It is only mentioned in the Methods section (LL396-403).\n>> We used temperature fluctuations to test whether Dnmt1 expression responds to rapid\nenvironmental changes, which can be encountered by invasive species during dispersal.\nTemperature is a biologically relevant and easily controlled parameter, which has already\nbeen shown to affect crayfish methylome (Tönges et al., 2021). We have now added a short\nexplanation in the Introduction (LL93-95) stating this rationale.\n25. L106: It is also not clear from the introduction why a biofloc environment was tested.\nWhat was the stressor here? Please clarify.\n>> Biofloc systems are semiautonomous environments containing diverse microbial and\nalgal communities that can influence crayfish methylome (Coutinho et al., 2025).\nTransitioning animals from a biofloc to a clearwater aquarium environment, thus mimics an\necologically relevant shift in water quality, nutrient composition, and microbial exposure. We\nhave now clarified this rationale in the Introduction (LL93-95).\n26. L107: was the Dnmt1 expression in hemocytes done with qPCR? qPCR is mentioned in\nthe next subsection as method, but not here, please clarify.\n>> Clarified as suggested.\n27. L117: via „in“ vitro\n>> Corrected as suggested.\n28. L127: replace knockdown with „KD“ as it is introduced.\n>> Corrected as suggested.\n29. L177: no space between 55 and % (general comment, please check throughout the\nmanuscript)\n>> Corrected as suggested, throughout the manuscript.\n30. L232: The other KD samples were taken 28 dpi. And here they were taken 1 and 6\nmonths. This is not mentioned in the methods. What is the reason for the 6 months?\n>> “1 month” corresponds to 28 days post-injection. The 6-month time point was included to\nassess the persistence of methylation changes following Dnmt1 KD. This has now been\nclarified in the Results. L247.\n31. LL245-247: In the methods is mentioned supplement material S6, where a lot of other\ngenes are listed. Why discussing only these genes presented here, while the other results\nare only mentioned in supplement figure S2d?\n>> The highlighted genes from Figure 4e (ProPO, SOD, Notch, klumpfuss) were selected for\ndiscussion because they are directly involved in arthropod hematopoiesis and immunity,\nwhile the rest of the validated genes have other functions. This has now been clarified in the\nResults (L260).\n32. L258: There is no figure s2f.\n>> Two panels were mistakenly labeled as S2d and there was no S2f in the Supplementary\nFigure. This has now been corrected.\n33. L329: replace knockdown with „KD“\n>> Corrected as suggested.\n34. L349: replace knockdown with „KD“\n>> Corrected as suggested.\n35. L351: do not start a sentence with abbreviated genus name.\n>> Corrected as suggested.\n36. L364: please specify whether the non-invasive population was from the same species\n>> The comparisons discussed refer to populations of the same species. For the tubeworm\nand mussel, the comparison is made between newly and long-established populations, while\nin the case of the ascidian, it compares invasive to non-invasive populations. We have\nclarified this in the Discussion (LL374-377).\n37. LL376-378: can you provide any hypothesis for the potential impact on adult\nneurogenesis?\n>> We have clarified this point in the Discussion (LL394-398). We suggest that a decrease in\nneuronal renewal, which mainly targets clusters innervating the olfactory and accessory\nlobes that are essential for detecting and integrating olfactory, visual and mechanosensory\nsignals (Beltz and Benton, 2017). This may alter sensory processing, stress responsiveness\nand overall behaviour, impacting adaptation to novel environments.\n38. L379: this is a very blunt final statement. Please specify how this research could benefit\nstrategies for invasion management, as this seems a bit far fetched.\n>> We have rewritten the final paragraph.\nReviewer #4\n>> We thank the reviewer for his/her/their support."}, {"round": 2, "reviewer_comments": {"reviewer_3": "We have reviewed the revised manuscript anf the authors' response letter. We are satisfied with the way the authors have addressed our previous comments. The additions regarding the methodology and study design, as well as the explanations in the introduction, have clearly improved the clarity of the manuscript.\nRegarding methodology, the sample size details are now included in the supplement, the parameters of bioinformatic analyses have been added and explained in detail. Statements regarding causality of invasive traits and epigenetic modification have been toned down. Important contextual information about epigenetic canalisation, EICA and GPG hypothesis was added in the introduction.\nWe recommend acceptance at this stage, assuming the minor changes below are made:\n1. We appreciate the clarification that the bioinformatic analyses were repeated. To strengthen the robustness, please specify how many times the analyses using random clustering were repeated.\n2. The addition of the hypothesis addresses our comments. Please consider expanding this section to cleary state the ecological parameters tested: temperature and water quality (different composition in nutrients and microbial exposure)", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Second round of revisions\nReviewer #3\nWe thank the reviewer for her very encouraging general comments. A detailed point-by-point\nresponse to the specific comments is provided below:\n1. We appreciate the clarification that the bioinformatic analyses were repeated. To strengthen\nthe robustness, please specify how many times the analyses using random clustering were\nrepeated.\n>> The bioinformatic analyses involving stochastic steps (including dimensionality reduction and\nclustering in Seurat) were repeated multiple times using identical input data and parameters and\nthe resulting clustering structure was consistent across runs. No explicit random seed was set\nduring these analyses. This information has now been clarified in the Methods section under\nscRNA-seq data analysis subsection.\n2. The addition of the hypothesis addresses our comments. Please consider expanding this\nsection to cleary state the ecological parameters tested: temperature and water quality (different\ncomposition in nutrients and microbial exposure)\n>> Ecological parameters tested are now stated at the end of the Introduction to increase clarity."}]

Decision letter