Introduction
Environmental changes are jeopardizing worldwide biodiversity. The increase in atmospheric CO2 concentration due to human greenhouse gas emissions is drastically modifying Earth's ecosystems (Convey & Peck Reference Convey and Peck2019). Climate change has caused a significant increase in atmospheric temperature, shifts in precipitation and atmospheric circulation patterns and a higher frequency of extreme events (e.g. droughts, floods or heatwaves; Convey & Peck Reference Convey and Peck2019). In this respect, isolated and highly fragmented areas of our planet, such as the polar regions and hence Antarctica, are likely to be thoroughly affected by such changes in terms of biodiversity composition and loss. The Antarctic terrestrial ecosystem is highly susceptible not only to phenological mismatches, but also to the introduction and establishment of alien species that alter the natural equilibria of native ones (e.g. Convey & Peck Reference Convey and Peck2019, Wauchope et al. Reference Wauchope, Shaw and Terauds2019). In contrast to other southern landmasses, Antarctica is more susceptible to climatic variations due to its environmental conditions. For instance, global warming firstly affects polar regions, leading to ice melting, which contributes to 1) the ice level increasing, 2) marine chemical properties alterations and 3) the appearance of ice-free areas, which allow for the establishment of non-native species (Convey & Peck Reference Convey and Peck2019) and the intercontinental dispersal of resident biota. In addition, even if Antarctica is protected under international regulations, spatially explicit conservation planning (i.e. the prioritization of planned actions) is still in its infancy (Wauchope et al. Reference Wauchope, Shaw and Terauds2019), whereas anthropogenic disturbance is increasing (Convey & Peck Reference Convey and Peck2019).
It has been suggested that arthropods' thermal sensitivity may follow a latitudinal gradient, with tropical species being more vulnerable to temperature increases than polar biota, because the former are already closer to their upper physiological optima, whereas the latter live well below this limit (Deutsch et al. Reference Deutsch, Tewksbury, Huey, Sheldon, Ghalambor, Haak and Martin2008). Moreover, other studies highlighted the importance of micro-environment conditions (e.g. nutrients, humidity, etc.) as the primary variable impacting species wellness and population growth during temperature shifts (Convey et al. Reference Convey, Pugh, Jackson, Murray, Ruhland, Xiong and Day2002, Bokhorst et al. Reference Bokhorst, Huiskes, Convey, Van Bodegom and Aerts2008).
How environmental changes and global warming may affect the resident biota's physiology has been studied, using molecular approaches, in some Antarctic marine groups, such as molluscs (Reed & Thatje Reference Reed and Thatje2015), echinoderms, crustaceans (González-Aravena et al. Reference González-Aravena, Calfio, Mercado, Morales-Lange, Bethke, De Lorgeril and Cárdenas2018) and bony fishes (Bilyk et al. Reference Bilyk, Vargas-Chacoff and Cheng2018). In comparison, very little is known on how the Antarctic terrestrial fauna endures increasing temperatures (Michaud et al. Reference Michaud, Benoit, Lopez-Martinez, Elnitsky, Lee and Denlinger2008, Everatt et al. Reference Everatt, Convey, Worland, Bale and Hayward2014), and almost no molecular data are available for microarthropods (Everatt et al. Reference Everatt, Convey, Worland, Bale and Hayward2013).
Collembola (= springtails) is one of the most represented groups of strictly terrestrial animals in Antarctica (McGaughran et al. Reference McGaughran, Stevens, Hogg and Carapelli2011). Springtails play a pivotal role in Antarctic soil functioning; therefore, significant efforts are required to understand how these taxa are responding to climate and environmental changes. A significant molecular survey (using microarrays) has been conducted on the effects of cold and dehydration stress on springtail species from the Maritime Antarctic (Burns et al. Reference Burns, Thorne, Hillyard, Clark, Convey and Worland2010). These should be expanded to include representative species from Continental Antarctica and to study, besides cold tolerance, the physiological and molecular mechanisms activated (if any) with short- and long-term heat exposure. These results will be relevant to more in-depth investigations of how basal Hexapoda respond to global warming and phenological mismatches (Everatt et al. Reference Everatt, Convey, Worland, Bale and Hayward2013).
The Antarctic springtail Cryptopygus terranovus is an endemic species distributed along the northern Victoria Land coast. It can be considered an important species for studying how global warming can threaten continental springtails, as it has been recently investigated on morphological and molecular grounds (data not published yet) and its distribution range covers an area where several international research stations are located (Caruso et al. Reference Caruso, Hogg, Carapelli, Frati and Bargagli2009). Phylogeographical and phylogenetic studies demonstrated that this species was present in Antarctica and was already well differentiated from other Antarctic springtail lineages during the mid- to late Miocene, implicating it as a long-term resident of the continent (Carapelli et al. Reference Carapelli, Leo and Frati2017). As such, C. terranovus managed to survive in local refugia during recent glacial cycles, becoming the predominant springtail species along a territory influenced by the volcanic activity of Mount Melbourne (Carapelli et al. Reference Carapelli, Leo and Frati2017). Several molecular population genetics studies have been recently conducted on the species (Fanciulli et al. Reference Fanciulli, Summa, Dallai and Frati2001, Carapelli et al. Reference Carapelli, Leo and Frati2017, Collins et al. Reference Collins, Hogg, Convey, Barnes and McDonald2019), and its phylogenetic relatedness to the better-studied and co-generic Cryptopygus antarcticus has been assessed (Carapelli et al. Reference Carapelli, Fanciulli, Frati and Leo2019). Everatt et al. (Reference Everatt, Convey, Worland, Bale and Hayward2013) established that C. antarcticus is able to survive during short-term heat exposure (72 h at 35°C) when subjected to a slow warming rate. However, the survival rate of C. antarcticus decreases during long periods (> 40 days) at 10°C. For these reasons, further investigations are needed to explore the molecular and physiological mechanisms that are activated during long-term heat exposure and are therefore responsible for, or at least associated with, heat endurance in Antarctic springtails. In this respect, we focused on the identification of pathways that are influenced in C. terranovus under artificially warm conditions by means of the first transcriptome analysis performed on this species.
Methods
Sample collection and preparation
Individuals of C. terranovus were sampled during the Italian National Antarctic Program (PNRA) expedition in the summer of 2018 at the Mario Zucchelli Station (MZS; Victoria Land, 74°42′02″S 164°06′45″E; at 70 m altitude). Individuals were subdivided in a control (CT) group and a treatment (HE) group. The temperature for the former was 5.4°C (SD = 3.27°C; data registered with a data logger for 20 days at the sampling location). The HE group was collected and stressed in plastic boxes at MZS with stones at 18°C for 20 days under controlled conditions (24 h full daylight, as expected during the Antarctic summer, and 62% relative humidity) and fed ad libitum with natural moss before analysis. Therefore, the average temperature difference during the experimental time between the CT and HE groups was ~12.5°C. Both groups were then transferred in RNAlater solution (Sigma-Aldrich) and stored at -20°C for further processing.
RNA extraction and sequencing
Three samples of C. terranovus were screened for each of the CT and HE groups (biological replicates). Each sample was composed of a pool of three individuals (i.e. for a total of nine specimens for each of the CT and HE groups), whose RNA was extracted with TRI Reagent® solution (Sigma-Aldrich) following the manufacturer's instructions. The concentration and quality of the RNA were checked by capillary electrophoresis using Agilent High Sensitivity RNA TapeStation on a BioAnalyzer 2100 instrument (Agilent Technologies). Library preparation was performed using Lexogen's SENSE™ mRNA-Seq Kit V2 as per the manufacturer's instructions. All of the libraries were sent to an external sequencing service (ARGO Open Lab Platform for Genome Sequencing, AREA Science Park, Trieste, Italy) to be sequenced on a NovaSeq 6000 system (Illumina; 100 bp paired end reads). Raw sequencing reads are available at the Sequence Read Archive (SRA; National Center for Biotechnology Information (NCBI)) with the BioProject ID PRJNA645792.
De novo transcriptome assembly and annotation
Raw sequences were quality checked using FastQC and MultiQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc, Ewels et al. Reference Ewels, Magnusson, Lundin and Käller2016). In order to tackle possible assembly bias due to overrepresented sequences and therefore reduce assembly times, read files were merged and normalized using BBNorm (BBTools: http://sourceforge.net/projects/bbmap) with the following flags: min = 1, max = 100, prefilter = t, fixspikes = t. De novo transcriptome assembly was performed with the Oyster River Protocol (ORP) v.2.3.1 (MacManes Reference MacManes2018) with default settings. Briefly, the protocol performs an initial trimming with error correction and then a de novo assembly using three different programs: Trinity v.2.8.4 (Grabherr et al. Reference Grabherr, Haas, Yassour, Levin, Thompson and Amit2011), Trans-ABySS v.2.0.1 (Robertson et al. Reference Robertson, Schein, Chiu, Corbett, Field and Jackman2010) and SPAdes v.3.13.0 (Bushmanova et al. Reference Bushmanova, Antipov, Lapidus and Prjibelski2019), merging their results as the final step.
In order to improve completeness assembly, reads were mapped back on the assembled transcriptome and unmapped reads were collected and mapped on the intermediate assemblies generated by ORP with Salmon v.0.13.1 (Patro et al. Reference Patro, Duggal, Love, Irizarry and Kingsford2017). Contigs with coverage > 10 were then added to the final transcriptome and sequences < 200 nt were removed. Cryptopygus terranovus rRNAs and mtDNAs available on GenBank (NCBI) in February 2020 were downloaded, clustered with cd-hit-est v.4.7 (Fu et al. Reference Fu, Niu, Zhu, Wu and Li2012) and used as database in a blastn+ v.2.7.1 (Camacho et al. Reference Camacho, Coulouris, Avagyan, Ma, Papadopoulos, Bealer and Madden2009), against which the whole transcriptome was queried in order to filter out high-similarity hits (e-value < 1e-50), minimizing ribosomal and mitochondrial contaminations and hence improving the transcript signal. Final completeness was assessed with BUSCO v.3 against the Arthropoda database (Seppey et al. Reference Seppey, Manni, Zdobnov and Kollmar2019). Transcript annotation was performed using the annotaM pipeline (https://gitlab.com/54mu/annotaM), which annotates the assemblies by querying several databases (UniprotKb, OrthoDB, PFAM) to recover the gene IDs, Gene Ontologies (GOs) and associated pathways.
Mapping, differentially expressed gene identification and hypergeometric tests
Gene expression values were estimated as transcripts per million (TPM) and counted with Salmon v.0.13.1 (Patro et al. Reference Patro, Duggal, Love, Irizarry and Kingsford2017) using the not-normalized reads in mapping-based mode. The NOISeq package (Tarazona et al. Reference Tarazona, Furió-Tarí, Turrà, Pietro, Nueda, Ferrer and Conesa2015) was used in the R environment to identify differentially expressed genes (DEGs) between the two conditions. The noiseqbio function was applied (Protocol S1) and DEGs with a probability cut-off of 0.9999 and log2-fold change > |2| were kept for statistical investigations. An enrichment analysis based on the hypergeometrical test of the correspondent GOs and PFAMs was performed (Protocols S2 & S3) and only the ones represented by a significant P-value (< 5 ×10-4) and observation counts > 3 were retained.
Results
Wild temperature trend
CT springtails were sampled in the wild environment after 20 days of temperature data acquisition. During this time, registered climate conditions of the soil (recorded every 10 min between 27 December 2017 and 15 January 2018) varied between 0.11°C and 12.60°C, with an average temperature of 5.4°C (SD = 3.27°C). Therefore, a difference of ~12.5°C was recorded between the CT and HE samples.
De novo assembled transcriptome
Paired-end Illumina sequencing resulted in ~14–41 million reads per sample (Table SI). After the filtering steps described in the ‘Methods’ section, the de novo assembled transcriptome had a total of 64,054 contigs with an average length of 369.25 nt (Table SII). The N50 statistic, defined as the sequence length of the shortest contig at 50% of the total transcriptome length (useful for assessing the mean quality of a transcriptome), was 345 nt, and the BUSCO report against the Arthropoda database indicated the completeness as acceptable (as C. terranovus is neither a model organism nor phylogenetically close to one of them) while also evidencing a certain degree of assembly fragmentation: 29.4% complete (21.7% single copy, 7.7% duplicated), 47.1% fragmented and 23.5% missing BUSCOs. The GC content in the final assembled transcriptome was 45.53 ± 6.08%.
Functional annotation
A total of 45,303 genes were annotated in at least one of the used databases, leaving 29.3% of transcript unsuccessfully annotated (Table SII). In detail, 42,309 genes were OrthoDB hits, 34,143 genes were Uniprot-Swissprot hits and 15,936 genes contained at least one PFAM domain (Fig. 1). Following the GO annotation, a total of 12,747 different GO terms were recovered, of which 7998 represent biological processes (BPs), 3317 represent molecular functions (MFs) and 1432 represent cellular components (CCs). GO terms were distributed across 13 different GO levels. Between the 5th and 13th levels, there were 78.3% GO terms for BPs, 52.3% terms for CCs and 68.2% terms for MFs, indicating that a good resolution was achieved by the functional annotation process (Fig. 2).
Fig. 1. Venn diagram of the annotated genes shared by and unique to each database. Numbers shown in the graph correspond to the number of contigs in each dataset. Orthologs represent the majority within them. A total of 13% of all annotations are common to all three of the investigated databases.
Fig. 2. Levels of representation of Gene Ontology terms for biological processes (BPs) in green, cellular components (CCs) in blue and molecular functions (MFs) in red. The maximum resolution was achieved between the 5th and 13th levels (78.3% of Gene Ontology terms assigned).
Differentially expressed genes and enrichment analysis
A total of 7637 significant DEGs were found, of which 3719 were upregulated and 3918 were downregulated in the HE samples (Table SII). Overall, 29% of the total DEGs lacked a meaningful functional annotation. Furthermore, both the principal component analysis and the heatmap displayed a clear clustering between the two conditions (Figs 3 & 4).
Fig. 3. Principal component analysis of control (CT) group (white dots) and heated (HT) group (black dots) samples.
Fig. 4. Heatmap summarizing the log10-transformed ratio of differentially expressed genes between the control (CT) group and the heated (HT) group obtained with the NOISeq package using a probability threshold of 0.9999.
A comparative assessment of GO classifications between the overall annotated genes and DEGs identified a limited number of differences. We considered only the most representative GOs, excluding the rarest (below the 0.5th percentile), and whether, among them, any particular difference was assessed (Fig. 5). The comparative measurement of the most expressed BPs, CCs and MFs between the HE and CT springtails showed no remarkable divergences, except for a frequency bias of a few GO (Figs 6–8).
Fig. 5. Abundance differences between the Gene Ontologies of differentially expressed genes (orange) and annotated genes (dark grey) at the 99.5th percentile. The terms that diverge between the two datasets are highlighted in bold.
Fig. 6. Biological process terms at the 99.5th percentile compared between the heated (HE) group (dark green) and the control (CT) group (light green). Expression differences between the two conditions are highlighted in bold.
Fig. 7. Cellular component terms at the 99.5th percentile compared between the heated (HE) group (dark blue) and the control (CT) group (light blue). Expression differences between the two conditions are highlighted in bold.
Fig. 8. Molecular function terms at the 99.5th percentile compared between the heated (HE) group (dark red) and the control (CT) group (light red). Expression differences between the two conditions are highlighted in bold.
The GO enrichment analysis on the annotated DEGs revealed the presence of 39 significant terms, classified as 19 MFs, 15 BPs and 5 CCs (Fig. S1). Overall, the majority of the enriched GOs displayed a noticeable expression difference between the HE and CT samples, except for some that did not show a clear dissimilarity between the two groups, probably due to a similar transcript co-expression. On the other hand, the PFAM enrichment analysis disclosed the presence of five significant domains. Among these, one was considered clearly perturbed because of the limited divergence between the two conditions (Fig. S2).
Discussion
The effects of thermal stress on polar terrestrial arthropods is still a relatively unexplored field of study. Although previous research shows contrasting outcomes on animal survival, micro-environmental conditions apparently play a pivotal role in population prosperity (Bokhorst et al. Reference Bokhorst, Huiskes, Convey, Van Bodegom and Aerts2008). Nutrients and predators were identified as the two major factors that influence polar invertebrate populations' viability in a situation characterized by rising temperatures (Bokhorst et al. Reference Bokhorst, Huiskes, Convey, Van Bodegom and Aerts2008). However, the effect of thermal stress at the gene level has never been investigated in detail. In this study, we used a de novo transcriptome analysis to identify genes and pathways specifically modulated following mid-term exposure to higher temperatures.
The lack of a reference genome or transcriptome hampered the analysis due to the production of partial and/or fragmented transcripts (Voshall & Moriyama Reference Voshall, Moriyama and Abdurakhmonov2018). Nevertheless, the fraction of the annotated transcripts was remarkable, and only a small fraction of contigs remained unassigned. In detail, 71.7% of the transcriptome was annotated in at least one of the considered databases, and 27.6% (12,522) of this was characterized by the three main databases used (Uniprot, OrthoDB and PFAM) (Fig. 1). This suggests that although C. terranovus is phylogenetically distant from most model species and especially from those with exhaustive genomic data available, protein families and domains are sufficiently conserved, allowing for solid automatic annotation.
The differential expression analysis revealed that only subtle dissimilarities could be observed between the two conditions, at variance with the fairly high level of inter-individual variability. This latter finding is probably associated with the remarkably plastic gene expression of this taxon, as registered in C. antarcticus, where subgroups of the main population managed to endure long periods of thermal stress better than others, despite the survival rate continuously decreasing (Everatt et al. Reference Everatt, Convey, Worland, Bale and Hayward2013). On the other hand, the mortality rate of both the CT and HE groups in captivity was well below 10%, and some egg clusters of C. terranovus were observed at the bottom of the box containers where specimens were reared.
In the comparative analysis of the 99.5th percentile of genes annotated in both conditions, a similar distribution of GO terms was observed, with few exceptions, indicating high intra-condition variability due to transcript expression plasticity. Despite this outcome, gene expression enrichment analysis provided significant insights into the modulation of gene expression following mid-term thermal stress.
Carbohydrate metabolism
GOs related to the sugar catabolic processes (GO:0004034, GO:0004553, GO:0046373, GO:004656), probably induced during glycolytic activity, appear to be underrepresented in HE compared to CT samples. Significantly, the transcripts involved in this process were identified as part of a digestion pathway (e.g. β-galactosidase, β-glucuronidase or aldose-1 epimerase) (Table SIII), probably being induced by an optimal nutritional status in the wild environment. These enzymes are, indeed, parts of pathways involved in carbohydrate hydrolysis or glycolytic activities (e.g. aldose-1 epimerase). However, carbohydrate catabolism is generally a typical defence mechanism adopted by Antarctic arthropods to endure extreme cold temperatures (Teets & Denlinger Reference Teets and Denlinger2014). In C. antarcticus, three main compounds are used as cryoprotectants: trehalose, glucose and glycerol (Elnitsky et al. Reference Elnitsky, Benoit, Denlinger and Lee2008). Despite our CT samples expressing a different carbohydrate catabolism pathway for trehalose and glucose, it is possible to hypothesize that this metabolic pathway may be activated as a response to low temperatures as an alternative mechanism that is parallel or connected to cryoprotectant production. In this respect, subsequent studies are necessary to investigate which cryoprotectant is used in the continental Antarctic springtail C. terranovus and to compare its carbohydrate profile with the co-generic species C. antarcticus.
Protein degradation
Overall, the HE group springtails showed enhanced protein catabolism (GO:0004021, GO:0004180, GO:0004181, GO:0042853, GO:0070573), as the expression of several peptidase transcripts suggests (e.g. GPTs (or ALTs), CPN, CPQ, CNDP2, VASH1 and DPEP1, homologous to several model organisms) (Table SIII). Many of these protein degradation genes are well studied in different animals, but they have never been found in association with thermal stress. As a few examples, the alanine-aminotransferase transcript is a well-known molecular target for cellular necrosis in mammalian serum (Washington & Van Hoosier Reference Washington, Van Hoosier, Suckow, Stevens and Wilson2012), but is has never been associated with Arthropoda heat exposure; the several carboxypeptidases and dipeptidase 1 detected herein act in the peptide degradation pathway and are conserved in many animal lineages, but they have never been related to cellular thermal stress. Heat exposure can cause protein damage and misfolding, leading to increased protein turnover (thus possibly explaining the upregulation of genes involved in protein degradation) or rearrangement following heat shock protein (HSP) activation. In addition, these latter molecules play a fundamental role in thermal stress responses and have been detected in all of the studied living organisms, categorized as 1) inducible (e.g. Hsp70) and 2) constitutive (e.g. Hspc70) forms (Roelofs et al. Reference Roelofs, Aarts, Schat and Van Straalen2008). In the Antarctic environment, the larvae of Belgica antarctica, the fish Trematomus bernacchii and the ciliate Euplotes focardii constitutively express such chaperones, which allow for year-round defence against frequent temperature shifts (Rinehart et al. Reference Rinehart, Hayward, Elnitsky, Sandro, Lee and Denlinger2006, Teets & Denlinger Reference Teets and Denlinger2014). Similarly, C. terranovus specimens subject to mid-term thermal stress seem to activate protein catabolism in the absence of the typical upregulation of HSP production. Indeed, during differential expression analyses, several transcripts annotated as HSPs were revealed to be expressed in the CT samples, as well as in the HE samples. Thus, we can assume that C. terranovus, similarly to B. antarctica larvae (i.e. the closest phylogenetic Antarctic species whose molecular chaperons have been deeply investigated), expresses HSPs constitutively and not as a response to variable environmental temperature (Rinehart et al. Reference Rinehart, Hayward, Elnitsky, Sandro, Lee and Denlinger2006). Nevertheless, our findings are based on collembolan individuals sampled during the Antarctic summer (i.e. the warmest season), although it would be of interest to study HSP expression in specimens approaching the coldest season for comparison.
Lipid activity
Lipid metabolism pathways appear to be upregulated following heat treatment. Previous studies indicated that dehydrated animals (i.e. the dipteran B. antarctica and the springtail Folsomia candida; Timmermans et al. Reference Timmermans, Roelofs, Nota, Ylstra and Holmstrup2009, Teets et al. Reference Teets, Peyton, Colinet, Renault, Kelley and Kawarasaki2012) display downregulation of fatty acid metabolism, similar to what happened in the C. terranovus CT samples. Our samples were collected in the wild in the absence of detailed humidity monitoring, and therefore it is not possible to assess whether this downregulation might be the consequence of dehydration stress. Moreover, in our analysis, no GO linked to a similar description was found in the CT group. Nonetheless, Thorne et al. (Reference Thorne, Burns, Fraser, Hillyard and Clark2010) found a similarly enhanced lipid metabolism in the Antarctic benthonic fish Harpagifer antarcticus after thermal exposure. These authors proposed that similar pathways are enhanced in order to rearrange cellular membranes after a temperature shock. Heated C. terranovus specimens showed both fatty acid synthase and lipase to be differentially expressed (Table SIII), indicating that the metabolism of these molecules was overexpressed. While C. terranovus is not phylogenetically related to H. antarcticus, they both upregulate these same pathways, possibly indicating an evolutionary convergence in cellular lipid rearrangement to endure heat exposure. However, more evidence should be gathered in order to assess this correspondence, and so more in-depth investigations are required.
Spermatid development
Despite evidence highlighting that aberrant environmental conditions can cause a reduction in the production of gametes, a process that is by no means restricted to springtails (Dallai Reference Dallai2014), stressed C. terranovus specimens did not show this expected reduction. Indeed, the stressed group was characterized by increased expression of genes related to a GO term designated for spermatid development (GO:0007286) and of several transcripts annotated as alanine-aminotransferase, the activity of which is highly increased in Drosophila nigromelanica sperm and ovarian cells during their development (Schneider & Chen Reference Schneider and Chen1981). Significantly, several transcripts related to gametogenesis were discovered in the treated animals (homologs to Ccdc63, SPAG6 and poe studied in model organisms), while, on the contrary, increased expression of a male infertility gene (CDYL) was observed in the CT group (Table SIII). This may suggest that the extreme environmental conditions simulated for HE samples did not negatively influence sexual development but, on the contrary, fostered spermatid production. This process could be related to the breeding conditions (inside a box container), where the proximity of individuals may enhance chemical signal production to promote aggregation and therefore stimulate sexual behaviour and spermatid production. However, the link between thermal exposure and sexual development must be clarified in the future in order to determine whether or not this long-term condition impacts sexual activity in continental Antarctic collembolans.
Conserved protein domains
The PFAM enrichment analysis disclosed a limited number of outcomes regarding the conserved protein domains in the C. terranovus specimens. In fact, a pentapeptide family domain and a protein of unknown function were found to be overrepresented in the HE group. The former is an amino acid repeat found in a number of different protein families and therefore it cannot be associated with any specific target.
Similarly, a domain of unknown function was more abundant in the CT samples. This was found to be associated to the YLP motif in several Drosophila proteins, and apart from the probable interaction of YLP motifs with dozens of tyrosine kinase enzymes, no further insights can be gained.
In addition, a mucin-like protein domain was characteristic of the CT groups. These proteins, which are well characterized in vertebrates, were found to be synthetized in several Drosophila tissues as well (i.e. salivary glands, midgut, Malpighian tubules, etc.) and often in relation with development (Syed et al. Reference Syed, Härd, Uv and van Dijk-Härd2008). An additional PFAM-enriched term, whose expression appeared to be disturbed but was slightly more abundant in the CT group, was marked as ‘insect cuticle protein’. In line with the mucin-like protein domain, it may indicate an association with tissue development. These two latter pieces of evidence, circumstantial as they are, suggest an increase in body development in the CT group that may be related to springtail wellness in wild environments, where development can proceed with regularity, at variance with individuals in stressed conditions. Nevertheless, these preliminary findings must be deepened in further investigations comparing how springtail development and hormonal levels vary between stressed and wild specimens.
Conclusions
Although Antarctic marine animal groups have been investigated in some detail in order to understand how gene expression is modified during environmental changes, data on Antarctic terrestrial fauna are scant. In this study, we analysed (to our knowledge for the first time) the transcriptome of an Antarctic collembolan in a situation of prolonged heat exposure. We showed that control specimens display an overrepresentation of pathways such as carbohydrate catabolism, reduced lipid metabolism activity and enhancements in tissue development, which have been also found to be modulated in other model organisms under experimental conditions. Moreover, we described some biological mechanisms that appear to be activated in thermally stressed C. terranovus individuals, where protein catabolism and fatty acid activity are promoted probably in response to protein misfolding and fresh nutrient availability. Surprisingly, we discovered that spermatid development appears to be improved in animals subject to heat stress, presumably because, during the experimental breeding condition, the proximity of individuals inside the box containers promoted chemical exchange favouring animal aggregation and, hence, a sexual response.
The low mortality rate observed in the stressed group, the observation that the animals are able to lay eggs even in unusually high-temperature conditions and the limited extent to which the animals from the HE and CT groups differed in terms of gene expression would lead to the hypothesis that the testing regime (18°C for 20 days) was not stringent enough to observe significant differences. Nevertheless, this temperature is substantially higher than what these animals usually experience in their natural conditions, where average daily temperatures are stably < 10°C, even during the Antarctic summer months (data not shown). This would support the notion that these animals are indeed characterized by ample phenotypic plasticity, which may allow them to survive even significant thermal stress. More severe stressing regimes will be adopted in the future in order to further test this hypothesis over prolonged durations.
In summary, the gene expression of this continental Antarctic springtail should be studied in more detail to: 1) assess which patterns of gene expression are activated during exposure to lower temperatures; 2) evaluate which transcripts are enhanced in short-term heat exposure and to determine whether HSPs are enhanced during this process (as happens in several other groups) or whether these chaperones are constitutively expressed as in B. antarctica; and 3) evaluate the extent to which the Antarctic springtail phenology is compromised during long-term heat exposure.
Acknowledgements
We wish to thank Professor Bettine van Vuuren and an anonymous reviewer for their constructive and helpful comments.
Financial support
This study was funded by the Italian National Antarctic Program (PNRA), grant number: PNRA16_00234.
Author contributions
AC collected the samples and supervised the experiment together with PGG; CL performed the experiments, CM extracted the RNA and built the Illumina libraries, CC and SG analysed the bioinformatic data and CC together with FN and AC wrote the paper. All of the authors participated in drafting and critically revising the paper.
Supplemental material
A supplemental methods section including three supplemental bioinformatic codes (Protocols S1–S3), two figures (Figs S1 & S2) and three tables (Tables SI–SIII) will be found at https://doi.org/10.1017/S0954102021000195.
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