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A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis)

Abstract

Background

The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required.

Methods

Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages.

Results

Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation.

Conclusions

We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Peer Review reports

Background

Copepods have been suggested as the most abundant animal group, with important roles in marine ecosystems [1, 2]. The salmon louse (Lepeophtheirus salmonis) is an ectoparasitic copepod on salmonids, with a life cycle that has eight developmental stages (instars) separated by moulting, consisting of two nauplius stages, one copepodid stage, two chalimus stages, two preadult stages and the adult stage [3, 4]. Salmon lice are a major challenge to cage-based aquaculture of salmonids and cause large economical losses each year [5]. The emergence of salmon lice resistances against several drugs makes the situation even worse [6, 7]. Developing novel anti-parasitic strategies is thus an urgent and vital issue. To achieve this, we require a thorough understanding of the molecular mechanism of life stages development of the salmon louse. Identifying key genes that influence or regulate the lifespan of the parasite is also of great importance for finding novel drug targets against salmon lice.

Moulting or ecdysis, the shedding and replacement of the exoskeleton, plays a crucial role in the survival and development of arthropods and has been extensively studied in insects. Moulting consists of different events, including detachment of the old cuticle, synthesis of new cuticle, shedding of the old cuticle, hardening of the new cuticle and absorption of the old cuticle. Steroid hormones such as 20-hydroxyecdysone (20E) play a crucial role in arthropod ecdysis by regulating a series of pathways [8, 9]. Synthesis of 20E and other steroid hormones from cholesterol via ecdysone as direct precursor to 20E is mediated via a conserved pathway of enzymatic reactions. This pathway includes 7,8-dehydrogenase, encoded by the gene neverland (nvd) in Drosophila melanogaster followed by a cascade of cytochrome P450 mono-oxygenases, encoded by the so called Halloween genes; phantom(phm), disembodied (dib), shadow(sad), shade (shd), and spook (spo) [1015].

The binding of moulting hormones to nuclear receptors leads to a complex hormonal cascade controlling moulting [9]. The ecdysone receptor (EcR) and the mammalian retinoid X receptor (RXR) with its insect homologue, Ultraspiricle (USP), belong to the group of nuclear receptors. RXR/USP and EcR form heterodimers which in turn bind to regulatory elements in the promoters of ecdysone responsive genes [16]. In insects, USP is an important regulator of metamorphosis, growth, development, and reproduction, in concert with other nuclear receptors [1719]. Furthermore, the polysaccharide chitin and other structural molecules, such as cuticle proteins, are major components of the arthropod exoskeleton. Chitin synthesis and recycling form a conserved pathway in insects, and coordinated regulation of chitin metabolism and cuticle formation is important for moulting [20].

The characterization of homologous structures and genes involved in ecdysis and ontogenesis has only recently gained traction in Copepoda. In early studies, the ultrastructure of the cuticle of moulting salmon louse larvae was visualized by electron microscopy [21] and ecdosteroid levels were measured during a single instar in Calanus pacificus [22]. Transcriptional profiling of the last juvenile instar of Calanus finmarchicus identified genes with expression profiles changing significantly over the course of the moulting cycle [23].

Within the Copepoda, moulting occurs cyclically during development until the adult stage through a sequence of instars that is thought to be evolutionarily conserved within this taxonomic group [4, 24, 25]. Based on the annotated genome sequence of the Atlantic salmon louse [26] and expressed sequence tags [27], a limited number of ecdysis-related genes have been characterized. From the 20E biosynthetic pathway, homologous sequences of the insect genes neverland (nvd) and all the Helloween genes are present in the genome but thus far, only orthologues of neverland (nvd)disembodied (dib) and shade (shd) have been partially characterized [28, 29].

In the salmon louse, orthologous genes coding for the EcR/USP pair of nuclear receptors (LsEcR/LsRXR) have been characterized. Two genes were characterized by RNA interference (RNAi) mediated gene knock-down and by measuring ontogenic and tissue-specific expression [3032]. Unlike in other crustaceans and insects, only a combined knock-down of LsEcR/LsRXR but not each gene individually, resulted in moulting arrest. Very recently, the nuclear receptor FushiTarazu Factor-1 (FTZ-F1) has been characterized in the salmon louse [33]. Two distinct transcript isoforms, αFTZ-F1 and βFTZ-F1, are expressed. Out of these, only the ablation of the most highly expressed isoform βFTZ-F1 resulted in altered phenotypes of moulting arrest and oocyte maturation as well as significant differential regulation of genes associated with proteolysis and chitin binding.

Recently, genes from the conserved chitin-biosynthetic pathway have also been identified in the salmon louse genome [34, 35]. Like insects, the salmon louse genome contains two homologous genes for chitin synthase, LsCHS1 and LsCHS2. Knock-down of LsCHS1 resulted in a lethal phenotype with cuticle deformation and knock-down of LsCHS2 affected the digestive system [36]. In another study, five genes of the same pathway and three additional putative chitin deacetylases were targeted, also yielding full abrogation of infectivity when targetting LsCHS1, fructose-6-phosphate aminotransferase (LsGFAT) and a putative chitin deacetylase (CDA5956) [35]. From the chitin catabolic pathway, three gene coding for chitinases have been identified (LsChi1, LsChi2, LsChi4). Knock-down of LsChi2 in larval stages resulted in reduced infectivity [30, 31].

Recent investigations on the impact of chitin synthesis inhibitors, compounds belonging to the benzoylurea family (for example diflubenzuron, lufenuron, teflubenzuron) demonstrate the importance of chitin metabolism for parasite survival and as a target for pest management [37, 38]. Still, there is only limited and often circumstantial knowledge of the molecular mechanisms driving developmental processes in copepods.

In recent years, high-throughput technologies have enabled us to study a large number of genes in parallel and thus facilitate the study of complex biological systems [39]. Being tremendously successful, high-throughput sequencing produces large volumes of data and has enabled a new era of genome research [40]. Our group has recently performed a comprehensive transcriptome time-series analysis using RNA sequencing data from three developmental stages of salmon lice (chalimus-1, chalimus-2 and preadult-1) [24] wherein we applied a method for improved developmental staging of samples by instar-age [41]. That way, we identified genes that may regulate development in this parasite.

A research area that is particularly important for systems biology is the study of dynamic interfaces and crosslinks between different processes and components of biological systems [42]. Recently, a great deal of attention has been devoted to the area of network-based analysis. Network analysis provides a powerful framework for studying a large number of interactions among biological processes and components. Gene co-expression networks (GCNs) have been widely used to capture and mine the interactions among components of the transcriptome [42, 43].

Signatures of hierarchical modularity have been suggested to be present in all cellular networks investigated so far, ranging from metabolic to protein–protein interaction and regulatory networks [44]. In gene co-expression networks, modules are defined as groups of genes with similar expression patterns and can be identified by using clustering methods [4547]. GCN modules have facilitated a better understanding of a number of biological phenomena [45, 48, 49], and an increasing number of studies based on GCN have been conducted to identify condition-specific gene modules and predict potential genes involved in a certain phenotype [5053].

In this study, by re-analyzing the staged time-series data produced by Eichner et al. [24], we aim at providing a framework for identifying important genes through GCN analysis and contributing to a better understanding of the molecular mechanisms of moulting in copepods. By combining GCN analysis, sample traits and annotation information from public databases we identified relevant modules and hub genes and propose novel candidates with association to moulting and development. For validation, we performed gene knock-down by RNA interference (RNAi) of five genes.

Methods

Gene expression data and genome annotation

A normalized gene expression matrix was generated from the RNA-seq data provided by Eichner et al. [24], by extracting samples from middle instar ages and old/moulting instar ages of chalimus-1, chalimus-2 and preadult-1 larvae (Fig. 1). Transcripts with low expression (not having at least 3 cpm in at least 3 samples) were excluded from the analysis. In this manuscript we are using Ensembl Metazoa stable identifiers, consisting of a 13 digit numerical suffix, with prefixes EMLSAG or EMLSAT, to unanimously refer to predicted genes and transcripts, respectively, in the L. salmonis salmonis genome annotation [26]. Gene annotation data were obtained from LiceBase [54].

Fig. 1
figure1

Grouping of sample data and photographs of representative L. salmonis chalimus-1, chalimus-2 and preadult-1 larvae. Within each stage, lice were divided into groups of same instar age: directly after moulting (young), in the middle of the stage (middle) and directly before the moult to the next stage (old/moulting). Moults are represented by a green arrow and a shedded exoskeleton. In this study, data from lice of the middle and old/moulting instar age were used

Identification of moulting-associated genes and transcription factor (TF) genes

By combining data from the published literature and LiceBase, we collected genes which are involved in the moulting of salmon lice or known to be associated with the moulting of other arthropods with high confidence. We named these genes as “moulting-associated genes”.

Gene Ontology (GO) annotation information for the salmon louse genes was obtained as previously described [24]. Any salmon louse gene that was annotated by GO terms related to transcription factor (TF) (GO:0006351, GO:0001071, GO:0008134, GO:0000988, and GO:0005667) or child-terms are annotated as TF genes.

Gene co-expression network (GCN) analysis for identifying important modules and genes associated with moulting and development of salmon louse

In this study, we define the modules and genes that might play a role in the regulation of moulting and development of salmon louse as “important modules” and “important genes”, and we proposed a workflow to identify these important modules and genes based on GCN analysis (Fig. 2). Using gene expression profiles, sample traits and gene annotation information as input, this workflow is used to predict the important modules and genes for moulting and development of salmon louse.

Fig. 2
figure2

The workflow of gene co-expression network (GCN) analysis for identifying important modules and genes. Methods are highlighted in light-green

GCN construction, module identification and module eigengene calculation

GCN construction and power parameter estimation

GCNs were constructed using the R package WGCNA [55]. A modified version of the biweight midcorrelation (bicor) [56] was adopted to calculate the absolute correlation between pairwise genes (transcripts) (Sij):

$$\begin{array}{@{}rcl@{}} S_{ij}=\left|bicor(x_{i}, x_{j})\right|, \end{array} $$
(1)

where xi denotes the expression profile across all samples of transcript i. The funnction bicor is implemented in the R package WGCNA.

By transforming the correlation by power function, we obtained the adjacency between pairwise transcripts (Aij):

$$\begin{array}{@{}rcl@{}} A_{ij}=S_{ij}^{\beta}, \end{array} $$
(2)

where β is the power parameter, and β is determined based on whether the corresponding co-expression network exhibits scale-free characteristics and has relatively high connectivities. We chose the suitable power parameter from integers ranging from 1 to 20 by plotting the signed scale-free topology fitting index R2 against different power parameters, and we also plotted the corresponding network mean connectivity against different power parameters. Details about how the power parameter β was estimated can be found in Additional file 1.

With the adjacency matrix A we can construct the co-expression network, where each node represents a gene, and the weight possessed by edges between nodes indicates the co-expression relationship between nodes. Although our data is from a transcriptome study we use the terms “gene co-expression network” and “eigengene” because transcript quantification was done based on gene-level counts [24].

We constructed three GCNs, based on the gene expression profiles from middle samples, old/moulting samples and all samples (samples from both middle instar ages and old/moulting instar ages).

GCN module identification and eigengene calculation

For each GCN, hierarchical clustering was performed for the nodes based on their adjacencies and a dendrogram was obtained. Using this dendrogram as input, a top-down algorithm cutreeDynamicTree was applied to identify gene modules. Each module was assigned a unique name as color. For each gene co-expression network, nodes that could not be assigned to any modules were moved to a module called “grey”. The grey module in each of the network was not considered in further analysis.

After identifying modules from each network, a sub-adjacency matrix can be extracted for all the gene members in each module. Then the eigengene for each module was computed as the eigenvector for the largest eigenvalue of the module gene expression matrix by the function moduleEigengenes in WGCNA.

Intramodular centrality measurements and intramodular hub identification

In this study, we adopted three types of centrality measurements to measure the centralities of nodes within each module and identified intramodular hubs.

Intramodular connectivity (kIM)

The connectivity of the ith node (ki) in the weighted network is defined as the sum of connection weights between node i and the other nodes [57]:

$$\begin{array}{@{}rcl@{}} k_{i}=\sum_{j\neq i}{A_{ij}}. \end{array} $$
(3)

Suppose that there are Q modules detected in a network, and they are labeled by q=1,2,…Q, so the connectivity of a node i within a module q is defined as intramodular connectivity \(\left (k_{i}^{(q)}\ \text {or}\ {kIM}_{i}^{(q)}\right)\):

$$\begin{array}{@{}rcl@{}} k_{i}^{(q)}={kIM}_{i}^{(q)} = \sum_{\substack{j\in M_{q} \\ j\neq i}} {A_{ij}^{(q)}}, \end{array} $$
(4)

where Mq denotes the set of node indices that correspond to the nodes in module q, and A(q) is the adjacency matrix of module q. High intramodular connectivity implies that a node could be a hub within the module.

Module membership / module eigengene-based connectivity (kME)

The module membership (or module eigengene-based connectivity) is defined as the value of correlation between module eigengene and the expression profile of the genes (or transcripts) assigned to this module [58]:

$$\begin{array}{@{}rcl@{}} {kME}_{i}^{(q)} = cor\left(x_{i}^{(q)}, E^{(q)} \right), \end{array} $$
(5)

where \(x_{i}^{(q)}\) specifies the expression profile in different samples of transcript i that is assigned to the module q, and E(q) denotes the eigengene of module q.

Since our gene co-expression networks were constructed based on the absolute correlation values between gene expression profiles, we used the absolute value of module membership to measure the centrality of each node within a module:

$$\begin{array}{@{}rcl@{}} {kME}_{i}^{(q)} = \left|cor\left(x_{i}^{(q)}, E^{(q)} \right)\right|. \end{array} $$
(6)

In addition, the module membership of a node for module q can be calculated for all nodes in the network:

$$\begin{array}{@{}rcl@{}} {kMEall}_{i}^{(q)}= \left|cor\left(x_{i}, E^{(q)} \right)\right|, \end{array} $$
(7)

and this definition can be used in the module preservation analysis. The details can be found in Additional file 1.

Intramodular weighted betweenness centrality (BC)

The betweenness centrality of a node in an unweighted network (or module) is the number of shortest paths between all other nodes in the network that pass through the node [59]. To calculate the betweenness centralities of nodes in our weighted networks, a generalization of betweenness centrality proposed by Brandes [60] was employed. The approach is implemented in the R package tnet [61].

Definition of intramodular hubs

We evaluated the centralities of nodes in each module, using intramodular connectivity, absolute module membership and intramodular weighted betweenness centrality. The nodes ranking among the highest ten percent in any of the three centrality measurements of all nodes within a module were defined as intramodular hubs. The node obtaining highest scores in all of the three centrality measurements was defined as “absolute hub”.

Based on the ranks of nodes in three types of centrality measurement, we can calculate the average rank of nodes within each module. Therefore, the absolute hub should have an average rank as 1.

Module preservation analysis

The preservation of a module between the reference network and a test network can be evaluated based on the alterations in connectivity patterns and density. A well-preservation module in two or more networks should have similar connectivity patterns and nodes in the module should remain being tightly connected. WGCNA provides a series of approaches to evaluate whether a module is preserved and reproducible in another network [62]. In this study, module preservation statistics were computed to compare the two networks constructed based on middle samples and old/moulting samples. For each module preservation statistic, permutation tests were performed to evaluate the significance of the observed value and a Z score was obtained. The Z scores for all of the module preservation statistics were integrated as a composite summary statistic Zsummary. Details about how to calculate module preservation statistics and Zsummary can be found in Additional file 1.

The networks were unsigned, and we set the number of permutation as 200. All the correlations were calculated using the biweight midcorrelation (bicor). Modules with a Zsummary smaller than 2 was regarded as non-preserved, while a Zsummary larger than 10 indicated that a module was well preserved across different networks. Since we aimed to identify modules playing a role in the regulation of moulting, the non-preserved modules from the moulting network were of particular interest.

Regularized logistic regression using module eigengenes as independent variables

We made use of the eigengenes of modules in the global network to perform logistic regression with an elastic-net penalty (α=0.5). This task was achieved by setting the binary dependent variable as the label of middle or old/moulting (old/moulting stages were labeled as 1), and using the eigengenes of each module as independent variables.

We used the R package glmnet [63] to perform this analysis, and we adopted the λ that gives minimum mean cross-validated error.

Integrating information from external databases and enrichment analysis

Data from FlyBase [64, 65] and GenomeRNAi [66] were extracted and used to identify homologous observable phenotypes and lethal phenotypes enriched modules.

To detect homologous sequences in D. melanogaster, we ran BLASTP with E-value cutoff as 1e-10 on the corresponding protein sequences of salmon louse transcripts against protein sequences from Drosophila. Only best hits were considered. After mapping the protein IDs of the homologues from Drosophila to gene IDs, RNAi knock-down phenotype information were mapped to data from GenomeRNAi. If a salmon louse protein had more than one Drosophila homologue with identical maximum bitscore, all the homologues were used to search for RNAi phenotypes. BLASTP searches of all salmon louse predicted amino-acid sequences were performed to find paralogues.

Enrichment analysis of modules

Based on the GO annotation file for salmon louse genes from LiceBase, GO enrichment analyses were performed for each modules identified in the middle, moulting and global network using the fisher statistic and the “elim” algorithm provided by R package topGO [67].

Furthermore, with the information from the Drosophila homologues-based transcript-phenotype list (Additional file 2-Table S1), we conducted two enrichment analyses for each module identified in all networks. The p-values of these enrichment analyses were obtained based on hypergeometric tests, to determine whether transcripts with homologue observable phenotypes or homologue lethal phenotypes in Drosophila were significantly enriched within a module. Based on the suggestions from [68], we used the raw p-values of our enrichment analyses, and the cutoff of p-values was set as 0.05 for all the enrichment analyses.

Selecting important modules for further analyses

We were interested in identifying gene modules which are likely to play a role in the moulting and development of salmon louse, and we chose important modules based on three analyses: the module preservation analysis, the regularized logistic regression analysis and the Drosophila homologues-based enrichment analysis. According to the guilt-by-association (GBA) heuristic [69], nodes in the moulting-associated transcripts-containing modules are more likely to play a role in the moulting and development of salmon louse, and we conducted a focused search among modules containing at least one known moulting-associated transcript. Therefore, moulting-associated transcripts-containing modules satisfying any of the following criteria were chosen for further studies: 1) non-preserved modules in the moulting network (Zsummary<2); 2) the eigengenes of modules from the global network obtained positive coefficients from the regularized logistic regression analysis (the module with largest coefficient value should be prioritized); 3) modules that are significantly enriched by transcripts with observable and lethal RNAi phenotypes from homologues (pvalue<0.05) (Fig. 2).

Selecting important genes as Knock-Down candidates from important modules

Since many researchers have proposed that hubs in a biological network tend to be more important [7072], we chose RNAi knock-down candidates among the hubs of the important modules. For each selected module, we gave prime consideration to the absolute hub. If no absolute hub was detected, knock-down candidates were chosen from other intramodular hubs. Hubs with less paralogues and little annotation information were then given priority.

Differential gene expression (DGE) analysis and regulatory impact factor (Rif) calculation

We calculated the regulatory impact factors (RIF) for all the transcripts annotated as TF, based on the metric proposed in [73].

The first step was to perform differential gene expression (DGE) analysis to compare the middle group and the old/moulting group, and the statistics used to test the null hypotheses were calculated based on standardized rank-sum Wilcoxon test. We computed the permutation adjusted p-values using the step-down maxT multiple testing procedures, which provide strong control of the family-wise Type I error rate (FWER). The functions are implemented in the R package multtest [74]. Transcripts with an adjusted p-value smaller than 0.05 were identified as differentially expressed between middle instar ages and old/moulting instar ages.

The first RIF value (RIF1) for fth TF transcript was defined as:

$$\begin{array}{@{}rcl@{}} RIF1_{f}= \frac{1}{n_{de}} \sum_{i=1}^{n_{de}}A_{i}{DE}_{i}{DW}_{if}^{2}, \end{array} $$
(8)

where nde is the number of DE transcripts; Ai represents the average expression of ith DE transcript across the two groups, and DEi is the statistics obtained from the previous DGE analysis. DWif is the abbreviation for differential wiring, which means the change of correlation between fth TF and the ith DE transcript across the two groups:

$$\begin{array}{@{}rcl@{}} {DW}_{if}={ro}_{if}-{rm}_{if}, \end{array} $$
(9)

where roif and rmif are the co-expression correlation between fth TF and the ith DE transcript in the old/moulting samples and the middle samples, respectively.

The second RIF value (RIF2) was computed as followed:

$$\begin{array}{@{}rcl@{}} RIF2_{f}=\frac{1}{n_{de}} \sum_{i=1}^{n_{de}}\left({EO}_{i}^{2}{ro}_{if}^{2}-{EM}_{i}^{2}{rm}_{if}^{2}\right), \end{array} $$
(10)

where \({EO}_{i}^{2}\) and \({EM}_{i}^{2}\) denote the square of the average expression value of the ith DE transcript in the old/moulting samples and the middle samples; \({ro}_{if}^{2}\) and \({rm}_{if}^{2}\) are the square of the co-expression correlation between fth TF and the ith DE transcript in the old/moulting samples and the middle samples.

RNA interference experiments

Double-stranded RNA synthesis

Double-stranded RNA (dsRNA) was produced using MEGAscript® RNAi Kit (Ambion) according to supplier’s instructions using the primers given in Table 1 with the prefixed T7 sequence. The control group was treated with the non target control fragment CPY185 [75].

Table 1 Primer sequences used in dsRNA production for RNA interference. The T7 sequence TAATACGACTCACTATAGGGAGA was prepended to the 5’-ends of all primers

RNA interference on nauplia and fish challenge with the emerging copepodids

RNA interference was conducted in nauplia as described in [30], but with 2 μg fragment. Infection was done in single tanks with 60 copepodids per fish on three fish for each fragment and for the control as described in [76] (10 C, full salinity). The remaining copepodids were stored on RNAlater® (Invitrogen) for later measurement of transcript down regulation. Sampling was done after 16 days when lice from control group were in chalimus or preadult-1 stage. All lice were sampled from fish and photographs were taken under the binocular in a drop of seawater with a cover slide on top. Number of lice and size measurements on photographs were recorded. Genes to be knocked down were: EMLSAG00000001458, EMLSAG00000003179 and EMLSAG00000005299. A second trial with knock-down larvae of EMLSAG00000001458 with 100 copepodids on three fish each was done. The outflow water from the tanks was filtered and lice in the flow out were counted two hours after infection and 24 hours after infection. One fish from knock-down and control group each were terminated three days after infection and lice were fixed on Karnovsky’s fixative for histological investigation. The other fish were terminated eight days after infection.

RNA interference on preadult lice

RNA interference in preadult-2 lice was done as described in [75]. In short, the fragment was injected into preadult-2 lice, which were put on fish again until most of the control lice showed its second pair of egg strings. All lice were sampled and photographed. The lice egg strings were laid into single flow through wells for hatching observation. Lice were either stored on RNAlater® for quantitative PCR (qPCR) measurements or on Karnovski’s reagent for histological investigation. Three different experiments were conducted, one with double-stranded RNA for EMLSAG00000001458, EMLSAG00000005299 and EMLSAG00000003179 as well as a control (35 days), one with dsRNA for EMLSAG00000004347 and a control (40 days) and one with dsRNA for EMLSAG00000008959 and a control (37 days). The cod trypsin RNA was used as a non-target control fragment [75].

RNA extraction, cDNA synthesis and qPCR measurements

Nauplia were divided into five (or four in case of control group from second infection trial) batches of 30 to 40 nauplia. RNA from nauplia was extracted by a combination of Trizol and RNAeasy micro kit as previously described [30]. RNA was frozen at -80 C until usage. cDNA synthesis was conducted using the AffinityScript QPCR cDNA Synthesis Kit (Agilent) according to suppliers recommendations. Gene expression of the target gene was measured by quantitative real-time PCR in control and knock-down group. qPCR was carried out in duplicates using the salmon louse elongation factor 1 α (LsEF1 α) [77] as well as Adenine Nucleotide Translocator 3 (LsADT3) [76] (in nauplia only) as a standard. SYBR® Green PCR master mix was used with the primers given in Table 2.

Table 2 Primers used for qPCR measurements

Applied Biosystems 7500 Fast Real-Time PCR system was used for thermal cycling and quantification in 10 μl reactions (initiation 50 C, 2 min, 95 C, 2 min, then 40 cycles of 95 C, 15 seconds, 60 C, 1 min). A melting curve 60 C to 90 C was performed. Relative gene expression was calculated using the differences in threshold cycle (CT) between gene of interest and standard genes.

Histology

Lice were prepared as described in [78] for histological investigation. For plastic embedding, lice were washed twice in phosphate buffered saline, dehydrated in a graded ethanol series, pre-infiltrated with Technovit/ethanol (50/50) for four hours (Technovit 7100, Heraeus Kulzer Technique) and infiltrated with Technovit and hardener overnight. Two micrometre thick sections were cut with a microtome (Leica RM 2165) and stained with toluidine blue (1% in 2% borax) for one minute. The stained sections were mounted using Mountex (Histolab Products).

Results

Identification of moulting-associated and tF genes

Among the transcripts in our RNA-seq data, we found 40 moulting-associated transcripts and 32 of them were retained after low expression filtering. The list of moulting-associated transcripts and the relevant publications can be found in Table 3.

Table 3 ID, annotation and relevant publications for the moulting-associated transcripts

There were 433 transcripts annotated as TF, and 231 TF were retained after low expression filtering (Additional file 2-Table S8).

GCN construction and module identification

To detect the genes that might be involved in the regulation of moulting and development, we analyzed the RNA-seq data sampled from the middle instar ages and old/moulting instar ages of chalimus-1, chalimus-2 and preadult-1 larvae (Fig. 1). Of the 45 samples, 18 samples were from middle instar ages, and 27 samples were from old/moulting instar ages. After filtering transcripts with low expressions, 7108 transcripts were retained for network analysis.

Three GCNs were constructed for different sample groups. The first GCN was generated using all samples labeled as “middle instar age”. Meanwhile, an “old/moulting instar age” GCN was created for all old/moulting samples. To further facilitate our analysis, a GCN based on all samples from middle and old/moulting instar ages was built. The three GCNs were thus denoted as middle network, moulting network, and global network, respectively.

We set the power parameter β=7 to make sure that the networks satisfied scale-free topology approximately while having relatively high mean connectivities (Fig. 3). The adjacency matrices of the three networks can be found in Additional file 7.

Fig. 3
figure3

Network scale-free topology and mean connectivity for different power parameters ranging from 1 to 20. Plots on the left show the scale-free topology fitting index (y-axis) as a function of the power (x-axis) in the middle, moulting, and global network, respectively. A horizontal line is added at y=0.85 in each plot. Plots on the right show the mean connectivity (y-axis) as a function of the power (x-axis) in the middle, moulting, and global network, respectively

In the module identification process, 83, 60 and 78 modules were found in the middle, moulting and global network, respectively, and the module sizes ranged from 32 to 333. There were 203, 444 and 506 genes assigned to the grey module of the middle, moulting and global network, respectively (Additional file 2-Table S2-S4). Genes in the grey modules were not considered for further analysis. Dendrogram and module identification results for the three networks were displayed in Additional file 3-Figure S1. Eigengenes for each module in all the three networks were also obtained.

The centralities and distribution of moulting-associated genes across modules

To preliminarily examine the essentiality of intramodular hubs, we calculated the centralities for the 32 moulting-associated transcripts. Based on our definition of intramodular hubs, there were 6, 12 and 8 moulting-associated transcripts identified as intramodular hubs in the middle, moulting and global network, respectively (Additional file 2-Table S5-S7). The transcript EMLSAT00000005083 (annotated to encode hexokinase type 2, Table 1) was identified as intramodular hub in all the three networks, and it was the absolute hub in the module “lightcyan1” of the global network.

We examined how the 32 moulting-associated transcripts were distributed across modules in the three networks. There were 25, 20 and 24 modules containing moulting-associated transcripts in the middle, moulting and global network, accounting for 30.1%, 33.3% and 30.8% in the three networks, respectively. The numbers of moulting-associated transcripts in these modules ranged from 1 to 4 (Additional file 2-Table S2-S4).

Module preservation analysis

To identify genes which may play a role in the moulting of salmon lice, we detected non-preserved modules from the moulting network based on module preservation analysis. Five modules from the moulting network were found as non-preserved, and the module sizes ranged from 41 to 100 (Additional file 2-Table S3). Strong correlations among genes in these non-preserved modules were only observed in the moulting network, and two non-preserved modules (yellowgreen and lavenderblush3) contained moulting-associated transcripts (EMLSAT00000008812 and EMLSAT00000012651) (Additional file 2-Table S6). Notably, the moulting-associated transcripts were also identified as intramodular hubs in these modules. The transcript EMLSAT00000008812 (annotated to encode chitinase, Table 1) was ranked eighth (based on connectivity) in the yellowgreen module, and the transcript EMLSAT00000012651 (annotated as EcR, Table 1) was ranked third (based on betweenness centrality) in the lavenderblush3 module. We thus hypothesized that transcripts in these two modules could be important for salmon louse moulting, and hubs from these modules should be considered as important.

Eight modules from the middle network were identified as non-preserved, and the module sizes ranged from 54 to 109 (Additional file 2-Table S2). Three non-preserved modules (darkseagreen4, brown4 and lightcyan1) were found containing one moulting-associated transcript (Additional file 2-Table S5). However, none of these moulting-associated transcripts were intramodular hubs in the middle network.

Regularized logistic regression analysis on the global co-expression network

To compare the intramodular overall gene pexression patterns between the middle sample group and old/moulting sample group, we performed elastic net regularization-based logistic regression using the eigengenes of module from the global network as independent variables. As a result, we found modules with eigengenes that were highly expressed in one sample group but lowly expressed in the other sample group. From the 78 module eigengenes, we identified 15 eigengenes with non-zero coefficient (ranging from -1.75 to 0.963), and six of the 15 corresponding modules contained one known moulting-associated transcripts (Additional file 2-Table S4). It was noteworthy that module steelblue possessed the largest positive coefficient and contained one moulting-associated transcript (EMLSAT00000001150) as intramodular hub, which was ranked second in the betweenness centrality measurement (Additional file 2-Table S7).

When checking the absolute value of regression coefficients, three modules (magenta, lightcyan, and ivory) were found with absolute coefficient larger than 1. The moulting-associated transcripts were found in two of the three modules (lightcyan and ivory). The coefficients of all the three modules were negative, indicating that genes in these modules exhibited much higher expressions in middle samples. Notably, two modules (indianred4 and lavenderblush3) with negative regression coefficients contained moulting-associated transcripts (EMLSAT00000000733 and EMLSAT00000008543) annotated as hormone receptor 3 (Hr3) and hormone receptor 4 (Hr4) (Table 1). In the module indianred4, EMLSAT00000000733 was ranked eighth in the connectivity measurement.

Differentially expressed transcripts between middle group and old/moulting group were found in all the modules with non-zero regression coefficients, and the proportions ranged from 36.9% to 98.4% (Additional file 2-Table S4).

Integrating information from external databases

We identified homologous genes in D. melanogaster for the salmon louse transcripts and then searched for RNAi phenotypes for these homologues in the GenomeRNAi database. We found homologous RNAi phenotypes for 3059 salmon louse transcripts. Additional file 2-Table S1 maps salmon louse transcripts to the RNAi phenotypes of the corresponding homologues in Drosophila.

Enrichment analysis of modules

Based on the GO annotation file for the salmon louse transcripts, we performed GO enrichment analysis for all the modules to preliminarily elucidate the biological functions of the modules. The GO term with smallest p-value in each category (Biological Process(BP), Molecular Function (MF) and Cellular Component(CC)) were recorded (Additional file 2-Table S2-S4).

To further identify modules which are more likely to contain important genes for lice development, we conducted enrichment analyses for all the modules based on the homologues-based transcript-phenotype list. The transcripts with observable RNAi phenotypes were significantly enriched in 16, 13, and 14 modules in the middle, moulting and global network, accounting for 19.3%, 21.7%, and 17.9% in total modules, respectively. Analogously, 14, 14, and 9 modules were detected as enriched by transcripts with lethal RNAi phenotypes in the middle, moulting and global network, accounting for 16.9%, 23.3% and 11.5% in total modules. We found a relatively large overlap between the two enrichment analyses: 10, 11 and 7 modules (accounting for 12.0%, 18.3% and 9.0% in total modules) were identified as being significantly enriched by both observable and lethal RNAi phenotypes in the middle, moulting and global network (Additional file 2-Table S2-S4).

DGE analysis and Rif calculation

All 45 samples were divided into middle and old/moulting groups to find DE transcripts. There were 1364 transcripts differentially expressed (DE) between the two groups. The list of DE transcripts facilitated calculation of the RIF scores for all transcripts with GO annotation as TF.

For the 231 TF transcripts, RIF scores were computed (Additional file 2-Table S8). It is noteworthy that EMLSAT00000003849 (annotated as forkhead box protein O (FOXO)) obtained highest RIF scores from both methods. This transcript is also an intramodular hub of a moulting-associated transcripts-containing module in both of the middle and moulting network.

Selecting important modules for further analyses

In the module preservation analysis, two modules (yellowgreen and lavenderblush3) from the moulting network were detected based on our criteria. In the regularized logistic regression analysis, two modules (steelblue and green) from the global network passed the criteria. In the homologues-based enrichment analysis, one (mediumpurple3), two (darkolivegreen and violet) and one (turquoise) module were found satisfying the criteria from the middle, moulting and global network, respectively. In summary, one, four and three modules from the middle, moulting and global network were selected for further analyses.

Examining intramodular hubs and selecting important genes as Knock-Down candidates from important modules

After determining important modules, we investigated the hubs of these modules to understand their roles in moult- ing and development of salmon louse. For each of the eight chosen modules, we examined their hub with highest average rank (Additional file 2-Table S12). The absolute hubs (EMLSAT00000003844 and EMLSAT00000001458) of the two non-preserved modules (lavenderblush3 and yellowgreen) selected from the moulting network are annotated as epithelial cell transforming 2 (ECT2) and Ras-related protein Rab-1A (RAB1A), respectively. For the four modules selected from the enrichment analysis, EMLSAT00000000929, EMLSAT00000005299, and EMLSAT00000012769 were identified as absolute hubs, annotated as high density lipoprotein-binding protein (HDLBP), ER membrane protein complex subunit 3 (EMC3) and laminin subunit beta-1 (LanB1). EMLSAT00000010555 had the highest average rank in the module (turquoise) from the global network, annotated as stress-induced-phosphoprotein 1 (STIP1). No absolute hubs were found in the two global modules selected from the regression analysis, and the nodes with highest average rank in these modules were EMLSAT00000007421 and EMLSAT00000012693, both of them were identified as differentially expressed transcripts between the middle group and old/moulting group. EMLSAT00000007421 was annotated as cuticular protein 62Bb (Cpr62Bb), and little annotation was found for the hub EMLSAT00000012693.

To validate the importance of genes in the selected modules in moulting and development of salmon louse within the limited accesses to RNAi experiments, we selected RNAi knock-down candidates from three important modules. Since the important modules were selected based on three analyses, we selected one module from each of the three analyses. Firstly, we choose the module yellowgreen from the two non-preserved modules (the other one is lavenderblush3) in the moulting network. The module yellowgreen had larger size then the module lavenderblush3, and the absolute hub of the module yellowgreen had higher score of the absolute module membership. From the two moulting modules (darkolivegreen and violet) selected from the enrichment analysis, we chose the module violet for further analysis, since it contained more transcripts annotated as TF, and these transcripts obtained higher scores in the regulatory impact factor analysis than those found in the module darkolivegreen. Furthermore, with regards to the proportion of transcripts annotated as TF, the module violet and yellowgreen ranked as first and third among all modules in the moulting network. Finally, we selected the module steelblue in the global network for further analysis, because the eigengene of this module obtained largest coefficient in the regularized logistic regression analysis. Details of the three selected modules can be found in Table 4 and Additional file 2.

Table 4 Information on selected modules for selection of knock-down candidates

For the module yellowgreen and violet from the moulting network, we chose the absolute hub for RNAi experiment. According to the criteria discussed in the method section, we chose another one hub without paralogues from each module to knock down.

No absolute hub was found in the module steelblue from the global network, and the hub (EMLSAT00000007421) with highest average rank was annotated to encode cuticle protein. Among the 12 intramodular hubs found in the module steelblue, three (EMLSAT00000012111, EMLSAT00000008158 and EMLSAT00000012113) were annotated to encode proteins with the chitin binding peritrophin-A domain (PF01607); four (EMLSAT00000007421, EMLSAT00000007422, EMLSAT00000009987, and EMLSAT00000010209) were annotated to encode cuticle proteins (PF00379); one (EMLSAT00000004870) was annotated to encode protein with the polyprenyl synthetase domain (PF00348), and the moulting-associated transcript (EMLSAT00000001150) was annotated to encode cytochrome P450 (PF00067) (Additional file 2-Table S10, Table 1). Among the three hubs with few annotation information, we chose one (EMLSAT00000004347) with least number of paralogues to knock down. The details of all the knock-down candidates are summarized in Table 5.

Table 5 Information of the selected knock-down candidate genes

RNA interference on nauplia and infection of salmon with the emerging copepodids

Measurement of gene expression in copepodids before infection showed down regulation of all targeted genes (t-test: p-value < 0.05) with varying knock-down efficiency. For genes EMLSAG00000001458, EMLSAG00000005299 and EMLSAG00000003179, efficiency was 94%, 84% and 89%, respectively. At termination after 16 days no lice were found on the fish infected with copepodids from EMLSAG00000001458 knock-down (KD) group (Fig. 4, left panel). There was no significant difference in the number of lice between the control group and EMLSAG00000005299-KD and EMLSAG00000003179-KD groups and the development of lice from all groups found on fish was similar (Fig. 4, right panel). No difference in the phenotype could be observed under the binocular or by size measurements.

Fig. 4
figure4

Number of L. salmonis per fish (left panel) and distribution of stage and instar age (right panel). Lice were collected at termination of the knock down experiment, 16 days post infection, dsRNA was introduced in nauplia larvae. pad= preadult, ch= chalimus. Sample names represent dsRNA target transcripts; KD1458 : EMLSAT00000001458, KD5299 : EMLSAT00000005299, KD3179 : EMLSAT00000003179. Error bars represent +−1 standard deviation, N=3

Second trial with eMLSAG00000001458 Knock-Down

Since no lice with EMLSAG00000001458-KD were found at termination after 16 days on the fish, we were interested in finding out whether this could be due to reduced infection success or due to problems with development and moulting. A second infection trial for qualitative measurement was done. Knock down efficiency measured in copepodids before infection was 95%. After two hours, 30, 37 and 33 lice were found in the filtered flow through water of tanks from fish of the control group, and 32, 57 and 35 lice were found from tanks of the knock-down group. After 24 hours, 9, 9 and 4 lice were found in the flow out from control fish, and 9, 8 and 4 lice were found from knock-down fish. No lice were found in the filters after three days. At termination of the first fish at day three after infection there were 10 lice on the control fish and 14 on the knock-down fish. These were sampled for histological investigation. No differences were observed in the histological sections (Additional file 3-Figure S3). Eight days after infection, lice had developed to chalimus-1 on control fish (13 on one fish, 39 on the other), but no lice were found on one of the fish with knock-down samples and two copepodids on the other fish.

Knock-Down in preadult lice

At sampling lice were in the adult stage. Down regulation was on average 77% for EMLSAG00000001458-KD group, 47% for EMLSAG00000003179-KD and 68% for EMLSAG00000004347-KD group. Lice from EMLSAG00000001458-KD group and EMLSAG00000008959-KD group had no egg string. Length measurements for body parameters (cephalothorax and genital segment length) as well as egg strings are shown in Table 6. Egg strings of all groups with egg strings present hatched and produced viable normal looking offspring. Histological sections were done for EMLSAG00000001458-KD and EMLSAG00000004347-KD lice. Histology of different tissues is shown in Additional file 3-Figure S2. EMLSAG00000001458-KD lice did not develop normal looking oocytes (Additional file 3-Figure S2 f) and the ovaries did not contain any oogonia (Additional file 3-Figure S2 j). The cellular structures of the subcuticular tissue of the cephalothorax were changed and only loose connection between cells was observed (Additional file 3-Figure S2 n), while the subcuticular tissue of the genital segment seemed not to be affected in the same way. At sampling, EMLSAG00000004347-KD lice showed a weak genital segment, which was easily squeezed and torn apart when handling the lice. In the histological sections, the cuticle and subcuticular tissue of the genital segment (Additional file 3-Figure S2 s) did not show obvious differences to the control louse.

Table 6 Observed phenotypes by RNAi for the selected knock-down candidates

Examining the RNA interference experiments data from liceBase

From LiceBase, RNAi experiments for 188 genes were collected, and 112 genes among them appeared in our three networks. 10 genes in six of the eight selected modules were found with observable RNAi phenotypes (including the RNAi experiments results from this study). One gene from the selected module darkolivegreen had been knocked down, but no phenotype was observed. No RNAi results were found for the genes in the selected module lavenderblush3 (Additional file 2-Table S13).

Notably, one hub (EMLSAG00000009839) from one non-preserved module (skyblue) of the moulting network show reduced survival in the RNAi experiments, although this module did not contain any known moulting-associated genes. The absolute hub (EMLSAG00000005382) of the module blue2 in the middle network show shorter eggstrings in RNAi experiments, and this module contained two known moulting-associated genes.

We also found RNAi experiment records of four genes (EMLSAG00000010968, EMLSAG00000006642, EMLSAG00000007048 and EMLSAG00000004159) which obtained highest average rank in four modules. These four modules did not satisfy any of the criteria for being an important module for salmon louse moulting and development, and the four genes did not show any observable phenotype in RNAi experiments (Additional file 2-Table S13).

Examining the modules where the rNAi candidates were selected

In the RNAi experiments, all five selected gene candidates show observable phenotypes, and we thus examined the three modules they were from. We plotted heatmaps of scaled gene expression profiles and barplot of scaled eigengene expression for each of the three module (Fig. 5).

Fig. 5
figure5

Heatmap of scaled gene expression profiles and barplot of scaled eigengene expression values for module yellowgreen extracted from the moulting network (a), module violet extracted from the moulting network (b), and module steelblue extracted from the global network (c). The heatmap color-codes gene expression values for each gene in a module: higher expression values are represented in red, and lower expression values are represented in greens according to the color legend. The barplot below each heatmap depicts the expression levels of module eigengenes in different samples (x-axis). The barplots are colored based on the name of each module

Gene expression profiles within a module were strongly correlated. Genes in the module yellowgreen tended to be highly expressed in the chalimus-1 old female samples, chalimus-1 moulting samples and preadult-1 old female samples. Genes in the module violet were highly expressed in the chalimus-1 male samples and chalimus-1 moulting samples. For the module steelblue from the global network, genes were highly expressed in almost all samples from the old and moulting instar stages (except two preadult-1 old male samples), especially in the chalimus-1 moulting samples. The preadult lice with the two genes of the module yellowgreen knocked down failed to develop eggstrings. Further study is necessary to understand the role of genes from the module yellowgreen in the fecundity of female lice.

The topological graph for each of three modules (Fig. 6, Additional file 46) shows that moulting-associated genes in the module yellowgreen and steelblue obtained relatively high average ranks, and they were tightly connected with other hubs. In the module violet, the moulting-associated gene obtained a low average rank, but the genes annotated as TF obtained high average ranks and were tightly connected with other hubs. For these modules, the proportion of differentially expressed genes was highest in the module steelblue identified from the global network.

Fig. 6
figure6

Graph representation of the modules yellowgreen extracted from the moulting network (a), violet extracted from the moulting network (b), and steelblue extracted from the global network (c). Nodes represent genes, edges represent correlation between nodes. For readability, only edges with absolute correlation > 0.84 are drawn. Nodes are labeled based on their average rank over three centrality statistics, node label font size is proportional to average nodes ranks. TF genes are colored in red, purple nodes represent moulting-associated genes from the literature. Differentially expressed genes between non-moulting and moulting groups are displayed as diamond shapes. Node diameter is proportional to weighted betweenness centrality. Annotation information of the top 10 genes based on rank is listed for each module

The enriched GO terms in the module yellowgreen included GO:0008152, GO:0001071 and GO:0005667, indicating nucleic acid transcription factor activity. The enriched GO terms in the module violet included GO:0006355, GO:0003677 and GO:0044454, indicating transcriptional regulation. In the module steelblue, the most enriched GO terms were GO:0042302, GO:0008061 and GO:0006030, indicating metabolic processes of chitin and cuticle.

Discussion

RNAi has enormously facilitated rapid and straightforward analysis of gene function for parasites and other organisms [7981], and whole-genome RNAi screens have been successfully applied to detect genes with important functions for many biological processes in Caenorhabditis elegans and mammalian cultured cells [8284]. Although a robust RNAi method for knocking down salmon louse genes has been established [81], genome-wide RNAi screening is both labour-intensive and time-consuming due to the parasitic lifestyle of salmon louse [3, 4]. Currently, biologists choose RNAi gene candidates subjectively based on their research interests, and little work has yet been carried out to develop bioinformatics methods for objectively predicting salmon louse genes that have a crucial role in biological processes of interest and are likely to show visible phenotypes when targeted in RNAi experiments.

In this study, we systematically analyzed the RNA-seq data of salmon lice from different life stages and proposed an approach (a workflow) for identifying important genes involved in the moulting and development of salmon louse (Fig. 2). Subsequently, RNAi experiments were performed on the genes identified by the network-based approach and gene annotation information. The results of our RNAi experiments and the RNAi records from LiceBase indicate the effectiveness of our approach.

The module preservation analysis allowed us to identify two important genes (EMLSAG00000001458 and EMLSAG00000008959 annotated as RAB1A and digestive organ expansion factor (DIEXF)), and both of the genes were from a non-preserved module (yellowgreen) in the moulting network. The non-preserved modules in the moulting network may be co-regulated and play an indispensable role in moulting or development of the salmon louse. Further studies are required to clarify the biological meaning of the non-preserved modules in the middle network as well as the well-preserved modules between the middle and moulting network.

In the regularized logistic regression analysis, all module eigengenes were calculated using the same method, thus they are on the same scale and it is feasible to identify the most important module by comparing the regression coefficients of eigengenes. We found that the module (steelblue) whose eigengene obtained largest coefficient was enriched for GO categories related to cuticle and chitin metabolic process. All the annotated hubs in this module are associated with chitin binding peritrophin-A domain, cuticle proteins, and cytochrome P450, which have been reported as important proteins for the moulting of arthropods [8588]. We knocked down a hub (EMLSAG00000004347) with little annotation and observed both reduced fecundity and fragile cuticle. Based on RNAi results and the annotations of other hubs in this module, we speculate that gene EMLSAG00000004347 may participate in building the louse exoskeleton during the moulting process to adult stage. Our approach offers an effective solution in proposing and annotating novel putative genes that play a role in the moulting process of salmon louse. Although we focused on analyzing the modules containing moulting-associated genes due to the limited access to RNAi experiments, the module preservation analysis and regularized logistic regression analysis identify important modules without taking any prior knowledge into account. These methods are suitable to analyze the expression data from less well-annotated organisms.

Instead of focusing on the moulting process directly, the emphasis of homologue-based enrichment analysis is on detecting important modules that are enriched for genes yielding observable phenotypic changes in another species. Four modules were identified in the first step. Besides the two RNAi experiments performed in this study, RNAi records were found for genes in each of the four modules. Strong RNAi phenotypes were observed on one and three genes in the module mediumpurple3 and module turquoise, respectively. Therefore, homologue-based phenotype enrichment analysis can contribute to rational selection of important modules, especially for studying less well-annotated organisms.

For scale free protein–protein interaction (PPI) networks, many groups have argued that highly connected hub nodes are more likely to be essential than sparsely connected nodes [8991]. Although the underlying reason is in dispute [92], the centrality-lethality rule [70] has been widely accepted. A recent study on centrality in GCNs arrived at a similar conclusion [93]. Since virtually no PPI data are available for the salmon louse, we focussed on the essentiality of hubs in GCNs instead. Taking the topological characteristics of weighted GCNs into consideration, we used three different methods to identify intramodular hubs. In many cases, the hubs identified with these three measurements were coherent and complementary, enabling us to define absolute hubs. This not only had the advantage of evaluating the intramodular centrality of nodes from different angles, but also increases robustness of our approach. 17 of the 32 moulting-associated genes were detected as intramodular hubs in the three GCNs, and a hexokinase orthologue was found as absolute intramodular hub in the global network and intramodular hub in the other two networks. For the two modules (yellowgreen and violet) from which we chose two hubs in each to knock down, we found that both of the two absolute hubs (EMLSAG00000001458 and EMLSAG00000005299) show stronger phenotypic consequences than the other middle-ranked hubs (EMLSAG00000008959 and EMLSAG00000003179). Interestingly, the absolute hub (EMLSAT00000005382) of another module containing moulting-associated transcripts has recently been identified as a novel intestinal heme scavenger receptor (LsHSCARB) with significant phenotypic effect on reproduction and body heme levels [94].

In addition to demonstrating the biological importance of intramodular hubs, RNAi experiments also highlight the role of our selected genes in moulting and development. Ablation of the RAB1A-like gene (EMLSAG00000001458) resulted in reduced survival and fecundity. Human RAB1 regulates vesicle trafficking between the endoplasmatic reticulum and the Golgi complex [95, 96]. As a member of the Ras guanosine triphosphatase (GTPase) protein family, Ras-related protein RAB1 has important roles in many biological processes, such as vesicle formation, translocation and targeting, autophagy-mediated host defense, and secretion of interleukin-8 [9799].

Small GTPases, including Ras GTPases, are versatile molecular switches that alternate between a GTP-bound and a GDP-bound conformation. The GTP-bound state is generally considered the active state in which the GTPase interacts with various effector proteins [100]. Few studies have been conducted on Rab GTPases in crustaceans but insect Rab proteins have a role in secretion of prothoracicotropic hormone (PTTH) [101, 102], an important regulator of ecdysteroidogenesis [103]. Recently, human RAB1A has been found to be involved in receptor-mediated endocytosis (RME) by regulating motility of early endocytotic vesicles [104]. While the mechanism of RME has not been studied in the salmon louse, the RAB1A-like gene may have a similar role in the uptake and trafficking of nutrients via receptors, such as LsHSCARB, which is itself an absolute hub and abundantly expressed in the intestinal epithelium of the parasite [94].

According to our module preservation analysis, another top-scoring hub is annotated as epithelial cell transforming 2 (ECT2) that is associated with GTPase activity [105107]. Human ECT2 is a guanine nucleotide exchange factor that mediates the exchange of guanosine diphosphate for guanosine triphosphate and thereby activates Rho GTPases [107]. ECT2, and its Drosophila orthologue Pebble (Pbl), have an essential function in cytokinesis and potentially cell polarity [108]. Loss-of-function and gain-of-function mutations in Pbl result in embryonic lethal phenotypes with altered cuticle structure [109]. Human ECT2 has been identified as an oncogene, its expression is highly elevated in different human tumors, and it promotes tumorigenesis in vivo and in vitro [110].

To our knowledge, the only other small GTPase-like gene targeted by RNAi in the salmon louse is Ras homolog Enriched in Brain (LsRheb) which is a part of the Target of Rapamycin (TOR) signaling pathway. The TOR pathway is important for regulating growth and development in many arthropods and ablation of LsRheb expression leads to complete abrogation of oocyte development [111]. Thus, we propose that small GTPases and interacting proteins are promising targets for understanding the metabolism and development of the salmon louse and other parasites and warrant inclusion in functional studies.

The top-scoring transcript (EMLSAT00000003849) by RIF analysis is an orthologue of the highly conserved TF FOXO. The importance of FOXO in metabolism, cellular proliferation, stress tolerance and lifespan has long been recognized in the model organisms D. melangaster and C. elegans, and mammals [112, 113]. TFs of the FOXO family are crucial regulators of cellular homeostasis that have a conserved role in modulating organismal aging and fitness [114]. More interestingly, several recent studies have demonstrated that FOXO-like TFs control growth and moulting in insects [115117]. A homologous gene in D. melanogaster(dFOXO) was reported to be involved in regulation of developmental timing through interaction with moulting hormone ecdysone [118]. Combining our analysis results with these published papers, we propose that it is worth investigating whether the FOXO-like TFs have a crucial role in salmon louse development.

Our findings provide support for the importance of intramodular hubs in GCNs. On the other hand, when looking at all other public RNAi experiments in LiceBase [54], we discovered four additional hub genes that had been tested previously, all of which had highest average rank distributed across four different modules. However, these modules passed none of our criteria for module selection, and negative results had been recorded. We thereby conclude that not all the intramodular hubs may be equally important, even if they have highest ranks, supporting the need for an initial step of module selection. Combining our RNAi results and public records, we argue that our rational approach is more likely to yield genes with measurable phenotypic effect under ablation of gene expression than random selection. Nonetheless, more work is needed to affirm the relationship between centrality and gene essentiality in this organism.

Neither the essential RAB1A-like nor FOXO-like genes are detected as DE, indicating that DGE analysis might not always be the best choice when it comes to identify genes that play a key role in regulating a certain phenotype. In a standard DGE analysis, only single genes are taken into account disregarding possible correlations. On the other hand, some genes linked to a phenotype or disease are not differentially expressed across samples [73, 119], because mutations or post-translational modifications may alter coding potential and function without affecting expression levels [120]. A powerful advantage of network-based analysis is that it can reveal interactions across different groups of samples, even in case of high within-group variability. Furthermore, GCN-based analysis circumvents the multiple testing problem that plagues conventional differential gene expression analysis. Nevertheless, it should be noted that the dataset we analyzed contains only three replicates per condition and may therefore provide limited power to detect DE transcripts. Furthermore, our dataset did not allow for detection of variation in transcript isoforms, for example αFTZ-F1 and βFTZ-F1 [33] as only gene-level quantification was performed [24] and only very few transcript variants have been identified in this organism. On the other hand, we assume our method readily lends itself to co-expression of transcript-level quantification and could equally profit from increased replication.

In summary, our results support the hypothesis that GCN-based approaches are effective in identifying genes with association to a phenotype of interest. The widely accepted view that hubs in biological networks are more likely to be essential has for the first time been successfully tested in a marine parasite. Because of the high level of modularity, it was necessary to break down our rational approach of candidate selection by GCN into a two-step process with selecting interesting modules first. In our opinion, improving prioritization of genes is in strong demand in functional genomics of parasites. This is due to the fact that slow parasite growth as well as labor- and time-intensive handling and collection procedures often render genome-wide functional assays intractable in host-parasite systems. We therefore propose that our selection method may guide gene selection towards candidates with high probability of success in functional studies of salmon lice and other parasites. Prospectively, new multi-factorial gene-expression data may also allow to transfer our approach to a broader range of phenotypes.

Availability of data and materials

The datasets analyzed in this study and code are available from the corresponding author on reasonable request. Supplementary data have been made available in figshare [121].

Abbreviations

20E:

20-hydroxyecdysone

BC:

Betweenness centrality

BLAST:

Basic local alignment search tool

BP:

Biological process

CT:

Threshold cycle

CC:

Cellular component

DE:

Differentially expressed

DGE:

Differential gene expression

dsRNA:

double-stranded RNA

DW:

Differential wiring

FWER:

Family-wise type I error rate

GCN:

Gene co-expression network

GO:

Gene ontology

KD:

Knock down

kIM:

Intramodular connectivity

kME:

Module eigengene-based connectivity

MF:

Molecular function

PPI:

Protein-protein interaction

qPCR:

quantitative PCR

RIF:

Regulatory impact factor

RME:

Receptor mediated endocytosis

RNAi:

RNA interference

RNA-seq:

RNA sequencing

TF:

Transcription factor

References

  1. 1

    Martin JH. The Possible Transport of Trace Metals Via Moulted Copepod Exoskeletons. Limnol Oceanogr. 1970; 15(5):756–61.

    CAS  Google Scholar 

  2. 2

    Turner JT. The importance of small planktonic copepods and their roles in pelagic marine food webs. Zool. Stud. 2004:255–66.

  3. 3

    Johnson SC, Albright LJ. The developmental stages of Lepeophtheirus salmonis (Krøyer, 1837) (Copepoda: Caligidae). Can J Zool. 1991; 69(4):929–50. https://doi.org/10.1139/z91-138.

    Google Scholar 

  4. 4

    Hamre LA, Eichner C, Caipang CMA, Dalvin ST, Bron JE, Nilsen F, Boxshall G, Skern-Mauritzen R. The Salmon Louse Lepeophtheirus salmonis (Copepoda: Caligidae) Life Cycle Has Only Two Chalimus Stages. PLoS ONE. 2013; 8(9). https://doi.org/10.1371/journal.pone.0073539.

  5. 5

    Abolofia J, Asche F, Wilen JE. The Cost of Lice: Quantifying the Impacts of Parasitic Sea Lice on Farmed Salmon. Mar Resour Econ. 2017; 32(3):329–49. https://doi.org/10.1086/691981.

    Google Scholar 

  6. 6

    Denholm I, Devine GJ, Horsberg TE, Sevatdal S, Fallang A, Nolan DV, Powell R. Analysis and management of resistance to chemotherapeutants in salmon lice, Lepeophtheirus salmonis (Copepoda: Caligidae). Pest Manag Sci. 2002; 58(6):528–36. https://doi.org/10.1002/ps.482.

    CAS  PubMed  Google Scholar 

  7. 7

    Aaen SM, Helgesen KO, Bakke MJ, Kaur K, Horsberg TE. Drug resistance in sea lice: a threat to salmonid aquaculture. Trends Parasitol. 2015; 31(2):72–81. https://doi.org/10.1016/j.pt.2014.12.006.

    CAS  PubMed  Google Scholar 

  8. 8

    Chang ES, Mykles DL. Regulation of crustacean molting: A review and our perspectives. Gen Comp Endocrinol. 2011; 172(3):323–30. https://doi.org/10.1016/j.ygcen.2011.04.003.

    CAS  PubMed  Google Scholar 

  9. 9

    Zitnan D, Kim Y-J, Zitnanová I, Roller L, Adams ME. Complex steroid-peptide-receptor cascade controls insect ecdysis,. GGen Comp Endocrinol. 2007; 153(1-3):88–96. https://doi.org/10.1016/j.ygcen.2007.04.002.

    CAS  Google Scholar 

  10. 10

    Yoshiyama T, Namiki T, Mita K, Kataoka H, Niwa R. Neverland is an evolutionally conserved Rieske-domain protein that is essential for ecdysone synthesis and insect growth. Development. 2006; 133(13):2565–74. https://doi.org/10.1242/dev.02428.

    CAS  PubMed  Google Scholar 

  11. 11

    Petryk A, Warren JT, Marqués G, Jarcho MP, Gilbert LI, Kahler J, Parvy JP, Li Y, Dauphin-Villemant C, O’Connor MB. Shade is the Drosophila P450 enzyme that mediates the hydroxylation of ecdysone to the steroid insect molting hormone 20-hydroxyecdysone. Proc Natl Acad Sci U S A. 2003; 100(SUPPL. 2):13773–78. https://doi.org/10.1073/PNAS.2336088100.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Niwa R, Matsuda T, Yoshiyama T, Namiki T, Mita K, Fujimoto Y, Kataoka H. CYP306A1, a cytochrome P450 enzyme, is essential for ecdysteroid biosynthesis in the prothoracic glands of Bombyx and Drosophila. J Biol Chem. 2004; 279(34):35942–49. https://doi.org/10.1074/JBC.M404514200.

    CAS  PubMed  Google Scholar 

  13. 13

    Warren JT, Petryk A, Marqués G, Jarcho M, Parvy JP, Dauphin-Villemant C, O’Connor MB, Gilbert LI. Molecular and biochemical characterization of two P450 enzymes in the ecdysteroidogenic pathway of Drosophila melanogaster. Proc Natl Acad Sci U S A. 2002; 99(17):11043–48. https://doi.org/10.1073/PNAS.162375799.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Warren JT, Petryk A, Marqués G, Parvy JP, Shinoda T, Itoyama K, Kobayashi J, Jarcho M, Li Y, O’Connor MB, Dauphin-Villemant C, Gilbert LI. Phantom encodes the 25-hydroxylase of Drosophila melanogaster and Bombyx mori: A P450 enzyme critical in ecdysone biosynthesis. Insect Biochem Mol Biol. 2004; 34(9):991–1010. https://doi.org/10.1016/J.IBMB.2004.06.009.

    CAS  PubMed  Google Scholar 

  15. 15

    Rewitz KF, Rybczynski R, Warren JT, Gilbert LI. The Halloween genes code for cytochrome P450 enzymes mediating synthesis of the insect moulting hormone. Biochem Soc Trans. 2006; 34(6):1256–60. https://doi.org/10.1042/BST0341256.

    CAS  PubMed  Google Scholar 

  16. 16

    Thomas HE, Stunnenberg HG, Stewart AF. Heterodimerization of the Drosophila ecdysone receptor with retinoid X receptor and ultraspiracle. Nature. 1993; 362(6419):471–75. https://doi.org/10.1038/362471a0.

    CAS  PubMed  Google Scholar 

  17. 17

    Hall BL, Thummel CS. The RXR homolog ultraspiracle is an essential component of the Drosophila ecdysone receptor. Development. 1998; 125(23):4709–17. https://doi.org/10.1242/dev.125.23.4709.

    CAS  PubMed  Google Scholar 

  18. 18

    Barchuk AR, Figueiredo VLC, Simões ZLP. Downregulation of ultraspiracle gene expression delays pupal development in honeybees. J Insect Physiol. 2008; 54(6):1035–40. https://doi.org/10.1016/j.jinsphys.2008.04.006.

    CAS  PubMed  Google Scholar 

  19. 19

    Xu J, Tan A, Palli SR. The function of nuclear receptors in regulation of female reproduction and embryogenesis in the red flour beetle, Tribolium castaneum. J Insect Physiol. 2010; 56(10):1471–80. https://doi.org/10.1016/j.jinsphys.2010.04.004.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Merzendorfer H, Zimoch L. Chitin metabolism in insects: structure, function and regulation of chitin synthases and chitinases. J Exp Biol. 2003; 206(24):4393–412. https://doi.org/10.1242/JEB.00709.

    CAS  PubMed  Google Scholar 

  21. 21

    Bron JE, Shinn AP, Sommerville C. Ultrastructure of the cuticle of the chalimus larva of the salmon louse Lepeophtheirus salmonis (Krøyer, 1837) (Copepoda: Caligidae). Contrib Zool. 2000; 69(1-2):39–49. https://doi.org/10.1163/18759866-0690102004.

    Google Scholar 

  22. 22

    Johnson C. Ecdysteroids in the oceanic copepod Calanus pacificus: Variation during molt cycle and change associated with diapause. Mar Ecol Prog Ser MAR ECOL-PROGR SER. 2003; 257:159–65. https://doi.org/10.3354/meps257159.

    CAS  Google Scholar 

  23. 23

    Tarrant AM, Baumgartner MF, Hansen BH, Altin D, Nordtug T, Olsen AJ. Transcriptional profiling of reproductive development, lipid storage and molting throughout the last juvenile stage of the marine copepod Calanus finmarchicus. Front Zool. 2014; 11(1):91. https://doi.org/10.1186/s12983-014-0091-8.

    PubMed  PubMed Central  Google Scholar 

  24. 24

    Eichner C, Dondrup M, Nilsen F. RNA sequencing reveals distinct gene expression patterns during the development of parasitic larval stages of the salmon louse (Lepeophtheirus salmonis). J Fish Dis. 2018; 41(6):1005–29. https://doi.org/10.1111/jfd.12770.

    CAS  PubMed  Google Scholar 

  25. 25

    Schram TA. Supplementary descriptions of the developmental stages of Lepeophtheirus salmonis (Kroyer, 1837) (Copepoda: Caligidae). In: Pathogens of Wild and Farmed Fish: Sea Lice. 1st edn. New York: Ellis Horwood Limited: 1993. p. 30–47.

    Google Scholar 

  26. 26

    Skern-Mauritzen R, Malde K, Eichner C, Dondrup M, Furmanek T, Besnier F, Komisarczuk AZ, Nuhn M, Dalvin S, Edvardsen RB, Klages S, Huettel B, Stueber K, Grotmol S, Karlsbakk E, Kersey P, Leong JS, Glover KA, Reinhardt R, Lien S, Jonassen I, Koop BF, Nilsen F. The salmon louse genome: Copepod features and parasitic adaptations. Genomics. 2021; 113(6):3666–80. https://doi.org/10.1016/j.ygeno.2021.08.002.

    CAS  PubMed  Google Scholar 

  27. 27

    Eichner C, Frost P, Dysvik B, Jonassen I, Kristiansen B, Nilsen F. Salmon louse (Lepeophtheirus salmonis) transcriptomes during post molting maturation and egg production, revealed using EST-sequencing and microarray analysis. BMC Genomics. 2008; 9(1):1–15. https://doi.org/10.1186/1471-2164-9-126.

    Google Scholar 

  28. 28

    Sandlund L, Kongshaug H, Horsberg TE, Male R, Nilsen F, Dalvin S. Identification and characterisation of the ecdysone biosynthetic genes neverland, disembodied and shade in the salmon louse Lepeophtheirus salmonis (Copepoda, Caligidae). PLoS ONE. 2018; 13(2):0191995. https://doi.org/10.1371/journal.pone.0191995.

    Google Scholar 

  29. 29

    Humble JL, Carmona-Antoñanzas G, McNair CM, Nelson DR, Bassett DI, Egholm I, Bron JE, Bekaert M, Sturm A. Genome-wide survey of cytochrome P450 genes in the salmon louse Lepeophtheirus salmonis (Krøyer, 1837). Parasites Vectors. 2019; 12(1):1–14. https://doi.org/10.1186/s13071-019-3808-x.

    Google Scholar 

  30. 30

    Eichner C, Nilsen F, Grotmol S, Dalvin S. A method for stable gene knock-down by RNA interference in larvae of the salmon louse (Lepeophtheirus salmonis). Exp Parasitol. 2014; 140:44–51. https://doi.org/10.1016/j.exppara.2014.03.014.

    CAS  PubMed  Google Scholar 

  31. 31

    Eichner C, Dalvin S, Skern-Mauritzen R, Malde K, Kongshaug H, Nilsen F. Characterization of a novel RXR receptor in the salmon louse (Lepeophtheirus salmonis, Copepoda) regulating growth and female reproduction. BMC Genomics. 2015; 16(1):81. https://doi.org/10.1186/s12864-015-1277-y.

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Sandlund L, Nilsen F, Male R, Dalvin S. The ecdysone receptor (EcR) is a major regulator of tissue development and growth in the marine salmonid ectoparasite, Lepeophtheirus salmonis (Copepoda, Caligidae). Mol Biochem Parasitol. 2016; 208(2):65–73. https://doi.org/10.1016/j.molbiopara.2016.06.007.

    CAS  PubMed  Google Scholar 

  33. 33

    Brunet J, Eichner C, Male R. The FTZ-F1 gene encodes two functionally distinct nuclear receptor isoforms in the ectoparasitic copepod salmon louse (Lepeophtheirus salmonis). PLoS ONE. 2021; 16(5 May):0251575. https://doi.org/10.1371/journal.pone.0251575.

    Google Scholar 

  34. 34

    Har ∂ardóttir HM, Male R, Nilsen F, Eichner C, Dondrup M, Dalvin S. Chitin synthesis and degradation in Lepeophtheirus salmonis: Molecular characterization and gene expression profile during synthesis of a new exoskeleton. Comp Biochem Physiol A Mol Integr Physiol. 2019; 227:123–33. https://doi.org/10.1016/j.cbpa.2018.10.008.

    Google Scholar 

  35. 35

    Braden L, Michaud D, Igboeli OO, Dondrup M, Hamre L, Dalvin S, Purcell SL, Kongshaug H, Eichner C, Nilsen F, Fast MD. Identification of critical enzymes in the salmon louse chitin synthesis pathway as revealed by RNA interference-mediated abrogation of infectivity. Int J Parasitol. 2020; 50(10):873–89. https://doi.org/10.1016/j.ijpara.2020.06.007.

    CAS  PubMed  Google Scholar 

  36. 36

    Har ∂ardóttir HM, Male R, Nilsen F, Dalvin S. Chitin synthases are critical for reproduction, molting, and digestion in the salmon louse (Lepeophtheirus salmonis). Life. 2021; 11(1):1–24. https://doi.org/10.3390/life11010047.

    Google Scholar 

  37. 37

    Poley JD, Braden LM, Messmer AM, Igboeli OO, Whyte SK, Macdonald A, Rodriguez J, Gameiro M, Rufener L, Bouvier J, Wadowska DW, Koop BF, Hosking BC, Fast MD. High level efficacy of lufenuron against sea lice (Lepeophtheirus salmonis) linked to rapid impact on moulting processes. Int J Parasitol Drugs Drug Resist. 2018; 8(2):174–88. https://doi.org/10.1016/j.ijpddr.2018.02.007.

    PubMed  PubMed Central  Google Scholar 

  38. 38

    Har ∂ardóttir HM, Male R, Nilsen F, Dalvin S. Effects of chitin synthesis inhibitor treatment on Lepeophtheirus salmonis (Copepoda, Caligidae) larvae. PLoS ONE. 2019; 14(9):0222520.

    Google Scholar 

  39. 39

    Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013; 9(1):640. https://doi.org/10.1038/msb.2012.61.

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Park ST, Kim J. Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing. Int Neurourol J. 2016; 20(Suppl 2):76–83. https://doi.org/10.5213/inj.1632742.371.

    CAS  Google Scholar 

  41. 41

    Eichner C, Hamre LA, Nilsen F. Instar growth and molt increments in Lepeophtheirus salmonis (copepoda: Caligidae) chalimus larvae. Parasitol Int. 2015; 64(1):86–96. https://doi.org/10.1016/j.parint.2014.10.006.

    PubMed  Google Scholar 

  42. 42

    Green S. Philosophy of Systems and Synthetic Biology, Winter 201 edn: Metaphysics Research Lab, Stanford University; 2019. https://plato.stanford.edu/archives/win2019/entries/systems-synthetic-biology/. Accessed 08 May 2019.

  43. 43

    Zheng Z-L, Zhao Y. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘CandidatusLiberibacter asiaticus’ infection. BMC Genomics. 2013; 14(1):27. https://doi.org/10.1186/1471-2164-14-27.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004; 5(2):101–13. https://doi.org/10.1038/nrg1272.

    PubMed  Google Scholar 

  45. 45

    Saelens W, Cannoodt R, Saeys Y. A comprehensive evaluation of module detection methods for gene expression data. Nat Commun. 2018; 9(1). https://doi.org/10.1038/s41467-018-03424-4.

  46. 46

    Zhang B, Horvath S. A General Framework for Weighted Gene Co-Expression Network Analysis. Stat Appl Genet Mol Biol. 2005; 4(1). https://doi.org/10.2202/1544-6115.1128.

  47. 47

    Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci. 2006; 103(47):17973–78. https://doi.org/10.1073/pnas.0605938103.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci U S A. 2006; 103(46):17402–07. https://doi.org/10.1073/pnas.0608396103.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Gargalovic PS, Imura M, Zhang B, Gharavi NM, Clark MJ, Pagnon J, Yang W-P, He A, Truong A, Patel S, Nelson SF, Horvath S, Berliner JA, Kirchgessner TG, Lusis AJ. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc Natl Acad Sci. 2006; 103(34):12741–46. https://doi.org/10.1073/pnas.0605457103.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2018; 19(4):575–92. https://doi.org/10.1093/bib/bbw139.

    CAS  PubMed  Google Scholar 

  51. 51

    Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003; 34(2):166–76. https://doi.org/10.1038/ng1165.

    CAS  PubMed  Google Scholar 

  52. 52

    Jiang J, Sun X, Wu W, Li L, Wu H, Zhang L, Yu G, Li Y. Construction and application of a co-expression network in Mycobacterium tuberculosis. Sci Rep. 2016; 6(1):28422. https://doi.org/10.1038/srep28422.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Kommadath A, te Pas MFW, Smits MA. Gene coexpression network analysis identifies genes and biological processes shared among anterior pituitary and brain areas that affect estrous behavior in dairy cows. J Dairy Sci. 2013; 96(4):2583–95. https://doi.org/10.3168/jds.2012-5814.

    CAS  PubMed  Google Scholar 

  54. 54

    Dondrup M. LiceBase. https://licebase.org. Accessed 03 May 2021.

  55. 55

    Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9. https://doi.org/10.1186/1471-2105-9-559.

  56. 56

    Langfelder P, Horvath S. Fast R Functions for Robust Correlations and Hierarchical Clustering. J Stat Softw. 2012; 46(11):11.

    Google Scholar 

  57. 57

    Steve H. Weighted Network Analysis: Applications in Genomics and Systems Biology: Springer; 2011, pp. 4–16. https://doi.org/10.1007/978-1-4419-8819-5.

  58. 58

    Horvath S, Dong J. Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol. 2008; 4(8):1000117.

    Google Scholar 

  59. 59

    Freeman L. A Set of Measures of Centrality Based on Betweenness. Sociometry. 1977; 40:35–41. https://doi.org/10.2307/3033543.

    Google Scholar 

  60. 60

    Brandes U. A faster algorithm for betweenness centrality. J Math Sociol. 2001; 25(2):163–77. https://doi.org/10.1080/0022250X.2001.9990249.

    Google Scholar 

  61. 61

    Opsahl T. Structure and Evolution of Weighted Networks. PhD thesis. New York: Queen Mary College University of London; 2009.

    Google Scholar 

  62. 62

    Langfelder P, Luo R, Oldham MC, Horvath S. Is My Network Module Preserved and Reproducible?,. PLoS Comput Biol. 2011; 7(1):1001057.

    Google Scholar 

  63. 63

    Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010; 33:1–22.

    PubMed  PubMed Central  Google Scholar 

  64. 64

    FlyBase. https://flybase.org/. Accessed 03 May 2021.

  65. 65

    Thurmond J, Goodman JL, Strelets VB, Attrill H, Gramates LS, Marygold SJ, Matthews BB, Millburn G, Antonazzo G, Trovisco V, Kaufman TC, Calvi BR, Perrimon N, Gelbart SR, Agapite J, Broll K, Crosby L, Dos Santos G, Emmert D, Falls K, Jenkins V, Sutherland C, Tabone C, Zhou P, Zytkovicz M, Brown N, Garapati P, Holmes A, Larkin A, Pilgrim C, Urbano P, Czoch B, Cripps R, Baker P. FlyBase 2.0: The next generation. Nucleic Acids Res. 2019; 47(D1):759–65. https://doi.org/10.1093/nar/gky1003.

    Google Scholar 

  66. 66

    Schmidt EE, Pelz O, Buhlmann S, Kerr G, Horn T, Boutros M. GenomeRNAi: A database for cell-based and in vivo RNAi phenotypes, 2013 update. Nucleic Acids Res. 2013; 41(D1):1021–26. https://doi.org/10.1093/nar/gks1170.

    Google Scholar 

  67. 67

    Alexa A, Rahnenführer J. topGO: Enrichment Analysis for Gene Ontology. R package version 2.34.0. 2018.

  68. 68

    Alexa A, Rahnenfuhrer J. Gene set enrichment analysis with topGO. 2020. https://www.bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO.pdf. Accessed 05 Mar 2020.

  69. 69

    Wolfe CJ, Kohane IS, Butte AJ. Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinformatics. 2005; 6(1):227. https://doi.org/10.1186/1471-2105-6-227.

    PubMed  PubMed Central  Google Scholar 

  70. 70

    Jeong H, Mason SP, Barabási A-L, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001; 411(6833):41–42. https://doi.org/10.1038/35075138.

    CAS  PubMed  Google Scholar 

  71. 71

    Hahn MW, Kern AD. Comparative Genomics of Centrality and Essentiality in Three Eukaryotic Protein-Interaction Networks. Mol Biol Evol. 2004; 22(4):803–06. https://doi.org/10.1093/molbev/msi072.

    PubMed  Google Scholar 

  72. 72

    Han J-DJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJM, Cusick ME, Roth FP, Vidal M. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature. 2004; 430(6995):88–93. https://doi.org/10.1038/nature02555.

    CAS  PubMed  Google Scholar 

  73. 73

    Reverter A, Hudson NJ, Nagaraj SH, Pérez-Enciso M, Dalrymple BP. Regulatory impact factors: Unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics. 2010; 26(7):896–04. https://doi.org/10.1093/bioinformatics/btq051.

    CAS  PubMed  Google Scholar 

  74. 74

    Pollard KS, Dudoit S, Van der Laan MJ. Multiple Testing Procedures: R multtest Package and Applications to Genomics. New York: U.C. Berkeley Division of Biostatistics Working Paper Series; 2004.

    Google Scholar 

  75. 75

    Dalvin S, Frost P, Biering E, Hamre LA, Eichner C, Krossøy B, Nilsen F. Functional characterisation of the maternal yolk-associated protein (LsYAP) utilising systemic RNA interference in the salmon louse (Lepeophtheirus salmonis) (Crustacea: Copepoda). Int J Parasitol. 2009; 39(13):1407–15. https://doi.org/10.1016/j.ijpara.2009.04.004.

    CAS  PubMed  Google Scholar 

  76. 76

    Eichner C, Øvergård AC, Nilsen F, Dalvin S. Molecular characterization and knock-down of salmon louse (Lepeophtheirus salmonis) prostaglandin E synthase. Exp Parasitol. 2015; 159:79–93. https://doi.org/10.1016/j.exppara.2015.09.001.

    CAS  PubMed  Google Scholar 

  77. 77

    Frost P, Nilsen F. Validation of reference genes for transcription profiling in the salmon louse, Lepeophtheirus salmonis, by quantitative real-time PCR. Vet Parasitol. 2003; 118(1):169–74. https://doi.org/10.1016/j.vetpar.2003.09.020.

    CAS  PubMed  Google Scholar 

  78. 78

    Eichner C, Harasimczuk E, Nilsen F, Grotmol S, Dalvin S. Molecular characterisation and functional analysis of LsChi2, a chitinase found in the salmon louse (Lepeophtheirus salmonis salmonis, Krøyer 1838). Exp Parasitol. 2015; 151-152:39–48. https://doi.org/10.1016/j.exppara.2015.01.011.

    CAS  PubMed  Google Scholar 

  79. 79

    Banerjee S, Banerjee A, Gill SS, Gupta OP, Dahuja A, Jain PK, Sirohi A. RNA Interference: A Novel Source of Resistance to Combat Plant Parasitic Nematodes. Front Plant Sci. 2017; 8:834. https://doi.org/10.3389/fpls.2017.00834.

    PubMed  PubMed Central  Google Scholar 

  80. 80

    Kolev NG, Tschudi C, Ullu E. RNA interference in protozoan parasites: achievements and challenges. Eukaryot Cell. 2011; 10(9):1156–63. https://doi.org/10.1128/EC.05114-11.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Bucher G, Scholten J, Klingler M. Parental RNAi in Tribolium (Coleoptera). Curr Biol. 2002; 12(3):85–86. https://doi.org/10.1016/S0960-9822(02)00666-8.

    Google Scholar 

  82. 82

    Dompe N, Rivers CS, Li L, Cordes S, Schwickart M, Punnoose EA, Amler L, Seshagiri S, Tang J, Modrusan Z, Davis DP. A whole-genome RNAi screen identifies an 8q22 gene cluster that inhibits death receptor-mediated apoptosis. Proc Natl Acad Sci U S A. 2011; 108(43):943–51. https://doi.org/10.1073/pnas.1100132108.

    Google Scholar 

  83. 83

    Parry DH, Xu J, Ruvkun G. A whole-genome RNAi Screen for C. elegans miRNA pathway genes. Curr Biol: CB. 2007; 17(23):2013–22. https://doi.org/10.1016/j.cub.2007.10.058.

    CAS  PubMed  Google Scholar 

  84. 84

    Marie C, Verkerke HP, Theodorescu D, Petri WA. A whole-genome RNAi screen uncovers a novel role for human potassium channels in cell killing by the parasite Entamoeba histolytica. Sci Rep. 2015; 5:13613. https://doi.org/10.1038/srep13613.

    PubMed  PubMed Central  Google Scholar 

  85. 85

    Hadley NF. The Arthropod Cuticle. Sci Am. 1986; 255(1):104–13.

    Google Scholar 

  86. 86

    Zhang X, Chang H, Dong Z, Zhang Y, Zhao D, Ye L, Xia Q, Zhao P. Comparative Proteome Analysis Reveals that Cuticular Proteins Analogous to Peritrophin-Motif Proteins are Involved in the Regeneration of Chitin Layer in the Silk Gland of Bombyx mori at the Molting Stage. PROTEOMICS. 2018; 18(19):1700389. https://doi.org/10.1002/pmic.201700389.

    Google Scholar 

  87. 87

    Noh MY, Muthukrishnan S, Kramer KJ, Arakane Y. A chitinase with two catalytic domains is required for organization of the cuticular extracellular matrix of a beetle. PLoS Genet. 2018; 14(3):1007307.

    Google Scholar 

  88. 88

    Aragon S, Claudinot S, Blais C, Maibeche M, Dauphin-Villemant C. Molting cycle-dependent expression of CYP4C15, a cytochrome P450 enzyme putatively involved in ecdysteroidogenesis in the crayfish, Orconectes limosus. Insect Biochem Mol Biol. 2002; 32(2):153–59. https://doi.org/10.1016/S0965-1748(01)00095-9.

    CAS  PubMed  Google Scholar 

  89. 89

    Said MR, Begley TJ, Oppenheim AV, Lauffenburger DA, Samson LD. Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A. 2004; 101(52):18006–11. https://doi.org/10.1073/pnas.0405996101.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Wuchty S. Interaction and domain networks of yeast. PROTEOMICS. 2002; 2(12):1715–23. https://doi.org/10.1002/1615-9861(200212)2:12<1715::AID-PROT1715>3.0.CO;2-O.

    CAS  PubMed  Google Scholar 

  91. 91

    Hahn MW, Kern AD. Comparative Genomics of Centrality and Essentiality in Three Eukaryotic Protein-Interaction Networks. Mol Biol Evol. 2005; 22(4):803–06. https://doi.org/10.1093/molbev/msi072.

    CAS  Google Scholar 

  92. 92

    Goymer P. Why do we need hubs?,. Nat Rev Genet. 2008; 9(9):651. https://doi.org/10.1038/nrg2450.

    Google Scholar 

  93. 93

    Borin GP, Carazzolle MF, Dos Santos RAC, Riaño-Pachón DM, Oliveira J. V. d. C.Gene Co-expression Network Reveals Potential New Genes Related to Sugarcane Bagasse Degradation in Trichoderma reesei RUT-30. Front Bioeng Biotechnol. 2018; 6:151. https://doi.org/10.3389/fbioe.2018.00151.

    PubMed  PubMed Central  Google Scholar 

  94. 94

    Heggland EI, Eichner C, Støve SI, Martinez A, Nilsen F, Dondrup M. A scavenger receptor B (CD36)-like protein is a potential mediator of intestinal heme absorption in the hematophagous ectoparasite Lepeophtheirus salmonis. Sci Rep. 2019; 9(1):4218. https://doi.org/10.1038/s41598-019-40590-x.

    PubMed  PubMed Central  Google Scholar 

  95. 95

    Moyer BD, Allan BB, Balch WE. Rab1 interaction with a GM130 effector complex regulates COPII vesicle cis-Golgi tethering. Traffic. 2001; 2(4):268–76. https://doi.org/10.1034/J.1600-0854.2001.1O007.X.

    CAS  PubMed  Google Scholar 

  96. 96

    Allan BB, Moyer BD, Balch WE. Rab1 recruitment of p115 into a cis-SNARE complex: Programming budding COPII vesicles for fusion. Science. 2000; 289(5478):444–48. https://doi.org/10.1126/science.289.5478.444.

    CAS  PubMed  Google Scholar 

  97. 97

    Goody RS, Müller MP, Wu YW. Mechanisms of action of Rab proteins, key regulators of intracellular vesicular transport. De Gruyter. 2017. https://doi.org/10.1515/hsz-2016-0274. https://www.degruyter.com/document/doi/10.1515/hsz-2016-0274/html.

  98. 98

    Huang J, Birmingham CL, Shahnazari S, Shiu J, Zheng YT, Smith AC, Campellone KG, Heo WD, Gruenheid S, Meyer T, Welch MD, Ktistakis NT, Kim PK, Klionsky DJ, Brumell JH. Antibacterial autophagy occurs at PtdIns(3)P-enriched domains of the endoplasmic reticulum and requires Rab1 GTPase. Autophagy. 2011; 7(1):17–26. https://doi.org/10.4161/auto.7.1.13840.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Dong N, Zhu Y, Lu Q, Hu L, Zheng Y, Shao F. Structurally distinct bacterial TBC-like GAPs link Arf GTPase to Rab1 inactivation to counteract host defenses. Cell. 2012; 150(5):1029–41. https://doi.org/10.1016/j.cell.2012.06.050.

    CAS  PubMed  Google Scholar 

  100. 100

    Takai Y, Sasaki T, Matozaki T. Small GTP-binding proteins: American Physiological SocietyBethesda, MD; 2001. https://doi.org/10.1152/physrev.2001.81.1.153. https://journals.physiology.org/doi/abs/10.1152/physrev.2001.81.1.153.

  101. 101

    Uno T, Sakamoto K, Isoyama Y, Hiragaki S, Uno Y, Kanamaru K, Yamagata H, Takagi M, Mizoguchi A, Takeda M. Relationship between the expression of Rab family GTPases and neuropeptide hormones in the brain of Bombyx mori. Histochem Cell Biol. 2013; 139(2):299–308. https://doi.org/10.1007/s00418-012-1021-5.

    CAS  PubMed  Google Scholar 

  102. 102

    Hiragaki S, Uno T, Takeda M. Putative regulatory mechanism of prothoracicotropic hormone (PTTH) secretion in the American cockroach, Periplaneta americana as inferred from co-localization of Rab8, PTTH, and protein kinase C in neurosecretory cells. Cell Tissue Res. 2009; 335(3):607–15. https://doi.org/10.1007/s00441-008-0747-9.

    CAS  PubMed  Google Scholar 

  103. 103

    Niwa YS, Niwa R. Transcriptional regulation of insect steroid hormone biosynthesis and its role in controlling timing of molting and metamorphosis. Dev Growth Differ. 2016; 58(1):94–105. https://doi.org/10.1111/dgd.12248.

    CAS  PubMed  Google Scholar 

  104. 104

    Mukhopadhyay A, Quiroz JA, Wolkoff AW. Rab1a regulates sorting of early endocytic vesicles. Am J Physiol Gastrointest Liver Physiol. 2014; 306(5):412. https://doi.org/10.1152/ajpgi.00118.2013.

    Google Scholar 

  105. 105

    Stenmark H, Olkkonen VM. The Rab GTPase family. Genome Biol. 2001; 2(5):3007–1. https://doi.org/10.1186/gb-2001-2-5-reviews3007.

    Google Scholar 

  106. 106

    Field MC, Horn D, Carrington M. Analysis of Small GTPase Function in Trypanosomes. In: Methods in Enzymology. 1st edn. Berlin: Academic Press: 2008. p. 57–76. https://doi.org/10.1016/S0076-6879(07)38005-1. https://linkinghub.elsevier.com/retrieve/pii/S0076687907380051.

    Google Scholar 

  107. 107

    Tatsumoto T, Xie X, Blumenthal R, Okamoto I, Miki T. Human ECT2 is an exchange factor for Rho GTPases, phosphorylated in G2/M phases, and involved in cytokinesis. J Cell Biol. 1999; 147(5):921–28. https://doi.org/10.1083/jcb.147.5.921.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Schmidt A, Hall A. Guanine nucleotide exchange factors for Rho GTPases: Turning on the switch: Cold Spring Harbor Laboratory Press; 2002. https://doi.org/10.1101/gad.1003302. http://genesdev.cshlp.org/content/16/13/1587.fullhttp://genesdev.cshlp.org/content/16/13/1587.

  109. 109

    Greer ER, Chao AT, Bejsovec A. Pebble/ECT2 RhoGEF negatively regulates the wingless/Wnt signaling pathway. Dev (Camb). 2013; 140(24):4937–46. https://doi.org/10.1242/dev.101303.

    CAS  Google Scholar 

  110. 110

    Fields AP, Justilien V. The guanine nucleotide exchange factor (GEF) Ect2 is an oncogene in human cancer. Adv Enzym Regul. 2010; 50(1):190–200. https://doi.org/10.1016/j.advenzreg.2009.10.010.

    Google Scholar 

  111. 111

    L S, H K, F N, S D. Molecular characterization and functional analysis of components of the TOR pathway of the salmon louse, Lepeophtheirus salmonis (Krøyer, 1838). Exp Parasitol. 2018; 188:83–92. https://doi.org/10.1016/J.EXPPARA.2018.04.004.

    Google Scholar 

  112. 112

    van der Horst A, Burgering BMT. Stressing the role of FoxO proteins in lifespan and disease. Nat Rev Mol Cell Biol. 2007; 8(6):440–50. https://doi.org/10.1038/nrm2190.

    CAS  PubMed  Google Scholar 

  113. 113

    Martins R, Lithgow GJ, Link W. Long live FOXO: unraveling the role of FOXO proteins in aging and longevity. Aging Cell. 2016; 15(2):196–207. https://doi.org/10.1111/acel.12427.

    CAS  PubMed  Google Scholar 

  114. 114

    Spellberg MJ, Marr MT. FOXO regulates RNA interference in Drosophila and protects from RNA virus infection. Proc Natl Acad Sci. 2015; 112(47):14587–92. https://doi.org/10.1073/pnas.1517124112.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Zeng B, Huang Y, Xu J, Shiotsuki T, Bai H, Palli SR, Huang Y, Tan A. The FOXO transcription factor controls insect growth and development by regulating juvenile hormone degradation in the silkworm, Bombyx mori. J Biol Chem. 2017; 292(28):11659–69. https://doi.org/10.1074/jbc.M117.777797.

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Cai M-J, Zhao W-L, Jing Y-P, Song Q, Zhang X-Q, Wang J-X, Zhao X-F. 20-hydroxyecdysone activates Forkhead box O to promote proteolysis during Helicoverpa armigera molting. Development. 2016; 143(6):1005–15. https://doi.org/10.1242/dev.128694.

    CAS  PubMed  Google Scholar 

  117. 117

    Hossain MS, Liu Y, Zhou S, Li K, Tian L, Li S. 20-Hydroxyecdysone-induced transcriptional activity of FoxO upregulates brummer and acid lipase-1 and promotes lipolysis in Bombyx fat body,. Insect Biochem Mol Biol. 2013; 43(9):829–38. https://doi.org/10.1016/j.ibmb.2013.06.007.

    CAS  PubMed  Google Scholar 

  118. 118

    Colombani J, Bianchini L, Layalle S, Pondeville E, Dauphin-Villemant C, Antoniewski C, Carré C, Noselli S, Léopold P. Antagonistic Actions of Ecdysone and Insulins Determine Final Size in Drosophila. Science. 2005; 310(5748):667–70. https://doi.org/10.1126/science.1119432.

    CAS  PubMed  Google Scholar 

  119. 119

    Hudson NJ, Reverter A, Dalrymple BP. A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation. PLoS Comput Biol. 2009; 5(5):1000382. https://doi.org/10.1371/journal.pcbi.1000382.

    Google Scholar 

  120. 120

    de la Fuente A. From ’differential expression’ to ’differential networking’ – identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010; 26(7):326–33. https://doi.org/10.1016/j.tig.2010.05.001.

    CAS  PubMed  Google Scholar 

  121. 121

    Zhou Z, Eichner C, Nilsen F, Jonassen I, Dondrup M. Additional Data for Gene co-expression network analysis facilitates identification of novel important genes for the moulting process of the Atlantic salmon louse Lepeophtheirus salmonis. figshare. 2021. https://doi.org/10.6084/m9.figshare.c.5375315.

  122. 122

    Chen J, Liang Z, Liang Y, Pang R, Zhang W. Conserved microRNAs miR-8-5p and miR-2a-3p modulate chitin biosynthesis in response to 20-hydroxyecdysone signaling in the brown planthopper, Nilaparvata lugens. Insect Biochem Mol Biol. 2013; 43(9):839–48. https://doi.org/10.1016/j.ibmb.2013.06.002.

    CAS  PubMed  Google Scholar 

  123. 123

    Surholt B. Formation of glucosamine-6-phosphate in chitin synthesis during ecdysis of the migratory locust, Locusta migratoria. Insect Biochem. 1975; 5(5):585–93. https://doi.org/10.1016/0020-1790(75)90040-2.

    CAS  Google Scholar 

  124. 124

    Oikari S, Makkonen K, Deen AJ, Tyni I, Kärnä R, Tammi RH, Tammi MI. Hexosamine biosynthesis in keratinocytes: roles of GFAT and GNPDA enzymes in the maintenance of UDP-GlcNAc content and hyaluronan synthesis. Glycobiology. 2016; 26(7):710–22. https://doi.org/10.1093/glycob/cww019.

    CAS  PubMed  Google Scholar 

  125. 125

    Vrba J, Macháéek J. Release of dissolved extracellular β-N-acetylglucosaminidase during crustacean moulting. Limnol Oceanogr. 1994; 39(3):712–16. https://doi.org/10.4319/lo.1994.39.3.0712.

    CAS  Google Scholar 

  126. 126

    Lyu Z, Chen J, Li Z, Cheng J, Wang C, Lin T. Knockdown of β-N-acetylglucosaminidase gene disrupts molting process in Heortia vitessoides Moore. Arch Insect Biochem Physiol. 2019; 101(4):21561. https://doi.org/10.1002/arch.21561.

    Google Scholar 

  127. 127

    Poley JD, Sutherland BJG, Jones SRM, Koop BF, Fast MD. Sex-biased gene expression and sequence conservation in Atlantic and Pacific salmon lice (Lepeophtheirus salmonis). BMC Genomics. 2016; 17(1):483. https://doi.org/10.1186/s12864-016-2835-7.

    PubMed  PubMed Central  Google Scholar 

  128. 128

    Chen J, Tang B, Chen H, Yao Q, Huang X, Chen J, Zhang D, Zhang W. Different Functions of the Insect Soluble and Membrane-Bound Trehalase Genes in Chitin Biosynthesis Revealed by RNA Interference. PLoS ONE. 2010; 5(4):10133.

    Google Scholar 

  129. 129

    Merzendorfer H. The cellular basis of chitin synthesis in fungi and insects: Common principles and differences. Eur J Cell Biol. 2011; 90(9):759–69. https://doi.org/10.1016/j.ejcb.2011.04.014.

    CAS  PubMed  Google Scholar 

  130. 130

    Frand AR, Russel S, Ruvkun G. Functional Genomic Analysis of C. elegans Molting. PLoS Biol. 2005; 3(10):312.

    Google Scholar 

  131. 131

    Schumann I, Hering L, Mayer G, Kenny N, Hui J. Halloween genes in panarthropods and the evolution of the early moulting pathway in Ecdysozoa. R Soc Open Sci. 2018. https://doi.org/10.1098/rsos.180888.

  132. 132

    Nakagawa Y, Henrich VC. Arthropod nuclear receptors and their role in molting. FEBS J. 2009; 276(21):6128–57. https://doi.org/10.1111/j.1742-4658.2009.07347.x.

    CAS  PubMed  Google Scholar 

  133. 133

    Lavorgna G, Karim FD, Thummel CS, Wu C. Potential role for a FTZ-F1 steroid receptor superfamily member in the control of Drosophila metamorphosis. Proc Natl Acad Sci. 1993; 90(7):3004–08. https://doi.org/10.1073/pnas.90.7.3004.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134

    Piulachs M-D, Pagone V, Bellés X. Key roles of the Broad-Complex gene in insect embryogenesis. Insect Biochem Mol Biol. 2010; 40(6):468–75. https://doi.org/10.1016/j.ibmb.2010.04.006.

    CAS  PubMed  Google Scholar 

  135. 135

    Maldonado-Aguayo W, Chávez-Mardones J, Gonçalves AT, Gallardo-Escárate C. Cathepsin Gene Family Reveals Transcriptome Patterns Related to the Infective Stages of the Salmon Louse Caligus rogercresseyi. PLoS ONE. 2015; 10(4):0123954.

    Google Scholar 

  136. 136

    Nagai C, Mabashi-Asazuma H, Nagasawa H, Nagata S. Identification and Characterization of Receptors for Ion Transport Peptide (ITP) and ITP-like (ITPL) in the Silkworm Bombyx mori*. J Biol Chem. 2014; 289(46):32166–77. https://doi.org/10.1074/jbc.M114.590646.

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137

    Webster SG, Keller R, Dircksen H. The CHH-superfamily of multifunctional peptide hormones controlling crustacean metabolism, osmoregulation, moulting, and reproduction. Gen Comp Endocrinol. 2012; 175(2):217–33. https://doi.org/10.1016/j.ygcen.2011.11.035.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We would like to thank Heidi Kongshaug, Lars Are Hamre and Per Gunnar Espedal for technical help in the laboratory, and we would like to thank Kjell Petersen for helpful comments and suggestions on the manuscript.

Funding

This project was funded by the Research Council of Norway, SFI-Sea Lice Research Centre, grant number 203513/ O30. This work was funded by the ELIXIR2 (270068) infrastructure grant from the Research Council of Norway to MD.

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Authors

Contributions

ZZ performed bioinformatics analyses and drafted the initial manuscript. MD and CE designed the study, provided and analyzed data. IJ and FN contributed to the design of the study and provided funding. CE performed RNAi experiments and drafted the corresponding methods and results. ZZ, MD and CE edited the manuscript. All authors read, revised and approved the final manuscript.

Corresponding author

Correspondence to Michael Dondrup.

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Ethics approval and consent to participate

All experiments were performed according to Norwegian animal welfare regulations with the approval of the governmental Norwegian Animal Research Authority (ID7704, no 2010/245410).

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

The methods used to estimate the power parameter and calculate the module preservation statistics.

Additional file 2

Table S1: Homologues-based transcript-phenotype list

Table S2-S3: Analysis results for the modules in the middle network and moulting network, including module size, enriched GO terms with smallest p-values, p-values of enrichment analyses based on homologues, module preservation Zsummary, number of known moulting-associated genes, proportion of TF and DE genes.

Table S4: Analysis results for the modules in the global network, including module size, enriched GO terms with smallest p-values, p-values of enrichment analyses based on homologues, regularized logistic regression coefficients, number of known moulting-associated genes, proportion of TF and DE genes.

Table S5-S7: Module assignment results for the known moulting-associated transcripts in the middle, moulting and global network; the ranks of these transcripts based on three types of centrality measurements within modules; whether they were intramodular hubs or not.

Table S8: Two types of RIF scores for the transcripts annotated as transcription factors

Table S9-S11: Centrality measurements, average ranks and annotations for the nodes in three selected modules (yellowgreen, steelblue and violet).

Table S12: Annotations of the nodes with highest average ranks from the selected modules.

Table S13: Available RNAi results for nodes from the eight selected important modules and nodes with high average ranks from modules without passing any criteria.

Additional file 3

Figure S2: Histological sections of adult female louse tissues from control samples and samples with selected genes knock-down.

Figure S3: Histological sections of copepodids sampled three days post infection.

Additional file 4

Visualization file for the selected important module “yellowgreen” from the moulting network.

Additional file 5

Visualization file for the selected important module “violet” from the moulting network.

Additional file 6

Visualization file for the selected important module “steelblue” from the global network.

Additional file 7

Supplementary data has been deposited in Figshare DOI: 10.6084/m9.figshare.c.5375315

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Zhou, Z., Eichner, C., Nilsen, F. et al. A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis). BMC Genomics 22, 832 (2021). https://doi.org/10.1186/s12864-021-08054-7

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Keywords

  • Gene co-expression network analysis
  • Regularized logistic regression
  • Differential co-expression analysis
  • Regulatory impact factor
  • Transcriptome
  • Salmon louse
  • Moult
  • RAB1A