Like many other sea creatures, the worm Platynereis dumerilii reproduces by dispersing eggs and sperm in the water. For these animals, timing is everything: if they fail coordinate their release, the precious reproductive cells will drift in the vastness of the ocean without ever meeting their male or female counterparts.

Internal clocks are a set of mechanisms that allow organisms to tune their internal processes to their environment. For example, the circadian clock helps many creatures to adapt to the cycle of day and night. This involves switching genes on and off according to the time of day. When a gene is activated, its information is copied into a molecule of RNA, which is then read to create proteins that will go on performing specific roles. To produce their eggs and sperm at the right time, P. dumerilii worms rely on a poorly understood internal clock which is synchronized by the moon cycle.

To investigate this ‘inner calendar’, Schenk, Bannister et al. developed a new technique that allows them to extract both RNA and proteins from the miniscule heads of the worms. The results showed that the internal clock synchronized by the lunar phases influenced the levels of many more proteins than RNA molecules. In comparison, other life events such as the worms becoming sexually mature, had a more similar impact on both protein and RNA regulation. This might suggest that the inner calendar that coordinates the worms with the moon cycle could work by changing protein, rather than RNA levels. The analysis also highlighted several molecular actors that may be essential for the worm’s inner clock to work properly. In the future, the new technique will help to dissect more finely how P. dumerilii and many other marine creatures stay synchronized with the moon, and spawn at the right time.

Appropriate timing is essential for reproductive success, especially in organisms that reproduce via external fertilisation. In order to maximise the chances of successful mating encounters, marine animals often rely on mass spawning events that require synchronisation of behaviour and gonadal maturation, both within individuals and on a population level (Fischer, 1984; Guest, 2008; Hoeger et al., 1999; Tessmar-Raible et al., 2011).

Many species achieve this synchronisation through the interplay of multiple timing systems that operate on different time scales, ranging from months or days to hours or even minutes (reviewed in Liedvogel et al., 2011; Tessmar-Raible et al., 2011). One timing cue used for synchronised reproduction in organisms ranging from brown algae and corals to bristle worms, echinoderms and vertebrates, is provided by the changing phases of the moon (Brady et al., 2016; Coppard and Campbell, 2005; Grant et al., 2009; Kaniewska et al., 2015; Kennedy and Pearse, 1975; Oldach et al., 2017; Rahman et al., 2004; Raible et al., 2017; Saavedra and Pousão-Ferreira, 2006; Saigusa, 1988; Skov et al., 2005). Depending on the species, the predominant physical cue interpreted by organisms is either gravitation or illumination. A study in reefs at two distinct lunar time points (full moon vs. new moon) has revealed a direct impact of moonlight illumination on the transcriptome of Acropora corals, but also found that light-induced transcriptome changes are most dramatic in mature animals, when light orchestrates the actual spawning event (Kaniewska et al., 2015). While pointing at the transcriptome as a readout for organismal changes, this study therefore also illustrates that in natural samples, the molecular changes relating to illumination can be tightly interwoven with internal changes (maturation and spawning), and might in addition mask endogenous monthly oscillations as they can be produced by endogenous timing systems.

Laboratory research on animals such as the marine annelid Platynereis dumerilii or the marine midge Clunio marinus has indeed revealed evidence for such internal timers, showing that nocturnal light (in nature provided by the moon) does not only impact directly on transcription (Zantke et al., 2015), but is both necessary and sufficient to entrain endogenous monthly (circalunar) oscillators (Kaiser et al., 2016; Zantke et al., 2013), which in turn orchestrate reproduction at distinct times of the lunar month (Figure 1a).

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Figure 1—source data 1

Sample IDs.

Table depicting maturation satge, sex and lunar phase of the samples included in this study.

https://doi.org/10.7554/eLife.41556.006

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Figure 1—source data 2

Differential expression comparisons.

Table showing the different comparions made for DET/DEP predictions.

https://doi.org/10.7554/eLife.41556.007

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In Platynereis dumerilli, sexually mature animals reproduce by engaging in stereotypical swimming behaviours classically referred to as the ‘nuptial dance’, the exact onset of which is signalled by sex pheromones and results in the coordinated release of gametes by male and female worms (Beckmann et al., 1995; Zeeck et al., 1994; Zeeck et al., 1998). Sexual dimorphisms exist in the production and response of mature worms to spawning pheromones, as well as on the level of gonadal and parapodial morphology. These differences are not obvious during the early and juvenile developmental stages, but develop within a few weeks to months prior to spawning (Figure 1a,b). The sex ratio of the worms remains constant within laboratory cultures, even under different temperature, feeding or light conditions and different population densities, consistent with genetic mechanisms underlying the sexual dimorphisms (Beckmann et al., 1995; Fischer and Dorresteijn, 2004; Schulz et al., 1989; Zeeck et al., 1994; Zeeck et al., 1998).

Studies from established molecular model systems have shown that sexual behaviour involves neurons expressing sexually-dimorphic markers, including fruitless and doublesex in Drosophila, and mab genes in Caenorhabditis (e.g. Kopp, 2012). In Platynereis, differential expression of neuropeptides in whole mature males and females suggests that sexual dimorphism at the level of hormonal regulation coincides with sexual maturation (Conzelmann et al., 2013). Given the roles of such neurohormones in other animals, it is likely that they act both locally within the brain, as well as in the periphery of the animals to orchestrate sexual dimorphism of the gonads and behaviour as the animals mature. However, no systematic information is available on sexual dimorphisms in the brain of Platynereis.

Importantly, the bristleworm brain also plays a crucial role in circadian and circalunar timing (Hauenschild, 1956a; Hauenschild, 1956b; Hauenschild, 1959; Hauenschild, 1960; Hauenschild, 1966; Hofmann, 1976; Hofmann and Schiedges, 1984; Schenk et al., 2016; Zantke et al., 2013). Its importance in reproductive timing is highlighted by the fact that tails of decapitated animals undergo sexual maturation within two weeks, but are no longer synchronised to the lunar phase (Hauenschild, 1966; Hauenschild, 1974; Schenk et al., 2016). The brain also possesses non-visual photoreceptors that likely receive light inputs for circalunar (and circadian) clock entrainment (Arendt et al., 2004; Tessmar-Raible et al., 2007; Zantke et al., 2013).

Taken together, these findings argue that the Platynereis brain integrates information on maturation, sexual differentiation, and circalunar phase in order to bring about lunar-synchronised reproduction. We therefore sought to identify the molecular signatures of these three major biological processes using an integrated molecular profiling approach, and to assess whether a circalunar component could be disentangled from the changes brought about by maturation and sexual differentiation. We carried out this analysis on two levels: First, we systematically assessed transcriptome signatures, building on our previous observation that the transcript levels of a subset of core circadian clock genes are influenced by the phase of the circalunar oscillator in Platynereis, a finding that has also been observed in corals and the reef fish Siganus guttatus (Raible et al., 2017; Altincicek and Vilcinskas, 2007 references cited therein). Second, we extended our analyses to the proteome level, reasoning that a combined quantitative transcriptomics and proteomics approach would allow us to obtain a better understanding of the major molecular changes that occur on different functional levels, and highlight molecules and pathways that are most prominent in the different processes.

This systematic approach revealed that maturation induces the largest amount of changes on both transcriptome and proteome levels, whereas the circalunar clock has a much stronger impact on the proteome than the transcriptome. Our analyses identified molecular functions, pathways and candidate genes that are diagnostic for either sex and/or maturation stage, and show that representative genes associated with these processes localise in distinct brain regions. We further identify new factors that are subject to circalunar clock-dependent changes on mRNA and/or protein level, and map their locations in the adult brain. Specifically, we demonstrate that Ependymin-related proteins (ERPs) are strongly affected on both transcript and protein levels by maturation and circalunar clock phases. We further show that the neuropeptide Whitnin/Proctolin, which recently has been correlated with the spawning event in the semilunar spawner abalone Haliotis asinina (York et al., 2012), is also regulated by the circalunar clock in Platynereis.

Taken together, our work provides a comprehensive co-evaluation of transcriptomic and proteomic regulation of stage, sex, and circalunar phase-matched tissue samples, and provides the first systematic insight into the distinct genes and molecular processes influenced by maturation, sexual differentiation and the circalunar clock in adult Platynereis dumerilii.

In order to gain first systematic insight into the molecular impact of the circalunar clock in the brain, we conducted a comprehensive analysis of changes in the Platynereis head at two distinct phases of the circalunar clock: new moon (NM) and free-running full moon (FRFM). The environmental conditions are the same at both circalunar phases, FRFM describes the chronobiological conditions in which the animals would normally be presented with a nocturnal light stimulus (‘full moon’- FM), which is however not given (compare Figure 1a vs. c). Thus, their endogenous circalunar clock is under free-running conditions (i.e. FRFM, see also Zantke et al., 2013). In order to disentangle these respective circalunar molecular changes from those primarily related to maturation or sexual differentiation, we performed this analysis separately for male and female worms, across three stages of maturation (Figure 1b, Figure 1—figure supplement 1). We started our analysis with gene expression profiling by RNA sequencing (RNA-Seq), to assess effects on the transcriptome. As outlined further below, we developed an extraction method that allowed us to subsequently complement this transcriptomic analysis with a comprehensive proteomic analysis on material from the identical samples (Figure 1d, Figure 1—figure supplement 2).

A maturation-stage and sex-locked lunar head transcriptome

As the current Platynereis dumerilii genome assembly is not yet optimal for unique mapping of short reads for quantitative analyses, we generated a mapping resource based on transcript data. Whereas previous Platynereis transcriptomes were generated from whole animals during various adult life stages (Conzelmann et al., 2013), or early developmental stages (Chou et al., 2016), we reasoned that this might result in under-representation of head-specific transcripts. Moreover, when using whole animal samples, the increase in germ cells during maturation (see e.g. Schenk and Hoeger, 2010; Schenk et al., 2016 and references cited therein) could obscure low abundance expression, which might be of regulatory importance.

Therefore, we generated a head reference transcriptome assembled from RNA-Seq data combined from heads sampled under various circalunar conditions and maturation stages (see Materials and methods). The FRFM condition refers to animals kept in a standard daily 16:8 hr light dark (LD) light cycle whose circalunar clock was previously entrained by nocturnal light for at least two months, but are not exposed to nocturnal light at the expected full moon time when sampling was performed. Such ‘free-running’ conditions can thus reveal effects solely caused by the endogenous circalunar clock (Zantke et al., 2013 and Figure 1c).

Our primary assembly (Pdu_HeadRef_TS_v1) was systematically curated to (i) remove redundancies, (ii) merge scaffold information provided by published Platynereis sequences, (iii) remove sequence contaminations (e.g. prokaryotic), and (iv) flag additional sequences with high similarity to other species (see Materials and methods section), resulting in our final reference sets (Pdu_HeadRef_TS_v4 and v5, see Figure 2, Figure 2—source data 1–3 and Materials and methods section).

Figure 2

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Figure 2—source data 1

Excluded transcripts.

Table containing the IDs of the 5,727 prokaryotic contaminats present in the TS_v4 as identified by Kraken; which were excluded from TS_v5 prior to differential expression analysis.

https://doi.org/10.7554/eLife.41556.009

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Figure 2—source data 2

Platynereis head reference transcriptome v1 base annotation.

Annotation of TS_v1, including blastn, blastx, GO-term and THMM predictions.

https://doi.org/10.7554/eLife.41556.010

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Figure 2—source data 3

Platynereis head reference transcriptome v4 interpro annotation.

Interpro annotation of TS_v4, including blastx-hist, GO-terms, and KEGG-terms.

https://doi.org/10.7554/eLife.41556.011

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Figure 2—source data 4

Transcriptome ENA accession numbers.

Full list of European Nucleotide Archive accession numbers for all sequencing raw data and transcriptome assemblies from this study.

https://doi.org/10.7554/eLife.41556.012

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To identify gene sets that were regulated by either one of the three sampled parameters (circalunar clock phase, maturation, and sex), and assess to which extent these gene sets were distinct, we performed differential expression analyses using DESeq2 (v1.10.0, Love et al., 2014) and EdgeR (v3.12.0, Robinson et al., 2010) (Figure 3a, b, Figure 3—figure supplements 1–9, Figure 1—figure supplement 1, Figure 1—source datas 1 and 2, Figure 3—source datas 1 and 2). Transcripts passing a Benjamin-Hochberg (BH) false discovery rate (FDR) of 10% were considered as significant (FDR, Benjamini and Hochberg, 1995). To compare the results of both methods, rank sum files were generated to combine the results of both DESeq2 and EdgeR differential expression testing, to yield the final lists of differentially expressed transcripts (DETs) for each condition (Figure 3—source datas 3, 4, 5, 9, 10 and 11). Of the three major comparison groups, that is maturation, sex biased and circalunar, maturation exhibits the largest number of DETs (Altincicek and Vilcinskas, 2007); 16.52% of all transcripts, Figure 3a). Sexual differentiation was characterised by 640 DETs (1.23%), whereas 63 DETs (0.12%) appear to distinguish the two sampled phases of the circalunar clock. Of the 640 sex-biased DETs, 149 (23.28%) were also regulated by maturation and six (0.94%) by the circalunar phase (Figure 3—source data 3–5); of the 63 circalunar DETs 34 (53.97%) were also regulated by maturation. Four transcripts were regulated in all three parameters (Figure 3a).

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Figure 3—source data 1

Platynereis head reference transcriptome v5 read counts.

Table containing the raw read count data for TS_v5. This table is used as input for differential expression analysis.

https://doi.org/10.7554/eLife.41556.024

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Figure 3—source data 2

Transcriptome: DESeq normalised read counts.

Normalised read counts of TS_v5 as calculated by the DESeq2 algorithm; used to build DET expression graphs.

https://doi.org/10.7554/eLife.41556.025

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Figure 3—source data 3

Platynereis head reference transcriptome v5 gene universe.

Table containing the "gene universe" for the TS_v5 used as background for GO-term enrichment analysis in Figs. 3 and S3a-k.

https://doi.org/10.7554/eLife.41556.026

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Figure 3—source data 4

Transcriptome: DESeq output tables.

File containing DESeq predicted DETs for all 14 comparision made. Each comparison is contained within a separate tab.

https://doi.org/10.7554/eLife.41556.027

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Figure 3—source data 5

Transcriptome: EdgeR output tables.

File containing EdgeR predicted DETs for all 14 comparision made. Each comparison is contained within a separate tab.

https://doi.org/10.7554/eLife.41556.028

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Figure 3—source data 6

Transcriptome: DESeq2-EdgeR rank sum files.

This file contains DESeq2-EdgeR rank sums for predicted DETs for all 14 comparision made plus the all maturation rank sum predictions. Each comparison is contained within a separate tab. Note that while DESeq2 calculates the log2 fold change as A/B; EdgeR calculates the log2 fold change as B/A. log2FoldChange: DESeq2; log2FC: EdgeR; padj: BH-adjusted p-value DESeq2; FDR: BH-adjusted p-value EdgeR.

https://doi.org/10.7554/eLife.41556.029

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Figure 3—source data 7

Transcriptome: Mfuzz cluster gene IDs with cluster membership values.

This file contains the soft clustering output of RNA expression values with Mfuzz. Each comparison, maturation, sex, and lunar contains the individual transcript ID and the membership values for each cluster ranging from 0 to 1.

https://doi.org/10.7554/eLife.41556.030

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Figure 3—source data 8

Transcriptome: all over- and under-represented GO-Terms from all comparisions.

Table containing the results of the GO-term analysis on transcript level. Individual GO-terms are shown with their respective p-values in each of the three comparisons made (maturation, sex, and lunar), as well as their ontolgy, i.e. Molecular Function, Biological Process, or Cellular Compartment.

https://doi.org/10.7554/eLife.41556.031

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Figure 3—source data 9

Transcriptome: over- and under-represented GO-Terms maturation comparison.

This file contains the over- and under-representet GO-terms for the maturation comparison on transcript level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.032

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Figure 3—source data 10

Transcriptome: over- and under-represented GO-Terms sex bias comparison.

This file contains the over- and under-representet GO-terms for the sex comparison on transcript level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.033

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Figure 3—source data 11

Transcriptome: over- and under-represented GO-Terms circalunar comparison.

This file contains the over- and under-representet GO-terms for the lunar comparison on transcript level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.034

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Figure 3—source data 12

Transcriptome: over-represented GO-Term unique for any of the three comparisons.

This file contains those over-represented GO-terms which are unique to a given comparison on transcript level, i.e. maturation, sex, or lunar, with p-values for each of the three comparision.

https://doi.org/10.7554/eLife.41556.035

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Figure 3—source data 13

All transcripts significantly regulated during maturation (maturation DETs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all transcripts significantly regulated during maturation.

https://doi.org/10.7554/eLife.41556.036

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Figure 3—source data 14

All transcripts significantly regulated during sexual differentiation (sex DETs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all transcripts significantly regulated between males and females.

https://doi.org/10.7554/eLife.41556.037

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Figure 3—source data 15

All transcripts significantly regulated between circalunar phases (lunar DETs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all transcripts significantly regulated between circalunar phases.

https://doi.org/10.7554/eLife.41556.038

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To further validate our quantitative sequencing results, we selected transcripts from the top 50 ranked DETs for additional qRT-PCR analyses, preferring those that also had evidence on the protein level (see below). These experiments showed that the differences observed in our sequencing approach generally validated well when assessed by qRT-PCR on independent samples (Figure 4a–d, Figure 4—figure supplement 1). Within these experiments, the tested circalunar candidates appeared more variable (Figure 4—figure supplement 1). We noted that the majority of circalunar candidate DETs exhibited generally lower overall expression levels (Figure 4—figure supplement 1) and showed additional susceptibility to strain specific polymorphisms or phase differences in their circalunar rhythm profiles, similar to previous findings (Zantke et al., 2014).

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Figure 4—source data 1

Expression values relative to sams and cdc5 as obtained by qRT-PCR.

This file contains the qRT-PCR expression values of the candidate genes relative to the expression of the arithmetic mean of the reference genes sams and cdc5. Each gene is contained in a separate tab.

https://doi.org/10.7554/eLife.41556.041

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A maturation-stage and sex locked circalunar head proteome

We next analysed the proteome with respect to the different sampling conditions. To enable robust comparison between all conditions, we defined a subset of ‘quantifiable protein’ comprising only those detected uniquely in at least 2 BR and in 3 out of the 5 TR per BR for all samples, yielding a total of 1,064 proteins; a number comparable to studies of the mosquito Aedes aegypti head proteome, applying similar stringency filters (1,139 proteins, Nunes et al., 2016). We tested for significantly different protein abundances using ROTS and LIMMA (Elo et al., 2008; Elo et al., 2009; Ritchie et al., 2015) statistics and combined the results from both tests using the same rank sum approach used previously for the transcriptome DET analyses (Figure 5—source datas 6–8 and 19–21). As for the transcriptome data, the largest proportion of proteins, that is 693 proteins were differentially expressed over the process of maturation, corresponding to 65.13% of quantifiable proteins (Figure 5a). Contrary to the transcriptome profiling results, however, sexual differentiation accounted for only 17 differentially expressed proteins (DEP, 1.60%). Conversely, circalunar phase affected 261 DEP (24.53%), significantly exceeding the amount observed in the transcriptomic analysis. Of the 17 sex biased proteins, 12 were regulated during maturation, as were 177 of the 261 (67.82%) circalunar proteins. Six proteins were affected by all three processes (Figure 5a).

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Figure 5—source data 1

Labelled proteomics: detected proteins and intensities, filtered and unfiltered.

Table contataining protein intensities and calculation of the normalised intensities for both, all 2,290 detected proteins and the BR and TR filtered final list containing 1,064 proteins used for DEP prediction. Used for DEP graphs.

https://doi.org/10.7554/eLife.41556.050

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Figure 5—source data 2

Platynereis head reference proteome v4 gene universe.

Table containing the "gene universe" for the Prot_v4 used as background for GO-term enrichment analysis in Figs. 5 and S6a-g.

https://doi.org/10.7554/eLife.41556.051

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Figure 5—source data 3

Proteome comparison: all over- and under-represented GO-Terms labelled proteomics.

Table containing the over-and under-represented GO-terms using all 2,290 proteins deteced in the labelled proteome dataset. GO-terms are shown with their respective p-values, ontology (Molecular Function, Biological Process, Cellular Compartment), and test direction, i.e. over- or under-represented.

https://doi.org/10.7554/eLife.41556.052

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Figure 5—source data 4

Proteome comparison: all over- and under-represented GO-Terms unlabelled proteomics.

Table containing the over-and under-represented GO-terms using all 3,847 proteins deteced in the unlabelled proteome dataset. GO-terms are shown with their respective p-values, ontology (Molecular Function, Biological Process, Cellular Compartment), and test direction, i.e. over- or under-represented.

https://doi.org/10.7554/eLife.41556.053

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Figure 5—source data 5

Proteome comparision: over- and under-represented GO-Terms shared between labelled and unlabelled proteomics.

Table containing the over-and under-represented GO-terms shared between the labelled 2,290 proteins and the unlabelled 3,847 proteins sets. GO-terms are shown with p-values, actual counts, expected counts, number of IDs per category for both set, respectively; as well as ontology (Molecular Function, Biological Process, Cellular Compartment).

https://doi.org/10.7554/eLife.41556.054

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Figure 5—source data 6

Proteome: ROTS results.

This file contains the ROTS predicted DEPs for all 14 comparision made. Each comparison is contained within a separate tab.

https://doi.org/10.7554/eLife.41556.055

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Figure 5—source data 7

Proteome: LIMMA results.

This file contains the LIMMA predicted DEPs for all 14 comparision made. Each comparison is contained within a separate tab.

https://doi.org/10.7554/eLife.41556.056

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Figure 5—source data 8

Proteome: ROTS-LIMMA rank sum files.

This file contains the ROTS-LIMMA rank sums for predicted DEPs for all 14 comparision made plus the all maturation rank sum predictions. Each comparison is contained within a separate tab.

https://doi.org/10.7554/eLife.41556.057

Download elife-41556-fig5-data8-v2.xlsx

Figure 5—source data 9

Proteome: Mfuzz cluster protein IDs with cluster membership values.

This file contains the soft clustering output of protein expression values with Mfuzz. Each comparison, maturation, sex, and lunar contains the individual transcript ID and the membership values for each cluster ranging from 0 to 1.

https://doi.org/10.7554/eLife.41556.058

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Figure 5—source data 10

Proteome: all over- and under-represented GO-Terms from all comparisions.

Table containing the results of the GO-term analysis on transcript level. Individual GO-terms are shown with their respective p-values in each of the three comparisons (maturation, sex, and lunar), as well as their ontolgy, i.e. Molecular Function, Biological Process, or Cellular Compartment.

https://doi.org/10.7554/eLife.41556.059

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Figure 5—source data 11

Proteome: over- and under-represented GO-Terms maturation comparison.

This file contains the over- and under-representet GO-terms for the maturation comparison on protein level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.060

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Figure 5—source data 12

Proteome: over- and under-represented GO-Terms sex bias comparison.

This file contains the over- and under-representet GO-terms for the sex comparison on protein level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.061

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Figure 5—source data 13

Proteome: over- and under-represented GO-Terms circalunar comparison.

This file contains the over- and under-representet GO-terms for the lunar comparison on protein level. Tabs for each of the three ontologies, i.e. Molecular Function, Biological Process, and Cellular Compartment.

https://doi.org/10.7554/eLife.41556.062

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Figure 5—source data 14

Lunar proteome repetion: detected proteins and intensities, filtered and unfiltered.

Table contataining protein intensities and calculation of the normalised intensities for both, all 2,092 detected proteins and the BR and TR filtered final list containing 1,671 proteins used for DEP prediction from the lunar proteome repetion experiment.

https://doi.org/10.7554/eLife.41556.063

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Figure 5—source data 15

Lunar proteome repetion: ROTS-LIMMA significant proteins.

This file contains the ROTS-LIMMA rank sums for predicted DEPs for the repeat lunar proteome experiment using only IM sample, containg the 1,671 filtered and analysed proteins.

https://doi.org/10.7554/eLife.41556.064

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Figure 5—source data 16

Lunar proteome set1-set2 ovelapping IDs ROTS-LIMMA significant proteins in set1.

This file contains the ROTS-LIMMA rank sums of DEPs predicted as significant in the first proteomics experiment (set1); using only the 1,017 proteins detected in both, the first (set1) and the second experiment (set2) as input data.

https://doi.org/10.7554/eLife.41556.065

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Figure 5—source data 17

Lunar proteome set1-set2 ovelapping IDs ROTS-LIMMA significant proteins in set2.

This file contains the ROTS-LIMMA rank sums of DEPs predicted as significant in the second proteomics experiment (set2); using only the 1,017 proteins detected in both, the first (set1) and the second experiment (set2) as input data.

https://doi.org/10.7554/eLife.41556.066

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Figure 5—source data 18

Lunar proteome: all ROTS-LIMMA significant proteins with BLASTx annotation.

This file contains all DEPs predicted as significantly circalunar different by either the first, the second, or both proteome experiments. All proteins have also a BLASTx annotions where possible. Positive identification is marked by '+' in the respective column.

https://doi.org/10.7554/eLife.41556.067

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Figure 5—source data 19

All proteins significantly regulated during maturation (maturation DEPs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all proteins significantly regulated during maturation.

https://doi.org/10.7554/eLife.41556.068

Download elife-41556-fig5-data19-v2.xlsx

Figure 5—source data 20

All proteins significantly regulated during sexual differentiation (sex DEPs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all proteins significantly regulated between males and females.

https://doi.org/10.7554/eLife.41556.069

Download elife-41556-fig5-data20-v2.xlsx

Figure 5—source data 21

All proteins significantly regulated between circalunar phases (lunar DEPs).

This file contains the IDs, mean expression values, top hit by blastX with e-value cut-off 10^-4, e-value corresponding to blast hit, number of identical/ similar AA or gaps in blast alignment ("identity", "positives", "gaps") for all proteins significantly regulated between circalunar phases.

https://doi.org/10.7554/eLife.41556.070

Download elife-41556-fig5-data21-v2.xlsx

We are aware that transcriptomic versus proteomic comparisons are of course different in that RNA sequencing will allow to quantify most transcript types present in a given sample, whereas quantitative proteomics can at present only measure the most abundant proteins present in this sample. This likely explains the difference in the number of sex-specific DETs vs. DEPs, but not the difference observed in the numbers of circalunar DETs vs. DEPs.

To further assimilate the proteomic data with the transcriptomic profiles, we performed the same type of soft cluster analysis of the significant DEPs using the Mfuzz algorithm (Futschik and Carlisle, 2005; Kumar and Futschik, 2007) to assess if co-regulated clusters existed for the respective processes. Maturation as well as circalunar DEPs each clustered robustly into five protein expression clusters, whereas the sex-specific DEPs showed only weak associations to three clusters (Figure 5b, gene lists Figure 5—source data 9). A comparison between protein and RNA expression clusters for the maturation process revealed similar dynamic groups (Figure 3b vs. 5b), however, these were not necessarily populated by corresponding transcripts and proteins, as only 40.12% (278 out of 693) of DETs showed a similar profile of regulation on the protein level. This finding suggests that transcript to protein changes are ‘offset’ in time for the majority of the transcripts. Comparisons between DET and DEP clusters were less robust for the sex-specific and circalunar categories, given that only few DEPs or DETs were identified, respectively.

GO enrichment analysis of the DEPs showed that out of a total of 2,448 GO terms present in all 2,290 detectable proteins, 121 GO terms were enriched. Of these, 75 (62.5%) GO terms were found to be enriched in maturation DEPs, 17 in sex biased DEPs, and 46 in circalunar DEPs. Like for the transcriptome, there was little overlap between the GO term enrichments across the three different analysed biological processes (Figure 5c, Figure 5—figure supplements 2–6, Figure 5—source data 10–13).

Characterisation of circalunar proteome regulated candidates reveals ependymin-related genes and prepro-whitnin as putative circalunar clock phase signalling molecules

We next started to characterise several circalunar DEP candidates and provide a showcase of five of them. To gain a better understanding as to where these proteins are synthesised and possibly active, we analysed their expression in adult heads. These expression analyses are based on whole mount in situ hybridisations, which we think is justified to do as we were interested in their place of origin.

A larger group that caught our attention consisted of iron binding proteins. We selected two of these for validation: Myohaemerythrin (myoHr), and Somaferritin. MyoHr is a representative of the Haemerythrins, small oxygen-binding non-haem iron proteins present in all phyla except for in Deuterostomes; myoHr in particular appears to be restricted to marine invertebrates (Coates and Decker, 2017; Costa-Paiva et al., 2017). Ferritins are intracellular iron storage proteins, which additional to their role iron metabolism have been implicated in oxidative stress resistance and immunology (Altincicek and Vilcinskas, 2007; Andrews et al., 1992; Pham and Winzerling, 2010).

Another circalunar DEP that caught our attention was annotated as Whitnin precursor. Whitnin is the mollusc orthologue of the insect neuropeptide Proctolin (Veenstra, 2010, Figure 7—figure supplement 1, Figure 7—source datas 1 and 2). Of note, prepro-whitnin was shown to be differentially regulated on transcript level in the nervous system of abalone Haliotis asinina over the course of two weeks. Maximal expression occurred just prior to the semi-circalunar spawning events of this organism (York et al., 2012). Whitnin is thus a potential candidate for relaying the circalunar signal to other cells. The last two DEPs we focused on both belong to the large family of Ependymin-related proteins. These are highly sequence-related and could either represent different alleles of the same gene or are the result of a recent gene duplication event (Figure 6a, Figure 6—figure supplement 1, Figure 6—source datas 1 and 2).

Figure 6 with 2 supplements see all

Download asset Open asset

Figure 6—source data 1

Ependymins alignment.

Fasta-file containing the ependymin protein alignement; used for the ERP trees in Figs. 6 and Figure 6-figure supplement 1.

https://doi.org/10.7554/eLife.41556.074

Download elife-41556-fig6-data1-v2.txt

Figure 6—source data 2

Ependymins alignment sequence IDs/accession numbers.

Table containing the TS_v5 IDs for the Platynereis Ependymins as well as the NCBI IDs for the other ERPs.

https://doi.org/10.7554/eLife.41556.075

Download elife-41556-fig6-data2-v2.csv

Ependymin-related proteins (ERPs) are a group of small extracellular glycoproteins originally identified as one of the most abundant proteins in teleost-fish cerebrospinal fluid (Shashoua, 1991; Suárez-Castillo and García-Arrarás, 2007). Later, ERPs were discovered in other vertebrates and due to their complete absence from all ecdysozoan genomes and transcriptomes analysed so far, were considered to be vertebrate specific (Suárez-Castillo and García-Arrarás, 2007; Andrews et al., 1992) references cited therein). However, recently ERPs have been identified in an increasing number of non-ecdysozoan invertebrates, including echinoderms, hemichordates, molluscs, annelids and even cnidarians (Hall et al., 2017; Suárez-Castillo and García-Arrarás, 2007). A re-occurring feature of ERPs are their multiple group-specific gene duplications (Hall et al., 2017; Suárez-Castillo and García-Arrarás, 2007). This can be on a higher level - such as the teleost-specific or echinoderm-specific multiplications, but also on the level of species (groups), as pointed out for the Acanthaster planci species group (Hall et al., 2017), or as we also observed in Platynereis dumerilii. We identified 15 ependymin-like sequences in the transcriptome, most of which cluster together in one specific subgroup (Figure 6a, Figure 6—figure supplement 1, Figure 6—source datas 1 and 2). Seven Pdu-ERPs were detected in the proteome, all of which were significantly differentially abundant between the two circalunar phases (Figure 5—source data 18). Of those, two showed correlated changes between transcript and proteome level (Figure 6b,b’ and Figure 6—figure supplement 2) and were subsequently investigated for their expression patterns.

Both Pdu-erps exhibited identical patterns, covering sensory appendages of the head; palpae and peristomial cirri, a large expression domain on the ventral side of the head, as well as an anterior and posterior domain in the dorsal brain (Figure 6c–e, Figure 6—figure supplement 2). The other investigated circalunar candidates exhibited overall different expression domains (Figures 6c–e and 7): Pre-pro-whitnin is expressed in four main regions. Besides expression in single cells of the peristomial cirri, it is expressed in two areas located in the medial forebrain (Figure 7a’,a’’), and one small paired cluster slightly anterior to and below the anterior pair of adult eyes (Figure 7a’’’).

Figure 7 with 1 supplement see all

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Figure 7—source data 1

Whinin-Proctolin alignment.

Fasta-file containing the Whitnin-Proctolin protein alignement; used for the tree in Figure 7-figure supplement 1.

https://doi.org/10.7554/eLife.41556.078

Download elife-41556-fig7-data1-v2.txt

Figure 7—source data 2

Whitnin-Proctolin alignment sequence IDs/accession numbers.

Table containing the TS_v5 IDs for the Platynereis Whitnin as well as the NCBI IDs for the Whitnin/Protolins used in file Figure 7-figure supplement 1.

https://doi.org/10.7554/eLife.41556.079

Download elife-41556-fig7-data2-v2.csv

Similarly, myohr was present in the medial brain and to a lesser part around the posterior eyes (Figure 7b’b’’). Different to whitnin and myohr, ferritin showed a broad expression in the head (Figure 7c’,c’’). The fact that the expression domains are already largely divergent among these few investigated circalunar candidates, suggests that the effect of the circalunar clock is not confined to specific regions of the head or brain.

In this study, we pioneered a combined proteomic and transcriptomic profiling approach to identify coordinated-yet specific changes associated with maturation, sex and circalunar phase in the marine annelid Platynereis dumerilii. The analyses profited from our newly established and benchmarked protocol for the isolation and subsequent quantitative analysis of RNA and protein from the same tissue sample, which does not compromise the complexity of the detected proteins. We expect this method to be widely applicable and be particularly useful for studies that are restricted by the amount of samples that can be obtained and analysed.

Using this protocol for Platynereis dumerilii, we find that ~ 17 percent of all analysed transcripts and ~66% of the analysed proteins undergo regulation during maturation. This indicates that the maturation process is the biggest driving force for changes on both transcriptome and proteome levels as worms approach the single circalunar-synchronised spawning event of their lifecycle. This finding is consistent with the fact that physiology, metabolism, body morphology and behaviour change dramatically during the maturation phase: Animals change from a benthic, solitary, and feeding life style to a sexually mature, anorexic state that prepares them for their ‘nuptial dance’, during which germ cells are released. While this process is accompanied by major morphological changes in the trunk (in particular, the generation of germ cells that amass to up to 40% of the body mass, accompanied by a deprivation of up to 60% of longitudinal muscle fibres), the head is known to be a major source of factors that orchestrate this transition (Hauenschild, 1956a; Hauenschild, 1956b; Hauenschild, 1966; Hofmann, 1976; Schenk et al., 2016). The observed changes in the head transcriptome and proteome strongly support this notion, and the recent identification of a major brain hormone component that regulates maturation onset and progression (Schenk et al., 2016) provides an interesting entry point to understand how these significant changes are hormonally regulated and conveyed to the entire body.

Maturation of Platynereis is also accompanied by a significant increase of overall eye volume (Fischer and Brökelmann, 1966). In line with the described expansion of the outer segments of the eye photoreceptors – that typically harbour the photopigments – we observed a significant increase in transcript abundance of the Platynereis r-opsin1 gene, a major opsin gene expressed in the adult eye photoreceptors (Arendt et al., 2002).

Comparing the observed transcriptome changes to the changes found in other species shows variable similarities, depending on brain regions and species. A similar study of the honeybee brain showed that ~40% of the assessed transcripts exhibited changes associated with maturation, the majority of which (~61%) were associated with caste behaviour (Whitfield et al., 2003). Other species show less pronounced changes, such as in the human neocortex, in which about 9% of the transcripts were reported to change during postnatal development (Kang et al., 2011); in zebrafish ~20% of the pituitary transcripts were differentially expressed during sexual maturation (He et al., 2014), while ~11% of the murine hippocampus transcriptome were differentially expressed during maturation (Bundy et al., 2017) and the Mediterranean fruit fly Ceratitis capitata exhibited up to 7% of head transcriptome changes during maturation (Gomulski et al., 2012).

Overall proteome changes in the brain during development/maturation show a distribution range in vertebrates from 2–8% (Ori et al., 2015; Walther and Mann, 2011), while 15–17% changed in the larval brain development of Bombyx mori and Apis mellifera, respectively (Hernández et al., 2012; Li et al., 2016).

Sexual differences in the brain transcriptome are highly dependent on the species studied. In guppies ~ 13% and in the beetle Callosobruchus maculatus ~17% of the brain`s transcripts have been described to be sex biased (Sharma et al., 2014; Immonen et al., 2017); while in birds around ~2% of the brain transcripts (Naurin et al., 2011) and human brain ~1.1% of the transcripts show a sex bias (Kang et al., 2011).

While the observed transcriptome and proteome changes in Platynereis dumerilii heads during development, growth and maturation and brain transcriptome changes between different sexes are thus within the range of expectations, we somewhat unexpectedly found that the circalunar phase has little impact on transcript expression changes, but more prominently affects protein level changes. Specifically, we find that only 0.12% of the transcripts we analysed are regulated. This is more than 20-fold less than what has been shown as an impact of the circadian clock on the regulation for brain/head transcriptome in several animals, for example Aedes aegypti (6.7%, Leming et al., 2014), Drosophila melanogaster (2.8%, Hughes et al., 2012) and Mus musculus (3–4%, Zhang et al., 2014). Directly equivalent studies on head/brain proteomes have not been performed, instead specific central nervous system parts of mice and rats have been investigated. For the murine suprachiasmatic nucleus, 13–20% of the identified and quantifiable proteins (871 and 2,112, respectively) exhibited circadian abundance changes (Chiang et al., 2014; Deery et al., 2009), compared to about 5% of transcriptome changes in the same tissue (Ueda et al., 2002). Thus, the proteomic changes we observe between two different circalunar time points (with a similar number of quantifiable proteins) are comparable to the circadian changes observed in the nervous system of other animals.

While it is possible that we underestimate the amount of circalunar transcriptomic changes due to limited sampling resolution (only sampling two circalunar time points and one circadian time point), as well as stringent multiple comparisons statistics (see tests in result section), it is not obvious why such an underestimation effect should be different for the proteome under the same conditions. Alternatively, it is also possible that the level of variability is higher in the transcriptome than in the proteome, which – due to the higher experimental noise - would lead to a lower number of detected transcripts compared to the proteome. However, we do not see evidence for such a scenario in our data.

We therefore suggest as the most parsimonious explanation that we underestimate the occurring circalunar changes in both transcriptome and proteome, but that the observed relative difference is representative. This implies that, differing from the known circadian clock studies, the circalunar oscillator has a significantly stronger impact on the proteome than on the transcriptome. Thus, the mechanisms responsible for circalunar reproductive timing possibly operate and might be coordinated more strongly at post transcriptional, translational/post-translational level(s), than on transcript level(s).

The data generated and analysed during this study are included in the published article, its supplementary information files, and the following repositories: The mass spectrometry proteomics raw data and the translated protein database FASTA file have been deposited in the PRIDE database repository (Vizcaíno et al., 2016) with the dataset identifier PXD010532. Transcriptome data (RNA-Seq read libraries) have been submitted to the sequence read archive (SRA) through ENA; accession numbers: see Figure 2—source data 4. Transcriptome data (assembled sequences): ENA repository; accession number: PRJEB27496. Sequences of cloned coding sequences validated in this study: GenBank repository; accession numbers: MH587646-MH587650, MH645920-MH645924, MH678618 and MH678619.

1. 1. Schenk S
   2. Stephanie C Bannister
   3. Fritz J Sedlazeck
   4. Dorothea Anrather

   (2019) NCBI GenBank

   ID MH645920. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH645920
2. 1. Schenk S
   2. Bannister S
   3. Fritz J Sedlazeck
   4. Dorothea Anrather

   (2019) NCBI GenBank

   ID MH678618. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH678618
3. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D
   5. Minh BQ

   (2019) European Nucleotide Archive

   ID PRJEB27496. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ebi.ac.uk/ena/data/view/PRJEB27496
4. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D
   5. Minh BQ
   6. Bileck A
   7. Hartl M
   8. von Haeseler A
   9. Gerner C
   10. Raible F
   11. Tessmar-Raible K

   (2019) EBI PRIDE

   ID PXD010532. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ebi.ac.uk/pride/archive/projects/PXD010532
5. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D

   (2019) NCBI GenBank

   ID MH587646. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH587646
6. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D

   (2019) NCBI GenBank

   ID MH587647. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH587647
7. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D

   (2019) NCBI GenBank

   ID MH587648. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH587648
8. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D

   (2019) NCBI GenBank

   ID MH587649. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH587649
9. 1. Schenk S
   2. Bannister S
   3. Sedlazeck FJ
   4. Anrather D

   (2019) NCBI GenBank

   ID MH587650. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

   https://www.ncbi.nlm.nih.gov/nuccore/MH587650
10. 1. Schenk S
    2. Bannister S
    3. Sedlazeck FJ
    4. Anrather D

    (2019) NCBI GenBank

    ID MH645921. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

    https://www.ncbi.nlm.nih.gov/nuccore/MH645921
11. 1. Schenk S
    2. Bannister S
    3. Sedlazeck FJ
    4. Anrather D

    (2019) NCBI GenBank

    ID MH645922. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

    https://www.ncbi.nlm.nih.gov/nuccore/MH645922
12. 1. Schenk S
    2. Bannister S
    3. Sedlazeck FJ
    4. Anrather D

    (2019) NCBI GenBank

    ID MH645923. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

    https://www.ncbi.nlm.nih.gov/nuccore/MH645923
13. 1. Schenk S
    2. Bannister S
    3. Sedlazeck FJ
    4. Anrather D

    (2019) NCBI GenBank

    ID MH645924. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

    https://www.ncbi.nlm.nih.gov/nuccore/MH645924
14. 1. Schenk S
    2. Bannister S
    3. Sedlazeck FJ
    4. Anrather D

    (2019) NCBI GenBank

    ID MH678619. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase.

    https://www.ncbi.nlm.nih.gov/nuccore/MH678619

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Author details

1. Sven Schenk

   1. Max F Perutz Laboratories, University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Stephanie C Bannister

   For correspondence

   sven.schenk@univie.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7689-5854

2. Stephanie C Bannister

   1. Max F Perutz Laboratories, University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria

   Present address

   Lexogen GmbH, Campus Vienna Biocenter, Vienna, Austria

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Sven Schenk

   For correspondence

   stephanie.bannister@univie.ac.at

   Competing interests

   is affiliated with Lexogen GmbH. The author has no other competing interests to declare.

3. Fritz J Sedlazeck

   Center of Integrative Bioinformatics Vienna, Max F Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna BioCenter, Vienna, Austria

   Present address

   Human Genome Sequencing Center, Baylor College of Medicine, Houston, United States

   Contribution

   Data curation, Software, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6040-2691

4. Dorothea Anrather

   1. Max F Perutz Laboratories, University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Mass Spectrometry Facility, Max F Perutz Laboratories, Vienna, Austria

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Bui Quang Minh

   Center of Integrative Bioinformatics Vienna, Max F Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna BioCenter, Vienna, Austria

   Present address

   1. Ecology and Evolution, Research School of Biology, Australian National University, Canberra, Australia
   2. Research School of Computer Science, Australian National University, Canberra, Australia

   Contribution

   Data curation, Software, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5535-6560

6. Andrea Bileck

   1. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Department of Analytical Chemistry, University of Vienna, Vienna, Austria

   Present address

   Department of Biomedical Research, University Hospital Bern, Bern, Switzerland

   Contribution

   Data curation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7053-8856

7. Markus Hartl

   1. Max F Perutz Laboratories, University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Mass Spectrometry Facility, Max F Perutz Laboratories, Vienna, Austria

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4970-7336

8. Arndt von Haeseler

   1. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Center of Integrative Bioinformatics Vienna, Max F Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna BioCenter, Vienna, Austria
   3. Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria

   Contribution

   Resources, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3366-4458

9. Christopher Gerner

   1. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria
   2. Department of Analytical Chemistry, University of Vienna, Vienna, Austria

   Contribution

   Resources, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

10. Florian Raible

    1. Max F Perutz Laboratories, University of Vienna, Vienna BioCenter, Vienna, Austria
    2. Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    florian.raible@mfpl.ac.at

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4515-6485

11. Kristin Tessmar-Raible

    Research Platform 'Rhythms of Life', University of Vienna, Vienna BioCenter, Vienna, Austria

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    kristin.tessmar@mfpl.ac.at

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8038-1741

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