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\begin{document}

\title{Diel metabolic tuning revealed by in situ transcriptome and proteome in
a vertically migratory copepod}



\author[1]{Amy Maas}%
\author[2]{Emma Timmins-Schiffman}%
\author[3]{Ann Tarrant}%
\author[2]{Brook Nunn}%
\author[2]{Jea Park}%
\author[1]{Leocadio Blanco-Bercial}%
\affil[1]{Bermuda Institute of Ocean Sciences}%
\affil[2]{University of Washington Department of Genome Sciences}%
\affil[3]{Woods Hole Oceanographic Institution}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Zooplankton undergo a vertical migration which exposes them to gradients
of light, temperature, oxygen and food availability on a predictable
daily schedule. Anticipating and responding to these environmental
conditions, which independently are known to influence metabolic rates,
likely has an appreciable effect on the delivery of waste products to
the distinctly different daytime (deep) and nighttime (surface)
habitats. Disentangling the co-varying and potentially synergistic
interactions on metabolic rates has proven difficult, despite the
importance of this migration to oceanic biogeochemical cycling. This
study examines the transcriptomic and proteomic profile of the
circumglobal migratory copepod, Pleuromamma xiphias, over the diel
cycle. The transcriptome showed a large number of up-regulated genes
during the middle of the day -- the period often considered to be of
lowest metabolic activity. There were proteomic and transcriptomic peaks
in oxidative stress response and muscle proteins after the periods of
migration, suggestive of a physiological consequence of migration. There
were changes in metabolic pathways over time, with increased ammonium
production signals during the evening and chitin synthesis and
degradation pathways during the day. Comparisons of patterns across the
paired datasets suggest that 1) estimates of physiological rates made in
the laboratory in steady state conditions that don't account for time of
day may not be adequate to predict in situ phenotypes 2) use of `omics
datasets to predict organismal phenotypes must be done cautiously as
highly dynamic patterns in the transcriptome and proteome are often
dampened and sometimes asynchronous at the enzyme or organismal level.%
\end{abstract}%



\sloppy


\textbf{Diel metabolic patterns revealed by in situ transcriptome and
proteome in a vertically migratory copepod}

Amy E. Maas\textsuperscript{1}, Emma
Timmins-Schiffman\textsuperscript{2}, Ann M. Tarrant\textsuperscript{3},
Brook L. Nunn\textsuperscript{2}, Jea Park\textsuperscript{2},~Leocadio
Blanco-Bercial\textsuperscript{1}

\textsuperscript{1} Bermuda Institute of Ocean Sciences, School of Ocean
Futures, Arizona State University, St. George's, Bermuda

\textsuperscript{2} Department of Genome Sciences, University of
Washington, Seattle, WA, USA

\textsuperscript{3} Biology Department, Woods Hole Oceanographic
Institution, Woods Hole, MA, USA

Abstract

Zooplankton undergo a diel vertical migration which exposes them to
gradients of light, temperature, oxygen and food availability on a
predictable daily schedule. Anticipating and responding to these
environmental conditions, which independently are known to influence
metabolic rates, likely has an appreciable effect on the delivery of
metabolic waste products to the distinctly different daytime (deep) and
nighttime (surface) habitats. Disentangling the co-varying and
potentially synergistic interactions on metabolic rates has proven
difficult, despite the importance of this migration to oceanic
biogeochemical cycling. This study examines the transcriptomic and
proteomic profiles of the circumglobal migratory
copepod,\emph{Pleuromamma xiphias,} over the diel cycle. The
transcriptome showed a large number of up-regulated genes during the
middle of the day -- the period often considered to be of lowest
zooplankton activity. During the day (9:00 and 15:00) there were
patterns of increased chitin synthesis and degradation in both the
transcriptome and proteome. At 09:00 and 22:00 there were increases in
myosin and vitellogenin proteins, which may relate to the stress of
migration and/or reproductive investment. There is an inferred switch,
based on protein expression, in broad metabolic processes, shifting from
electron transport system in the day, to glycolysis and glycogen
mobilization in the afternoon and evening. These observations provide
evidence of the diel impact of DVM on transcriptomic and proteomic
pathways that likely influence metabolic processes and subsequent
excretion products, and clarify how this behavior results in the direct
rapid transport of active metabolisms that are motored from the surface
to the deep ocean.

Introduction

In the ocean, changes in light level are a cue for one of the largest
migrations on earth (Forward 1988; Hays 2003; Longhurst 1967). As the
sun rises, millions of zooplankton migrate to deeper water, away from
the light, reducing the risk of visual predation. This behavior, which
varies across regions and among taxa, is thought to be driven by
trade-offs between simultaneously increased feeding opportunities and
predation risk at higher light levels (Antezana 2009; Benoit-Bird \&
Moline 2021; Pinti \emph{et al.} 2019). During their descent to their
daytime habitat, migrators encounter increasingly deoxygenated, colder
waters, with lower food availability; phytoplankton are absent from the
deep ocean and both particulate organic carbon and total organismal
biomass tends to drop exponentially with depth (Buesseler \emph{et
al.}2007; Hern\selectlanguage{ngerman}ández-León \emph{et al.} 2020). Each of these factors
independently affects zooplankton metabolism, with extensive literature
devoted to analyses of critical oxygen partial pressure, temperature
coefficients (Q\textsubscript{10}), and specific dynamic action
(increase in metabolic rate after consumption of food; Hochachka \&
Somero 2002; Ikeda 2014; Kiørboe \emph{et al.} 1985; Seibel \emph{et
al.} 2021; Svetlichny \& Hubareva 2005; Thor 2000). Recent efforts have
additionally demonstrated diel rhythms in metabolic enzyme activity and
oxygen consumption, both in controlled laboratory conditions where
extrinsic environmental cues including food and light were manipulated
(Häfker \emph{et al.} 2017; Maas \emph{et al.} 2018; Piccolin \emph{et
al.} 2020; Teschke \emph{et al.} 2011), and in wild-caught organisms
captured across diel cycles (De Pitta \emph{et al.} 2013;
Tarrant\emph{et al.} 2021).

The zooplankton migration supplies an estimated 15-40\% of the global
flux of carbon into the midwater region (Archibald \emph{et al.} 2019;
Bianchi \emph{et al.} 2013a; Steinberg \emph{et al.} 2000), and a
similar percentage of nitrogen (Al-Mutairi \& Landry 2001;
Bianchi\emph{et al.} 2014; Schnetzer \& Steinberg 2002), via the
``active flux'' pathway of the biological pump. Calculations of these
fluxes are based on application of physiological rate measurements from
surface caught organisms to an estimated migratory biomass and typically
are exclusively corrected for temperature at depth (e.g. Kwong \emph{et
al.}2020; Maas \emph{et al.} 2021; Steinberg \emph{et al.} 2008),
although some studies are now starting to include the effects of
variations in oxygen level (e.g. Kiko \& Hauss 2019). Experiments
developed to estimate metabolic rates are typically conducted with
surface-caught individuals under surface (oxygenated and relatively
warm) or deep (cold, sometimes deoxygenated) conditions (see metanalysis
of Ikeda 2014); possible daily cycles in these rates associated with
migratory effort, food availability, or circadian regulation are usually
ignored in biogeochemical modeling (Archibald \emph{et al.} 2019; Burd
\emph{et al.} 2010). The current assumption, however, is that an
underlying circadian clock and the local environmental conditions
together influence migratory behavior (Bianchi \emph{et al.} 2013b;
Häfker\emph{et al.} 2022; Pinti \emph{et al.} 2019). In addition,
circadian regulation of metabolism, which acts independently of
environmental changes in pressure, temperature, oxygen and food
availability, appears to moderate the physiology of zooplankton across
their vertical migration (Häfker \emph{et al.} 2017; Maas \emph{et al.}
2018; Teschke\emph{et al.} 2011). In light of all of these co-varying
parameters, predicting metabolic patterns of migrators over the course
of a day is profoundly difficult.

Despite the difficulties, more precisely estimating the timing of the
release of carbon and nitrogen across the diel cycle and across depths
has clear biogeochemical relevance. The nighttime surface metabolic rate
of zooplankton influences the recycling of nutrients for photosynthesis
in the euphotic zone, while metabolic waste production at depth may
provide a substantive portion of the labile nutrients available in the
midwater (Archibald \emph{et al.} 2019; Kelly \emph{et al.} 2019;
Steinberg \& Landry 2017). The dissolved excreta of migratory copepods
has been shown to include labile highly nutritive components, including
vitamins (Maas \emph{et al.} 2020; Shoemaker \emph{et al.} 2020;
Valdés\emph{et al.} 2017), which would have distinctly different
ecological impacts if produced at depth versus being recycled in the
euphotic zone. Variations in the types of metabolic waste, or temporal
pulses in their production, would thus have profound implications for
the local ecosystems across the depth range that migratory zooplankton
inhabit. The location of nitrogenous waste production is of particular
relevance, as it delivers nitrogen for recycling back into primary
production in surface waters, or to support alternate metabolic
pathways, such as anammox -- the anaerobic oxidation of ammonium, in the
midwater (Bianchi\emph{et al.} 2014; Bronk \& Steinberg 2008; Valdés
\emph{et al.} 2018).

Broadly, understanding of the physiological underpinnings and
consequences of DVM on zooplankton metabolism still lags far behind
similar observations of migration in terrestrial systems, where there
are known consequences for blood-oxygen binding, oxidative stress,
metabolic substrate use, overall metabolism and broad patterns of gene
expression (Doyle \emph{et al.} 2022; Gerson \emph{et al.} 2020;
Gutiérrez \emph{et al.} 2019; Olsen \emph{et al.} 2021). Work in the
Sargasso Sea with the copepod \emph{Pleuromamma xiphias} has shown that
this abundant migratory species, which is found at 50-200 m depth during
the night and 400-700 m during the day, exhibits true circadian patterns
in oxygen consumption rate. Continuous observation over three days in
constant dark laboratory conditions revealed a substantial (40\%) daily
change in oxygen consumption rate, a peak during dawn, and lowest levels
during the evening (Maas \emph{et al.} 2018). \emph{Pleuromamma
xiphias}sampled directly from the field at different times of day showed
diel variation in the activity of metabolic enzymes associated with
oxygen consumption (citrate synthase and the electron transport system)
and ammonium metabolism (glutamate dehydrogenase; Tarrant \emph{et
al.}2021). However, during short duration incubations of these
wild-caught\emph{P. xiphias} held at common environmental conditions,
investigators were unable to detect diel oscillations in oxygen
consumption rate. Furthermore, the enzyme patterns detected in the field
were inverse in pattern to those observed in dark:dark conditions in the
laboratory, with peaks during the late afternoon and early evening.
Combined, these studies both demonstrate the difficulty of determining
instantaneous changes in metabolic state in organismal studies of small
wild-caught organisms, and also suggest potential interactive effects of
the various ecological drivers that migrators experience over diel
cycles.

To gain further insight into diel transitions in metabolism, we have
conducted transcriptomic and proteomic analyses of \emph{P.
xiphias}caught across the diel cycle, using flash frozen copepods
collected at the same time as individuals used in previously published
measurements of oxygen consumption, ammonium excretion, fecal pellet
production, and enzyme activity (Tarrant \emph{et al.} 2021). This
design thus allows simultaneous analysis of periodicity in both the
transcriptomic and proteomic datasets, and an assessment of how these
markers relate to metabolic rates.

Based on previously measured diel variation in enzyme activity levels,
we hypothesized that we would observe complex regulatory mechanisms at
the transcriptomic and proteomic level associated with periods of
``rest'' during the day, and recovery from migration during dawn and
dusk. Informed by previously observed differences in fecal pellet
production and enzyme activity (Tarrant et al. 2021), we additionally
hypothesized that we would observe diel changes in digestion and
excretion pathways as a consequence of different prey fields (high food
concentrations at the surface at night and lower concentrations at depth
during the day).

Materials and Methods

2.1 Sample Collection

To determine diel patterns of gene expression and protein abundance in a
migratory copepod, \emph{Pleuromamma xiphias} were collected during a
cruise aboard the \emph{R/V Atlantic Explorer} from May 20-22, 2019 in
the waters off Bermuda. The objective was to sample individuals from
their natural location in the water column at as close to in situ
conditions as possible over the course of their diel migration. During
the evening, when the animals resided near the surface, organisms were
captured using a 1-m diameter Reeve net (Reeve 1981) deployed to 200 m
depth, with 150 \selectlanguage{greek}µ\selectlanguage{english}m mesh, a 20-L cod end, and a miniSTAR-ODDI temperature
and depth sensor. During the daytime, after animals had migrated into
their colder, deeper water, tows were conducted from 400-600 m depth
using a 1-m\textsuperscript{2} MOCNESS equipped with 150 \selectlanguage{greek}µ\selectlanguage{english}m mesh nets
and a custom-built thermally-insulated closing cod end. For both shallow
and deep-caught organisms, time in the net was maximally 1.25 hours at a
constant temperature. On deck, copepods were rapidly selected from the
cod end and placed in filtered seawater. Adult female \emph{P.
xiphias}were identified using a stereomicroscope and flash-frozen for
transcriptomic and proteomic analyses (\textless{}20 minutes processing
time). Time points were 22:00 (evening, after the upward migration) on
May 21st and 02:00 (midnight), 09:00 (morning, after the downward
migration), 15:00 (mid-afternoon), and 22:00 on May 22nd (local time;
sunrise during this cruise was \textasciitilde{}06:00 and sunset was
\textasciitilde{}20:00). Individuals from the two separate 22:00 time
points were analyzed for the subsequent analyses (total n=8). All
transcriptomic and proteomic analyses were done on whole individual
copepods to capture biological variation.

2.2 Transcriptomics

Pairwise differential expression analysis, cluster analysis and network
analysis were conducted to explicitly examine differences in expression
associated with time of day. To accomplish this, RNA was extracted from
eight individuals from each time point, then the best six extractions
(highest quality on Bioanalyzer) were sequenced on an Illumina
NovaSeq6000 (s2-300) as 150 bp paired-end reads (30M reads per sample).
Adapter sequences were trimmed, and low-quality reads were removed
(detailed methods in SF1).

Multiple transcriptome assemblies were constructed with the Trinity
pipeline (v2.3.2; Haas \emph{et al.} 2013), then compared using BUSCO,
and evaluated on the basis of high completeness (BUSCO score;
Sim\selectlanguage{ngerman}ão\emph{et al.} 2015), higher e90n50, and a lower number of total
transcripts (see SF1 for detailed methods and statistics). The assembly
chosen for use was built from one individual copepod captured at 02:00
with no transcript clustering. This transcriptome was annotated
following the Trinotate (v3.2.0) pipeline using a local NCBInr blastx
database (updated Feb 3, 2020). The assembly was filtered to include
only sequences that were annotated as metazoan (see SF2 for annotation).

To enable differential expression analysis, reads were mapped against
the reference transcriptome using Bowtie2 (v.2.2.9; Langmead \& Salzberg
2012), estimates of abundances were made with RSEM (v1.3.0; Li \& Dewey
2011), and pairwise comparison of differential expression (DE) among all
time points was performed using an edgeR analysis in R v.3.6.0
(Robinson\emph{et al.} 2010) following the pipeline packaged with
Trinity (v.2.3.2; Haas \emph{et al.} 2013). Differences were considered
significant if the log2-fold change was \textgreater{} 2 (corresponding
to a four-fold change in expression), and both the false discovery rate
and p value were \textless{} 0.05.

All transcripts for which there were no counts in any library were
removed from the dataset, leaving 21,863 transcripts for downstream
analyses in R v. 4.1.0. Hierarchical clustering was applied to these
transcripts. First, read abundances were averaged across replicates
(n=6) within timepoints. The average clustering method was used on a
Bray-Curtis dissimilarity matrix produced with the vegan package (v
2.5-7; Oksanen \emph{et al.} 2020). Tree cut-off height was set at 0.5.
The data were formatted using the package reshape (Wickham 2007) and
plotted in ggplot2 (Wickham 2016) with a loess smoothing curve for each
cluster.

Two forms of ordination analyses were performed on the dataset.
Nonmetric multidimensional scaling (NMDS) was performed on a Bray-Curtis
dissimilarity matrix based on the log(x+1)-transformed transcriptomic
data using the vegan package. A second ordination analysis, discriminant
analysis of principal components (DAPC), was applied to minimize the
within-group/time point variability and better reveal the transcriptomic
differences that could be ascribed to time of day in the adegenet
package (Jombart 2008). The top 60 transcripts weighted along Linear
Discriminant (LD) 1 and 2 of the DAPC analysis were exported for further
analysis of the transcripts whose expression patterns were more strongly
correlated with time point.

Weighted gene correlation network analysis (WGCNA) was performed on the
full transcriptomic dataset following Langfelder and Horvath (2008), and
using an interactive GUI (detailed in SF1).

Gene Ontology enrichment (GO enrichment) analyses were run to determine
significantly (p-value \selectlanguage{english}[?] 0.01) enriched categories of biological process
(BP), molecular function (MF), and cellular component (CC) for all of
the differential expression comparisons, as well as WGCNA modules and
hierarchical clusters of interest using the GoSeq package in R
(Young\emph{et al.} 2012). The most specific terms were identified with
the CompGO visualization tool
(https://meta.yeastrc.org/compgo\_TS6a\_Trinity/pages/goAnalysisForm.jsp
) (Timmins-Schiffman \emph{et al.} 2017).

Proteomics

Four copepods from each point were selected for proteomics analysis.
Copepods were individually homogenized, and proteins were extracted and
digested from the homogenates (see SF1). Proteomics mass spectrometry
was performed on each copepod peptide digest, in triplicate, on a
Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific).
Reverse-phase high performance liquid chromatography was performed
in-line with the mass spectrometer operated in data dependent
acquisition mode (details of mass spectrometry settings available in
SF1). Chromatographic and MS consistency were monitored with regular
injections of a quality control mix that included BSA and Pierce's
Peptide Retention Time Calibration mix in the Skyline software
(MacLean\emph{et al.} 2010).

To create a reference proteome, sequences from the reference
transcriptome (described above) were translated using Transdecoder v.
2.0.1 (github.com/Transdecoder) and concatenated with standard
laboratory contaminant sequences from the cRAPome (Mellacheruvu \emph{et
al.} 2013). Raw files of acquired mass spectra were then searched
against the reference proteome using Comet v. 2019.01 rev. 4
(Eng\emph{et al.} 2015; Eng \emph{et al.} 2013), with the following
parameters: concatenated decoy search; peptide mass tolerance = 20 ppm;
search enzyme = trypsin; allowed missed cleavages = 2; fragment bin
tolerance = 0.02; fragment bin offset = 0. Probability scores for
peptide and protein detection were assigned using PeptideProphet and
ProteinProphet (Deutsch \emph{et al.} 2015). Consensus protein
inferences for proteins with a false discovery rate \textless{} 0.01 or
less were determined using Abacus (Fermin \emph{et al.} 2011). Technical
replicates that did not group with the other replicates in a group were
excluded from analysis (n=3).

The proteomics dataset was analyzed in parallel to the transcriptomics
dataset as described above, using NMDS, DAPC, hierarchical clustering,
and WGCNA. The hierarchical clustering tree height was cut at 0.5 for
proteomics. Proteins were annotated with the Uniprot trembl database
(downloaded May 2019) using BLASTp with an e-value cut-off of 1E-10.
QSpec (Choi \emph{et al.} 2008) was used to detect differentially
abundant proteins between grouped day and night time points, as well as
pairwise between all time points. Proteins were considered to be
significantly differentially abundant if \textbar{}log fold
change\textbar{} was greater than 0.5 and the
\textbar{}z-statistic\textbar{} was greater than 2. Enrichment analysis
was performed on differentially abundant proteins, on protein clusters
from the hierarchical clustering, and on proteins in WGCNA modules using
CompGO (Timmins-Schiffman \emph{et al.} 2017). Gene Ontology terms for
proteomics were considered significantly enriched at a p-value of \selectlanguage{english}[?] 0.1.
The compGO portal can be accessed at the following
url:\emph{https://meta.yeastrc.org/compgo\_emma\_sub\_copepod/pages/goAnalysisForm.jsp}.

2.4 Integrating Datasets

To explore associations between the `omics datasets and the broader
metabolic pathways that influence biogeochemical cycling, particularly
ammonium production and respiration, we investigated specific pathways
in the transcriptome and proteome, plotting expression and abundance
patterns that were compared with enzyme and organismal level
measurements from Tarrant \emph{et al.} (2021). Chitin biosynthesis and
degradation processes were queried using the inferred crustacean
pathways described in Zhang \emph{et al.} (2021).

3. Results

3.1 Transcriptomics

The transcriptomic assembly used for gene expression analysis was
generated from a single individual with no transcript clustering, and
initially consisted of 123,272 genes and 174,733 transcripts (see SF2
for transcript counts and annotation). The assembly had high N50 (1275
bp), e90n50 (1764), and BUSCO completeness (C:96\% {[}S:51.7\%,
D:44.3\%{]}, F:1.7\%, M:2.3) scores. Annotation of this ``raw''
transcriptome indicated contamination, likely due to the gut contents of
these wild caught organisms. For gene expression analysis, the assembly
was filtered to only retain transcripts that were annotated as metazoan
in origin (21,872 genes and 38,517 transcripts) while those that were
non-metazoan (3,043) and unannotated (133,173) were removed. On average,
49\% of all sequences from the samples mapped back uniquely onto the
filtered reference transcriptome, while only 2.5\% mapped back to
multiple contigs (details about trimming, assembly, annotation, and
mapping are all available in SF1).

When total gene expression was statistically investigated, the time
points were significantly different based on an ANOSIM analysis (R =
0.2498; p-value = 0.003, NMDS SF3), and day versus night clustering was
significant (R = 0.1654 and p=0.011). Hierarchical clustering of total
transcript expression among all samples resulted in 48 statistically
distinct clusters of gene expression patterns over the diel cycle (SF2;
SF3). The two predominant expression patterns were a peak during the
middle of the day (15:00) and reduced expression in the middle of the
day (15:00). Likewise, the WGCNA module-trait correlation analysis
similarly suggested that the most abundant gene expression patterns were
associated with a peak at 15:00 (SF2; SF3). Clustering (DAPC analysis;
Figure 1A) was most significantly attributable to a distinct gene
expression pattern at 15:00 (LD1 axis, 86.5\% of the variation), with no
clear progression between sequential time points.

In pairwise comparisons, a total of 7,956 differentially expressed genes
(DEG) were identified (Table1). This constitutes 39\% of the transcripts
analyzed. Many of the genes were differentially expressed in multiple
pairwise comparisons, resulting in 15,566 significant relationships;
96\% of these represented the upregulation of a gene at the 15:00 time
point, and 3\% were downregulation of a gene at the 15:00 time point. Of
the 327 genes that were downregulated in pairwise comparisons at 15:00,
there were no statistically significant enriched GO terms.

During the mid-day peak in transcription (15:00) numerous transcripts
associated with glycolysis, gluconeogenesis, protein and fatty acid
catabolism based on their GO annotation were upregulated (SF3).
Oxidative stress response genes, including peroxidases and superoxide
dismutase, were additionally upregulated in this period, along with a
subset of the heat shock proteins. Various forms of vitellogenin were
significantly upregulated at 15:00. These observations were
statistically supported by the GO enrichment analysis of the pairwise
comparisons to 15:00, which demonstrated that the upregulated genes were
enriched for GO terms associated with protein metabolic process
(especially proteolysis), lipid transport, response to oxidative stress
and motor activity (SF4). The GO enrichment analysis also suggested
cellular detoxification, immune response, ionic transport and a suite of
biological regulation processes as transcriptionally upregulated during
the middle of the day. Finally, a number of core circadian genes
(period, PER; timeless, TIM; and cryptochrome 1, CRY1) peaked in the
middle of the day with statistically significant patterns in gene
expression (Fig. 2)

Beyond the overarching pattern of a mid-day peak in transcription at
15:00, there were a set of transcripts that were upregulated in the
09:00 time point, particularly in contrast to the 22:00 time point.
These transcripts were frequently observed as also upregulated in the
15:00 pairwise comparisons suggesting a ``daytime'' set of upregulated
pathways. Specifically, during the day at depth (9:00 and 15:00 time
points) 92 genes were upregulated compared to one of the night time
points. Of these upregulated DE genes, almost half (44) were associated
with muscle tissue and contractile function (i.e. actin, myosin,
smoothelin, tropomodulin, paladin, and troponin). There was also
upregulation of numerous (15) transcripts associated with smooth muscle,
collagen, cuticle proteins, cuticlin, and peritrophin -- proteins that
are thought to be associated with the production of fecal pellets (which
involves packaging of material in a chitinous package within the gut).
Motor activity was overrepresented in the GO enrichment analysis of
genes with higher expression during the day (SF4). These patterns were
even more pronounced when assessing genes that were upregulated during
the 15:00 time point. Almost all sequences annotated as associated with
muscle tissue and contractile function were upregulated in the middle of
the day (15:00), relative to at least one other time point. Most enzymes
in the inferred crustacean chitin metabolism and degradation pathways
(Zhang \emph{et al.} 2021) were also significantly upregulated in the
middle of the day (SF2), as well as the sequences identified as being
potentially associated with the cuticle and the peritrophic membrane.

3.2 Proteomics

Across all time points, 2214 proteins were identified with high
confidence (see SF5 for protein counts and annotation). Hierarchical
clustering resulted in 27 statistically distinct clusters of protein
abundance (SF2). Proteomes differed significantly by time point and
globally between night and day (ANOSIM by time point: R = 0.4611 with
p-value = 0.001, night vs. day: R = 0.2484 with p-value = 0.013; NMDS
SF3). In the proteome DAPC analysis, much of the variation appeared to
be associated with time point (which correlated with LD1, 74.1\% of the
variation). DAPC analysis also revealed differences within the
quantitative proteome between night and day, which also represents the
depth of sampling (correlated with LD2; Fig. 1B; 24\% of the variation
in protein abundance). In pairwise differential abundance comparisons
between time points there were 133 unique proteins that were
significantly differentially abundant (SF5). When time points were
clustered by day and night (i.e., 09:00 and 15:00 vs 22:00 and 02:00)
there were 36 differentially abundant proteins.

Across analyses (e.g., clustering, WGCNA, QSpec), specific markers of
the nighttime proteome and daytime proteome of \emph{P. xiphias} were
revealed. At night (i.e., 22:00 and 02:00), when \emph{P. xiphias} was
found nearer the surface and was presumably actively feeding, the
proteome shifted towards functions of glycolysis followed by changes in
amino acid biosynthesis. Specifically, during the evening (22:00), when
the copepods come to the surface, there was a peak in abundance of
glucose-6-phosphate 1-dehydrogenase, glyceraldehyde-3 phosphate
dehydrogenase, pyruvate kinase, and phosphoglycerate mutase. While the
latter three enzymes are involved in the glycolysis pathway,
glucose-6-phosphate 1-dehydrogenase catalyzes the first step in the
pentose phosphate pathway, an alternate pathway for the breakdown of
glucose-6-phosphate, which finishes with the glycolytic intermediate
glyceraldehyde 3-phosphate. Many protein clusters show peaks at the
22:00 time point (clusters 1, 6, 8, 9, 14, 16, 26; SF3Fig2) and the
diverse functions of proteins included in these clusters may point to a
time of high metabolic activity. In the later part of the night (02:00)
the protein betaine-homocysteine S-methyltransferase (BHMT) peaked
(compared to all other time points). BHMT is involved in amino acid
biosynthesis, specifically of the amino acid L-methionine, and is part
of the larger cysteine and methionine metabolism pathway. In humans,
supplements of betaine increase power (Cholewa \emph{et al.} 2013), and
similarly, the increased BHMT may be indicative of high levels of
reactant to produce increased betaine prior to the downward migration.
Also at 02:00, the abundance of the enzyme responsible for the
rate-limiting step of the methionine cycle, S-adenosylmethionine
synthetase, was decreased in comparison with all other time points. The
cluster analysis reveals some groups of proteins that peak in abundance
at 02:00, including molecular chaperones (cluster 7) and a variety of
ATPases (cluster 15).

When the copepods were at depth (\textasciitilde{}400-600 m) during the
daylight hours (09:00 and 15:00 sampling time points), their proteomes
shifted towards chitin metabolism, alternate pathways of carbohydrate
metabolism, amino acid metabolism, and mitochondrial respiration.~
Multiple observations suggest an increase in chitin metabolism in the
daytime proteome. In the enrichment of cluster 12 (SF3Fig2; proteins
elevated in the daytime), ``chitin metabolism'' proteins included two
isoforms of protein obstructor-E (a cuticular protein). Cuticle protein
6 was increased in abundance at 15:00 (compared to 22:00) and chitinase
4 was suppressed at 15:00 compared to all other timepoints. Levels of
carbonic anhydrase, which has a variety of roles including pH regulation
and physiological homeostasis, increased in the morning (09:00 vs.
02:00) and then rose again as the day progressed (15:00 vs. 09:00 and
02:00). Potential evidence of gluconeogenesis was also present during
the daytime, with the inclusion of pyruvate carboxylase in
daytime-peaking cluster 21 and glyceraldehyde 3-phosphate dehydrogenase,
which is active in the gluconeogenesis and glycolysis pathways, in
clusters 11 and 13 (SF3Fig2). Proteins in the WGCNA blue module (SF5)
trended towards higher abundance during the day and were most strongly
correlated with night:day (correlation coefficient of -0.552). This
module was enriched for GO terms tricarboxylic acid cycle (e.g.,
oxoglutarate dehydrogenase, malate dehydrogenase) and electron transport
chain (e.g., cytochrome c oxidase, electron transfer flavoprotein).
Cytochrome c, a mitochondrial protein involved in the electron transport
chain, was detected at significantly elevated levels during the day
(09:00 vs. 02:00 and 22:00; 15:00 vs. 02:00 and 22:00).

Some proteins were statistically more abundant in pairwise differential
analyses at both the 09:00 and 22:00 time points relative to 02:00 and
15:00. Both periods, directly after the migrations, had higher levels of
multiple isoforms of vitellogenin, a major egg yolk protein precursor,
the increased abundance of which was responsible for the enrichment of
the GO term lipid transport (biological process) in the nighttime
proteome and lipid transporter activity (molecular function) in the
WGCNA brown module (SF5). The brown module, which was moderately
correlated with hour (correlation coefficient of 0.453) and night/day
(0.492), contained many proteins with elevated abundance at 22:00 and at
09:00, and was also enriched with proteins that matched to GO terms
proteasome complex, Golgi-associated vesicle membrane, isomerase
activity, and threonine-type endopeptidase activity. Proteins involved
in muscle contraction, including myosin, troponin and tropomyosin were
frequently upregulated in the 09:00 and 22:00 time points. At 09:00 vs.
02:00 other muscle proteins detected at increased abundance included
actin, smoothelin, a calponin-homology domain-containing protein, and a
protein unc-45 homolog (also elevated at 09:00 vs 22:00). Hemerythrin, a
protein that binds oxygen, among other functions, was also elevated at
09:00 and at 22:00 vs.15:00. Finally, the protein peroxiredoxin, which
is associated with oxidative stress, was upregulated at 22:00.

3.3 Integrated Transcriptomics and Proteomics

In a comparison of differentially abundant markers between `omics
datasets, 31 unique transcripts were also differentially abundant in the
protein dataset. The temporal pattern, or direction of regulation, was
not always conserved between transcription and translation. Often
(\textgreater{}70\%), the `omics markers followed opposite patterns,
which suggests a temporal shift in the regulation of transcripts and
proteins; transcripts did not always peak before proteins for the same
marker (SF3).

Most of the transcripts and proteins that represented specific enzymes
that are frequently used as proxies for organismal level processes, such
as glutamate dehydrogenase for ammonium excretion or citrate synthase
and the electron transport system enzymes for oxidative respiration,
were not significantly different between time points in the pairwise
statistical comparisons. This was inconsistent with the observed
statistical differences in enzyme activity that were made during
excretion experiments conducted with organisms flash frozen at the same
time as our samples (Fig. 3).

An additionally interesting pattern was the presence of transcripts with
low coefficients of variation (e.g., stable expression across samples)
for which the corresponding proteins demonstrated large changes in
abundance. These may suggest proteins that are post-transcriptionally
regulated. These proteins are described in SF3 and include, for example,
electron transfer flavoprotein, signal peptidase complex catalytic
subunit, and ADP ribosylation factor.

Discussion

Diel vertical migration of metazoan animals is a common behavior
observed across phyla in marine and limnological systems. With 70\% of
the earth covered in water, this means that a diverse set of ecosystems
are biogeochemically and trophically impacted by this phenomenon. In
this study, we leverage a suite of thousands of molecular markers
(transcripts and proteins) to better understand how the physiology of
the abundant circumglobal copepod, \emph{Pleuromamma xiphias} , varies
over the course of the day. Our findings suggest that migratory species
are responding to the variety of changing environmental factors over
their diel cycle by partitioning components of their metabolic and
repair processes to different periods of the day, with important
implications for their biogeochemical and ecological roles (Fig. 4).
There are distinct metabolic processes upregulated by these copepods
while feeding at the surface at night and during the ``non-feeding''
period at depth during the day, as well as responses to the periods of
migration. The proteomic and transcriptomic signaling associated with
these cycles is often temporally uncoupled. As this species is a
dominant migrator in the region, responsible for an average of 23\% of
the migratory population biomass (Steinberg et al. 2000), more
explicitly detailing how the physiology of \emph{P. xiphias} responds to
the suite of co-varying environmental forces over the diel cycle has
implications for our understanding of the local active flux of carbon
and nitrogen in the Sargasso Sea. Echoing the complexity demonstrated in
terrestrial and model organisms, these results suggest that estimating
biological rates from `omics in metazoans will never be straightforward.
These findings do, however, position \emph{P. xiphias} as a model for
increasing our understanding of how diel changes in migrator physiology
contribute to the spatial and temporal complexity of midwater ecology
and global biogeochemical cycles.

One limitation of our design was that it integrated signals across
multiple tissue types, potentially masking distinct diel patterns of
stimulation in different tissue types. It was, however, reflective of
the ``whole organism'' response that is relevant to the broader
zooplankton physiology and biogeochemical literature (Ikeda 2014;
Steinberg \& Landry 2017), as well as the co-captured organismal level
metrics (Tarrant \emph{et al.} 2021). The integration of these
synchronously collected datasets additionally emphasizes that sampling
at the transcript, protein, enzyme, and organismal (i.e. oxygen
consumption, ammonium excretion) level provides very different
perspectives of organismal physiology, making mechanistic descriptions
or biogeochemical predictions from any single level of organization
difficult. Oceanographers are seeking ways to implement `omics tools to
more comprehensively understand the physiology of marine zooplankton
(Lenz \emph{et al.} 2021; Matos \emph{et al.} 2020; Tarrant \emph{et
al.} 2019), which are classically difficult to capture, culture, and
mechanistically understand. The fact that the transcriptomics patterns
were not closely correlated with patterns at the other levels
(proteomics, enzymatic or organismal) raises concerns the present
application of transcriptomic approaches in oceanographic studies to
infer physiological activity, and to derive biogeochemical significance.
In the light of these results, and as has been previously demonstrated
in terrestrial and model organisms (Kassahn \emph{et al.} 2009; Vogel \&
Marcotte 2012), it appears that combined approaches likely provide the
best understanding of migrator physiology and the associated
consequences for pelagic ecology.

Circadian Signals

The transcriptomic and proteomic responses documented here provide a
detailed look at how \emph{P. xiphias} gene expression and protein
abundance shifts throughout the diel cycle. While we did not
specifically study the circadian rhythm of this copepod, we detected
transcripts of most of the core circadian genes previously observed in
copepods. Many known circadian genes were differentially expressed in
our pairwise comparisons and peaked in the middle of the day, including
period (PER), timeless (TIM), cryptochrome-1 (CRY1) and aryl hydrocarbon
receptor nuclear translocator-like protein 1 (ARNTL, also called BMAL1
or cycle; Fig. 2). Two other core circadian genes, CLOCK and
cryptochrome-2 (CRY2), showed antiphase expression with a nadir
mid-afternoon, although these changes were not statistically
significant. The trend of CLOCK being in antiphase with PER, is
consistent with prior analyses in sub-polar and temperate populations of
the copepod \emph{Calanus finmarchicus} (Hafker \emph{et al.} 2017;
Huppe \emph{et al.} 2020). In studies of \emph{C.
finmarchicus,}variations in day length, light field, season, and depth
of capture result in differences in the timing of circadian gene
expression, indicating a complex interaction between the environment and
circadian signaling. Further studies of copepods from a variety of
environments are called for to complement previous studies conducted in
\emph{C. finmarchicus} , which have shown that photoperiod, and
potentially food moderate the expression of circadian regulatory genes
(Hafker \emph{et al.} 2017; Hafker \emph{et al.} 2018). Genes identified
as having rhythmicity in polar copepods, including those associated with
neurotransmitters, oxido-reduction, carbohydrate metabolism, lipid
metabolism, and proteolysis processes (Payton \emph{et al.} 2021), were
also upregulated at 15:00 in our dataset. This suggests that these are
robust indicators of diel rhythmicity across copepods, despite
substantial differences in light level and species ecology.

Migration

As per our hypothesis, there appear to be proteomic responses that
distinctly correlate to specific periods of migration. Interestingly,
some of these were additionally present in the transcriptome throughout
the middle of the day resulting in patterns that distinguish the middle
of the night from all other time points. For example, myosin and other
motor activity proteins showed complex patterns of expression that
appear to be linked to migration. Transcripts associated with motor and
contractile function were upregulated throughout the daytime period.
This is supported by the transcriptomic upregulation of myosin, actin
and various other transcripts associated with the motor function GO term
at the 15:00 time point. In the proteome, many isoforms of myosin peaked
after the migrations (9:00 and 22:00), while smoothelin, tropomyosin and
actin were more abundant during the daytime points (9:00 and 15:00).
Repair of the muscular machinery after the effort of diel vertical
migration seems a potential cause of this signal during the periods
after upward and downward migration. The peaks in the middle of the day,
when swimming activity is thought to be at its lowest as organisms avoid
detection by visual predators, may suggest that during this period the
musculature continues to be under repair. Circadian patterns in muscle
repair and regeneration during the rest period are, in fact, well
documented in mammalian literature (reviewed in: Chatterjee \& Ma 2016).
Consequently, the observed daily cycles in investment in muscle tissue
are perhaps unsurprising for organisms that undergo such extensive
migrations, but are highly relevant for analysis of prior work in
migrators since actin, in particular, was historically used as a
housekeeping gene in RNA studies (reviewed by Tarrant \emph{et
al.}2019).

Similar to the patterns observed in myosin and motor activity,
vitellogenin transcripts were statistically more abundant at 15:00, and
vitellogenin protein peaks occurred at both migratory time points (09:00
and 22:00) in pairwise differential abundance analysis. Vitellogenin is
ubiquitous in copepods, which possess multiple homologs of the gene in
their genomes (Hwang \emph{et al.} 2010; Hwang \emph{et al.} 2009;
Lee\emph{et al.} 2008; Lee \emph{et al.} 2016; Semmouri \emph{et
al.}2020). Vitellogenin RNA shows variation in expression level across
developmental stages (Lee \emph{et al.} 2008) and molt cycles
(Tarrant\emph{et al.} 2014), and is expressed in both mature male and
female copepods but at much higher levels in females than males (Hwang
\emph{et al.} 2010; Hwang \emph{et al.} 2009; Lee \emph{et al.} 2008;
Lee\emph{et al.} 2016). \emph{Pleuromamma xiphias} have asynchronous
oogenesis (egg production) meaning all stages of unfertilized oocytes
are present within all mature females. All individuals used in this
study were mature adult females, and consequently were all 1) engaged in
vitellogenesis (production of yolk bodies) in their eggs (Eckelbarger \&
Blades-Eckelbarger 2005; Niehoff 2007), and 2) not molting anymore. The
variation in vitellogenin thus could be related to patterns in
allocation of energy to reproductive investment over the daily cycle.
Alternatively, vitellogenin has additionally been shown to act as an
antioxidant, as well as to play a role in the immune system in other
copepod taxa (Zhang \emph{et al.} 2011). In the copepod \emph{Tigriopus
kingsejongensis,} previous researchers have suggested that homologs of
vitellogenin had other physiological roles, including potential
immune-related activity (Hwang \emph{et al.} 2010; Hwang \emph{et
al.}2009; Lee \emph{et al.} 2016). Consequently, it is possible that
some of the vitellogenin patterns observed in our protein dataset are
associated with antioxidant or immune defense activity in \emph{P.
xiphias} , which may be differentially regulated during DVM.

Finally, in both the transcriptome and the proteome there were signals
of oxidative stress response, a common consequence of acute exercise
(Simioni \emph{et al.} 2018), after the periods of migration and during
the day. In the transcriptome this was characterized by upregulation of
heat shock proteins, ubiquitins, superoxide dismutase, and hemicentin.
In the proteome there were increased abundances in ubiquitin-like
modifier-activating enzyme, hemerythrin, multiple forms of
peroxiredoxin, a glutaredoxin domain-containing protein, and
glucose-6-phosphate dehydrogenase. The former proteins are well known
components of the oxidative stress response across taxa, and the latter
may indicate a need to control oxidative stress via the production of
NADPH (Preville \emph{et al.} 1999). There was stronger signal of
oxidative stress response in the proteome after the upward migration
(22:00), which may be due to the increased temperatures experienced
after arriving in the evening surface habitat in conjunction with
migratory effort, or a distinction between the effort of upward versus
downward swimming. Redox state has, however, been suggested to provide
non-transcriptional control over diel rhythmicity especially via
peroxiredoxin proteins (Reddy \& Rey 2014). The prevalence of peaks in
the peroxiredoxin proteins at 22:00, along with genes associated with
ubiquitination, response to oxidative stress and oxidation-reduction
processes, have been observed in our studies and others (Maas \emph{et
al.} 2018; Payton \emph{et al.} 2021). This suggests that, beyond being
a consequence of migratory activity, there may be a role of
oxidation-reduction cycles in the diel rhythmic signaling in
zooplankton.

Midwater Daytime Activity

The daytime has classically been considered a period of quiescence for
copepods that are waiting out the day avoiding predators, with lower
feeding activity, lower temperatures, and reduced oxygen thought to
contribute to various types of behavioral and metabolic suppression
(Lampert 1989; Ohman 1988; Seibel 2011; Torgersen 2003). Our dataset
suggests the daytime is, however, a period of complex and distinct
cellular activity. The highly upregulated transcriptomic response at
15:00 has not been previously reported for zooplankton and implies that
during the daytime midwater animals are not simply ``resting'' in a
semi-dormant state, but are actively engaged in transcription, repair
and biological regulation. Prior enzymatic analyses indicate that the
mid-day (15:00) is a peak for the electron transport system
(Tarrant\emph{et al.} 2021), while the current data reveals an
upregulation in transcripts associated with glycolysis, gluconeogenesis,
protein and fatty acid catabolism, the electron transport chain, lipase
activity, cell growth/development, lipid metabolism or transport, and
protein maturation/processing. Processes elevated in the proteome during
the day include carbohydrate metabolism, amino acid metabolism, and
electron transport chain.

Additionally, `omics markers suggest that the copepods are actively
engaged in processes that involve chitin metabolism and binding during
the daytime. Specifically, analysis of the transcriptome reveals that
the mid-afternoon time point (15:00) is the peak in the differential
expression of a suite of chitin biosynthesis transcripts including
trehalase (TRE), chitin synthase (CHS), glutamine-fructose-6-phosphate
aminotransferase (GFAT), glycogen phosphorylase and phosphoglucomutase.
Chitin metabolism proteins (e.g. protein obstructor E, cuticle protein
6) were also elevated during daytime time points. These genes and their
associated proteins are known to be involved the production of the
intestinal peritrophic matrix (Zhang \emph{et al.} 2021), which produces
the chitinous covering of fecal pellets. In measurements made during the
same cruise (Tarrant \emph{et al.} 2021) fecal pellet production was
highest during the middle of the night (02:00), but remained at
\textasciitilde{}50\% at depth during the morning (09:00). Thus, the
increased chitin synthesis and metabolism during the daytime period may
reflect the recovery of this membrane after fecal pellet expulsion.
Alternative roles for chitin metabolic processes include repair or
growth of the exoskeletal structures. Once they reach an adult
stage\emph{Pleuromamma} sp. have been documented to experience cuticular
thickening and modification in the postmolt and intermolt stage,
although they no longer molt (Park 1995). Shifts in chitin metabolism
proteins, which were concurrent with increases in some muscle proteins,
could thus be indicative of an investment in tissue and exoskeleton
repair during this time. Additionally, some of the chitin degradation
transcripts, like beta-N-acetylglucosaminidase and chitinase, were also
upregulated in the mid-afternoon and could be associated with
exoskeletal repair, or could similarly be indicative of the digestion of
crustacean prey items. Krill that undergo DVM similarly show changes in
gene expression suggesting an up-regulation of chitin synthesis and
catabolism during the day (Biscontin \emph{et al.} 2019), further
suggesting a general pattern in migratory Crustacea.

Daily Metabolic Partitioning

As predicted, there was partitioning of metabolic processes that was
obvious in the proteomic dataset. During the day proteins associated
with the electron transport system (ETS; cytochrome c and NADH
dehydrogenase) were increased in abundance. This is consistent with the
previous findings of increase electron transport enzyme assay activity
in the mid-day (Tarrant \emph{et al.} 2021), and peak in oxygen
consumption during daytime hours (6:00-12:00) in constant dark
laboratory conditions (Maas \emph{et al.} 2018). Fructose bisphosphate
aldolase, a glycolysis enzyme, is more abundant in the middle of the day
(15:00), and right after the migration (22:00) a suite of proteins
associated with glycolysis including glyceraldehyde-3-phosphate
dehydrogenase and pyruvate kinase, as well as glycogen phosphorylase,
which is the first enzyme of glycogenolysis and provides
glucose-6-phosphate for glycolysis, are more abundant. These proteins
suggest an increase in carbohydrate metabolism in the evening,
transitioning to the mobilization of glycogen in the later evening,
additionally supported by the increased abundance of a protein involved
in the pentose phosphate pathway at 22:00. This pattern of metabolic
partitioning matches well with the observed transcriptomic patterning in
polar krill \emph{Euphausia superba} (Biscontin \emph{et al.} 2019),
where ETS peaked in the later afternoon, and was followed by glycogen
mobilization and glycolysis in the early evening.

4.5 Data Integration

The expansive upregulation of transcripts during the middle of the day,
which was the overwhelming signal in the transcriptomic dataset, points
to mid-afternoon being a period of regulation and repair of the cellular
machinery. The profound differential expression of transcripts did not,
however, typically translate to changes at the protein level, suggesting
that the transcripts were produced in response to or expectation of a
need to replace proteins to maintain a relatively stable level.
Estimations of transcript expression do not provide an accurate picture
of the true biological state, as mRNA profiles do not capture regulatory
processes or post transcriptional modification that directly influence
the amount of active protein (reviewed in: de Sousa Abreu \emph{et
al.}2009; Maier \emph{et al.} 2009; Vogel \& Marcotte 2012).
Unfortunately, transcriptomics tend to yield extreme highs and lows in
transcript abundances- resulting in exaggerated log fold changes.
Additionally, turnover time for proteins is generally substantially
longer than that of RNA, so subtle shifts in their abundance could
signal the production of transcripts that allowed for replacement,
maintaining the observed steady state.

To add to the complexity of data interpretation, the assembled
transcriptome often had multiple ``genes'' that had the same annotation,
and the redundant annotations propagated to the protein level where
various protein sequences were observed with similar names. These likely
represent splice variants, as is typical in de novo assembled copepod
transcriptomes (Tarrant \emph{et al.} 2019), and is supported by the
relatively high proportion of duplication within the assembly (BUSCO
score; 44.3\%). We chose not to cluster these sequences by a similarity
threshold to allow observation of variation in the expression patterns.
It is likely that these sequences perform distinct roles that have not
yet been elucidated. These variants would be the result of
post-transcriptional modification that produces distinct mature mRNA and
subsequent proteins that are tuned to different environmental conditions
or different cell lines. Alternative splicing (one of the forms of
post-transcriptional modification) has been observed in response to
thermal or salinity stress in metazoan animal species including fish and
rats (Huang \emph{et al.} 2022; Tan \emph{et al.} 2019; Tian \emph{et
al.} 2020). It is possible that the changes in environment that are part
of the diel migration result in the preferential production of different
isoforms at surface and depth. Exploring this possibility, which would
require a \emph{P. xiphias} genome assembly, would be instructive for
our understanding of the structure and function of transcriptomes in
planktonic species.

The observed variations in pattern from transcript to protein to enzyme
assay emphasize the complexity of metabolic regulation (Figure 4).
Although enzyme assays typically provide unlimited substrate, they are
influenced by the presence of phosphorylation, inhibitor and promotor
molecules within the copepod cells, demonstrating a further level of
biological regulation of diel patterning (Reddy \& Rey 2014;
Thurley\emph{et al.} 2017), which has been observed in marine primary
producers (Tan \emph{et al.} 2020; Welkie \emph{et al.} 2019). Finally,
the time lag for protein production and the variable turnover rate for
protein degradation can exacerbate discontinuities between protein
levels and enzyme activity levels. Combined, our findings suggest that
although total oxygen consumption and ammonium excretion do not change
detectably over the course of the day (Tarrant et al. 2021), the
cellular machinery of the copepods are undergoing complex and distinct
processes throughout the day, with potential profound responses for the
production of waste products at the surface and at depth. While diel
variation in fecal pellet production is already accounted for in
biogeochemical studies (Schnetzer \& Steinberg 2002; Steinberg et al.
2023), diel variation in excretion of dissolved metabolites is typically
ignored and merits further investigation.

As the field of oceanography increasingly explores the use of
transcriptomic, proteomic, and enzyme assays as proxies for ecological
function, it is imperative to document and acknowledge the diel
variability and asynchronous patterning of these markers of metabolism.
Our results suggest that in copepods, post-transcriptional and
post-translational modifications may play a large role in metabolic
regulation. Consequently `omics datasets could be difficult to use to
directly predict biogeochemically relevant rates of carbon or nitrogen
consumption and waste production. Thurley et al. (2017) found that
metabolic pathways with circadian rhythms are often propagated by
rhythmic but asynchronous patterning across multiple enzymes.
Consequently, phenotype (what matters functionally in
biogeochemistry/ecology) is controlled by a complex set of temporally
asynchronous enzymatic processes. However, the strength of the `omics
analyses, which are high-throughput and more temporally precise, is that
they can be used to prioritize enzymes for rate-measurements as we move
to more intensive molecular analyses in the ocean environment. The
cautionary tale is that fully integrated datasets are required to
understand diel physiology. If the ``products'' (i.e. simple functions
like oxygen consumption) are measured independently, the diel controls
may not be obvious. In contrast, if analyses are only made at the
transcriptome level, it may appear that there would be profound
physiological activity in the middle of the day.~Exploring the phase lag
between various proteins, enzymes and organismal function, especially
waste production, would require higher temporal resolution sampling, but
will be important for the application of `omics and enzymes as
biogeochemical proxies in the future.

Acknowledgements

We would like to thank Captain George Gunther and the crew of
the\emph{R/V Atlantic Explorer} , as well as cruise participants Hannah
Gossner, Andrea Miccoli, Lindsey Cunningham, Susanne Neuer, and Nora
McNamara-Bordewick for the assistance with diel copepod sampling. We are
grateful for the efforts of Jason Kapit and his team of engineers who
designed and fabricated our closing cod ends. Alex Federation provided
assistance with the mass spectrometry. This work is supported in part by
the University of Washington's Proteomics Resource (UWPR95794) and the
UW BioStatistics department provided guidance on the combined 'omics
analysis\emph{.} Funding this work was supported by the National Science
Foundation Grants OCE-1829318 (to AEM, LBB, ETS, BLN) and OCE-1829378
(to AMT).

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Data Accessibility

Cruise data and metadata including net tow data and environmental
conditions are available at BCO-DMO project number 764114. Transcriptome
and raw gene expression data are available at NCBI PRJNA766852. This
Transcriptome Shotgun Assembly version described in this paper is the
first version, GJVP01000000. Raw mass spectrometry and supporting files
for analysis were deposited to the ProteomeXchange Consortium via PRIDE
with the dataset identifier PXD021156 (reviewer log-in
credentials:\emph{reviewer83975@ebi.ac.uk}, password = 7hySyEDg )

Author Contributions

A.E.M, L.B.-B., A.T. and E.T.-S. conceived and planned the experiments.
A.E.M, L.B.-B., A.T., B.N. and E.T.-S. participated in the cruise
execution and animal collection. A.T. and E.T.-S. carried out laboratory
work. A.E.M, J.P. and E.T.-S. were responsible for bioinformatics
analysis. A.E.M and E.T.-S. took the lead in writing the manuscript. All
authors provided critical feedback and helped shape the research,
analysis and manuscript.\emph{}

\emph{Table 1. The directionality and number of transcripts that were
significantly differentially expressed, the number of significantly
enriched GO terms (P \textless{}0.05) associated with those transcripts,
and the number of differentially abundant proteins in pairwise
comparisons. Detailed lists of transcripts and proteins that were
significantly different between treatments are available in
supplementary materials SF2 and SF5 respectively. A list of all the
enriched GO terms is available in SF4.}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllll@{}}
\toprule
& & Transcripts (SF3) & Transcripts (SF3) & Transcripts (SF3) &
Transcripts (SF3) & & Trans. GO terms (SF4) & Trans. GO terms (SF4) &
Trans. GO terms (SF4) & Trans. GO terms (SF4) & & Proteins (SF5) &
Proteins (SF5) & Proteins (SF5) & Proteins (SF5)\tabularnewline
\midrule
\endhead
& & Upregulated & Upregulated & Upregulated & Upregulated & &
Upregulated & Upregulated & Upregulated & Upregulated & & Upregulated &
Upregulated & Upregulated & Upregulated\tabularnewline
& & 15 & 2 & 9 & 22 & & 15 & 2 & 9 & 22 & & 15 & 2 & 9 &
22\tabularnewline
Downregulated & 15 & & 151 & 100 & 216 & 15 & & 0 & 0 & 0 & 15 & & 24 &
18 & 22\tabularnewline
& 2 & 3939 & & 2 & 3 & 2 & 61 & & 0 & 0 & 2 & 30 & & 28 &
57\tabularnewline
& 9 & 3823 & 5 & & 25 & 9 & 79 & 0 & & 1 & 9 & 5 & 2 & &
16\tabularnewline
& 22 & 7168 & 36 & 98 & & 22 & 166 & 2 & 19 & & 22 & 15 & 3 & 9
&\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\emph{Figure 1. Discriminant analysis of principal components (DAPC) of
the transcriptomics (left, n=6) and proteomics (right n=4, except 22:00
when n=8) datasets. Each large circle represents the centroid of a
predefined group (time point). Smaller shapes connected to the larger
one show the expression pattern for an individual copepod within the
group. Daytime time points are yellow or orange; nighttime are in blue
or gray. The percentage of variance explained by the linear discriminant
(LD) is noted within each axis label.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\emph{Figure 2: Patterns of gene expression for core circadian genes.
Time points that were statistically higher in any pairwise comparison
are demarcated by an asterisk and error bars represent standard error.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\emph{Figure 3. Gene expression (top), protein abundance (middle) and
enzyme activity (bottom, modified from Tarrant et al. 2021) for citrate
synthase (left) and glutamate dehydrogenase (middle)} \emph{and the
first enzyme associated with the electron transport system: NADH
dehydrogenase (right). Patterns of transcript expression and protein
abundance were not statistically significant in pairwise comparisons
(with the exception of NADH dehydrogenase which was significantly higher
in the daytime proteome compared to the night time). For each analysis,
enzyme activity significantly varied over the course of the day.
Clusters of statistical significance are demarcated by letters and error
bars represent standard error. Samples collected during the day at depth
are demarcated in white while those from the surface at night are in
black.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\emph{Figure 4: Patterns of differential gene expression and protein
abundance in the context of environmental sampling. Animals were sampled
across the course of the day (lower x-axis) from the local depths
(y-axis) experienced profound differences in food type/availability
(fluorescence as a proxy for photosynthetic plankton density),
temperature and oxygen (right panel, from CTD cast). Although copepods
primarily experienced low light levels, they responded to the diel cycle
(upper x-axis demarcates sunrise and sunset times) with migrations.
These environmental and behavioral distinctions among sampling points
resulted in distinct changes in the transcriptome (blue) and proteome
(orange), suggestive of diel partitioning of repair, migratory effort,
and metabolism that may have substantial consequences for biogeochemical
and ecological signatures across the daily cycle.}

Supplementary Files

1. Detailed transcriptomic, proteomic and bioinformatics methods

2. Transcriptome and differential expression

3. Detailed `omics results and figures

4. Transcriptome Go Enrichment results

5. Proteome and differential abundance

\selectlanguage{english}
\FloatBarrier
\end{document}

