# Daily proteome variations highlight sustained metabolic activity in cone cells of Nrl knockout mice

Cristina Sandu, Supitcha Pannengpetch, Prasong Khaenam, Montri Yasawong, Piyada Na Nakorn, Sarawut Lapmanee, Marie-Paule Felder-Schmittbuhl, Prapimpun Wongchitrat

► To cite this version:

Cristina Sandu, Supitcha Pannengpetch, Prasong Khaenam, Montri Yasawong, Piyada Na Nakorn, et al.. Daily proteome variations highlight sustained metabolic activity in cone cells of Nrl knockout mice. Experimental Eye Research, 2025, 253, pp.110284. 10.1016/j.exer.2025.110284. hal-05234487

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Research article

# Daily proteome variations highlight sustained metabolic activity in cone cells of *Nrl* knockout mice

Cristina Sandu<sup>a,1</sup>, Supitcha Pannengpetch<sup>b,1</sup>, Prasong Khaenam<sup>c</sup>, Montri Yasawong<sup>d</sup>, Piyada Na Nakorn<sup>b</sup>, Sarawut Lapmanee<sup>e</sup>, Marie-Paule Felder-Schmittbuhl<sup>a,*</sup>, Prapimpun Wongchitrat<sup>b,**</sup><sup>ID</sup>

<figure><img src="image_2.png" /></figure>

ARTICLE INFO

Keywords:

Cone photoreceptor cells

Daily rhythm

Protein expression

Retinal clock

A B S T R A C T

Vision is a highly rhythmic function adapted to daily changes in light intensity. Rhythms disruption is known to compromise retinal health and visual function. This study investigates expression patterns of cone proteins over the 24-h daily cycle in order to understand the molecular bases of cone cyclic physiology. Cones were isolated by vibratome-sectioning from *Nrl* knockout mice at four time points across the 24-h LD (Light-Dark) cycle and protein extracts were quantified by label-free LC-MS/MS. The resulting protein data was then submitted to MetaCycle analysis to identify proteins with rhythmic expression patterns and associated functions. Cyclic profiles were further validated by SWATH-MS analysis. A total of 1208 proteins were identified. Rhythmic expression patterns were found for 319 proteins, categorized into four clusters based on intensity variation. SWATH-MS analysis validated the approach. Functional enrichment analysis revealed proteins critical for photoreceptor function, including those involved in phototransduction, oxidative phosphorylation, RNA processing, proteostasis, transport, synaptic function and cilia biogenesis. These findings provide a unique dataset of rhythmic cone proteins, potentially crucial for elucidating cone cell physiology and visual function. This knowledge can empower future research on preventing and treating vision impairment.

1. Introduction

Vision impairment is a major public health issue in many countries
around the world, with an increasing incidence of blindness associated
with extending life expectancy (Bourne et al., 2017). Design of appro-
priate curative measures requires extensive knowledge of eye physi-
ology, and in particular of the retina, a major target in blinding diseases.
More specifically, photoreceptors – rods and cones - play major roles by
detecting light under low and high light intensity, respectively, and are
the most frequently affected in inherited blinding diseases. In addition to
photoreceptors, the retina contains a wide variety of cell types, which

are organized into precise circuits to treat visual information in a highly
dynamic manner, converting it into information-rich cell-specific elec-
trical signals and sending them to the brain. Although cones are
responsible for visual acuity and color vision, hence the most essential
players in human vision, their physiology is not yet well understood
(Verra et al., 2022).

Building up on the top of this organization, the visual function also
displays the capacity to adapt to the alternation of day and night, which
is associated with billion-fold differences in the intensity of light striking
the retina (Bhoi et al., 2023). This property is mediated by another level
of dynamic involving molecular and cellular processes throughout the

<footnote><sup>a</sup> Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, Strasbourg, France</footnote>

<footnote><sup>b</sup> Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand</footnote>

<footnote><sup>c</sup> Center for Standardization and Product Validation, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand</footnote>

<footnote><sup>d</sup> Program on Environmental Toxicology, Chulabhorn Graduate Institute, Bangkok, Thailand</footnote>

<footnote><sup>e</sup> Division of Physiology, Chulabhorn International College of Medicine, Thammasat University, Pathumthani, Thailand</footnote>

<footnote>* Corresponding author. Centre National de la Recherche Scientifique, Université de Strasbourg Institut des Neurosciences Cellulaires et Intégratives, 8, Allée du Général Rouvillois, F-67000, Strasbourg, France</footnote>

<footnote>** Corresponding author. Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 999 Phutthamonthon 4 Road, Salaya, Phutthamonthon, Nakhon Pathom, 73170, Thailand</footnote>

<footnote>E-mail address: prapimpun.won@mahidol.ac.th (P. Wongchitrat).</footnote>

<footnote n="1">¹ These authors contributed equally to this work.</footnote>
retina and occurring at a daily time-scale. The 24-h rhythmicity in the mammalian retina was demonstrated 28 years ago to be controlled by a circadian clock located within the tissue, which is directly entrained by the light-dark (LD) cycle (Tosini and Menaker, 1996). Intrinsic 24-h rhythms are programmed by molecular clocks widely distributed in mammalian tissues/cells and coordinated by a hypothalamic master clock, itself aligned with the light-dark cycle through the retina (Finger et al., 2020). A wide array (about 43% of all protein-encoding genes in mice (Zhang et al., 2014)) of ubiquitous and tissue-specific genes are controlled by circadian clocks, in agreement with the need for different organs to fulfill different temporally controlled tasks. Modern lifestyle, with its uncontrolled use of artificial light, shift work, or jet lag, perturbs synchronization with the LD cycle and insidiously affects mental function and long-term health (Logan and McClung, 2019). Because rhythmicity is central to retina physiology and visual function, this organ itself represents a major target for impairments induced by misalignment with the LD cycle (Huang et al., 2024; Mathew et al., 2022).

Photoreceptors are highly metabolically active, polarized cells with specialized outer segments containing the molecular machinery sustaining phototransduction (Liu et al., 2007). Daytime exposure to light and phototransduction triggers oxidative stress in rods and cones. Likely as a protective response, photoreceptors completely renew their outer segments within a few days. This requires a light- and clock-controlled burst of phagocytosis by the underlying retinal pigment epithelium (RPE) every day in the morning in rods and cones (Bobu and Hicks, 2009; Krigel et al., 2010). In rods, it is compensated by extensive reintegration of phototransduction elements including rhodopsin (90% of outer segment protein content) and discs into the basis of outer segments (Nemet et al., 2015). This process was shown to be controlled also by both light and the circadian clock (Hsu et al., 2015; Rose et al., 2024). Rhythms in retinal gene expression that are driven by the LD cycle were actually shown to be in major part regulated by the local circadian clock (Storch et al., 2007). The retina clock is a network of coupled cellular oscillators (Jaeger et al., 2015) in which cones were shown to play a particular role: they display oscillating expression of clock genes and autonomous rhythmic capacity (Liu et al., 2012; Sandu et al., 2020). However, the regulation of cone physiology by the circadian clock and/or the LD cycle is barely understood. By using mice carrying a double mutation in the *Per1* and *Per2* clock genes, our previous study showed that a functional molecular clock is required for proper development of cones (Ait-Hmyed et al., 2013) and for the rod outer segment phagocytosis peak to occur at light onset (Milicevic et al., 2021). Also, retina-specific impairment of the circadian clock leads to the shortening of cone survival (Baba et al., 2018). Together, these results indicate that 24-h rhythms and circadian clocks profoundly regulate retina/cone physiology. Nevertheless, the underlying mechanisms have thus far only been addressed by transcriptome studies, which have been demonstrated in the circadian field to generally allow the detection of approximately 50% of regulatory processes (Parnell et al., 2021).

The objective of this research is to explore the protein content of cones, as a marker of biological functions, across the LD cycle. We have used a proteomics approach to identify proteins that are rhythmically expressed in photoreceptor layers of the *Nrl* knockout (*Nrl*<sup>-/-</sup>) mouse, a model that develops only cones and no rods, overcoming the difficulty of studying cones due to their very low number in rodents (1–3%) (Mears et al., 2001; Nikonov et al., 2005). Functional analysis of proteins with rhythmic profiles highlights the high metabolic activity of cones in support of distinct processes related to visual function.

## 2. Materials and methods

### 2.1. Animals

*Nrl*<sup>-/-</sup> mice in which all rods are replaced by cones were used in this study (Mears et al., 2001). Animal breeding, housing, and animal experimental procedures were carried out according to the European

Parliament and The Council of the European Union Directive (2010/63/EU), to institutional ethical guidelines (E-67-218-38) and the Laboratory Animal Care and Use Committee of Mahidol University (COA.NO.MU-IACUC,2023/009). The generation of the *Nrl*<sup>-/-</sup> mice was fully described in our previous report (Krigel et al., 2010). Animals were housed in the Chronobiotron animal facility (UAR 3415, Strasbourg) under a 12 h Light/12 h Dark LD cycle (300 lx during daytime) with the light on at Zeitgeber time (ZT) 0. Animals had free access to standard food and water, and the ambient temperature was maintained at 22 ± 1 °C.

### 2.2. Retinal dissection and cone photoreceptor cells isolation over the 24-h cycle

Forty-eight *Nrl*<sup>-/-</sup> mice (males and females) were reared until 4 to 5-month-old and were randomly assigned to 4 time points of the 24-h LD cycle: ZT02 (early daytime, n = 12 mice), ZT06 (middle of daytime, n = 12 mice), ZT14 (after 2h of lights off, n = 12 mice) and ZT18 (middle of the nighttime, n = 12 mice). The daytime samples (ZT02, ZT06) were collected under housing-light conditions, whereas nighttime samples (ZT14, ZT18) were collected in the dark under dim red light. The sampling scheme is illustrated in Fig. 1. Animals were euthanized in a carbon dioxide chamber, and eyes were enucleated from the orbital cavity. The eyeballs were transferred into ice-cold Hank's balanced salt solution (HBSS) and hemisected equatorially by scissors to remove the anterior part of the eye containing the lens, iris, cornea, and vitreous body. The intact whole retina was carefully separated from the RPE and released from the posterior eyecup. Cone photoreceptor layers were immediately isolated by the vibratome sectioning technique (Jaeger et al., 2015). Briefly, the dissected retina was flat-embedded onto 3% agarose in PBS using 2% low melting point agarose in HBSS, with the photoreceptor layer facing downward. Agarose is used instead of gelatin to avoid contamination. The embedded retina was sectioned using a vibratome (Vibratome Series 1000 Tissue sectioning system, Technical Products International, Inc., West Bend, WI, USA) and a double-edged razor blade under a dissecting microscope in an ice-cold HBSS bath, as previously described (Jaeger et al., 2015). The inner retina and intermediary sections (100 ± 30 µm total) were discarded, and the outer retina section containing the cone photoreceptor cells was finally collected by sectioning through the underlying agarose block (Supplementary Fig. S1). Photoreceptor layer sections from both eyes of three mice at the same time point were pooled and considered as a single sample (final n = 4 for each time point). The samples were snap-frozen on dry ice and stored at -80 °C until used.

### 2.3. Protein extraction and preparation for LC-MS/MS

The total protein extraction procedure was performed according to our previous reports (Polvat et al., 2023; Promthep et al., 2022). Sixteen photoreceptor layer samples from four time points were homogenized in ice-cold lysis buffer containing 30 mM Tris-HCl (pH 8.5), 7 M urea, 2 M thiourea, 4% CHAPS and 1% protease inhibitors cocktail using manual tissue homogenizer with microtube pestles. The agarose and cell debris in the sample solution were removed by centrifuging at 14,000 g for 30 min at 4 °C. The supernatant was collected and cleaned up using a clean-up kit (GE Healthcare, Chicago, IL, USA) following the manufacturer's instructions. The protein pellets were then dissolved in 8 M urea and the protein concentrations were determined by Bradford protein assay using Bio-Rad Protein assay (Bio-Rad Laboratory, Hercules, CA, USA) according to the manufacturer's instructions. Protein samples in equal amounts (50 µg) were next subjected to the in-solution digestion process with a reduction buffer (100 mM dithiothreitol in 100 mM triethylammonium bicarbonate (TEAB)) for 30 min at 37 °C. For alkyl-ation, 100 mM iodoacetamide in 100 mM TEAB was added to the sample and then incubated in the dark for 30 min at room temperature and reincubated with a reduction buffer for 15 min. Then, trypsin (Trypsin
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** A systematic workflow of the cone cells sampling and proteomics experiment assessing daily rhythms in protein levels in mouse cone cells. Created with BioRender.com.</figcaption></figure>

Gold, Promega Corporation, Madison, WI, USA) was added for tryptic digestion at 1:50 (w/w enzyme to substrate ratio) overnight at 37 °C. Digested samples were dried up in the Nitrogen Evaporator (Organomation, Berlin, MA, USA). The tryptic peptide pellet was re-dissolved in 0.1% formic acid (FA) and cleaned up by a C18 Zip tip. Finally, cleaned peptide concentration was measured by a NanoDrop™ One Microvolume UV-Vis Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) at 205 nm, and samples were prepared with a final concentration of 1 µg/µL for MS injection. The samples were stored at -80 °C until used.

## 2.4. LC-MS/MS analysis

A schematic of the label-free quantification (LFQ) proteomics experiment is detailed in Fig. 1. Tryptic peptides were analyzed by LC-MS/MS on a Dionex Ultimate 3000 RSLCnano system (Thermo Fisher Scientific, Waltham, MA, USA) combined with CaptiveSpray source/Quadrupole ion trap mass spectrometry (model Q-ToF Compact, Bruker, Berlin, Germany). Equal amounts (1 µg) of digested peptides were loaded onto Nano-trap Acclaim PepMap™100 C18 column (100 µm i.d. × 2 cm, 5 µm, 100 Å pore size) (Thermo Fisher Scientific, Waltham, MA, USA) and were then separated on a PepMap™100 C18 column (75 µm i. d. × 500 mm, 3 µm) (Thermo Fisher Scientific, Waltham, MA, USA). The loading pump solvent consisted of 0.05% TFA in 2% acetonitrile with a linear gradient of 2–95% Solvent B over 160 min at a flow rate of 300 nL/min and a column temperature of 60 °C. The gradient of solvent B settings and conditions used were as follows: 0–5 min: 2% B, 5–125 min:35% B, 125–145 min:55% B, 145–155 min:95% B, 155–160 min:2% B. The flow rate of the drying gas was 5 L/min at 150 °C, and the pressure of the nebulizer gas was 0.2 bar. The MS acquisition rate was 6 Hz with a range of 150–2200 m/z for mass scanning in positive ionization mode.

The raw MS files were processed with the MaxQuant software (version 1.6.2.10, Max Planck Institute of Biochemistry, Munich, Germany) using the integrated Andromeda search engine. MS data were

analyzed by blast against the UniProt *Mus musculus* protein database. A search parameter was defined to include fixed modification (carbamidomethyl cysteine) and variable modifications (N-terminal acetylation and methionine oxidation). Peptide identification was set as Trypsin/P with a maximum of two missing cleavages and a false discovery rate (FDR) of 1% of the protein level. The time-of-flight (TOF) MS/MS match tolerance was set as 0.5 Da, and for LFQ, the ‘Match between run’ option was selected. MaxQuant LFQ algorithm generated the intensity of each matching protein, which was used for further bioinformatics and statistical analysis.

## 2.5. SWATH-MS analysis

SWATH-MS was used to perform targeted label-free Nano-LC QTOF for the biomarker qualification phase. The procedure was conducted in accordance with the previously published protocol (Polvat et al., 2023). Peptides were analyzed on an LC-MS/MS system including a Nano-liquid chromato-graph (Dionex Ultimate 3000, RSLCnano System, Thermo Scientific, MA, USA) in combination with a Captive Spray source/Quadrupole ion trap mass spectrometer (Model Q-ToF Compact, Bruker, Germany). One microgram of peptides was separated by a PepMap100 C18 LC analytical column (75 µm i.d. × 500 mm, 3 µm) after enrichment on a Nano-trap Acclaim PepMap™100 C18 column (100 µm i.d. × 2 cm, 5 µm, 100 Å pore size). Mobile phase A consisted of 0.1% FA in water (HPLC grade); mobile phase B consisted of 0.08% FA in 80% acetonitrile. The loading pump solvent consisted of 0.05% TFA in 2% acetonitrile; the temperature was 60 °C, and the flow rate was 300 nL/min. The gradient of mobile phase B was: 2% at 0–5 min, 35% at 125 min, 55% at 145 min, 95% at 155 min, and 2% at 155.1–160 min. The instrument was tuned for SWATH mode MS experiments to provide a quadrupole resolution of 25 Da/mass selection. An isolation width of 25 Da was set in a looped mode over the full mass range (150–2200 m/z) scan and 32 overlapping windows were constructed. An accumulation time of 100 ms was set for each of the fragment ions, resulting in a total duty cycle of 3.0 s.
The SWATH data were processed and analyzed using Skyline version 19.1.0.193 software (MacCoss Lab, Seattle, WA, USA). All peaks were checked manually for correct integration and peptide intensity was clarified as the peak area based on the sum of all transitions. Peptide intensity was determined manually using the total area sums (TAS) method. All mass spectrometry proteomics data (both LC MS/MS and SWATH-MS data) have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD054284.

## 2.6. Bioinformatics analysis

The rhythmicity of protein expression was assessed using the meta2d function in the MetaCycle R package (Wu et al., 2016) with the period fixed at 24 h and the amplitude and phase as free parameters. In this study, proteins were considered rhythmically expressed when the LS BH. Q-value (Benjamini-Hochberg corrected P-value) was <0.1 (0.006 < LS P-value <0.05).

We used the fuzzy c-means algorithm in the Mfuzz R package to profile rhythmic proteins according to their expression patterns (Kumar and Futschik, 2007). The average intensity value for each protein at each time point was used as the input. After standardization, each protein was assigned a unique cluster according to its membership value.

Gene ontology (GO) and pathway enrichment analyses of the whole cone proteome were performed using g:Profiler (Reimand et al., 2007) using the *Mus musculus* transcriptome as a reference background. An adjusted p-value <0.05 was set as a threshold for significantly enriched pathways using the g:SCS method to correct for multiple testing.

We used the Search Tool for the Retrieval of Interacting Genes/ Proteins (Szklarczyk et al., 2015) (STRING; https://string-db.org) to perform functional analysis of proteins with rhythmic expression in cones and to construct the protein-protein interaction (PPI) network. Only pathways and terms with an FDR less than 0.05 were considered to be significantly enriched.

This study employed Phase Set Enrichment Analysis (PSEA) to investigate daily rhythmic gene pathways using 7796 GO Biological Process (BP) terms and 1031 GO Cellular Component terms (CC) (Castanza et al., 2023). PSEA was conducted using PSEA version 1.1 with the following parameters: a minimum gene set size of 10, simulations of 100,000, a p-value threshold of 0.01, and identified significantly enriched pathways with robust daily expression patterns (Zhang et al., 2016). Harmonic regression was employed to predict proteome acrophases, while the Kuiper test was used to assess enrichment against a uniform background. In order to ensure the reliability of the results, gene sets with fewer than ten genes were excluded.

## 2.7. Statistical analysis

Data from SWATH-MS over 24 h were analyzed by nonlinear least-square fitting of a 24-h sinusoid (Cosinor analysis) $y = y0 + c \cdot \cos[2\pi(t-\varphi)/24]$, with $y0$ – mesor, $c$ – amplitude and $\varphi$ – acrophase (Nelson et al., 1979) and by One-way ANOVA, by using SigmaPlot 12 software (Systat Software, San Jose, CA, USA). A statistically significant rhythmic pattern or time-effect was assumed with P-values less than 0.05.

# 3. Results

## 3.1. Protein profile of Nrl⁻/⁻ photoreceptor layers

To examine the daily rhythmic expression of the cone photoreceptor cell proteome we isolated photoreceptor layers by vibratome sectioning (Jaeger et al., 2015), adapted to Nrl⁻/⁻ retinas (Supplementary Fig. S1). The photoreceptor layer was collected at four time points, and the extracted proteins were analyzed by LC-MS/MS using label-free quantification. A total of 1208 proteins were identified as being quantitatively detected at all studied time points (Supplementary Table S1).

Gene ontology and pathway analysis of these 1208 proteins (Supplementary Table S2) showed strong enrichment in functions related to energy metabolism [Oxidative phosphorylation, The citric acid (TCA) cycle and respiratory electron transport], synaptic activity (Synaptic vesicle cycle), proteostasis (Cytoplasmic translation, Protein folding) among others. This analysis also highlighted enrichment in components of photoreceptors, with Cellular compartments such as the Photoreceptor inner segment, Photoreceptor outer segment, and Photoreceptor ribbon synapse. We evaluated potential contamination by proteins known to be enriched in Müller glial cells (Grosche et al., 2016) since the processes of these cells extend up to the outer limiting membrane. Indeed, CD44, Glutamine synthase (Glu1), Glial fibrillary acidic protein (Gfap), Glutamate transporters (Solute carrier family 1 member 2 and 3; Slc1a2 and Slc1a3 – Glast and Glt1), Cellular retinaldehyde binding protein (Rlbp1 or Cralbp), Vimentin (Vim) were all detected in our dataset. Since our sections comprised the whole photoreceptor layer including synapses, we logically also detected PKC-alpha (PRKCA) the marker of rod bipolar cells known to be present at this level in the Nrl⁻/⁻ retina (Strettoi et al., 2004). However, only a minor contribution from the RPE was found since we could not detect the retinoid isomerase-ohydrolase Rpe65, the Mertk kinase or bestrophin 1 (Best1), but the enriched peptides corresponding to known enriched genes in the RPE: Cbfa2t3, Atp1b1, Tmem27 and Ctsd (Bennis et al., 2015). Altogether, these data confirm that our sample collection method was effective for photoreceptors and is in line with the highly active metabolism occurring in cones (Ingram et al., 2020) and the sustained protein synthesis and trafficking towards outer segments (Radhakrishnan et al., 2022).

Principal component analysis (PCA) of the proteome data revealed minor in-group variation of protein expression but, essentially, PC1 (39.24%) showed marked differences in expression between groups (Fig. 2), with each of the daytime samples (ZT02 and ZT06) and nighttime samples (ZT14 and ZT18) clustering together. This result indicates that the cone proteome is strongly influenced by the time of day and that the main variations in protein expression are due to light versus night. It also suggests that more profound differences occur between the two day-time points than between ZT14 and ZT18 protein content (Fig. 2).

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Principal component analysis (PCA) of LFQ intensity of proteins from individual samples of each time point. The percentage of variance is indicated in parenthesis. Light and dark phases are indicated by white and black bars respectively.</figcaption></figure>
## 3.2. Identification of rhythmic proteins

Rhythms in cone protein levels were identified using the MetaCycle R package which revealed 319 (~26% of the 1208 quantified proteins) proteins with rhythmic expression patterns across the four time points (Supplementary Table S3). Expression profiles of these oscillating proteins (Fig. 3) were quite diverse but also showed two major patterns with the highest expression either during the day or during the night time points.

STRING analysis of the 319 proteins with rhythmic expression revealed that 315 proteins of our list matched with the database, with a Protein-Protein-Enrichment P-value <1.0e-16. It highlighted a major enrichment in GO terms and pathways related to energy metabolism: for instance, Aerobic respiration, with 29 associated proteins from our list and oxidative phosphorylation (20 proteins) or inner mitochondrial membrane protein complex (23 proteins) (Fig. 4 and Supplementary Table S4). STRING analysis also revealed enrichment in proteins linked to RNA metabolism (RNA binding, Spliceosome) and also neuronal activity (Synapse, Myelin sheath) (Fig. 4). Noteworthy, numerous genes belonging to the synapse cellular component GO term displayed interactions with other terms and pathways such as translation, RNA splicing, transport and vacuolar proton-transporting V-type ATPase complex (Supplementary Fig. S2). Further examination of the STRING results highlighted the clustering of rhythmic proteins into diverse cellular components relevant for photoreceptor function such as: i) photoreceptor inner segment, which includes the S-opsin (Opn1sw) and is strongly linked with signal transduction proteins from the circadian entrainment pathway; ii) chaperone complex (Sokolov et al., 2019) and proteasome complex (Tolone et al., 2023) that are relevant for the high rate of protein synthesis/degradation and transport, inherent to permanent turnover of outer segments.

## 3.3. Phase-distribution of protein expression and of biological functions

The Mfuzz clustering algorithm was applied to the expression data of the 319 rhythmic proteins in order to reveal dynamics of expression patterns. The distribution of peaks and troughs allowed distinction of four clusters containing 77, 105, 40, and 97 proteins and revealing the diversity of expression profiles (Fig. 5 and Supplementary Table S3). In Cluster 1, protein expression exhibited the highest intensities during daytime (ZT02 or ZT06) and the lowest during nighttime (ZT14 or ZT18). The proteins in Cluster 2 reached the peak levels of intensity after lights off at ZT14 or ZT18. The expression pattern of proteins in Cluster 3 was increased at ZT06 or ZT14 and expression was lowest at ZT02 and ZT18. In contrast, the proteins in Cluster 4 reached their maxima at the end of the night (ZT18) and/or the beginning of the day (ZT02).

To get further insight into the distribution of biological functions in cones over the 24-h cycle we used an integrated approach, Phase Set Enrichment Analysis (PSEA), concentrating on biological processes and cellular components (Zhang et al., 2016) (Fig. 6 and Supplementary Table S5). Unlike what appeared in the analysis of the whole group of 319 proteins, energy metabolism was not clearly identified upon PSEA, likely because of the phase dispersion of the distinct involved genes. Yet, “mitochondrion” and “energy derivation by oxidation of organic

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The rhythmic proteins in mouse cone cells. The heatmap indicates the intensity of protein rhythmicity in cones during the light (white bar; ZT02 and ZT06) and dark (black bar; ZT14 and ZT18) cycle. Each row represents a protein identified as oscillating (319 proteins in total). Each column represents a single time point with 4 independent samples. The color bar indicates z-scored. Blue to red represents protein expression from min to max. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Functional enrichment analysis of rhythmic proteins using Gene Ontology (GO) terms, KEGG and Reactome pathways. (A) Top 10 enriched GO Biological Processes, (B) Top 10 enriched GO Molecular Functions, (C) Top 10 enriched GO Cellular Components, (D) Top 10 enriched KEGG pathways, and (E) Top 10 enriched Reactome pathways. The green line represents the significance level cut-off (adjusted P < 0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

compound" clustered together to approximately ZT10. By contrast, a number of functions displayed striking enrichment and were specifically associated with a time window of the 24-h cycle. For instance, translation-related terms (cytoplasmic translation, ribosome biogenesis) clustered together around ZT02-3, followed by processes linked to RNA (RNA processing, RNA splicing, spliceosome complex) around ZT04-5. Transport-associated processes (intracellular transport, regulation of transport) were found uniquely around ZT16-17, whereas GO terms linked to neuronal morphology (axon, synapse, pre- and post-synapse) clustered around the transition between night and day. Thus, biological functions in cones appear under marked rhythmic control across the light-dark cycle.

### 3.4. Confirmation of rhythmic patterns by SWATH-MS

We performed SWATH-MS to validate the rhythmic protein expression established by MetaCycle analysis. A list of 52/319 proteins belonging to the four clusters and representative of the distinct pathways highlighted above was selected. Rhythmicity was confirmed for all of them, both by Cosinor analysis and by One-way ANOVA (Supplementary Table S6). Phases determined by Cosinor analysis mainly coincided with those of the *Mfuzz* cluster to which proteins belonged. Thus, the MetaCycle for this set of data was a valuable approach to identifying rhythmic protein expression patterns.

We more particularly focused on proteins with rhythmic expression

and relevant for cone function (Fig. 7). These include elements of the phototransduction cascade such as S-opsin (Opn1sw), alpha- and gamma-subunits of cone transducin (Gnat2, Gngt2). They also included proteins associated with the phototransduction cascade such as phosducin (Pdc), Unc119 and Inosine-5'-monophosphate dehydrogenase 1 (Impdh1), the rate-limiting enzyme in GTP synthesis. Importantly, in the 319 protein/gene list, 7 overlapped with the RetNet list of eye disease-related genes (Daiger et al., 1998). In addition to Opn1sw, Gnat2, Unc119 and Impdh1, these include hexokinase 1 (Hk1) involved in retinitis pigmentosa and macular dystrophy (Yuan et al., 2017), harmonic (Ush1c) responsible for severe deafness and blindness and able to affect cones in Usher syndrome type 1 (Trouillet et al., 2018), as well as chaperonin-containing TCP-1, subunit 2 (Cct2) involved in Leber congenital amaurosis, a retinopathy associated with macular degeneration (Minegishi et al., 2016). Thus, a number of structural and functional components of photoreceptors, including cones, display rhythmic daily expression levels, indicating this is important for photoreceptor health.

### 4. Discussion

In the present study, we used the “pure-cone” Nr1f1⁻/⁻ mouse to characterize the proteome of cones across the 24-h LD cycle. By performing label-free quantification of proteins extracted from freshly isolated photoreceptor layers we find that about one-fourth of detected
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Clustering of rhythmic proteins in mouse cone cells. The expression patterns of the rhythmic proteins (319) were analyzed by Mfuzz R package. Four clusters were identified based on the similarity in expression patterns. The top panel shows intensity changes of rhythmic protein expression at each cluster during the light (white bar; ZT02 and ZT06) and dark (black bar; ZT14 and ZT18) cycle. The bottom panel shows the heatmap of the z-score normalized intensity level and time course of the rhythmic cone proteins in the corresponding cluster. Blue to red represents protein expression from min to max. White bars: light phase; black bars: dark phase. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

proteins display rhythmic expression, with broad distribution of peak phases. Functional analysis highlights salient rhythms in proteins linked to energy metabolism and RNA- and protein-biosynthesis. It also shows the clustering of biological functions across the LD cycle, with RNA and protein biosynthesis-related proteins mainly expressed at the beginning of the day, together with processes related to synaptic activity, and expression of transport-associated proteins at the beginning of the night. Our data provide evidence for extensive adaptation of cone biology to the 24-h cycle, with marked variations in metabolic activity and energy needs, likely imposed by the changes in environmental lighting.

The *Nrl*⁻¹⁻² model has been instrumental in investigating cone physiology and pathology (Li et al., 2019; Rajala et al., 2013; Zhu et al., 2003), including their rhythmic properties (Krigel et al., 2010; Sandu et al., 2020) in an otherwise rod-dominated mouse retina. Here it allowed characterizing the 24-h proteome (to our knowledge, the first) in mouse cones without extensive contamination from neighboring cell types, except for a minor contribution from Müller glial cells. Our data show robust cyclic changes in protein levels with a 24-h period, detected by the Lomb-Scargle periodogram method hosted in the MetaCycle package and that allows analyzing unevenly spaced data. Importantly, 90 % of our list of 319 proteins with periodic expression were also detected as rhythmic using RAIN algorithm (Thaben and Westermark, 2014), another approach adapted to our dataset (data not shown), and 52/319 were fully validated following SWATH analysis. Our data also present some common point with previously published results. Among the 14 proteins described by Moller et al. (2017) to vary between ZT6 and ZT18 in mouse whole retinas, one photoreceptor specific protein, phosducin (Pdc), was also present in our dataset, also with highest expression at night.

Photoreceptors are among the most metabolically active cells in the body (Okawa et al., 2008) with metabolic needs highly depending on ambient illumination. Energy consumption in the retina is in major part devoted to ion transport to compensate the permanent depolarization of photoreceptors under darkness, by pumping out cations that enter outer

segment light-dependent channels (Okawa et al., 2008). This energy demand stays high under daylight in cones because they never saturate (Ingram et al., 2020), hence a marked need for ATP throughout the 24-h LD cycle. Metabolism, and in particular oxidative phosphorylation and aerobic respiration, are the main enriched functions and pathways (Supplementary Tables S2 and S4, Fig. 5) in the proteome of cones (1208 proteins) and also in the list of 319 proteins with rhythmic expression, supporting the fact that ATP consumption is a hallmark of cone biological activity. Peak expression of proteins related to these functions is grossly distributed all over the 24-h cycle but a major phase for energy metabolism terms is detected around ZT11 ("Mitochondron", "Energy derivation by oxidation of organic compound"), likely in line with the increased energy demand during the dark phase (Ingram et al., 2020). Noteworthy, glycolysis-related pathways are less evident in our datasets, although it was shown that photoreceptor highly rely on it for their energy needs: this could be due to the fact that glycolysis is less crucial for cone health (that mainly uses OXPHOS) than for rods (Pan et al., 2021).

Robust metabolic activity and associated oxidative stress as well as phototoxicity generate a higher risk for impaired proteostasis in photoreceptors (Faber and Roepman, 2019). Removal of toxic phototransduction byproducts is counterbalanced by continuous renewal of the outer segments by incorporating newly synthesized discs/membranes/phototransduction elements at the outer segment base, and shedding the oldest ones at the cell's tip to be phagocytosed by the underlying RPE (Moran et al., 2022). This process in turn requires a high rate of protein biosynthesis in the inner segment and reliable transport and targeting pathways. Not surprisingly, RNA and protein biosynthesis were also among the main functions to show rhythmic occurrence with major peaks at the beginning of the day (Fig. 6). Indeed, phagocytosis of cone outer segments in *Nrl*⁻¹⁻² takes place as a daily morning surge (Krigel et al., 2010) that might, based on the present data, be followed by massive protein regeneration in these cells.

Inherent to massive protein synthesis in photoreceptors are the
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Phase distribution of biological functions in rhythmic cone proteins over the 24-h light-dark cycle. Phases were calculated using Set Enrichment Analysis (PSEA) for rhythmic proteins. The polar plots of the phase distribution of the representative Gene Ontology term are presented, with the Biological Processes highlighted in red (outer cycle) and the Cellular Components highlighted in blue (inner cycle). The grey background indicates the dark phase. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Validation of expression pattern of rhythmic proteins by SWATH-MS analysis. Daily expression profiles of representative proteins (Gnat2, Gngt2, Opn1sw, Hk1, Unc119, Pdc, Ush1c, Impdh1) from mouse cone cells. The x-axis represents the sampling time points, and the y-axis represents the intensity of SWATH-MS analysis. Black line: best-fitted sinewave function for significant Cosinor analysis. Grey rectangles: dark phase.</figcaption></figure>

processes of protein folding, trafficking and targeting to the appropriate cellular compartment (Nemet et al., 2015; Sokolov et al., 2019). Molecular functions highlighted in the cone proteome show marked enrichment in proteins linked to folding or unfolding and chaperone activity (Supplement Table S2), further underlining the intense protein synthesis activity occurring in cones. Also, transport-linked proteins are markedly enriched in our dataset and related processes appear to occur mainly at the beginning of the night, indicating that trafficking through

the highly compartmentalized cones also supports adaptation to the LD cycle. There is a constant flux of new materials moving from the inner segment to the outer segment. Transit of the phototransduction machinery from its site of synthesis (inner segments) to outer segments is quite complex, involving both vesicular transport for transmembrane proteins (e.g.: rhodopsin) and interaction with trafficking chaperones for lipidated proteins (Pearring et al., 2013). The latter mechanisms have been mainly documented in rods, with alpha-transducin being
carried by UNC119 and the beta-gamma subunits by phosducin (Pearring et al., 2013). This holds also true for the translocation of transducin back into the inner segment, under bright light (Calvert et al., 2006). How transducin subunits are transported in cones is not completely understood (Bales et al., 2020). Our data in cones show maximal levels of the cone transducin gamma- and alpha-subunits around the transition between night and day, together with those of the UNC119 and phosducin chaperonins (Fig. 7), suggesting that similar modes of transport might take place in cones than in rods, with a rhythmic pattern. This remains, however, to be demonstrated, both for the transport towards the outer segment and the translocation back.

Besides transport, analysis of rhythmic proteome put forward peak phases of synapse-related proteins around the transition between night and day. The contact between cones and cone (depolarizing) bipolar cells and horizontal cells involves ribbon synapses and complex molecular architectures ensuring optimized regulation of neurotransmitter release and visual function. Numbers and ultrastructure of rod ribbons are known to undergo changes according to time of day, in fish and mice (Spiwoks-Becker et al., 2004; Vollrath and Spiwoks-Becker, 1996). Whereas it is unknown for mammals, ribbons undergo daily changes in cones in fish (Wagner and Djamgoz, 1993). Thus, it might be that the increased expression of synaptic proteins in *Nrl*⁻⁻ cones at dawn also underlies the reorganization of ribbons in the cone pathway.

Although rhythmic functions in the retina are obviously controlled by the LD cycle, the endogenous retinal circadian clock was also demonstrated to play an important role in the control of cyclic changes, including in gene expression (Felder-Schmittbuhl et al., 2017). Some data support the presence of a clock in cones themselves (Baba et al., 2018; Liu et al., 2012; Sandu et al., 2020). Also, a number of rhythmic gene expression patterns are retained in total darkness in the zebrafish cones (Zang et al., 2021). These clock-regulated cone genes encode arrestins, GRK7s, recoverins and RGS9s, all involved in shutting off the light response. Rhythmic profiles in LD were found for the orthologues in mice (Zang et al., 2021). These components did not prove rhythmic in our MetaCycle analysis but were detected in the proteome data; Arrestin 3, Recoverin, Rgs9, and Grk1 (Lyubarsky et al., 2000). Their expression levels proved rhythmic upon Cosinor analysis and ANOVA (not shown), further confirming the cyclic expression of phototransduction actors. Whether these proteins are also controlled by the circadian clock remains to be demonstrated but since the clock machinery is the local relay of LD cycle-related time cues (Storch et al., 2007), this is likely to be the case.

In this study, SWATH-MS was used for protein validation. This technique was particularly adapted to our limited amounts of cone tissue samples. In addition, it provides complete protein quantification by systematically capturing multiple unique peptides per protein and monitoring different fragment ions for confirmation. By combining the extensive coverage of shotgun proteomics with the precision of targeted methods, SWATH-MS thus allows the reliable identification and quantification of thousands of proteins in a single analysis. However, the technique has its limitations because it relies on comprehensive spectral libraries for peptide identification and quantification. Our current spectral library is restricted to tryptic peptides, which limits our analyses to urea-soluble proteins. This limitation potentially excludes important cone proteins that may require alternative extraction methods or enzymatic digestion strategies to be effectively quantified. Moreover, it is also possible that proteins that are very low in abundance in a given sample may be missed due to the sensitivity of the technique. This may require a larger amount of sample and an additional time point for study.

In summary, our results show that gene expression in cones is under pervasive control by the light-dark cycle, underlying profound changes in cone (metabolic, anabolic) activity and structure, and ultimately supporting visual function. These data are likely to open new perspectives in the understanding of retinal diseases, in particular inherited degenerations since part of their causative genes are submitted to such control.

### CRediT authorship contribution statement

**Cristina Sandu:** Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. **Supitcha Pannengpetch:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. **Prasong Khaenam:** Visualization, Investigation, Formal analysis, Data curation. **Montri Yasawong:** Visualization, Formal analysis, Data curation. **Piyada Na Nakorn:** Formal analysis. **Sarawut Lapmanee:** Investigation. **Marie-Paule Felder-Schmittbuhl:** Writing – review & editing, Writing – original draft, Visualization, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Pra-pimpun Wongchitrat:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Funding

This project was funded with support from Mahidol University (Basic Research Fund: fiscal year 2023 grant to PW), the Franco-Thai Mobility Programme (PHC SIAM) and the Centre National de la Recherche Scientifique (CNRS, core grant to MPFS), France.

### Declaration of competing interests

The authors declare that they have no relevant financial or nonfinancial interests to disclose.

### Acknowledgments

We specially thank the Proteomics Center, Faculty of Medical Technology, Mahidol University, for providing proteomics approach facilities and Louise Martineau for helping with RAIN analysis. The authors are also grateful to Dr. David Hicks for critical reading of the manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.exer.2025.110284.

### Data availability

Data will be made available on request.

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