Short Exposure to Full Moonlight Has a Long‐Term Impact on Brassica juncea Cell Activity and Growth

ABSTRACT

Lunar farming, often regarded as a myth, is regularly practiced in many places around the world (e.g., India) where framers organized their agricultural activities according to moon phases. Early and recent work showed that exposure to moonlight affects the life cycle of plants, from seed germination and vegetative growth to fruit maturation and dispersal. Here we addressed the long‐term effect of short exposure to full moonlight (FML) on cellular activities in Brassica juncea by analyzing protein and metabolite profiles immediately after 3‐night‐exposure (3NE) or 7 and 15 days after exposure (DAE) to FML. This study shows an increase in nuclear size following 3NE to FML, which was accompanied by changes in protein and metabolite profiles. We identified significant alterations in protein and metabolite profiles between FML and dark‐treated plants in conjunction with developmental stages, which persisted long after exposure to FML. Most notable are the changes in composition of metabolite interconversion enzymes (MIEs) at various developmental stages which were intensified in FML‐treated plants. Changes in MIEs were accompanied by significant alterations in metabolite composition and level, particularly at 15DAE, including branched‐chain amino acids (e.g., valine, leucine), multiple sugars (raffinose, glucose, sucrose) as well as the tricarboxylic acid (TCA) cycle intermediates malic acid and citric acid. Thus, our results show that short‐term exposure to FML triggers a developmental switch resulting in a long‐term impact on plant performance that brings about an increase in cell activities and consequently enhanced growth. Our results call for meticulous research on this lunar phenomenon and its potential to enhance crop plant growth and development.

Summary statement

The study of the impact of an overlooked yet prominent environmental factor, the moon, on growth of mustard plants revealed that short‐time exposure to moonlight initiates a developmental switch that subsequently influences cellular activities over the long term, resulting in improved growth.

1. Introduction

Traditional lunar farming constitutes a unique agricultural approach where planting, harvesting, and agronomical activities (pruning, weeding, grafting, transplanting, etc.) are synchronized with the phases of the moon. By aligning agricultural activities with the lunar phases, practitioners of traditional lunar farming aim to improve crop growth and enhance yields (Zürcher 2011). The longevity of traditional lunar farming spans centuries across diverse cultures, and it has been preserved as a cultural and historical tradition (Folkard 1884). While this practice carries significant cultural weight, scientific validation of its efficacy remains limited. Most modern agricultural practices are firmly anchored in rigorous scientific research, relying on evidence‐based methodologies. In contrast, traditional lunar farming, although rich in heritage, is often viewed more as a cultural or historical tradition and often regarded as a myth, than a method substantiated by contemporary scientific rigor (Mayoral et al. 2020). Consequently, the influence of this conspicuous environmental factor, the moon, on plant cell biology as well as on plant performance under field conditions has hardly been investigated (Sivasankar and Thimmaiah 2021).

Very few studies have explored the effect moonlight has on the plant life cycle, and results have been conflicting (reviewed in Zürcher 2011; Sivasankar and Thimmaiah 2021). Remarkably, plants may be exposed to 80%–100% of the full moon's luminance for over 5 h each night across eight consecutive nights every month (Supporting Information S1: Figure 2B). Yet, research delving into the effects of this significant environmental factor on plant biology and ecology remains sparse. This knowledge gap is striking, considering the potential influence that such consistent and prolonged exposure to moonlight could have on plant developmental processes and ecological interactions. Previous reports pointed out that the state of the moon at the time of sowing appeared to play an important role in seed germination, vegetative growth, and flowering (reviewed in Zürcher 2011; Sivasankar and Thimmaiah 2021). For example, early studies showed that the seeds of multiple crop plants, including vegetables and cereals, sown 2 days before the full moon displayed better germination and post‐germination growth and produced a better harvest than those emerging from seeds sown 2 days before the new moon (Kolisko 1936). Germination and early growth of some tropical trees appear to be connected to the timing of sowing in relation to moon phases (Zürcher 2011). Multiple mechanisms have been suggested to explain the effect of moonlight on plant growth, including the breakdown of starch by diastases (Semmens 1923), variation in water absorption during seed imbibition (Brown and Chow 1973; Spruyt, Verbelen, and de Greef 1987), and gaseous exchange (Graviou 1978), as well as the effect of the moon on the movement of sap in plants (Restrepo 2004). Recent scientific reports highlighted a possible link between the lunar cycle and plant physiology and phenology. Accordingly, a study on Cereus peruvianus (Peruvian apple cactus) showed that under a long‐day photoperiod, large‐sized flowers open almost exclusively at night, in a 24 h rhythm, over 3–4 days that span the cycle of the full moon (Ben‐Attia et al. 2016). Likewise, pollination of Ephedra foeminea by dipterans and lepidopterans coincides with the full moon of July (Rydin and Bolinder 2015). A study by Breitler et al. (2020) provides strong evidence for the effect of moonlight on plant biochemistry and molecular biology revealing a massive transcriptional variation in Coffea arabica under full moonlight (FML) conditions. Among the genes affected by FML are core clock genes, stress‐responsive genes, and the photoreceptor phototropin1 (PHOT1). The enhanced expression of multiple stress‐responsive genes, such as redox and heat shock protein (HSP) genes, suggested that FML is perceived by the plant as a “stress” signal. A recent study by Singiri et al. (2023) demonstrated notable changes in genome organization associated with changes in DNA methylation and histone modification in tobacco and mustard (Brassica juncea) plants following exposure to FML. Moreover, there was a significant increase in primary metabolites linked to stress, such as proline and raffinose. Additionally, the study observed heightened expression of stress‐associated proteins and photoreceptors, including phytochrome B and phototropin 2, indicating a cellular response to moonlight exposure.

Conceivably, the sunlight reflected by the moon at a similar spectrum might be acting as an environmental signal, rather than an energy source, which is perceived by the plant, via photoreceptors to induce plant response and variation in cellular function. It should be noted, however, that the wavelength ratio may differ between sunlight and moonlight. Thus, while the red:far red ratio of sunlight is around 1.2, that of the moonlight is around 0.2 (Breitler et al. 2020). To examine the biological significance of moonlight on crop plant physiology and cell biology, we chose the crucifer, Arabidopsis relative Brassica juncea (AABB, 2n = 36) for our investigation. Commonly known as Indian mustard, B. juncea is an above‐ground crop with natural allopolyploid characteristics, originating from two diploid species: B. rapa (AA, 2n = 20) and B. nigra (BB, 2n = 16) (Nagaharu 1935). The data presented here showed that short‐term exposure to moonlight has a long‐term effect on B. juncea cell activities as determined by proteome and metabolome analyses and consequently on plant growth and development.

2. Materials and Methods

2.1. Plant Growth Conditions and Exposure to Moonlight

B. juncea seeds were planted in small pots filled with standard gardening soil composed of peat and perlite mixed in a ratio of 2:1. The growth process took place in a growth room under controlled conditions, with a light intensity of ~150 μmol m^–2 s^–1, humidity maintained at 65%–75%, a temperature of 25 ± 1°C, and a photoperiod of 14/10 h (day/night). Ten‐day‐old B. juncea seedlings were placed in the experimental site (the roof of the building covered with asphalt sheets) and exposed to dark or FML for three consecutive nights, 5 h each night (Figure 1A), starting a day before a full moon as described (Singiri et al. 2023). The temperature at the experimental location (Supporting Information S1: Figure 1) was monitored using a USB iButton Reader, DS9490# (MAXIM, China). The data regarding the timing of moonrise, moonset, meridian passage, and sunrise/sunset (based on Time and Date, https://www.timeanddate.com/moon/@293396?month=4& year=2020) as well as the timing of exposure initiation and termination are given in Supporting Information S1: Figure 2A. Samples (four biological replicates) were collected immediately after exposure (3NE) at the experimental site, frozen immediately in liquid nitrogen and kept at –80°C until used for analyzing proteins and metabolites, or fixed in acetic acid–ethanol (1:3 v/v) and kept at –20°C, for nuclei analysis. Plants were then placed in a growth room for further growth and sampled at 7 and 15 days after exposure (7 DAE, and 15 DAE respectively; Figure 1A) as described above. At 15DAE, plants were harvested, and multiple parameters of growth were measured including root and shoot length and dry weight. The experiment was repeated four times, with four replicates each, during the full moon periods of June 13–15, August 10–12, and September 9–11 in the year 2022 and August 30 to September 1, in the year 2023. For proteome and metabolome analyses we have used plant material from the experiment conducted in August 30 to September 1, 2023.

Figure 1.

A schematic representation of the experimental setting. (A) B. juncea 10‐day‐old seedlings were subjected for three consecutive nights (3NE) to FML or dark, 5 h each night, and placed there after in a growth room. Sample leaves were taken on the third night (3NE), 7 days after exposure (7DAE), and at 15DAE for further analysis. (B, C) FML induces changes in nuclear size. Leaves of B. juncea plants exposed to dark or to FML for 3 consecutive nights (3NE) were fixed in acetic acid: ethanol (1:3), nuclei were prepared, stained with DAPI and visualized under a confocal microscope (B). (C) Average diameter of nuclei prepared from the indicated treated plants (n = 100). Vertical bars represent the standard deviation. The p‐value was determined using Student's unpaired t‐test (GraphPad software).

2.2. Nuclei Isolation and Confocal Microscope Inspection

Nuclei were prepared from fixed leaves of B. juncea using the method described by Saxena, Fowke and King (1985). Briefly, leaves were chopped using a razor blade in a nuclei isolation buffer (NIB) (10 mM MES‐KOH, pH 5.5, 0.2 M sucrose, 2.5 mM EDTA, 2.5 mM dithiothreitol, 0.1 mM spermine, 10 mM NaCl, 10 mM KCl, 0.15% Tritron X‐100). The homogenate was gently stirred for 45 min at 4°C and filtered through 100 μm nylon mesh followed by 30 μm nylon mesh. The filtered extract was centrifuged for 8 min at 2000 rpm at 4°C. The pellet was gently washed to remove the upper chloroplast layer, and nuclei pellets were recovered and washed twice with NIB buffer, fixed in ethanol–acetic acid (3:1 v/v) and stored at −20°C until further use. Nuclei were stained for 10 min with 10 μg/mL diamidino‐phenyl‐indole (DAPI), washed twice with 2× SSC and mounted in Vectashield (Vector Laboratories, Burlingame, CA, USA). Nuclei size measurements were carried out using a confocal microscope (Zeiss LSM 900), and the data were processed using Excel software (Microsoft, Redmond, WA, USA).

2.3. Proteome Analysis

Proteome analysis was performed on samples collected from plants exposed to dark or FML at 3NE, 7DAE and 15DAE. Each sample (50 mg) was extracted with 100 µL of NETN buffer (100 mM NaCl, 1 mM EDTA, 20 mM Tris, pH 8.0, and 0.5% NP‐40) on ice, centrifuged at 4°C at high speed for 10 min and 50 µL of supernatants were collected and stored at –20°C until used for comparative, quantitative proteome analysis. The analysis was performed by the proteomic services of the Smoler Protein Research Center at the Technion, Haifa, Israel using LC‐MS/MS on LTQ Orbitrap (ThermoFisher Scientific, Waltham, MA, USA; https://proteomics.net.technion.ac.il/proteomic-services). Protein identification and quantification were done by using MaxQuant, using Arabidopsis thaliana proteins from uniport as a reference. Quantification and normalization were performed using the Label‐free quantification (LFQ) method. Proteins marked as “only identified by site” were filtered out. In an additional step, proteins having at least two peptides and expressed in at least two replicates of at least one treatment group were retained. A protein was considered differentially expressed if it had a nominal p‐value < 0.05 and absolute fold change > 1.5. PCA and Student's t‐tests were performed using Metaboanalyst 6.0 (Xia and Wishart 2016). Venn diagram was generated by Venny 2.1 (Oliveros 2007‐2015).

2.4. Metabolome Analysis

Metabolic analysis was performed on samples collected from plants exposed to dark or FML at 3NE, 7DAE and 15DAE. Gas chromatography‐mass spectrometry (GC‐MS) was used to quantify primary metabolites in five separate replicates, essentially as described (Lisec et al. 2006). Briefly, lyophilized leaf samples were extracted with a precooled mix containing methanol, chloroform, and MiliQ H₂O (2.5:1:1 v/v/v, respectively) supplemented with sorbitol and Ribitol as the internal standard (4.5 µg/mL), vortexed and incubated for 10 min at 25°C on an orbital shaker. Samples were sonicated for 10 min in an ultra‐sonication bath at room temperature followed by centrifugation at high speed (10 min, 16,000 × g). The supernatant was collected, 300 µL MiliQ H₂O and 300 µL of chloroform were vortexed for 10 s, and centrifuged for 5 min at high speed. The upper phase was collected, lyophilized, and subjected to derivatization. Derivatization was performed by adding 40 µL of methoxyaminhydrochloride (20 mg/mL in Pyridine) to the lyophilized samples followed by incubation for 2 h at 37°C on an orbital shaker. Later 70 µL of MSTFA and 7 µL of Alkane mix were added and incubated for 30 min at 37°C with constant shaking. Samples were subjected to GC‐MS analysis (Agilent Ltd. Santa Clara, CA, USA) as described (Lisec et al. 2006; Reshef Fait and Agam 2019). Separation was carried out on a Thermo Scientific DSQ II GC/MS using a Factor Four capillary VF‐5ms column (Agilent Ltd. Santa Clara, CA, USA). The acquired chromatograms and mass spectra were evaluated using Xcalibur (version 2.0.7) software and metabolites were identified and annotated using the Mass Spectral and Retention Time Index libraries available from the Max‐Planck Institute for Plant Physiology, Golm, Germany (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/msri/gmd_msri.html, accessed on May 11, 2021). A metabolite was considered differentially present if it had an unadjusted p‐value < 0.05 and fold change > 1.5. The metabolite level was calculated by normalizing the intensity of the peak of each metabolite to the sorbitol standard and initial weight of samples. PCA and Student's t‐tests were performed using Metaboanalyst 6.0 (Xia and Wishart 2016). Venn diagram was generated by Venny 2.1 (Oliveros 2007‐2015).

3. Results

3.1. Exposure to FML Induces an Increase in Nuclear Size

B. juncea seedlings were subjected to FML and dark conditions for three consecutive nights (3NE), 5 h each (Figure 1A) and the effect of FML on genome organization was analyzed. Accordingly, nuclei isolated from leaves of B. juncea exposed to FML or dark at 3NE were stained with DAPI, inspected under a confocal microscope (Figure 1B) and nuclear diameter was measured (n = 100; Figure 1C). Results showed a notable increase in the nuclear size of FML‐treated plants, as compared to dark plants. Thus, the average nuclear diameter for dark and FML‐treated plants was 20.49 and 24.23 µm, respectively. The ~1.18‐fold increase in nuclear diameter under FML compared to dark treatment accounts for ~1.65‐fold increase in the nuclear volume (considering the nucleus as a sphere, V = 4/3πr³).

3.2. Long Term FML Impact on B. Juncea Cellular Activities: Proteome Analysis

Proteome analysis was performed on B. juncea seedlings subjected to FML and dark conditions for 3 consecutive nights, 5 h each and samples were collected on the third night (3NE). Seedlings were then placed in a growth room and sampled 7 and 15 days after exposure (7DAE and 15DAE, respectively, Figure 1A). Proteins were extracted from leaf samples and subjected to proteome analysis and identified by Discoverer software against the Arabidopsis thaliana from Uniprot databases. This analysis identified a total of 1852 proteins (Supporting Information S2: Table 1) and after filtering for proteins identified by at least two peptides and are expressed in at least two replicates of at least one treatment group, 1407 proteins were documented as expressed proteins (Supporting Information S2: Table 2). Comparative analysis was performed for proteins expressed under FML and dark treatments. To gain insights into the overall proteomic landscape, principal component analysis (PCA) was performed, which provided a comprehensive overview of the protein profiles across different samples. Thus, the PCA score plot (Figure 2A) revealed distinct patterns, with PC1 separating samples mainly according to developmental stages, while PC2 separating samples essentially according to treatments.

Figure 2.

The impact of FML exposure on protein expression. (A) PCA score plot demonstrating clustering of proteins according to developmental stages and treatments. Venn Diagram of upregulated (B) and downregulated (C) proteins under FML versus dark at the indicated developmental stages. Venn diagram was performed with Venny 2.1 software. [Color figure can be viewed at wileyonlinelibrary.com]

To visualize the differentially expressed proteins (DEPs) across all groups, Venn diagrams were constructed to illustrate both upregulated (Table 1) and downregulated proteins (Supporting Information S2: Tables 3–5) under FML compared to dark condition (Figure 2B,C). No DEP is shared by plants sampled at the three developmental stages. Two upregulated proteins are shared between 3NE and 15DAE, namely Peroxidase 42 (related to At4g21960 gene product) and the 26S proteasome regulatory subunit RPN13 (related to At2g26590 gene product) (Table 1), while one downregulated protein was shared between 3NE and 15DAE, namely Phosphoethanolamine N‐methyltransferase 2 (Supporting Information S2: Tables 3 and 5). Notably, no DEP was shared between 7DAE and 3NE or between 7DAE and 15DAE. This lack of overlap suggests that there is no proteomic memory induced by FML that persists in B. juncea 1 week or 2 weeks after exposure.

Table 1.

List of differentially expressed proteins upregulated in FML‐treated plants when compared to Dark in Brassica juncea at 3NE, 7DAE, and 15DAE (AiD, absent in the dark; FC > 1.5, p‐value < 0.05). Cellular location based on UniProt. Cyto, cytosol; Nuc, nucleus; Plasmo, plasmodesmata; Chl, chloroplast; Mito, mitochondria; Perox, peroxisome; Gol, Golgi apparatus; CW, cell wall; Mem, membrane; ER, endoplasmic reticulum; Vac, vacuole; Sec, secreted; Apo, apoplast.

At Gene ID	Protein ID	Protein name	Gene name	Location	FC FML/Dark
3NE
AT5G42970	Q8L5U0	COP9 signalosome complex subunit 4	CSN4	Cyto, Nuc	AiD
AT4G21960	Q9SB81	Peroxidase 42	PER42	Sec	AiD
AT1G63610	Q9CAC8	At1g63610	F24D7.19		AiD
AT2G27720	P51407	Large ribosomal subunit protein P2z	RPP2A	Cyto	1.8
AT1G07140	Q9LMK7	Ran‐binding protein 1 homolog a	RANBP1A	Nuc	1.72
AT2G26590	O48726	26S proteasome subunit RPN13	RPN13	Cyto, Nuc	1.72
AT5G24020	Q9MBA2	Septum site‐determining protein minD	MIND1	Chl	1.66
AT5G42890	Q9FMN0	Sterol carrier protein 2	SCP2	Perox	1.63
AT1G10522	Q9XIK0	Protein PLASTID REDOX INSENSITIVE 2	PRIN2	Chl	1.61
AT3G23450	Q9LW52	Transmembrane protein	KRATOS	Sec	1.6
AT3G10090	Q9SR73	Small ribosomal subunit protein eS28z/eS28y	RPS28B	Cyto	1.56
AT1G31160	Q8GYJ9	Histidine triad nucleotide‐binding 2	HINT	Chl	1.53
AT4G13670	A1A6M1	Protein disulfide isomerase pTAC5	PTAC5	Chl	1.52
AT3G54900	Q84Y95	Monothiol glutaredoxin‐S14	GRXS14	Chl	1.5
7DAE
AT2G05620	Q9SL05	Protein proton gradient regulation 5	PGR5	Chl	AiD
AT5G08080	Q8VZU2	Syntaxin‐132	SYP132	Mem	AiD
AT5G48930	Q9FI78	Shikimate O‐hydroxycinnamoyltransferase	HST	Cyto, Mem	AiD
AT1G20200	Q9LNU4	26S proteasome non‐ATPase regulatory subunit 3 homolog A	RPN3A	Cyto, Nuc	AiD
AT5G13110	Q9FY99	Gluc‐6‐phosphate1‐dehydrogenase 2	G6PD2	Chl	2.25
ATCG00340	P56767	Photosystem I P700 chlorophyll a apoprotein A2	psaB	Chl	2.17
ATCG00350	P56766	Photosystem I P700 chlorophyll a apoprotein A1	psaA	Chl	1.96
AT1G66580	Q93W22	Large ribosomal subunit protein uL16x	RPL10C	Cyto	1.9
AT5G12860	Q9LXV3	Dicarboxylate transporter 1	DIT1	Chl	1.89
ATCG00270	P56761	Photosystem II D2 protein	psbD	Chl	1.88
AT5G22620	Q9FNJ9	Probable 2‐carboxy‐d‐arabinitol‐1‐phosphata	At5g22620	Chl	1.85
AT1G62020	Q94 A40	Coatomer subunit alpha‐1	At1g62020	Cyto, Golg	1.78
ATCG00380	P56799	Small ribosomal subunit protein uS4c	rps4	Chl	1.74
AT4G39080	Q8W4S4	V‐type proton ATPase subunit a3	VHA‐a3	Vac	1.63
AT2G20580	Q9SIV2	26S proteasome non‐ATPase regulatory subunit 2 homolog A	RPN1A	Cyto, Nuc	1.62
AT3G08580	P31167	ADP, ATP carrier protein 1	AAC1	Mito	1.6
AT1G37130	P11035	Nitrate reductase [NADH] 2	NIA2	Nuc, Mito	1.6
AT5G66760	O82663	Succinate dehydrogenase subunit 1	SDH1‐1	Mito	1.59
AT2G05070	Q9S7J7	Chlorophyll a‐b binding protein 2.2	LHCB2.2	Chl	1.58
ATCG00020	P83755	Photosystem II protein D1	psbA	Chl	1.53
AT1G15690	P31414	Pyrophosphate‐energized vacuolar membrane proton pump 1	AVP1	Vac	1.52
AT3G62290	Q9M1P5	ADP‐ribosylation factor	ARFA1E	Gol	1.51
AT3G11130	Q0WNJ6	Clathrin heavy chain 1	CHC1	Mem	1.51
15DAE
AT3G20370	Q9LTQ5	TRAF/MATH protein	At3g20370	Plasmo	AiD
AT1G09620	F4I116	Leucine‐‐tRNA ligase	At1g09620	Cyto	AiD
AT2G26590	O48726	26S proteasome subunit RPN13	RPN13	Cyto, Nuc	AiD
AT2G30390	O04921	Ferrochelatase‐2	FC2	Chl	AiD
AT2G22240	Q38862	Inositol‐3‐phosphate synthase isozym 2	IPS2	Cyto	3.58
AT5G01600	Q39101	Ferritin‐1	FER1	Chl	3.55
AT5G26780	Q94C74	Serine hydroxymethyltransferase 2	SHMT2	Mito	3.34
AT4G21960	Q9SB81	Peroxidase 42	PER42	Sec	3.01
AT5G28020	Q9SXS7	Cysteine synthase D2	CYSD2	Cyto	2.5
AT3G02730	Q9XFH8	Thioredoxin F1	At3g02730	Chl	2.5
AT5G23440	Q9FHL4	Ferredoxin‐thioredoxin reductase A1	FTRA1	Chl	2.44
AT5G10170	Q9LX12	inositol 3‐phosphate synthase isozym 3	IPS3	Cyto	2.09
AT2G04700	Q9SJ89	Ferredoxin‐thioredoxin reductase	FTRC	Chl	2.06
AT1G74880	Q9S829	NAD(P)H‐quinone oxidoreductase O	ndhO	Chl	1.81
AT4G14880	P47998	Cysteine synthase 1	OASA1	Cyto	1.71
AT4G30270	P24806	Xyloglucan endotransglucosylase	XTH24	CW	1.66
AT5G60360	Q8H166	Thiol protease aleurain	ALEU	Vac	1.65
AT2G25080	P52032	Phospholipid hydroperoxide glutathione peroxidase 1	GPX1	Chl	1.63
AT5G66570	P23321	Oxygen‐evolving enhancer protein 1‐1	PSBO1	Chl	1.6
AT3G59760	Q43725	Cysteine synthase	OASC	Mito	1.57
AT2G30870	P42761	Glutathione S‐transferase F10	GSTF10	Apo	1.5
AT5G04590	Q9LZ66	Assimilatory sulfite reductase	SIR	Apo	1.5

A total of 24 DEPs (FC > < 1.5, p < 0.05) were found in 3NE with 14 and 10 proteins upregulated and downregulated, respectively under FML compared to dark condition (Supporting Information S2: Table 3). Samples derived from 7DAE exhibited 36 DEPs including 23 upregulated and 13 downregulated proteins (Supporting Information S2: Table 4), while 15DAE showed 47 DEPs with 22 and 25 upregulated and downregulated proteins, respectively, under FML compared to dark (Supporting Information S2: Table 5). Thus, the persistence of DEPs between dark‐ and FML‐treated plants even 1 week and 2 weeks after exposure suggests a long‐term effect of FML on protein profiles of B. juncea plants.

PCA analysis of each treatment, dark or FML, across all groups revealed clustering according to developmental stages with the most apparent separation between groups under FML treatment (Figure 3A). Comparing upregulated proteins between the groups under dark and FML treatment showed (Figure 3B) that in all comparisons the developmental stage has a prominent effect on protein expression with FML treatment significantly increasing the number of DEPs. Accordingly, 15DAE FML‐treated plants displayed 319 and 252 DEPs compared to 3NE and 7DAE FML‐treated plants, respectively (FC > < 1.5; p < 0.05), while 15DAE dark‐treated plants showed 175 and 50 DEPs compared to 3NE and 7DAE, respectively (Supporting Information S2: Tables 6–11).

Figure 3.

PCA score plots demonstrating variance among developmental stages and treatments. (A) PCA score plot demonstrating the variance between developmental stages under Dark (left panel) and FML (right panel) treatments. (B) Bar graph demonstrating the number of DEPs between various developmental stages in the dark and FML‐treated plants. (C, D) Protein class categorization (by Panther bioinformatics, Thomas et al. 2022). (C) FML versus dark comparison of differentially expressed metabolite interconversion enzymes (MIEs) up or downregulated at the indicated developmental stages. (D) Pairwise comparison between developmental stages of the number of MIEs up or downregulated in the dark‐ and FML‐treated plants. [Color figure can be viewed at wileyonlinelibrary.com]

Categorization for protein class identified a major class whose composition was affected by the treatments in conjunction with developmental stages, namely metabolite interconversion enzymes (MIEs). Thus, a comparison of MIEs between treatments, FML versus Dark at 3NE, 7DAE, and 15DAE revealed an interesting picture where, in all developmental stages, the number of up and down‐regulated MIEs is almost equal suggesting that a switch in MIEs composition is induced following exposure to FML (Figure 3C). Further comparison of MIEs between developmental stages in the dark‐ or FML‐treated plants showed that FML‐treated plants exhibit a high accumulation of MIEs in 15DAE as compared to 3NE and 7DAE; high expression of MIEs is also seen in the dark‐treated plants in 15DAE as compared to 3NE (Figure 3D). Thus, developmental stages have a notable effect on the level and composition of MIEs, which is intensified following exposure to FML.

We examined the effect of FML on the abundance of circadian proteins. We have selected all proteins in the proteome data identified by GO slim bioinformatic platform as related to circadian rhythm under the biological process category. The proteomic analysis of B. juncea revealed no significant alterations in all circadian‐related proteins (such as RBG7/AtGRP7, RBG8/AtGRP8, UVR8, PHOT1, PHOT2) between plants subjected to dark conditions and those treated with FML immediately after exposure (3NE) (Supporting Information S1: Figure 3). At 7DAE and 15DAE most of the circadian‐related proteins showed no significant variation in abundance between dark and FML except for serine hydroxymethyltransferase 1 (SHM1) whose level was slightly decreased (FC FML/dark = 0.79; p = 0.0095) at 7DAE, and slightly increased (FC FML/dark = 1.24; p = 0.0051) at 15DAE (Supporting Information S1: Figure 3). Similarly, the Chloroplast stem‐loop binding protein of 41 kDa A (CSP41A) is slightly decreased (FC FML/dark = 0.85; p = 0.0029) at 7DAE, while CSP41B is slightly increased (FC FML/dark = 1.26; p = 0.0054) at 15DAE. Although the role of circadian proteins in regulating gene expression cannot be excluded, the observation that their relative abundance remains largely unchanged immediately after exposure to FML (3NE) indicates that these proteins may not be the main contributors to the alterations in gene expression. Instead, it is plausible that changes observed in protein and metabolite profiles are regulated, at least partly, by epigenetic mechanism(s) that extensively modify genome organization and chromatin structure and function following exposure to FML.

3.3. Long‐Term Lunar Impact on B. juncea Cellular Activities: Metabolic Analysis

Samples used for proteome analysis were also used for metabolic analysis. Accordingly, samples derived from 3NE, 7DAE, and 15DAE plants were subjected to GCMS. The original signal data listed a total of 130 metabolites, which were normalized against sorbitol and the initial weight of the samples (Supporting Information S2: Tables 12 and 13). PCA score plot (Figure 4A) provided a valuable overview of the differences in metabolic profiles between samples. The PC1 representing 46.4% of the variance separated samples mainly according to developmental stages, while PC2 (20.7%) separated samples according to treatments. PCA analysis of each treatment across development, namely dark versus FML revealed apparent clustering according to developmental stages (Figure 4B).

Figure 4.

FML impact on biosynthesis of metabolites. (A) PCA score plot demonstrating clustering of metabolites according to developmental stages for the indicated treatments. (B) PCA score plot demonstrating the variance in metabolites between developmental stages under Dark (left panel) and FML (right panel) treatments. Venn Diagrams are shown for upregulated (C) and downregulated (D) metabolites under FML versus dark at the indicated developmental stages. [Color figure can be viewed at wileyonlinelibrary.com]

A total of 13 DEMs (FC > < 1.5; p < 0.05) were found in 3NE with 10 and 3 metabolites upregulated and downregulated, respectively, under FML compared to dark conditions (Table 2). Among upregulated metabolites are five amino acids including proline, often up accumulated in response to various abiotic stresses and glutamine. Samples derived from 7DAE exhibited six DEMs including five Upregulated and one downregulated metabolite, while 15DAE showed 43 DEMs with 32 and 11 upregulated and downregulated metabolites, respectively, under FML compared to dark. Thus, the increase in DEMs 2 weeks after exposure suggests a long‐lasting effect of FML on cellular activities in B. juncea plants.

Table 2.

Lists of DEMs between FML and dark‐treated B. juncea plants at 3NE, 7DAE, and 15DAE (FC > < 1.5; p < 0.05).

3NE
Metabolites	FC FML/DARK	p value	UP/DOWN
Amino acids
Glutamine	8.8922	5.E‐03	UP
Glutamic acid	4.6408	9.E‐03	UP
Proline	2.5039	5.E‐03	UP
Aspartic acid	1.7407	3.E‐02	UP
Serine	1.7067	6.E‐03	UP
Sugar
Lactose	–1.8053	1.E‐02	DOWN
Organic acids
Phosphoric acid monomethyl ester	1.7559	1.E‐02	UP
2‐Imidazolidone‐4‐carboxylic acid	1.5199	3.E‐02	UP
Others
Putrescine	3.0143	2.E‐02	UP
Resveratrol	1.8218	5.E‐02	UP
Sarcosine	2.1664	1.E‐02	UP
Galactonic acid	−1.5865	3.E‐02	DOWN
Secologanin	−2.8488	2.E‐03	DOWN
7DAE
Metabolites	FC FML/DARK	p value	UP/DOWN
Sugars
Raffinose	1.9341	1.E‐04	UP
Melezitose	1.6897	3.E‐03	UP
Lactose_1_1MeOX	−4.738	2.E‐02	DOWN
Organic acids
Lactic acid	2.5031	2E‐05	UP
Citric acid	2.0891	3E‐04	UP
Malic acid	1.8986	3E‐04	UP
15DAE
Metabolites	FC FML/DARK	p value	UP/DOWN
Amino acids
Valine	2.1263	2E‐05	UP
Isoleucine	2.1121	7E‐04	UP
Leucine	2.107	8E‐05	UP
Tyrosine	2.0994	1E‐02	UP
Glutamine	−3.3017	3E‐02	DOWN
Serine	−2.2744	2E‐02	DOWN
Sugars
Fructose	2.2778	8.E‐06	UP
Mannitol	2.335	3.E‐06	UP
Galactose	2.3441	3.E‐06	UP
Glucopyranose, D	15.611	5.E‐04	UP
Glucose	2.3	1.E‐04	UP
Melezitose	3.0128	1.E‐05	UP
N,N'‐Diacetylchitobiose	2.2696	1.E‐03	UP
Raffinose	3.0496	4.E‐05	UP
Sorbose	2.2862	8.E‐06	UP
Sucrose	1.9198	3.E‐03	UP
Arabitol	−1.9019	7.E‐03	DOWN
Fructose‐6‐phosphate	−3.7593	2.E‐04	DOWN
Glucose‐6‐phosphate	−3.9365	9.E‐05	DOWN
Mannose‐6‐phosphate	−3.8478	8.E‐05	DOWN
Ribose	−1.6956	2.E‐03	DOWN
Organic acids
Benzoic acid, 2,3‐dihydroxy	1.8301	4E‐03	UP
Citric acid	2.8021	2E‐06	UP
Dehydroascorbic acid	1.6331	5E‐04	UP
Galactonic acid‐1,4‐lactone	2.4564	3E‐05	UP
Gluconic acid‐1,5‐lactone	2.298	1E‐04	UP
Glutaric acid, 2‐oxo	3.6327	3E‐05	UP
Glyceric acid	2.2418	1E‐06	UP
Glyoxylic acid	1.6608	4E‐03	UP
Ascorbic acid	2.7543	4.E‐04	UP
Lactic acid	1.6548	5E‐02	UP
Malic acid	2.5681	5E‐05	UP
Quinic acid, 5‐caffeoyl	1.6942	1E‐02	UP
Lyxonic acid‐1,4‐lactone	−2.1908	6E‐06	DOWN
Phosphoric acid monomethyl ester	−3.0826	1E‐03	DOWN
Others
Diethylenglycol	2.2279	8.E‐03	UP
Erythritol	2.2128	4.E‐02	UP
Glucopyranoside, 1‐O‐methyl‐, beta‐D‐	1.7295	3.E‐02	UP
Lumichrome	2.3277	3.E‐03	UP
Uracil	1.8349	6.E‐05	UP
Norleucine	2.1329	6E‐04	UP
Uridine	–1.6405	4.E‐03	DOWN
Tryptamine	–2.2338	5E‐04	DOWN

To visualize the DEMs across all groups, Venn diagrams were constructed to illustrate both upregulated and downregulated proteins under FML versus dark conditions (Figure 4C,D). Five upregulated metabolites were found to be shared between 7DAE and 15DAE and none with 3NE. These metabolites include raffinose, melezitose, citric acid, malic acid and lactic acid. Notably, raffinose has been implicated in plant drought tolerance (Li et al. 2020) while citric acid and malic acid are intermediate products of the TCA cycle, and their exogenous application can improve crop plant growth and yield particularly under various abiotic stress conditions (Tahjib‐Ul‐Arif et al. 2021). This lack of overlap suggests that there is no metabolic memory induced by FML that persists in B. juncea 1 week or 2 weeks after exposure.

4. Discussion

Rhythmic exposure to moonlight is believed to affect the life cycle of plants, from seed germination to fruit maturation (reviewed in Zürcher 2011; Sivasankar and Thimmaiah 2021). This is demonstrated by lunar farming, which is still practiced in certain places around the world where farmers are using the lunar cycle to organize their agricultural activities. Although lunar farming has been regarded as a myth having no scientific support (Mayoral et al. 2020), the results presented here provide further evidence that plants do respond to FML and significantly change their developmental program demonstrated by significant changes in cellular activities. Indeed, previous and current investigations revealed significant enhancement in all measured growth parameters of B. juncea seedlings following exposure to FML for three consecutive nights (Singiri et al. 2023; Supporting Information S1: Figure 4). This observation aligns with previous research demonstrating the positive effects of moonlight on plant growth, germination, and harvest outcomes (Kolisko 1936; Semmens 1923). These findings support the notion that lunar phases at sowing time can substantially influence various aspects of plant development, encompassing germination, vegetative growth, and flowering (Zürcher 2011), and call for meticulous research on this overlooked conspicuous environmental factor, the moon, and its effect on plant biology in agroecosystems (Sivasankar and Thimmaiah 2021).

The variations observed in the proteome and metabolome of B. juncea indicate that FML is perceived by plants and promotes a major response, which is demonstrated by variations in protein and metabolite profiles. The increase in free amino acids such as glutamine, proline, and glutamic acid immediately after exposure (3NE) has been reported extensively as a notable response of plants subjected to multiple biotic and abiotic stresses (Batista‐Silva et al. 2019), and the accumulation of free amino acids has been implicated in increasing tolerance to adverse environmental conditions, such as drought, heat, and salinity (Rai 2002). Apart from their fundamental role as the building blocks for protein synthesis, amino acids also serve as precursors for the synthesis of specialized metabolites such as polyamines (e.g., putrescine) from arginine (Janowitz, Kneifel, and Piotrowski 2003). Particularly, proline, a well‐known amino acid, that is accumulated under abiotic stresses, contributes to enhanced plant tolerance. It serves multiple roles, acting as an osmolyte, scavenging reactive oxygen species (ROS), and functioning as a molecular chaperone (Ghosh et al. 2022). We observed an increase in the polyamine putrescine soon after exposure to FML (3NE) but not thereafter (7DAE, 15DAE). Polyamines play crucial roles in plant growth and development, and adaptation to environmental stresses; in general, improved plant growth and metabolism have been often related to increased polyamine production and higher polyamine content (reviewed in González‐Hernández et al. 2022). Our findings align with a previous study by Singiri et al. (2023), which showed changes in metabolite profile, including an increase in multiple amino acids (e.g., proline), sugars (e.g., raffinose), and putrescine in tobacco plants exposed to FML. Notably, multiple stress‐related metabolites were elevated immediately following exposure to FML (3NE), yet the finding that B. juncea seedlings exposed to FML performed better and showed enhancement of growth (Singiri et al. 2023; Supporting Information S1: Figure 3), suggests that the observed increase in stress metabolites may not reflect a genuine “stress” response but rather a developmental switch eventually leading to enhancement of growth and development.

Variations between dark and FML‐treated plants persisted at 7 and 15 DAE where plants display upregulation of the TCA cycle intermediates, namely, malic acid and citric acid, and multiple sugars particularly at 15DAE, such as glucose and sucrose. These metabolites might reflect intensive metabolic activities that could enhance growth. Indeed, sucrose and glucose serve as signals for high carbon availability that stimulate plant growth and development (Lastdrager, Hanson, and Smeekens 2014; Wingler and Henriques 2022). Other metabolites upregulated in FML‐treated plants at 15DAE include the sugar alcohol mannitol, often used as a photoassimilate and phloem translocated carbohydrate in certain plants. Mannitol is synthesized in higher plants from mannose‐6‐phosphate through the activity of a NADPH‐mannose‐6‐phosphate reductase (Loescher et al. 1992) resulting in production of NADP⁺ that functions as the final electron acceptor of the photosynthetic electron transport chain (Goss and Hanke 2014). Consistent with upregulation of mannitol we observed downregulation of its precursor mannose‐6‐phosphate in FML‐treated plants at 15DAE (Table 2). Mannitol is among the most abundant molecules for energy and carbon storage found in various organisms including algae and plants. Notably, mannitol‐producing plants exhibit a high degree of salt tolerance due to their activity as a compatible solute (Stoop, Williamson, and MASONPHARR 1996). Interestingly, FML‐treated plants at 15DAE display elevated levels of the branched‐chain amino acids (BCAAs) valine, leucine, and isoleucine, essential amino acids that are not synthesized in animals. In animals, catabolism of BCAAs may yield NADH and FADH2 which can be utilized for ATP production (Ye et al. 2020). In plants, BCAAs are increased under conditions of energy starvation, such as darkness and drought (Law et al. 2018; Shim et al. 2023); under such conditions, catabolism of BCAA appears to be important for maintaining minimal ATP production (Law et al. 2018; Izumi and Ishida 2019). Taken together, the elevated levels of sugars and amino acids observed at 15DAE may contribute to greater energy availability, which could be utilized to promote plant growth and development.

The proteomic analysis demonstrated a conspicuous response of B. juncea to FML. Most intriguing is the changes in MIE composition at all developmental stages following exposure to FML. Thus, the number of MIEs upregulated and downregulated following exposure to FML is almost equal at all developmental stages, but the composition of MIEs has been altered. This is probably due to a developmental switch triggered by the moonlight demonstrated by increasing nuclei size accompanying epigenetic modifications (Singiri et al. 2023), often seen in stress‐induced developmental reprogramming (Zhao et al. 2001; Tessadori et al. 2007; Damri et al. 2009; Florentin, Damri, and Grafi 2013; Ichihashi et al. 2020). This leads to changes in protein and metabolite profiles and consequently to enhanced growth (Supporting Information S1: Figure 3). Proteins like COP9 signalosome subunit 4 (CSN4), known for its role in auxin signaling and root development (Pacurar et al. 2017) significantly upregulated under FML treatment at 3NE but not later at 7 and 15 DAE. At 7DAE proteins involved in auxin homeostasis (Shikimate O‐hydroxycinnamoyltransferase) or glucose metabolic process (glucose‐6‐phosphate 1‐dehydrogenase 2) were upregulated while in 15DAE proteins involved in metabolite modification, photosynthesis and development were upregulated. Notably, TRAF/MATH (Tumor Necrosis Factor Receptor‐Associated Factor) protein (related to Arabidopsis AtTRAF29) upregulated in FML‐treated plants only at 15DAE, belongs to a large family of proteins (encoded by 77 genes in Arabidopsis) implicated in regulating plant development and stress responses (Qi et al. 2022); though the specific function of many TRAF/MATH proteins is presently unknown. As with metabolites, proteins are mainly separated according to developmental stages and their composition and levels at each stage are affected by the treatment. The hypothesis that changes in cellular properties between dark and FML‐treated plants will be most prevalent immediately after exposure, at 3NE, and diminished thereafter is thus rejected as changes between treatments persisted and even intensified at later stages of development. This suggests that short‐term exposure to FML triggers developmental reprogramming that underly long‐term effects on plant cell activity and growth. Long‐term effects have been reported for abscisic acid (ABA) in which its spray on grape berries during the pre‐véraison stage had a long‐term impact on gene expression and metabolic profile (Villalobos‐González et al. 2016). Similarly to the effect of FML, the application of ABA in Arabidopsis induces extensive chromatin remodeling in various cell types including guard cells, root and mesophyll cells (Seller and Schroeder 2023).

In conclusion, our research significantly contributes to the understanding of moonlight influence on the crop plant B. juncea and provides further scientific support for lunar farming. By demonstrating the long‐term alterations in protein and metabolite profiles, we provide insights into the plant's adaptive responses and the underlying signalling mechanisms triggered by FML exposure. These findings pave the way for further investigations into the specific signaling pathways and regulatory networks modulated by FML, ultimately leading to a better understanding of this intriguing lunar phenomenon and its impact on plant biology.

A proposed model (Figure 5) explaining the influence of the FML on plant growth is centered on a FML‐induced developmental switch. Plants perceive FML signals via photoreceptors (e.g., phototropins). The absorbed light is interpreted as a “stress‐like” signal that activates a developmental switch, which promotes chromatin reorganization and an altered gene expression program. These changes lead to long‐lasting alterations in plant cell activities, and in protein and metabolite profiles resulting in accumulation of sugars, amino acids, TCA cycle intermediates and other metabolites leading to increase in energy availability. Consequently, the effect on plant growth and development is dependent on the actual environmental condition sensed by the plant. Accordingly, if the plant senses stress the increased availability of resources will be used for growth compensation and recovery from the stress, but in the case where stress is not apparent, the abundant building blocks and readily available energy sources will be channeled for growth enhancement. The plant appears to have repurposed the signaling pathways triggered by FML to optimize growth and development, taking advantage of the abundant resources for direct growth enhancement in the absence of genuine stress. This model highlights the potential role of FML as a signal/cue, warranting further investigation into the specific signaling pathways and regulatory networks that activate the developmental switch for a deeper understanding of moonlight's influence on plant biology.

Figure 5.

A proposed model to explain the long‐term impacts of short‐term exposure to full moonlight (FML) on plants. The moonlight is perceived by plants via photoreceptors and acts as an environmental signal triggering a ‘stress‐like’ developmental switch (DS) in plants. Turning on DS promotes chromatin reorganization and reprograming in gene expression, leading to long‐lasting alterations in plant cell activities, adjusted protein profiles, and modified metabolite levels bringing about accumulation of sugars, amino acids, TCA cycle intermediates and increase in energy availability. The outcome is dependent on the actual environment senses by the plant. Accordingly, if the plant senses stress (e.g., heat, salt, etc.) then energy/growth resources generated following DS will be channeled to compensate/catch‐up growth but in the case of “no stress” (e.g., FML) they will be channeled for enhancement of growth. [Color figure can be viewed at wileyonlinelibrary.com]

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting information.

Supporting information.

Data Availability Statement

The data that supports the findings of this study are available in the supplementary material of this article.