Skip to main content
NCBI home page
BMC Plant Biology logoLink to BMC Plant Biology
. 2025 Sep 30;25:1236. doi: 10.1186/s12870-025-07179-1

Integrated of metabolome and transcriptome analysis reveals the effect of artificial light at night on Platanus orientalis L.

Zhiyuan Lv 1,2, Yue Hu 1,2, Hanyu Zhou 1,2, Chong Hu 1,2, Rong Liang 3, Lijiao Ai 1,2,✉, Lichao Tian 1,2,✉
PMCID: PMC12487231  PMID: 41029479

Abstract

Background

Artificial light at night (ALAN) and its associated biological impacts have regularly been characterized as predominantly urban issues. ALAN delays the senescence of plane tree leaves in cities, resulting in the longer retention of green leaves. Some studies have described the effects of ALAN on plane trees at the physiological and biochemical levels. However, there is a lack of research at the molecular level.

Results

In this study, we performed metabolic and transcriptomic analyses on plane green leaves with and without streetlight, as well as natural yellowing leaves away from the streetlight. In green leaves exposed to streetlights, numerous genes associated with auxin and cytokinins that inhibit leaf senescence were activated, while most genes associated with salicylic acid, jasmonic acid, ethylene and brassinolide that promote senescence were suppressed. Some candidate senescence-associated genes (SAGs) were identified, and brassinolide (6alpha-hydroxy-castasterone and castasterone), jasmonic acid ((-)-jasmonic acid and methyl jasmonate) and most flavonoids were significantly enriched in yellowing leaves. Combined analyses of transcriptome and metabolome data revealed that compared with the two green leaf groups, biosynthesis of amino acid, phenylpropanoid biosynthesis, tryptophan metabolism and flavonoid biosynthesis were the most significantly enriched metabolic pathway in yellowing leaves. Especially for phenylpropanoid biosynthesis and flavonoid biosynthesis related to leaf yellowing pigment accumulation, most of DEGs and DAMs are upregulated in yellowing leaves, but downregulated in green leaves under streetlight.

Conclusion

Our results indicate that ALAN retarded plant leaf senescence mainly by altering plant hormone levels and inhibiting metabolic pathways such as flavonoid biosynthesis, betalain biosynthesis, caffeine metabolism and phenylpropanoid biosynthesis. This study provides insights into how streetlights affect plane trees.

Supplementary information

The online version contains supplementary material available at 10.1186/s12870-025-07179-1.

Keywords: ALAN, Light pollution, Plant hormones, Leaf senescence, Urban ecology

Introduction

Organisms on earth have evolved an internal endogenous oscillator called a circadian clock that enables their development and physiology to align internal biological processes with daily rhythms [1, 2]. However, the sharp increase in light pollution caused by artificial light at night (ALAN) has altered the natural light environment at night in large areas of the earth over the last century [3]. The intensity of artificial light is still rapidly increasing at a rate of approximately 6% per year [4, 5]. ALAN has been regarded as a significant anthropogenic environmental pressure [6–8]. The extensive use of ALAN interferes with the regulation of the circadian rhythm by natural light and affects the physiological processes of many organisms [9]. ALAN interferes with the nocturnal migration of birds; the commuting behavior of bats; the sea-finding behavior of sea turtles; and the abundance, richness and pollination of insects [10–14]. In recent years, an increasing number of studies have shown that ALAN effects plant physiology, phenology, growth morphology, and resource allocation. Street lighting alters the flowering, growth and yield of ornamental plants and crops [15, 16], and causes bud bursts in deciduous trees [17]. ALAN can also influence plant growth and resource allocation by inducing photosynthetic responses, thereby altering the plant community structure [18, 19]. In a plant community, common plant species benefit more from ALAN than rare plants, and the positive response of alien species to ALAN is often stronger than that of local species [20]. One of the typical and most intuitive effects of ALAN that has been observed on plants is the delay in leaf senescence and the retention of leaves for longer periods in several deciduous tree species close to light sources [21–23].

The plane (Platanus orientalis L.) tree is one of the most widely planted urban tree species in Asia and Europe [24, 25]. With the rapid development of urbanization, there is no doubt that cities are the areas most seriously affected by light pollution. As the main street tree, the plane is particularly sensitive to artificial light, and it is also one of the earliest plants to be identified as affected by ALAN [21]. The most intuitive effect of ALAN on planes is that it causes plants to retain their leaves longer in winter (January). Light is an essential environmental factor for plant growth, development, and the biosynthesis of phytochemicals [26]. ALAN interferes with plant photoreceptors such as phytochromes, cryptochromes and phototropins, which are essential for regulating plant physiological and developmental responses [22, 27]. Over a short duration, ALAN can enhance photosynthetic capacity and promote plant growth [28]. The photosynthetic processes of plants growing under artificial lighting often change because street lighting usually cannot mimic the spectrum and energy of sunlight [29]. However, continuous ALAN can also cause many negative and adverse effects, such as the inhibition of photooxidative damage repair, chlorosis, high starch yield and stress induction [30, 31]. Several studies have shown that artificial light at night has significant effects on the plane at physiological and biochemical levels. Street lighting was shown to change the chlorophyll content and gas exchange parameters of plane leaves at night [32]. Compared with plane trees without street lighting, plane leaves illuminated by streetlights have a lower carbon dioxide assimilation rate, higher chlorophyll content and lower starch content [33].

However, there has been no research on the effect of ALAN on planes at the molecular level. In this study, we used transcriptomic and metabolomic analyses to clarify the effects of ALAN on plane. ALAN prolonged the defoliation time of plane leaves by altering phytohormone levels and inhibiting senescence-related metabolic pathways. Our results expand our understanding of the effects of ALAN on plants.

Materials and methods

Plant materials and sampling

Platanus orientalis L. is planted as a street tree on Shuangyuan Avenue (29.822 N, 106.445 E) in Beibei District, Chongqing, China. In December, the leaves of P. orientalis turn yellow with age, but the leaves remain green under streetlight. The light intensity of streetlight is ~ 20 lx. The duration of street light is from 6 pm to 7:30 am the next day in winter, and from 8 pm to 6 am the next day in summer. In the middle stage of leaf aging, there are both green and yellowed leaves on the tree. In this study, the green leaves far away from the streetlight (GA), the green leaves under the streetlight (GU) and the yellowing leaves far away from the streetlight (YA) were each collected. Each sample has five leaves from a single tree. All three groups contained three biological replicates. The samples were stored in liquid nitrogen for subsequent use.

RNA extraction, library preparation, and sequencing

Total RNA was extracted using TRIzol reagent (Invitrogen, CA, USA) following the manufacturer’s instructions, and it was then subjected to quality assessment by an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA) and RNase-free agarose gel electrophoresis. Sequencing libraries were generated using NEBNext Ultra™ RNA Library Prep Kit for Illumina (NEB, USA) and 2 µg of total RNA for each Library. The Library preparations were sequenced using the NovaSeq 6000 sequencing system (Illumina) in PE mode, created by Biomarker Technologies Corporation (Beijing, China). The raw reads were cleaned by removing adaptor sequences, empty reads and low-quality sequences (reads with unknown sequences “N” or less than 25 bp). The clean reads were then assembled into non-redundant transcripts using Trinity [34].

Transcriptome analysis

Gene expression was estimated by fragments per kilobase of transcript per million fragments mapped (FPKM) values. The differentially expressed genes (DEGs) were identified by DESeq2 v1.6.3 software [35], with a false discovery rate (FDR) < 0.01 and absolute fold change ≥ 2. Gene function was annotated based on the following databases: Non-Redundant Protein Sequence Database (NR), Pfam database, Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot protein database, COG/KOG database and Gene Ontology (GO).

Metabolome analysis

The same samples used for transcriptome sequencing were used for metabolome analysis. The frozen leaf samples were ground into powder by a mixer mill for 1.0 min at 30 Hz. Each powder sample was weighed to 50 mg and extracted overnight at 4 °C with 1.0 mL of methanol–acetonitrile–water (2:2:1, v/v/v) containing 20 mg/L Lidocaine for the internal standard. After centrifugation for 10 min at 10,000 × g, the supernatant was collected and filtrated, after which 500 µL of the supernatant was transferred to an EP tube for freeze-drying. The freeze-dried metabolites were redissolved in 160 µL of acetonitrile: water (1:1, v/v). The samples were centrifuged again at 10,000 × g for 10 min at 4 °C. The supernatant was collected for subsequent liquid chromatography with tandem mass spectrometry (LC‒MS/MS) analysis. The quality control samples were prepared by mixing 10 µL of extract from each sample.

The analysis was performed using an ultra-performance liquid chromatography (UPLC) system (Waters, Manchester, UK) and an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm, Waters, UK). The mobile phase was composed of solvent A (water with 0.1% (v/v) formic acid) and solvent B (acetonitrile with 0.1% (v/v) formic acid), and the flow rate of the mobile phases was 400 µL/min. The gradient program was set as follows: 98% solvent A and 2% solvent B at 0 min. We kept the initial conditions of 98% solvent A and 2% solvent B for 0.25 min. We performed a Linear gradient to 2% solvent A and 98% solvent B within 10 min, and the composition of 2% A and 98% B was maintained for 3 min. Subsequently, it was adjusted to 98% solvent A and 2% solvent B within 0.1 min and kept for 1.9 min. The injection volume was 1 µL, and the column temperature was 40 °C.

Statistical analysis

Principal component analysis (PCA) was performed on BMKCloud (www.biocloud.net). The VIP value of the model was calculated using multiple cross-validation. The method of combining the difference multiple, the p value and the VIP value of the OPLS-DA model was adopted to screen the differentially accumulated metabolites (DAMs), and the screening criteria were FC > ≥ 1.5 or fold change ≤ 0.5, P value < 0.05 and VIP > 1. The significantly enriched KEGG pathways of the differentially abundant metabolites were calculated using a hypergeometric distribution test.

Results

Light delayed the yellowing of the leaves of P. orientalis

From December to January of the following year in Chongqing, China, the leaves of plane trees gradually withered and fell. However, on the streets of the city, we saw linear plane trees that intermittently appeared green, and the green trees happened to be located by streetlights (Fig. 1). Not all the leaves of plane trees under streetlights were green, and only the leaves in the range of light were. It was very rare for the leaves of plane trees that were not under streetlights to be green, and these plants were randomly distributed. This phenomenon indicates that the illumination of streetlights delayed leaf yellowing.

Fig. 1.

Fig. 1

Open in a new tab

Artificial light delayed the yellowing of the leaves of P. orientalis. A The green leaves are located near the streetlight. B The growth of plane tree under streetlight. C The growth of plane tree not under streetlight

Transcriptome sequencing of the effect of light on plane tree leaves

To investigate the molecular mechanism underlying the effect of light on plane tree leaves, we conducted RNA-seq analysis on three types of leaves, namely, green leaves under streetlight (GU), green leaves away from streetlight (GA) and yellowing leaves away from streetlight (YA) (Fig. 2A). A total of 6,371 differentially expressed genes (DEGs) were identified in GA vs. GU, of which 3,317 were upregulated and 3,054 were downregulated (Fig. 2B). Compared to yellowing leaves (YA), there were more DEGs in GU (9,628) than in GA (3,938). KEGG pathway analysis showed that both up- and downregulated genes related to plant–pathogen interaction and plant hormone signal transduction were enriched in the GA vs. GU comparison (Fig. 2C, Table S1−2). In addition, DEGs in the flavonoid biosynthesis, stilbenoid, diarylheptanoid and gingerol biosynthesis pathway were downregulated. From normal green leaves to yellowing leaves, namely, GA vs. YA, downregulated expressed genes were enriched mainly in photosynthesis–antenna proteins, phenylpropanoid biosynthesis, and plant hormone signal transduction pathways, while upregulated genes were enriched mainly in ribosome, fatty acid degradation, tryptophan metabolism and carbon metabolism (Fig. 2D, Table S3-4). In the GU vs. YA, upregulated expressed genes were mainly enriched in phenylpropanoid biosynthesis and carotenoid biosynthesis, and downregulated expressed genes were mainly enriched in plant–pathogen interaction, plant hormone signal transduction, flavonoid biosynthesis, starch and sucrose metabolism, and photosynthesis–antenna proteins (Fig. 2E, Table S5-6).

Fig. 2.

Fig. 2

Open in a new tab

Transcriptome sequencing analysis of three types of plane tree leaves. A Plane tree leaves used for RNA seq. GA: the green leaves far away from the streetlight, GU: the green leaves under the streetlight, YA: the yellowing leaves far away from the streetlight. B Statistical analysis of the number of differentially expressed genes (DEGs) in three comparative groups. C-E Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs was performed between three comparative groups

Analysis of leaf senescence-associated genes

Plant hormones play a crucial regulatory role in the yellowing process of plant leaves. Auxin and cytokinins (CK) are considered to inhibit plant leaf senescence. A large number of auxin- and CK- related genes were significantly upregulated in GU (Fig. 3A, B). Abscisic acid (ABA) acts as a plant hormone in response to a variety of abiotic stresses, and most of its related genes were upregulated in long-light leaves (GU) (Fig. 3C). Jasmonic acid (JA), salicylic acid (SA), ethylene (ET) and brassinolide (BR) are considered to be phytohormones that promote plant senescence, and most of the genes in these signaling pathways were significantly upregulated in senescent leaves (Fig. 3D-G).

Fig. 3.

Fig. 3

Open in a new tab

Expression profile of DEGs related to plant hormone biosynthesis and signal transduction. CK: cytokinins, ABA: abscisic acid, JA: jasmonic acid, ET: ethylene, SA: salicylic acid, BR: brassinolide

In order to comprehensively identify the genes related to plane tree leaf senescence, all the senescence-associated genes (SAGs) in the Leaf Senescence Database (LSD3.0) were used to BLASTP the local plane tree protein data with an E-value threshold of 1e-10. Finally, 5955 differentially expressed SAGs were obtained. These SAGs were divided into six groups according to the trend of gene expression (Fig. 4). The genes in the second group were significantly upregulated in yellowing leaves, and the enriched GO terms included mainly ion transport, ion transmembrane transport, cation transmembrane transport, oxidation reduction process and response to chemical (Figure S1). The fourth group of genes were significantly upregulated in green leaves under streetlight, and the DEGs were mainly enriched in the terms of protein kinase activity, protein serine/threonine kinase activity, plasma membrane, RNA modification and macromolecule modification. Similarly, the third group of DEGs that were significantly downregulated in the green leaves under streetlights were enriched mainly in the terms of protein kinase activity, protein serine/threonine kinase activity, DNA-binding transcription factor activity, macromolecule modification and signal transduction (Figure S1). The DEGs in the sixth group were expressed at a low level in the yellowing leaves and were significantly enriched in the terms of hydrolase activity, hydrolyzing O − glycosyl compounds, the regulation of transcription, DNA-templated, extracellular region and auxin-activated signaling pathway (Figure S1).

Fig. 4.

Fig. 4

Open in a new tab

Heatmaps of leaf senescence-associated genes in three different groups

Metabolic analysis of the effect of streetlights on plane tree leaves

To further investigate the differences in metabolites among GA, GU and YA, the metabolites of these three groups were detected using an untargeted metabolome approach. A Principal component analysis (PCA) of the metabolites among the three different leaf groups revealed that their metabolomes were clearly different (Fig. 5A). A total of 4,215 metabolites were detected (Table 1). The differentially accumulated metabolites (DAMs) between pairs of samples (GA vs. GU, GA vs. YA and GU vs. YA) were determined based on the variable importance in projection (VIP) >1, P < 0.05 and fold change ≥ 1.5 or ≤ 0.5. A total of 1,988 DAMs were identified in all three comparison groups.

Fig. 5.

Fig. 5

Open in a new tab

Enrichment analysis of differentially abundant metabolites. A PCA analysis of metabolome data for GA, GU and YA. B-D KEGG pathway enrichment analysis of DAMs in GA vs. GU, GA vs. YA and GU vs. YA, respectively

Table 1.

Statistics on the number of differentially accumulated metabolites (DAMs) in three different groups

DAM_Set Total_num Diff_num Up-num Down-num
GA vs. GU 4215 970 296 674
GA vs. YA 4215 872 586 286
GU vs. YA 4215 1187 898 289
Open in a new tab

In the GA vs. YA and GU vs. YA comparisons, the common KEGG pathways of DAM enrichment were betalain biosynthesis, phenylpropanoid biosynthesis, caffeine metabolism, tyrosine metabolism and glucosinolate biosynthesis. This suggests that these metabolic pathways may promote leaf senescence (Fig. 5C, D). The metabolic pathways of flavonoid biosynthesis, isoflavonoid biosynthesis, phenylpropanoid biosynthesis and phenylalanine metabolism may contribute to the accumulation of yellowing pigments in plants and are inhibited in GU (Fig. 5B, D). Compared with GA, metabolic pathways such as galactose metabolism, fatty acid biosynthesis, and taurine and hypotaurine metabolism were activated in green leaf samples under streetlights. However, the photosynthesis pathway was inhibited in GU (Fig. 5B).

The statistical analysis of the fold change of DAMs showed that avermitilol, 7-oxateasterone and alisol C monoacetate were significantly enriched in GU, while (R)−2,3-dihydroxy-3-methylpentanoate, 1-diphosinositol pentakisphosphateand, trans-Δ2-11-methyl-dodecenoic acid, 3,4-dimethoxycinnamic acid and O-1821 were significantly decreased in GU (Fig. 6A, C, D). Trans-Δ2-11-methyl-dodecenoic acid, 3,4-dimethoxycinnamic acid, O-1821 and tormentic acid were significantly enriched in YA (Fig. 6B-D). Castasterone and 6alpha-hydroxy-castasterone in BR signaling pathway and (-)-jasmonic acid and methyl jasmonate in JA signaling pathway were significantly accumulated in YA (Fig. 6E). In addition, jasmone, jasmonic acid and (-)-jasmonoyl-L-isoleucine also have high concentrations in YA (Fig. 6E). Consistent with the results of KEGG pathway enrichment analysis, most of the flavonoid DAMs significantly enriched in YA (Fig. 6F).

Fig. 6.

Fig. 6

Open in a new tab

DAMs analysis in three comparison groups. A-C Upregulation and downregulation of the top 10 DAMs in GA vs. GU, GA vs. YA and GU vs. YA, respectively. The fold change of DAMs was treated by log conversion (log2FC). D Differential metabolites significantly affected by streetlights. The fold change of DAMs was treated by log conversion (log10FC). E Metabolites related to brassinolide and jasmonic acid are enriched in yellowed leaves. The fold change of DAMs was treated by ln conversion (lnFC). F Heatmap of the DAMs of flavonoids in three comparison groups

Combined metabolome and transcriptome analysis of the effect of streetlights on plants

To further explore the relationship between gene expression and metabolite accumulation in the three comparison groups, correlation tests and conjoint biological annotations were conducted. Combined analyses of transcriptome and metabolome data revealed that compared with the two green leaf groups, biosynthesis of amino acid, phenylpropanoid biosynthesis, tryptophan metabolism and flavonoid biosynthesis were the most significantly enriched metabolic pathway in yellowing leaves (Fig. 7). Especially for phenylpropanoid biosynthesis and flavonoid biosynthesis related to leaf yellowing pigment accumulation, most of DEGs and DAMs are up-regulated in YA, but down-regulated in GU (Table S7, 8).

Fig. 7.

Fig. 7

Open in a new tab

Integration of transcriptome and metabolome to analyze the top 10 KEGG pathways enriched for DEGs and DAMs in the three comparison groups

Discussion

Light pollution caused by artificial lighting has a wide range of effects on organisms, from individuals and populations to communities and even ecosystems. ALAN disrupts natural daily, seasonal and lunar light cycles, as experienced by a diverse of organisms [36]. As urbanization continues to expand, the problem of light pollution is becoming increasingly prominent. ALAN and its associated biological and ecological impacts have been characterized as predominantly urban issues [37]. The impact of ALAN on the growth and development of many plants, such as regards maple, plane and camphor plants, has attracted increasing amounts of attention [21, 22, 38]. A few studies have focused on the effects of nighttime light on the physiological and biochemical levels of plants, with different light intensities and spectra affecting the rate of carbon dioxide assimilation, chlorophyll content, starch content, nitrogen and water uptake [33, 39]. The effects of ALAN on the phenology and physiology of different plants are different, and even the opposite results appear [40]. Plane tree is highly sensitive to nighttime lighting. Prolonging the time of defoliation is the most obvious effect of ALAN on plane trees [27]. Chlorophyll content was higher in lighted plants, this is because street lighting hinders the natural reduction of chlorophyll biosynthesis in plants at night [32, 33]. In addition, lighted plants (including Platanus) showed higher CO2 assimilation rates during the night compared to the control [32]. The increase in chlorophyll content and net photosynthetic rate (at least slowing down degradation rate and activity decay) of plants illuminated by street lights results in leaves remaining green for a longer period of time in winter. However, retaining active foliage in deciduous trees during the winter may cause freezing damage to plant organs, such as leaves, and the early bursting of buds [27]. However, in areas such as southern China, where there is no severe freezing, the above situation rarely occurs. Although ALAN’s ability to delay senescence in plane tree leaves is often quite conspicuous, it remains unclear whether it has any significant consequences for the individual trees. In any case, ALAN disrupts the natural growth and development of plants, which is definitely detrimental to them.

In this study, we investigated the effects of ALAN on plane at the molecular level. The transcriptomic and metabolomic analyses were conducted on three sets of plane samples, namely, green leaves away from streetlights, green leaves under streetlights and yellowing leaves away from streetlights. Plant hormones regulate various growth and developmental processes in plants, including senescence. Auxin, CK and gibberellins (GAs) are typically thought to delay senescence, while ET, ABA, SA, JA and BR are generally considered to be senescence-promoting hormones [41]. In green leaves under streetlights, not only were auxin- and CK-related genes significantly upregulated, but ABA-related genes were as well. Although ABA is a senescence-promoting hormone, it is also a hormone that responds to a variety of abiotic stresses [42]. Green leaves under streetlights may have been subjected to cold stress in winter resulting in the upregulated expression of ABA-related genes. Genes associated with SA, JA, ET and BR biosynthesis as well as signal transduction were significantly upregulated in yellowing leaves away from streetlights. Then, the plane tree senescence-associated genes (SAGs) were screened and divided into six groups according to the trend of gene expression. In yellowing leaves, the main GO terms enriched for DEGs were ion transport, ion transmembrane transport, cation transmembrane transport, oxidation reduction process and response to chemical. In green leaves under streetlight, the DEGs were mainly enriched in the terms protein kinase activity, protein serine/threonine kinase activity, plasma membrane, RNA modification and macromolecule modification. These terms indicate that plant tissues are in an active state. Transcriptomic and metabolomic analyses indicate that biosynthesis of amino acid, phenylpropanoid biosynthesis, tryptophan metabolism and flavonoid biosynthesis were the most significantly enriched metabolic pathway in yellowing leaves. Among them, most DEGs and DAMs in phenylpropanoid biosynthesis and flavonoid biosynthesis are up-regulated in YA, but down-regulated in GU. This shows that these two pathways are the major metabolic pathways contributing to plant leaf yellowing or senescence.

Light pollution is a growing ecological concern in cities, with a variety of biological species proven to be affected. Prolonged defoliation in the winter caused by the interference of circadian rhythm is the most obvious effect of night lighting on plane trees. In this study, we found that ALAN retarded plant leaf senescence mainly by altering plant hormone levels and inhibiting metabolic pathways such as flavonoid biosynthesis, betalain biosynthesis, caffeine metabolism and phenylpropanoid biosynthesis.

Conclusions

ALAN is a major urban problem that disrupts plane tree growth rhythms and delays leaf senescence. In this study, we performed metabolic and transcriptomic analyses on plane green leaves with and without streetlights. Our results indicate that ALAN retarded plant leaf senescence mainly by altering plant hormone levels and inhibiting leaf senescence-related metabolic pathways. This study provides insights into how streetlights affect plane trees.

Supplementary information

12870_2025_7179_MOESM1_ESM.tif (1.4MB, tif)

Supplementary Material 1. Figure S1. Enrichment analysis of GO terms for leaf senescence-associated DEGs in G2, G4, and G6

12870_2025_7179_MOESM2_ESM.xlsx (174.2KB, xlsx)

Supplementary Material 2. Table S1 KEGG-enriched pathway of upregulated expressed genes in GA vs.GU. Table S2 KEGG-enriched pathway of downregulated expressed genes in GA vs.GU. Table S3 KEGG-enriched pathway of upregulated expressed genes in GA vs. YA. Table S4 KEGG-enriched pathway of downregulated expressed genes in GA vs. YA. Table S5 KEGG-enriched pathway of upregulated expressed genes in GU vs. YA. Table S6 KEGG-enriched pathway of downregulated expressed genes in GU vs. YA. Table S7 The integrated transcriptome and metabolome statistics of DAM and DEGs in GA vs. GU for ko00940 and ko00941 KEGG pathways. Table S8 The integrated transcriptome and metabolome statistics of DAM and DEGs in GU vs. YA and GA vs. YA for ko00940 and ko00941 KEGG pathways.

Acknowledgements

Not applicable.

Authors’ contributions

L.T., L.A. and Z.L. contributed to the conception and design of the study. Z.L., C.H. and R.L. collected and processed the samples. Z.L., Y.H., H.Z. and C.H. analyzed the transcriptome and metabolome data. Y.H., H.Z. and R.L. for data visualization. Z.L., L.T. and L.A. wrote the article. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that they have no conflict of interest. This work was funded by the Performance Incentive and Guidance Special Project for Chongqing Scientific Research Institutes (2023-KJ02-07380, 2023-KJ02-08381).

Data availability

The raw data have been deposited in the Genome Sequence Archive in National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, under accession number CRA015140, which are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA015140.

Declarations

Ethics approval and consent to participate

The plant materials were provided by the local regulatory agency and collaborator of this study, the Landscape and Greening Management Office of Beibei District. The plants materials were all artifcially planted and the experimental research comply with relevant institutional, national, and international guidelines and legislation. The plants materials we used were neither wild nor endangered.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Lijiao Ai, Email: alj01461@foxmail.com.

Lichao Tian, Email: tianlc0730@163.com.

References

  • 1.Dodd AN, Salathia N, Hall A, Kévei E, Tóth R, Nagy F, Hibberd JM, Millar AJ, Webb AAR. Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science. 2005;309(5734):630–3. [DOI] [PubMed] [Google Scholar]
  • 2.Venkat A, Muneer S. Role of circadian rhythms in major plant metabolic and signaling pathways. Front Plant Sci. 2022. 10.3389/fpls.2022.836244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Kyba CCM, Kuester T, Sánchez de Miguel A, Baugh K, Jechow A, Hölker F, Bennie J, Elvidge CD, Gaston KJ, Guanter L. Artificially lit surface of Earth at night increasing in radiance and extent. Sci Adv. 2017;3(11):e1701528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hölker F, Wolter C, Perkin EK, Tockner K. Light pollution as a biodiversity threat. Trends Ecol Evol. 2010;25(12):681–2. [DOI] [PubMed] [Google Scholar]
  • 5.Sánchez de Miguel A, Bennie J, Rosenfeld E, Dzurjak S, Gaston KJ. First Estimation of global trends in nocturnal power emissions reveals acceleration of light pollution. Remote Sens. 2021;13(16):3311. [Google Scholar]
  • 6.Gaston KJ, Gaston S, Bennie J, Hopkins J. Benefits and costs of artificial nighttime lighting of the environment. Environ Rev. 2015;23(1):14–23. [Google Scholar]
  • 7.Hölker F, Moss T, Griefahn B, Kloas W, Voigt CC, Henckel D, Hänel A, Kappeler PM, Völker S, Schwope A, et al. The dark side of light: a transdisciplinary research agenda for light pollution policy. Ecol Soc. 2010. 10.5751/ES-03685-150413. [Google Scholar]
  • 8.Gaston KJ, Duffy JP, Gaston S, Bennie J, Davies TW. Human alteration of natural light cycles: causes and ecological consequences. Oecologia. 2014;176(4):917–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Sanders D, Frago E, Kehoe R, Patterson C, Gaston KJ. A meta-analysis of biological impacts of artificial light at night. Nat Ecol Evol. 2021;5(1):74–81. [DOI] [PubMed] [Google Scholar]
  • 10.Stone EL, Jones G, Harris S. Street lighting disturbs commuting bats. Curr Biol. 2009;19(13):1123–7. [DOI] [PubMed] [Google Scholar]
  • 11.Van Doren BM, Horton KG, Dokter AM, Klinck H, Elbin SB, Farnsworth A. High-intensity urban light installation dramatically alters nocturnal bird migration. Proc Natl Acad Sci U S A. 2017;114(42):11175–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Berry M, Booth D, Limpus C. Artificial lighting and disrupted sea-finding behaviour in hatchling loggerhead turtles (Caretta caretta) on the Woongarra coast, south-east Queensland, Australia. Aust J Zool. 2013;61:137. [Google Scholar]
  • 13.Macgregor CJ, Pocock MJO, Fox R, Evans DM. Effects of street lighting technologies on the success and quality of pollination in a nocturnally pollinated plant. Ecosphere. 2019;10(1):e02550. [Google Scholar]
  • 14.Knop E, Zoller L, Ryser R, Gerpe C, Hörler M, Fontaine C. Artificial light at night as a new threat to pollination. Nature. 2017;548(7666):206–9. [DOI] [PubMed] [Google Scholar]
  • 15.Cathey HM, Campbell LE. Effectiveness of five vision-lighting sources on photo-regulation of 22 species of ornamental plants. J Am Soc Hortic Sci. 1975;100(1):65–71. [Google Scholar]
  • 16.ChiLing C, YunHam S, ChiaJen L, YahnChir L. Effect of night illumination on growth and yield of soybean. J Taiwan Agric Res. 2009;58:146–54. [Google Scholar]
  • 17.Ffrench-Constant RH, Somers-Yeates R, Bennie J, Economou T, Hodgson D, Spalding A, McGregor PK. Light pollution is associated with earlier tree budburst across the United Kingdom. Proc Biol Sci. 2016;283(1833): 20160813. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Bennie J, Davies TW, Cruse D, Bell F, Gaston KJ. Artificial light at night alters grassland vegetation species composition and phenology. J Appl Ecol. 2018;55(1):442–50. [Google Scholar]
  • 19.Speißer B, Liu Y, van Kleunen M. Biomass responses of widely and less-widely naturalized alien plants to artificial light at night. J Ecol. 2021;109(4):1819–27. [Google Scholar]
  • 20.Liu Y, Speißer B, Knop E, van Kleunen M. The matthew effect: common species become more common and rare ones become more rare in response to artificial light at night. Glob Chang Biol. 2022;28(11):3674–82. [DOI] [PubMed] [Google Scholar]
  • 21.Matzke EB. The effect of street lights in delaying leaf-fall in certain trees. Am J Bot. 1936. 10.1002/j.1537-2197.1936.tb09009.x. [Google Scholar]
  • 22.Škvareninová J, Tuhárska M, Škvarenina J, Babálová D, Slobodníková L, Slobodník B, Středová H, Minďaš J. Effects of light pollution on tree phenology in the urban environment. Moravian Geogr Rep. 2017;25(4):282–90. [Google Scholar]
  • 23.Schroeder C. Tree foliation affected by street lights. Arborist News. 1945;10:1–3. [Google Scholar]
  • 24.Pourkhabbaz A, Rastin N, Olbrich A, Langenfeld-Heyser R, Polle A. Influence of environmental pollution on leaf properties of urban plane trees, Platanus orientalis L. Bull Environ Contam Toxicol. 2010;85(3):251–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Li X, Gong X, Zhang C, Feng T, Ouyang Z, Peng Y. Comparison of PM10 emission from co-combustion of Platanus orientalis leaf and wood in different seasons with coal. Fuel Process Technol. 2022;234: 107334. [Google Scholar]
  • 26.Proietti S, Moscatello S, Riccio F, Downey P, Battistelli A. Continuous lighting promotes plant growth, light conversion efficiency, and nutritional quality of Eruca vesicaria (L.) cav. in controlled environment with minor effects due to light quality. Front Plant Sci. 2021. 10.3389/fpls.2021.730119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bennie J, Davies TW, Cruse D, Gaston KJ. Ecological effects of artificial light at night on wild plants. J Ecol. 2016;104(3):611–20. [Google Scholar]
  • 28.Gendron JM, Leung CC, Liu W. Energy as a seasonal signal for growth and reproduction. Curr Opin Plant Biol. 2021;63: 102092. [DOI] [PubMed] [Google Scholar]
  • 29.Darko E, Heydarizadeh P, Schoefs B, Sabzalian MR. Photosynthesis under artificial light: the shift in primary and secondary metabolism. Philos Trans R Soc Lond B Biol Sci. 2014;369(1640): 20130243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Bhatta M, Sandro P, Smith MR, Delaney O, Voss-Fels KP, Gutierrez L, Hickey LT. Need for speed: manipulating plant growth to accelerate breeding cycles. Curr Opin Plant Biol. 2021;60: 101986. [DOI] [PubMed] [Google Scholar]
  • 31.Bucher SF, Uhde L, Weigelt A, Cesarz S, Eisenhauer N, Gebler A, Kyba C, Römermann C, Shatwell T, Hines J. Artificial light at night decreases plant diversity and performance in experimental grassland communities. Philos Trans R Soc Lond B Biol Sci. 2023;378(1892): 20220358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lo Piccolo E, Lauria G, Remorini D. Urban lighting alters chlorophyll metabolism And Romotes CO2 assimilation during the night in Tilia Plathyphyllos scop. And Platanus x Acerifolia (Aiton) willd. Agrochimica. Volume 65. Pisa: Pisa University; 2021. pp. 389–400. [Google Scholar]
  • 33.Lo Piccolo E, Lauria G, Guidi L, Remorini D, Massai R, Landi M. Shedding light on the effects of LED streetlamps on trees in urban areas: friends or foes? Sci Total Environ. 2023;865: 161200. [DOI] [PubMed] [Google Scholar]
  • 34.Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Gaston KJ, Davies TW, Nedelec SL, Holt LA. Impacts of artificial light at night on biological timings. Annu Rev Ecol Evol Syst. 2017;48(1):49–68. [Google Scholar]
  • 37.Gaston KJ, Ackermann S, Bennie J, Cox DTC, Phillips BB, Sánchez de Miguel A, Sanders D. Pervasiveness of biological impacts of artificial light at night. Integr Comp Biol. 2021;61(3):1098–110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Li X, Chen B, Wang H, Zheng G, Yang D, Miao X, Xu C. Effects of urban nighttime light on the growth of Cinnamomum Camphora. Chin J Appl Ecol. 2019;30(7):2284–90. [DOI] [PubMed] [Google Scholar]
  • 39.Liu P, Cao B, Wang Y, Wei Z, Ye J, Wei H. Spectral effect of streetlamps on urban trees: a simulated study on tissue water, nitrogen, and carbohydrate contents in maple and oak. PLoS One. 2021;16(3):e0248463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Friulla L, Varone L. Artificial light at night (ALAN) as an emerging urban stressor for tree phenology and physiology: a review. Urban Sci. 2025;9(1): 14. [Google Scholar]
  • 41.Zhang Y-M, Guo P, Xia X, Guo H, Li Z. Multiple layers of regulation on leaf senescence: new advances and perspectives. Front Plant Sci. 2021. 10.3389/fpls.2021.788996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lim PO, Kim HJ, Gil Nam H. Leaf senescence. Annu Rev Plant Biol. 2007;58:115–36. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12870_2025_7179_MOESM1_ESM.tif (1.4MB, tif)

Supplementary Material 1. Figure S1. Enrichment analysis of GO terms for leaf senescence-associated DEGs in G2, G4, and G6

12870_2025_7179_MOESM2_ESM.xlsx (174.2KB, xlsx)

Supplementary Material 2. Table S1 KEGG-enriched pathway of upregulated expressed genes in GA vs.GU. Table S2 KEGG-enriched pathway of downregulated expressed genes in GA vs.GU. Table S3 KEGG-enriched pathway of upregulated expressed genes in GA vs. YA. Table S4 KEGG-enriched pathway of downregulated expressed genes in GA vs. YA. Table S5 KEGG-enriched pathway of upregulated expressed genes in GU vs. YA. Table S6 KEGG-enriched pathway of downregulated expressed genes in GU vs. YA. Table S7 The integrated transcriptome and metabolome statistics of DAM and DEGs in GA vs. GU for ko00940 and ko00941 KEGG pathways. Table S8 The integrated transcriptome and metabolome statistics of DAM and DEGs in GU vs. YA and GA vs. YA for ko00940 and ko00941 KEGG pathways.

Data Availability Statement

The raw data have been deposited in the Genome Sequence Archive in National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, under accession number CRA015140, which are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA015140.

Articles from BMC Plant Biology are provided here courtesy of BMC

RESOURCES