Full-length transcriptome sequencing of Arabidopsis seedlings provided new insights into autophagic regulation of photosynthesis

Full-length transcriptome sequencing of Arabidopsis seedlings provided new insights into autophagic regulation of photosynthesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Full-length transcriptome sequencing of Arabidopsis seedlings provided new insights into autophagic regulation of photosynthesis Song Wang, Yunfeng Shi, Yanhui Zhou, Weiming Hu, Fen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4041050/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Autophagy is a highly conserved eukaryotic pathway and plays a crucial role in cell survival under stress. Here, we applied a full-length transcriptome approach to study Arabidopsis autophagy mutant (atg5-1) exposed to nitrogen-starvation, using Oxford Nanopore Technologies. A total of 39,033 transcripts were identified, including 11,356 new transcripts. In addition, alternative splicing (AS) events and lncRNAs were also detected between Col-0 (WT) and atg5-1. Differentially expressed transcripts enrichment shown that autophagy upregulates the expression of many stress-responsive genes and inhibits transcription of photosynthesis-associated genes. The results of qRT-PCR showed that the expression patterns of photosynthesis-related genes in the atg5-1 were different under the conditions of nitrogen starvation and carbon starvation. Under nitrogen starvation treatment, many genes related to photosynthesis also exhibited AS. The chlorophyll fluorescence images showed that the Fv/Fm and ΦPSII of old atg5-1 leaves were significantly reduced after nitrogen starvation treatment, but the Y(NPQ) indices were significantly increased compared to those of WT. Taken together, the results provide new insights into how new transcripts, lncRNAs and AS (alternative splicing) are involved in plant autophagy through full-length transcriptome sequencing and suggest a new potential link between autophagy and photosynthesis. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Biological sciences/Plant sciences Biological sciences/Plant sciences/Photosynthesis Biological sciences/Plant sciences/Photosynthesis/Photosystem ii Full-length transcriptome autophagy photosynthesis qRT-PCR. Arabidopsis thaliana Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In response to stress, plants use a variety of adaptive responses to maintain an adequate supply of nutrients for growth, development, reproduction, and protection. One important pathway involves the autophagic turnover of intracellular substances, which is essential for the proper processing of unnecessary or dysfunctional substances in the cells and the subsequent reuse of the nutrient components 1,2 . To date, three different types of autophagy have been discovered in plants, including microautophagy, macroautophagy, and mega-autophagy 3,4 . The best-studied type of autophagy in plants is macroautophagy, in which autophagosomes are formed and then fuse with vacuoles to break down cargos 5,6 . Over the past decade, more than 40 ATG (autophagy-related gene) proteins have been identified in the canonical macroautophagy pathway in Arabidopsis thaliana 1,7 . These proteins can be classified into four groups: the ATG1/ATG13 kinase complex, the phosphatidylinositol 3-kinase (PI3K) complex, the ATG9 complex, and the ATG8 and ATG12 ubiquitin-like conjugation systems 6,8-10 . To date, many regulatory factors have been described as being involved in the regulation of autophagy in plants, including TOR, SnRK1, FREE1, and SH3P2 11-14 . In addition, multiomic methods have been used to dissect autophagy regulatory networks, including transcriptomic, metabolomic, and proteomic methods 15-18 . Research is increasingly showing that autophagy acts as a protective strategy for plants to cope with a variety of stresses. Under stress, the expression of many ATG genes is rapidly upregulated simultaneously to promote autophagy, recycle nutrients, and remove harmful debris to maintain cell homeostasis 19,20 . Conversely, autophagy mutants (e.g., atg5-1 ) become chlorotic or show premature senescence under stress 17,21,22 , further indicating the important role of autophagy in plant resistance to abiotic stress. However, until now, only a few transcriptional regulators involved in these processes have been identified in Arabidopsis 8 . Transcriptional regulation is an important step in regulating eukaryotic gene expression. In Arabidopsis, transcriptional changes in ATG genes are frequently observed during plant development and adaptation to environmental changes 23-28 . Using conventional transcriptome technology, Masclaux-Daubresse et al revealed connections between autophagy and salicylic acid biosynthesis and response, cytokinin perception, oxidative stress, and plant defense 15 . In another transcriptomic study, Minina et al found that genes associated with necrotrophic pathogens and oxidative stress were abundant in ATG5 - or ATG7 -overexpressing plants 17 . However, the exploration of the transcriptional regulation of autophagy genes by third-generation sequencing has not been reported. Compared with traditional second-generation transcriptome sequencing, third-generation full-length sequencing based on the Oxford Nanopore Technologies (ONT) platform can more accurately and variably splice alternative transcripts (AS), long noncoding RNA (lncRNAs) and their target genes 29 . In addition, both genes and transcripts can be quantitatively analyzed simultaneously. A recent study showed that this technique provides better-quality raw data and more accurate estimates of transcription levels than the PacBio technique 29 . The ONT platform has been widely applied in whole-genome sequencing, but has rarely been used in full-length transcriptome sequencing 30 . In this study, we generated 12 full-length transcriptomes of wild-type Arabidopsis thaliana and the autophagy mutant atg5-1 under low and high nitrogen level using the ONT platform. Based on the above data, we first analyzed the differentially expressed transcripts (DETs) between different groups. Compared to atg5-1 , WT had a large number of stress-related DETs that showed increased expression after nitrogen starvation, while the expression of photosynthesis-related DETs decreased. The index obtained from the chlorophyll fluorescence images also further proved the difference between WT and atg5-1 in terms of photosynthetic capacity. The above sequencing results were further verified by determination of photosynthetic indicators. Although several articles on the autophagy-related transcriptome have been published, no studies on dynamic AS events and lncRNA-regulated target genes have been published. In this study, we examined AS events and identified lncRNAs under nitrogen starvation. AS is a common way to generate protein diversity after transcription 31 . Several studies have provided overwhelming amount of unassembled data for ASs events, as they produce all kinds of rich and high-quality transcripts of different lengths, such as AS events that occur in response to drought stress 32 and those that occur during development 33,34 . AS enriches the diversity of the transcriptome and proteome and offers more flexibility in transcriptional regulation 35 . In this study, we found that a many of genes related to photosynthesis exhibited AS. LncRNAs are involved in gene transcription and posttranscriptional regulation in eukaryotes 36-39 . LncRNAs can regulate target genes by being transcribed, sequestering microRNAs and proteins, or acting as guide RNAs to recruit proteins 40,41 . LncRNAs have been closely implicated in metabolism, flowering, fertility, and the biological and abiotic stress response in plants 42-44 . We also found differences in the expression of several lncRNAs after nitrogen starvation between WT and atg5-1 . Taken together, our results provide a basis for further understanding the mechanism of autophagy regulation and the relationship between autophagy and photosynthesis. Results Analysis of ONT sequencing datasets. To examine the defined transcriptional regulation of autophagy in Arabidopsis after nitrogen starvation, Col-0 and atg5-1 mutants were subjected to ONT full length- transcriptome sequencing. After sequencing, the original fastq data were filtered for short fragments and low-quality reads, resulting in clean data. A total of 62 GB of clean data was produced with 4,522,054 to 5,597,930 reads, which had an average length between 1,176 and 1,265 nt (Table S1). Full-length reads accounted for more than 93.12% of the total clean reads after filtering rRNAs in each sample (Table S2). These reads had an N50 of 1,295-1,473 nt, and a maximum length of 15,138-257,481 nt. Through this dataset by the ONT sequencing platform, we identified 11,356 novel transcripts, 1,306 novel genes, and 375 lncRNAs. Principal component analysis (PCA) and hierarchical cluster analysis based on the CPM of all transcripts in 12 samples showed a large genotypic effect as well as a treatment effect (Figure 1A). To further investigate the degree of variability of the gene expression level distribution in each sample and to visually compare the overall gene expression levels of different samples, we constructed boxplot to visualize the CPM distribution (Figure 1B). DET identification and functional analysis. To assess transcriptomic changes under nitrogen starvation, DETs were identified with an at least 1.5-fold difference in expression and an FDR less than 0.05 for four comparisons, Col-0+N (A) versus atg5-1 +N (B), Col-0+N versus Col-0-N (C), atg5-1 +N versus atg5-1 -N (D), and Col-0-N versus atg5-1 -N. Interestingly, WT showed a substantial alteration in transcript expression after low-nitrogen treatment compared to nitrogen-rich conditions. In contrast, the mutation of atg5-1 resulted in fewer changers in transcript expression under +/- N (Figure 2, Table S3). For the comparison between the C and D groups, we obtained a total of 5,797 DETs, of which 2,837 were upregulated and 2,960 were downregulated in group D (Table S3). To select the DETs that were specifically expressed in C versus D, we removed the DETs that were already present in A versus B. On this basis, a total of 5,545 DETs were found, of which 2,719 and 2,826 DETs were upregulated and downregulated in group D, respectively (Table S4). To analyze the function of these DETs, GO enrichment analyses were performed. The results showed that these transcripts were associated with different functions in the biological processes category. Among the DETs under nitrogen starvation, the upregulated transcripts were mainly associated with response to stimuli, response to stress, and response to chemical processes (Figure 3A, 3B), and the downregulated transcripts were mainly related to stimulus response, abiotic stimulus response, and photosynthesis (Figure 3C, 3D). The above results not only show that autophagy is involved in the regulation of a wide range of stimuli regulation, whether upregulated or downregulated, but also has a significant impact on photosynthesis. In addition, to identify the metabolic pathways underlying nitrogen deficiency, a KEGG pathway enrichment analysis was performed. In addition to the secondary metabolic pathways and biosynthesis, the upregulated DETs in WT were associated with phenylpropanoid biosynthesis (Figure 4A). Carbon metabolism and photosynthesis were identified as being involved with downregulated transcripts (Figure 4B). Based on the KEGG and GO database, we selected 10 genes associated with photosynthesis and their expression after nitrogen starvation for further study, which contain photosynthetic electron transport chainsⅠ (PSⅠ) and and Ⅱ (PSⅡ). Similar to the results of RNA-seq, most of the photosynthesis genes measured (9/10) were expressed at higher levels in atg5-1 than in Col-0 after -N treatment (Figure 5). In addition to -N treatment, -C is also one of the conventional means to induce autophagy. Therefore, we further investigated the expression levels of these photosynthesis genes before and after +/-C treatment in autophagy mutants. Unlike the -N treatment, only one PS1 gene, namely PSAH, was expressed higher in atg5-1 than in Col-0 after -C treatment (Figure S1). However, the expression of atg5-1 in most of the measured PSPSII-realated genes (4/5), such as CYTC6A , PPL1 , PSB01 and PSB27 , was lower than that in Col-0 after -C treatment (Figure S1). Determination of the photosynthetic index. Given that the abundances of transcripts associated with photosynthesis were higher in the atg5-1 background, we assume that autophagy may play an important role in regulation of photosynthesis. To confirm this hypothesis, we used a chlorophyll fluorescence imaging system to analyze the photosynthetic indices of the abovementioned materials, under dark treatment, which was used to induce autophagy 58 . Compared with nitrogen starvation, shading stress can prevent plant photosynthesis and induce autophagy more quickly, which can better measure its photosynthetic index. As shown in Figure 6 and Figure S2, no significant difference was observed in the photosynthetic indices including Fv/Fm between Col-0 and atg5-1 in both young and old leaves under normal conditions. Under fixed- carbon starvation, a sharp increase in Y(NO) and a decrease in Fv/Fm and ΦPSII) were observed in old leaves of the atg5-1 mutant compared to young leaves of Col-0 and atg5-1 and old leaves of Col-0, respectively (Figure 6, 7A, S2). Fv/Fm refers to the maximum quantum efficiency of photosystem II. The results showed that Fv/Fm was approximately 0.7 in the young leaves of WT and atg5-1 , and +/- C led to significant difference between them. In contrast, Fv/Fm decreased rapidly to approximately 0.27 in the old leaves of atg5-1 (Figure 6, 7A, S2). Both the quantum yield of regulated energy dissipation ( Y ( NPQ )) and the quantum yield of nonregulated energy dissipation ( Y ( NO )) are indicators of energy dissipation during photosynthesis. In the normal growth environment without nutritional stress, the Y ( NPQ ) values of WT and atg5-1 were both 0. However, under the carbon conditions, the Y ( NPQ ) value of young WT leaves was significantly higher than that of young atg5-1 leaves. Surprisingly, Y ( NPQ ) showed opposite indicators in the old leaves of both (Figure 6, 7A, S2). Same as Y ( NPQ ), WT and atg5-1 show a similar Y ( NO ), around 6.5 to 6.9, under nonstress conditions. (Figure 6, 7A, S2). After carbon starvation, the Y ( NO ) values of the WT and mutant did not change significantly in young leaves but increased significantly in old leaves, the value for atg5-1 increased to approximately 0.88, which was significantly higher than the value of 0.74 was observed for WT. In addition, we also measured the ΦPSII, an indicator of shows the actual photosynthetic quantum yield of plants. The results showed that the ΦPSII values of WT and atg5-1 in plants without stress treatment were stable between 0.51 and 0.52 in old and young leaves of atg5-1 decreased to 0.06 and 0.35, respectively (Figure 6, 7A, S2). Furthermore, we compare the differences between the relative electron transport rate ( rETR (II)) and ΦPSII under gradually increasing light intensity from 0 to 1251 μmol·m -2 ·s -1 . Apparently, the young leaves showed similar pattern for rETR (II) and Φ PSII between WT and atg5-1 (Figure 7B, 7D). In contrast, in old leaves under fixed-carbon starvation, atg5-1 rETR (II) and ΦPSII were lower in atg5-1 than in WT (Figure 7C, 7E). To determine whether the change in photosynthetic indices in old atg5-1 leaves was caused by the degradation of chlorophyll, we examined the chlorophyll contents of plant leaves. Under normal growth conditions, the chlorophyll content in old leaves of atg5-1 was slightly lower than that in old WT leaves, but there was no significant difference in new leaves (Figure 7F). A similar trend was observed after nutritional stress (Figure 7F). Characterization of AS events. Increasing evidence indicates that AS plays a crucial role in plant development and stress. Five categories of AS events were identified in this study, the most common event was intron retention (41.76% to 48.47%), and the least abundant event was mutually exclusive exons (0.27% to 0.7%) (Figure 8A). Under high nitrogen levels, the number of AS events was higher in WT than in atg5-1 , particularly intron retention events (Figure 8A). In contrast, there was no significant difference in the total number of AS between the WT and the mutant atg5-1 under low nitrogen levels, but the proportion of intron retention events was higher in WT (Figure 8A). Furthermore, we comprehensively compared AS events among the four groups. The overall trend was more consistent with the results for DETs; that is, WT had more AS events (A vs. C, 643) under nitrogen starvation, while atg5-1 is relatively few (B vs. D, 510) (Figure 8C, D). Regardless of which groups were compared, intron retention was the most common AS mode. To select the AS that changed specifically between C vs. D, we removed the AS that was already present between A vs. B. We finally screened 254 AS that changed specifically in C vs D group from the 674 AS events (Table S5, Figure 8B). Next, we performed GO enrichment analyses on the 254 genes with AS events. Surprisingly, some transcripts of these AS events were also linked to photosynthesis. For example, several genes were associated with the photosynthetic system, chloroplast thylakoid membrane, etc. (Figure 8C, 8D; Table S6). Characterization of lncRNAs. LncRNAs were identified by CNCI, CPAT, CPC, and Pfam analysis, and a total of 375 lncRNAs were detected using all four methods (Figure S3). LncRNAs were classified and mapped according to their position on the reference genome annotation information (gff). All lncRNAs were classified into three categories with 233 lncRNAs, 54 antisense lncRNAs, 20 intronic lncRNAs, and 88 sense lncRNAs (Figure S3). Among the 375 lncRNAs, 43 were differentially expressed in the C vs. D group, and 20/23 were up-/downregulated in Col-0 under nitrogen starvation (Table S7). Furthermore, 6 lncRNAs were predicted to target autophagy-related genes (Table S7), which may play a negative feedback mechanism role during autophagy occurrence. Discussion Autophagy plays a key role in plant resistance to nutrient stress. Nitrogen is a necessary macroelement for plants and nitrogen deficiency leads to significant changes in the transcriptome in plants. To accurately characterize gene expression patterns and molecular traits under nitrogen starvation, some studies have assessed transcriptomic changes using the Illumina sequencing platform 15,17,59 Because mRNA transcripts are the direct template for translation, identifying changes that affect the abundance of autophagy transcripts under nitrogen starvation conditions is an important task. In this study, we performed full-length RNA sequencing for Col-0 and atg5-1 with or without nitrogen in Arabidopsis. A total of 39,033 transcripts were detected, including 11,356 novel transcripts. We identified 2,719 upregulated and 2,826 DETs in atg5-1 under nitrogen starvation by comparative transcriptomic analysis (Table S4). By analyzing DETs, some potential molecular regulatory networks could be revealed. We found that there were only 412 DETs from WT and the autophagic mutant atg5-1 when sufficient nitrogen was present (Table S3). Accordingly, there is a large number of DETs (5797) under nitrogen starvation (Table S3). This result may suggest that autophagy is necessary to maintain plant homeostasis under nitrogen starvation, while it appears to have little effect on plant growth, at least at the seedling stage under sufficient nitrogen. In addition, we found that there was no significant difference in the expression of most transcripts of atg5-1 between the+/- N conditions (Table S3). This further suggests that autophagy is essential for the regulation of plant nutritional stress. To further analyze the function of up- and downregulated DETs between the C group ( atg5-1 +N versus atg5-1 -N) and D group (Col-0-N versus atg5-1 -N ), we performed the GO and KEGG enrichment analyses. From the GO results, we found that many transcripts in both up- and downregulated DETs were involved in stimulus (Figure 3). Autophagy has two roles in regulating plant abiotic stress: it can upregulate the expression of some stress response genes and also downregulate the expression of other stress genes. In combination with GO and KEGG analyses, the results showed that the autophagy mutant atg5-1 had many of photosynthesis-related transcripts that were significantly upregulated under nitrogen starvation (Figure 3, 4). This indicates that plants weaken photosynthesis through autophagy to cope with nitrogen starvation and even abiotic stress. The results of RNA-seq were further validated by qRT-PCR (Figure 5). From the current research, we have not been able to determine the cause of the increase in the expression of photosynthesis-related genes in the low-nitrogen environment, and it may be that too high photosynthesis will cause damage to plants. Conversely, the expression of most PSⅡ relater-genes in mutants decreased significantly after -C treatment, suggesting that autophagy may affect photosynthesis by affecting the expression of PSⅡ-related genes. It may be that autophagy is involved in PSII photoprotection and repair under stress/dark conditions and ultimately affects photosynthetic efficiency 60,61 . The difference in the expression of photosynthesis genes in ATG5-1 under low nitrogen and low carbon conditions indicates the complexity of autophagy in regulating photosynthesis, which may involve the influence of autophagy on plant C/N balance. There should be more experimental evidence at the protein level in the future to validate the results at these transcriptional levels. The PSII- related genes involved in this study, such as CYTC6A , PPL1 , PSB01 , and PSK27 , may be degraded by autophagy as potential target genes. Based on the results of GO and KEGG analyses, WT plants showed downregulated of a large number of photosynthetic genes compared to atg5-1 , which may indicate that autophagy has a direct or indirect relationship in regulating the transcription of photosynthesis-related genes. So far, there are few reports on autophagy and photosynthesis in plants. A recent study has shown that plants can reduce high-intensity light damage by accumulating ROS through pexophagy and microautophagy 62 . This result indicates a relationship between autophagy and high-light stress, which also suggest that autophagy is somehow related to photosynthesis. Using chlorophyll fluorescence imaging, we further confirmed that autophagy can regulate photosynthesis in plants by an unknown mechanism. Fv/Fm and ΦPSⅡ represent the maximum and real quantum efficiency of photosystem II, respectively. The higher the value is, the stronger the photosynthesis. We found that after stress treatment, Fv/Fm and ΦPSⅡ decreased significantly in the old leaves of autophagic mutant atg5-1 , reflecting the decrease in photosynthesis (Figure 7A). The same trend could be seen under different lighting conditions (Figure 7C). Y ( NPQ ) and Y ( NO ) represent the regulatory and nonregulatory energy dissipation of PSII, respectively. High Y ( NPQ ) indicates the plants have a strong photoprotection mechanism, such as heat dissipation and other forms of dissipation of excess light energy 63 . In normally growing plants (WT and atg5-1 ), we found that the Y ( NPQ ) values were all 0, which may be due to the lack of excess light energy such that the photoprotective mechanism was not activated. However, in the young leaves after nitrogen starvation, the WT Y ( NPQ ) value increased to 0.08, while that for atg5-1 was still 0 (Figure 7A). This may be due to the weakening of photosynthesis caused by stress, suggesting that WT protected plants better under strong light. A high Y ( NO ) value indicates that the photochemical PSII reaction and protective regulatory mechanism play no role and that excessive excitation energy damages PSII 63 . Apparently, the Y ( NO ) value of the old leaves of atg5-1 after nitrogen starvation reached 0.88, while that of the WT leaves was 0.74, further indicating that the photoprotective ability of the autophagic mutant was weaker (Figure 7A). Chlorophyll levels can also be an indicator of the photosynthetic capacity of plants. We found no significant change in chlorophyll content in young WT leaves and atg5-1 after treatment. A decrease in chlorophyll content in old atg5-1 old leaves was observed during normal growth, which may indicate that autophagy does not regulate photosynthesis by controlling chlorophyll levels in plants under nutrient stress. Curiously, there was no significant difference in chlorophyll content between the WT and the mutant in both old and new leaves after nitrogen stress (Figure 7F), suggesting that autophagy may not be involved in chlorophyll degradation in the light-avoidance reaction. However, studies have shown that the selective autophagy receptor NBR1 targets and degrades translocon at the outer envelopem embrane of chloroplasts (TOC) under some abiotic stresses such as UV-B irradiation and heat stress 64 . This suggests that autophagy, as one of the pathways of chloroplast degradation, only works under specific conditions, which requires further research. Notably, all changes in photosynthetic indices occurred in relatively old leaves, while there were only minor changes in new and young leaves. This suggests that plants respond to stress by preferentially transporting nutrients to young leaves to ensure plant survival. When the autophagy pathway is blocked, plants cannot gain energy from the breakdown of intracellular substances, leading to senescence and death of old leaves. As we all know, chloroplasts are the most important sites for photosynthesis. Two new studies have found that autophagy is extensively involved in chloroplast degradation under abiotic stress, which may be one of the main reasons for the decline in plant photosynthetic capacity 64,65 . In eukaryotes, AS is a posttranscriptional regulation mechanism for the production of new transcripts. In this study, we also examined the two events of WT and atg5-1 under +/-N conditions. The overall result was similar to the trend for DETs observed previously, that is, WT showed more genes becoming subject to AS under nitrogen starvation, while atg5-1 was relatively insensitive. GO enrichment analyses revealed that numerous genes related to photosynthesis had AS events (Figure 7C; Table S6). This suggests that autophagy plays an important role in the breakdown of chloroplasts and photosynthetic system II. Interestingly, a gene called CV (AT2G25625) associated with the photosynthetic membrane was previously reported to regulate plant senescence independently of the autophagic pathway 66 . This may be because different isoforms of the gene are reversely broken down by autophagy. In addition, we identified 20 and 23 lncRNAs that were up- and downregulated in WT under nitrogen deficiency stress. In these lncRNAs, we found 6 lncRNAs with the potential to regulate autophagy. The target genes of these lncRNAs all contain autophagy-related genes, particularly ATG8 , which may be involved in autophagy formation. It is worth noting that four agt8 genes were separately regulated by different lncRNAs, suggesting that atg8 genes may exhibit different expression patterns during stress generation (Table S7). Among the 4 lncRNAs that regulate atg8 , three were upregulated and one is downregulated, suggesting that these different lncRNAs play a role in promoting/inhibiting autophagy genes. There are currently no relevant reports in plants, nor do our results provide clues as to how lncRNAs regulate autophagy. Materials And Methods Plant materials and treatment. The seeds of the Arabidopsis ecotypes Col-0 and atg5-1 45 (Purchased from ABRC (https://abrc.osu.edu/)) were surface sterilized with 1% sodium hypochlorite after 3 days of stratification at 4°C. The sterilized seeds were grown in a triangular flask with liquid MS culture medium for 5 days under a long- day photoperiod (16 h light/8 h dark) with a continuous temperature of 22°C and shaking at 100 rpm. Plants that grew well transferred to MS or nitrogen-depleted liquid medium for an additional 2 days, as control and treatment groups, respectively. RNA-seq library construction and Nanopore sequencing. The experimental procedure was performed according to the standard protocol provided by ONT. Briefly, RNA from Arabidopsis seedlings was extracted with a plant RNA isolation kit (RC401-01, Vazyme). A cDNA PCR barcoding kit (SQK-PCS109 with SQK-PBK004, ONT) was used for sample cDNA generation, barcoding and sequencing. A total of 12 samples, including 3 biological replicates of each treatment or control, were sequenced. For each sample, 2 ng of polyA+ RNA was used in reverse transcription and strand switching, and 5 µL of reverse transcribed RNA was used to select for full-length transcripts. The cDNA product was amplified for 14 cycles with LongAmp® Taq DNA Polymerase (NEB). Then, adapters were added to cDNA samples by T4 DNA ligase (NEB). The final cDNA libraries were added to FLO-MIN109 flow cells, and sequenced on the PromethION platform from Biomarker Technology Company (Beijing, China). Raw data processing and genome mapping. The low-quality reads (Q-score < 6, length < 200 bp) were filtered and ribosomal RNAs were discarded after mapping to the rRNA database (https://www.arb-silva.de). After trimming adapter primers, the full-length nonchimeric transcripts were mapped to the Arabidopsis TAIR10 reference genome by minmap2 46 and further polished to obtain consensus sequences by pinfish (https://github.com/nanoporetech/pinfish). Principal component analysis (PCA) was performed using BMKCloud ( www.biocloud.net ). Differential gene/transcript expression analysis. The counts per million (CPM) calculation method was used to standardize the number of reads versus the genome as a data indicator to measure the expression level of transcripts. The non-redundant full-length sequences were compared to the reference transcriptome, and the quantitative results of all transcripts transcribed from the gene were counted as the quantitative results of the gene, which were also calculated by the CPM method. For the quantitative results of transcripts and genes, DESeq2 was used for difference analysis and the p-value was corrected by Benjamini-Hochberg method 47 to obtain the false discovery rate (FDR). Transcripts with a fold change (FC) ≥ 1.5 and FDR < 0.05 were selected for further analysis. Alternative splicing analysis. Transcripts were validated against known reference transcript annotations using gffcompare 48 . AS events were detected by the AStalavista tool 49 . Different types of AS were identified, including an alternative 3' and an alternative 5' splice site, as well as exon skipping, intron retention, and mutually exclusive exons. Identification of long noncoding RNAs. We identified CDSs of polished nonredundant isoforms using TransDecoder software 50 . Prediction of lncRNAs was performed using four methods, Coding Potential Calculator (CPC) 51 , Coding-Non-Coding Index (CNCI) 52 , Coding Potential Assessment Tool (CPAT) 53 , and Pfam. These four methods were used in combination to screen out the noncoding RNA sequences of noncoding proteins from the predicted RNA sequences of coding proteins. The screening criteria for lncRNA candidates were based on transcript length and exon number (greater than 200 bp in length with more than two exons). LncRNAs were then classified as intergenic lncRNA (also called lincRNAs), antisense lncRNAs, sense lncRNAs, ord intronic lncRNAs. Target genes regulated by the identified lncRNAs were also predicted using the LncTar software 54 . Functional annotation and enrichment analysis. Gene/transcript functions were annotated based on the GO 55 and KEGG databases. We used the GOseq R package 56 and KOBAS software 57 to perform GO and KEGG enrichment analyses on DEGs, respectively. Measurement of photosynthetic index and chlorophyll content. Four-week-old Arabidopsis plants were subjected to measurement of the photosynthetic index and chlorophyll content. Chlorophyll fluorescence images were obtained using an IMAGING-PAM system (WALZ, Germany). The maximum quantum efficiency of photosystem II (Fv/Fm) was measured after 20 min dark adaptation with a saturating light pulse with a photosynthetic photon flux density (PFD) of 6,000 µmol m -2 s -1 for 1 s. Then, the plants were exposed to an actinic PFD of 56 µmol m -2 s -1 . Saturation flashes were applied 10 times every 20 s, and the effective quantum efficiency of PSII (ΦPSII) after 224s of light adaptation was used for data analysis. The photosynthetic electric transport rate (ETR) was determined by continuous irradiation with gradually increasing light intensity (0 – 1,251 µmol m -2 s -1 ) in 12 steps (20 s for each stage). We applied three replicates for each sample. For determination of chlorophyll content, 0.1g of freshly-harvested leaves was put into 5 ml of anhydrous ethanol, and stored in a dark incubator at 4°C for 48h to extract chlorophyll (mg/L). Chla=13.95*A665-6.88*A649; Chlb=24.96*A649-7.32*A665. There were 3 replicates per sample. RNA Isolation and qRT-PCR. The 7-day-old seedlings were treated with -N (4 d) or -C (2 d) respectively. Samples are quickly frozen in liquid nitrogen and then RNA is extracted. The primers of qRT-PCR of the 10 genes involved in photosynthesisare are listed in Table S8.A rapid RNA isolation kit (Vazyme, China) was used to extract total RNA of samples. Total RNAs were used for complementary cDNA synthesis using SuperScript II QRT SuperMix (Vazyme. China) in accordance with the manufacturer’s instructions. The qRT-PCR analysis was performed on a Bio-Rad CFX96 using the 2×ChamQ universal SYBR qPCR Master Mix (Vazyme. China). The PCR reaction conditions were as follows: 95 ℃ for 30 s, followed by 40 cycles of 95 ℃ for 5 s, and 60 ℃ for 30 s, and 2 − ΔΔCT was used to reckon the relative expression levels of genes. The experiments were repeated three times. Declarations Ethics statements The authors confirm that experimental research on the Arabidopsis thaliana used in this research, complied with institutional, national, and international guidelinesand legislation. Data availability The raw sequencing data have been deposited in the National Genomics Data Center (NGDC), Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation (https://ngdc.cncb.ac.cn/), under the accession number PRJCA018751 (https://ngdc.cncb.ac.cn/gsa/s/essO5v6w). Acknowledgments We thank the technical assistance from Biomarker Technologies (Beijing, China) for Oxford Nanopore Full-length Sequencing and data analysis. We would like to appreciate the reviewers’ comments and helpful suggestions. Author contributions FL and WH conceived, designed the research and revised the manuscript. SW performed the experiments, interpreted the data, and wrote the manuscript. YS performed the bioinformatic analysis. YZ performed qRT-PCR experiments. All authors have read and approved the final version of the manuscript. Funding This work was supported by the grants from the Lushan Botanical Garden, Chinese Academy of Sciences [No.2021ZWZX09 to Song Wang], by grant from the Natural Science Foundation of Jiangxi Province [No.20232BAB215003 to Song Wang ] by grant from the National Natural Science Foundation of China [32160099 to Dr. Weiming Hu; 32100297 to Dr. Fen Liu], and by grant from Jiangxi high-level and urgently needed overseas talent introduction plan [20212BCJ25025 to Dr. Weiming Hu; 20212BCJ25024 to Dr. Fen Liu]. Competing interests The authors declare no competing interests References Marshall, R. S. & Vierstra, R. D. Autophagy: The Master of Bulk and Selective Recycling. Annu Rev Plant Biol 69 , 173-208, doi:10.1146/annurev-arplant-042817-040606 (2018). McLoughlin, F. et al. Autophagy Plays Prominent Roles in Amino Acid, Nucleotide, and Carbohydrate Metabolism during Fixed-Carbon Starvation in Maize. Plant Cell 32 , 2699-2724, doi:10.1105/tpc.20.00226 (2020). van Doorn, W. G. & Papini, A. Ultrastructure of autophagy in plant cells: a review. Autophagy 9 , 1922-1936, doi:10.4161/auto.26275 (2013). Machado, S. R. & Rodrigues, T. M. Autophagy and vacuolar biogenesis during the nectary development. Planta 250 , 519-533, doi:10.1007/s00425-019-03190-7 (2019). Feng, Y., He, D., Yao, Z. & Klionsky, D. J. The machinery of macroautophagy. Cell Res 24 , 24-41, doi:10.1038/cr.2013.168 (2014). Wang, S., Hu, W. & Liu, F. Autophagy in the Lifetime of Plants: From Seed to Seed. Int J Mol Sci 23 , doi:10.3390/ijms231911410 (2022). Jiang, W. et al. Key Regulators of Autophagosome Closure. Cells 10 , doi:10.3390/cells10112814 (2021). Yang, C., Luo, M., Zhuang, X., Li, F. & Gao, C. Transcriptional and Epigenetic Regulation of Autophagy in Plants. Trends Genet 36 , 676-688, doi:10.1016/j.tig.2020.06.013 (2020). Liu, F. et al. AUTOPHAGY-RELATED14 and Its Associated Phosphatidylinositol 3-Kinase Complex Promote Autophagy in Arabidopsis. Plant Cell 32 , 3939-3960, doi:10.1105/tpc.20.00285 (2020). Cheng, S., Wang, Q., Manghwar, H. & Liu, F. Autophagy-Mediated Regulation of Different Meristems in Plants. Int J Mol Sci 23 , doi:10.3390/ijms23116236 (2022). Pu, Y., Luo, X. & Bassham, D. C. TOR-Dependent and -Independent Pathways Regulate Autophagy in Arabidopsis thaliana. Front Plant Sci 8 , 1204, doi:10.3389/fpls.2017.01204 (2017). Soto-Burgos, J. & Bassham, D. C. SnRK1 activates autophagy via the TOR signaling pathway in Arabidopsis thaliana. PLoS One 12 , e0182591, doi:10.1371/journal.pone.0182591 (2017). Zhuang, X. et al. A BAR-domain protein SH3P2, which binds to phosphatidylinositol 3-phosphate and ATG8, regulates autophagosome formation in Arabidopsis. Plant Cell 25 , 4596-4615, doi:10.1105/tpc.113.118307 (2013). Gao, C. et al. Dual roles of an Arabidopsis ESCRT component FREE1 in regulating vacuolar protein transport and autophagic degradation. Proc Natl Acad Sci U S A 112 , 1886-1891, doi:10.1073/pnas.1421271112 (2015). Masclaux-Daubresse, C. et al. Stitching together the Multiple Dimensions of Autophagy Using Metabolomics and Transcriptomics Reveals Impacts on Metabolism, Development, and Plant Responses to the Environment in Arabidopsis. Plant Cell 26 , 1857-1877, doi:10.1105/tpc.114.124677 (2014). Avin-Wittenberg, T. et al. Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation. Plant Cell 27 , 306-322, doi:10.1105/tpc.114.134205 (2015). Minina, E. A. et al. Transcriptional stimulation of rate-limiting components of the autophagic pathway improves plant fitness. J Exp Bot 69 , 1415-1432, doi:10.1093/jxb/ery010 (2018). Havé, M. et al. Proteomic and lipidomic analyses of the Arabidopsis atg5 autophagy mutant reveal major changes in endoplasmic reticulum and peroxisome metabolisms and in lipid composition. New Phytol 223 , 1461-1477, doi:10.1111/nph.15913 (2019). Signorelli, S., Tarkowski Ł, P., Van den Ende, W. & Bassham, D. C. Linking Autophagy to Abiotic and Biotic Stress Responses. Trends Plant Sci 24 , 413-430, doi:10.1016/j.tplants.2019.02.001 (2019). Avin-Wittenberg, T. Autophagy and its role in plant abiotic stress management. Plant Cell Environ 42 , 1045-1053, doi:10.1111/pce.13404 (2019). Xiong, Y., Contento, A. L. & Bassham, D. C. AtATG18a is required for the formation of autophagosomes during nutrient stress and senescence in Arabidopsis thaliana. Plant J 42 , 535-546, doi:10.1111/j.1365-313X.2005.02397.x (2005). Liu, Y., Xiong, Y. & Bassham, D. C. Autophagy is required for tolerance of drought and salt stress in plants. Autophagy 5 , 954-963, doi:10.4161/auto.5.7.9290 (2009). Chen, L. et al. Autophagy contributes to regulation of the hypoxia response during submergence in Arabidopsis thaliana. Autophagy 11 , 2233-2246, doi:10.1080/15548627.2015.1112483 (2015). Eguchi, M., Kimura, K., Makino, A. & Ishida, H. Autophagy is induced under Zn limitation and contributes to Zn-limited stress tolerance in Arabidopsis (Arabidopsis thaliana). Soil Science and Plant Nutrition 63 , 342-350, doi:10.1080/00380768.2017.1360750 (2017). Di Berardino, J. et al. Autophagy controls resource allocation and protein storage accumulation in Arabidopsis seeds. J Exp Bot 69 , 1403-1414, doi:10.1093/jxb/ery012 (2018). Yang, C. et al. HY5-HDA9 Module Transcriptionally Regulates Plant Autophagy in Response to Light-to-Dark Conversion and Nitrogen Starvation. Mol Plant 15 , 1632-1634, doi:10.1016/j.molp.2022.09.015 (2022). Bedu, M., Marmagne, A., Masclaux-Daubresse, C. & Chardon, F. Transcriptional Plasticity of Autophagy-Related Genes Correlates with the Genetic Response to Nitrate Starvation in Arabidopsis Thaliana. Cells 9 , doi:10.3390/cells9041021 (2020). Sedaghatmehr, M. et al. A regulatory role of autophagy for resetting the memory of heat stress in plants. Plant Cell Environ 42 , 1054-1064, doi:10.1111/pce.13426 (2019). Cui, J. et al. Analysis and comprehensive comparison of PacBio and nanopore-based RNA sequencing of the Arabidopsis transcriptome. Plant Methods 16 , 85, doi:10.1186/s13007-020-00629-x (2020). Hou, C. et al. Comparative Analyses of Full-Length Transcriptomes Reveal Gnetum luofuense Stem Developmental Dynamics. Front Genet 12 , 615284, doi:10.3389/fgene.2021.615284 (2021). Zhao, L. et al. Analysis of Transcriptome and Epitranscriptome in Plants Using PacBio Iso-Seq and Nanopore-Based Direct RNA Sequencing. Front Genet 10 , 253, doi:10.3389/fgene.2019.00253 (2019). Abdel-Ghany, S. E. et al. A survey of the sorghum transcriptome using single-molecule long reads. Nat Commun 7 , 11706, doi:10.1038/ncomms11706 (2016). Wang, M. et al. A global survey of alternative splicing in allopolyploid cotton: landscape, complexity and regulation. New Phytol 217 , 163-178, doi:10.1111/nph.14762 (2018). Yang, Z. et al. Identification and genetic analysis of alternative splicing of long non-coding RNAs in tomato initial flowering stage. Genomics 112 , 897-907, doi:10.1016/j.ygeno.2019.06.005 (2020). Zhang, Y. et al. Molecular Basis of the Distinct Metabolic Features in Shoot Tips and Roots of Tea Plants (Camellia sinensis): Characterization of MYB Regulator for Root Theanine Synthesis. J Agric Food Chem 69 , 3415-3429, doi:10.1021/acs.jafc.0c07572 (2021). Mercer, T. R., Dinger, M. E. & Mattick, J. S. Long non-coding RNAs: insights into functions. Nat Rev Genet 10 , 155-159, doi:10.1038/nrg2521 (2009). Bonasio, R. & Shiekhattar, R. Regulation of transcription by long noncoding RNAs. Annu Rev Genet 48 , 433-455, doi:10.1146/annurev-genet-120213-092323 (2014). Yadav, V. K., Sawant, S. V., Yadav, A., Jalmi, S. K. & Kerkar, S. Genome-wide analysis of long non-coding RNAs under diel light exhibits role in floral development and the circadian clock in Arabidopsis thaliana. Int J Biol Macromol 223 , 1693-1704, doi:10.1016/j.ijbiomac.2022.09.295 (2022). Karlik, E., Ari, S. & Gozukirmizi, N. LncRNAs: genetic and epigenetic effects in plants. Biotechnology & Biotechnological Equipment 33 , 1-11, doi:10.1080/13102818.2019.1581085 (2019). Chekanova, J. A. Long non-coding RNAs and their functions in plants. Curr Opin Plant Biol 27 , 207-216, doi:10.1016/j.pbi.2015.08.003 (2015). Rai, M. I., Alam, M., Lightfoot, D. A., Gurha, P. & Afzal, A. J. Classification and experimental identification of plant long non-coding RNAs. Genomics 111 , 997-1005, doi:10.1016/j.ygeno.2018.04.014 (2019). Zhao, X. et al. Global identification of Arabidopsis lncRNAs reveals the regulation of MAF4 by a natural antisense RNA. Nat Commun 9 , 5056, doi:10.1038/s41467-018-07500-7 (2018). Jampala, P., Garhewal, A. & Lodha, M. Functions of long non-coding RNA in Arabidopsis thaliana. Plant Signal Behav 16 , 1925440, doi:10.1080/15592324.2021.1925440 (2021). Zhao, Z. et al. Long Non-Coding RNAs: New Players in Plants. Int J Mol Sci 23 , doi:10.3390/ijms23169301 (2022). Thompson, A. R., Doelling, J. H., Suttangkakul, A. & Vierstra, R. D. Autophagic nutrient recycling in Arabidopsis directed by the ATG8 and ATG12 conjugation pathways. Plant Physiol 138 , 2097-2110, doi:10.1104/pp.105.060673 (2005). Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34 , 3094-3100, doi:10.1093/bioinformatics/bty191 (2018). Ferreira, J. A. The Benjamini-Hochberg Method in the Case of Discrete Test Statistics. The International Journal of Biostatistics 3 , 1065-1065 (2007). Pertea, G. & Pertea, M. GFF Utilities: GffRead and GffCompare. F1000Res 9 , doi:10.12688/f1000research.23297.2 (2020). Foissac, S. & Sammeth, M. ASTALAVISTA: dynamic and flexible analysis of alternative splicing events in custom gene datasets. Nucleic Acids Res 35 , W297-299, doi:10.1093/nar/gkm311 (2007). Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8 , 1494-1512, doi:10.1038/nprot.2013.084 (2013). Kong, L. et al. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35 , W345-349, doi:10.1093/nar/gkm391 (2007). Sun, L. et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Research 41 , e166-e166, doi:10.1093/nar/gkt646 (2013). Wang, L. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res 41 , e74, doi:10.1093/nar/gkt006 (2013). Li, J. et al. LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Brief Bioinform 16 , 806-812, doi:10.1093/bib/bbu048 (2015). Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25 , 25-29, doi:10.1038/75556 (2000). Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11 , R14, doi:10.1186/gb-2010-11-2-r14 (2010). Mao, X., Cai, T., Olyarchuk, J. G. & Wei, L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21 , 3787-3793, doi:10.1093/bioinformatics/bti430 (2005). Wang, Y. et al. Autophagy contributes to leaf starch degradation. Plant Cell 25 , 1383-1399, doi:10.1105/tpc.112.108993 (2013). Shi, Y., Yu, B., Cheng, S., Hu, W. & Liu, F. The Change in Whole-Genome Methylation and Transcriptome Profile under Autophagy Defect and Nitrogen Starvation. Int J Mol Sci 24 , doi:10.3390/ijms241814047 (2023). Cao, J. et al. Autophagic pathway contributes to low-nitrogen tolerance by optimizing nitrogen uptake and utilization in tomato. Hortic Res 9 , uhac068, doi:10.1093/hr/uhac068 (2022). Barros, J. A. S. et al. Autophagy Deficiency Compromises Alternative Pathways of Respiration following Energy Deprivation in Arabidopsis thaliana. Plant Physiol 175 , 62-76, doi:10.1104/pp.16.01576 (2017). Oikawa, K. et al. Pexophagy suppresses ROS-induced damage in leaf cells under high-intensity light. Nat Commun 13 , 7493, doi:10.1038/s41467-022-35138-z (2022). Kramer, D. M., Johnson, G., Kiirats, O. & Edwards, G. E. New Fluorescence Parameters for the Determination of QA Redox State and Excitation Energy Fluxes. Photosynth Res 79 , 209, doi:10.1023/B:PRES.0000015391.99477.0d (2004). Wan, C. et al. Selective autophagy regulates chloroplast protein import and promotes plant stress tolerance. Embo j 42 , e112534, doi:10.15252/embj.2022112534 (2023). Lee, H. N. et al. The autophagy receptor NBR1 directs the clearance of photodamaged chloroplasts. eLife 12 , e86030, doi:10.7554/eLife.86030 (2023). Wang, S. & Blumwald, E. Stress-induced chloroplast degradation in Arabidopsis is regulated via a process independent of autophagy and senescence-associated vacuoles. Plant Cell 26 , 4875-4888, doi:10.1105/tpc.114.133116 (2014). Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg FigureS2.jpg FigureS3.jpg TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Apr, 2024 Reviews received at journal 28 Mar, 2024 Reviewers agreed at journal 26 Mar, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Editor invited by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 08 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4041050","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":282323769,"identity":"0aab6111-bca7-4bae-bab9-79ca9a21859d","order_by":0,"name":"Song Wang","email":"","orcid":"","institution":"Jiangxi province and Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Wang","suffix":""},{"id":282323770,"identity":"88c62087-bf11-4e86-8a01-d64c05133116","order_by":1,"name":"Yunfeng Shi","email":"","orcid":"","institution":"Jiangxi province and Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Shi","suffix":""},{"id":282323771,"identity":"b297c10c-3cac-488e-a6d8-978e73b41752","order_by":2,"name":"Yanhui Zhou","email":"","orcid":"","institution":"Jiangxi province and Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Zhou","suffix":""},{"id":282323772,"identity":"554df152-d474-4518-8817-a9f15fb49905","order_by":3,"name":"Weiming Hu","email":"","orcid":"","institution":"Jiangxi province and Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Hu","suffix":""},{"id":282323773,"identity":"b31a5942-c020-4d90-b636-5beaf6fce4f1","order_by":4,"name":"Fen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlklEQVRIiWNgGAWjYBACPmYGhg8fChgSiNfCxszAOHOGAUlaGBgYZ/OQpoWdx7DZxqAuT96B+dkDIh0G1JJjcLjY8ACbuQGxWswf5xgcSNzYwMMmQbwtFgZ1pGphMGBOnM9AvBa2wsYeoF8MmNnMiNPCz394Y8OPCmCItTc/I04LHBgcJk09EMg3kKxlFIyCUTAKRgoAAEEAI4oCwKrSAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangxi province and Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Fen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-03-08 09:41:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4041050/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4041050/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53416976,"identity":"054a9b9f-17d6-42cf-b37c-33dcd9974f62","added_by":"auto","created_at":"2024-03-25 18:01:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273678,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of transcript expression profiles of 12 samples. A, B, C, D indicated Col-0, \u003cem\u003eatg5-1\u003c/em\u003e, Col-0-N, and \u003cem\u003eatg5-1\u003c/em\u003e-N, respectively. \u003cstrong\u003eA\u003c/strong\u003e, Principal component analysis (PCA); \u003cstrong\u003eB\u003c/strong\u003e, CPM boxplot of each sample gene.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/eddeff3ba96de9b495588f6c.jpg"},{"id":53418752,"identity":"ca3179d2-60f9-4558-9902-d8f4635e420c","added_by":"auto","created_at":"2024-03-25 18:09:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":738220,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes in each group. A, B, C, D stand for Col-0+N versus \u003cem\u003eatg5-1\u003c/em\u003e+N, Col-0+N versus Col-0-N, \u003cem\u003eatg5-1\u003c/em\u003e+N versus \u003cem\u003eatg5-1\u003c/em\u003e-N, and Col-0-N versus \u003cem\u003eatg5-1\u003c/em\u003e-N respectively.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/c61e75dafd1ccb08e7c03fd5.jpg"},{"id":53416992,"identity":"54746b27-f78e-4db7-a471-cc1e494c9fbb","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1785964,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analyses using DETs between C group vs D group.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/78b2ab8a535edccea40d5dae.jpg"},{"id":53416978,"identity":"0303d889-a1ab-4f5f-a9b0-c6fa410d7697","added_by":"auto","created_at":"2024-03-25 18:01:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155212,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment of DETs between C group vs D group.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/af9aa9a41ad028095d917805.jpg"},{"id":53416987,"identity":"56d86c62-569f-4a63-9a42-e4ff280d008f","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":622814,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis(+/-N) of the 10 photosynthesis-related genes was performed using qRT-PCR. Error bars represent the stand errors with three replications and the lowercase letter above the bar indicates a significant difference (α=0.05, LSD) among the treatments.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/151b4bd1c9b3ce331ca1fa89.jpg"},{"id":53416982,"identity":"89d8ba29-8ac6-4f37-934e-5153ee8dc633","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2275102,"visible":true,"origin":"","legend":"\u003cp\u003eThe chlorophyll fluorescence images. The red circle indicates the site where NPQ occurs.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/6ac98364a83c6ecb6e174b86.jpg"},{"id":53416989,"identity":"02132389-533e-49c8-a6f4-904556ee7637","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1008405,"visible":true,"origin":"","legend":"\u003cp\u003ePhotosynthetic index statistics. \u003cstrong\u003eA\u003c/strong\u003e, Photosynthetic index under the condition the condition of carbon starvation at 56 μmol·m\u003csup\u003e-2\u003c/sup\u003e·s\u003csup\u003e-1\u003c/sup\u003e. \u003cstrong\u003eB\u003c/strong\u003e (Yong leaf), \u003cstrong\u003eC \u003c/strong\u003e(Old leaf), Photosynthetic electron transfer rate (ETR) under different photosynthetically active radiation (0 μmol·m\u003csup\u003e-2\u003c/sup\u003e·s\u003csup\u003e-1\u003c/sup\u003e-1251 μmol·m\u003csup\u003e-2\u003c/sup\u003e·s\u003csup\u003e-1\u003c/sup\u003e). PPF means photosynthetic photon flux. \u003cstrong\u003eD\u003c/strong\u003e (Yong leaf), \u003cstrong\u003eE \u003c/strong\u003e(Old leaf), ΦPSⅡ under different photosynthetically active radiation. \u003cstrong\u003eF\u003c/strong\u003e, Determination of chlorophyll content.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/d30711b38955c6eb5c6699b2.jpg"},{"id":53416990,"identity":"250b4e8f-fa29-497f-b4b6-26f3ae09a219","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3797421,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of alternative splicing events (AS) and functional analysis. \u003cstrong\u003eA\u003c/strong\u003e, Statistics of the number of AS events in each sample. \u003cstrong\u003eB\u003c/strong\u003e. Comparison of AS events between different groups C vs D(S) indicated that the AS event occurs only in the C vs D group. \u003cstrong\u003eC\u003c/strong\u003e, Filtered differential alternative splicing events between groups. A3SS, A5SS, IR, ES, MES, and ALL means alternative 3' splice site, alternative 5' splice site, intron retention, exon skipping, mutually exclusive exon, and total AS events, respectively.\u003cstrong\u003e D\u003c/strong\u003e. Many genes involved in AS events are involved in photosynthesis.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/0135a8115ae1dfcaf6cca514.jpg"},{"id":53419984,"identity":"b56c6d2c-025f-40a8-889d-8a52ab87bb93","added_by":"auto","created_at":"2024-03-25 18:17:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1256564,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/7e724f67-9c35-406d-a3ca-0e826c9c03e7.pdf"},{"id":53416977,"identity":"5f625d0e-fe05-4373-b711-69b82ba03c6f","added_by":"auto","created_at":"2024-03-25 18:01:46","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":654063,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/ae3bdd063e03763888411422.jpg"},{"id":53416981,"identity":"d915c038-6d4d-422a-8487-09710c94481c","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170371,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/2189484a286b55954fe25636.jpg"},{"id":53416991,"identity":"fd60bf25-eb56-436a-a9d7-8b463c3710fc","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":104163,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/a685f63956d10b25e38ffc38.jpg"},{"id":53416985,"identity":"21656e0a-aecd-4f3b-896f-a8eed7dc9982","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11164,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/41afbd4d382fd56e74a84e0d.xlsx"},{"id":53416980,"identity":"a13fc217-c140-4384-9a37-436f30f25c3c","added_by":"auto","created_at":"2024-03-25 18:01:46","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11114,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/51fcc0a1e9b75a685c101697.xlsx"},{"id":53416983,"identity":"0149e9eb-f4e7-4ba9-a05f-168d470cdb1d","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4494011,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/c56cf46ac59391dda611cf1f.xlsx"},{"id":53418753,"identity":"c821c952-d8c8-47e3-87a4-2668b7a5db54","added_by":"auto","created_at":"2024-03-25 18:09:47","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":742538,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/3183aa75e21cb66c76f4b51d.xlsx"},{"id":53416993,"identity":"ff173407-d2d3-4943-ade4-c8b92b05f3a6","added_by":"auto","created_at":"2024-03-25 18:01:48","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":320791,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/0e01e99efb6a98e68a7d55e8.xlsx"},{"id":53416988,"identity":"77ab191e-a4a2-4d80-bbfb-ef65df16b79d","added_by":"auto","created_at":"2024-03-25 18:01:47","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":24421,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/d21b6f534f022eda0b7c1e42.xlsx"},{"id":53416995,"identity":"a256db94-7933-4f53-b2ad-210a829018ef","added_by":"auto","created_at":"2024-03-25 18:01:48","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":57728,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/cfedf42134615ee240faaed9.xlsx"},{"id":53418754,"identity":"85202c11-7559-4353-80f0-f7f2e491537b","added_by":"auto","created_at":"2024-03-25 18:09:48","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":10913,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4041050/v1/c6121082820be88dd18c078b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Full-length transcriptome sequencing of Arabidopsis seedlings provided new insights into autophagic regulation of photosynthesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn response to stress, plants use a variety of adaptive responses to maintain an adequate supply of nutrients for growth, development, reproduction, and protection. One important pathway involves the autophagic turnover of intracellular substances, which is essential for the proper processing of unnecessary or dysfunctional substances in the cells and the subsequent reuse of the nutrient components\u003csup\u003e1,2\u003c/sup\u003e. To date, three different types of autophagy have been discovered in plants, including microautophagy, macroautophagy, and mega-autophagy\u003csup\u003e3,4\u003c/sup\u003e.\u0026nbsp;The best-studied type of autophagy in plants is macroautophagy, in which autophagosomes are formed and then fuse with vacuoles to break down cargos\u003csup\u003e5,6\u003c/sup\u003e. Over the past decade, more than 40 ATG (autophagy-related gene) proteins have been identified in the canonical macroautophagy pathway in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e\u003csup\u003e1,7\u003c/sup\u003e. These proteins can be classified into four groups: the ATG1/ATG13 kinase complex, the phosphatidylinositol 3-kinase (PI3K) complex, the ATG9 complex, and the ATG8 and ATG12 ubiquitin-like conjugation systems\u003csup\u003e6,8-10\u003c/sup\u003e. To date, many regulatory factors have been described as being involved in the regulation of autophagy in plants, including TOR, SnRK1, FREE1, and SH3P2\u003csup\u003e11-14\u003c/sup\u003e. In addition,\u0026nbsp;multiomic\u0026nbsp;methods have been used to dissect autophagy regulatory networks, including transcriptomic, metabolomic, and proteomic methods\u003csup\u003e15-18\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch is increasingly showing that autophagy acts as a protective strategy for plants to cope with a variety of stresses. Under stress, the expression of many \u003cem\u003eATG\u0026nbsp;\u003c/em\u003egenes is rapidly upregulated simultaneously to promote autophagy, recycle nutrients, and remove harmful debris to maintain cell homeostasis\u003csup\u003e19,20\u003c/sup\u003e. Conversely, autophagy mutants (e.g., \u003cem\u003eatg5-1\u003c/em\u003e) become chlorotic or show premature senescence under stress\u003csup\u003e17,21,22\u003c/sup\u003e, further indicating the important role of autophagy in plant resistance to abiotic stress. However, until now, only a few transcriptional regulators involved in these processes have been identified in \u003cem\u003eArabidopsis\u003c/em\u003e\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTranscriptional regulation is an important step in regulating eukaryotic gene expression. In Arabidopsis, transcriptional changes in \u003cem\u003eATG\u003c/em\u003e genes are frequently observed during plant development and adaptation to environmental changes\u003csup\u003e23-28\u003c/sup\u003e. \u0026nbsp; Using conventional transcriptome technology, Masclaux-Daubresse et al revealed connections between autophagy and salicylic acid biosynthesis and response, cytokinin perception, oxidative stress, and plant defense\u003csup\u003e15\u003c/sup\u003e. In another transcriptomic study, Minina et al found that genes associated with necrotrophic pathogens and oxidative stress were abundant in \u003cem\u003eATG5\u003c/em\u003e- or \u003cem\u003eATG7\u003c/em\u003e-overexpressing plants\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;However, the exploration of the transcriptional regulation of autophagy genes by third-generation sequencing has not been reported. Compared with traditional second-generation transcriptome sequencing, third-generation full-length sequencing based on the Oxford Nanopore Technologies (ONT) platform can more accurately and variably splice alternative transcripts (AS), long noncoding RNA (lncRNAs) and their target genes\u003csup\u003e29\u003c/sup\u003e. In addition, both genes and transcripts can be quantitatively analyzed simultaneously. A recent study showed that this technique provides better-quality raw data and more accurate estimates of transcription levels than the PacBio technique\u003csup\u003e29\u003c/sup\u003e. The ONT platform has been widely applied in whole-genome sequencing, but has rarely been used in full-length transcriptome sequencing\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we generated 12 full-length transcriptomes of wild-type \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and the autophagy mutant \u003cem\u003eatg5-1\u003c/em\u003e under low and high nitrogen level using the ONT platform. Based on the above data, we first analyzed the differentially expressed transcripts (DETs) between different groups. Compared to \u003cem\u003eatg5-1\u003c/em\u003e, WT had a large number of stress-related DETs that showed increased expression after nitrogen starvation, while the expression of photosynthesis-related DETs decreased. The index obtained from the chlorophyll fluorescence images also further proved the difference between WT and \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003ein terms of photosynthetic capacity. The above sequencing results were further verified by determination of photosynthetic indicators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough several articles on the autophagy-related transcriptome have been published, no studies on dynamic AS events and lncRNA-regulated target genes have been published. In this study, we examined AS events and identified lncRNAs under nitrogen starvation. AS is a common way to generate protein diversity after transcription\u003csup\u003e31\u003c/sup\u003e. Several studies have provided overwhelming amount of unassembled data for ASs events, as they produce all kinds of rich and high-quality transcripts of different lengths, such as AS events that occur in response to drought stress\u003csup\u003e32\u003c/sup\u003e and those that occur during development\u003csup\u003e33,34\u003c/sup\u003e.\u0026nbsp;AS enriches the diversity of the transcriptome and proteome and offers more flexibility in transcriptional regulation\u003csup\u003e35\u003c/sup\u003e. In this study, we found that a many of genes related to photosynthesis exhibited AS. LncRNAs are involved in gene transcription and posttranscriptional regulation in eukaryotes\u003csup\u003e36-39\u003c/sup\u003e. LncRNAs can regulate target genes by being transcribed, sequestering microRNAs and proteins, or acting as guide RNAs to recruit proteins\u003csup\u003e40,41\u003c/sup\u003e. LncRNAs have been closely implicated in metabolism, flowering, fertility, and the biological and abiotic stress response in plants\u003csup\u003e42-44\u003c/sup\u003e. We also found differences in the expression of several lncRNAs after nitrogen starvation between WT and\u003cem\u003e\u0026nbsp;atg5-1\u003c/em\u003e. Taken together, our results provide a basis for further understanding the mechanism of autophagy regulation and the relationship between autophagy and photosynthesis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of ONT sequencing datasets. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eTo examine the defined transcriptional regulation of autophagy in Arabidopsis after nitrogen starvation, Col-0 and \u003cem\u003eatg5-1\u003c/em\u003e mutants were subjected to ONT full length- transcriptome sequencing. After sequencing, the original fastq data were filtered for short fragments and low-quality reads, resulting in clean data. A total of 62 GB of clean data was produced with 4,522,054 to 5,597,930 reads, which had an average length between 1,176 and 1,265 nt (Table S1). Full-length reads accounted for more than 93.12% of the total clean reads after filtering rRNAs in each sample (Table S2). These reads had an N50 of 1,295-1,473 nt, and a maximum length of 15,138-257,481 nt. Through this dataset by the ONT sequencing platform, we identified 11,356 novel transcripts, 1,306 novel genes, and 375 lncRNAs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) and hierarchical cluster analysis based on the CPM of all transcripts in 12 samples showed a large genotypic effect as well as a treatment effect (Figure 1A). To further investigate the degree of variability of the gene expression level distribution in each sample and to visually compare the overall gene expression levels of different samples, we constructed boxplot to visualize the CPM distribution (Figure 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDET identification and functional analysis. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eTo assess transcriptomic changes under nitrogen starvation, DETs were identified with an at least 1.5-fold difference in expression and an FDR less than 0.05 for four comparisons, Col-0+N (A) versus \u003cem\u003eatg5-1\u003c/em\u003e+N (B), Col-0+N versus Col-0-N (C), \u003cem\u003eatg5-1\u003c/em\u003e +N versus \u003cem\u003eatg5-1\u003c/em\u003e-N (D), and Col-0-N versus \u003cem\u003eatg5-1\u003c/em\u003e-N. Interestingly, WT showed a substantial alteration in transcript expression after low-nitrogen treatment compared to nitrogen-rich conditions. In contrast, the mutation of \u003cem\u003eatg5-1\u003c/em\u003e resulted in fewer changers in transcript expression under +/- N (Figure 2, Table S3).\u003c/p\u003e\n\u003cp\u003eFor the comparison between the C and D groups, we obtained a total of 5,797 DETs, of which 2,837 were upregulated and 2,960 were downregulated in group D (Table S3). To select the DETs that were specifically expressed in C versus D, we removed the DETs that were already present in A versus B. On this basis, a total of 5,545 DETs were found, of which 2,719 and 2,826 DETs were upregulated and downregulated in group D, respectively (Table S4). To analyze the function of these DETs, GO enrichment\u0026nbsp;analyses\u0026nbsp;were performed. The results showed that these transcripts were associated with different functions in the biological processes category. Among the DETs under nitrogen starvation, the upregulated transcripts were mainly associated with response to stimuli, response to stress, and response to chemical processes (Figure 3A, 3B), and the downregulated transcripts were mainly related to stimulus response, abiotic stimulus response, and photosynthesis (Figure 3C, 3D). The above results not only show that autophagy is involved in the regulation of a wide range of stimuli regulation, whether upregulated or downregulated, but also has a significant impact on photosynthesis.\u003c/p\u003e\n\u003cp\u003eIn addition, to identify the metabolic pathways underlying nitrogen deficiency, a KEGG pathway enrichment analysis was performed. In addition to the secondary metabolic pathways and biosynthesis, the upregulated DETs in WT were associated with phenylpropanoid biosynthesis (Figure 4A). Carbon metabolism and photosynthesis were identified as being involved with downregulated transcripts (Figure 4B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the KEGG and GO database, we selected 10 genes associated with photosynthesis and their expression after nitrogen starvation for further study, which contain photosynthetic electron transport chainsⅠ (PSⅠ) and and Ⅱ (PSⅡ).\u0026nbsp;Similar to the results of RNA-seq, most of the photosynthesis genes measured (9/10) were expressed at higher levels in \u003cem\u003eatg5-1\u003c/em\u003e than in Col-0 after -N treatment (Figure 5). In addition to -N treatment, -C is also one of the conventional means to induce autophagy. Therefore, we further investigated the expression levels of these photosynthesis genes before and after +/-C treatment in autophagy mutants. Unlike the -N treatment, only one PS1 gene, namely PSAH, was expressed higher in \u003cem\u003eatg5-1\u003c/em\u003e than in Col-0 after -C treatment (Figure S1). However, the expression of \u003cem\u003eatg5-1\u003c/em\u003e in most of the measured PSPSII-realated genes (4/5), such as \u003cem\u003eCYTC6A\u003c/em\u003e, \u003cem\u003ePPL1\u003c/em\u003e, \u003cem\u003ePSB01\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePSB27\u003c/em\u003e, was lower than that in Col-0 after -C treatment (Figure S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of the photosynthetic index. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eGiven that the abundances of transcripts associated with photosynthesis were higher in the \u003cem\u003eatg5-1\u003c/em\u003e background, we assume that autophagy may play an important role in regulation of photosynthesis. To confirm this hypothesis, we used a chlorophyll fluorescence imaging system to analyze the photosynthetic indices of the abovementioned materials, under dark treatment, which was used to induce autophagy\u0026nbsp;\u003csup\u003e58\u003c/sup\u003e. Compared with nitrogen starvation, shading stress can prevent plant photosynthesis and induce autophagy more quickly, which can better measure its photosynthetic index. As shown in Figure 6 and Figure S2, no significant difference was observed in the photosynthetic indices including Fv/Fm between Col-0 and \u003cem\u003eatg5-1\u003c/em\u003e in both young and old leaves under normal conditions. Under fixed- carbon starvation, a sharp increase in \u003cem\u003eY(NO)\u003c/em\u003e and a decrease in Fv/Fm and \u0026Phi;PSII) were observed in old leaves of the \u003cem\u003eatg5-1\u003c/em\u003e mutant compared to young leaves of Col-0 and \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003eand old leaves of Col-0, respectively (Figure 6, 7A, S2). Fv/Fm refers to the maximum quantum efficiency of photosystem II. The results showed that Fv/Fm was approximately 0.7 in the young leaves of WT and \u003cem\u003eatg5-1\u003c/em\u003e, and +/- C led to significant difference between them. In contrast, Fv/Fm decreased rapidly to approximately 0.27 in the old leaves of \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003e(Figure 6, 7A, S2). Both the quantum yield of regulated energy dissipation (\u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e)) and the quantum yield of nonregulated energy dissipation (\u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e)) are indicators of energy dissipation during photosynthesis. In the normal growth environment without nutritional stress, the \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) values of WT and \u003cem\u003eatg5-1\u003c/em\u003e were both 0. However, under the carbon conditions, the \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) value of young WT leaves was significantly higher than that of young \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003eleaves. Surprisingly, \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) showed opposite indicators in the old leaves of both (Figure 6, 7A, S2). Same as \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e), WT and \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003eshow a similar \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e), around 6.5 to 6.9, under nonstress conditions. (Figure 6, 7A, S2). After carbon starvation, the \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e) values of the WT and mutant did not change significantly in young leaves but increased significantly in old leaves, the value for \u003cem\u003eatg5-1\u003c/em\u003e increased to\u0026nbsp;approximately\u0026nbsp;0.88, which was significantly higher than the value of 0.74 was observed for WT. In addition, we also measured the \u0026Phi;PSII, an indicator of shows the actual photosynthetic quantum yield of plants. The results showed that the \u0026Phi;PSII values of WT and \u003cem\u003eatg5-1\u003c/em\u003e in plants without stress treatment were stable between 0.51 and 0.52 in old and young leaves of \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003edecreased to 0.06 and 0.35, respectively (Figure 6, 7A, S2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we compare the differences between the relative electron transport rate (\u003cem\u003erETR\u003c/em\u003e (II)) and \u0026Phi;PSII under gradually increasing light intensity from 0 to 1251 \u0026mu;mol\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e. Apparently, the young leaves showed similar pattern for \u003cem\u003erETR\u003c/em\u003e (II) and \u0026Phi; PSII between WT and \u003cem\u003eatg5-1\u003c/em\u003e (Figure 7B, 7D). In contrast, in old leaves under fixed-carbon starvation, \u003cem\u003eatg5-1 rETR\u003c/em\u003e (II) and \u0026Phi;PSII were lower in \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003ethan in WT (Figure 7C, 7E).\u003c/p\u003e\n\u003cp\u003eTo determine whether the change in photosynthetic indices in old \u003cem\u003eatg5-1\u003c/em\u003e leaves was caused by the degradation of chlorophyll, we examined the chlorophyll contents of plant leaves. Under normal growth conditions, the chlorophyll content in old leaves of \u003cem\u003eatg5-1\u003c/em\u003e was slightly lower than that in old WT leaves, but there was no significant difference in new leaves (Figure 7F). A similar trend was observed after nutritional stress (Figure 7F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of AS events. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eIncreasing evidence indicates that AS plays a crucial role in plant development and stress. Five categories of AS events were identified in this study, the most common event was intron retention (41.76% to 48.47%), and the least abundant event was mutually exclusive exons (0.27% to 0.7%) (Figure 8A). Under high nitrogen levels, the number of AS events was higher in WT than in \u003cem\u003eatg5-1\u003c/em\u003e, particularly intron retention events (Figure 8A). In contrast, there was no significant difference in the total number of AS between the WT and the mutant \u003cem\u003eatg5-1\u003c/em\u003e under low nitrogen levels, but the proportion of intron retention events was higher in WT (Figure 8A). Furthermore, we comprehensively compared AS events among the four groups. The overall trend was more consistent with the results for DETs; that is, WT had more AS events (A vs. C, 643) under nitrogen starvation, while \u003cem\u003eatg5-1\u003c/em\u003e is relatively few (B vs. D, 510) (Figure 8C, D). Regardless of which groups were compared, intron retention was the most common AS mode.\u003c/p\u003e\n\u003cp\u003eTo select the AS that changed specifically between C vs. D, we removed the AS that was already present between A vs. B. We finally screened 254 AS that changed specifically in C vs D group from the 674 AS events (Table S5, Figure 8B). Next, we performed GO enrichment\u0026nbsp;analyses\u0026nbsp;on the 254 genes with AS events. Surprisingly, some transcripts of these AS events were also linked to photosynthesis. For example, several genes were associated with the photosynthetic system, chloroplast thylakoid membrane, etc. (Figure 8C, 8D; Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of lncRNAs. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eLncRNAs were identified by CNCI, CPAT, CPC, and Pfam analysis, and a total of 375 lncRNAs were detected using all four methods (Figure S3). LncRNAs were classified and mapped according to their position on the reference genome annotation information (gff). All lncRNAs were classified into three categories with 233 lncRNAs, 54 antisense lncRNAs, 20 intronic lncRNAs, and 88 sense lncRNAs (Figure S3). Among the 375 lncRNAs, 43 were differentially expressed in the C vs. D group, and 20/23 were up-/downregulated in Col-0 under nitrogen starvation (Table S7). Furthermore, 6 lncRNAs were predicted to target autophagy-related genes (Table S7), which may play a negative feedback mechanism role during autophagy occurrence.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAutophagy plays a key role in plant resistance to nutrient stress. Nitrogen is a necessary macroelement for plants and nitrogen deficiency leads to significant changes in the transcriptome in plants. To accurately characterize gene expression patterns and molecular traits under nitrogen starvation, some studies have assessed transcriptomic changes using the Illumina sequencing platform\u003csup\u003e15,17,59\u003c/sup\u003e Because mRNA transcripts are the direct template for translation, identifying changes that affect the abundance of autophagy transcripts under nitrogen starvation conditions is an important task. In this study, we performed full-length RNA sequencing for Col-0 and \u003cem\u003eatg5-1\u003c/em\u003e with or without nitrogen in Arabidopsis. A total of 39,033 transcripts were detected, including 11,356 novel transcripts. We identified 2,719 upregulated and 2,826 DETs in\u003cem\u003e\u0026nbsp;atg5-1\u003c/em\u003e under nitrogen starvation by comparative transcriptomic analysis (Table S4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy analyzing DETs, some potential molecular regulatory networks could be revealed. We found that there were only 412 DETs from WT and the autophagic mutant \u003cem\u003eatg5-1\u003c/em\u003e when sufficient nitrogen was present (Table S3). Accordingly, there is a large number of DETs (5797) under nitrogen starvation (Table S3). This result may suggest that autophagy is necessary to maintain plant homeostasis under nitrogen starvation, while it appears to have little effect on plant growth, at least at the seedling stage under sufficient nitrogen. In addition, we found that there was no significant difference in the expression of most transcripts of \u003cem\u003eatg5-1\u003c/em\u003e between the+/- N conditions (Table S3). This further suggests that autophagy is essential for the regulation of plant nutritional stress.\u003c/p\u003e\n\u003cp\u003eTo further analyze the function of up- and downregulated DETs between the C group (\u003cem\u003eatg5-1\u003c/em\u003e+N versus \u003cem\u003eatg5-1\u003c/em\u003e-N) and D group (Col-0-N versus \u003cem\u003eatg5-1\u003c/em\u003e-N ), we performed the GO and KEGG enrichment analyses. From the GO results, we found that many transcripts in both up- and downregulated DETs were involved in stimulus (Figure 3). Autophagy has two roles in regulating plant abiotic stress: it can upregulate the expression of some stress response genes and also downregulate the expression of other stress genes. In combination with GO and KEGG analyses, the results showed that the autophagy mutant \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003ehad many of photosynthesis-related transcripts that were significantly upregulated under nitrogen starvation (Figure 3, 4). This indicates that plants weaken photosynthesis through autophagy to cope with nitrogen starvation and even abiotic stress. The results of RNA-seq were further validated by qRT-PCR (Figure 5). From the current research, we have not been able to determine the cause of the increase in the expression of photosynthesis-related genes in the low-nitrogen environment, and it may be that too high photosynthesis will cause damage to plants. Conversely, the expression of most PSⅡ relater-genes in mutants decreased significantly after -C treatment, suggesting that autophagy may affect photosynthesis by affecting the expression of PSⅡ-related genes. It may be that autophagy is involved in PSII photoprotection and repair under stress/dark conditions and ultimately affects photosynthetic efficiency\u003csup\u003e60,61\u003c/sup\u003e. The difference in the expression of photosynthesis genes in ATG5-1 under low nitrogen and low carbon conditions indicates the complexity of autophagy in regulating photosynthesis, which may involve the influence of autophagy on plant C/N balance. There should be more experimental evidence at the protein level in the future to validate the results at these transcriptional levels. The PSII- related genes involved in this study, such as \u003cem\u003eCYTC6A\u003c/em\u003e, \u003cem\u003ePPL1\u003c/em\u003e, \u003cem\u003ePSB01\u003c/em\u003e, and \u003cem\u003ePSK27\u003c/em\u003e, may be degraded by autophagy as potential target genes.\u003c/p\u003e\n\u003cp\u003eBased on the results of GO and KEGG analyses, WT plants showed downregulated of a large number of photosynthetic genes compared to \u003cem\u003eatg5-1\u003c/em\u003e, which may indicate that autophagy has a direct or indirect relationship in regulating the transcription of photosynthesis-related genes. So far, there are few reports on autophagy and photosynthesis in plants. A recent study has shown that plants can reduce high-intensity light damage by accumulating ROS through pexophagy and microautophagy\u003csup\u003e62\u003c/sup\u003e.\u0026nbsp;This result indicates a relationship between autophagy and high-light stress, which also suggest that autophagy is somehow related to photosynthesis. Using chlorophyll fluorescence imaging, we further confirmed that autophagy can regulate photosynthesis in plants by an unknown mechanism. Fv/Fm and \u0026Phi;PSⅡ represent the maximum and real quantum efficiency of photosystem II, respectively. The higher the value is, the stronger the photosynthesis. We found that after stress treatment, Fv/Fm and \u0026Phi;PSⅡ decreased significantly in the old leaves of autophagic mutant \u003cem\u003eatg5-1\u003c/em\u003e, reflecting the decrease in photosynthesis (Figure 7A). The same trend could be seen under different lighting conditions (Figure 7C). \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) and\u003cem\u003e\u0026nbsp;Y\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e) represent the regulatory and nonregulatory energy dissipation of PSII, respectively. High \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) indicates the plants have a strong photoprotection mechanism, such as heat dissipation and other forms of dissipation of excess light energy\u003csup\u003e63\u003c/sup\u003e. In normally growing plants (WT and \u003cem\u003eatg5-1\u003c/em\u003e), we found that the \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) values were all 0, which may be due to the lack of excess light energy such that the photoprotective mechanism was not activated. However, in the young leaves after nitrogen starvation, the WT \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNPQ\u003c/em\u003e) value increased to 0.08, while that for \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003ewas still 0 (Figure 7A). This may be due to the weakening of photosynthesis caused by stress, suggesting that WT protected plants better under strong light. A high \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e) value indicates that the photochemical PSII reaction and protective regulatory mechanism play no role and that excessive excitation energy damages PSII\u0026nbsp;\u003csup\u003e63\u003c/sup\u003e. Apparently, the \u003cem\u003eY\u003c/em\u003e(\u003cem\u003eNO\u003c/em\u003e) value of the old leaves of \u003cem\u003eatg5-1\u003c/em\u003e after nitrogen starvation reached 0.88, while that of the WT leaves was 0.74, further indicating that the photoprotective ability of the autophagic mutant was weaker (Figure 7A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChlorophyll levels can also be an indicator of the photosynthetic capacity of plants. We found no significant change in chlorophyll content in young WT leaves and \u003cem\u003eatg5-1\u003c/em\u003e after treatment. A decrease in chlorophyll content in old \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003eold leaves was observed during normal growth, which may indicate that autophagy does not regulate photosynthesis by controlling chlorophyll levels in plants under nutrient stress. Curiously, there was no significant difference in chlorophyll content between the WT and the mutant in both old and new leaves after nitrogen stress (Figure 7F), suggesting that autophagy may not be involved in chlorophyll degradation in the light-avoidance reaction. However, studies have shown that the selective autophagy receptor NBR1 targets and degrades translocon at the outer envelopem embrane of chloroplasts (TOC) under some abiotic stresses such as UV-B irradiation and heat stress\u003csup\u003e64\u003c/sup\u003e. This suggests that autophagy, as one of the pathways of chloroplast degradation, only works under specific conditions, which requires further research. Notably, all changes in photosynthetic indices occurred in relatively old leaves, while there were only minor changes in new and young leaves. This suggests that plants respond to stress by preferentially transporting nutrients to young leaves to ensure plant survival. When the autophagy pathway is blocked, plants cannot gain energy from the breakdown of intracellular substances, leading to senescence and death of old leaves. As we all know, chloroplasts are the most important sites for photosynthesis. Two new studies have found that autophagy is extensively involved in chloroplast degradation under abiotic stress, which may be one of the main reasons for the decline in plant photosynthetic capacity\u003csup\u003e64,65\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn eukaryotes, AS is a posttranscriptional regulation mechanism for the production of new transcripts. In this study, we also examined the two events of WT and \u003cem\u003eatg5-1\u0026nbsp;\u003c/em\u003eunder +/-N conditions. The overall result was similar to the trend for DETs observed previously, that is, WT showed more genes becoming subject to AS under nitrogen starvation, while \u003cem\u003eatg5-1\u003c/em\u003e was relatively insensitive. GO enrichment\u0026nbsp;analyses\u0026nbsp;revealed that numerous genes related to photosynthesis had AS events (Figure 7C; Table S6). This suggests that autophagy plays an important role in the breakdown of chloroplasts and photosynthetic system II. Interestingly, a gene called CV (AT2G25625) associated with the photosynthetic membrane was previously reported to regulate plant senescence independently of the autophagic pathway\u003csup\u003e66\u003c/sup\u003e. This may be because different isoforms of the gene are reversely broken down by autophagy.\u003c/p\u003e\n\u003cp\u003eIn addition, we identified 20 and 23 lncRNAs that were up- and downregulated in WT under nitrogen deficiency stress. In these lncRNAs, we found 6 lncRNAs with the potential to regulate autophagy. The target genes of these lncRNAs all contain autophagy-related genes, particularly \u003cem\u003eATG8\u003c/em\u003e, which may be involved in autophagy formation. It is worth noting that four \u003cem\u003eagt8\u0026nbsp;\u003c/em\u003egenes were separately regulated by different lncRNAs, suggesting that \u003cem\u003eatg8\u0026nbsp;\u003c/em\u003egenes may exhibit different expression patterns during stress generation (Table S7). Among the 4 lncRNAs that regulate \u003cem\u003eatg8\u003c/em\u003e, three were upregulated and one is downregulated, suggesting that these different lncRNAs play a role in promoting/inhibiting autophagy genes. There are currently no relevant reports in plants, nor do our results provide clues as to how lncRNAs regulate autophagy.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials and treatment. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThe seeds of the \u003cem\u003eArabidopsis\u003c/em\u003e ecotypes Col-0 and \u003cem\u003eatg5-1\u003c/em\u003e\u003csup\u003e45\u003c/sup\u003e (Purchased from ABRC (https://abrc.osu.edu/)) were surface sterilized with 1% sodium hypochlorite after 3 days of stratification at 4\u0026deg;C. The sterilized seeds were grown in a triangular flask with liquid MS culture medium for 5 days under a long- day photoperiod (16 h light/8 h dark) with a continuous temperature of 22\u0026deg;C and shaking at 100 rpm. Plants that grew well transferred to MS or nitrogen-depleted liquid medium for an additional 2 days, as control and treatment groups, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq library construction and Nanopore sequencing. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe experimental procedure was performed according to the standard protocol provided by ONT. Briefly, RNA from Arabidopsis seedlings was extracted with a plant RNA isolation kit (RC401-01, Vazyme). A cDNA PCR barcoding kit (SQK-PCS109 with SQK-PBK004, ONT) was used for sample cDNA generation, barcoding and sequencing. A total of 12 samples, including 3 biological replicates of each treatment or control, were sequenced. For each sample, 2 ng of polyA+ RNA was used in reverse transcription and strand switching, and 5 \u0026micro;L of reverse transcribed RNA was used to select for full-length transcripts. The cDNA product was amplified for 14 cycles with LongAmp\u0026reg; Taq DNA Polymerase (NEB). Then, adapters were added to cDNA samples by T4 DNA ligase (NEB). The final cDNA libraries were added to FLO-MIN109 flow cells, and sequenced on the PromethION platform from Biomarker Technology Company (Beijing, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRaw data processing and genome mapping. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThe low-quality reads (Q-score \u0026lt; 6, length \u0026lt; 200 bp) were filtered and ribosomal RNAs were discarded after mapping to the rRNA database (https://www.arb-silva.de). After trimming adapter primers, the full-length nonchimeric transcripts were mapped to the Arabidopsis TAIR10 reference genome by minmap2\u003csup\u003e46\u003c/sup\u003e and further polished to obtain consensus sequences by pinfish (https://github.com/nanoporetech/pinfish). Principal component analysis (PCA) was performed using BMKCloud (\u003ca href=\"http://www.biocloud.net\"\u003ewww.biocloud.net\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene/transcript expression analysis. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThe counts per million (CPM) calculation method was used to standardize the number of reads versus the genome as a data indicator to measure the expression level of transcripts. The non-redundant full-length sequences were compared to the reference transcriptome, and the quantitative results of all transcripts transcribed from the gene were counted as the quantitative results of the gene, which were also calculated by the CPM method. For the quantitative results of transcripts and genes, DESeq2 was used for difference analysis and the p-value was corrected by Benjamini-Hochberg method\u003csup\u003e47\u003c/sup\u003e to obtain the false discovery rate (FDR). Transcripts with a fold change (FC) \u0026ge; 1.5 and FDR \u0026lt; 0.05 were selected for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative splicing analysis. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eTranscripts were validated against known reference transcript annotations using gffcompare\u003csup\u003e48\u003c/sup\u003e. AS events were detected by the AStalavista tool\u003csup\u003e49\u003c/sup\u003e. Different types of AS were identified, including an alternative 3\u0026apos; and an alternative 5\u0026apos; splice site, as well as exon skipping, intron retention, and mutually exclusive exons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of long noncoding RNAs. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eWe identified CDSs of polished nonredundant isoforms using TransDecoder software\u003csup\u003e50\u003c/sup\u003e. Prediction of lncRNAs was performed using four methods, Coding Potential Calculator (CPC)\u003csup\u003e51\u003c/sup\u003e, Coding-Non-Coding Index (CNCI)\u003csup\u003e52\u003c/sup\u003e, Coding Potential Assessment Tool (CPAT)\u003csup\u003e53\u003c/sup\u003e, and Pfam. These four methods were used in combination to screen out the noncoding RNA sequences of noncoding proteins from the predicted RNA sequences of coding proteins.\u0026nbsp;The screening criteria for lncRNA candidates were based on transcript length and exon number (greater than 200 bp in length with more than two exons). LncRNAs were then classified as intergenic lncRNA (also called lincRNAs), antisense lncRNAs, sense lncRNAs, ord intronic lncRNAs. Target genes regulated by the identified lncRNAs were also predicted using the LncTar software\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation and enrichment analysis. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eGene/transcript functions were annotated based on the GO\u0026nbsp;\u003csup\u003e55\u003c/sup\u003e and KEGG databases. We used the GOseq R package\u003csup\u003e56\u003c/sup\u003e and KOBAS software\u003csup\u003e57\u003c/sup\u003e to perform GO and KEGG enrichment analyses on DEGs, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of photosynthetic index and chlorophyll content. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eFour-week-old Arabidopsis plants were subjected to measurement of the photosynthetic index and chlorophyll content. Chlorophyll fluorescence images were obtained using an IMAGING-PAM system (WALZ, Germany). The maximum quantum efficiency of photosystem II (Fv/Fm) was measured after 20 min dark adaptation with a saturating light pulse with a photosynthetic photon flux density (PFD) of 6,000 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e for 1 s. Then, the plants were exposed to an actinic PFD of 56 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e. Saturation flashes were applied 10 times every 20 s, and the effective quantum efficiency of PSII (\u0026Phi;PSII) after 224s of light adaptation was used for data analysis. The photosynthetic electric transport rate (ETR) was determined by continuous irradiation with gradually increasing light intensity (0 \u0026ndash; 1,251 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) in 12 steps (20 s for each stage). We applied three replicates for each sample.\u003c/p\u003e\n\u003cp\u003eFor determination of chlorophyll content, 0.1g of freshly-harvested leaves was put into 5 ml of anhydrous ethanol, and stored in a dark incubator at 4\u0026deg;C for 48h to extract chlorophyll (mg/L). Chla=13.95*A665-6.88*A649; Chlb=24.96*A649-7.32*A665. There were 3 replicates per sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Isolation and qRT-PCR. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe 7-day-old seedlings were treated with -N (4 d) or -C (2 d) respectively. Samples are quickly frozen in liquid nitrogen and then RNA is extracted. The primers of qRT-PCR of the 10 genes involved in photosynthesisare are listed in Table S8.A rapid RNA isolation kit (Vazyme, China) was used to extract total RNA of samples. Total RNAs were used for complementary cDNA synthesis using SuperScript II QRT SuperMix (Vazyme. China) in accordance with the manufacturer\u0026rsquo;s instructions. The qRT-PCR analysis was performed on a Bio-Rad CFX96 using the 2\u0026times;ChamQ universal SYBR qPCR Master Mix (Vazyme. China). The PCR reaction conditions were as follows: 95 ℃ for 30 s, followed by 40 cycles of 95 ℃ for 5 s, and 60 ℃ for 30 s, and 2\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003csup\u003e\u0026Delta;\u0026Delta;CT\u003c/sup\u003e was used to reckon the relative expression levels of genes. The experiments were repeated three times.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statements\u0026nbsp;\u003c/strong\u003eThe authors confirm that experimental research on the Arabidopsis thaliana used in this research, complied with institutional, national, and international guidelinesand legislation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe raw sequencing data have been deposited in the National Genomics Data Center (NGDC), Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation (https://ngdc.cncb.ac.cn/), under the accession number PRJCA018751 (https://ngdc.cncb.ac.cn/gsa/s/essO5v6w).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the technical assistance from Biomarker Technologies (Beijing, China) for Oxford Nanopore Full-length Sequencing and data analysis. We would like to appreciate the reviewers\u0026rsquo; comments and helpful suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFL and WH conceived, designed the research and revised the manuscript. SW performed the experiments, interpreted the data, and wrote the manuscript. YS performed the bioinformatic analysis. YZ performed qRT-PCR experiments. All authors have read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grants from the Lushan Botanical Garden, Chinese Academy of Sciences [No.2021ZWZX09 to Song Wang], by grant from the Natural Science Foundation of Jiangxi Province [No.20232BAB215003 to Song Wang ] by grant from the National Natural Science Foundation of China [32160099 to Dr. Weiming Hu; 32100297 to Dr. Fen Liu], and by grant from Jiangxi high-level and urgently needed overseas talent introduction plan [20212BCJ25025 to Dr. Weiming Hu; 20212BCJ25024 to Dr. Fen Liu].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarshall, R. S. \u0026amp; Vierstra, R. D. Autophagy: The Master of Bulk and Selective Recycling. \u003cem\u003eAnnu Rev Plant Biol\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 173-208, doi:10.1146/annurev-arplant-042817-040606 (2018).\u003c/li\u003e\n\u003cli\u003eMcLoughlin, F.\u003cem\u003e et al.\u003c/em\u003e Autophagy Plays Prominent Roles in Amino Acid, Nucleotide, and Carbohydrate Metabolism during Fixed-Carbon Starvation in Maize. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 2699-2724, doi:10.1105/tpc.20.00226 (2020).\u003c/li\u003e\n\u003cli\u003evan Doorn, W. G. \u0026amp; Papini, A. Ultrastructure of autophagy in plant cells: a review. \u003cem\u003eAutophagy\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 1922-1936, doi:10.4161/auto.26275 (2013).\u003c/li\u003e\n\u003cli\u003eMachado, S. R. \u0026amp; Rodrigues, T. M. Autophagy and vacuolar biogenesis during the nectary development. \u003cem\u003ePlanta\u003c/em\u003e\u003cstrong\u003e250\u003c/strong\u003e, 519-533, doi:10.1007/s00425-019-03190-7 (2019).\u003c/li\u003e\n\u003cli\u003eFeng, Y., He, D., Yao, Z. \u0026amp; Klionsky, D. J. The machinery of macroautophagy. \u003cem\u003eCell Res\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 24-41, doi:10.1038/cr.2013.168 (2014).\u003c/li\u003e\n\u003cli\u003eWang, S., Hu, W. \u0026amp; Liu, F. Autophagy in the Lifetime of Plants: From Seed to Seed. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, doi:10.3390/ijms231911410 (2022).\u003c/li\u003e\n\u003cli\u003eJiang, W.\u003cem\u003e et al.\u003c/em\u003e Key Regulators of Autophagosome Closure. \u003cem\u003eCells\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, doi:10.3390/cells10112814 (2021).\u003c/li\u003e\n\u003cli\u003eYang, C., Luo, M., Zhuang, X., Li, F. \u0026amp; Gao, C. Transcriptional and Epigenetic Regulation of Autophagy in Plants. \u003cem\u003eTrends Genet\u003c/em\u003e\u003cstrong\u003e36\u003c/strong\u003e, 676-688, doi:10.1016/j.tig.2020.06.013 (2020).\u003c/li\u003e\n\u003cli\u003eLiu, F.\u003cem\u003e et al.\u003c/em\u003e AUTOPHAGY-RELATED14 and Its Associated Phosphatidylinositol 3-Kinase Complex Promote Autophagy in Arabidopsis. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 3939-3960, doi:10.1105/tpc.20.00285 (2020).\u003c/li\u003e\n\u003cli\u003eCheng, S., Wang, Q., Manghwar, H. \u0026amp; Liu, F. Autophagy-Mediated Regulation of Different Meristems in Plants. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, doi:10.3390/ijms23116236 (2022).\u003c/li\u003e\n\u003cli\u003ePu, Y., Luo, X. \u0026amp; Bassham, D. C. TOR-Dependent and -Independent Pathways Regulate Autophagy in Arabidopsis thaliana. \u003cem\u003eFront Plant Sci\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 1204, doi:10.3389/fpls.2017.01204 (2017).\u003c/li\u003e\n\u003cli\u003eSoto-Burgos, J. \u0026amp; Bassham, D. C. SnRK1 activates autophagy via the TOR signaling pathway in Arabidopsis thaliana. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, e0182591, doi:10.1371/journal.pone.0182591 (2017).\u003c/li\u003e\n\u003cli\u003eZhuang, X.\u003cem\u003e et al.\u003c/em\u003e A BAR-domain protein SH3P2, which binds to phosphatidylinositol 3-phosphate and ATG8, regulates autophagosome formation in Arabidopsis. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 4596-4615, doi:10.1105/tpc.113.118307 (2013).\u003c/li\u003e\n\u003cli\u003eGao, C.\u003cem\u003e et al.\u003c/em\u003e Dual roles of an Arabidopsis ESCRT component FREE1 in regulating vacuolar protein transport and autophagic degradation. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e\u003cstrong\u003e112\u003c/strong\u003e, 1886-1891, doi:10.1073/pnas.1421271112 (2015).\u003c/li\u003e\n\u003cli\u003eMasclaux-Daubresse, C.\u003cem\u003e et al.\u003c/em\u003e Stitching together the Multiple Dimensions of Autophagy Using Metabolomics and Transcriptomics Reveals Impacts on Metabolism, Development, and Plant Responses to the Environment in Arabidopsis. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 1857-1877, doi:10.1105/tpc.114.124677 (2014).\u003c/li\u003e\n\u003cli\u003eAvin-Wittenberg, T.\u003cem\u003e et al.\u003c/em\u003e Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 306-322, doi:10.1105/tpc.114.134205 (2015).\u003c/li\u003e\n\u003cli\u003eMinina, E. A.\u003cem\u003e et al.\u003c/em\u003e Transcriptional stimulation of rate-limiting components of the autophagic pathway improves plant fitness. \u003cem\u003eJ Exp Bot\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 1415-1432, doi:10.1093/jxb/ery010 (2018).\u003c/li\u003e\n\u003cli\u003eHav\u0026eacute;, M.\u003cem\u003e et al.\u003c/em\u003e Proteomic and lipidomic analyses of the Arabidopsis atg5 autophagy mutant reveal major changes in endoplasmic reticulum and peroxisome metabolisms and in lipid composition. \u003cem\u003eNew Phytol\u003c/em\u003e\u003cstrong\u003e223\u003c/strong\u003e, 1461-1477, doi:10.1111/nph.15913 (2019).\u003c/li\u003e\n\u003cli\u003eSignorelli, S., Tarkowski Ł, P., Van den Ende, W. \u0026amp; Bassham, D. C. Linking Autophagy to Abiotic and Biotic Stress Responses. \u003cem\u003eTrends Plant Sci\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 413-430, doi:10.1016/j.tplants.2019.02.001 (2019).\u003c/li\u003e\n\u003cli\u003eAvin-Wittenberg, T. Autophagy and its role in plant abiotic stress management. \u003cem\u003ePlant Cell Environ\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 1045-1053, doi:10.1111/pce.13404 (2019).\u003c/li\u003e\n\u003cli\u003eXiong, Y., Contento, A. L. \u0026amp; Bassham, D. C. AtATG18a is required for the formation of autophagosomes during nutrient stress and senescence in Arabidopsis thaliana. \u003cem\u003ePlant J\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 535-546, doi:10.1111/j.1365-313X.2005.02397.x (2005).\u003c/li\u003e\n\u003cli\u003eLiu, Y., Xiong, Y. \u0026amp; Bassham, D. C. Autophagy is required for tolerance of drought and salt stress in plants. \u003cem\u003eAutophagy\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e, 954-963, doi:10.4161/auto.5.7.9290 (2009).\u003c/li\u003e\n\u003cli\u003eChen, L.\u003cem\u003e et al.\u003c/em\u003e Autophagy contributes to regulation of the hypoxia response during submergence in Arabidopsis thaliana. \u003cem\u003eAutophagy\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 2233-2246, doi:10.1080/15548627.2015.1112483 (2015).\u003c/li\u003e\n\u003cli\u003eEguchi, M., Kimura, K., Makino, A. \u0026amp; Ishida, H. Autophagy is induced under Zn limitation and contributes to Zn-limited stress tolerance in Arabidopsis (Arabidopsis thaliana). \u003cem\u003eSoil Science and Plant Nutrition\u003c/em\u003e\u003cstrong\u003e63\u003c/strong\u003e, 342-350, doi:10.1080/00380768.2017.1360750 (2017).\u003c/li\u003e\n\u003cli\u003eDi Berardino, J.\u003cem\u003e et al.\u003c/em\u003e Autophagy controls resource allocation and protein storage accumulation in Arabidopsis seeds. \u003cem\u003eJ Exp Bot\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 1403-1414, doi:10.1093/jxb/ery012 (2018).\u003c/li\u003e\n\u003cli\u003eYang, C.\u003cem\u003e et al.\u003c/em\u003e HY5-HDA9 Module Transcriptionally Regulates Plant Autophagy in Response to Light-to-Dark Conversion and Nitrogen Starvation. \u003cem\u003eMol Plant\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 1632-1634, doi:10.1016/j.molp.2022.09.015 (2022).\u003c/li\u003e\n\u003cli\u003eBedu, M., Marmagne, A., Masclaux-Daubresse, C. \u0026amp; Chardon, F. Transcriptional Plasticity of Autophagy-Related Genes Correlates with the Genetic Response to Nitrate Starvation in Arabidopsis Thaliana. \u003cem\u003eCells\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, doi:10.3390/cells9041021 (2020).\u003c/li\u003e\n\u003cli\u003eSedaghatmehr, M.\u003cem\u003e et al.\u003c/em\u003e A regulatory role of autophagy for resetting the memory of heat stress in plants. \u003cem\u003ePlant Cell Environ\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 1054-1064, doi:10.1111/pce.13426 (2019).\u003c/li\u003e\n\u003cli\u003eCui, J.\u003cem\u003e et al.\u003c/em\u003e Analysis and comprehensive comparison of PacBio and nanopore-based RNA sequencing of the Arabidopsis transcriptome. \u003cem\u003ePlant Methods\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 85, doi:10.1186/s13007-020-00629-x (2020).\u003c/li\u003e\n\u003cli\u003eHou, C.\u003cem\u003e et al.\u003c/em\u003e Comparative Analyses of Full-Length Transcriptomes Reveal Gnetum luofuense Stem Developmental Dynamics. \u003cem\u003eFront Genet\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 615284, doi:10.3389/fgene.2021.615284 (2021).\u003c/li\u003e\n\u003cli\u003eZhao, L.\u003cem\u003e et al.\u003c/em\u003e Analysis of Transcriptome and Epitranscriptome in Plants Using PacBio Iso-Seq and Nanopore-Based Direct RNA Sequencing. \u003cem\u003eFront Genet\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 253, doi:10.3389/fgene.2019.00253 (2019).\u003c/li\u003e\n\u003cli\u003eAbdel-Ghany, S. E.\u003cem\u003e et al.\u003c/em\u003e A survey of the sorghum transcriptome using single-molecule long reads. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 11706, doi:10.1038/ncomms11706 (2016).\u003c/li\u003e\n\u003cli\u003eWang, M.\u003cem\u003e et al.\u003c/em\u003e A global survey of alternative splicing in allopolyploid cotton: landscape, complexity and regulation. \u003cem\u003eNew Phytol\u003c/em\u003e\u003cstrong\u003e217\u003c/strong\u003e, 163-178, doi:10.1111/nph.14762 (2018).\u003c/li\u003e\n\u003cli\u003eYang, Z.\u003cem\u003e et al.\u003c/em\u003e Identification and genetic analysis of alternative splicing of long non-coding RNAs in tomato initial flowering stage. \u003cem\u003eGenomics\u003c/em\u003e\u003cstrong\u003e112\u003c/strong\u003e, 897-907, doi:10.1016/j.ygeno.2019.06.005 (2020).\u003c/li\u003e\n\u003cli\u003eZhang, Y.\u003cem\u003e et al.\u003c/em\u003e Molecular Basis of the Distinct Metabolic Features in Shoot Tips and Roots of Tea Plants (Camellia sinensis): Characterization of MYB Regulator for Root Theanine Synthesis. \u003cem\u003eJ Agric Food Chem\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 3415-3429, doi:10.1021/acs.jafc.0c07572 (2021).\u003c/li\u003e\n\u003cli\u003eMercer, T. R., Dinger, M. E. \u0026amp; Mattick, J. S. Long non-coding RNAs: insights into functions. \u003cem\u003eNat Rev Genet\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 155-159, doi:10.1038/nrg2521 (2009).\u003c/li\u003e\n\u003cli\u003eBonasio, R. \u0026amp; Shiekhattar, R. Regulation of transcription by long noncoding RNAs. \u003cem\u003eAnnu Rev Genet\u003c/em\u003e\u003cstrong\u003e48\u003c/strong\u003e, 433-455, doi:10.1146/annurev-genet-120213-092323 (2014).\u003c/li\u003e\n\u003cli\u003eYadav, V. K., Sawant, S. V., Yadav, A., Jalmi, S. K. \u0026amp; Kerkar, S. Genome-wide analysis of long non-coding RNAs under diel light exhibits role in floral development and the circadian clock in Arabidopsis thaliana. \u003cem\u003eInt J Biol Macromol\u003c/em\u003e\u003cstrong\u003e223\u003c/strong\u003e, 1693-1704, doi:10.1016/j.ijbiomac.2022.09.295 (2022).\u003c/li\u003e\n\u003cli\u003eKarlik, E., Ari, S. \u0026amp; Gozukirmizi, N. LncRNAs: genetic and epigenetic effects in plants. \u003cem\u003eBiotechnology \u0026amp; Biotechnological Equipment\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 1-11, doi:10.1080/13102818.2019.1581085 (2019).\u003c/li\u003e\n\u003cli\u003eChekanova, J. A. Long non-coding RNAs and their functions in plants. \u003cem\u003eCurr Opin Plant Biol\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 207-216, doi:10.1016/j.pbi.2015.08.003 (2015).\u003c/li\u003e\n\u003cli\u003eRai, M. I., Alam, M., Lightfoot, D. A., Gurha, P. \u0026amp; Afzal, A. J. Classification and experimental identification of plant long non-coding RNAs. \u003cem\u003eGenomics\u003c/em\u003e\u003cstrong\u003e111\u003c/strong\u003e, 997-1005, doi:10.1016/j.ygeno.2018.04.014 (2019).\u003c/li\u003e\n\u003cli\u003eZhao, X.\u003cem\u003e et al.\u003c/em\u003e Global identification of Arabidopsis lncRNAs reveals the regulation of MAF4 by a natural antisense RNA. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 5056, doi:10.1038/s41467-018-07500-7 (2018).\u003c/li\u003e\n\u003cli\u003eJampala, P., Garhewal, A. \u0026amp; Lodha, M. Functions of long non-coding RNA in Arabidopsis thaliana. \u003cem\u003ePlant Signal Behav\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 1925440, doi:10.1080/15592324.2021.1925440 (2021).\u003c/li\u003e\n\u003cli\u003eZhao, Z.\u003cem\u003e et al.\u003c/em\u003e Long Non-Coding RNAs: New Players in Plants. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, doi:10.3390/ijms23169301 (2022).\u003c/li\u003e\n\u003cli\u003eThompson, A. R., Doelling, J. H., Suttangkakul, A. \u0026amp; Vierstra, R. D. Autophagic nutrient recycling in Arabidopsis directed by the ATG8 and ATG12 conjugation pathways. \u003cem\u003ePlant Physiol\u003c/em\u003e\u003cstrong\u003e138\u003c/strong\u003e, 2097-2110, doi:10.1104/pp.105.060673 (2005).\u003c/li\u003e\n\u003cli\u003eLi, H. Minimap2: pairwise alignment for nucleotide sequences. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, 3094-3100, doi:10.1093/bioinformatics/bty191 (2018).\u003c/li\u003e\n\u003cli\u003eFerreira, J. A. The Benjamini-Hochberg Method in the Case of Discrete Test Statistics. \u003cem\u003eThe International Journal of Biostatistics\u003c/em\u003e\u003cstrong\u003e3\u003c/strong\u003e, 1065-1065 (2007).\u003c/li\u003e\n\u003cli\u003ePertea, G. \u0026amp; Pertea, M. GFF Utilities: GffRead and GffCompare. \u003cem\u003eF1000Res\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, doi:10.12688/f1000research.23297.2 (2020).\u003c/li\u003e\n\u003cli\u003eFoissac, S. \u0026amp; Sammeth, M. ASTALAVISTA: dynamic and flexible analysis of alternative splicing events in custom gene datasets. \u003cem\u003eNucleic Acids Res\u003c/em\u003e\u003cstrong\u003e35\u003c/strong\u003e, W297-299, doi:10.1093/nar/gkm311 (2007).\u003c/li\u003e\n\u003cli\u003eHaas, B. J.\u003cem\u003e et al.\u003c/em\u003e De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. \u003cem\u003eNat Protoc\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 1494-1512, doi:10.1038/nprot.2013.084 (2013).\u003c/li\u003e\n\u003cli\u003eKong, L.\u003cem\u003e et al.\u003c/em\u003e CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. \u003cem\u003eNucleic Acids Res\u003c/em\u003e\u003cstrong\u003e35\u003c/strong\u003e, W345-349, doi:10.1093/nar/gkm391 (2007).\u003c/li\u003e\n\u003cli\u003eSun, L.\u003cem\u003e et al.\u003c/em\u003e Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. \u003cem\u003eNucleic Acids Research\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, e166-e166, doi:10.1093/nar/gkt646 (2013).\u003c/li\u003e\n\u003cli\u003eWang, L.\u003cem\u003e et al.\u003c/em\u003e CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. \u003cem\u003eNucleic Acids Res\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, e74, doi:10.1093/nar/gkt006 (2013).\u003c/li\u003e\n\u003cli\u003eLi, J.\u003cem\u003e et al.\u003c/em\u003e LncTar: a tool for predicting the RNA targets of long noncoding RNAs. \u003cem\u003eBrief Bioinform\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 806-812, doi:10.1093/bib/bbu048 (2015).\u003c/li\u003e\n\u003cli\u003eAshburner, M.\u003cem\u003e et al.\u003c/em\u003e Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. \u003cem\u003eNat Genet\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 25-29, doi:10.1038/75556 (2000).\u003c/li\u003e\n\u003cli\u003eYoung, M. D., Wakefield, M. J., Smyth, G. K. \u0026amp; Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. \u003cem\u003eGenome Biol\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, R14, doi:10.1186/gb-2010-11-2-r14 (2010).\u003c/li\u003e\n\u003cli\u003eMao, X., Cai, T., Olyarchuk, J. G. \u0026amp; Wei, L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 3787-3793, doi:10.1093/bioinformatics/bti430 (2005).\u003c/li\u003e\n\u003cli\u003eWang, Y.\u003cem\u003e et al.\u003c/em\u003e Autophagy contributes to leaf starch degradation. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 1383-1399, doi:10.1105/tpc.112.108993 (2013).\u003c/li\u003e\n\u003cli\u003eShi, Y., Yu, B., Cheng, S., Hu, W. \u0026amp; Liu, F. The Change in Whole-Genome Methylation and Transcriptome Profile under Autophagy Defect and Nitrogen Starvation. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, doi:10.3390/ijms241814047 (2023).\u003c/li\u003e\n\u003cli\u003eCao, J.\u003cem\u003e et al.\u003c/em\u003e Autophagic pathway contributes to low-nitrogen tolerance by optimizing nitrogen uptake and utilization in tomato. \u003cem\u003eHortic Res\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, uhac068, doi:10.1093/hr/uhac068 (2022).\u003c/li\u003e\n\u003cli\u003eBarros, J. A. S.\u003cem\u003e et al.\u003c/em\u003e Autophagy Deficiency Compromises Alternative Pathways of Respiration following Energy Deprivation in Arabidopsis thaliana. \u003cem\u003ePlant Physiol\u003c/em\u003e\u003cstrong\u003e175\u003c/strong\u003e, 62-76, doi:10.1104/pp.16.01576 (2017).\u003c/li\u003e\n\u003cli\u003eOikawa, K.\u003cem\u003e et al.\u003c/em\u003e Pexophagy suppresses ROS-induced damage in leaf cells under high-intensity light. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 7493, doi:10.1038/s41467-022-35138-z (2022).\u003c/li\u003e\n\u003cli\u003eKramer, D. M., Johnson, G., Kiirats, O. \u0026amp; Edwards, G. E. New Fluorescence Parameters for the Determination of QA Redox State and Excitation Energy Fluxes. \u003cem\u003ePhotosynth Res\u003c/em\u003e\u003cstrong\u003e79\u003c/strong\u003e, 209, doi:10.1023/B:PRES.0000015391.99477.0d (2004).\u003c/li\u003e\n\u003cli\u003eWan, C.\u003cem\u003e et al.\u003c/em\u003e Selective autophagy regulates chloroplast protein import and promotes plant stress tolerance. \u003cem\u003eEmbo j\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, e112534, doi:10.15252/embj.2022112534 (2023).\u003c/li\u003e\n\u003cli\u003eLee, H. N.\u003cem\u003e et al.\u003c/em\u003e The autophagy receptor NBR1 directs the clearance of photodamaged chloroplasts. \u003cem\u003eeLife\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, e86030, doi:10.7554/eLife.86030 (2023).\u003c/li\u003e\n\u003cli\u003eWang, S. \u0026amp; Blumwald, E. Stress-induced chloroplast degradation in Arabidopsis is regulated via a process independent of autophagy and senescence-associated vacuoles. \u003cem\u003ePlant Cell\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 4875-4888, doi:10.1105/tpc.114.133116 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Full-length transcriptome; autophagy; photosynthesis; qRT-PCR.; Arabidopsis thaliana","lastPublishedDoi":"10.21203/rs.3.rs-4041050/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4041050/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Autophagy is a highly conserved eukaryotic pathway and plays a crucial role in cell survival under stress. Here, we applied a full-length transcriptome approach to study Arabidopsis autophagy mutant (atg5-1) exposed to nitrogen-starvation, using Oxford Nanopore Technologies. A total of 39,033 transcripts were identified, including 11,356 new transcripts. In addition, alternative splicing (AS) events and lncRNAs were also detected between Col-0 (WT) and atg5-1. Differentially expressed transcripts enrichment shown that autophagy upregulates the expression of many stress-responsive genes and inhibits transcription of photosynthesis-associated genes. The results of qRT-PCR showed that the expression patterns of photosynthesis-related genes in the atg5-1 were different under the conditions of nitrogen starvation and carbon starvation. Under nitrogen starvation treatment, many genes related to photosynthesis also exhibited AS. The chlorophyll fluorescence images showed that the Fv/Fm and ΦPSII of old atg5-1 leaves were significantly reduced after nitrogen starvation treatment, but the Y(NPQ) indices were significantly increased compared to those of WT. Taken together, the results provide new insights into how new transcripts, lncRNAs and AS (alternative splicing) are involved in plant autophagy through full-length transcriptome sequencing and suggest a new potential link between autophagy and photosynthesis.","manuscriptTitle":"Full-length transcriptome sequencing of Arabidopsis seedlings provided new insights into autophagic regulation of photosynthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:01:41","doi":"10.21203/rs.3.rs-4041050/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-09T08:08:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-28T08:15:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"fe6fd649-a6a5-436d-98e5-d6c53b6fe523_SNPRID","date":"2024-03-27T03:40:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c508709c-87d1-4b4b-ac09-2072bd0d128d_SNPRID","date":"2024-03-21T15:00:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-20T23:20:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T23:08:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-20T04:42:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T04:37:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-08T09:22:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"705a1480-48cc-4360-b4b4-e0204e9c8ea3","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":29727874,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":29727875,"name":"Biological sciences/Computational biology and bioinformatics/Gene regulatory networks"},{"id":29727876,"name":"Biological sciences/Plant sciences"},{"id":29727877,"name":"Biological sciences/Plant sciences/Photosynthesis"},{"id":29727878,"name":"Biological sciences/Plant sciences/Photosynthesis/Photosystem ii"}],"tags":[],"updatedAt":"2024-06-20T18:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 18:01:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4041050","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4041050","identity":"rs-4041050","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}