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. 2024 Mar 28;19(3):e0298264. doi: 10.1371/journal.pone.0298264

Similarity and dissimilarity in alterations of the gene expression profile associated with inhalational anesthesia between sevoflurane and desflurane

Takehiro Nogi 1, Kousuke Uranishi 2, Ayumu Suzuki 2, Masataka Hirasaki 3, Tina Nakamura 1, Tomiei Kazama 1, Hiroshi Nagasaka 1, Akihiko Okuda 2,*, Tsutomu Mieda 1,*
Editor: Shin Yamazaki4
PMCID: PMC10977671  PMID: 38547201

Abstract

Although sevoflurane is one of the most commonly used inhalational anesthetic agents, the popularity of desflurane is increasing to a level similar to that of sevoflurane. Inhalational anesthesia generally activates and represses the expression of genes related to xenobiotic metabolism and immune response, respectively. However, there has been no comprehensive comparison of the effects of sevoflurane and desflurane on the expression of these genes. Thus, we used a next-generation sequencing method to compare alterations in the global gene expression profiles in the livers of rats subjected to inhalational anesthesia by sevoflurane or desflurane. Our bioinformatics analyses revealed that sevoflurane and, to a greater extent, desflurane significantly activated genes related to xenobiotic metabolism. Our analyses also revealed that both anesthetic agents, especially sevoflurane, downregulated many genes related to immune response.

Introduction

Inhalational anesthesia using halogenated anesthetic agents is a common method of inducing general anesthesia [1,2]. Sevoflurane is currently one of the most commonly used inhalational anesthetic agents because of its numerous beneficial characteristics in the clinic [3,4]. For example, sevoflurane is extremely refractory to being dissolved in the blood, leading to its elimination from the lung as vapor [5,6]. Furthermore, sevoflurane, unlike other halogenated anesthetic agents, does not produce trifluoroacetic acid, which induces severe hepatic injury [7,8]. Despite this, desflurane, another halogenated anesthetic agent, is rapidly gaining popularity, and the prevalence of inhalational anesthesia using desflurane is approaching that of sevoflurane. Although desflurane has some disadvantages, including causing respiratory tract irritation [9–13], an obvious advantage of its clinical use is the relatively rapid induction of an anesthetic state upon its administration and the rapid recovery of patients from that state after the cessation of its supply compared with other anesthetic agents [14–19]. These effects of desflurane are related to its marked low solubility in the blood, which is lower than that of sevoflurane [6,20,21]. An additional notable characteristic of desflurane is its extreme resistance to degradation and biotransformation [6,22–24], further elevating its safety level for clinical use. Thus, sevoflurane and desflurane have beneficial features as inhalational anesthetic agents.

To date, several studies have demonstrated that inhalation anesthesia is associated with the transcriptional activation of genes related to xenobiotic metabolism [25,26]. In addition, inhalation anesthesia reduces the expression of immune response-related genes [27–29]. However, whether these anesthetic agents have different influences on these genes is unknown. In this study, we addressed this question by comparing alterations in the global gene expression profiles of livers from rats subjected to inhalational anesthesia with desflurane or sevoflurane. Consistent with a previous report, we found that inducing general anesthesia with sevoflurane led to significant activation of genes related to xenobiotic metabolizing enzymes [25,26]. We also found that desflurane activated these genes more prominently than sevoflurane. Furthermore, our data revealed that sevoflurane and desflurane downregulated many genes related to immune response, and that the repressive effect of sevoflurane on these genes was more profound than that of desflurane.

Materials and methods

Animal experiments

Male Wistar rats (6 weeks old, 140–160 g body weight) purchased from Japan SLC Inc. (Hamamatsu, Japan) were housed in plastic cages with free access to food and water at 23°C under controlled lighting (12:12-hour light/dark cycle: lights on at 7:00 AM) for at least 1 week to acclimatize. Rats were deprived of food and water for 2 hours prior to experimentation and then subjected to inhalational anesthesia via nose cones using sevoflurane (4.5% gas-air mixture) [25] or desflurane (6.0% gas-air mixture) [23] with a 3 L/min flow of 50% oxygen. During anesthesia, rats were allowed to breathe spontaneously, and the value of saturation of percutaneous oxgen measured using a pulse oximeter through the lower extremities of rats was strictly controlled so as not to drop below 98%. In addition, the body temperatures of anesthetized rats were maintained at 36.5–37.5°C. After 6 hours, the rats subjected to inhalational anesthesia with sevoflurane or desflurane were sacrificed by decapitation, and the left lateral lobe of the liver was quickly isolated from each rat. Then, a portion of each liver was immersed in RNA later after rinsing with phosphate-buffered saline, and stored at 4°C. For control rat livers, rats subjected to inhalational anesthesia with sevoflurane or desflurane were immediately sacrificed after the loss of consciousness, and liver specimens were prepared and stored as described above. All rats, including those used as controls, were sacrificed at approximately 3:00 PM to avoid the effect of differences in the circadian rhythm phase on gene expression. The protocol for these experiments was approved by the Institutional Review Board on the Ethics of Saitama Medical University (permission numbers 3301, 3547, and 3796).

RNA preparation and reverse transcription

Total RNAs were prepared from the livers of rats subjected to inhalational anesthesia with sevoflurane or desflurane for 6 hours or less than 1 minute using an RNeasy Midi Kit (Qiagen, Venlo, Netherlands) according to the manufacturer’s instructions. RNAs were then used to obtain cDNAs by reverse transcription as described previously [30].

Quantitative PCR

cDNAs obtained by reverse transcription were used for quantitative PCR (qPCR) using the following TaqMan probes. Cyp2b1: Rn01457880_m1; Por: Rn00580820_m1; Alas1: Rn00577936_m1; Irf1: Rn01456791_m1; Mx2: Rn01444341_m1; Ccl6: Rn01456400_m1; Il33: Rn01759835_m1; and Gapdh: Rn01775763_g1. qPCR was performed in triplicate using livers from 12 rats (3 rat livers for each condition), and the results were normalized to Gapdh expression levels.

RNA sequencing

The integrity of total RNA was checked using 4200 TapeStation (Agilent Technologies) before generating the library. Libraries were prepared with total RNAs from four samples (N = 1 for each condition) using a Stranded Total RNA Prep, Ligation with Ribo-Zero Plus Kit (Illumina, San Diego, CA) according to the manufacturer’s instructions. RNA sequencing was performed on a NovoSeq 6000 system (Illumina, Albany, NY), by paired-end 101 bp reads, with 40–60 M reads for each sample. Sequence reads were trimmed to remove low-quality sequences and adapter sequences using sickle 1.33 (parameter -q 30 -l 20). Trimmed reads were then mapped to the rn6 reference genome using HISAT2 (version 2.1.0) with default parameters. The mapped reads were sorted using SAMtools (version 1.10). After removing small RNA genes whose lengths were equal to or shorter than 200 base pairs from the gene list of the Genomic Annotation Resource (https://hgdownload.soe.ucsc.edu/goldenPath/rn6/bigZips/genes/rn6.refGene.gtf.gz), the gene transfer format was used for read count extraction and normalization by StringTie (version 2.1.2). To identify genes activated by sevoflurane or desflurane, genes whose values of transcripts per kilobase million (TPM) were higher than two in the anesthetized state were selected from the list. Then, genes whose TPM values in the anesthetized state were more than 2-fold higher than those obtained from respective control rats were selected. Likewise, genes whose TPM values were higher than two in the control state were used as the starting list to identify genes repressed by anesthesia.

Gene ontology and gene set enrichment analyses

Gene Ontology (GO) analysis was performed using DAVID web tools (http://david.abcc.ncifcrf.gov). Gene Set Enrichment Analysis (GSEA) [31] was conducted according to the method described on the GSEA homepage (http://www.gsea-msigdb.org/gsea/index.jsp) using three different platforms of gene sets, “biological process of Gene Ontology”, “Kyoto Encyclopedia of Genes and Genome”, and “Reactome Pathway Database”.

Statistical analysis

All data from qPCR were subjected to the Student’s t-test (two-tailed) to examine statistical significance. The following marks were used to indicate the extent of statistical significance: ***, P<0.001; **, P<0.01; *, P<0.05; NS (not significant), P>0.05.

Results

Genome-wide expression analyses of livers from rats subjected to inhalational anesthesia

We conducted comprehensive gene expression analyses using next-generation sequencing to compare alterations in the global expression profiles of rat livers caused by the inhalational anesthetic agents sevoflurane and desflurane (S1 Fig). Our data revealed that 201 and 282 genes were transcriptionally activated more than 2-fold by sevoflurane and desflurane, respectively, in which 59 genes were commonly activated by both anesthetic agents (Fig 1A, S1 Table). Next, these upregulated gene sets were assigned to GO classification to correlate gene expression changes with overall molecular functions. These analyses yielded 3 and 14 specific GO terms related to sevoflurane (Fig 1B) and desflurane (Fig 1C), respectively, whose p-values were less than 10−3. As expected, two GO terms related to the response to drug treatment (marked in green and pink) were obtained by sevoflurane and desflurane treatments, although desflurane had a higher statistical significance than sevoflurane. One-on-one comparisons with a heat map revealed that genes activated by inhalation anesthesia among the members constituting the GO term “response to xenobiotic stimulus” did not overlap much between the sevoflurane and desflurane groups (Fig 1D). However, unexpectedly, regression analysis suggested a high correlation (R2 = 0.9371) between these two groups (S2A Fig, upper panel), even though a substantial number of genes were activated specifically by sevoflurane or desflurane. As a possible explanation for this apparent discrepancy, we considered that the profound activation of the Cyp2b1 gene by both anesthetic agents may skew the proper evaluation of the data. Consistent with this notion, the analysis demonstrated no apparent correlation between these two groups in cases where data related to Cyp2b1 were removed as an outlier from the gene list (S2A Fig, lower panel).

Fig 1. GO analyses of genes upregulated by inhalational anesthesia.

Fig 1

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(A) A Venn diagram showing a comparison of genes upregulated more than 2-fold by sevoflurane or desflurane. P-value for the significance of the overlap between two gene sets was calculated by a hypergeometric test. Lists of these genes are provided using their official gene symbols in S1 Table. (B, C) Genes whose expression was activated more than 2-fold by sevoflurane (B) or desflurane (C) were individually subjected to GO analyses using DAVID web tools (http://david.abcc.ncifcrf.gov). GO terms with a p-value less than 10−3 were selected and subjected to AmiGo2 analyses (http://amigo.geneontology.org/amigo/landing) to eliminate synonymous terms. GO terms identical between sevoflurane and desflurane treatments were marked in distinct colors (green and pink). (D) Heatmap showing a comparison of altered expression levels of xenobiotic metabolism-related genes activated by sevoflurane and/or desflurane. Genes that contributed to the identification of the GO term “response to xenobiotic stimulus (0009410)” by sevoflurane treatment or desflurane treatment were combined to generate a gene list of the heatmap.

We also conducted analyses for genes downregulated by anesthetic agent treatment (Figs 2A and S1, S1 Table). Compared with the upregulated genes, genes downregulated more than 2-fold than their respective controls showed less intensive overlap between the two anesthetic agents. GO analyses of these downregulated gene sets yielded 17 and 5 specific GO terms related to sevoflurane (Fig 2B) and desflurane (Fig 2C), respectively, whose p-values were less than 10−3. Consistent with the less intensive overlap between sevoflurane and desflurane with respect to downregulated genes, no common GO term was obtained. Notably, many of the GO terms associated with sevoflurane were related to immunological reaction (indicated by red letters). Because this finding most probably reflects the repression of immune response-related genes in blood cells, such as lymphocytes that were included in the liver samples, these results indicate that immunological response may be impaired by sevoflurane treatment. Unlike sevoflurane treatment, no apparent biological relatedness was evident among five GO terms associated with desflurane treatment, none of which were related to immune response, suggesting that desflurane may exert no, or at least less, significant repression of genes related to immune response compared with sevoflurane. Unexpectedly, the same GO term, “response to xenobiotic stimuli” (GO:0009410) obtained for genes activated by desflurane and sevoflurane, was also obtained in the analyses of genes downregulated by desflurane, suggesting that inhalation anesthesia with desflurane induces more complex responses in the liver than originally anticipated. Although GO analyses did not provide any indication of the alleviation of immunological responses by desflurane treatment, we manually inspected our RNA sequence data to determine whether expression levels of immunological response-related genes were not affected by desflurane. First, we used a gene set in which genes downregulated more than 2-fold by sevoflurane treatment were selected from among the members of the GO term “defense response to virus” (GO:0051607) for the analysis. A heatmap visualization of gene expression revealed that none of these genes were noticeably activated by desflurane, but many showed reduced expression levels, although the magnitude of downregulation was much less significant compared with that induced by sevoflurane (Fig 2D). Likewise, many genes that contributed to the GO terms “immune response” (GO:0006955) and/or “response to bacterium” (GO:0009617) as sevoflurane treatment-specific terms also had a tendency to be downregulated by desflurane (S3 Fig). These data indicate that the repressing effect of immune response-related genes by inhalation anesthesia was not specific to sevoflurane, but the magnitude of the repressive effect was greater for sevoflurane than desflurane. Although the manual inspection of our RNA-sequence data indicated that sevoflurane as well as desflurane led to the repression of immunological response-related genes, regression analyses revealed that there were no (GO:0051607 and GO:0006955) (S2B and S3A Figs) or weak (GO:0009617) (S3B Fig) correlations in expression changes between sevoflurane and desflurane treatments. These data indicated that, with a few exceptions, genes that were downregulated strongly or weakly by sevoflurane were not downregulated strongly and weakly by desflurane, suggesting that desflurane is not simply an anesthetic agent that downregulates immune-related genes more weakly than sevoflurane.

Fig 2. GO analyses of genes downregulated by inhalational anesthesia.

Fig 2

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(A) A Venn diagram showing a comparison of genes downregulated more than 2-fold by sevoflurane or desflurane. P-value for the significance of the overlap between two gene sets was calculated by a hypergeometric test. Lists of these genes are provided using their official gene symbols in S1 Table. (B, C) Genes whose expression was downregulated more than 2-fold by sevoflurane (B) or desflurane (C) were individually subjected to GO analyses as in Fig 1B and 1C. GO terms related to immune response in (B) are indicated in red. (D) Heatmap showing a comparison of alterations in the expression levels of immune response-related genes by sevoflurane (left column) and desflurane (right column). Genes that contributed to the identification of the GO term “defense response to virus (0051607)” as a sevoflurane treatment-specific term were used for the analyses.

Identification of gene sets coordinately regulated by desflurane and/or sevoflurane via gene set enrichment analysis

In addition to the above GO analyses, we also conducted GSEA to assess similarities and differences in phenotypic changes that occurred in rat livers, including blood cells subjected to inhalational anesthesia by sevoflurane or desflurane (S3 Fig). In the analyses, we used three publicly available databases, “biological process of Gene Ontology”, “Kyoto Encyclopedia of Genes and Genome”, and “Reactome Pathway Database”. First, we found that three gene sets related to xenobiotic metabolism including “DRUG_METABOLISM_CYTOCHROME_P450” were identified as gene sets activated specifically by desflurane treatment (S3A Fig). Of note, none of these gene sets were identified by GSEA using RNAs from the livers of rats in the sevoflurane group, even though such terms were identified by GO analyses. These results indicate that xenobiotic metabolism-related genes may not be as coordinately regulated by sevoflurane compared with desflurane. This notion is consistent with the data shown in Fig 1A and 1B where GO terms related to xenobiotic metabolism were statistically more significant in the desflurane group compared with the sevoflurane group. We also found that numerous terms related to immunological reactions were identified in the gene sets downregulated by sevoflurane and desflurane. Of note, some terms, including “CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION” were commonly identified in both groups. However, the effect of sevoflurane treatment appeared to be more profound than desflurane treatment on the basis of the total number of identified terms related to immunological reactions (43 and 15 terms for sevoflurane and desflurane treatments, respectively) and normalized enriched score (NES) (number of terms whose NES values were lower than −2.0 = 31 and 1 for sevoflurane and desflurane treatments, respectively). These findings were consistent with the GO analysis data shown in Fig 2B and 2C, where many and no terms related to immune response were obtained for the sevoflurane and desflurane-treated rat livers, respectively. Fig 3 shows representative snapshots of GSEA showing a tendency for the positive regulation of genes constituting the term “DRUG_METABOLISM_CYTOCHROME_P450” by desflurane treatment (Fig 3A) and the negative regulation of genes constituting the term “ADAPTIVE_IMMUNE_RESPONSE” by sevoflurane treatment (Fig 3B). In addition, Fig 3C shows snapshots of GSEA showing a tendency for the negative regulation of genes constituting the term “CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION”, which was a commonly identified term in the sevoflurane (left panel) and desflurane (right panel) groups. These two snapshots demonstrated that genes constituting this term were subjected to greater negative regulation by sevoflurane than by desflurane.

Fig 3. GSEA.

Fig 3

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(A) Snapshot showing a tendency for the positive regulation of genes constituting the term “DRUG_METABOLISM_CYTOCHROME_P450” after treatment with desflurane. Among 47 members of the term included in the list of our RNA-sequence data, 29 and 18 genes were up- and downregulated by desflurane treatment, respectively. A list of genes denoted as leading-edge genes by the analysis is provided in S2A Table. (B) Snapshot showing a tendency for the negative regulation of genes constituting the term “adaptive immune response” after treatment with sevoflurane. Among 353 members of the term included in the list of our RNA-sequence data, 90 and 263 genes were up- and downregulated by sevoflurane treatment, respectively. The regulatory mode of 21 genes could not be determined because there was no expression in certain samples. A list of leading-edge genes is provided in S2B Table. (C) Snapshots showing a tendency for the negative regulation of genes constituting the term “CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION” after treatment with sevoflurane (left panel) and desflurane (right panel). Among 217 members of the term included in the list of our RNA-sequence data, 132 and 106 genes were downregulated and 50 and 80 genes were upregulated by sevoflurane and desflurane, respectively. The regulatory modes of 35 (for sevoflurane) and 31 (for desflurane) genes could not be determined because there was no expression in certain samples. The lists of genes denoted as leading-edge genes for the treatments of sevoflurane and desflurane are provided in S2C and S2D Table, respectively.

Validation of global gene expression analysis data by the qPCR of representative genes

Next, we conducted qPCR analyses of genes whose expression levels were significantly up- or downregulated by sevoflurane and/or desflurane by means of global gene expression analyses. Specifically, we selected three genes (Cyp2b1, Por, and Alas1) (Fig 4A) and four genes (Irf1, Mx2, Ccl6, and Il33) (Fig 4B) as representative of xenobiotic metabolism and immune response, respectively. First, we confirmed that xenobiotic metabolism-related genes were significantly activated by both inhalation anesthetic agents. Our qPCR data of immune response-related genes also recapitulated the data from the RNA-sequencing analyses. Indeed, our qPCR data confirmed the significant downregulation of the expression of Irf1 and Mx2 by sevoflurane and desflurane, which were suggested to be downregulated significantly by both anesthetic agents in the RNA-sequencing analyses. Likewise, our qPCR analyses confirmed the sevoflurane treatment-specific downregulation of the expression of Ccl6 and IL33, which were specifically repressed by sevoflurane, but not desflurane, in the RNA-sequence analyses.

Fig 4. qPCR analyses of representative genes whose expression was significantly altered by inhalational anesthesia.

Fig 4

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qPCR analyses of the expression of three (A) and four (B) genes as representative genes for xenobiotic metabolism and immune response, respectively. Data from control rats in which livers were recovered immediately after the loss of consciousness by sevoflurane or desflurane treatment were arbitrarily set to one. Data represent the mean ± SD of three independent experiments. The Student’s t-test (two-tailed) was conducted to examine statistical significance. ***, P<0.001; **, P<0.01; *, P<0.05; NS, P>0.05.

Discussion

Sevoflurane and desflurane are commonly used inhalational anesthetic agents in modern anesthesia practice [32,33]. Inhalation anesthesia in general is known to induce the activation and repression of genes related to xenobiotic metabolism and immune response, respectively [25–29]. However, because these two halogenated anesthetics have never been compared comprehensively with respect to alterations in the expression levels of genes related to xenobiotic metabolism and immune response, we conducted next-generation sequence analyses using mRNAs from the livers of rats subjected to inhalational anesthesia using sevoflurane or desflurane. Our GO analyses of RNA-sequence data revealed that both anesthetic agents significantly activated numerous genes related to xenobiotic metabolism. These analyses also indicated that desflurane activated these genes to a greater extent than sevoflurane, which was confirmed by GSEA. Given that the magnitude of the activation of xenobiotic metabolism genes parallels the level of protection of the host against xenobiotic-mediated toxicity, these data suggest that a higher level of protection via the xenobiotic metabolizing system might be required in the host when administering desflurane compared with sevoflurane.

Unlike the activated gene sets, no common GO terms were obtained in the analyses of genes downregulated by sevoflurane and desflurane. Although no obvious biological relatedness was apparent among five GO terms obtained in the analyses of downregulated genes by desflurane, we found that most GO terms obtained with sevoflurane were related to immune response, indicating that sevoflurane treatment may be strongly linked to immunosuppression. However, a heatmap visualization revealed that desflurane also reduced the expression of immune response genes, albeit less intensively compared with sevoflurane. Our data from GSEA were consistent with the heatmap visualization data, further corroborating the notion that both anesthetic agents repressed immune response-related genes, although sevoflurane exerted a more pronounced repressing effect compared with desflurane.

Multiple studies have demonstrated that volatile anesthetic agents exhibit immunosuppressive effects [27–29]. Therefore, surgeons and anesthesiologists prefer to avoid surgery coupled with general anesthesia for patients vaccinated within the last 2 or 3 weeks because of concerns regarding the insufficient acquisition of immunity by the vaccine. However, because our data suggest that desflurane exerts a less intensive immunosuppressive effect than sevoflurane, our future studies will investigate whether there is a significant difference in the specific immunoprotective ability of rats subjected to inhalation anesthesia with sevoflurane or desflurane after immunization with a vaccine such as that for COVID-19 or influenza virus.

Supporting information

S1 Fig. Scatter plot of RNA sequence data.

Upper and lower panels show data obtained from experiments of inhalational anesthesia using sevoflurane and desflurane, respectively. These scatter plots were generated after removing genes whose lengths are equal or shorter than 200 base pairs from the gene list of RNA sequence data. Numerical values shown on the X- and Y-axes are TPM values from RNA sequence data. Genes whose TPM values were increased or decreased more than 2-fold by inhalational anesthesia using sevoflurane or desflurane are indicated as red and blue dots, respectively. The numbers of genes upregulated by 6 hours treatment with sevoflurane and desflurane were 210 and 282, respectively, of which 59 genes overlapped, and 329 and 141 genes were downregulated by sevoflurane and desflurane treatments, respectively, with 31 overlapping genes. S1 Table shows a list of these genes with their official gene symbols.

(PDF)

pone.0298264.s001.pdf (351.9KB, pdf)
S2 Fig. Comparison of the repressing effect on specific gene sets between sevoflurane and desflurane by regression analysis.

(A, B) Coefficient of determination was calculated using the genes in Fig 1D that were upregulated by sevoflurane and/or desflurane more than 2-fold among the members of the GO term “response to xenobiotic stimulus (0009410)” (A, upper panel) and genes shown in Fig 2D that were downregulated by sevoflurane more than 2-fold among the members of the GO term “defense response to virus (0051607)” (B). Lower panel in A shows the result after the removal of Cyp2b1 gene data as an outlier in the gene set.

(PDF)

pone.0298264.s002.pdf (227.3KB, pdf)
S3 Fig. Comparisons of expression of immune response-related genes between sevoflurane and desflurane treatments.

(A, B) Effect of desflurane treatment on the expressions of genes that contributed to the identification of immune response-related terms as sevoflurane treatment-specific GO terms. Genes downregulated more than 2-fold by sevoflurane treatment were selected among genes constituting the GO terms “immune response (0006952)” (A) and “response to bacterium (0009617)” (B). Relative expression levels in the livers of rats treated with desflurane for 6 hours compared to the control were demonstrated by a heatmap (right column) along with data obtained by the analyses of livers of rats treated with sevoflurane (left column). Panels shown under each heatmap represent regression analyses for the calculation of the coefficient of determination.

(PDF)

pone.0298264.s003.pdf (236.4KB, pdf)
S4 Fig. GSEA of RNA sequence data from livers of rats subjected to inhalational anesthesia.

(A) A list of gene sets identified by GSEA as significantly activated gene sets by inhalational anesthesia using sevoflurane or desflurane. Three distinct publicly available databases, “biological process of Gene Ontology”, “Kyoto Encyclopedia of Genes and Genome”, and “Reactome Pathway Database” were used for the analyses, in which the top twenty terms according to their NES values were selected from positively-regulated gene sets with the analyses using each platform, but terms whose p-values were greater than 0.05 were eliminated from the list. Terms related to xenobiotic metabolism are shown in light blue. (B) A list of gene sets identified by GSEA as significantly repressed gene sets by inhalational anesthesia using sevoflurane or desflurane. The same criteria used in (A) were used. Commonly identified gene sets by treatment with sevoflurane or desflurane are shown in green. Terms related to immune response are indicated by red font.

(PDF)

pone.0298264.s004.pdf (327.4KB, pdf)
S1 Table. Gene lists that were up- or down-regulated by sevoflurane and/or desflurane.

(PDF)

pone.0298264.s005.pdf (220.5KB, pdf)
S2 Table. Gene lists denoted as leading-edge genes by GSEA.

(PDF)

pone.0298264.s006.pdf (186.2KB, pdf)

Acknowledgments

The authors are indebted to Nahomi Nakahara for her technical assistance. The authors also thank Macrogen, Inc. for the RNA-sequencing analyses. We thank J. Ludovic Croxford, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Data Availability

RNA sequence data used for this study were deposited in Gene Expression Omnibus-NCBI under accession number GSE244436.

Funding Statement

Takehiro Nogi is the recipient of in-house grants from Saitama Medical University (Internal Grants, 21-B-1-07 and 23-B-1-15) and Saitama Medical University Hospital (02-E-1-08). Akihiko Okuda is the recipient of a grant from the Japan Society for the Promotion of Science (KAKENHI: grant number 23H02678). However, these funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Shin Yamazaki

28 Jun 2023

PONE-D-23-16299Similarity and dissimilarity in alteration of gene expression profile associated with inhalational anesthesia between sevoflurane and desfluranePLOS ONE

Dear Dr. Okuda,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript was reviewed by three reviewers who have specialized research fields in anesthesia, GO analysis, and circadian rhythm. All reviewers expressed the same concern about the time of tissue sampling and a potential difference in tissue sampling time between treated and control rats. Each reviewer also expressed other concerns. I only invite you to submit a revised manuscript if you can address all of the reviewers’ concerns. Redoing the experiments and analysis may be necessary to fully address those concerns (in case you have different sampling times between treated and control rats).

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This work was supported in part by in-house grants from Saitama Medical University (Internal Grant 21-B-1-07) and Saitama Medical University Hospital (02-E-1-08) to TNo. This work was also supported in part by a grant from the Japan Society for the Promotion of Science KAKENHI (grant number 23H02678) to AO."

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

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Reviewer #1: The authors state that different anesthetic agents (desflurane compared to sevoflurane) have different transcriptomic signatures. They show this by anesthetizing rats for 6 hours with the aforementioned agents and then sacrificing the animals and performing bulk RNA sequencing and qPCR on the tissue of interest, which in this case is the liver. They use briefly induced rats as controls for this study.

The authors further claim that the transcriptomic signature of desflurane and sevoflurane are comparable in their effect on circadian rhythm and drug metabolizing genes and that the only difference was the expression of genes related to cholesterol biosynthesis, which were repressed by sevoflurane, but not by desflurane.

In order to be able to make the statement that circadian rhythm genes are differentially expressed after anesthesia with either sevoflurane or desflurane the authors need to control for circadian timing of the tissue harvesting. The authors do not share any information on harvesting time of the control group samples or the anesthetized samples.

From the methods the impression is made that rats were anesthetized at ZTx for 6hr and sacrificed at ZTx+6, whereas the control group was briefly induced at ZTx and immediately sacrificed. If this is the case, the harvested tissue of the anesthetized rats would be at a different circadian phase, and therefore would have different circadian rhythm gene expression than the controls. However this would be the effect of harvesting time rather than that of the anesthetic used.

In addition this raises the question whether any of the expression changes that the authors see is caused by other oscillating factors, like corticosterone levels for instance. We know that drug metabolism changes throughout the 24-h period, so the difference in gene expression for drug metabolism might also be attributed to the harvesting time. It doesn't seem like the authors have controlled for, or addressed this possible confounder.

Furthermore, the authors do not state the number of animals used for any of their experiments, and this makes it impossible to asses whether the study is well powered or not.

As a minor issue with the availability of sequencing data, the reviewers were not provided a token to access the data, and GEO would not provide one but they said that the publisher would provide one. I didn't follow that up with the journal however.

Reviewer #2: The manuscript described the transcriptome comparison between two inhalational anesthesia agents, sevoflurane and desflurane. From GO analysis and gene set enrichment analysis (GSEA), Nogi et al. identified that they shared similar transcriptome changes. Congruent with previous sevoflurane studies (Gene 2004 Sakamoto et al., Biomed Res. 2009 Nakazato el al.), their RNA-seq study in sevoflurane and desflurane also identified differentially expressed genes (DEGs) in circadian rhythm and drug metabolism pathways. Furthermore, in their downregulated gene dataset, they found that the cholesterol biosynthetic pathway is only responsive to sevoflurane treatment. Since the two anesthesia agents are commonly used and sevoflurane does not tend to induce hepatotoxicity, these transcriptome studies are valuable for understanding the pattern of DEG which causes hepatotoxicity. However, there are some concerns to improve the manuscript.

Major issues:

1. In general, the result section needs to provide more details. Specially, there is no information about the computational and bioinformatic pipelines for RNA-seq data process and DEG identification. Please, provide them in the method section.

2. The RNA sequencing data should not contain small RNA signals because these are depleted during size selection step to remove adaptors. However, there are small RNA signals at Table S1 (e.g. miRNA genes) and Figure 3S (e.g. MIRNA_MEDIATED_GENE_SILENCING_BY_INHIBITION_OF_TRANSLATION). Please, count aligned reads without small RNA genes (<= 200bp) and recalculate TPM values for GO and GSEA analyses. For the analyses, use liver expressed background gene sets that do not include the small RNA genes.

3. In the method section, zeitgeber times are not very clear to collect 0-hour and 6-hour treatment samples. If there is 6-hour gap between the samples, there is additional factor of time that alters the expression of gene sets related to daily circadian rhythms including sevoflurane and desflurane treatments. The phase of the circadian rhythm during treatment with either anesthetic could very likely be the driver of the differential gene expression if there is 6-hour gap between the samples. To reach the same conclusion, the author needs to subtract the rhythmic gene expression changes.

Minor issues

1. Main figures need higher resolution since it is very hard to read the figures.

2. For qPCR experiments, provide if t-tests were one- or two-tailed. Also, note how many samples are used.

3. For Fig. 3, provide more explanation in the figure legend to understand the figure.

4. For Fig. S2B and Fig. 2D, heatmaps showed similar reduced expression at the GO terms. It is better to include plots that show expression correlations with R2 and p-values.

5. For the Venn diagrams at fig 1C and 2C, provide hypergeometric p-values to show how significantly the gene sets are overlapped.

Reviewer #3: This paper concerns differences in gene expression in response to two different inhalation anaesthetic agents (sevoflurane and desflurane). I must admit to being a little confused about the overall point of the study and the results presented. While differences in a number of different gene expression profiles are presented (particularly for genes involved in the molecular mechanics of the circadian clock and in drug metabolism) there were no differences in the expression changes between the two agents. The authors do report a downregulation in the expression of cholersterol biosynthesis genes in response to sevoflurane but I am unsure of the potential clinical significance of this finding.

One significant omission from the manuscript was any description of the timing of the administration of the two agents. Particularly with respect to the analysis of circadian gene expression profiles this is very important. Time of day will have a profound influence on the level of expression of clock genes (and also potentially on the influence of the anaesthetic agents on the expression of clock genes).

A detailed description of the timing and standardization of the administration of GA needs to be including, as does a description of what the implications of different timings might mean in the discussion.

I did not get a clear idea of the point of the manuscript. I think the manuscript would benefit from a substantial re-work in order to make it clear why the authors would expect changes in the gene expression profiles they have seen and what the scientific and clinical relevance of this changes might be.

Other more minor comments include

Introduction

1. GA is not sedation.

2. Isoflurane is the most commonly used inhalational agent in the world not sevoflurane

**********

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Reviewer #1: No

Reviewer #2: Yes: Chang Hoon Lee

Reviewer #3: No

**********

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PLoS One. 2024 Mar 28;19(3):e0298264. doi: 10.1371/journal.pone.0298264.r002

Author response to Decision Letter 0

6 Nov 2023

Response to Reviewers

Reviewer #1:

First comment from reviewer #1

In order to be able to make the statement that circadian rhythm genes are differentially expressed after anesthesia with either sevoflurane or desflurane the authors need to control for circadian timing of the tissue harvesting. The authors do not share any information on harvesting time of the control group samples or the anesthetized samples.

From the methods the impression is made that rats were anesthetized at ZTx for 6hr and sacrificed at ZTx+6, whereas the control group was briefly induced at ZTx and immediately sacrificed. If this is the case, the harvested tissue of the anesthetized rats would be at a different circadian phase, and therefore would have different circadian rhythm gene expression than the controls. However this would be the effect of harvesting time rather than that of the anesthetic used.

As this reviewer pointed out, we had harvested livers from control and experimental rats at 9 am and 3 pm, respectively. Therefore, we conducted the same experiments in which all livers, including those in the control group, were harvested at 3 pm. Then, we compared the expression levels of circadian genes by quantitative PCR using RNAs prepared from the newly harvested liver samples. We found minimal differences in the expression levels between liver RNAs from control and anesthetized rats, indicating that the marked activation of circadian rhythm-related genes we observed previously were not due to inhalation anesthesia but rather the difference in their circadian rhythm phase. Because of this finding, we repeated the analyses using data from the newly conducted RNA-sequencing to revise our manuscript.

Second comment from reviewer #1

In addition, this raises the question whether any of the expression changes that the authors see is caused by other oscillating factors, like corticosterone levels for instance. We know that drug metabolism changes throughout the 24-h period, so the difference in gene expression for drug metabolism might also be attributed to the harvesting time. It doesn't seem like the authors have controlled for, or addressed this possible confounder.

As this reviewer stated, we found that not only canonical circadian genes but also a substantial number of genes including those for xenobiotic metabolism such as Alas1 and Por were substantially affected by the timing of tissue harvesting. However, unlike circadian genes, the anesthetic procedure-dependent activation of these genes was still evident in the newly conducted analyses, albeit to a lower level than before.

Third comment from reviewer #1

Furthermore, the authors do not state the number of animals used for any of their experiments, and this makes it impossible to assess whether the study is well powered or not.

Although N=1 for the RNA-seq data, quantitative PCR used to validate the RNA-seq data were conducted with N=3. We described this explicitly in Methods section in the revised text (page 6 lines 16 to 18 for quantitative PCR and page 7 lines 3 to 5 for RNA-seq data).

Fourth comment from reviewer #1

As a minor issue with the availability of sequencing data, the reviewers were not provided a token to access the data, and GEO would not provide one but they said that the publisher would provide one. I didn't follow that up with the journal however.

We must apologize for this issue. We did not know that we had to create and provide a token so that any reviewers can access the private data. We have rectified this and provide a token (snqnkssarfwjjip) for our RNA sequence data (GSE244436). Once again, we would like to apologize any inconvenience that this reviewer encountered because of this issue.

 

Reviewer #2:

First comment from reviewer #2

In general, the result section needs to provide more details. Specially, there is no information about the computational and bioinformatic pipelines for RNA-seq data process and DEG identification. Please, provide them in the method section.

According to this reviewer’s comment, we provided the details of pipelines for the RNA-seq data process and DEG identification in the Methods section (page 7 line 1 to page 8 line 4). We also added information about the timing of tissue harvesting in the Methods section (page 5 line 18 to page 6 line 2)

Second comment from reviewer #2

The RNA sequencing data should not contain small RNA signals because these are depleted during size selection step to remove adaptors. However, there are small RNA signals at Table S1 (e.g. miRNA genes) and Figure 3S (e.g. MIRNA_MEDIATED_GENE_SILENCING_BY_INHIBITION_OF_TRANSLATION). Please, count aligned reads without small RNA genes (<= 200bp) and recalculate TPM values for GO and GSEA analyses. For the analyses, use liver expressed background gene sets that do not include the small RNA genes.

Because we did not know how to delete the data of the small RNA genes (≤200 bp), we removed all non-coding RNAs from the list including longer RNAs (>200 bp) from the list using BioMart web tool (https://asia.ensembl.org/info/data/biomart/index.html) as described on page 7 lines 14 to 15 and conducted GO and GSEA analyses in response to this reviewer’s comment. We hope that these procedures are satisfactory for this reviewer.

Third comment from reviewer #2

In the method section, zeitgeber times are not very clear to collect 0-hour and 6-hour treatment samples. If there is 6-hour gap between the samples, there is additional factor of time that alters the expression of gene sets related to daily circadian rhythms including sevoflurane and desflurane treatments. The phase of the circadian rhythm during treatment with either anesthetic could very likely be the driver of the differential gene expression if there is 6-hour gap between the samples. To reach the same conclusion, the author needs to subtract the rhythmic gene expression changes.

As this reviewer and other reviewers pointed out, there was a 6-hour gap in liver harvesting time between the control and experimental rats. Therefore, we conducted the same experiments in which all livers, including those of the control group, were harvested at the same time, at 3 pm. Then, we compared the expression levels of circadian genes by quantitative PCR using RNAs prepared from newly harvested liver samples and found only minimal differences in their expression levels between liver RNAs from anesthetized rats and control rats. This indicated that the marked activation of circadian rhythm-related genes we observed previously was not due to inhalation anesthesia but rather to the difference in their circadian rhythm phase. Because of this finding, we repeated the analyses using data from newly conducted RNA-sequencing to revise our manuscript.

Fourth comment from reviewer #2

Main figures need higher resolution since it is very hard to read the figures.

In the revised manuscript, we provided figures with a high resolution (600 dot per inch). We hope that the resolution of the newly provided files is satisfactory.

Fifth comment from reviewer #2

For qPCR experiments, provide if t-tests were one- or two-tailed. Also, note how many samples are used.

A two-tailed t-test (N=3) was conducted for all experiments. We described this in Methods section (page 8 lines 15 to 17) as well as in legends of Figure 4 (page 34 lines 4 to 6) in the revised text.

Sixth comment from reviewer #2

For Fig. 3, provide more explanation in the figure legend to understand the figure.

We have provided information related to the number of genes included in each gene set and how many genes were activated or repressed. We hope that our response is satisfactory.

Seventh comment from reviewer #2

For Fig. S2B and Fig. 2D, heatmaps showed similar reduced expression at the GO terms. It is better to include plots that show expression correlations with R2 and p-values.

We provided these data in Fig. S2 and S3 in the revised manuscript. These regression analyses revealed that neither xenobiotic metabolism-related genes nor immune response-related genes had a noticeable correlation related to variations in their expression levels between the sevoflurane and desflurane groups. We described about these results in the text (page 11 lines 1 to 7, pages 13 lines 5 to 11 and page 18 lines 10 to 17).

Eighth comment from reviewer #2

For the Venn diagrams at fig 1C and 2C, provide hypergeometric p-values to show how significantly the gene sets are overlapped.

As suggested, we provided these data in Fig. 1A and Fig. 2A in the revised manuscript.

 

Reviewer #3:

First comment from reviewer #3

This paper concerns differences in gene expression in response to two different inhalation anaesthetic agents (sevoflurane and desflurane). I must admit to being a little confused about the overall point of the study and the results presented. While differences in a number of different gene expression profiles are presented (particularly for genes involved in the molecular mechanics of the circadian clock and in drug metabolism) there were no differences in the expression changes between the two agents. The authors do report a downregulation in the expression of cholersterol biosynthesis genes in response to sevoflurane but I am unsure of the potential clinical significance of this finding. One significant omission from the manuscript was any description of the timing of the administration of the two agents. Particularly with respect to the analysis of circadian gene expression profiles this is very important. Time of day will have a profound influence on the level of expression of clock genes (and also potentially on the influence of the anaesthetic agents on the expression of clock genes).

As this reviewer and other reviewers pointed out, there was a 6-hour gap in liver harvesting time between the control and experimental rats. Therefore, we conducted the same experiments in which all livers, including those of the control group, were harvested at the same time, at 3 pm. Then, we compared the expression levels of circadian genes by quantitative PCR using RNAs prepared from newly harvested liver samples and found only minimal differences in their expression levels between liver RNAs from anesthetized rats and control rats. This indicated that the marked activation of circadian rhythm-related genes we observed previously was not due to inhalation anesthesia but rather to the difference in their circadian rhythm phase. Because of this finding, we repeated the analyses using data from newly conducted RNA-sequencing.

GO analyses using data from the newly conducted RNA-sequencing revealed that terms related to circadian rhythm were no longer significant terms with sevoflurane-treated sample, although the term “circadian regulation of gene expression (GO:0032922)” was identified with desflurane-treated sample in the 12th position from the top. Unexpectedly, the GO term “cholesterol biosynthetic process (0006695)” was not identified with samples from rats treated with sevoflurane, indicating that a 6-hour gap in tissue harvesting not only affected canonical circadian genes in their expression levels but also affected many other genes whose expressions oscillate between day and night. These findings indicate that adjusting the timing of tissue harvesting within the day are critical when comparing control and experimental animals.

We would like to thank this reviewer and other reviewers because we would not have conducted the RNA-sequencing analyses again without these invaluable comments.

Second comment from reviewer #3

I did not get a clear idea of the point of the manuscript. I think the manuscript would benefit from a substantial re-work in order to make it clear why the authors would expect changes in the gene expression profiles they have seen and what the scientific and clinical relevance of this changes might be.

We have intensively edited the text, especially in the Abstract (page 2 lines 2 to 9) and Introduction (page 4 lines 3 to 9) in response to this reviewer’s comment. We edited the text with a focus on the comparison of the effects of sevoflurane and desflurane on the expressions of genes related to xenobiotic metabolism and immune response, because no comprehensive comparison has been reported to date. We hope the newly edited text is satisfactory.

Third comment from reviewer #3

GA is not sedation.

To avoid confusion, we eliminated this expression from the text.

Fourth comment from reviewer #3

Isoflurane is the most commonly used inhalational agent in the world not sevoflurane

As suggested, we changed the expression from “the most---” to “one of the most---” (first line in the abstract and second sentence in the Introduction section).

Attachment

Submitted filename: Response to reviewers (PONE-D-23-16299).docx

pone.0298264.s007.docx (34.3KB, docx)

Decision Letter 1

Shin Yamazaki

18 Dec 2023

PONE-D-23-16299R1Similarity and dissimilarity in alterations of the gene expression profile associated with inhalational anesthesia between sevoflurane and desfluranePLOS ONE

Dear Dr. Okuda,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Shin Yamazaki, Ph.D.

Section Editor

PLOS ONE

Additional Editor Comments:

First, I apologize for taking a significant amount of time to evaluate your amended manuscript. Your amended manuscript was reviewed by two reviewers who also reviewed your original manuscript. Both reviewers have several important suggestions. Please revise the manuscript accordingly. To address the comments by reviewer #1, extensive manuscript revision and language editing are necessary. I believe this will make your manuscript legible, therefore. I strongly encourage you to do so. PLoS ONE doesn’t perform language editing, so the accepted manuscript will be published as is. After you amend the manuscript, please use a scientific language editing service. When you submit, please provide the service you have used.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors compared two different volatile anesthetics (sevoflurane and desflurane) and compared the transcriptomic response to these anesthetic by next-gen sequencing of the rat liver. Their findings suggest that the differentially expressed genes of these anesthetics have different gene ontology profiles, and that the profile belonging to sevoflurane seems to be suggestive of a decreased immune response profile. Compared to the last submission of this paper, the findings have really changed, and I think this paper is a better representation of what is happening in the liver after anesthesia with sevoflurane or desflurane. Even though I believe the science is sound and the findings and conclusions are now in line with the experimental setup, the way it is presented is very difficult to follow. Below I have highlighted some segments that are examples, but the manuscript is overwhelming with these kind of sentencing constructs. Based on the difficulty of following the manuscript I fear that the interesting findings will be lost to confusion.

example 1: this following segment is one sentence.

"In accordance with these data, regression analysis demonstrated no apparent correlation in alterations of the expressions of these genes between sevoflurane and desflurane treatments in case that data of Cyp2b1 expression were removed as an outlier in the gene set (S2A Fig, lower panel), although an analysis of all genes shown in Figure 1D suggests a high correlation (R2=0.9371) because of the marked activation of Cyp2b1 gene expression by both anesthetic agents (S2A Fig, upper panel)."

They are saying that the expression patterns between the two modalities (sevoflurane vs desflurane) show now apparent correlation when the expression of Cyp2b1 is excluded from the analysis. Apparently the expression of Cyp2b1 is an outlier that skews the correlation analysis significantly.

example 2: this following segment is one sentence.

"Although no GO term related to immunological response was obtained in the analyses of genes downregulated by desflurane, we inspected the gene expression levels in the livers of rats subjected to inhalational anesthesia with desflurane as to genes that were contributed to the identification of the GO term, “defense response to virus” (GO:0051607) as sevoflurane- specific terms because of their downregulation more than 2-fold by sevoflurane treatment."

I think it's clear that they wanted to compare the expression levels of a gene set from a sevoflurane specific GO term between the two different treatments, but the wording does not make that easy to understand.

Example 3: this following segment is one sentence.

Regression analyses revealed that, although there was a weak correlation between genes that contributed to the identification of the GO term, “response to bacterium” (GO:0009617) as a sevoflurane treatment-specific term (R2=0.2605) (S3B Fig), no appreciable statistical significance was apparent with other gene sets (genes downregulated more than 2-fold by sevoflurane treatment among genes comprising the terms of GO:0051607 and GO:0006955) (S2B Fig and S3A Fig).

I had a very difficult time following what the authors were trying to say here.

Reviewer #2: The revised manuscript has appropriately addressed most of my comments. However, there is one aspect where the authors need to make a correction in their approach.

To delete the data of the small RNA genes (≤200 bp), removing all non-coding RNAs is not the optimal approach. While it effectively eliminates most small RNAs, it could also lead to the depletion of all long non-coding RNAs, remaining some small RNAs originate from protein-coding genes.

To identify small RNA genes (≤200bp), you can utilize a GTF file from Ensembl that aligns with your reference genome. After calculating transcript lengths, filter out all small transcripts (≤200bp) from the GTF file. Using the refined GTF file, you can get a list of genes that transcribe RNAs ≥200bp. Consequently, you can accurately count your aligned reads, excluding small RNAs (≤200bp).

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2024 Mar 28;19(3):e0298264. doi: 10.1371/journal.pone.0298264.r004

Author response to Decision Letter 1

9 Jan 2024

Response to Reviewers

Reviewer #1:

First comment from reviewer #1

The way it is presented is very difficult to follow. Below I have highlighted some segments that are examples.

example 1: this following segment is one sentence.

"In accordance with these data, regression analysis demonstrated no apparent correlation in alterations of the expressions of these genes between sevoflurane and desflurane treatments in case that data of Cyp2b1 expression were removed as an outlier in the gene set (S2A Fig, lower panel), although an analysis of all genes shown in Figure 1D suggests a high correlation (R2=0.9371) because of the marked activation of Cyp2b1 gene expression by both anesthetic agents (S2A Fig, upper panel).

We agree the English in this section was extremely difficult to follow as it was difficult to explain the data explicitly, because the regression analyses conducted according to a comment from another reviewer with a gene set shown in Figure 1D apparently contradicted the result obtained from the heatmap visualization. However, in the revised manuscript, this section of the text was edited extensively with the aid of professional English editing service so that it can be understood more easily (page 10 lines 1 to 8). We hope that newly edited text is satisfactory.

example 2: this following segment is one sentence.

"Although no GO term related to immunological response was obtained in the analyses of genes downregulated by desflurane, we inspected the gene expression levels in the livers of rats subjected to inhalational anesthesia with desflurane as to genes that were contributed to the identification of the GO term, “defense response to virus” (GO:0051607) as sevoflurane- specific terms because of their downregulation more than 2-fold by sevoflurane treatment."

Again, we agree this section was extremely difficult to follow. We did not use an entire gene set constituting the GO term “defense response to virus”, but rather selected genes with expression levels reduced more than 2-fold by sevoflurane treatment from the gene set. Accordingly, we have rewritten the text in the revised manuscript with the aid of a professional editing service (page 12 lines 10 to 16) and we hope this is now easier to understand.

Example 3: this following segment is one sentence.

Regression analyses revealed that, although there was a weak correlation between genes that contributed to the identification of the GO term, “response to bacterium” (GO:0009617) as a sevoflurane treatment-specific term (R2=0.2605) (S3B Fig), no appreciable statistical significance was apparent with other gene sets (genes downregulated more than 2-fold by sevoflurane treatment among genes comprising the terms of GO:0051607 and GO:0006955) (S2B Fig and S3A Fig).

We also edited this portion extensively so that it is easier to understand (page 13 lines 7 to 15). We hope that newly edited text is satisfactory for this reviewer.

Second comment from reviewer #1

The manuscript is overwhelming with these kind of sentencing constructs.

In the revised manuscript, we tried to avoid making overstatements. In particular, we removed the following sentences from the Discussion section, because we felt that they were too exaggerated and could be removed without affecting the scientific content.

“This finding might alter the current concept that one of major side-effects of general anesthesia with a volatile anesthetic agent is immunosuppression.”

Reviewer #2:

Comment from reviewer #2

The revised manuscript has appropriately addressed most of my comments. However, there is one aspect where the authors need to make a correction in their approach.

To delete the data of the small RNA genes (≤200 bp), removing all non-coding RNAs is not the optimal approach. While it effectively eliminates most small RNAs, it could also lead to the depletion of all long non-coding RNAs, remaining some small RNAs originate from protein-coding genes.

According to this reviewer’s comment, we removed the small RNA genes (≤200 bp) from the gene list instead of removing all the non-coding genes, and re-conducted all the analyses shown in Figures 1-3 and all the supplementary figures and tables.

Attachment

Submitted filename: Nogi et al. response to reviewers.docx

pone.0298264.s008.docx (28.2KB, docx)

Decision Letter 2

Shin Yamazaki

23 Jan 2024

Similarity and dissimilarity in alterations of the gene expression profile associated with inhalational anesthesia between sevoflurane and desflurane

PONE-D-23-16299R2

Dear Dr. Okuda,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Shin Yamazaki, Ph.D.

Section Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Between the first and second resubmission of this paper I feel the authors have addressed all my previous comments in a acceptable fashion. And I will be happy to support the submission of this paper. I think it adds interesting new insight in the effects of inhalation anesthetics in peripheral organs.

Reviewer #2: In the revised manuscript, the authors has appropriately addressed all my comments as reanalyzing data.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Acceptance letter

Shin Yamazaki

19 Mar 2024

PONE-D-23-16299R2

PLOS ONE

Dear Dr. Okuda,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

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Section Editor

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Associated Data

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

    Supplementary Materials

    S1 Fig. Scatter plot of RNA sequence data.

    Upper and lower panels show data obtained from experiments of inhalational anesthesia using sevoflurane and desflurane, respectively. These scatter plots were generated after removing genes whose lengths are equal or shorter than 200 base pairs from the gene list of RNA sequence data. Numerical values shown on the X- and Y-axes are TPM values from RNA sequence data. Genes whose TPM values were increased or decreased more than 2-fold by inhalational anesthesia using sevoflurane or desflurane are indicated as red and blue dots, respectively. The numbers of genes upregulated by 6 hours treatment with sevoflurane and desflurane were 210 and 282, respectively, of which 59 genes overlapped, and 329 and 141 genes were downregulated by sevoflurane and desflurane treatments, respectively, with 31 overlapping genes. S1 Table shows a list of these genes with their official gene symbols.

    (PDF)

    pone.0298264.s001.pdf (351.9KB, pdf)
    S2 Fig. Comparison of the repressing effect on specific gene sets between sevoflurane and desflurane by regression analysis.

    (A, B) Coefficient of determination was calculated using the genes in Fig 1D that were upregulated by sevoflurane and/or desflurane more than 2-fold among the members of the GO term “response to xenobiotic stimulus (0009410)” (A, upper panel) and genes shown in Fig 2D that were downregulated by sevoflurane more than 2-fold among the members of the GO term “defense response to virus (0051607)” (B). Lower panel in A shows the result after the removal of Cyp2b1 gene data as an outlier in the gene set.

    (PDF)

    pone.0298264.s002.pdf (227.3KB, pdf)
    S3 Fig. Comparisons of expression of immune response-related genes between sevoflurane and desflurane treatments.

    (A, B) Effect of desflurane treatment on the expressions of genes that contributed to the identification of immune response-related terms as sevoflurane treatment-specific GO terms. Genes downregulated more than 2-fold by sevoflurane treatment were selected among genes constituting the GO terms “immune response (0006952)” (A) and “response to bacterium (0009617)” (B). Relative expression levels in the livers of rats treated with desflurane for 6 hours compared to the control were demonstrated by a heatmap (right column) along with data obtained by the analyses of livers of rats treated with sevoflurane (left column). Panels shown under each heatmap represent regression analyses for the calculation of the coefficient of determination.

    (PDF)

    pone.0298264.s003.pdf (236.4KB, pdf)
    S4 Fig. GSEA of RNA sequence data from livers of rats subjected to inhalational anesthesia.

    (A) A list of gene sets identified by GSEA as significantly activated gene sets by inhalational anesthesia using sevoflurane or desflurane. Three distinct publicly available databases, “biological process of Gene Ontology”, “Kyoto Encyclopedia of Genes and Genome”, and “Reactome Pathway Database” were used for the analyses, in which the top twenty terms according to their NES values were selected from positively-regulated gene sets with the analyses using each platform, but terms whose p-values were greater than 0.05 were eliminated from the list. Terms related to xenobiotic metabolism are shown in light blue. (B) A list of gene sets identified by GSEA as significantly repressed gene sets by inhalational anesthesia using sevoflurane or desflurane. The same criteria used in (A) were used. Commonly identified gene sets by treatment with sevoflurane or desflurane are shown in green. Terms related to immune response are indicated by red font.

    (PDF)

    pone.0298264.s004.pdf (327.4KB, pdf)
    S1 Table. Gene lists that were up- or down-regulated by sevoflurane and/or desflurane.

    (PDF)

    pone.0298264.s005.pdf (220.5KB, pdf)
    S2 Table. Gene lists denoted as leading-edge genes by GSEA.

    (PDF)

    pone.0298264.s006.pdf (186.2KB, pdf)
    Attachment

    Submitted filename: Response to reviewers (PONE-D-23-16299).docx

    pone.0298264.s007.docx (34.3KB, docx)
    Attachment

    Submitted filename: Nogi et al. response to reviewers.docx

    pone.0298264.s008.docx (28.2KB, docx)

    Data Availability Statement

    RNA sequence data used for this study were deposited in Gene Expression Omnibus-NCBI under accession number GSE244436.

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