Modelling 16:8 Intermittent Fasting and Breakfast-Skipping in Mouse Adipocytes 3T3-L1 In Vitro: A Transcriptomics Study

Modelling 16:8 Intermittent Fasting and Breakfast-Skipping in Mouse Adipocytes 3T3-L1 In Vitro: A Transcriptomics Study | 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 Research Article Modelling 16:8 Intermittent Fasting and Breakfast-Skipping in Mouse Adipocytes 3T3-L1 In Vitro: A Transcriptomics Study Pei Han Er, Jin Yi Chye, Geetha Letchumanan, Yee-How Say This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7322953/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Obesity, a chronic metabolic disease linked to multiple disorders, lacks effective treatments. Intermittent fasting (IF), especially time-restricted feeding (TRF), is a promising dietary strategy. This study investigated the effects of various IF/TRF regimens on 3T3-L1 adipocytes and transcriptomic changes. Methods 3T3-L1 cells were differentiated into adipocytes for 7 d and synchronized with 200 nM dexamethasone before 24 h treatments: (1) Control [high glucose (4.5 g/L DMEM), 15% bovine calf serum (BCS)], (2) 16 h fasting/8 h feeding IF [control medium ZT3–ZT11; low glucose (1.0 g/L) and low serum (1% BCS) ZT12–ZT2], (3) “Distributed” IF [medium glucose (2.75 g/L), medium serum (8% BCS) ZT0–ZT24], (4) Breakfast-skipping (BS) [low glucose/low serum ZT1–ZT5; control medium ZT6–ZT0]. Lipid accumulation was assessed by Oil Red O staining; whole transcriptome sequencing was performed. Results The 16/8 IF regimen showed the greatest lipid reduction (74.41% vs . control; p = 0.007) with upregulation of lipolysis genes ( Tgm2 , Notch2 ) and downregulation of adipogenesis and glycolysis genes ( Ccnd1 , Ldha ). Enriched pathways included TGF-β, p53, and apelin signaling. The BS group showed minimal effect (98.53% vs . control; p = 0.999) and downregulation of mitochondrial genes ( mt-Rnr1 , mt-Rnr2 ), indicating increased glucose uptake and reduced fatty acid oxidation. Conclusion Differentiated 3T3-L1 adipocytes are a useful in vitro model for IF/TRF studies. 16/8 IF regimen was the most effective in reducing lipid content, compared to “distributed” IF and BS regimens within a 24h-period, consistent with the significant modulation of genes promoting lipolysis and inhibiting adipogenesis and glycolysis. Intermittent fasting Time-restricted feeding 3T3-L1 adipocytes Obesity Transcriptomics Lipid metabolism Figures Figure 1 Figure 2 Figure 3 1. Introduction Obesity is a complex chronic disease characterized by an excessive and abnormal accumulation of adipose tissue (AT) in the body and defined by a BMI of 30 or above [ 1 ]. It is considered a major public health challenge because obesity can cause elevated risks for several chronic diseases, including heart disease and type 2 diabetes, and increased risks of certain cancers [ 2 ]. Although obesity is a preventable condition, its prevalence is steadily increasing across the globe. According to the World Health Organization (WHO), around 16% of adults aged 18 years old and above were obese in 2022. Between 1990 and 2022, the global percentage of children and adolescents aged 5–19 with obesity surged from 2–8%, marking a four-fold increase. Simultaneously, the proportion of adults aged 18 and older with obesity more than doubled, rising from 7–16% [ 1 ]. Thus, the increasing prevalence of obesity underscores the urgent need for effective treatment options. While there are established treatments for obesity, including lifestyle modifications, pharmacotherapies, and bariatric surgery, several factors underscore the continued need for research into obesity treatments that are not only effective but also more practical [ 3 , 4 ]. One of the factors is that there are challenges with the widespread adoption and long-term adherence to these standard obesity treatments. While lifestyle modifications are effective, most diabetes patients find it difficult to maintain substantial weight loss [ 3 , 5 ]. Moreover, there are issues with consistent adherence to pharmacotherapies, which uses medications like glucagon-like peptide-1 (GLP-1) receptor agonist, due to their limited availability, high cost, and the potential for relapse after discontinuing treatment [ 5 ]. Bariatric surgery, being the most effective option, also requires dietary interventions, which led to similar challenges faced by lifestyle modifications [ 5 ]. In this context, intermittent fasting (IF), which refers to an eating pattern that involves regular durations of voluntary fasting and eating, has emerged as a promising dietary intervention for obesity [ 6 ]. There are different types of IFs, including time-restricted feeding (TRF), 5:2 diet, and alternate-day fasting (ADF) [ 7 , 8 ]. Among these methods, TRF, such as the 16/8 diet (eating for 8 hours and fasting for 16 hours), is the most common form of IF [ 9 ]. Although fasting has been practiced since ancient times among various communities for religious, cultural, or therapeutic reasons, IF, which mainly focuses on when you eat, is different from traditional fasting, which focuses on what you eat (caloric restriction) [ 10 , 11 ]. A recent study demonstrated that IF can significantly upregulate expression of key adipogenic marker genes and lipid regulators within adipocytes, such as FABP4 and PLIN1 [ 12 ]. Besides, IF was shown to have upregulated the expression of genes involved in lipolysis, including PDK4 and PNPLA7 , in both human and mice [ 13 ]. Moreover, other studies employing murine adipocytes derived from cell lines, such as C3H10T1/2 and 3T3-L1, have also simulated IF/TRF in vitro [ 14 , 15 ]. Liang et al. (2021) demonstrated that IF can significantly reduce the expression of inflammatory markers (CRP, IL-1β, and IL-18) in 3T3-L1 adipocytes, suggesting a potential underlying modulation of gene expression for these inflammatory mediators [ 14 ]. This is important as chronic, low-grade inflammation, often associated with obesity, impairs healthy AT functions and leads to insulin resistance [ 16 ]. Besides, IF was also shown to activate the p53 signalling pathway, which promotes apoptosis, directly in C3H10T1/2 and SGBS adipocytes, leading to the upregulation of p53-dependent target genes [ 15 ]. This suggests that IF induces adipocyte apoptosis and leads to the reduction in adiposity, which may be attributed to the reduced adipocyte number by apoptosis. Paradoxically, this same activation is also linked to a repression of adipocyte catabolism and oxidative gene expression, highlighting the complex role of IF in managing obesity [ 15 ]. However, significant gaps persist in our understanding of the direct differential effects on gene expression and specific molecular pathways of IF/TRF within adipocytes as there is a lack of in vitro studies specifically on this cell type. Adipocytes, a key cell type involved in obesity, are specialized cells of AT, which are responsible for storing excess energy in the form of lipids and can expand unlimitedly in accordance with metabolic needs. These adipocytes possess their own circadian clocks that regulate key functions of AT, including the expression and secretion of adipokines, thermogenesis, browning, inflammation, lipolysis, and adipogenesis [ 17 ]. Mouse 3T3-L1 preadipocytes, a well-established cell line, are widely used as an in vitro model for studying obesity-related therapeutic agents and molecular pathways. This is attributed to their ability to differentiate into mature adipocytes and perform many key functions of adipocytes, including adipogenic gene expression, lipid storage, adipokine secretion, and lipolysis [ 18 , 19 ]. This cell line is also more cost-effective and easier to culture compared to freshly isolated cells, making it a powerful tool for studying the cellular and molecular responses to interventions for obesity, such as IF/TRF. Therefore, this study aims to investigate the direct effect of IF/TRF in vitro using mouse adipocyte 3T3-L1 cell line and to investigate its effect on global gene expression (transcriptomics). This is important as in vivo studies, which involve complex inter-tissue communication and hormonal influences, may obscure the cell-autonomous effects of IF/TRF directly on adipocytes. With the knowledge of these direct cellular effects, researchers can develop more targeted and effective interventions for obesity. Hence, the objectives of this study include: (1) investigating the differential effects of various IF/TRF regimens, including 16/8 IF, “Distributed” IF, and breakfast-skipping (BS), on intracellular lipid accumulation rate in differentiated 3T3-L1 adipocytes; and (2) assessing the effects of selected IF/TRF regimen on their global gene expression profiles using Whole Transcriptome Sequencing (WTS). 2. Materials and Methods 2.1 3T3-L1 Cell Culture and Treatment Paradigm 3T3-L1 fibroblasts were differentiated into mature adipocytes over a 10-day period at 37°C, 5% (v/v) CO 2 , according to [ 19 ]. On Day 0, cells were seeded into a 12-well plate at a density of 5 × 10 4 cells/well in Pre-adipocyte Expansion Medium (PEM) containing 90% (v/v) DMEM with 4.5 g/L glucose and 10% (v/v) Bovine Calf Serum (BCS; TICO Europe, Netherlands). Cell growth and confluency were monitored using an inverted microscope (Nikon Eclipse TS100, Japan). Day 2 started when cell confluency reached approximately 75%. Cells were rinsed twice with 1× Phosphate-Buffered Saline (PBS), and Differentiation Medium (DM) containing 90% (v/v) DMEM with 4.5 g/L glucose, 10% (v/v) BCS, 1 µg/mL insulin (Nacalai Tesque, Japan), 0.25 µM dexamethasone (Nacalai Tesque, Japan), 2 µM rosiglitazone (TCI Chemicals, Japan), and 0.5 mM isobutylmethylxanthine (Merck, Germany) was added to each well. On Day 4, DM was replaced by Adipocyte Maintenance Medium (AMM) containing 85% (v/v) DMEM with 4.5 g/L glucose, 15% (v/v) BCS, and 1 µg/mL Insulin after rinsing the cells with PBS. On Day 6, AMM was replaced by DM to further induce differentiation after rinsing the cells with PBS. On Day 8, DM was replaced by AMM, following PBS rinses, to further induce lipid accumulation. On Day 8, the circadian clock of the cells was synchronized with 200 nM dexamethasone for 30 min at room temperature (RT) [ 20 ], followed by rinsing with AMM. Then, the differentiated 3T3-L1 adipocytes were subjected to four groups of treatment regimens during a 24-hour period, with the sunrise time of 0700 [Zeitgeber Time 0 (ZT0)] and sunset time of 1900 [Zeitgeber Time 12 (ZT12)]. The four groups were: 1. Control group - AMM with 4.5 g/L glucose (control medium); 2. 16h fasting/8h feeding IF – control medium from ZT3 to ZT11, and switched to AMM with low glucose (1.0 g/L in DMEM) and low serum (1% BCS) from ZT12 to ZT2 (next day); 3. "Distributed" IF – AMM with medium glucose (2.75 g/L in DMEM) and medium serum (8% BCS) from ZT0 to ZT24; 4. Breakfast-skipping BS – AMM with low glucose and low serum from ZT1 to ZT5, and switched to control medium from ZT6 to ZT0 (next day). 2.2 Lipid Accumulation Assay After 24 h treatment, the spent medium was discarded from the wells. Cells were then rinsed twice with PBS, and 1 mL of 4% (w/v) paraformaldehyde (PFA; Sigma-Aldrich, MO, USA) was added to each well to fix the cells. The plate was incubated at RT for 1 hour. Meanwhile, a 0.5% (w/v) Oil Red O (ORO; Sigma-Aldrich, MO, USA) stock solution was diluted with sterile deionized water in a 3:2 ratio (ORO:water) and was incubated at RT for 20 minutes. The solution was filter-sterilized prior to use. After incubation, PFA was discarded, and fixed cells were stained with 1 mL of the filtered, diluted 0.5% (w/v) ORO solution. The plate was then incubated at RT for 20 min. Subsequently, the solution was discarded, and cells were washed twice with PBS. Stained lipids in the cells were observed and imaged under an inverted microscope (Nikon Eclipse TS100, Japan) at 100× and 200× magnification. After microscopy, PBS was discarded, and 1 mL of isopropanol was added into the wells to dissolve the stained lipids. The plate was incubated at RT for 20 minutes. Following that, 100 µL aliquot from each well was transferred to a 96-well plate. Absorbance was measured at 540 nm with an Infinite® M Plex microplate reader (Tecan, Switzerland), whereby isopropanol was used as a blank, to quantify the intracellular lipid content. Lipid accumulation rate was calculated relative to the control group using the formula: Lipid accumulation rate (%) = [average absorbance of treatment group - average blank/average absorbance of control group - average blank] ×100% 2.3 RNA Extraction Total RNA was extracted from treated 3T3-L1 adipocytes of the control group, 16/8 IF group (most effective treatment), and breakfast-skipping group (least effective treatment) using the NucleoSpin® RNA extraction kit (MACHEREY-NAGEL, Germany) following the manufacturer’s protocol with slight modifications. After 24 h treatment, cells were lysed by adding 350 µL of Lysis Buffer RA1 for 15 min. The lysate was then transferred to a 1.5 mL microcentrifuge tube and homogenized by passing it through a 21G needle attached to a syringe for 10 times, cleared by centrifugation through a NucleoSpin® Filter at 11,000 g for 1 min, and 350 µL 70% ethanol was added to the filtrate and mixed. The mixture was then loaded onto NucleoSpin® RNA Column and centrifuged for 30 sec at 11,000 g , column then placed in a new collection tube, 350 µL membrane desalting buffer added, and centrifuged for 1 min at 11,000 g . Digestion of DNA was performed by adding 95 µL of DNase reaction mixture directly onto the silica membrane of the column and incubating at RT for 15 min, column washed with 200 µL Buffer RAW2 (centrifuged at 11,000 g for 30 sec), followed by two washes with 600 µL (centrifuged at 11,000 g for 30 sec) and 250 µL (centrifuged at 11,000 g for 2 min) of Buffer RA3. The column was spun dry in the final wash step, and finally, the purified RNA was eluted from the column adding 60 µL of RNase-free H 2 O and centrifuging for 1 min at 11,000 g. Subsequently, RNA quality and quantity assessments were performed using the BioDrop µLite + Microvolume Spectrophotometer (Biochrom, UK) and non-denaturing 1% agarose gel electrophoresis in TAE buffer (0.5 mM, 0.02 M Tris, 0.01 M glacial acetic acid). All extracted RNA was stored at − 80°C until use. 2.4 Whole Transcriptome Sequencing (WTS) and Differential Gene Expression (DEG) Analysis The RNA samples were sent to AGTC Genomics Sdn. Bhd. (Kuala Lumpur, Malaysia) for WTS. A total of two biological replicates per group were used for WTS analysis. Prior to sequencing, RNA sample quality was assessed to ensure optimal data generation. RNA concentration was determined using the Qubit™ RNA High Sensitivity (HS) assay kit and the Qubit Fluorometer 4.0 (Invitrogen). RNA purity (A260/280 and A260/230 ratios) was evaluated using the Implen NanoQuant Spectrophotometer (Implen, Germany). RNA quality was further assessed using the LabChip™ RNA Assay Reagent Kit (Revvity) and the LabChip® GX Touch™ nucleic acid analyzer (Revvity), which yielded RNA Quality Scores (RQS) ranging from 6.8 to 7.3 (out of 10 = most intact), indicating “pass” scores. The samples were then sequenced on Illumina NovaSeq 6000 system (Illumina, CA, USA) system, with 20 million reads. Libraries were normalized to 1 nM and pooled before loading into the NovaSeq 6000 sequencing platform. Illumina DRAGEN encompassed an RNA-seq (splicing-aware) aligner, along with RNA-specific analysis components for gene expression quantification and gene fusion detection. For differential gene expression (DEG) analysis, the transcript quantification file was imported into R. DEG calling was performed using DESeq2 [ 21 ]. Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using clusterProfiler [ 22 ]. Reference genome used in this analysis was Mus musculus genome assembly GRCm38: GCF_000001635.20 downloaded from NCBI. 2.5 Statistical Analysis All statistical tests were conducted in the R statistical environment, v4.0.3. All results were presented as mean ± SD based on three independent experiments, each with triplicate readings. Differences in adipocytes lipid accumulation rate (%) between treatment groups relative to the control group were analyzed by one-way analysis of variance (ANOVA) followed by Dunnett’s post-hoc test. All hypothesis testing was two-sided, and a p -value < 0.05 was considered statistically significant. 3. Results 3.1 Lipid Accumulation Assay Figure 1 A shows ORO-stained 3T3-L1 adipocytes with different treatment regimens under 100× and 200× magnifications. The morphology of the adipocytes was similar between the control group and the rest of the treatment groups. However, it can be observed that the 16/8 IF treated cells had the least intensity of red color staining compared with other treatment groups, while the BS treated cells had the most intense red color staining among all treatment regimens. Figure 1 B shows that the 16/8 IF group had the most reduction in the average percentage of lipid accumulation rate relative to the control group (-25.59%), followed by the “distributed” IF group (-15.21%), and BS group (-1.47%). ANOVA showed that the differences among the groups were statistically significant ( p = 0.006), and Dunnett’s post hoc test indicated that lipid accumulation rate was significantly reduced in the 16/8 IF group ( p = 0.007) and the distributed IF group ( p = 0.048), but not in the breakfast-skipping group ( p = 0.999), relative to the control group. [Figure 1 here] 3.2 Whole Transcriptome Sequencing (WTS) and Differential Gene Expression (DEG) Analysis Figure 2 shows the comprehensive transcriptomic analysis of 3T3-L1 adipocytes in 16/8 IF group compared to control group. Out of 13,652 DEGs, 85 were significantly upregulated and 130 were significantly downregulated in response to 16/8 IF treatment (Fig. 2 A). The heatmap analysis showed similar expression patterns between treatment duplicates, signifying replicability (Fig. 2 B). The Gene Ontology (GO) enrichment analysis highlighted significantly modulated biological processes relevant to adipocyte function, including lipid droplet regulation (Fig. 2 C). The KEGG pathway enrichment analysis identified several impacted metabolic and signaling pathways, including the TGF-β and p53 signaling pathways (Fig. 2 D). [Figure 2 here] Table 1 and Table 2 show the top 10 significantly upregulated and downregulated DEGs, respectively, between 16/8 IF treatment and control. Among them, Tgm2 was most significantly upregulated, followed by Notch2 , and Sema5a , while Ccnd1 was most significantly downregulated, followed by Ldha gene, and Fkbp5 gene. Table 1 The top 10 significantly upregulated DEGs identified between 16/8 IF group and control group. Gene Full Name Official Symbol Brief Gene Function (Sayers et al., 2025) baseMean log2FoldChange lfcSE stat p -value Adjusted p -value Transglutaminase 2, C polypeptide Tgm2 • Catalyze calcium-dependent cross-linking of proteins • Involved in extracellular matrix stabilization and apoptosis 1614.388 1.549 0.083 18.763 1.53E-78 1.84E-74 Notch 2 Notch2 • Enable NF-κB binding activity and enzyme binding activity 4824.175 1.085 0.059 18.504 1.90E-76 1.15E-72 Sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Sema5a • Enable axon guidance receptor activity and semaphorin receptor binding activity • Negatively regulate endothelial cell apoptotic process • Regulate signal transduction 2136.618 1.372 0.075 18.302 7.94E-75 3.19E-71 Glucoside xylosyltransferase 2 Gxylt2 • Predicted to enable UDP-xylosyltransferase activity • Predicted to involve in O-glycan processing 3089.608 1.042 0.066 15.864 1.13E-56 1.94E-53 Carboxylesterase 1A Ces1a • Predicted to enable carboxylesterase activity and sterol ester esterase activity • Predicted to be active in lipid droplet 500.5828 2.345 0.149 15.702 1.47E-55 2.21E-52 Deltex 4, E3 ubiquitin ligase Dtx4 • Predicted to enable ubiquitin protein ligase activity • Predicted to involve in Notch signaling pathway 701.8275 1.816 0.116 15.642 3.75E-55 4.53E-52 Teneurin transmembrane protein 4 Tenm4 • Enable protein homodimerization activity 1673.635 1.214 0.078 15.529 2.20E-54 2.42E-51 Interferon gamma inducible protein 30 Ifi30 • Predicted to enable oxidoreductase activity • Involved in antigen processing and presentation of exogenous peptide antigen via MHC class I 764.194 1.683 0.109 15.490 4.06E-54 4.08E-51 Leucine-rich repeats and immunoglobulin-like domains 1 Lrig1 • Involved in inner action, otolith morphogenesis, and sensory perception 1158.579 1.296 0.090 14.399 5.25E-47 3.96E-44 Ral guanine nucleotide dissociation stimulator Ralgds • Predicted to enable guanyl-nucleotide exchange factor activity and Ras protein signal transduction 1709.671 1.157 0.081 14.332 1.39E-46 9.31E-44 Table 2 The top 10 significantly downregulated DEGs identified between 16/8 IF group and control group. Gene Full Name Official Symbol Brief Gene Function (Sayers et al., 2025) baseMean log2FoldChange lfcSE stat p -value Adjusted p -value Cyclin D1 Ccnd1 • Promote G1/S phase transition • Negatively regulates epithelial cell proliferation and transcription 3699.779 -1.064 0.065 -16.483 4.87E-61 1.17E-57 Lactate dehydrogenase A Ldha • Encode protein that catalyzes the interconversion of pyruvate and lactate in anaerobic glycolysis 5436.927 -1.104 0.068 -16.301 9.79E-60 1.97E-56 FK506 binding protein 5 Fkbp5 • Predicted to enable heat shock protein binding activity, peptidyl-prolyl cis-trans isomerase activity, and protein-macromolecule adaptor activity 1093.184 -1.378 0.093 -14.836 8.58E-50 7.97E-47 Inhibitor of DNA binding 3 Id3 • Enable bHLH transcription factor binding activity • Enable transcription regulator inhibitor activity • Involved in several processes, including negative regulation of myoblast differentiation and negative regulation of macromolecule biosynthetic process 360.248 -2.211 0.160 -13.845 1.37E-43 7.88E-41 Guanine deaminase Gda • Enable guanine deaminase activity • Involved in allantoin metabolic process, amide catabolic process, and nucleobase-containing small molecule metabolic process 1128.861 -1.156 0.091 -12.676 8.07E-37 3.75E-34 Inhibitor of DNA binding 1, HLH protein Id1 • Enable transcription regulator inhibitor activity 368.638 -1.913 0.157 -12.174 4.25E-34 1.55E-31 Solute carrier family 25 (mitochondrial carrier, adenine nucleotide translocator), member 5 Slc25a5 • Encode a transmembrane domain-containing protein of the mitochondrial inner membrane 1539.524 -1.032 0.088 -11.771 5.48E-32 1.84E-29 H4 clustered histone 14 Hist2h4 • Encode a replication-dependent histone of the histone H4 family 1640.862 -1.008 0.086 -11.743 7.65E-32 2.49E-29 Lipoprotein lipase Lpl • Enable lipoprotein lipase activity • Involved in several processes including cytokines production and triglyceride catabolic process 1224.481 -1.008 0.088 -11.456 2.20E-30 6.48E-28 ADAMT like 4 Adamtsl4 • Encode for ADAMTS-like proteins 769.6562 -1.179 0.106 -11.073 1.70E-28 4.55E-26 Figure 3 shows the comprehensive transcriptomic analysis of 3T3-L1 adipocytes in BS group compared to control. Out of 13,652 DEGs, only three were significantly downregulated and none were upregulated (Fig. 3 A). The heatmap analysis showed similar expression patterns between treatment duplicates, signifying replicability (Fig. 3 B). No biological process was identified through GO enrichment analysis, and the KEGG pathway enrichment analysis identified only two impacted metabolic and signaling pathways, consistent with the minimal overall gene expression changes (Fig. 3 C). [Figure 3 here] Table 3 shows the three significantly downregulated ribosomal DEGs between BS treatment and control, namely mt-Rnr2 followed by mt-Rnr2 , and Lars 2. Table 3 The significant DEGs identified between breakfast-skipping group and control group. Gene Full Name Official Symbol Brief Gene Function (Sayers et al., 2025) baseMean log2FoldChange lfcSE stat p -value Adjusted p -value 16S rRNA, mitochondrial mt-Rnr2 • Predicted to be a structural constituent of ribosome and part of mitochondrial large ribosomal subunit 4419.984 -1.284 0.072 -17.774 1.12E-70 4.91E-67 12S rRNA, mitochondrial mt-Rnr1 • Predicted to be a structural constituent of ribosome and part of mitochondrial small ribosomal subunit 5974.083 -1.099 0.068 -16.098 2.65E-58 5.82E-55 Leucyl-tRNA synthetase, mitochondrial Lars2 • Predicted to enable leucine-tRNA ligase activity 3212.527 -1.191 0.150 -7.918 2.41E-15 2.12E-12 4. Discussion This study investigated the effect of IF/TRF in vitro using mouse adipocyte 3T3-L1 cell line and its effect on global gene expression (transcriptomics). The findings from lipid accumulation assay reveal that among the tested regimens, the 16/8 IF group was the most effective in reducing lipid storage, followed by “distributed” IF group, and lastly breakfast-skipping group. This suggests that 16/8 IF regimen has the highest therapeutic potential for obesity. This aligns with previous literature demonstrating the efficacy of 16/8 IF in decreasing fat mass in vivo , which directly reflects the reduction of overall lipid accumulation in the body’s AT. Although there were no significant changes in total cholesterol, HDL-C, and LDL-C, a decrease in circulating triglycerides was observed [ 23 ]. This further supports that 16/8 IF regimen improves lipid metabolism, consistent with the findings obtained on adipocyte lipid storage. The effectiveness of the 16/8 IF regimen may be attributed to the concept of “metabolic switching”. During the fasting state, glucose, which is the primary energy source, becomes limited. This forces the cells to utilize ketone bodies, which are derived from fatty acids converted from the triglycerides stored in AT, for energy. In this context, the 16-hour nutrient deprivation period of the regimen has successfully induced this switch in 3T3-L1 adipocyte, stimulating lipolysis, and lead to the observed significant reduction in lipid accumulation [ 10 ]. The DEG analysis findings provided molecular evidence supporting this concept. Notably, Ccnd1 , which encodes cyclin D1, was the most significantly downregulated in the 16/8 IF group. According to Wu et al. (2019), cyclin D1 inhibits lipolysis and promotes lipid accumulation by decreasing lipophagy [ 24 ]. Moreover, Tgm2 , which encodes transglutaminase 2 (TG2), was the most significantly upregulated in the 16/8 IF group. This aligns with a study demonstrating that TG2 is a negative regulator of adipogenesis, where a low TG2 level can increase and accelerate lipid accumulation in vivo [ 25 ]. Additionally, 16/8 IF has significantly downregulated Ldha , which encodes lactate dehydrogenase-A responsible for converting pyruvate to lactate in glycolysis [ 26 ]. Besides, Notch2 , which encodes NOTCH2 receptor, was significantly upregulated in the 16/8 IF group. Studies demonstrated that NOTCH2 signaling can decrease glycolysis in bone cells, indicating that Notch2 is potentially increasing lipolysis indirectly by influencing glucose utilization [ 27 ]. Moreover, the KEGG pathway enrichment analysis showed that the TGF-β signaling pathway was significantly enriched. This aligns with previous studies showing its ability in inhibiting lipid accumulation and adipogenesis [ 28 – 30 ]. Subsequently, the apelin signaling pathway was also found to be significantly enriched. This aligns with previous in vivo studies demonstrating its ability in promoting lipolysis by regulating the expression of perilipin and in inhibiting adipogenesis by regulating the expression of PPARγ [ 31 , 32 ]. Collectively, these findings suggest that 16/8 IF can potentially promote lipolysis, inhibit glycolysis, and inhibit adipogenesis, which is consistent with the observed lipid accumulation rate in this group. However, the DEG analysis also revealed more complex gene regulations. For example, Dtx4 was significantly upregulated in the 16/8 IF group, but previous studies demonstrated that DTX4 can promote adipogenesis in 3T3-L1 cells [ 33 ]. This contradiction, where a gene typically associated with promoting adipogenesis is upregulated under a condition with reduced lipid accumulation, suggests a more complex regulatory role for the DEGs in response to IF/TRF. Besides, the KEGG pathway enrichment analysis showed that the p53 signaling pathway was significantly enriched. This aligns with a study demonstrating an activation of the p53 signaling pathway under IF treatment, which suggests that the reduced lipid accumulation is attributed to the ability of IF in increasing adipocyte apoptosis in addition to promoting lipolysis [ 15 ]. Moreover, the effectiveness of the 16/8 IF regimen may also be attributed to its alignment with the adipocyte’s natural circadian rhythm, an innate timing mechanism that anticipates energy consumption during daylight hours [ 34 ]. The circadian clock regulates the expression of genes involved in lipid metabolism, such as Pnpla3 and Cpt1 [ 35 , 36 ]. Hence, the 16/8 IF regimen, which aligns with the feeding and fasting cycles of the circadian rhythm, can optimize the efficiency of the lipid-regulating pathways and further contribute to the observed reduction in adipocyte lipid accumulation. While the DEG analysis did not show any differentially expressed core circadian clock genes like CLOCK and BMAL1 , it is important to note that many downstream genes, such as Ccnd1 and Ldha , are known to be regulated by these clock genes [ 37 , 38 ]. In contrast, the lipid accumulation assay findings suggest that the BS regimen, which demonstrated little to no reduction in lipid storage, has the lowest therapeutic potential for obesity. This aligns with previous literature demonstrating that breakfast-skipping regimen increased body weight gain in vivo [ 39 ]. Moreover, Kim et al. (2021) demonstrated that the BS regimen significantly increased epididymal AT weight and hepatic lipids in rats [ 40 ]. Chen et al. (2021) also reported an increased lipid profile (higher triglycerides, higher total cholesterol, higher LDL-C, and lower HDL-C) in breakfast skippers compared to non-breakfast skippers in vivo [ 41 ]. This evidence further suggests that the BS regimen may lead to increased lipid storage rather than reducing it. These negative effects of the BS regimen may be attributed to its misalignment with the adipocyte’s natural circadian rhythm. Skipping breakfast, a misaligned meal pattern, can disturb the peripheral clock and change the expression of clock-controlled genes involved in lipid metabolism [ 35 , 41 , 42 ]. This shifts the balance towards increased lipogenesis and reduced fatty acid oxidation, thereby favoring lipid accumulation. While the DEG analysis did not show any differentially expressed core circadian clock genes, it is important to note that mt-Rnr2 and mt-Rnr1 genes, which are among the few significantly downregulated genes in the breakfast-skipping group, are known to be clock-controlled [ 43 , 44 ]. mt-Rnr2 encodes mitochondrial 16S rRNA, Humanin (HN) peptide, and small humanin-like peptides (SHLPs)[ 45 ]. Intracellularly, HN binds to insulin-like growth factor binding protein 3 and inhibits insulin-stimulated glucose uptake [ 46 ]. Hence, the downregulation of mt-Rnr2 and its downstream effects on insulin sensitivity would then lead to an increased glucose uptake by the 3T3-L1 adipocytes. Elevated glucose uptake and glycolysis favors lipid accumulation, which is consistent with the observed lipid accumulation rate in this group. Moreover, mt-Rnr1 encodes the mitochondrial 12S rRNA and mitochondrial open reading frame of the 12S rRNA type-c (MOTS-c) peptide [ 45 ]. MOTS-c can regulate lipid metabolism by increasing β-oxidation of fatty acids [ 47 ]. Therefore, the downregulation of mt-Rnr1 would lead to lower MOTS-c levels, impairing fatty acid oxidation pathway and further favoring lipid accumulation in cells. Collectively, these transcriptional changes align with the observed minimal reduction in lipid accumulation rate in this group. This study has several limitations. First, the treatment duration in this study was relatively short (24 h) compared to the long-term nature of human dietary interventions, which may occur over months or years [ 48 ]. Although acute cellular responses were observed in this study, the findings may not fully capture the potential long-term effects of IF/TRF on lipid metabolism and gene expression in vivo . Future studies with extended treatment durations could provide a more comprehensive understanding of the adaptive responses of adipocytes to IF/TRF. Second, the use of 3T3-L1 murine adipocytes in this study provides a controlled system for mechanistic investigation of the effects of IF/TRF. However, there are key differences in their physiology and function that limit their direct relevance to human adipocytes or complex human adipocytes within a complete organism, which is influenced by systemic factors such as hormonal signaling [ 49 ]. According to Kersten (2023), the human body undergoes significant hormonal shifts during fasting, influencing AT metabolism in vivo [ 50 ]. Also, Defour et al. (2020) reported that the effect of fasting on gene expression was more remarkable in mice compared to humans despite the longer fasting duration in human volunteers [ 13 ], highlighting the complexities of translating findings directly from murine models to human physiology. Future studies should employ more physiologically relevant in vitro models, such as human Simpson-Golabi-Behmel syndrome (SGBS) cell line, or in vivo models, such as non-primate models, to validate these findings and assess their translational relevance. 6. Conclusions This study reveals the effects of different IF/TRF regimens: 16/8 IF, “distributed” IF, and BS, on lipid accumulation and global gene expression in mouse 3T3-L1 adipocytes. The findings from the lipid accumulation assay indicate that the 16/8 IF treatment was the most effective in reducing lipid storage, highlighting its significant therapeutic potential for managing obesity. This was strongly supported by the DEG analysis, which revealed that genes promoting lipolysis as well as inhibiting adipogenesis and glycolysis were significantly modulated, consistent with the lipid accumulation rate observed. While some complex gene regulations suggest additional mechanisms contributing to lipid reduction, the overall molecular effects align with the observed lipid accumulation. Conversely, BS treatment was the least effective in reducing lipid storage, suggesting its potential negative impact on metabolic health. The DEG analysis suggests that this treatment can increase glucose uptake and decrease fatty acid oxidation, favoring lipid accumulation, consistent with the lipid accumulation rate observed. Future research should focus on extended treatment durations and employ more physiologically relevant in-vivo models to validate these findings and comprehensively understand the long-term adaptive responses and systemic implications of various IF/TRF regimens, especially the 16/8 IF regimen. Understanding these differential cellular effects can guide the development of more targeted and effective interventions for obesity. Declarations Funding This work was supported by the Malaysian Ministry of Higher Education Fundamental Grant Research Scheme FRGS/1/2022/STG01/SYUC/02/1. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Competing interest The authors declare that they have no competing interests. Author contributions Conceptualization: YHS; Methodology: YHS; Data collection: PHE, JYC, and GL; Formal analysis: PHE, JYC, and GL; Writing - original draft preparation: PHE, JYC, and YHS; Writing - review and editing: PHE, JYC, and YHS; Supervision: YHS, and GL. All authors have read and agreed to the published version of the manuscript. Ethics approval Not applicable Consent to participate Not applicable Consent to publish Not applicable References World Health Organization (WHO) (2025) Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Blüher M (2019) Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol 15:288–298. https://doi.org/10.1038/s41574-019-0176-8 Heffron SP, Parham JS, Pendse J, Alemán JO (2020) Treatment of Obesity in Mitigating Metabolic Risk. 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Front Nutr 8:681436. https://doi.org/10.3389/fnut.2021.681436 Chen L, Li X, Du X, et al (2021) Cross-sectional association of meal skipping with lipid profiles and blood glucose in Chinese adults. Nutrition 90:111245. https://doi.org/10.1016/j.nut.2021.111245 Hashimoto T, Endo Y (2013) Cyclic restricted feeding enhances lipid storage in 3 T3-L1 adipocytes. Lipids Health Dis 12:76. https://doi.org/10.1186/1476-511X-12-76 Horiguchi M, Yoshihara K, Mizukami Y, et al (2025) The Diurnal Variation in Mitochondrial Gene in Human Type 2 Diabetic Mesenchymal Stem Cell Grafts. Int J Mol Sci 26:719. https://doi.org/10.3390/ijms26020719 Jacobi D, Liu S, Burkewitz K, et al (2015) Hepatic Bmal1 Regulates Rhythmic Mitochondrial Dynamics and Promotes Metabolic Fitness. Cell Metab 22:709–720. https://doi.org/10.1016/j.cmet.2015.08.006 Kal S, Mahata S, Jati S, Mahata SK (2024) Mitochondrial-derived peptides: Antidiabetic functions and evolutionary perspectives. Peptides 172:171147. https://doi.org/10.1016/j.peptides.2023.171147 Yamada PM, Mehta HH, Hwang D, et al (2010) Evidence of a Role for Insulin-Like Growth Factor Binding Protein (IGFBP)-3 in Metabolic Regulation. Endocrinology 151:5741–5750. https://doi.org/10.1210/en.2010-0672 Kim S, Miller B, Mehta HH, et al (2019) The mitochondrial‐derived peptide MOTS‐c is a regulator of plasma metabolites and enhances insulin sensitivity. Physiol Rep 7:. https://doi.org/10.14814/phy2.14171 Mirmiran P, Bahadoran Z, Gaeini Z (2021) Common Limitations and Challenges of Dietary Clinical Trials for Translation into Clinical Practices. Int J Endocrinol Metab 19:. https://doi.org/10.5812/ijem.108170 Börgeson E, Boucher J, Hagberg CE (2022) Of mice and men: Pinpointing species differences in adipose tissue biology. Front Cell Dev Biol 10:1003118. https://doi.org/10.3389/fcell.2022.1003118 Kersten S (2023) The impact of fasting on adipose tissue metabolism. Biochim Biophys Acta BBA - Mol Cell Biol Lipids 1868:159262. https://doi.org/10.1016/j.bbalip.2022.159262 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7322953","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498720541,"identity":"bd4132bd-70c9-47bd-84ed-ac498ed772a5","order_by":0,"name":"Pei Han Er","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"Han","lastName":"Er","suffix":""},{"id":498720542,"identity":"8f521e9a-8251-460d-a543-d090e610aa30","order_by":1,"name":"Jin Yi Chye","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Yi","lastName":"Chye","suffix":""},{"id":498720543,"identity":"b335d9ae-c8b0-471e-90ca-17a58c6ba3fa","order_by":2,"name":"Geetha Letchumanan","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Geetha","middleName":"","lastName":"Letchumanan","suffix":""},{"id":498720544,"identity":"f7877749-f69b-44eb-9ae2-c1d382142a07","order_by":3,"name":"Yee-How Say","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNhAhh+AfIFKLMelaEhuI1mJwvPnYg49tdenzZ+Qek/jZxiDHdyOBdTMPPi1njqUbzmw7nLvhRl6aZG8bg7HkjQS223i13Mgxk+bddiB3g0SO2Q3eNobEDQS13H//DailLl1+Ro7Zzb9tDPWEtdzgYQNqYU5gAFp3G2hLggEhLZJn0swkZ/47bLjhzBvz3zLnJAxnnnnYdnMOHi18xw8/k/hwpk5evj3H2PBNmY083/HkYzfe4NGicACZx8gmASIbmPA5TL4BhfsHqvUHHi2jYBSMglEw4gAAMFFUfZhSuBsAAAAASUVORK5CYII=","orcid":"","institution":"Sunway University","correspondingAuthor":true,"prefix":"","firstName":"Yee-How","middleName":"","lastName":"Say","suffix":""}],"badges":[],"createdAt":"2025-08-08 03:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7322953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7322953/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88857419,"identity":"2f6c1557-d3f1-4e25-9d0a-40a480d5e3fc","added_by":"auto","created_at":"2025-08-12 07:04:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1164294,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph of the lipid accumulation rate (%) of different treatment groups relative to the control group.\u003cstrong\u003e \u003c/strong\u003eData was expressed as mean ± SD (\u003cem\u003en \u003c/em\u003e= 6) based on six independent experiments, each with triplicate readings. A one-way ANOVA followed by Dunnett’s \u003cem\u003epost hoc\u003c/em\u003e test was used to compare each treatment group to the control. Significant differences compared to the control group were indicated (#\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7322953/v1/ddd7b57273c8fdbeb71c63fb.png"},{"id":88857417,"identity":"a33b3795-de1a-4f2e-831c-03e91a8d3edc","added_by":"auto","created_at":"2025-08-12 07:04:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":465539,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomics analysis of 16/8 IF treatment effects in 3T3-L1 adipocyte.\u003cstrong\u003e \u003c/strong\u003eA. Volcano plot showing DEGs between 16/8 IF group and control group. Red points indicated significantly upregulated genes, blue points indicated significantly downregulated genes (p-adjusted\u0026lt;0.05, Fold change\u0026gt;2 or \u0026lt;-2); B. Heatmap showing the expression patterns of the first 20 DEGs between 16/8 IF group and control group. Red indicated upregulated, and blue indicated downregulated; C. Dot plot of significant Gene Ontology (GO) terms identified using significant DEGs between 16/8 IF group and control group; D. Dot plot of the significant KEGG pathways enriched in DEGs between 16/8 IF group and control group; E. Enrichment map of the GO terms enriched in DEGs between 16/8 IF group and control group\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7322953/v1/e56c8310a1e3eaff73accb3d.png"},{"id":88857416,"identity":"96fbeb77-7ad4-48f3-a525-e62e59e96154","added_by":"auto","created_at":"2025-08-12 07:04:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85060,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomics analysis of breakfast-skipping (BS) treatment effects in 3T3-L1 adipocyte.\u003cstrong\u003e \u003c/strong\u003eA. Volcano plot showing DEGs between BS group and control group. Red points indicate significantly upregulated genes, blue points indicate significantly downregulated genes (p-adjusted\u0026lt;0.05, Fold change\u0026gt;2 or \u0026lt;-2); B. Heatmap showing the expression patterns of DEGs between BS group and control group. Red indicates upregulated, and blue indicates downregulated; C. Dot plot of the significant KEGG pathways enriched in DEGs between BS group and control group\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7322953/v1/92ae3fbbfdf25b8f0ee1cf61.png"},{"id":88859596,"identity":"adb506e4-6ab1-44ab-a435-733457cfe256","added_by":"auto","created_at":"2025-08-12 07:28:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2505310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322953/v1/e95ebc99-fe03-4442-bc7a-6dcde5b22bef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modelling 16:8 Intermittent Fasting and Breakfast-Skipping in Mouse Adipocytes 3T3-L1 In Vitro: A Transcriptomics Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObesity is a complex chronic disease characterized by an excessive and abnormal accumulation of adipose tissue (AT) in the body and defined by a BMI of 30 or above [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is considered a major public health challenge because obesity can cause elevated risks for several chronic diseases, including heart disease and type 2 diabetes, and increased risks of certain cancers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although obesity is a preventable condition, its prevalence is steadily increasing across the globe. According to the World Health Organization (WHO), around 16% of adults aged 18 years old and above were obese in 2022. Between 1990 and 2022, the global percentage of children and adolescents aged 5\u0026ndash;19 with obesity surged from 2\u0026ndash;8%, marking a four-fold increase. Simultaneously, the proportion of adults aged 18 and older with obesity more than doubled, rising from 7\u0026ndash;16% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thus, the increasing prevalence of obesity underscores the urgent need for effective treatment options.\u003c/p\u003e\u003cp\u003eWhile there are established treatments for obesity, including lifestyle modifications, pharmacotherapies, and bariatric surgery, several factors underscore the continued need for research into obesity treatments that are not only effective but also more practical [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. One of the factors is that there are challenges with the widespread adoption and long-term adherence to these standard obesity treatments. While lifestyle modifications are effective, most diabetes patients find it difficult to maintain substantial weight loss [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, there are issues with consistent adherence to pharmacotherapies, which uses medications like glucagon-like peptide-1 (GLP-1) receptor agonist, due to their limited availability, high cost, and the potential for relapse after discontinuing treatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Bariatric surgery, being the most effective option, also requires dietary interventions, which led to similar challenges faced by lifestyle modifications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this context, intermittent fasting (IF), which refers to an eating pattern that involves regular durations of voluntary fasting and eating, has emerged as a promising dietary intervention for obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. There are different types of IFs, including time-restricted feeding (TRF), 5:2 diet, and alternate-day fasting (ADF) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these methods, TRF, such as the 16/8 diet (eating for 8 hours and fasting for 16 hours), is the most common form of IF [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although fasting has been practiced since ancient times among various communities for religious, cultural, or therapeutic reasons, IF, which mainly focuses on when you eat, is different from traditional fasting, which focuses on what you eat (caloric restriction) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent study demonstrated that IF can significantly upregulate expression of key adipogenic marker genes and lipid regulators within adipocytes, such as \u003cem\u003eFABP4\u003c/em\u003e and \u003cem\u003ePLIN1\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Besides, IF was shown to have upregulated the expression of genes involved in lipolysis, including \u003cem\u003ePDK4\u003c/em\u003e and \u003cem\u003ePNPLA7\u003c/em\u003e, in both human and mice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, other studies employing murine adipocytes derived from cell lines, such as C3H10T1/2 and 3T3-L1, have also simulated IF/TRF \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Liang et al. (2021) demonstrated that IF can significantly reduce the expression of inflammatory markers (CRP, IL-1β, and IL-18) in 3T3-L1 adipocytes, suggesting a potential underlying modulation of gene expression for these inflammatory mediators [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is important as chronic, low-grade inflammation, often associated with obesity, impairs healthy AT functions and leads to insulin resistance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Besides, IF was also shown to activate the p53 signalling pathway, which promotes apoptosis, directly in C3H10T1/2 and SGBS adipocytes, leading to the upregulation of p53-dependent target genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This suggests that IF induces adipocyte apoptosis and leads to the reduction in adiposity, which may be attributed to the reduced adipocyte number by apoptosis. Paradoxically, this same activation is also linked to a repression of adipocyte catabolism and oxidative gene expression, highlighting the complex role of IF in managing obesity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, significant gaps persist in our understanding of the direct differential effects on gene expression and specific molecular pathways of IF/TRF within adipocytes as there is a lack of \u003cem\u003ein vitro\u003c/em\u003e studies specifically on this cell type. Adipocytes, a key cell type involved in obesity, are specialized cells of AT, which are responsible for storing excess energy in the form of lipids and can expand unlimitedly in accordance with metabolic needs. These adipocytes possess their own circadian clocks that regulate key functions of AT, including the expression and secretion of adipokines, thermogenesis, browning, inflammation, lipolysis, and adipogenesis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Mouse 3T3-L1 preadipocytes, a well-established cell line, are widely used as an \u003cem\u003ein vitro\u003c/em\u003e model for studying obesity-related therapeutic agents and molecular pathways. This is attributed to their ability to differentiate into mature adipocytes and perform many key functions of adipocytes, including adipogenic gene expression, lipid storage, adipokine secretion, and lipolysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This cell line is also more cost-effective and easier to culture compared to freshly isolated cells, making it a powerful tool for studying the cellular and molecular responses to interventions for obesity, such as IF/TRF. Therefore, this study aims to investigate the direct effect of IF/TRF \u003cem\u003ein vitro\u003c/em\u003e using mouse adipocyte 3T3-L1 cell line and to investigate its effect on global gene expression (transcriptomics). This is important as \u003cem\u003ein vivo\u003c/em\u003e studies, which involve complex inter-tissue communication and hormonal influences, may obscure the cell-autonomous effects of IF/TRF directly on adipocytes. With the knowledge of these direct cellular effects, researchers can develop more targeted and effective interventions for obesity. Hence, the objectives of this study include: (1) investigating the differential effects of various IF/TRF regimens, including 16/8 IF, \u0026ldquo;Distributed\u0026rdquo; IF, and breakfast-skipping (BS), on intracellular lipid accumulation rate in differentiated 3T3-L1 adipocytes; and (2) assessing the effects of selected IF/TRF regimen on their global gene expression profiles using Whole Transcriptome Sequencing (WTS).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 3T3-L1 Cell Culture and Treatment Paradigm\u003c/h2\u003e\u003cp\u003e3T3-L1 fibroblasts were differentiated into mature adipocytes over a 10-day period at 37\u0026deg;C, 5% (v/v) CO\u003csub\u003e2\u003c/sub\u003e, according to [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. On Day 0, cells were seeded into a 12-well plate at a density of 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well in Pre-adipocyte Expansion Medium (PEM) containing 90% (v/v) DMEM with 4.5 g/L glucose and 10% (v/v) Bovine Calf Serum (BCS; TICO Europe, Netherlands). Cell growth and confluency were monitored using an inverted microscope (Nikon Eclipse TS100, Japan). Day 2 started when cell confluency reached approximately 75%. Cells were rinsed twice with 1\u0026times; Phosphate-Buffered Saline (PBS), and Differentiation Medium (DM) containing 90% (v/v) DMEM with 4.5 g/L glucose, 10% (v/v) BCS, 1 \u0026micro;g/mL insulin (Nacalai Tesque, Japan), 0.25 \u0026micro;M dexamethasone (Nacalai Tesque, Japan), 2 \u0026micro;M rosiglitazone (TCI Chemicals, Japan), and 0.5 mM isobutylmethylxanthine (Merck, Germany) was added to each well. On Day 4, DM was replaced by Adipocyte Maintenance Medium (AMM) containing 85% (v/v) DMEM with 4.5 g/L glucose, 15% (v/v) BCS, and 1 \u0026micro;g/mL Insulin after rinsing the cells with PBS. On Day 6, AMM was replaced by DM to further induce differentiation after rinsing the cells with PBS. On Day 8, DM was replaced by AMM, following PBS rinses, to further induce lipid accumulation.\u003c/p\u003e\u003cp\u003eOn Day 8, the circadian clock of the cells was synchronized with 200 nM dexamethasone for 30 min at room temperature (RT) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], followed by rinsing with AMM. Then, the differentiated 3T3-L1 adipocytes were subjected to four groups of treatment regimens during a 24-hour period, with the sunrise time of 0700 [Zeitgeber Time 0 (ZT0)] and sunset time of 1900 [Zeitgeber Time 12 (ZT12)]. The four groups were: 1. Control group - AMM with 4.5 g/L glucose (control medium); 2. 16h fasting/8h feeding IF \u0026ndash; control medium from ZT3 to ZT11, and switched to AMM with low glucose (1.0 g/L in DMEM) and low serum (1% BCS) from ZT12 to ZT2 (next day); 3. \"Distributed\" IF \u0026ndash; AMM with medium glucose (2.75 g/L in DMEM) and medium serum (8% BCS) from ZT0 to ZT24; 4. Breakfast-skipping BS \u0026ndash; AMM with low glucose and low serum from ZT1 to ZT5, and switched to control medium from ZT6 to ZT0 (next day).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Lipid Accumulation Assay\u003c/h2\u003e\u003cp\u003eAfter 24 h treatment, the spent medium was discarded from the wells. Cells were then rinsed twice with PBS, and 1 mL of 4% (w/v) paraformaldehyde (PFA; Sigma-Aldrich, MO, USA) was added to each well to fix the cells. The plate was incubated at RT for 1 hour. Meanwhile, a 0.5% (w/v) Oil Red O (ORO; Sigma-Aldrich, MO, USA) stock solution was diluted with sterile deionized water in a 3:2 ratio (ORO:water) and was incubated at RT for 20 minutes. The solution was filter-sterilized prior to use. After incubation, PFA was discarded, and fixed cells were stained with 1 mL of the filtered, diluted 0.5% (w/v) ORO solution. The plate was then incubated at RT for 20 min. Subsequently, the solution was discarded, and cells were washed twice with PBS. Stained lipids in the cells were observed and imaged under an inverted microscope (Nikon Eclipse TS100, Japan) at 100\u0026times; and 200\u0026times; magnification. After microscopy, PBS was discarded, and 1 mL of isopropanol was added into the wells to dissolve the stained lipids. The plate was incubated at RT for 20 minutes. Following that, 100 \u0026micro;L aliquot from each well was transferred to a 96-well plate. Absorbance was measured at 540 nm with an Infinite\u0026reg; M Plex microplate reader (Tecan, Switzerland), whereby isopropanol was used as a blank, to quantify the intracellular lipid content. Lipid accumulation rate was calculated relative to the control group using the formula: Lipid accumulation rate (%) = [average absorbance of treatment group - average blank/average absorbance of control group - average blank] \u0026times;100%\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 RNA Extraction\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from treated 3T3-L1 adipocytes of the control group, 16/8 IF group (most effective treatment), and breakfast-skipping group (least effective treatment) using the NucleoSpin\u0026reg; RNA extraction kit (MACHEREY-NAGEL, Germany) following the manufacturer\u0026rsquo;s protocol with slight modifications. After 24 h treatment, cells were lysed by adding 350 \u0026micro;L of Lysis Buffer RA1 for 15 min. The lysate was then transferred to a 1.5 mL microcentrifuge tube and homogenized by passing it through a 21G needle attached to a syringe for 10 times, cleared by centrifugation through a NucleoSpin\u0026reg; Filter at 11,000 \u003cem\u003eg\u003c/em\u003e for 1 min, and 350 \u0026micro;L 70% ethanol was added to the filtrate and mixed. The mixture was then loaded onto NucleoSpin\u0026reg; RNA Column and centrifuged for 30 sec at 11,000 \u003cem\u003eg\u003c/em\u003e, column then placed in a new collection tube, 350 \u0026micro;L membrane desalting buffer added, and centrifuged for 1 min at 11,000 \u003cem\u003eg\u003c/em\u003e. Digestion of DNA was performed by adding 95 \u0026micro;L of DNase reaction mixture directly onto the silica membrane of the column and incubating at RT for 15 min, column washed with 200 \u0026micro;L Buffer RAW2 (centrifuged at 11,000 \u003cem\u003eg\u003c/em\u003e for 30 sec), followed by two washes with 600 \u0026micro;L (centrifuged at 11,000 \u003cem\u003eg\u003c/em\u003e for 30 sec) and 250 \u0026micro;L (centrifuged at 11,000 \u003cem\u003eg\u003c/em\u003e for 2 min) of Buffer RA3. The column was spun dry in the final wash step, and finally, the purified RNA was eluted from the column adding 60 \u0026micro;L of RNase-free H\u003csub\u003e2\u003c/sub\u003eO and centrifuging for 1 min at 11,000 \u003cem\u003eg.\u003c/em\u003e Subsequently, RNA quality and quantity assessments were performed using the BioDrop \u0026micro;Lite\u0026thinsp;+\u0026thinsp;Microvolume Spectrophotometer (Biochrom, UK) and non-denaturing 1% agarose gel electrophoresis in TAE buffer (0.5 mM, 0.02 M Tris, 0.01 M glacial acetic acid). All extracted RNA was stored at \u0026minus;\u0026thinsp;80\u0026deg;C until use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Whole Transcriptome Sequencing (WTS) and Differential Gene Expression (DEG) Analysis\u003c/h2\u003e\u003cp\u003eThe RNA samples were sent to AGTC Genomics Sdn. Bhd. (Kuala Lumpur, Malaysia) for WTS. A total of two biological replicates per group were used for WTS analysis. Prior to sequencing, RNA sample quality was assessed to ensure optimal data generation. RNA concentration was determined using the Qubit\u0026trade; RNA High Sensitivity (HS) assay kit and the Qubit Fluorometer 4.0 (Invitrogen). RNA purity (A260/280 and A260/230 ratios) was evaluated using the Implen NanoQuant Spectrophotometer (Implen, Germany). RNA quality was further assessed using the LabChip\u0026trade; RNA Assay Reagent Kit (Revvity) and the LabChip\u0026reg; GX Touch\u0026trade; nucleic acid analyzer (Revvity), which yielded RNA Quality Scores (RQS) ranging from 6.8 to 7.3 (out of 10\u0026thinsp;=\u0026thinsp;most intact), indicating \u0026ldquo;pass\u0026rdquo; scores. The samples were then sequenced on Illumina NovaSeq 6000 system (Illumina, CA, USA) system, with 20\u0026nbsp;million reads. Libraries were normalized to 1 nM and pooled before loading into the NovaSeq 6000 sequencing platform. Illumina DRAGEN encompassed an RNA-seq (splicing-aware) aligner, along with RNA-specific analysis components for gene expression quantification and gene fusion detection. For differential gene expression (DEG) analysis, the transcript quantification file was imported into R. DEG calling was performed using DESeq2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using clusterProfiler [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reference genome used in this analysis was \u003cem\u003eMus musculus\u003c/em\u003e genome assembly GRCm38: GCF_000001635.20 downloaded from NCBI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical tests were conducted in the R statistical environment, v4.0.3. All results were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD based on three independent experiments, each with triplicate readings. Differences in adipocytes lipid accumulation rate (%) between treatment groups relative to the control group were analyzed by one-way analysis of variance (ANOVA) followed by Dunnett\u0026rsquo;s \u003cem\u003epost-hoc\u003c/em\u003e test. All hypothesis testing was two-sided, and a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Lipid Accumulation Assay\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows ORO-stained 3T3-L1 adipocytes with different treatment regimens under 100\u0026times; and 200\u0026times; magnifications. The morphology of the adipocytes was similar between the control group and the rest of the treatment groups. However, it can be observed that the 16/8 IF treated cells had the least intensity of red color staining compared with other treatment groups, while the BS treated cells had the most intense red color staining among all treatment regimens. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows that the 16/8 IF group had the most reduction in the average percentage of lipid accumulation rate relative to the control group (-25.59%), followed by the \u0026ldquo;distributed\u0026rdquo; IF group (-15.21%), and BS group (-1.47%). ANOVA showed that the differences among the groups were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and Dunnett\u0026rsquo;s \u003cem\u003epost hoc\u003c/em\u003e test indicated that lipid accumulation rate was significantly reduced in the 16/8 IF group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and the distributed IF group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), but not in the breakfast-skipping group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.999), relative to the control group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Whole Transcriptome Sequencing (WTS) and Differential Gene Expression (DEG) Analysis\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the comprehensive transcriptomic analysis of 3T3-L1 adipocytes in 16/8 IF group compared to control group. Out of 13,652 DEGs, 85 were significantly upregulated and 130 were significantly downregulated in response to 16/8 IF treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heatmap analysis showed similar expression patterns between treatment duplicates, signifying replicability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The Gene Ontology (GO) enrichment analysis highlighted significantly modulated biological processes relevant to adipocyte function, including lipid droplet regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The KEGG pathway enrichment analysis identified several impacted metabolic and signaling pathways, including the TGF-β and p53 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the top 10 significantly upregulated and downregulated DEGs, respectively, between 16/8 IF treatment and control. Among them, \u003cem\u003eTgm2\u003c/em\u003e was most significantly upregulated, followed by \u003cem\u003eNotch2\u003c/em\u003e, and \u003cem\u003eSema5a\u003c/em\u003e, while \u003cem\u003eCcnd1\u003c/em\u003e was most significantly downregulated, followed by \u003cem\u003eLdha\u003c/em\u003e gene, and \u003cem\u003eFkbp5\u003c/em\u003e gene.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe top 10 significantly upregulated DEGs identified between 16/8 IF group and control group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Full Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOfficial Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrief Gene Function (Sayers et al., 2025)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebaseMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elog2FoldChange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003elfcSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003estat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransglutaminase 2, C polypeptide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTgm2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Catalyze calcium-dependent cross-linking of proteins\u003c/p\u003e\u003cp\u003e\u0026bull; Involved in extracellular matrix stabilization and apoptosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1614.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.53E-78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.84E-74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNotch 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNotch2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable NF-κB binding activity and enzyme binding activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4824.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.90E-76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.15E-72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSema5a\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable axon guidance receptor activity and semaphorin receptor binding activity\u003c/p\u003e\u003cp\u003e\u0026bull; Negatively regulate endothelial cell apoptotic process\u003c/p\u003e\u003cp\u003e\u0026bull; Regulate signal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2136.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.94E-75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.19E-71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucoside xylosyltransferase 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGxylt2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable UDP-xylosyltransferase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Predicted to involve in O-glycan processing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3089.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.13E-56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.94E-53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarboxylesterase 1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCes1a\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable carboxylesterase activity and sterol ester esterase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Predicted to be active in lipid droplet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e500.5828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.47E-55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.21E-52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltex 4, E3 ubiquitin ligase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDtx4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable ubiquitin protein ligase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Predicted to involve in Notch signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e701.8275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.75E-55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.53E-52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeneurin transmembrane protein 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTenm4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable protein homodimerization activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1673.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.20E-54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.42E-51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterferon gamma inducible protein 30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIfi30\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable oxidoreductase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Involved in antigen processing and presentation of exogenous peptide antigen via MHC class I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e764.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.06E-54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.08E-51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeucine-rich repeats and immunoglobulin-like domains 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLrig1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Involved in inner action, otolith morphogenesis, and sensory perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1158.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.25E-47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.96E-44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRal guanine nucleotide dissociation stimulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRalgds\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable guanyl-nucleotide exchange factor activity and Ras protein signal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1709.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39E-46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.31E-44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe top 10 significantly downregulated DEGs identified between 16/8 IF group and control group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Full Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOfficial Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrief Gene Function (Sayers et al., 2025)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebaseMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elog2FoldChange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003elfcSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003estat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyclin D1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCcnd1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Promote G1/S phase transition\u003c/p\u003e\u003cp\u003e\u0026bull; Negatively regulates epithelial cell proliferation and transcription\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3699.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-16.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.87E-61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.17E-57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate dehydrogenase A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLdha\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Encode protein that catalyzes the interconversion of pyruvate and lactate in anaerobic glycolysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5436.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-16.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.79E-60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.97E-56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFK506 binding protein 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFkbp5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable heat shock protein binding activity, peptidyl-prolyl cis-trans isomerase activity, and protein-macromolecule adaptor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1093.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-14.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.58E-50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.97E-47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhibitor of DNA binding 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eId3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable bHLH transcription factor binding activity\u003c/p\u003e\u003cp\u003e\u0026bull; Enable transcription regulator inhibitor activity\u003c/p\u003e\u003cp\u003e\u0026bull; Involved in several processes, including negative regulation of myoblast differentiation and negative regulation of macromolecule biosynthetic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e360.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-13.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.37E-43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.88E-41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuanine deaminase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable guanine deaminase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Involved in allantoin metabolic process, amide catabolic process, and nucleobase-containing small molecule metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1128.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.07E-37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.75E-34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhibitor of DNA binding 1, HLH protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eId1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable transcription regulator inhibitor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e368.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.25E-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.55E-31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolute carrier family 25 (mitochondrial carrier, adenine nucleotide translocator), member 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSlc25a5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Encode a transmembrane domain-containing protein of the mitochondrial inner membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1539.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.48E-32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.84E-29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4 clustered histone 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHist2h4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Encode a replication-dependent histone of the histone H4 family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1640.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.65E-32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.49E-29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipoprotein lipase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLpl\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Enable lipoprotein lipase activity\u003c/p\u003e\u003cp\u003e\u0026bull; Involved in several processes including cytokines production and triglyceride catabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1224.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.20E-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.48E-28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADAMT like 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAdamtsl4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Encode for ADAMTS-like proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e769.6562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.70E-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.55E-26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the comprehensive transcriptomic analysis of 3T3-L1 adipocytes in BS group compared to control. Out of 13,652 DEGs, only three were significantly downregulated and none were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The heatmap analysis showed similar expression patterns between treatment duplicates, signifying replicability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). No biological process was identified through GO enrichment analysis, and the KEGG pathway enrichment analysis identified only two impacted metabolic and signaling pathways, consistent with the minimal overall gene expression changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the three significantly downregulated ribosomal DEGs between BS treatment and control, namely \u003cem\u003emt-Rnr2\u003c/em\u003e followed by \u003cem\u003emt-Rnr2\u003c/em\u003e, and \u003cem\u003eLars 2.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe significant DEGs identified between breakfast-skipping group and control group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Full Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOfficial Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrief Gene Function (Sayers et al., 2025)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebaseMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elog2FoldChange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003elfcSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003estat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16S rRNA, mitochondrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emt-Rnr2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to be a structural constituent of ribosome and part of mitochondrial large ribosomal subunit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4419.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-17.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12E-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.91E-67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12S rRNA, mitochondrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003emt-Rnr1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to be a structural constituent of ribosome and part of mitochondrial small ribosomal subunit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5974.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-16.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.65E-58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.82E-55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeucyl-tRNA synthetase, mitochondrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLars2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Predicted to enable leucine-tRNA ligase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3212.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-7.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.41E-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.12E-12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated the effect of IF/TRF \u003cem\u003ein vitro\u003c/em\u003e using mouse adipocyte 3T3-L1 cell line and its effect on global gene expression (transcriptomics). The findings from lipid accumulation assay reveal that among the tested regimens, the 16/8 IF group was the most effective in reducing lipid storage, followed by \u0026ldquo;distributed\u0026rdquo; IF group, and lastly breakfast-skipping group. This suggests that 16/8 IF regimen has the highest therapeutic potential for obesity. This aligns with previous literature demonstrating the efficacy of 16/8 IF in decreasing fat mass \u003cem\u003ein vivo\u003c/em\u003e, which directly reflects the reduction of overall lipid accumulation in the body\u0026rsquo;s AT. Although there were no significant changes in total cholesterol, HDL-C, and LDL-C, a decrease in circulating triglycerides was observed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This further supports that 16/8 IF regimen improves lipid metabolism, consistent with the findings obtained on adipocyte lipid storage. The effectiveness of the 16/8 IF regimen may be attributed to the concept of \u0026ldquo;metabolic switching\u0026rdquo;. During the fasting state, glucose, which is the primary energy source, becomes limited. This forces the cells to utilize ketone bodies, which are derived from fatty acids converted from the triglycerides stored in AT, for energy. In this context, the 16-hour nutrient deprivation period of the regimen has successfully induced this switch in 3T3-L1 adipocyte, stimulating lipolysis, and lead to the observed significant reduction in lipid accumulation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe DEG analysis findings provided molecular evidence supporting this concept. Notably, \u003cem\u003eCcnd1\u003c/em\u003e, which encodes cyclin D1, was the most significantly downregulated in the 16/8 IF group. According to Wu et al. (2019), cyclin D1 inhibits lipolysis and promotes lipid accumulation by decreasing lipophagy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, \u003cem\u003eTgm2\u003c/em\u003e, which encodes transglutaminase 2 (TG2), was the most significantly upregulated in the 16/8 IF group. This aligns with a study demonstrating that TG2 is a negative regulator of adipogenesis, where a low TG2 level can increase and accelerate lipid accumulation \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, 16/8 IF has significantly downregulated \u003cem\u003eLdha\u003c/em\u003e, which encodes lactate dehydrogenase-A responsible for converting pyruvate to lactate in glycolysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Besides, \u003cem\u003eNotch2\u003c/em\u003e, which encodes NOTCH2 receptor, was significantly upregulated in the 16/8 IF group. Studies demonstrated that NOTCH2 signaling can decrease glycolysis in bone cells, indicating that \u003cem\u003eNotch2\u003c/em\u003e is potentially increasing lipolysis indirectly by influencing glucose utilization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, the KEGG pathway enrichment analysis showed that the TGF-β signaling pathway was significantly enriched. This aligns with previous studies showing its ability in inhibiting lipid accumulation and adipogenesis [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Subsequently, the apelin signaling pathway was also found to be significantly enriched. This aligns with previous \u003cem\u003ein vivo\u003c/em\u003e studies demonstrating its ability in promoting lipolysis by regulating the expression of perilipin and in inhibiting adipogenesis by regulating the expression of PPARγ [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Collectively, these findings suggest that 16/8 IF can potentially promote lipolysis, inhibit glycolysis, and inhibit adipogenesis, which is consistent with the observed lipid accumulation rate in this group.\u003c/p\u003e\u003cp\u003eHowever, the DEG analysis also revealed more complex gene regulations. For example, \u003cem\u003eDtx4\u003c/em\u003e was significantly upregulated in the 16/8 IF group, but previous studies demonstrated that DTX4 can promote adipogenesis in 3T3-L1 cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This contradiction, where a gene typically associated with promoting adipogenesis is upregulated under a condition with reduced lipid accumulation, suggests a more complex regulatory role for the DEGs in response to IF/TRF. Besides, the KEGG pathway enrichment analysis showed that the p53 signaling pathway was significantly enriched. This aligns with a study demonstrating an activation of the p53 signaling pathway under IF treatment, which suggests that the reduced lipid accumulation is attributed to the ability of IF in increasing adipocyte apoptosis in addition to promoting lipolysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, the effectiveness of the 16/8 IF regimen may also be attributed to its alignment with the adipocyte\u0026rsquo;s natural circadian rhythm, an innate timing mechanism that anticipates energy consumption during daylight hours [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The circadian clock regulates the expression of genes involved in lipid metabolism, such as \u003cem\u003ePnpla3 and Cpt1\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Hence, the 16/8 IF regimen, which aligns with the feeding and fasting cycles of the circadian rhythm, can optimize the efficiency of the lipid-regulating pathways and further contribute to the observed reduction in adipocyte lipid accumulation. While the DEG analysis did not show any differentially expressed core circadian clock genes like \u003cem\u003eCLOCK\u003c/em\u003e and \u003cem\u003eBMAL1\u003c/em\u003e, it is important to note that many downstream genes, such as \u003cem\u003eCcnd1\u003c/em\u003e and \u003cem\u003eLdha\u003c/em\u003e, are known to be regulated by these clock genes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, the lipid accumulation assay findings suggest that the BS regimen, which demonstrated little to no reduction in lipid storage, has the lowest therapeutic potential for obesity. This aligns with previous literature demonstrating that breakfast-skipping regimen increased body weight gain \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, Kim et al. (2021) demonstrated that the BS regimen significantly increased epididymal AT weight and hepatic lipids in rats [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Chen et al. (2021) also reported an increased lipid profile (higher triglycerides, higher total cholesterol, higher LDL-C, and lower HDL-C) in breakfast skippers compared to non-breakfast skippers \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This evidence further suggests that the BS regimen may lead to increased lipid storage rather than reducing it. These negative effects of the BS regimen may be attributed to its misalignment with the adipocyte\u0026rsquo;s natural circadian rhythm. Skipping breakfast, a misaligned meal pattern, can disturb the peripheral clock and change the expression of clock-controlled genes involved in lipid metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This shifts the balance towards increased lipogenesis and reduced fatty acid oxidation, thereby favoring lipid accumulation.\u003c/p\u003e\u003cp\u003eWhile the DEG analysis did not show any differentially expressed core circadian clock genes, it is important to note that \u003cem\u003emt-Rnr2\u003c/em\u003e and \u003cem\u003emt-Rnr1\u003c/em\u003e genes, which are among the few significantly downregulated genes in the breakfast-skipping group, are known to be clock-controlled [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003emt-Rnr2\u003c/em\u003e encodes mitochondrial 16S rRNA, Humanin (HN) peptide, and small humanin-like peptides (SHLPs)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Intracellularly, HN binds to insulin-like growth factor binding protein 3 and inhibits insulin-stimulated glucose uptake [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Hence, the downregulation of \u003cem\u003emt-Rnr2\u003c/em\u003e and its downstream effects on insulin sensitivity would then lead to an increased glucose uptake by the 3T3-L1 adipocytes. Elevated glucose uptake and glycolysis favors lipid accumulation, which is consistent with the observed lipid accumulation rate in this group. Moreover, \u003cem\u003emt-Rnr1\u003c/em\u003e encodes the mitochondrial 12S rRNA and mitochondrial open reading frame of the 12S rRNA type-c (MOTS-c) peptide [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. MOTS-c can regulate lipid metabolism by increasing β-oxidation of fatty acids [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, the downregulation of \u003cem\u003emt-Rnr1\u003c/em\u003e would lead to lower MOTS-c levels, impairing fatty acid oxidation pathway and further favoring lipid accumulation in cells. Collectively, these transcriptional changes align with the observed minimal reduction in lipid accumulation rate in this group.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the treatment duration in this study was relatively short (24 h) compared to the long-term nature of human dietary interventions, which may occur over months or years [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Although acute cellular responses were observed in this study, the findings may not fully capture the potential long-term effects of IF/TRF on lipid metabolism and gene expression \u003cem\u003ein vivo\u003c/em\u003e. Future studies with extended treatment durations could provide a more comprehensive understanding of the adaptive responses of adipocytes to IF/TRF. Second, the use of 3T3-L1 murine adipocytes in this study provides a controlled system for mechanistic investigation of the effects of IF/TRF. However, there are key differences in their physiology and function that limit their direct relevance to human adipocytes or complex human adipocytes within a complete organism, which is influenced by systemic factors such as hormonal signaling [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. According to Kersten (2023), the human body undergoes significant hormonal shifts during fasting, influencing AT metabolism \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Also, Defour et al. (2020) reported that the effect of fasting on gene expression was more remarkable in mice compared to humans despite the longer fasting duration in human volunteers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], highlighting the complexities of translating findings directly from murine models to human physiology. Future studies should employ more physiologically relevant \u003cem\u003ein vitro\u003c/em\u003e models, such as human Simpson-Golabi-Behmel syndrome (SGBS) cell line, or \u003cem\u003ein vivo\u003c/em\u003e models, such as non-primate models, to validate these findings and assess their translational relevance.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study reveals the effects of different IF/TRF regimens: 16/8 IF, \u0026ldquo;distributed\u0026rdquo; IF, and BS, on lipid accumulation and global gene expression in mouse 3T3-L1 adipocytes. The findings from the lipid accumulation assay indicate that the 16/8 IF treatment was the most effective in reducing lipid storage, highlighting its significant therapeutic potential for managing obesity. This was strongly supported by the DEG analysis, which revealed that genes promoting lipolysis as well as inhibiting adipogenesis and glycolysis were significantly modulated, consistent with the lipid accumulation rate observed. While some complex gene regulations suggest additional mechanisms contributing to lipid reduction, the overall molecular effects align with the observed lipid accumulation. Conversely, BS treatment was the least effective in reducing lipid storage, suggesting its potential negative impact on metabolic health. The DEG analysis suggests that this treatment can increase glucose uptake and decrease fatty acid oxidation, favoring lipid accumulation, consistent with the lipid accumulation rate observed. Future research should focus on extended treatment durations and employ more physiologically relevant \u003cem\u003ein-vivo\u003c/em\u003e models to validate these findings and comprehensively understand the long-term adaptive responses and systemic implications of various IF/TRF regimens, especially the 16/8 IF regimen. Understanding these differential cellular effects can guide the development of more targeted and effective interventions for obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Malaysian Ministry of Higher Education Fundamental Grant Research Scheme FRGS/1/2022/STG01/SYUC/02/1. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: YHS; Methodology: YHS; Data collection: PHE, JYC, and GL; Formal analysis: PHE, JYC, and GL; Writing - original draft preparation: PHE, JYC, and YHS; Writing - review and editing: PHE, JYC, and YHS; Supervision: YHS, and GL. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization (WHO) (2025) Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.\u003c/li\u003e\n\u003cli\u003eBl\u0026uuml;her M (2019) Obesity: global epidemiology and pathogenesis. 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Biochim Biophys Acta BBA - Mol Cell Biol Lipids 1868:159262. https://doi.org/10.1016/j.bbalip.2022.159262\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intermittent fasting, Time-restricted feeding, 3T3-L1 adipocytes, Obesity, Transcriptomics, Lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-7322953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7322953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObesity, a chronic metabolic disease linked to multiple disorders, lacks effective treatments. Intermittent fasting (IF), especially time-restricted feeding (TRF), is a promising dietary strategy. This study investigated the effects of various IF/TRF regimens on 3T3-L1 adipocytes and transcriptomic changes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e3T3-L1 cells were differentiated into adipocytes for 7 d and synchronized with 200 nM dexamethasone before 24 h treatments: (1) Control [high glucose (4.5 g/L DMEM), 15% bovine calf serum (BCS)], (2) 16 h fasting/8 h feeding IF [control medium ZT3\u0026ndash;ZT11; low glucose (1.0 g/L) and low serum (1% BCS) ZT12\u0026ndash;ZT2], (3) \u0026ldquo;Distributed\u0026rdquo; IF [medium glucose (2.75 g/L), medium serum (8% BCS) ZT0\u0026ndash;ZT24], (4) Breakfast-skipping (BS) [low glucose/low serum ZT1\u0026ndash;ZT5; control medium ZT6\u0026ndash;ZT0]. Lipid accumulation was assessed by Oil Red O staining; whole transcriptome sequencing was performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe 16/8 IF regimen showed the greatest lipid reduction (74.41% \u003cem\u003evs\u003c/em\u003e. control; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) with upregulation of lipolysis genes (\u003cem\u003eTgm2\u003c/em\u003e, \u003cem\u003eNotch2\u003c/em\u003e) and downregulation of adipogenesis and glycolysis genes (\u003cem\u003eCcnd1\u003c/em\u003e, \u003cem\u003eLdha\u003c/em\u003e). Enriched pathways included TGF-β, p53, and apelin signaling. The BS group showed minimal effect (98.53% \u003cem\u003evs\u003c/em\u003e. control; p\u0026thinsp;=\u0026thinsp;0.999) and downregulation of mitochondrial genes (\u003cem\u003emt-Rnr1\u003c/em\u003e, \u003cem\u003emt-Rnr2\u003c/em\u003e), indicating increased glucose uptake and reduced fatty acid oxidation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDifferentiated 3T3-L1 adipocytes are a useful \u003cem\u003ein vitro\u003c/em\u003e model for IF/TRF studies. 16/8 IF regimen was the most effective in reducing lipid content, compared to \u0026ldquo;distributed\u0026rdquo; IF and BS regimens within a 24h-period, consistent with the significant modulation of genes promoting lipolysis and inhibiting adipogenesis and glycolysis.\u003c/p\u003e","manuscriptTitle":"Modelling 16:8 Intermittent Fasting and Breakfast-Skipping in Mouse Adipocytes 3T3-L1 In Vitro: A Transcriptomics Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 07:04:44","doi":"10.21203/rs.3.rs-7322953/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d28107b-81e0-46a1-b6ef-ed6d421b4b20","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-12T07:04:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 07:04:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7322953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7322953","identity":"rs-7322953","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}