Elucidating altered neural molecular mechanisms in mice using transcriptomics underlying metabolic disorders induced cognitive and depressive disorders

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However, there is a dearth of molecular studies that deal with the underlying neural mechanisms using relevant animal models of MetDs-induced neurological and psychiatric disorders. We modeled MetDs-like condition in C57BL/6 Ncrl mice by feeding a 60% high fructose diet (Hfr) for 56 weeks. Significant changes were observed in various MetD-related physiological parameters between the Hfr diet and the control group except for glucose intolerance. Prolong Hfr diet induced some of the metabolic disorder like phenotype including aging except type-2 diabetes. But 10 days of chronic unpredictable mild stress (CUMS) paradigm induced mild insulin intolerance in oral glucose tolerance test. Further the animals were found to develop neurological and cognitive impairment and major depressive disorder like phenotype. Transcriptomic analysis led to uncover underlying molecular changes into the prefrontal cortex region of mice. The pattern of differentially expressed genes (DEGs) was strikingly different in the Hfr group compared to the Ctrl group, thus correlating the phenotype, i.e. MetD-induced mood and cognitive disorders. Pathway analysis of the DEGs indicated perturbations in cellular metabolism, inflammation, innate immunity, neurogenesis, vasculogenesis, ion channels, and neuronal signaling. In addition, altered epigenetic regulators appear to mediate the stress-induced precipitation of metabolic and neuropsychiatric disorders. The outcome of our study supports the hypothesis of disease susceptibility due to lifestyle changes involving a high-calorie diet and chronic stress. Metabolic disorders chronic stress Differentially Expressed Genes Neuropsychiatric disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Metabolic disorders (MetDs) is a condition characterized by a combination of symptoms that gradually impair an individual's overall well-being. Approximately 25% of the global population, regardless of age or gender, suffer from MetDs (Marengo et al., 2016 ). MetDs is associated with several risk factors including hypertension, cardiovascular diseases, dyslipidemia, and non-alcoholic fatty liver diseases. (Marengo et al., 2016 ), atherosclerosis, cancer (Gallagher and LeRoith, 2015 ), cerebrovascular (Hariharan et al., 2022 ) chronic inflammation, and neuropsychiatric disorders (van Sloten et al., 2020 ). The factors that contribute to MetDs can differ from person to person and may be influenced by environmental exposure or genetic traits. Unhealthy habits, a sedentary lifestyle, poor dietary choices, family history, socioeconomic status, and education are all potential factors that can lead to the development of MetDs. It is important to note that the genetic makeup of living individuals is a result of dietary habits that go back thousands of years, as mutations take place over many thousands of years. Therefore, the increase in fructose consumption poses a significant challenge to our conservative genes, and current habits can disrupt the balance of our body functions leading to disease. These limitations are more concerning when we consider that the sudden increase in sugar consumption after industrialization has been accompanied by a remarkable decrease in physical exercise. The growth of MetDs is fueled by the widespread availability of easily accessible, high-calorie food from global franchises, in recent times, fructose has become a popular sweetener in processed foods and soft drinks, often marketed as a "safe and healthy" option. However, new studies have shown that fructose is a significant contributor to metabolic disorders in humans (Lyssiotis and Cantley, 2013 ; Gomez-Pinilla et al., 2021 ). Fructose is usually added in addition to generic sugar, although fructose is one of the three common sugars, it is not directly involved in most metabolic processes to generate energy (ATP). Fructose is directly absorbed in the intestinal lumen, where it is transported by Glucose transporter 5 (GLUT5) (DeBosch et al., 2014 ) from the luminal side and Glucose transporter 2 (GLUT2) (Leturque et al., 2005 ) from the basolateral side, subsequently through the portal circulation, fructose transported to the liver, where its uptake and metabolism in hepatocyte were facilitated through Glucose transporter 8 (GLUT8) (DeBosch et al., 2014 ). Approximately 50% of fructose is converted into glucose, ~ 15 to 20% is converted into fatty acids, and the remaining 15 to 20% of fructose is converted into hepatic glycogen. The three key enzymes namely (I) fructokinase C (FFKC) also named ketohexokinase (KHK) catalyze phosphorylation of the fructose into Fructose-1P (II) subsequently, aldolase B enzyme converts Fructose-1P into di-hidroxyacetone-phosphate (DHAP) (III) Thiokinase (TKFC) responsible for the conversion into the glyceraldehyde-3-phosphate.It is important to note that, these Trioses are also involved in regulating lipid synthesis, glycolysis,glycogenesis, and gluconeogenesis (Akram and Hamid, 2013 ). The molecular mechanisms behind high fructose-induced metabolic disorders (MetDs) and brain disorders have been limited by conventional approaches that focus on isolated molecular events. This has led to delays in major advances. Because of these limitations, nutrient-based prevention and treatment strategies for common complex disorders have been hindered.Our study uses systems nutrigenomics to unveil the complex molecular interactions influenced by a high fructose diet. Our aim to uncover the potential molecular mechanisms by examining the effects of fructose diet on psycho-neuropathogenesis, transcriptomic approach was utilized. The outcome of the studyleads to better insight into the novel molecular mechanisms underlying prolonged high fructose-induced MetDs-mediated neuropsychiatric disorders. Our study could pave the way for developing effective strategies to alleviate common human diseases. Materials and Method All the experimental procedures with mice were approved by the Institutional Animal Ethics Committee (CCMB/IAEC/33-2021) of the CSIR-Centre for Cellular and Molecular Biology (CCMB), and conducted in accordance with guidelines established by the Committee for Control and Supervision of Experiments on Animals (CCSEA), Ministry of Fisheries, Animal Husbandry and Dairying, Government of India. ARRIVE guidelines were followed for the preparation of the manuscript. C57bl/6 mice-Ncrl (will be called C57 henceforth) were procured from the Charles River Laboratories USA) and bred and maintained in the CSIR-CCMB Animal House Facility, Hyderabad. 3 to 4 mice were housed in each individually ventilated cage system with a 12h light/12h dark cycle, temperature (23 ± 2°C), relative humidity (60%), and ad libitum access to food and water. Two-month-old animals were randomly divided into two groups followed by the baseline behavior experiments for depression, anxiety, and cognitive disorders and biochemical parameters including fasting glucose, triglycerides cholesterol, etc. One group of animals was fed on a 60% High Fructose Diet (Hfr) Composition D11707 (modified AIN − 76A) (Modified AIN-76A Rodent Diet with 65%kcal% Fructose) and another group of animals was fed a Control diet (D11708B). Hfr diet and control diet obtained from Research Diet ( https://researchdiets.com/en/search?q=D11708B ). The animals were fed high high-fructose diet throughout the experiment (56 weeks) (Fig. 1 ). Animals after being subjected to the 10 days chronic unpredictable mild stress paradigm at 52 weeks were grouped as (I) High fructose Unstressed (HfrUST) (II) High fructose stressed (HfrST) (III) Control Unstressed (CoUST) (IV) Control stressed (CoST) (Fig. 1 ). Biochemical analysis: The serum profile was done using TransAsia ERBA EM 200 automatic robotic spectrophotometry, a fully automated, random access, and discrete clinical chemistry analyzer. The following serum parameters were assayed (I) Triglyceride (TG 440, Cat No # XSYS0041), (II) Cholesterol (CHOL 5x50; Cat No. # BLT00034; CHOL 1000; Cat No. # BLT00035, CHOL250; Cat No.# BLT00036), (III) HDL DIRECT; HDL C 160, Cat No. # XSYS0043, HDL C 360 XL-1000, Cat No. # XSYS0078) (IV) ALT/SGPT (ALT/GPT30, Cat No# XSYS0017; ALT/GPT 564 XL-1000; Cat No# XSY0074), (V) AST/GOT (AST/GOT330 Cat No. # XSYS0016; AST/GOT 564 XL-1000, Cat No# XSYS0073), (VI) Bilirubin (BL00011) (VII) Creatinine (CREA 275, Cat No. # XSYS0024, CREA 564 XL-1000, Cat No. # XSYS0076), (VIII) Alkaline Phosphatase (ALP110; Cat No. # XSY0002) and (IX) UREA(UREA 1000, Cat No. # BLT 00060, UREA 250, Cat No. # BLT 00061). Glucose was measured by Accu-Chek Active (Roche, India). Anxiety-like behavior : Mice were assessed for anxiety-like behavior by Open Field Test (OFT), which is a very common and straightforward test to asses anxiety and exploratory-like behavior in mice (Gould et al., 2009 ; Prut and Belzung, 2003 ). OFT was conducted in an arena of a rectangular wooden box (35 X 45 X 30 cm), the arena was virtually divided into the central and peripheral zones. One day before test mice were habituated to the arena, each mouse was allowed to explore the arena for 5 minutes, where time spent by mice in each zone was measured using Ethovision 3.1 (Noldus, Netherlands). The percent time spent in the central zone was calculated as follows $$\:\left\{\right[Time\:sepnt\:in\:the\:central\:zone9\left(s\right)/Total\:time\:i.e\:300\:s\left]\:X\:100\right\}\:\left(1\right)$$ Tested mice spending less time in the central zone as compared to the control is considered anxiety-like behavior. Depression-like behaviour: Mice were assessed for depression-like behavior by the Tail Suspension Test (TST) (Steru et al., 1985 ; Cryan et al., 2005 ). TST is based on the assumption that under stressful conditions animals will try to escape. The test involves the suspension of the mice by their tail in three-sided wooden small chambers, and their body suspended in the air and facing downwards. Mice are suspended for 6 minutes till immobility where they are unable to escape or hold any surface, the tape should be applied at 3/4th position from the base of the tail, and suspend the mice by placing the free end of the tape on the bar. During the test agility and immobility of the animal were video recorded by the camera. At the end of the session return the animals to their home cage and tape remove gently. $$\:\left\{\right[Time\:sepnt\:in\:the\:immobility\:stage\left(s\right)/Total\:time\:i.e\:360\:s\left]\:X\:100\right\}\:\left(2\right)$$ Memory assessment: To assess the cognitive ability of the mice Novel Object Recognition Test (NORT) was performed(Ennaceur and Delacour, 1988 ). In brief, mice were initially habituated for one day in an empty arena. During the training period mice were allowed to explore the two identical objects placed at the opposite corners in a rectangular wooden box (35 X 45 X 30 cm) for 5 minutes. The mice were returned to their cage for one hour after training, while one of the familiar objects was replaced with a novel object, and the mouse was allowed to explore the object again for 5 minutes. The Recognition Index (RI) of the mice for the novel object was calculated using Eq. (3) where Tn and Tf are time spent with the novel object and familiar object respectively(Soni et al., 2021 ). $$\:RI=\left(\frac{Tn}{Tn+Tf}\right)x100\:\:\:\left(3\right)$$ The discrimination Index (DI) of the mice was calculated using Eq. (4) where Fn and Ff are the frequency of visiting the novel object and familiar object. $$\:DI=\left(\frac{Fn}{Ff+Fn}\right)x100\:\:\:\left(4\right)$$ Oral Glucose Tolerance Test (OGTT) The Oral Glucose Tolerance Test (OGTT) is a highly sensitive and specific test used to determine glucose intolerance, which can be shown by post-challenge glucose excursion. We measured 2-hour plasma glucose levels, a criterion for glucose intolerance on the OGTT (Sakamoto et al., 2013 ; Sakaguchi et al., 2016 ). The mice were fasted for 6 hours before the oral glucose tolerance test. The glucose bolus was administered orally in a saline solution (20%) at a dose of 2g/kg body weight. The postprandial glucose Area Under Curve (PG-AUC) was calculated using the trapezoidal rule to approximate postprandial glucose (PG) levels. The PG levels were measured every 30 minutes. The PG level at X minute was defined as PG (X min). The reference PG (X) and PG (AUC) were calculated. $$\:PG\:\left(AUC\right)mg-\frac{h}{dl}=\frac{1}{4}*(PG\left(0\right)+PG\left(30\:minute\right)*2+PG\left(60\:minute\right)*3+PG\left(120\:minute\right)*2\:(5)$$ Sacrificing the animals: The mice were euthanized by cervical dislocation 24 hours after the last behavioral test. The brain was immediately removed from the skull and rinsed in ice-cold, sterile 1x PBS. The brain was sliced (1 mm thickness) on a mouse brain matrix (Zivic rodent brain slicer matrix). The prefrontal cortex was microdissected andsnap-frozen in liquid N2, followed by storage at -80°C. RNA isolation from the prefrontal cortex region of the brain: RNA was isolatedusing a mir Vana™ isolation kit (Cat No. #AM1560; Thermo Fisher Scientific) as per the manufacturer’s protocol.To prepare the RNA samples, 10x DNase I buffer and one unit of DNase I enzyme (New England Biolabs) was added to a maximum of 5 µg RNA, for DNase treatment. The samples were incubated at 37°C for 15 minutes and then the enzyme was inactivated at 70°C for 15 minutes. After that, the DNase-treated RNA was quantified by measuring the absorption at 260 nm using a NanoDrop 2000 spectrophotometer. Each biological replicate had 3µg of RNA, with RNA Integrity Numbers (RIN) > 9 that were sent for sequencing at the CCMB RNA-Seq Facility. mRNA library preparation: The MGIEasy RNA Library Prep Set (MGI) was used for library preparation following the manufacturer’s instructions. Initially, 500 ng of total RNA was used, and the rRNA was depleted using an MGIEasy rRNA depletion kit.Following rRNA depletion, samples were fragmented and reverse transcribed. The second strand was then synthesized and converted into cDNA. The kit provided DNA Clean Beads for DNA purification, followed by end repair and A-tailing. The samples were then barcoded, adaptor-ligated, and subjected to purification. Adaptor-specific primers were used for amplification, and quantification was done using a Qubit dsDNA high-sensitivity kit from (Thermo Scientific). The size of the sample fragments was determined using a 4200 TapeStation (Agilent). Denaturation and circularization of 1pMol dsDNA were performed to generate single-stranded circular DNA, followed by the creation of DNA Nano Balls using Rolling cycle amplification. The DNBs were sequenced on an MGISEQ-2000 sequencer (MGI) using the PE100 recipe, after being placed on a patterned flow cell. Data processing and analysis: The sample quality was checked with FastQC. MGI adapters and low-quality reads were removed from raw sequencing reads using cutadapt. Reads with quality scores less than 20 and smaller than 36 bp were discarded. The processed reads were then mapped to the mouse genome mm10 using hisat2 with default parameters. Uniquely aligned reads were counted using the feature counts of the Subread package. There were 55487 genes in the gtf file, downloaded from Ensembl, for which we had count information. Genes with a total read count of 10 across allthe samples were removed resulting in 31299 genes for further analysis. The analysis of Differentially Expressed Genes (DEGs) was carried out using DESeq2. Genes with adjusted p-value 0.5 were considered differentially expressed. For the PCA plot and heat map, the raw read counts were log normalized, available with the DESeq2 package. Functional enrichment analysis: For functional enrichment analysis, clusterProfiler was used for GO term enrichment. We only used the Biological process for GO term enrichment analysis. Similar enriched terms were further merged using the ‘simplify’ function of cluster Profiler with a similarity cutoff set to 0.7. ‘p-adjust’ was used as a feature to select representative terms and ‘min’ was used to select features. ‘Wang’ was used as a method to measure similarity. ClusterProfiler was also used for the KEGG pathways enrichment analysis. Validation of RNA seq data throughgene expression analysis:: A set of mice, CoUST (n = 8), CoST (n = 8), HfrUST 9n = 8), and HfrST (n = 8) were used for the gene expression analysis. In brief, mice were euthanized by cervical dislocation and brain tissues were micro-dissected. Total RNA was isolated from the prefrontal cortex region of the mice using mirVana™ (miRNA Isolation Kit, without phenol; catalog number #: AM1561). The cDNA was synthesized using the prime scriptTM strand cDNA synthesis kit as per manufacturer protocol (TAKARA, Catalogue # 6110A). The quantitative polymerase chain reaction (qPCR) was performed using a specific target gene primer set (Table S1 ) and TB green premix Ex TaqTM II green master mix TIi RNase plus (TAKARA, Catalogue# RR820A) as per the manufacturer’s protocol. The Polymerase Chain reactions (PCR) were set up in triplicates in the MicroAmp optical 384 well plate (Applied Biosystems) in ViiATM7 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). β-actin and TBP (TATA-box binding protein) were used as housekeeping genes in different experiments for normalization. The data were analyzed using the ΔΔCt approach and normalized to the β-actin mRNA level (Khandelwal et al., 2019 ). Statistical analysis: All the statistical analysis was carried out using The GraphPad Prism software (Version 8.0.2 San Diego, USA). The significance of the difference was determined between the two groups using a 2-tailed Student’s T-test. All values are presented as mean ± standard deviation (SD) and p < 0.05 is considered a significant difference from each other. The significance of the difference in the data sets involving 4 groups and 2 variables was determined using One-way ANOVA Tuckey’s post-hoc test, assuming an interval of 95% confidence (p < 0.05). The data resulting in a p-value less than 0.05 were considered significantlydifferent from each other. Results To begin with, the animals were randomly assigned to two groups: (I) the control chow diet group (Control) and (II) the high fructose diet group (Hfr), and before putting the mice in group ii on the prolonged Hfr diet, the baseline parameters were measured. No significant difference was observed in any of the parameters studied (FigS1.). Mice on prolonged periods (44 weeks) of Hfr diet intake failed to show diabetes-like phenotype but developed other MetDs such as hyperlipidemia and reduced lean mass (Fig. 2 ). Mice on Hfr diet developed some of the Metabolic Disorder (MetDs) and premature aging-like phenotype: There was no significant difference in the body weight between the control group and the Hfr diet group till50 weeks of age. However, animals in the Hfr diet group exhibited a significant reduction in body weight once subjected to chronic unpredictable mild stress (CUMS) for 10 days i.e. at 56 weeks (Fig. 2 a.). EchoMRI scanning of the entire body showed no significant difference in fat mass (Fig. 2 c.),although the animals on the Hfr diet exhibited a significant decrease in lean mass (Fig. 2 d.).The biochemical analysis ofserumcollected at 44 weeksrevealed that the animals on the Hfr diet had significantly higher levels of triglycerides (Fig. 2 e.), total cholesterol (Fig. 2 f.), High-Density Lipoprotein (HDL) (Fig. 2 g.), and Low-Density Lipoprotein (LDL) (Fig. 2 h.), indicating hyperlipidemia. Animals on Hfr diet also showed a significantly high concentration of serum glutamic pyruvate transaminase (SGPT) (Fig. 2 i.) andserum glutamic oxaloacetic transaminase (SGOT) (Fig. 2 j.) levels, indicating compromised liver function. The alkaline phosphatase level was significantly high in Hfr group (Fig. 2 m.) and so was the level of creatinine, the markers of kidney function (Fig. 2 l.) in the Hfr diet group,thus indicatingaffected kidney function. Unlike earlier reports of Hfr diet-induced type 2 diabetes (T2D), our study failed to exhibit hyperglycemia phenotype following prolonged Hfr diet intake (data not shown). Even the oral glucose tolerance test (OGTT) showed no body insulin response against the sugar bolusbetweenthe control and Hfr group of animals (Fig. 2 n.). Thus, the phenotype characterization revealed that though the animals on a prolonged Hfr diet failed to develop diabetes, they developed other MetDs such as reduced lean mass and hyperlipidemia. The most remarkable finding we have is that the animals on a prolonged Hfr diet started showingvisible signs of premature aging, such asthe grey, ruffled, and lusterless appearance of hairs, the hallmark of aging (Fig. 2 b.). Thus, the animals on a prolonged Hfr diet developed some MetDs phenotypes, even though not T2D-like phenotypes. Chronic Unpredictable Mild Stress (CUMS)induced impaired glucose tolerance in mice on Hfr diet: Chronic stress is one of the causative factors for T2D. Since the mice on a prolonged Hfr diet failed to show an increase in blood glucose level or diabetes-like phenotype, a 10-dayCUMS paradigm was used to see if it can precipitate the T2D-like phenotype too, in mice on the Hfr diet. At the end of CUMS paradigm, OGTT was performed. Unlike the negative OGTT result we got before subjecting the animals to CUMS exposure (Fig. 2 n.), post-CUMS OGTT data analysis showed that the high fructose stressed animals (HfrST) developedmild T2D-like phenotype, albeit other groups of animals such as (I) (HfrUST), (II) (CoUST) and (III) (CoST) failed to develop the T2D-like phenotype (Fig. 3 a.). Further to corroborate the development of the T2D-like phenotype, we separately performed statistical analysis using one-way ANOVA multiple comparison t-test on the blood glucose levels at 120th minute that showed significantly high blood glucose level in HfrST in comparison to the other groups of animal (n = 8; p < 0.05; Fig. 3 b.). Moreover, we compared area under curve (AUC) between HfrST and CoST groups that revealed that the AUC of the HfrST is significantly more than that of the CoST (n = 8; p < 0.05; Fig. 3 c & 3 d). CUMS induced major depressive disorder (MDD) like-phenotype in mice on Hfr diet : To monitor the appearance of the depressive-like behaviour induced in the animals on the Hfr diet due to the CUMS treatment, we performed Force Swim Test (FST) and monitored the immobility of the mice which revealed that the immobility in HfrST animals was significantly higher than the HfrUST (n = 8;p < 0.001),CoST (n = 8;p < 0.03) (Fig. 4 a.). Furthermore, we noticed that the CoST showed significantly more inactive duration than CoUST (n = 8; p < 0.02;) (Fig. 4 a.) suggesting that the CUMS treatment induced depression-like behavior in both groups irrespective of the diet. Additionally, we performed a Tail Suspension Test (TST) which is also frequently used to investigate depressive disorders. Similarly,in agreement with the previous observation we observed HfrST spent significantly more inactive duration than HfrUST (n = 8; p < 0.05) (Fig. 4 b.). Interestingly, CoST also spent more inactive duration than CoUST (n = 8; p < 0.05) (Fig. 4 b.) suggesting the CUMS treatment-induced depression-like behaviour in both groups irrespective of the diet. However, no significant difference was observed between HfrST andCoST groups. The bar represents the mean ± SD of the group and the p-value shows the level of significance. CUMS induced memory or cognitive impairment-like phenotype in mice on Hfr diet: The cognitive ability of the mice was assessed by the Novel Object Recognition Test (NORT) in which calculation of the Recognition Index (RI) revealed the total time spent by the animals with novel objects during the test. There was no significant difference in Recognition Index (RI) between HfrST and HfrUST (Fig. 4 c.). Moreover, there was a significant differencebetween CoST and CoUST (n = 8; p < 0.01) (Fig. 4 c.) group. Additionally, the RI of the HfrUST was significantly less than the CoUST (n = 8; p < 0.01) (Fig. 4 c.). The discrimination index (DI) tells about theability of the animal to differentiate between familiar objects and novel objects. DI score of the HfrST was significantly less than the HfrUST (n = 8; p = 0.02) (Fig. 4 d.). Similarly the DI score of the CoST was significantly lower thanCoUST (n = 8; p = 0.03) (Fig. 4 d.). An additional observation was noticed that DI score of HfrST was significantly less than the CoST (n = 8; p = 0.02) revealed that HfrST animals spent significantly less time than the CoST animals (Fig. 4 d.). Furthermore, we noticed that the DI score of the HfrUST significantly less than the CoUST animals (n = 8; p = 0.03) suggesting the effect of a high fructose diet on the cognitive ability of the animals (Fig. 4 d.). Uncovering the molecular mechanism underlying Hfr diet and CUMS induced neuropsychiatric disorderslike phenotype: Mice were fed a high fructose (Hfr) diet for almost 52 weeks. Then they were subjected to chronic unpredictable mild stress treatment (CUMS) to induce the T2D-like phenotype, followed by behaviour analysis which revealed that these mice developed various neuropsychiatric disorders like phenotypes, including depressive and cognitive disorders, as shown in Fig. 4 . Several studies have shown that the prefrontal cortex region of the brain is most consistently affected bydepressive disorders (Pizzagalli and Roberts, 2022 ). To uncover the underlying molecular mechanisms, transcriptional profiling of the prefrontal cortex region was done using RNA-Seqon the three biological replicates from each group. The quality check (QC) analysis demonstrated low technical variability across samples with high coverage (40 million paired reads per sample). The results of a principal component analysis (PCA) indicated a clear-cut separation between the four groups (PC1 = 49%, PC2 = 15% variance) (Fig. 5 a.). As anticipated, there was a noticeable variance was observed at the transcriptome level, particularly in response to a high fructose diet (Fig. 5 a.). Interestingly, both stressed groups, regardless of the diet, i.e., the high fructose stressed group (HfrST) and the control stressed group (CoST), were relatively closer to each other (as shown in Fig. 5 a.). We conducted a Differential Expression Analysis using all the samples, taking Stress and a high fructose diet as covariance. This led us to identify in total 702 differentially expressed genes (DEGs) and transcripts (at log 2 FC = 0.3, Padj < 0.05) between the CoST group and CoUST including 259 upregulated genes and 443 downregulated genes (Table 1 ). The comparison between HfrUST and HfrST revealed 248 DEGs, including 103 upregulated and 145 downregulated genes (Table 1 ). As expected, given their close proximity on the PCA plot, only 16 DEGs were observed between the HfrST and CoST groups, with four upregulated and 12 downregulated genes (Table 1 ). Table 1 The total Differential Expression of the Genes (DEGs) in the prefrontal cortex region while comparing the different groups. Group Total DEGs Upregulated Downregulated HfrUST vs CoUST 572 289 283 HfrST vs HfrUST 248 103 145 CoST vs CoUST 702 259 443 HfrST vs CoST 16 4 12 A comparison between CoUST and HfrUST was performed to elucidate the effect of a high fructose diet. A total of 572 DEGs were found, including 289 upregulated genes and 283 downregulated genes (Table 1 ). Comparative Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genome(KEGG) Pathways analysis Gene Ontology (GO) analysis of the overall DEGs transcripts was performed using ShinyGo (Fig. 5 b.). The upregulated genes transcript of the comparison between HfrST vs HfrUST enriched in the following pathways, including epithelial tube morphogenesis, vasculogenesis, morphogenesis of the branching structure, regulation of the osteoblast differentiation, sensory organs morphogenesis, stem cell differentiation, neural crest cell differentiation, NOTCH signaling pathways, etc. (Fig. 5 b.). Similarly downregulated genes of the comparison between HfrST vs HfrUST were enriched in vasculogenesis, regulation of epithelial cell differentiation and proliferation, regulation of epidermis development, reactive oxygen species metabolic process, intrinsic apoptotic pathways etc. (Fig. 5 b.). Only one pathway transcriptional misregulation in cancer was revealed by KEGG pathway analysis (Fig. 5 d.). Furthermore, we also performed an Ingenuity Pathway Analysis (IPA) by QIAGEN on the upregulated DEGs transcript of the comparison between HfrST and HfrUST. The analysis revealed that several pro-inflammatory cytokines like interleukins (IL6, IL1A, IL12B), Toll-like receptor-2 (Tlr2), Interferon-gamma inducible protein 16 (IFl16), and chemokine C-C motif chemokine ligand-4 (Ccl4) and many more are implicated in metabolic and neuropsychiatric (refer to Fig S3 for details). Hfr dietand CUMS together perturbed microRNA expression pattern IPA analysis on (HfrST versus HfrUST) revealedseveral microRNAs (miRNAs) including mir-30, mir-124 mir-126, mir-129, mir-130,mir-132, mir146, mir-148, mir-154, mir-155, and mir-221, mir-467 in network ( FigS3.). These miRNAs act as transcriptional regulators of the various proinflammatory cytokines, chemokines, and genes including IL-1, IL-6, IL-18, Tnfα, ICAM-1, and VCAM-1. The previous studies reported diverse roles of these microRNAs such asmir-30 family implicated in several neuropsychiatric disorders (Kumar and Li, 2022 ; Khandelwal et al., 2019 ); mir-124 involve in regulating neuronal cell proliferation and differentiation in the adult brain (Han et al., 2019 ); mir-129 regulates the glucose metabolism (Chen et al., 2018 ); mir-130 has been implicated in the endothelial permeability of the brain’s microvasculature (Y. Wang et al., 2018 ); mir-148 reported to regulate the glial cell proliferation through EGFR/MAPK signaling pathway(M. Wang et al., 2020 ); mir-154 involved in glial cell proliferation (Zhao et al., 2017 ); mir-467; has been implicated in hyperglycemia-induced inflammation (Gajeton et al., 2021 ) (FigS3.). We observed the presence of the lethal 7 (let-7) microRNA which is the largest family of microRNA regulating the expression of various genes of different pathways such as AMPK, mTOR, etc which are involved in regulating various biological processes such as cancer, aging, differentiation, neuronal cell proliferation, and brain metabolism (FigS3.). Validation of the expression of genes perturbed due to the Hfr diet and CUMS First of all, we tried to validate the DEGs that were uncovered by our transcriptomic study. Interleukin 6 (IL6) is an inflammatory cytokine that has been widely demonstrated that play a role in neuroinflammation. We observed the transcriptomic expression level was significantly increased in the animals on high fructose diet exposed to the stress paradigm (HfrST) in comparison to the other group of animals (Fig. 6 a.). However, the expression of the interleukin 18 (Il 8) did not change between groups (Fig. 6 b.). We did not observe any changes in the histone class III (HDAC III) transcriptome, however, Reddy et al. 2018 reported a change in the expression of the histone class III genes in rats on a high fructose diet. Therefore, we decided to examine the expression of HDAC class III in the prefrontal cortex region of mice.Histone deacetylase class III (Sirtuin 1–7) is known as a metabolic sensor among them we checked mRNA expression of the sirtuin 1, sirtuin 6, and sirtuin 7. There was no significant difference was observed in the expression of sirtuin 1 and sirtuin 7 (Fig. 6 c & e). However, the m RNA expression of the sirtuin 6 (SIRT 6) was significantly reduced in the high fructose stressed (HfrST) group than the control stressed group (CoST) (Fig. 6 d). Furthermore, We noticed that the High fructose unstressed (HfrUST) group also showed a significant reduction in comparison to the control unstressed groups (CoUST) (Fig. 6 d.). Hfrand CUMS perturbed expression of genes regulatingautophagy Autophagy is an important lysosome-mediated highly regulated conserved catabolic process responsible for clearing the damaged organelles and maintaining intracellular homeostasis. Metabolic disorders are characterized by metabolic disarrangement and intracellular stress (oxidative stress, endoplasmic stress, and inflammation) due to the accumulation of damaged organelles (Kitada and Koya, 2021 ). We validateda few genes that are commonly used as markers for autophagy such as Bax is a member of the Bcl-2 family of proteins and a core regulator of autophagy (Karch et al., 2017 ) mRNA expression of the Bax gene was significantly reducedin the animals on high fructose stressed (HfrST) in comparison to the high fructose unstressed (HfrUST) (Fig. 6 f.) and similarly control stressed (CoST) in comparison to the control unstressed (Fig. 6 f.).However, the expression of the beclin-1 gene was not perturbed (Fig. 6 g). The Leucyl-tRNA synthetase-2 (Lars2) enzyme, catalyzes the aminoacylation of the mitochondrial tRNA, and mRNA expression was significantly reduced in CoST thanin CoUST (Fig. 6 g.). Hfr and CUMS affect synaptic plasticity and neuronal disorders The mRNA expression of theAdamts 19 (Disintegrinand Metalloproteinase with Thrombospondin motifs), which is implicated in various biological functions such as cell adhesion, and extracellular matrix organization, the inflammatory responsewas downregulated in high fructose stressed (HfrST) compared to the Control stressed (CoST) group (Fig. 6 i.). Interestingly high fructose Unstress (HfrUST) also showed significantly reduced expression than control unstressed (CoUST) Fig. 6 i.). The Apold1 gene encodes apolipoprotein L domain containing 1 (Vascular early response gene protein) and regulates several processes such as endothelial cell signaling, angiogenesis, and vascular function (Freson, 2023 ) expression significantly decreased in the HfrST group of micein comparison to the CoST, and also, the expression of the gene was significantly decreased in the HfrUST group compared to the CoUST group (Fig. 6 j.). The matrix metalloproteinases 2 (Mmp 2) gene plays an important role, in maintaining blood-brain integrity, and was found to be significantly reduced in the HfrUST groups in comparison to the CoUST group (Fig. 6 k.). Surprisingly, it was noticed that expression was significantly increased ~ 1.5 in HfrST than the HfrUST (Fig. 6 k.). We also observed that the mRNA expression of the Zink Finger protein 36 (Zfp36) was significantly reduced in the CoST than the CoUST (Fig. 5 k.) and a similar trend was noticed when a comparison was performed to CoUST and HfrUST also Fig. 6 k.). Our finding indicates that the Hfr diet along with stress is able to attenuate the expression of various genes resulting in the onset of neuropsychiatric disorders. Transcriptomics approach uncovered the molecular response due to the Hfr diet: Since the Hfr diet itself induced MetDs such as reduced lean mass, hyperlipidemia, liver and kidney functions, and accelerated aging phenotype, which in turn also induced neurological disorder i.e. cognitive impairment, to uncover the underlying molecular mechanism the transcriptomic changes in the critical brain region PFC was analyzed. The Gene Ontology (GO) of the DEGs showed the enrichment of genes in different biological pathways implicated in neural aging and neurological disorders. The upregulated genes in HfrUST compared to CoUST animals were found enriched in pathways such as memory, axonogenesis, synapse assembly, regulation of the G protein-coupled receptor signaling pathway, regulation of synapse organization, regulation of synapse structure or activity, cell junction assembly, synaptic plasticity, regulation of neurogenesis, synaptic transmission glutamatergic, etc. However, the downregulated genes (HfrUST vs CoUST) (FigS2a.). Similarly, KEGG pathways analysis (log 2 FC = 0.3, p adj <0.05) showed enrichment of pathways involved in the neuroactive ligand-receptor interaction, calcium signaling pathways, regulation of lipolysis in adipocytes, axon guidance, gap junction, proteoglycan in cancer, cAMP signaling pathways, chemical carcinogenesis receptor activation, thyroid hormone signaling pathways, morphine addiction pathways, etc(FigS3b.). Hfr diet perturbed genes expressions that regulate glucose homeostasis/ neurotransmitter synthesis The Glycogen Synthase Kinase-3 beta ( Gsk3β) is a serine/threonine protein kinase that plays a role in various signaling pathways, including AMPK, Wnt/β-catenin, phosphoinositide 3-kinase (PI3K), mammalian target of rapamycin (mTOR), Ras/Raf/MEK/ ERK and NOTCH, etc. Gsk3β is involved in regulating metabolism and a cell cycle has been linked to neurological disorders such as mood disorders and bipolar disorders.mRNA expression of Gsk3β was reduced significantly in high fructose (HfrUST) compared to control (CoUST) (Fig. 7 a.). Glutamate decarboxylase (GAD1) is responsible for the synthesis of gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter(Mitchell et al., 2015 ), reduced significantly in high fructose (HfrUST) compared to control (CoUST) (Fig. 7 a.). Similarly, Glutamate-ammonia ligase (GLUL) catalyzes the synthesis of glutamine from glutamate; its, expression was reduced significantly in mice on the Hfr diet(Fig. 7 a.). Interestingly, GABA-T expression, a mitochondrial enzyme primarily found in GABAergic neurons, which catalyzes the degradation of GABA into glutamate and succinic semialdehyde, significantly increased in mice on a high fructose diet (Fig. 7 a.). The alpha-2-adrenergic receptor (ADRA 2) is a type of adrenergic receptor that plays a role in regulating exocytosis and neurotransmitter cycling. The mRNA expression was significantly increased in mice on a high fructose diet compared to the control group (Fig. 7 a.). Hfr diet dysregulates transcription factors involved in angiogenesis, neurogenesis, & neuroplasticity The cAMP-response element-binding protein (CREBP) is a transcriptional coactivator of many different transcriptional factors involved in regulating various cellular functions. mRNA expression was significantly increased in mice on the Hfr diet compared to the control mice (Fig. 7 a.). Furthermore, Vascular endothelial growth factor (VEGF) has been implicated in angiogenesis (Melincovici et al., 2018 ), and was significantly reduced in the high fructose diet group (Fig. 7 a.). Additionally, SOX9 and HES5 mRNA expression was significantly increased, while Nestin, ZNF7, and SP7 (Osterix) expression were significantly decreased in mice on the high fructose diet compared to control mice (Fig. 7 a.). Hfr diet-induced neuroinflammation and innate immunity response Chronic inflammation is a key feature of metabolic disorders that leads to the activation of cytokines, chemokines, and inflammasomes(Vandanmagsar et al., 2011 ; Wen et al., 2012 ). In the current study, we found that the expression of the Nlrp3, TLR4Tumor necrosis factor 8 (Tnfrs8),, Cxcl12 (C-X-C motif Chemokine Ligand 12) which is produced by glial cells significantly increased in mice on the Hfr diet compared to the control mice (Fig. 7 b.). Discussion It is well known that MetDs in the long run causes vascular complications (Rask-Madsen and Kahn, 2012 ), including cerebrovascular (Hanefeld et al., 2016 ), which in turn appear to result inneurological (Farooqui et al., 2012 ) and psychiatric disorders (Frisardi et al., 2010 ). However, there is a dearth of reports on the molecular mechanisms involved in metabolic disorder-induced cerebrovascular (Iadecola and Gottesman, 2019 ) and neuropsychiatric disorders (Kan et al., 2022 ). Thus, our effort in this direction led us to uncover the molecular basis of cerebrovascular, neuroinflammatory, and neuroglial changes, while establishing a mouse model that mimics Hfr diet and/or stress-induced MetD and consequently develop neuropsychiatric disorders. As per our knowledge, the current study is one of its kind that unraveled the underlying neural molecular mechanisms using a high throughput transcriptomic approach, induced by lifestyle changes (Hfr diet +/- Stress) inone of the affected critical neural regions, the PFC. The analysis of the altered transcriptome data, listed as the differentially regulated genes (DEGs) between different groups in Tables S3 – S8, and validation of several key genes of the major pathways affected, led us to suggest the reprogramming of the neuroglial molecular responses and the circuitry, by the prolonged Hfr diet intake and also due to the chronic 10-days stress component added on top of it.In this study, we showed that like the previous reports using a mouse model of MetDs, even the prolonged period (46 weeks) on theHfr diet could able to induce some of the MetDs-like phenotype in mice, such as increased levels of serum triglycerides, total cholesterol, low-density lipid, together with the reduction in lean mass and body weight. Surprisingly, hyperglycemia or T2D, the most common MetDs could not be induced in our mice unlike that reported by several studies. However, those studies used a rat model in which even a few weeks to months on the Hfr diet was enough to induce the MetDs phenotype (Chan et al., 2021 ). Some of the studies that used mice had to add a high-fat diet, together with the Hfr diet to get the MetDs phenotype(Taskinen et al., 2019 ). Our study on mice could not replicate the T2D-like phenotype thatwe successfully modeled in the rat (Reddy et al 2016), where Sprague-Dawley rats developed MetDs, including hyperglycemia, in just 8 weeks on the HFr diet. It could be because the mice have a high basal metabolic rate, they are more active, running around, and hanging to the cage roof or cover than the sluggish rats in animal house cage conditions. This was the reason we continued feeding our experimental mice on the Hfr diet for almost a year and kept checking theirserum blood sugar levels. However, even though the mice did not develop hyperglycemia in the end, they developed other MetDs-like phenotypes such as hyperlipidemia and reduction in lean mass and body weight, which usually accompanies diabetes. Interestingly, after just 10 days of exposure to CUMS, these mice on the Hfr diet showed reduced insulin response at the 120th minute in OGTT, compared to the animals on a normal chow diet also exposed to CUMS. Thus, our findings suggest that the Hfr diet for almost a year itself might not have induced a full-blown MetDs phenotype including T2D, but it made the mice susceptible to MetDs, and, another environmental challenge like chronic stress could easily induce the diabetes-like phenotype. It is noteworthy that the animals on a prolonged Hfr diet quickly developed insulin resistance when exposed to just 10 days of chronic mild stress, unlike the control mice on a normal chow diet upon exposure to the same stress paradigm. Hfr diet and CUMS exposure induceneuropsychiatric disorder-like phenotype The chronic stress appears to induce severe changes in the brain of these mice on the Hfr diet, which we think had already become susceptible/vulnerable because of the prolonged Hfr diet-induced physiological and neural changes, including neuroinflammation and compromised neurotrophic support. This explanation is based onthe outcome of the analysis of the high throughput transcriptomic data between the two groups (see Table S1 ), which revealed severalpathways affected in the Hfr diet + Stress (HfrST) group compared to the Hfr diet + Unstressed (HfrUST) group of animals.Thus, the borderline MetDs itself was unable to cause enough changes in the brain and induce the neurological and psychiatric disorder-like phenotype in mice on the Hfr diet, except for the affected memory in the NORT task (Fig. 2 d.). But adding the stress component to mimic the modern-day lifestyle, triggered both mood disorders(depression, anxiety, Fig. 2 a& 2 b.) and further cognitive impairment (Fig. 2 c& 2 d.) like phenotype. The depression and cognitive impairment phenotype as shown in HfrST mice (Fig. 2 a & 2 b.) also appeared in the Control Stressed (CoST) animals (Fig. 2 a& 2 b) even though these animals failed to develop insulin resistance. However, the animals of the HfrST group showed more severe neurological and psychiatric disorder phenotypes (degree of changes and degree of significance P values), compared to the CoST group of animals, asclearly evident in the results. Thedata suggest that prolonged intake of the Hfr diet made the animals more susceptible or vulnerable to chronic stress. The appearance of mild cognitive impairment and depression-like phenotype in the CoST animals exposed to just 10 days of stress could be attributed to their advanced age (~ 56 weeks), as mice of this age are known to be more susceptible to stress. The physiological responses observed appear to depend on the age of the animals as well as the duration and concentration of the fructose intake. High fructose diet-induced accelerated aging Another highlight of the study is that a prolonged Hfr diet could induce an accelerated aging-like phenotype (ruffled and grey hair, reduced shining of the coat with rough texture(Fig. 2 b.), in addition to causing some of the MetDs (Fig. 2 .) including hyperlipidemia. So, even though a diabetes-like hyperglycemic state was not achieved just by feeding mice on the Hfr diet, it appeared to have altered the physiological homeostasis indicating compromised health (as reflected by the affected liver and kidney functions in test results on the serum samples), in addition to the reduced lean mass. The altered physiology might have driven the animals to the accelerated aging-like phenotype, as evidenced by course and grey hair, compared to the smooth and soft one in control mice on the normal chow diet (Fig. 2 .). The lean mass (protein mass) was much reduced in mice on the prolonged HFr diet, compared to the mice in the control diet group, as shown by our EchoMRI findings (Fig. 2 d.), which is a hallmark feature of MetDs as well as aging, Aging is also known to be the result of built upof tissue inflammation and thus the Hfr diet-induced aging phenotype could well be the result of an accumulation of pro-inflammatory molecules, as we find these genes upregulated in the list of DEGs in the Hfr group of animals.The analysis of the transcriptomic data on the brain region studied revealed hundreds of DEGs in the HfrUST vs CoUST group, just due to the effect of diet change, and interestingly the Ingenuity Pathway Analysis (IPA) led to the enrichment of some of the pathways and networks that are usually associated with the cellular senescence and organismal aging. Some of the altered genes were the ones that control genomic integrity, DNA repair, cell cycle regulation, cancer, etc., which are typically associated with aging (see Tables S6 & S7). High fructose diet aggravatedsystemic neuroinflammation Inflammation is known to be associated with MetDs, and also with most of the neurological and psychiatric disorders. Our transcriptomic analysis revealed the upregulation ofseveral pro-inflammatory genes in the PFC region of mice from both HfrUST as well as HfrST groups (as shown clearly in the Results section and TablesS4 & S5), where the validated results of these DEGs in brain samples of the individual mouse is shown. Of these altered genes, some that cause neuroinflammation were upregulated even without stress by the Hfr diet itself; these were Toll-like receptor 4 ( Tlr4 ), Nod-like receptor 4 ( Nlrp4 ) and Tumor necrosis factor receptor superfamily 8 ( Tnfrs 8 ). Under neuroinflammatory conditions (Fig. 7 b), TNF is released thereby enhancing the glial cell activation through various signaling pathways, including the activation of NFkB. These in turn, activate microglia and induce the production of proinflammatory cytokines such as IL6, IL1, and IFN-γ (Raffaele et al., 2020 ). This suggests that the long-term consumption of high amounts of fructose can cause neuroinflammation inducing neuroglial changes leading to susceptibility to neuropsychiatric disorders. Chemokines too have a positive role in neuroinflammation. One of these, C-X-C motif ligand 12 ( Cxcl12 ) plays a role by attracting the leukocytes from the blood-brain barrier (Li and Ransohoff, 2008 ). Interestingly, the enhanced expression of the Cxcl12 in the animals on a high fructose diet, which we validated too, suggests its role in increasing the inflammation in the PFC area of the brain (Fig. 7 b.). However, chronic stress-induced severe neurological and psychiatric disorder phenotype in mice on the prolonged Hfr diet appears to be due to a further increase in the levels of some other pro-inflammatory molecules (interleukins, cytokines, chemokines) and key inflammasomes (as listed in the DEGs in the corresponding tables). One of the inflammasomes Nlrp6, which was differentially downregulated in the HfrST vs HfrUST group (Table S4), is the novel and interesting finding of our study. The key inflammasomes Nlrp3 and Nlrp4 have been reported in earlier studies to be upregulated in inflammation conditions, but Nlrp6 has been shown to work differently. Further studies will be required to find out why, unlike other inflammasomes, Nlrp6 is downregulated in the brain region investigated in our study. Unlike the findings in earlier studies and that in our Lepr db/db mice brain (unpublished finding from our lab), Nlrp3 was not regulated in our transcriptomic data set from the Hfr mouse model. However, in an independent experiment, we could see the upregulation in the level of Nlrp 3 in the prefrontal cortex region of the Hfr diet or Hfr diet + stress group (Fig. 7 b.). High fructose diet and CUMS affect various signaling pathways The analysis of the transcriptome data also revealed the significant impact of the long-term Hfr diet on a number of critical cellular processes neurometabolism, pathways in cellular homeostasis, neuroinflammation, innate immune function, cell-cell communication, cell proliferation and differentiation, neuronal signaling, insulin signaling, GABA metabolism, Wnt signaling, Notch signaling, G protein coupled receptor signaling, S100 family signaling pathways, myelination signaling pathways, memory, axonogenesis, synapse assembly or structure, synaptogenesis, synapse organization signaling pathways, innate immunity, cerebrovascular, cell proliferation, JAK-STAT signaling, cardiomyopathy, transforming growth factor (TGF) beta signaling, mitogen-activated protein kinases (MAPK) signaling, platelet-derived growth factor (PDGF) signaling, vascular endothelial growth facotr, toll-like receptor (TLR) signaling, brain-derived neurotrophic factor (BDNF) signaling, pertubed in the prefrontal cortex of the mice on Hfr diet. Cellular processes and signaling pathways affected in HfrST animals compared to HfrUST ones were as follows (Fig.S2) : FAK signaling, CREB signaling in neurons, S100 family signaling, cancer signaling, glioma invasiveness signaling, Hif1a signaling, G-protein coupled receptor signaling, synaptogenesis signaling, neuroinflammation signaling, myelination signaling, serotonin receptor signaling, attenuated antioxidant action of vitamin C signaling, NRF2-mediated oxidative stress signaling, senescence pathway, DNA damage-induced 14-3-3 signaling, PI3K/AKT signaling, Sirtuin signaling pathway, WNT/b Catenin signaling, Nitric oxide signaling, white adipose tissue brown signaling, acute phase response signaling, regulation of the epithelial Mesenchyme signaling, adipogenesis pathway, angiopoietin signaling, NFkB activation by viruses pathway, Toll-like receptor signaling, ferroptosis signaling, orexin signaling, embryonic stem cell signaling, synaptic long-term potentiation signaling, endothelin 1 signaling, neurovascular coupling signaling, cAMP-mediated signaling, and GABAergic receptor signaling. Our study also revealed the broad effect of the high fructose diet on cell signaling pathways altered differentially in the brain region of the Hfr diet Stressed versus Hfr diet Unstressed group (HfrST vs HfrUST), compared to mice of the Control diet Stressed versus Control diet Unstressed group (CoST vs CoUST), as shown in Fig.S3. Most of the neuropsychiatric disorders are associated with altered neurotransmitter signaling and function. GABA is a major inhibitory neurotransmitter implicated in anxiety, depression, schizophrenia, and other neuropsychiatric disorders (Jewett and Sharma, 2023 ). In our study, reduced expression of the glutamic acid decarboxylase ( Gad 1) in the animals on the Hfr diet exposed to stress, might be causing an attenuation in the production of GABA from L-glutamic acid, resulting in a depression-like phenotype in HfrST groups (Fig. 6 a.). GSk3 β is a key molecule that mediates the regulation of PI3K/AKT/ or AMPK and Wnt signaling pathways, which are known to modulate neuronal cell proliferation, differentiation, migration, and plasticity. Since enzymes involved in cell survival and neuroplasticity are relevant to neurotrophic factor dysregulation, the PI3K/AKT/GSK3 pathway might act as an important signaling mechanism for neuroprotection in depression (Kitagishi et al., 2012 ). Our study revealed that there was a decrease in the mRNA expression of GSk3 β in the PFC region of the HfrST groups of animals (Fig. 7 a). This indicates that prolonged feeding of the high fructose diet appears to cause disruption of various signaling pathways in PFC resulting in the loss of cellular homeostasis, thus inducing the onset of neuropsychiatric disorders such as depression, anxiety, and cognitive loss in the animals' on high fructose diet. We validated a number of altered genes in the HfrST versus HfrUST group; these genes belong to the pathways that control not only inflammation and apoptosis but also angiogenesis and neurotrophic function, which several groups have reported to be associated with the etiopathology of the neuropsychiatric disorders (Table S1 ). Vascular endothelial growth factor ( Vegf) is a potential angiogenic factor responsible for angiogenesis and has a neurotrophic effect too. Vegf expression in the brain is linked to neuroprotection, cognitive, and aging phenotype through the PI3K/AKT pathway (W. Zhang et al., 2021 ). Our finding of its attenuated expression in the prefrontal cortical region of the HfrST group (Table S1 ) suggests the reduction in angiogenesis (Fig. 7 a), which might have caused perturbation of the cerebrovascular physiology leading to the neuropsychiatric disorder phenotype. Metabolic disorders including diabetes, hypertension, and perturbed lipid profiles are known to activate various cellular processes such as oxidative stress, insulin resistance, and inflammatory pathways. Autophagy is a lysosomal-mediated degradation process that plays an important role in maintaining the homeostasis of the cellular metabolic process (Moulis and Vindis, 2018 ). Bax is a BCL2-associated apoptotic regulator involved in regulating various cellular functions (L. Zhang et al., 2000 ). We observed that the expression of the Bax gene was reduced in the stressed group of animals (CoST) in comparison to the Control Unstressed (CoUST)ones (Fig. 6 f), and HfrST in comparison to the HFrUST, thus suggesting stress-induced apoptotic pathways (Fig. 6 f). High fructose and CUMS modulate the expression of transcription/ epigenetic factors : Dysregulation of transcription factors Dysregulation in the transcription regulatory network has been reported in some of the transcriptomic studies done on brain samples from rodent models of fructose-induced MeDs (Meng, 2016) (Table S11). Our data also corroborates this; several transcription factors (TFs) (Table S9) and epigenetic regulators (Table S10) were found altered in the Hfr diet and/or stress-induced cerebrovascular and neural changes. Many transcription factors are getting altered; some of these have been earlier reported, such as the ones belonging to the cAMP-CREB pathway involved in neuroplasticity regulation, cognition, and cognitive disorder, in addition to depression and related mood disorders (for reference see Table S8). One of the interesting families of transcription factors that were found altered in the stressed groups with or without the Hfr diet was the Tcf family; we found 3 members of this family getting dysregulated in the prefrontal cortex region, compared between different treatment groups (Table S8). It will be interesting in the future to investigate these family members of TFs as many members are affected. A few of these family members such as Tcf7l2 , which is the strongest and most reliable signal for T2D found in human GWAS (Voight, 2010) (Table S8). One more highlighting feature of our study is the discovery of alternatively spliced variants, and dysregulation of some of the key splicing regulators and splice regulator binding proteins. The splicing regulators appear to be one such class. In one of the recent studies (Meng et al 2016) it was found that 20% of the genes altered in the hippocampus were at the isoform or alternative splicing levels rather than in the overall expression of genes. The authors report that fructose reprograms the rat brain network inducing cognitive disorder phenotype by engaging core TFs, epigenetic regulators like DNA methyltransferases, and splicing factors such as Bicc1, Prpf31 , and Rbpms (Meng, 2016). Our data also showed dysregulation in one of the important Splicing factors Srsf5 , as shown by another group in fructose-induced changes in the brain as shown in Table S11 (Zhang et al. 2021 ).Another class of regulators we found altered in PFC in the Hfr group and also the Hfr Stressed group are the ones involved in epigenetic mechanisms of gene regulation. Epigenetic modulation Epigenetic changes are reversible and work via alterations in DNA and histone modifications and chromatin remodeling mechanisms (Table S9&S10). The dysregulation of epigenetic regulators and the epigenetic and transcription regulatory mechanism have been shown in recent years to play an important role in diverse stress-induced changes in the brain and in the etiopathogenesis of neurological and psychiatric disorders (Tsankova et al 2009). Unlike that shown in some previous reports and in our Lepr db/db mouse model, where many Sirtuins belonging to the epigenetic regulators of class III HDACs were found altered in the brain (unpublished finding from our lab), we could not find an alteration in any of the seven Situins in the RNA-Seq data in the mouse Hfr diet model that developed neuropsychiatric disorder like phenotype upon chronic stress exposure. However, we went ahead and mapped a few of the Sirtuins and found Sirt 6 only to be dysregulated (downregulated), which is known for its neuroprotective role (Fig. 6 c). Since Sirt6 negatively regulates genes that code for the pro-inflammatory cytokines and inflammasomes, we suggest that the downregulated Sirt 6 might have driven the transcription level of these Sirt6 targets resulting in heightened neuroinflammatory condition. This might have affected the prefrontal cortical circuitry, leading to alteration in neuroglial response, neurogenesis, and neuroplasticity (as deduced from the corresponding pathway genes dysregulated in our data sets. This, in turn, led to the neuropsychiatric phenotype in mice on Hfr diet + stress, i.e. cognitive impairment and depressive disorder. In the Lepr db/db mouse model studied by us, we found that Nlrp3 and Nlrp4 , the key inflammasomes, were highly upregulated (unpublished finding from our lab). However, in the transcriptomic data from our HfrUST and HfrSt groups, we could not find these genes regulated. Using the leftover RNA after making the library for RNA-Seq, we prepared the cDNA and ran qPCRs. The analysis revealed even the Nlrp3 was upregulated in our samples following the prolonged Hfr diet (Fig. 6 b). Since epigenetic mechanisms play an important role in gene-environment interaction and a number of these are found dysregulated in the brain regions of chronic stress-induced neuropsychiatric disorders models (Chakravarty S et al 2014 Int Rev Neurobiol), it is pertinent to look for the dysregulation in epigenetic regulators in PFC of animals from HfrST vs HfrUST and HFrUST vs CoUST groups. The ones found downregulated in the groups on Hfr diet and Hfr diet + Stress mice were histone lysine methyltransferases Ezh2, Dot1l , that target H3K27 di/tri methylation and K3K79 mono/di/tri methylation, respectively), and histone lysine demethylase Kdm6b that targets H3K27 di/tri methylation) (Table S9). Downregulation of Ezh2 (transcriptional repressor) and Dot1l (transcriptional activator) might have altered a large number of gene targets in the brain regions affected by MetDs and induced neuropsychiatric disorders (Table S9). These epigenetic regulators are known to regulate transcription and thus control processes as diverse as neuroglial response, development, cell cycle progression, somatic reprogramming, neural stem cell proliferation, differentiation, neurogenesis, and DNA damage repair. The involvement of the DNA methylation-based epigenetic mechanisms in the hypothalamus region of rats after the fructose diet-induced behavioural change, i.e. cognitive impairment, has recently been demonstrated that can regulate the transcriptome. Additionally, transcriptional regulators such as Atf3, Junb, Zbtb16, and Parp9, as well as microRNAs like rno-miR-421 and rno-miR-143, were found to co-occur with DNMTs, suggesting a transregulation mechanism (Meng, 2016) (Table S11). An increasing amount of evidence suggests that neurological and psychiatric disorders are influenced by epigenetics (reviewed in Tsankova, 2007).In particular, disruption in cell metabolism appears to be a key factor in epigenetic changes related to cognitive function (Tyagi, 2015). Furthermore, Sirtuins (Sirt 1–7) NAD+-dependent histone deacetylases of class III, are involved in regulating many important biological processes such as cell metabolism, cell senescence, proliferation, apoptosis, DNA repair and calorie restriction (Song and Kim, 2016 ). Sirt 1 and Sirt 6 are reported to be involved in regulating the metabolism (Liu et al., 2008 ), apart from that expression of the Sirt 7 was also attenuated in the striatum of the rat that developed metabolic disorder-induced psychiatric disorder (Reddy, 2016). In the current study, we found a decrease in Sirt 6 expression in the prefrontal cortex region of mice on the Hfr diet exposed to CUMS, as shown. There were no significant changes in Sirt 1 and Sirt 7 expressions. Hfr diet and CUMS together induced neuropschyiatric disorder-like phenotype through various mechanisms Additionally, the expression of the cytokine IL 6 (Fig. 6 a) appears to be involved in MetDs-induced neuroinflammation but no change was observed in the expression of the IL18 between the groups. Adamts19 plays a role in synaptic plasticity, neurodegenerative and neurological disorders; Apold, Mmp17, involved in angiogenesis, invasion, metastasis, and avoidance of immune surveillance, were found attenuated in the animals on the Hfr diet exposed to CUMS (Fig. 6 ).To sum up, our study concludes that the MetDs by Hfr diet for a prolonged period itself can bring about changes in brain reactions affecting neuroglial functions and neuroplasticity, by affecting inflammatory, neurotrophic, neurogenic, and vasculogenic Pathways. Chronic stress on top of it appears to make these changes more severe and, additional alterations in some other genes and pathways thus cause more severe neurological and psychiatric phenotypes. So unlike previous studies, where metabolic disorder-induced vascular disorders or changes were focused on the cardiovascular system mostly, for the first time our study reflects on cerebrovascular changes, the change in the endothelium lining, endothelial cell biology, its maintenance by altering several genes involved in the cerebrovascular physiology and blood-brain barrier function. The leaky barrier thus allows the movement of some of the peripheral immune and/or inflammatory molecules to the brain parenchyma. Some of these molecules are involved in cerebrovascular and downstream pathophysiology, which we uncovered here, shown in the tables listing the differentially expressed genes (DEGs). Earlier studies have also shown some of these changes, but for the first time, we are showing many other molecules dysregulated in the pathway.Ours is the first report of the prolonged Hfr diet +/- chronic stress-induced transcriptomic changes in the prefrontal cortex, one of the brain regions implicated in the rodent models of neuropsychiatric disorder-like phenotype. This investigation led us to get better molecular insight into the MetDs-induced changes in the brain.The outcome provides molecular evidence supporting the ability of fructose, or/and stress to disrupt the critical neural genes and the fundamental physiological processes; some of these genes are insulin, IL6, Nlrp3, Tlr4, CxCl12, Tnfrs8, Vegf, Crebp, Gsk3β, and Gad1, which are involved in various pathways including neurometabolism, neuroinflammatory signaling, vasculogenesis, neural cell proliferation, differentiation, etc. The majority of these pathways have been implicated in metabolic disorders and/ or neuropsychiatric disorders. Conclusion Prolonged HFr diet intake induced not only the MetDs-like phenotype in mice, but also the aging-like phenotype. Further lifestyle changes such as chronic stress not only induced mild insulin tolerance but also induced neuropsychiatric disorder-like phenotype like cognitive decline and depression. The exposure to CUMS affected the expression of a number of critical genes in various neural and neurovascular pathways, including synaptic signaling, cytokine signaling, vasculogenesis, synaptic transmission, myelination, NFκB signaling, Toll-like receptor signaling, etc. in the prefrontal cortex region of the Hfr diet mice; that, in turn, might have triggered the severe neuropsychiatric disorder like phenotype. The outcome led us to better molecular insight into the MetDs-induced molecular changes in the brain. The outcome provides evidence supporting the ability of fructose, or/and stress to disrupt the critical neural genes and the fundamental physiological processes. Abbreviations MetDs Metabolic disorders CUMS Chronic Unpredictable Mild Stress Hfr High Fructose Diet HfrUST High Fructose Unstressed HfrST High Fructose stressed CoUST Control Unstressed CoST Control Stressed OGTT Oral Glucose tolerance test RNA-Seq RNA sequencing OGTT Oral Glucose tolerance test DEGs Differentially Expressed Genes. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Financial interests The authors declare they have no conflict and financial interests. Funding The study was supported by funding from the Council for Scientific and Industrial Research (NCP/MLP0139). Author Contribution SS: Experimental design, Animal experiments, Data acquisition, Analysis and Interpretation of Results, Manuscript writing; NKS: Transcriptomics Data Analysis; SVK: Transcriptomic Data analysis; UAB: Experiment, Pathway analysis; DTS: Transcriptomics data acquisition and supervision of analysis; SC: Animal Experiments, Analysis, and Interpretation of Results; AK: Conceptualization and Experimental design, Reagents, Analysis and Interpretation of Results, Manuscript writing, Supervision of the overall project work. Acknowledgement This research was supported under the Council of Scientific and Industrial Research (CSIR) Major lab projects [NCP/MLP0139 to A.K.). The authors acknowledge Shashikant Patel for providing critical comments to improve the final manuscript. In addition, the authors would like to specially acknowledge N. Sai Ram of the Centre for Cellular and Molecular Biology (CCMB), Hyderabad, for the maintenance and care of animals throughout the study period; Mahesh Anumalla and Dr. V. Venugopal Rao for the technical assistance in biochemical assays. Data Availability All the data generated as well as analyzed in this study are included in this published article [and its supplementary information files]. Additionally, The reviewers may view the data GSE272983 at:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE272983 References Akram, M., & Hamid, A. (2013). Mini review on fructose metabolism. Obes Res Clin Pract , 7(2), e89-e94. doi:10.1016/j.orcp.2012.11.002. Chan, A. M. L., Ng, A. M. H., Mohd Yunus, M. H., Idrus, R. B. H., Law, J. X., Yazid, M. D., et al. (2021). Recent Developments in Rodent Models of High-Fructose Diet-Induced Metabolic Syndrome: A Systematic Review. Nutrients , 13(8). doi:10.3390/nu13082497. Chen, D., Wang, H., Chen, J., Li, Z., Li, S., Hu, Z., et al. (2018). 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Supplementary Files SupplementaryFinal.docx Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2025 Read the published version in Metabolic Brain Disease → Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviews received at journal 25 Nov, 2024 Reviews received at journal 19 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 15 Nov, 2024 Submission checks completed at journal 15 Nov, 2024 First submitted to journal 01 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5373067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382991527,"identity":"56be9d67-01b8-49fd-8b59-6503e490d4fa","order_by":0,"name":"Sachin Singh","email":"","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":false,"prefix":"","firstName":"Sachin","middleName":"","lastName":"Singh","suffix":""},{"id":382991528,"identity":"71f3d8a0-233a-41c1-9a60-8a41ac39f809","order_by":1,"name":"Nitesh Kumar Singh","email":"","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":false,"prefix":"","firstName":"Nitesh","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":382991529,"identity":"c50c7c21-27a2-424a-bb78-825ecf84fbe1","order_by":2,"name":"SriVidya Kottappali","email":"","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":false,"prefix":"","firstName":"SriVidya","middleName":"","lastName":"Kottappali","suffix":""},{"id":382991530,"identity":"17957ec5-22e5-4675-b47b-1cf246f4f2f5","order_by":3,"name":"Unis Ahmad Bhat","email":"","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":false,"prefix":"","firstName":"Unis","middleName":"Ahmad","lastName":"Bhat","suffix":""},{"id":382991531,"identity":"047b87c0-9814-47c9-9d28-a5d8f2fbb994","order_by":4,"name":"Divya Tej Sowpati","email":"","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"Tej","lastName":"Sowpati","suffix":""},{"id":382991532,"identity":"a9d348ef-c1fa-49c5-b30e-7ead4650fce5","order_by":5,"name":"Sumana Chakravarty","email":"","orcid":"","institution":"CSIR-Indian Institute of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Sumana","middleName":"","lastName":"Chakravarty","suffix":""},{"id":382991533,"identity":"70740bd9-7256-44a8-826b-7b2bbdc5f99b","order_by":6,"name":"Arvind Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACHjBpA2cxsBGpJY10LYcRWggC/p4zpht+7jkvzz/t7AHmioo6Bj7pBvxaJM72mN3seXbbcMbtvATGM2cOM7DJHCBgzXkesxs8B24nGEjnGDA2th1gYJNIwK9DHqjl5p8D56Ba/tUR1mIAdNhtngMHoFoamAlrMTxzrOy2zIFksF8ONhw7zENQi9yZ5G033xywk+efnXvwYUNNnZz8DAJaUMABBuJjZxSMglEwCkYBPgAAeE9BB49Hn84AAAAASUVORK5CYII=","orcid":"","institution":"Centre for Cellular and Molecular Biology","correspondingAuthor":true,"prefix":"","firstName":"Arvind","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-11-01 11:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5373067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5373067/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11011-025-01648-0","type":"published","date":"2025-06-17T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70313434,"identity":"87d3ad33-cc5c-4d1a-8495-a4e08b6f6fe1","added_by":"auto","created_at":"2024-12-02 04:54:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":783941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe schematic diagram represents the experimental strategy\u003c/strong\u003e (a) Experimental timeline (b) Strategy for Neuropsychiatric behavior evaluation (C) Chronic Unpredictable Mild Stress paradigm (CUMS) stressor\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/7065f46758a431d16f8bc164.jpg"},{"id":70312864,"identity":"68bc3738-f44f-4e49-a337-45bf1de17882","added_by":"auto","created_at":"2024-12-02 04:46:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1659344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement of the body weight and various biochemical parameters of the C57 animals fed a high fructose diet for 46 weeks (control (n=20) and high fructose diet (n=20):\u003c/strong\u003e(a) The graph represents the mean body weight of the animals measured at different time points. Animals were kept on the control diet (N=20) and 60% high fructose diet (N=20). (b) Animals fed a high fructose diet showed early signs of aging (appearance of relatively more grey hair) when compared with control mice at 44 weeks of age. EchoMRI whole body scanning of the animals (c) Fat mass (d) Lean mass performed at 44 weeks of age. Comparison of the biochemical parameters of the animals fed a high fructose diet with control animalsat 44 weeks of age (e) total cholesterol (f) High-Density Lipid (HDL) (g) (Low-density Lipid (LDL) (h) Serum Glutamic Pyruvic Transaminase (SGPT) (i) Serum Glutamic Oxaloacetic Transaminase (SGOT) represent (j) Bilirubin (k) Creatinine (l) Alkaline Phosphatase (m) Oral Glucose Tolerance test (OGTT) was performed on the animals on control diet and 60% high fructose diet versus animals on the control diet at 44 weeks of age. Glucose was measured at 6 hours of fasting, then at 30, 60, 90, and 120 minutes followed by the oral administration of the glucose bolus dissolved in normal saline solution (20% glucose solution) at doses of 2g/kg of body weight. (The Y-axis represents glucose concentration (mg/dl) and the X-axis representsa time interval). The bar represents the mean±SD of the group, and symbols denote individual values (the number of animals varies from 8 to 20 ; p-values less than 0.05 are considered significant differences).\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/acf660b9d6713e3a73df0553.jpg"},{"id":70313433,"identity":"2bb2ab7e-dc80-483e-956c-75e11258045d","added_by":"auto","created_at":"2024-12-02 04:54:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOral Glucose Tolerance Test (OGTT) was performed on High fructose and control diet-fed animals, followed by the Chronic Unpredictable Mild Stress (CUMS) paradigm at 54 weeks of age:\u0026nbsp;\u003c/strong\u003eThe high fructose (Hfr) and control diet-fed animals were divided into two groups following Chronic Unpredictable Mild Stress (CUMS) paradigm namely (I) Control Unstress (CoUST), (II) Control Stress (CoST), (III) High-fructose Unstress (HfrUST), and (IV) High fructose stress (HfrST) group. (a) OGTT was performed on the CoUST (n=8), CoST (n=8), HfrUST (n=8), and HfrUST (n=8). The Blood glucose level was measured from tail vain after 6 hours of fasting and then at 30, 60, 90, and 120-minute time intervals followed by the oral administration of the glucose bolus dissolved in normal saline solution (20% glucose solution) at doses of 2g/kg of body weight. (b) The blood glucose levels at the 120\u003csup\u003eth\u003c/sup\u003e minute were compared, which reflects the physiological response of the HfrST group of the animals was significantly reduced in comparison to the other group of animals including HfrST and HfrUST, CoST. (c)Comparison of theArea Under Curve (AUC) between different groups. (The bar shows the mean±SD, the p-value shows the level of significance, and statistical analysis was performed using ANOVA and Tukey's\u0026nbsp;\u003cem\u003epost hoc\u003c/em\u003e\u0026nbsp;test).(d) Comparison of the OGTT between HfrST and CoST groups. (Multiple t-tests were performed (p\u0026lt;0.05 was considered significant).\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/c953c22b73c1389bcaab79a8.jpg"},{"id":70312859,"identity":"e18747c4-bc4c-4b8d-ab6a-490d86a12e80","added_by":"auto","created_at":"2024-12-02 04:46:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":505810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChronic unpredictable mild stress paradigm (CUMS) induces neuropsychiatric behavior perturbations in different groups (CoUST, CoST, HfrUST, and HfrST) (n=8):\u003c/strong\u003e(a) Force swim test (FST) and (b) Tail suspension test (TST) were conducted to evaluate the depressive-like behavior of the mice.Novel Object Recognition Test (NORT) was conducted to assess the effect of CUMS on the cognitive ability of the mice (c) Recognition index (RI) (d) and Discrimination Index (DI) were calculated using equations3 and 4 respectively. The working memory of the mice was evaluated after 1 hour of training by replacing the familiar object with a novel object. The bar shows the mean±SD of the group and the symbol denotes the individual value, the p\u0026lt; 0.05 is considered as a level of significance. Statistical analysis was performed by using ANOVA and Tukey's \u003cem\u003epost hoc\u003c/em\u003e test.\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/17d1566b02a24302ff1221a7.jpg"},{"id":70312865,"identity":"c2ba5b05-1364-4161-a03e-23fada33a571","added_by":"auto","created_at":"2024-12-02 04:46:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1177288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic profiling of the prefrontal cortex region of the mice revealed differentially expressed pathways and molecules underlying due to the CUMS and high fructose diet:\u003c/strong\u003e(a)The Principal Component Analysis (PCA) of the Differentially expressed genes (DEGs) RNA-seq data of the prefrontal cortex region of the mouse brain. The different groups (I) Control unstressed (CoUST, n=3), (II) Control Stressed (CoST, n=3), (III) High fructose Unstressed (HfrUST, n=3), (IV) High fructose Stressed (HfrST, n=3). The PCA plot displays the characteristics of samples based on gene expression, where each dot represents a sample, with three biological replicates corresponding to each group. Three biological replicates correspond to each group. The X-axis represents the first principal component (PC1), which explains 49% of the variance, and the Y-axis represents the second principal component (PC2), which explains 15% of the variance. (b) Gene Ontology (GO) analysis was performed on the Differentially Expressed Genes (DEGs). The Y-axis represents the top enriched pathways and the X-axis represents the groups. The Statistical criteria for the DEGs werelog2 fold change =0.3; and p-adjusted values \u0026lt;0.05. \u0026nbsp;the size of the circle is directly proportional to the number of gene count; and the color indicates the −log10 (\u003cem\u003ep\u003c/em\u003e-value) of GO terms. (c) The Venn diagram showed the exclusive and overlap of DEGs, on comparison between different groups (p\u0026lt;0.05 and log 2-fold change is set at 0.3). (d) Kyoto Encyclopedia of the Genes and Genome (KEGG) pathway analysis of the DEGs between different groups(p\u0026lt;0.05 and log 2-fold change is set at 0.3). The Y-axis represents the number of pathways and the X-axis represents the enrichment factor, indicating the proportion of the annotated gene to all genes in that pathway.\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/05126b10150180bd50bb9e1f.jpg"},{"id":70313435,"identity":"e4003a91-1ce8-47db-9beb-f5e1144c7958","added_by":"auto","created_at":"2024-12-02 04:54:33","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1442707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression level of genes involved in different pathways\u003c/strong\u003e; such as inflammatory, Epigenetic regulator, apoptosis, and signaling pathways. Control unstressed (CoUST, n=8), (II) Control Stressed (CoST, n=8), (III) High fructose Unstressed (HfrUST, n=8), (IV) High fructose Stressed (HfrST, n=8). The expression levels of genes were measured using quantitative polymerase chain reaction using cDNA and gene-specific primers. The bar shows the mean±SD of the group while symbols show the individual. (Statistical analysis t-test was performed; p\u0026lt;0.05, considered as level of the significance).\u003c/p\u003e","description":"","filename":"Fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/00eff7310c5043010227385a.jpg"},{"id":70312862,"identity":"b00168ec-49c5-4b1e-aa8a-6b18d86b8f80","added_by":"auto","created_at":"2024-12-02 04:46:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":610971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of the high fructose diet on the expression level of the genes implicated in different pathways\u003c/strong\u003e; such as Wnt/beta-catenin, neurotransmitter synthesis\u0026amp; signaling, angiogenesis, and neuroinflammation. The expression levels of genes were measured using quantitative polymerase chain reaction using cDNA and gene-specific primers. The bar shows the mean±SD of the group while symbols show the individual. (n=8; p\u0026lt;0.05, considered as the level of the significance) when were compared with each other.\u003c/p\u003e","description":"","filename":"Fig7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/c19b93a4b8895842b7a1ef3e.jpg"},{"id":85231394,"identity":"0aea5dd0-fcea-408d-bed5-ff49955ff311","added_by":"auto","created_at":"2025-06-23 16:07:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8710050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/18545322-c323-432e-9cdb-7ddd3c4d00dd.pdf"},{"id":70312866,"identity":"9dc0a7dd-3526-4db5-b065-c5404dfbffd7","added_by":"auto","created_at":"2024-12-02 04:46:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5485017,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5373067/v1/1c7329a150b7b791d6e6d93e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating altered neural molecular mechanisms in mice using transcriptomics underlying metabolic disorders induced cognitive and depressive disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic disorders (MetDs) is a condition characterized by a combination of symptoms that gradually impair an individual's overall well-being. Approximately 25% of the global population, regardless of age or gender, suffer from MetDs (Marengo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). MetDs is associated with several risk factors including hypertension, cardiovascular diseases, dyslipidemia, and non-alcoholic fatty liver diseases. (Marengo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), atherosclerosis, cancer (Gallagher and LeRoith, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), cerebrovascular (Hariharan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) chronic inflammation, and neuropsychiatric disorders (van Sloten et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The factors that contribute to MetDs can differ from person to person and may be influenced by environmental exposure or genetic traits. Unhealthy habits, a sedentary lifestyle, poor dietary choices, family history, socioeconomic status, and education are all potential factors that can lead to the development of MetDs. It is important to note that the genetic makeup of living individuals is a result of dietary habits that go back thousands of years, as mutations take place over many thousands of years. Therefore, the increase in fructose consumption poses a significant challenge to our conservative genes, and current habits can disrupt the balance of our body functions leading to disease. These limitations are more concerning when we consider that the sudden increase in sugar consumption after industrialization has been accompanied by a remarkable decrease in physical exercise.\u003c/p\u003e \u003cp\u003eThe growth of MetDs is fueled by the widespread availability of easily accessible, high-calorie food from global franchises, in recent times, fructose has become a popular sweetener in processed foods and soft drinks, often marketed as a \"safe and healthy\" option. However, new studies have shown that fructose is a significant contributor to metabolic disorders in humans (Lyssiotis and Cantley, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gomez-Pinilla et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fructose is usually added in addition to generic sugar, although fructose is one of the three common sugars, it is not directly involved in most metabolic processes to generate energy (ATP). Fructose is directly absorbed in the intestinal lumen, where it is transported by Glucose transporter 5 (GLUT5) (DeBosch et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) from the luminal side and Glucose transporter 2 (GLUT2) (Leturque et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) from the basolateral side, subsequently through the portal circulation, fructose transported to the liver, where its uptake and metabolism in hepatocyte were facilitated through Glucose transporter 8 (GLUT8) (DeBosch et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Approximately 50% of fructose is converted into glucose, ~\u0026thinsp;15 to 20% is converted into fatty acids, and the remaining 15 to 20% of fructose is converted into hepatic glycogen. The three key enzymes namely (I) fructokinase C (FFKC) also named ketohexokinase (KHK) catalyze phosphorylation of the fructose into Fructose-1P (II) subsequently, aldolase B enzyme converts Fructose-1P into di-hidroxyacetone-phosphate (DHAP) (III) Thiokinase (TKFC) responsible for the conversion into the glyceraldehyde-3-phosphate.It is important to note that, these Trioses are also involved in regulating lipid synthesis, glycolysis,glycogenesis, and gluconeogenesis (Akram and Hamid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The molecular mechanisms behind high fructose-induced metabolic disorders (MetDs) and brain disorders have been limited by conventional approaches that focus on isolated molecular events. This has led to delays in major advances. Because of these limitations, nutrient-based prevention and treatment strategies for common complex disorders have been hindered.Our study uses systems nutrigenomics to unveil the complex molecular interactions influenced by a high fructose diet. Our aim to uncover the potential molecular mechanisms by examining the effects of fructose diet on psycho-neuropathogenesis, transcriptomic approach was utilized. The outcome of the studyleads to better insight into the novel molecular mechanisms underlying prolonged high fructose-induced MetDs-mediated neuropsychiatric disorders. Our study could pave the way for developing effective strategies to alleviate common human diseases.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cp\u003e All the experimental procedures with mice were approved by the Institutional Animal Ethics Committee (CCMB/IAEC/33-2021) of the CSIR-Centre for Cellular and Molecular Biology (CCMB), and conducted in accordance with guidelines established by the Committee for Control and Supervision of Experiments on Animals (CCSEA), Ministry of Fisheries, Animal Husbandry and Dairying, Government of India. ARRIVE guidelines were followed for the preparation of the manuscript. C57bl/6 mice-Ncrl (will be called C57 henceforth) were procured from the Charles River Laboratories USA) and bred and maintained in the CSIR-CCMB Animal House Facility, Hyderabad. 3 to 4 mice were housed in each individually ventilated cage system with a 12h light/12h dark cycle, temperature (23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C), relative humidity (60%), and ad libitum access to food and water. Two-month-old animals were randomly divided into two groups followed by the baseline behavior experiments for depression, anxiety, and cognitive disorders and biochemical parameters including fasting glucose, triglycerides cholesterol, etc. One group of animals was fed on a 60% High Fructose Diet (Hfr) Composition D11707 (modified AIN \u0026minus;\u0026thinsp;76A) (Modified AIN-76A Rodent Diet with 65%kcal% Fructose) and another group of animals was fed a Control diet (D11708B). Hfr diet and control diet obtained from Research Diet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://researchdiets.com/en/search?q=D11708B\u003c/span\u003e\u003cspan address=\"https://researchdiets.com/en/search?q=D11708B\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The animals were fed high high-fructose diet throughout the experiment (56 weeks) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Animals after being subjected to the 10 days chronic unpredictable mild stress paradigm at 52 weeks were grouped as (I) High fructose Unstressed (HfrUST) (II) High fructose stressed (HfrST) (III) Control Unstressed (CoUST) (IV) Control stressed (CoST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical analysis:\u003c/h2\u003e \u003cp\u003eThe serum profile was done using TransAsia ERBA EM 200 automatic robotic spectrophotometry, a fully automated, random access, and discrete clinical chemistry analyzer. The following serum parameters were assayed (I) Triglyceride (TG 440, Cat No # XSYS0041), (II) Cholesterol (CHOL 5x50; Cat No. # BLT00034; CHOL 1000; Cat No. # BLT00035, CHOL250; Cat No.# BLT00036), (III) HDL DIRECT; HDL C 160, Cat No. # XSYS0043, HDL C 360 XL-1000, Cat No. # XSYS0078) (IV) ALT/SGPT (ALT/GPT30, Cat No# XSYS0017; ALT/GPT 564 XL-1000; Cat No# XSY0074), (V) AST/GOT (AST/GOT330 Cat No. # XSYS0016; AST/GOT 564 XL-1000, Cat No# XSYS0073), (VI) Bilirubin (BL00011) (VII) Creatinine (CREA 275, Cat No. # XSYS0024, CREA 564 XL-1000, Cat No. # XSYS0076), (VIII) Alkaline Phosphatase (ALP110; Cat No. # XSY0002) and (IX) UREA(UREA 1000, Cat No. # BLT 00060, UREA 250, Cat No. # BLT 00061). Glucose was measured by Accu-Chek Active (Roche, India).\u003c/p\u003e \u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eAnxiety-like behavior\u003c/b\u003e:\u003c/div\u003e \u003cp\u003eMice were assessed for anxiety-like behavior by Open Field Test (OFT), which is a very common and straightforward test to asses anxiety and exploratory-like behavior in mice (Gould et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Prut and Belzung, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). OFT was conducted in an arena of a rectangular wooden box (35 X 45 X 30 cm), the arena was virtually divided into the central and peripheral zones. One day before test mice were habituated to the arena, each mouse was allowed to explore the arena for 5 minutes, where time spent by mice in each zone was measured using Ethovision 3.1 (Noldus, Netherlands). The percent time spent in the central zone was calculated as follows\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left\\{\\right[Time\\:sepnt\\:in\\:the\\:central\\:zone9\\left(s\\right)/Total\\:time\\:i.e\\:300\\:s\\left]\\:X\\:100\\right\\}\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTested mice spending less time in the central zone as compared to the control is considered anxiety-like behavior.\u003c/p\u003e\n\u003ch3\u003eDepression-like behaviour:\u003c/h3\u003e\n\u003cp\u003eMice were assessed for depression-like behavior by the Tail Suspension Test (TST) (Steru et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Cryan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). TST is based on the assumption that under stressful conditions animals will try to escape. The test involves the suspension of the mice by their tail in three-sided wooden small chambers, and their body suspended in the air and facing downwards. Mice are suspended for 6 minutes till immobility where they are unable to escape or hold any surface, the tape should be applied at 3/4th position from the base of the tail, and suspend the mice by placing the free end of the tape on the bar. During the test agility and immobility of the animal were video recorded by the camera. At the end of the session return the animals to their home cage and tape remove gently.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\left\\{\\right[Time\\:sepnt\\:in\\:the\\:immobility\\:stage\\left(s\\right)/Total\\:time\\:i.e\\:360\\:s\\left]\\:X\\:100\\right\\}\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMemory assessment:\u003c/h3\u003e\n\u003cp\u003eTo assess the cognitive ability of the mice Novel Object Recognition Test (NORT) was performed(Ennaceur and Delacour, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In brief, mice were initially habituated for one day in an empty arena. During the training period mice were allowed to explore the two identical objects placed at the opposite corners in a rectangular wooden box (35 X 45 X 30 cm) for 5 minutes. The mice were returned to their cage for one hour after training, while one of the familiar objects was replaced with a novel object, and the mouse was allowed to explore the object again for 5 minutes. The Recognition Index (RI) of the mice for the novel object was calculated using Eq.\u0026nbsp;(3) where \u003cem\u003eTn\u003c/em\u003e and \u003cem\u003eTf\u003c/em\u003e are time spent with the novel object and familiar object respectively(Soni et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:RI=\\left(\\frac{Tn}{Tn+Tf}\\right)x100\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe discrimination Index (DI) of the mice was calculated using Eq.\u0026nbsp;(4) where Fn and Ff are the frequency of visiting the novel object and familiar object.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:DI=\\left(\\frac{Fn}{Ff+Fn}\\right)x100\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eOral Glucose Tolerance Test (OGTT)\u003c/h3\u003e\n\u003cp\u003eThe Oral Glucose Tolerance Test (OGTT) is a highly sensitive and specific test used to determine glucose intolerance, which can be shown by post-challenge glucose excursion. We measured 2-hour plasma glucose levels, a criterion for glucose intolerance on the OGTT (Sakamoto et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sakaguchi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The mice were fasted for 6 hours before the oral glucose tolerance test. The glucose bolus was administered orally in a saline solution (20%) at a dose of 2g/kg body weight. The postprandial glucose Area Under Curve (PG-AUC) was calculated using the trapezoidal rule to approximate postprandial glucose (PG) levels. The PG levels were measured every 30 minutes. The PG level at X minute was defined as PG (X min). The reference PG (X) and PG (AUC) were calculated.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:PG\\:\\left(AUC\\right)mg-\\frac{h}{dl}=\\frac{1}{4}*(PG\\left(0\\right)+PG\\left(30\\:minute\\right)*2+PG\\left(60\\:minute\\right)*3+PG\\left(120\\:minute\\right)*2\\:(5)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSacrificing the animals:\u003c/h2\u003e \u003cp\u003eThe mice were euthanized by cervical dislocation 24 hours after the last behavioral test. The brain was immediately removed from the skull and rinsed in ice-cold, sterile 1x PBS. The brain was sliced (1 mm thickness) on a mouse brain matrix (Zivic rodent brain slicer matrix). The prefrontal cortex was microdissected andsnap-frozen in liquid N2, followed by storage at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA isolation from the prefrontal cortex region of the brain:\u003c/h3\u003e\n\u003cp\u003eRNA was isolatedusing a \u003cem\u003emir\u003c/em\u003eVana\u0026trade; isolation kit (Cat No. #AM1560; Thermo Fisher Scientific) as per the manufacturer\u0026rsquo;s protocol.To prepare the RNA samples, 10x DNase I buffer and one unit of DNase I enzyme (New England Biolabs) was added to a maximum of 5 \u0026micro;g RNA, for DNase treatment. The samples were incubated at 37\u0026deg;C for 15 minutes and then the enzyme was inactivated at 70\u0026deg;C for 15 minutes. After that, the DNase-treated RNA was quantified by measuring the absorption at 260 nm using a NanoDrop 2000 spectrophotometer. Each biological replicate had 3\u0026micro;g of RNA, with RNA Integrity Numbers (RIN)\u0026thinsp;\u0026gt;\u0026thinsp;9 that were sent for sequencing at the CCMB RNA-Seq Facility.\u003c/p\u003e\n\u003ch3\u003emRNA library preparation:\u003c/h3\u003e\n\u003cp\u003eThe MGIEasy RNA Library Prep Set (MGI) was used for library preparation following the manufacturer\u0026rsquo;s instructions. Initially, 500 ng of total RNA was used, and the rRNA was depleted using an MGIEasy rRNA depletion kit.Following rRNA depletion, samples were fragmented and reverse transcribed. The second strand was then synthesized and converted into cDNA. The kit provided DNA Clean Beads for DNA purification, followed by end repair and A-tailing. The samples were then barcoded, adaptor-ligated, and subjected to purification. Adaptor-specific primers were used for amplification, and quantification was done using a Qubit dsDNA high-sensitivity kit from (Thermo Scientific). The size of the sample fragments was determined using a 4200 TapeStation (Agilent). Denaturation and circularization of 1pMol dsDNA were performed to generate single-stranded circular DNA, followed by the creation of DNA Nano Balls using Rolling cycle amplification. The DNBs were sequenced on an MGISEQ-2000 sequencer (MGI) using the PE100 recipe, after being placed on a patterned flow cell.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis:\u003c/h2\u003e \u003cp\u003eThe sample quality was checked with FastQC. MGI adapters and low-quality reads were removed from raw sequencing reads using cutadapt. Reads with quality scores less than 20 and smaller than 36 bp were discarded. The processed reads were then mapped to the mouse genome mm10 using hisat2 with default parameters. Uniquely aligned reads were counted using the feature counts of the Subread package. There were 55487 genes in the gtf file, downloaded from Ensembl, for which we had count information. Genes with a total read count of 10 across allthe samples were removed resulting in 31299 genes for further analysis. The analysis of Differentially Expressed Genes (DEGs) was carried out using DESeq2. Genes with adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log\u003csub\u003e2\u003c/sub\u003e Fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5 were considered differentially expressed. For the PCA plot and heat map, the raw read counts were log normalized, available with the DESeq2 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis:\u003c/h2\u003e \u003cp\u003eFor functional enrichment analysis, clusterProfiler was used for GO term enrichment. We only used the Biological process for GO term enrichment analysis. Similar enriched terms were further merged using the \u0026lsquo;simplify\u0026rsquo; function of cluster Profiler with a similarity cutoff set to 0.7. \u0026lsquo;p-adjust\u0026rsquo; was used as a feature to select representative terms and \u0026lsquo;min\u0026rsquo; was used to select features. \u0026lsquo;Wang\u0026rsquo; was used as a method to measure similarity. ClusterProfiler was also used for the KEGG pathways enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eValidation of RNA seq data throughgene expression analysis::\u003c/h2\u003e \u003cp\u003eA set of mice, CoUST (n\u0026thinsp;=\u0026thinsp;8), CoST (n\u0026thinsp;=\u0026thinsp;8), HfrUST 9n\u0026thinsp;=\u0026thinsp;8), and HfrST (n\u0026thinsp;=\u0026thinsp;8) were used for the gene expression analysis. In brief, mice were euthanized by cervical dislocation and brain tissues were micro-dissected. Total RNA was isolated from the prefrontal cortex region of the mice using mirVana\u0026trade; (miRNA Isolation Kit, without phenol; catalog number #: AM1561). The cDNA was synthesized using the prime scriptTM strand cDNA synthesis kit as per manufacturer protocol (TAKARA, Catalogue # 6110A). The quantitative polymerase chain reaction (qPCR) was performed using a specific target gene primer set (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and TB green premix Ex TaqTM II green master mix TIi RNase plus (TAKARA, Catalogue# RR820A) as per the manufacturer\u0026rsquo;s protocol. The Polymerase Chain reactions (PCR) were set up in triplicates in the MicroAmp optical 384 well plate (Applied Biosystems) in ViiATM7 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). β-actin and TBP (TATA-box binding protein) were used as housekeeping genes in different experiments for normalization. The data were analyzed using the ΔΔCt approach and normalized to the β-actin mRNA level (Khandelwal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eAll the statistical analysis was carried out using The GraphPad Prism software (Version 8.0.2 San Diego, USA). The significance of the difference was determined between the two groups using a 2-tailed Student\u0026rsquo;s T-test. All values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered a significant difference from each other. The significance of the difference in the data sets involving 4 groups and 2 variables was determined using One-way ANOVA Tuckey\u0026rsquo;s post-hoc test, assuming an interval of 95% confidence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The data resulting in a p-value less than 0.05 were considered significantlydifferent from each other.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTo begin with, the animals were randomly assigned to two groups: (I) the control chow diet group (Control) and (II) the high fructose diet group (Hfr), and before putting the mice in group ii on the prolonged Hfr diet, the baseline parameters were measured. No significant difference was observed in any of the parameters studied (FigS1.). Mice on prolonged periods (44 weeks) of Hfr diet intake failed to show diabetes-like phenotype but developed other MetDs such as hyperlipidemia and reduced lean mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMice on Hfr diet developed some of the Metabolic Disorder (MetDs) and premature aging-like phenotype:\u003c/h2\u003e \u003cp\u003eThere was no significant difference in the body weight between the control group and the Hfr diet group till50 weeks of age. However, animals in the Hfr diet group exhibited a significant reduction in body weight once subjected to chronic unpredictable mild stress (CUMS) for 10 days i.e. at 56 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea.). EchoMRI scanning of the entire body showed no significant difference in fat mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec.),although the animals on the Hfr diet exhibited a significant decrease in lean mass (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed.).The biochemical analysis ofserumcollected at 44 weeksrevealed that the animals on the Hfr diet had significantly higher levels of triglycerides (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee.), total cholesterol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef.), High-Density Lipoprotein (HDL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg.), and Low-Density Lipoprotein (LDL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh.), indicating hyperlipidemia.\u003c/p\u003e \u003cp\u003eAnimals on Hfr diet also showed a significantly high concentration of serum glutamic pyruvate transaminase (SGPT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei.) andserum glutamic oxaloacetic transaminase (SGOT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej.) levels, indicating compromised liver function. The alkaline phosphatase level was significantly high in Hfr group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em.) and so was the level of creatinine, the markers of kidney function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el.) in the Hfr diet group,thus indicatingaffected kidney function.\u003c/p\u003e \u003cp\u003eUnlike earlier reports of Hfr diet-induced type 2 diabetes (T2D), our study failed to exhibit hyperglycemia phenotype following prolonged Hfr diet intake (data not shown). Even the oral glucose tolerance test (OGTT) showed no body insulin response against the sugar bolusbetweenthe control and Hfr group of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003en.). Thus, the phenotype characterization revealed that though the animals on a prolonged Hfr diet failed to develop diabetes, they developed other MetDs such as reduced lean mass and hyperlipidemia.\u003c/p\u003e \u003cp\u003eThe most remarkable finding we have is that the animals on a prolonged Hfr diet started showingvisible signs of premature aging, such asthe grey, ruffled, and lusterless appearance of hairs, the hallmark of aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.).\u003c/p\u003e \u003cp\u003eThus, the animals on a prolonged Hfr diet developed some MetDs phenotypes, even though not T2D-like phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eChronic Unpredictable Mild Stress (CUMS)induced impaired glucose tolerance in mice on Hfr diet:\u003c/h2\u003e \u003cp\u003eChronic stress is one of the causative factors for T2D. Since the mice on a prolonged Hfr diet failed to show an increase in blood glucose level or diabetes-like phenotype, a 10-dayCUMS paradigm was used to see if it can precipitate the T2D-like phenotype too, in mice on the Hfr diet. At the end of CUMS paradigm, OGTT was performed. Unlike the negative OGTT result we got before subjecting the animals to CUMS exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003en.), post-CUMS OGTT data analysis showed that the high fructose stressed animals (HfrST) developedmild T2D-like phenotype, albeit other groups of animals such as (I) (HfrUST), (II) (CoUST) and (III) (CoST) failed to develop the T2D-like phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea.). Further to corroborate the development of the T2D-like phenotype, we separately performed statistical analysis using one-way ANOVA multiple comparison t-test on the blood glucose levels at 120th minute that showed significantly high blood glucose level in HfrST in comparison to the other groups of animal (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.). Moreover, we compared area under curve (AUC) between HfrST and CoST groups that revealed that the AUC of the HfrST is significantly more than that of the CoST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u0026amp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eCUMS induced major depressive disorder (MDD) like-phenotype in mice on Hfr diet\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eTo monitor the appearance of the depressive-like behaviour induced in the animals on the Hfr diet due to the CUMS treatment, we performed Force Swim Test (FST) and monitored the immobility of the mice which revealed that the immobility in HfrST animals was significantly higher than the HfrUST (n\u0026thinsp;=\u0026thinsp;8;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001),CoST (n\u0026thinsp;=\u0026thinsp;8;p\u0026thinsp;\u0026lt;\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea.). Furthermore, we noticed that the CoST showed significantly more inactive duration than CoUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.02;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea.) suggesting that the CUMS treatment induced depression-like behavior in both groups irrespective of the diet.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we performed a Tail Suspension Test (TST) which is also frequently used to investigate depressive disorders. Similarly,in agreement with the previous observation we observed HfrST spent significantly more inactive duration than HfrUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.). Interestingly, CoST also spent more inactive duration than CoUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.) suggesting the CUMS treatment-induced depression-like behaviour in both groups irrespective of the diet. However, no significant difference was observed between HfrST andCoST groups. The bar represents the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of the group and the p-value shows the level of significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCUMS induced memory or cognitive impairment-like phenotype in mice on Hfr diet:\u003c/h2\u003e \u003cp\u003eThe cognitive ability of the mice was assessed by the Novel Object Recognition Test (NORT) in which calculation of the Recognition Index (RI) revealed the total time spent by the animals with novel objects during the test. There was no significant difference in Recognition Index (RI) between HfrST and HfrUST (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec.). Moreover, there was a significant differencebetween CoST and CoUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec.) group. Additionally, the RI of the HfrUST was significantly less than the CoUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec.).\u003c/p\u003e \u003cp\u003eThe discrimination index (DI) tells about theability of the animal to differentiate between familiar objects and novel objects. DI score of the HfrST was significantly less than the HfrUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed.). Similarly the DI score of the CoST was significantly lower thanCoUST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed.). An additional observation was noticed that DI score of HfrST was significantly less than the CoST (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.02) revealed that HfrST animals spent significantly less time than the CoST animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed.). Furthermore, we noticed that the DI score of the HfrUST significantly less than the CoUST animals (n\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.03) suggesting the effect of a high fructose diet on the cognitive ability of the animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUncovering the molecular mechanism underlying Hfr diet and CUMS induced neuropsychiatric disorderslike phenotype:\u003c/h2\u003e \u003cp\u003eMice were fed a high fructose (Hfr) diet for almost 52 weeks. Then they were subjected to chronic unpredictable mild stress treatment (CUMS) to induce the T2D-like phenotype, followed by behaviour analysis which revealed that these mice developed various neuropsychiatric disorders like phenotypes, including depressive and cognitive disorders, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have shown that the prefrontal cortex region of the brain is most consistently affected bydepressive disorders (Pizzagalli and Roberts, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To uncover the underlying molecular mechanisms, transcriptional profiling of the prefrontal cortex region was done using RNA-Seqon the three biological replicates from each group. The quality check (QC) analysis demonstrated low technical variability across samples with high coverage (40\u0026nbsp;million paired reads per sample). The results of a principal component analysis (PCA) indicated a clear-cut separation between the four groups (PC1\u0026thinsp;=\u0026thinsp;49%, PC2\u0026thinsp;=\u0026thinsp;15% variance) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea.). As anticipated, there was a noticeable variance was observed at the transcriptome level, particularly in response to a high fructose diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea.). Interestingly, both stressed groups, regardless of the diet, i.e., the high fructose stressed group (HfrST) and the control stressed group (CoST), were relatively closer to each other (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea.). We conducted a Differential Expression Analysis using all the samples, taking Stress and a high fructose diet as covariance. This led us to identify in total 702 differentially expressed genes (DEGs) and transcripts (at log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.3, Padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the CoST group and CoUST including 259 upregulated genes and 443 downregulated genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The comparison between HfrUST and HfrST revealed 248 DEGs, including 103 upregulated and 145 downregulated genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As expected, given their close proximity on the PCA plot, only 16 DEGs were observed between the HfrST and CoST groups, with four upregulated and 12 downregulated genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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 total Differential Expression of the Genes (DEGs) in the prefrontal cortex region while comparing the different groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal DEGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpregulated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDownregulated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHfrUST vs CoUST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHfrST vs HfrUST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoST vs CoUST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHfrST vs CoST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\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\u003eA comparison between CoUST and HfrUST was performed to elucidate the effect of a high fructose diet. A total of 572 DEGs were found, including 289 upregulated genes and 283 downregulated genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComparative Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genome(KEGG) Pathways analysis\u003c/strong\u003e \u003cp\u003eGene Ontology (GO) analysis of the overall DEGs transcripts was performed using ShinyGo (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb.). The upregulated genes transcript of the comparison between HfrST vs HfrUST enriched in the following pathways, including epithelial tube morphogenesis, vasculogenesis, morphogenesis of the branching structure, regulation of the osteoblast differentiation, sensory organs morphogenesis, stem cell differentiation, neural crest cell differentiation, NOTCH signaling pathways, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb.). Similarly downregulated genes of the comparison between HfrST vs HfrUST were enriched in vasculogenesis, regulation of epithelial cell differentiation and proliferation, regulation of epidermis development, reactive oxygen species metabolic process, intrinsic apoptotic pathways etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb.).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOnly one pathway transcriptional misregulation in cancer was revealed by KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed.). Furthermore, we also performed an Ingenuity Pathway Analysis (IPA) by QIAGEN on the upregulated DEGs transcript of the comparison between HfrST and HfrUST. The analysis revealed that several pro-inflammatory cytokines like interleukins (IL6, IL1A, IL12B), Toll-like receptor-2 (Tlr2), Interferon-gamma inducible protein 16 (IFl16), and chemokine C-C motif chemokine ligand-4 (Ccl4) and many more are implicated in metabolic and neuropsychiatric (refer to Fig S3 for details).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr dietand CUMS together perturbed microRNA expression pattern\u003c/strong\u003e \u003cp\u003eIPA analysis on (HfrST versus HfrUST) revealedseveral microRNAs (miRNAs) including mir-30, mir-124 mir-126, mir-129, mir-130,mir-132, mir146, mir-148, mir-154, mir-155, and mir-221, mir-467 in network ( FigS3.). These miRNAs act as transcriptional regulators of the various proinflammatory cytokines, chemokines, and genes including IL-1, IL-6, IL-18, Tnfα, ICAM-1, and VCAM-1. The previous studies reported diverse roles of these microRNAs such asmir-30 family implicated in several neuropsychiatric disorders (Kumar and Li, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khandelwal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); mir-124 involve in regulating neuronal cell proliferation and differentiation in the adult brain (Han et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); mir-129 regulates the glucose metabolism (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); mir-130 has been implicated in the endothelial permeability of the brain\u0026rsquo;s microvasculature (Y. Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); mir-148 reported to regulate the glial cell proliferation through EGFR/MAPK signaling pathway(M. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); mir-154 involved in glial cell proliferation (Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); mir-467; has been implicated in hyperglycemia-induced inflammation (Gajeton et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (FigS3.). We observed the presence of the lethal 7 (let-7) microRNA which is the largest family of microRNA regulating the expression of various genes of different pathways such as AMPK, mTOR, etc which are involved in regulating various biological processes such as cancer, aging, differentiation, neuronal cell proliferation, and brain metabolism (FigS3.).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eValidation of the expression of genes perturbed due to the Hfr diet and CUMS\u003c/strong\u003e \u003cp\u003eFirst of all, we tried to validate the DEGs that were uncovered by our transcriptomic study. Interleukin 6 (IL6) is an inflammatory cytokine that has been widely demonstrated that play a role in neuroinflammation. We observed the transcriptomic expression level was significantly increased in the animals on high fructose diet exposed to the stress paradigm (HfrST) in comparison to the other group of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea.). However, the expression of the interleukin 18 (Il 8) did not change between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eWe did not observe any changes in the histone class III (HDAC III) transcriptome, however, Reddy et al. 2018 reported a change in the expression of the histone class III genes in rats on a high fructose diet. Therefore, we decided to examine the expression of HDAC class III in the prefrontal cortex region of mice.Histone deacetylase class III (Sirtuin 1\u0026ndash;7) is known as a metabolic sensor among them we checked mRNA expression of the sirtuin 1, sirtuin 6, and sirtuin 7. There was no significant difference was observed in the expression of sirtuin 1 and sirtuin 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec \u0026amp; e). However, the m RNA expression of the sirtuin 6 (SIRT 6) was significantly reduced in the high fructose stressed (HfrST) group than the control stressed group (CoST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Furthermore, We noticed that the High fructose unstressed (HfrUST) group also showed a significant reduction in comparison to the control unstressed groups (CoUST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed.).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfrand CUMS perturbed expression of genes regulatingautophagy\u003c/strong\u003e \u003cp\u003eAutophagy is an important lysosome-mediated highly regulated conserved catabolic process responsible for clearing the damaged organelles and maintaining intracellular homeostasis. Metabolic disorders are characterized by metabolic disarrangement and intracellular stress (oxidative stress, endoplasmic stress, and inflammation) due to the accumulation of damaged organelles (Kitada and Koya, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We validateda few genes that are commonly used as markers for autophagy such as Bax is a member of the Bcl-2 family of proteins and a core regulator of autophagy (Karch et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) mRNA expression of the Bax gene was significantly reducedin the animals on high fructose stressed (HfrST) in comparison to the high fructose unstressed (HfrUST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef.) and similarly control stressed (CoST) in comparison to the control unstressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef.).However, the expression of the beclin-1 gene was not perturbed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). The Leucyl-tRNA synthetase-2 (Lars2) enzyme, catalyzes the aminoacylation of the mitochondrial tRNA, and mRNA expression was significantly reduced in CoST thanin CoUST (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg.).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr and CUMS affect synaptic plasticity and neuronal disorders\u003c/strong\u003e \u003cp\u003eThe mRNA expression of theAdamts 19 (Disintegrinand Metalloproteinase with Thrombospondin motifs), which is implicated in various biological functions such as cell adhesion, and extracellular matrix organization, the inflammatory responsewas downregulated in high fructose stressed (HfrST) compared to the Control stressed (CoST) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei.). Interestingly high fructose Unstress (HfrUST) also showed significantly reduced expression than control unstressed (CoUST) Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei.). The Apold1 gene encodes apolipoprotein L domain containing 1 (Vascular early response gene protein) and regulates several processes such as endothelial cell signaling, angiogenesis, and vascular function (Freson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) expression significantly decreased in the HfrST group of micein comparison to the CoST, and also, the expression of the gene was significantly decreased in the HfrUST group compared to the CoUST group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej.). The matrix metalloproteinases 2 (Mmp 2) gene plays an important role, in maintaining blood-brain integrity, and was found to be significantly reduced in the HfrUST groups in comparison to the CoUST group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek.). Surprisingly, it was noticed that expression was significantly increased\u0026thinsp;~\u0026thinsp;1.5 in HfrST than the HfrUST (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek.). We also observed that the mRNA expression of the Zink Finger protein 36 (Zfp36) was significantly reduced in the CoST than the CoUST (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek.) and a similar trend was noticed when a comparison was performed to CoUST and HfrUST also Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek.). Our finding indicates that the Hfr diet along with stress is able to attenuate the expression of various genes resulting in the onset of neuropsychiatric disorders.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomics approach uncovered the molecular response due to the Hfr diet:\u003c/h2\u003e \u003cp\u003eSince the Hfr diet itself induced MetDs such as reduced lean mass, hyperlipidemia, liver and kidney functions, and accelerated aging phenotype, which in turn also induced neurological disorder i.e. cognitive impairment, to uncover the underlying molecular mechanism the transcriptomic changes in the critical brain region PFC was analyzed. The Gene Ontology (GO) of the DEGs showed the enrichment of genes in different biological pathways implicated in neural aging and neurological disorders. The upregulated genes in HfrUST compared to CoUST animals were found enriched in pathways such as memory, axonogenesis, synapse assembly, regulation of the G protein-coupled receptor signaling pathway, regulation of synapse organization, regulation of synapse structure or activity, cell junction assembly, synaptic plasticity, regulation of neurogenesis, synaptic transmission glutamatergic, etc. However, the downregulated genes (HfrUST vs CoUST) (FigS2a.).\u003c/p\u003e \u003cp\u003eSimilarly, KEGG pathways analysis (log\u003csub\u003e2\u003c/sub\u003e FC\u0026thinsp;=\u0026thinsp;0.3, p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05) showed enrichment of pathways involved in the neuroactive ligand-receptor interaction, calcium signaling pathways, regulation of lipolysis in adipocytes, axon guidance, gap junction, proteoglycan in cancer, cAMP signaling pathways, chemical carcinogenesis receptor activation, thyroid hormone signaling pathways, morphine addiction pathways, etc(FigS3b.).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr diet perturbed genes expressions that regulate glucose homeostasis/ neurotransmitter synthesis\u003c/strong\u003e \u003cp\u003eThe Glycogen Synthase Kinase-3 beta ( Gsk3β) is a serine/threonine protein kinase that plays a role in various signaling pathways, including AMPK, Wnt/β-catenin, phosphoinositide 3-kinase (PI3K), mammalian target of rapamycin (mTOR), Ras/Raf/MEK/ ERK and NOTCH, etc. Gsk3β is involved in regulating metabolism and a cell cycle has been linked to neurological disorders such as mood disorders and bipolar disorders.mRNA expression of Gsk3β was reduced significantly in high fructose (HfrUST) compared to control (CoUST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). Glutamate decarboxylase (GAD1) is responsible for the synthesis of gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter(Mitchell et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reduced significantly in high fructose (HfrUST) compared to control (CoUST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). Similarly, Glutamate-ammonia ligase (GLUL) catalyzes the synthesis of glutamine from glutamate; its, expression was reduced significantly in mice on the Hfr diet(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). Interestingly, GABA-T expression, a mitochondrial enzyme primarily found in GABAergic neurons, which catalyzes the degradation of GABA into glutamate and succinic semialdehyde, significantly increased in mice on a high fructose diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). The alpha-2-adrenergic receptor (ADRA 2) is a type of adrenergic receptor that plays a role in regulating exocytosis and neurotransmitter cycling. The mRNA expression was significantly increased in mice on a high fructose diet compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr diet dysregulates transcription factors involved in angiogenesis, neurogenesis, \u0026amp; neuroplasticity\u003c/strong\u003e \u003cp\u003eThe cAMP-response element-binding protein (CREBP) is a transcriptional coactivator of many different transcriptional factors involved in regulating various cellular functions. mRNA expression was significantly increased in mice on the Hfr diet compared to the control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). Furthermore, Vascular endothelial growth factor (VEGF) has been implicated in angiogenesis (Melincovici et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and was significantly reduced in the high fructose diet group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.). Additionally, SOX9 and HES5 mRNA expression was significantly increased, while Nestin, ZNF7, and SP7 (Osterix) expression were significantly decreased in mice on the high fructose diet compared to control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea.).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr diet-induced neuroinflammation and innate immunity response\u003c/strong\u003e \u003cp\u003eChronic inflammation is a key feature of metabolic disorders that leads to the activation of cytokines, chemokines, and inflammasomes(Vandanmagsar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the current study, we found that the expression of the Nlrp3, TLR4Tumor necrosis factor 8 (Tnfrs8),, Cxcl12 (C-X-C motif Chemokine Ligand 12) which is produced by glial cells significantly increased in mice on the Hfr diet compared to the control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb.).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is well known that MetDs in the long run causes vascular complications (Rask-Madsen and Kahn, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), including cerebrovascular (Hanefeld et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which in turn appear to result inneurological (Farooqui et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and psychiatric disorders (Frisardi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, there is a dearth of reports on the molecular mechanisms involved in metabolic disorder-induced cerebrovascular (Iadecola and Gottesman, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and neuropsychiatric disorders (Kan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, our effort in this direction led us to uncover the molecular basis of cerebrovascular, neuroinflammatory, and neuroglial changes, while establishing a mouse model that mimics Hfr diet and/or stress-induced MetD and consequently develop neuropsychiatric disorders.\u003c/p\u003e \u003cp\u003eAs per our knowledge, the current study is one of its kind that unraveled the underlying neural molecular mechanisms using a high throughput transcriptomic approach, induced by lifestyle changes (Hfr diet +/- Stress) inone of the affected critical neural regions, the PFC. The analysis of the altered transcriptome data, listed as the differentially regulated genes (DEGs) between different groups in Tables S3 \u0026ndash; S8, and validation of several key genes of the major pathways affected, led us to suggest the reprogramming of the neuroglial molecular responses and the circuitry, by the prolonged Hfr diet intake and also due to the chronic 10-days stress component added on top of it.In this study, we showed that like the previous reports using a mouse model of MetDs, even the prolonged period (46 weeks) on theHfr diet could able to induce some of the MetDs-like phenotype in mice, such as increased levels of serum triglycerides, total cholesterol, low-density lipid, together with the reduction in lean mass and body weight. Surprisingly, hyperglycemia or T2D, the most common MetDs could not be induced in our mice unlike that reported by several studies. However, those studies used a rat model in which even a few weeks to months on the Hfr diet was enough to induce the MetDs phenotype (Chan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some of the studies that used mice had to add a high-fat diet, together with the Hfr diet to get the MetDs phenotype(Taskinen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study on mice could not replicate the T2D-like phenotype thatwe successfully modeled in the rat (Reddy et al 2016), where Sprague-Dawley rats developed MetDs, including hyperglycemia, in just 8 weeks on the HFr diet. It could be because the mice have a high basal metabolic rate, they are more active, running around, and hanging to the cage roof or cover than the sluggish rats in animal house cage conditions. This was the reason we continued feeding our experimental mice on the Hfr diet for almost a year and kept checking theirserum blood sugar levels. However, even though the mice did not develop hyperglycemia in the end, they developed other MetDs-like phenotypes such as hyperlipidemia and reduction in lean mass and body weight, which usually accompanies diabetes.\u003c/p\u003e \u003cp\u003eInterestingly, after just 10 days of exposure to CUMS, these mice on the Hfr diet showed reduced insulin response at the 120th minute in OGTT, compared to the animals on a normal chow diet also exposed to CUMS. Thus, our findings suggest that the Hfr diet for almost a year itself might not have induced a full-blown MetDs phenotype including T2D, but it made the mice susceptible to MetDs, and, another environmental challenge like chronic stress could easily induce the diabetes-like phenotype. It is noteworthy that the animals on a prolonged Hfr diet quickly developed insulin resistance when exposed to just 10 days of chronic mild stress, unlike the control mice on a normal chow diet upon exposure to the same stress paradigm.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr diet and CUMS exposure induceneuropsychiatric disorder-like phenotype\u003c/strong\u003e \u003cp\u003eThe chronic stress appears to induce severe changes in the brain of these mice on the Hfr diet, which we think had already become susceptible/vulnerable because of the prolonged Hfr diet-induced physiological and neural changes, including neuroinflammation and compromised neurotrophic support. This explanation is based onthe outcome of the analysis of the high throughput transcriptomic data between the two groups (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), which revealed severalpathways affected in the Hfr diet\u0026thinsp;+\u0026thinsp;Stress (HfrST) group compared to the Hfr diet\u0026thinsp;+\u0026thinsp;Unstressed (HfrUST) group of animals.Thus, the borderline MetDs itself was unable to cause enough changes in the brain and induce the neurological and psychiatric disorder-like phenotype in mice on the Hfr diet, except for the affected memory in the NORT task (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed.). But adding the stress component to mimic the modern-day lifestyle, triggered both mood disorders(depression, anxiety, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.) and further cognitive impairment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec\u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed.) like phenotype.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe depression and cognitive impairment phenotype as shown in HfrST mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.) also appeared in the Control Stressed (CoST) animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) even though these animals failed to develop insulin resistance. However, the animals of the HfrST group showed more severe neurological and psychiatric disorder phenotypes (degree of changes and degree of significance P values), compared to the CoST group of animals, asclearly evident in the results. Thedata suggest that prolonged intake of the Hfr diet made the animals more susceptible or vulnerable to chronic stress. The appearance of mild cognitive impairment and depression-like phenotype in the CoST animals exposed to just 10 days of stress could be attributed to their advanced age (~\u0026thinsp;56 weeks), as mice of this age are known to be more susceptible to stress. The physiological responses observed appear to depend on the age of the animals as well as the duration and concentration of the fructose intake.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh fructose diet-induced accelerated aging\u003c/strong\u003e \u003cp\u003eAnother highlight of the study is that a prolonged Hfr diet could induce an accelerated aging-like phenotype (ruffled and grey hair, reduced shining of the coat with rough texture(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.), in addition to causing some of the MetDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.) including hyperlipidemia. So, even though a diabetes-like hyperglycemic state was not achieved just by feeding mice on the Hfr diet, it appeared to have altered the physiological homeostasis indicating compromised health (as reflected by the affected liver and kidney functions in test results on the serum samples), in addition to the reduced lean mass. The altered physiology might have driven the animals to the accelerated aging-like phenotype, as evidenced by course and grey hair, compared to the smooth and soft one in control mice on the normal chow diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.). The lean mass (protein mass) was much reduced in mice on the prolonged HFr diet, compared to the mice in the control diet group, as shown by our EchoMRI findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed.), which is a hallmark feature of MetDs as well as aging, Aging is also known to be the result of built upof tissue inflammation and thus the Hfr diet-induced aging phenotype could well be the result of an accumulation of pro-inflammatory molecules, as we find these genes upregulated in the list of DEGs in the Hfr group of animals.The analysis of the transcriptomic data on the brain region studied revealed hundreds of DEGs in the HfrUST vs CoUST group, just due to the effect of diet change, and interestingly the Ingenuity Pathway Analysis (IPA) led to the enrichment of some of the pathways and networks that are usually associated with the cellular senescence and organismal aging. Some of the altered genes were the ones that control genomic integrity, DNA repair, cell cycle regulation, cancer, etc., which are typically associated with aging (see Tables S6 \u0026amp; S7).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh fructose diet aggravatedsystemic neuroinflammation\u003c/strong\u003e \u003cp\u003eInflammation is known to be associated with MetDs, and also with most of the neurological and psychiatric disorders. Our transcriptomic analysis revealed the upregulation ofseveral pro-inflammatory genes in the PFC region of mice from both HfrUST as well as HfrST groups (as shown clearly in the \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003eResults\u003c/span\u003e section and TablesS4 \u0026amp; S5), where the validated results of these DEGs in brain samples of the individual mouse is shown. Of these altered genes, some that cause neuroinflammation were upregulated even without stress by the Hfr diet itself; these were Toll-like receptor 4 (\u003cem\u003eTlr4\u003c/em\u003e), Nod-like receptor 4 (\u003cem\u003eNlrp4\u003c/em\u003e) and Tumor necrosis factor receptor superfamily 8 (\u003cem\u003eTnfrs 8\u003c/em\u003e). Under neuroinflammatory conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), TNF is released thereby enhancing the glial cell activation through various signaling pathways, including the activation of NFkB. These in turn, activate microglia and induce the production of proinflammatory cytokines such as IL6, IL1, and IFN-γ (Raffaele et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This suggests that the long-term consumption of high amounts of fructose can cause neuroinflammation inducing neuroglial changes leading to susceptibility to neuropsychiatric disorders. Chemokines too have a positive role in neuroinflammation. One of these, C-X-C motif ligand 12 (\u003cem\u003eCxcl12\u003c/em\u003e) plays a role by attracting the leukocytes from the blood-brain barrier (Li and Ransohoff, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Interestingly, the enhanced expression of the \u003cem\u003eCxcl12\u003c/em\u003e in the animals on a high fructose diet, which we validated too, suggests its role in increasing the inflammation in the PFC area of the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb.).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHowever, chronic stress-induced severe neurological and psychiatric disorder phenotype in mice on the prolonged Hfr diet appears to be due to a further increase in the levels of some other pro-inflammatory molecules (interleukins, cytokines, chemokines) and key inflammasomes (as listed in the DEGs in the corresponding tables). One of the inflammasomes Nlrp6, which was differentially downregulated in the HfrST vs HfrUST group (Table S4), is the novel and interesting finding of our study. The key inflammasomes \u003cem\u003eNlrp3\u003c/em\u003e and \u003cem\u003eNlrp4\u003c/em\u003e have been reported in earlier studies to be upregulated in inflammation conditions, but \u003cem\u003eNlrp6\u003c/em\u003e has been shown to work differently. Further studies will be required to find out why, unlike other inflammasomes, \u003cem\u003eNlrp6\u003c/em\u003e is downregulated in the brain region investigated in our study. Unlike the findings in earlier studies and that in our \u003cem\u003eLepr\u003c/em\u003e\u003csup\u003e\u003cem\u003edb/db\u003c/em\u003e\u003c/sup\u003e mice brain (unpublished finding from our lab), \u003cem\u003eNlrp3\u003c/em\u003e was not regulated in our transcriptomic data set from the Hfr mouse model. However, in an independent experiment, we could see the upregulation in the level of \u003cem\u003eNlrp\u003c/em\u003e3 in the prefrontal cortex region of the Hfr diet or Hfr diet\u0026thinsp;+\u0026thinsp;stress group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb.).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh fructose diet and CUMS affect various signaling pathways\u003c/strong\u003e \u003cp\u003eThe analysis of the transcriptome data also revealed the significant impact of the long-term Hfr diet on a number of critical cellular processes neurometabolism, pathways in cellular homeostasis, neuroinflammation, innate immune function, cell-cell communication, cell proliferation and differentiation, neuronal signaling, insulin signaling, GABA metabolism, Wnt signaling, Notch signaling, G protein coupled receptor signaling, S100 family signaling pathways, myelination signaling pathways, memory, axonogenesis, synapse assembly or structure, synaptogenesis, synapse organization signaling pathways, innate immunity, cerebrovascular, cell proliferation, JAK-STAT signaling, cardiomyopathy, transforming growth factor (TGF) beta signaling, mitogen-activated protein kinases (MAPK) signaling, platelet-derived growth factor (PDGF) signaling, vascular endothelial growth facotr, toll-like receptor (TLR) signaling, brain-derived neurotrophic factor (BDNF) signaling, pertubed in the prefrontal cortex of the mice on Hfr diet.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eCellular processes and signaling pathways affected in HfrST animals compared to HfrUST ones were as follows (Fig.S2) : FAK signaling, CREB signaling in neurons, S100 family signaling, cancer signaling, glioma invasiveness signaling, Hif1a signaling, G-protein coupled receptor signaling, synaptogenesis signaling, neuroinflammation signaling, myelination signaling, serotonin receptor signaling, attenuated antioxidant action of vitamin C signaling, NRF2-mediated oxidative stress signaling, senescence pathway, DNA damage-induced 14-3-3 signaling, PI3K/AKT signaling, Sirtuin signaling pathway, WNT/b Catenin signaling, Nitric oxide signaling, white adipose tissue brown signaling, acute phase response signaling, regulation of the epithelial Mesenchyme signaling, adipogenesis pathway, angiopoietin signaling, NFkB activation by viruses pathway, Toll-like receptor signaling, ferroptosis signaling, orexin signaling, embryonic stem cell signaling, synaptic long-term potentiation signaling, endothelin 1 signaling, neurovascular coupling signaling, cAMP-mediated signaling, and GABAergic receptor signaling.\u003c/p\u003e \u003cp\u003eOur study also revealed the broad effect of the high fructose diet on cell signaling pathways altered differentially in the brain region of the Hfr diet Stressed versus Hfr diet Unstressed group (HfrST vs HfrUST), compared to mice of the Control diet Stressed versus Control diet Unstressed group (CoST vs CoUST), as shown in Fig.S3. Most of the neuropsychiatric disorders are associated with altered neurotransmitter signaling and function. GABA is a major inhibitory neurotransmitter implicated in anxiety, depression, schizophrenia, and other neuropsychiatric disorders (Jewett and Sharma, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, reduced expression of the glutamic acid decarboxylase (\u003cem\u003eGad 1)\u003c/em\u003e in the animals on the Hfr diet exposed to stress, might be causing an attenuation in the production of GABA from L-glutamic acid, resulting in a depression-like phenotype in HfrST groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea.).\u003c/p\u003e \u003cp\u003eGSk3 β is a key molecule that mediates the regulation of PI3K/AKT/ or AMPK and Wnt signaling pathways, which are known to modulate neuronal cell proliferation, differentiation, migration, and plasticity. Since enzymes involved in cell survival and neuroplasticity are relevant to neurotrophic factor dysregulation, the PI3K/AKT/GSK3 pathway might act as an important signaling mechanism for neuroprotection in depression (Kitagishi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Our study revealed that there was a decrease in the mRNA expression of GSk3 β in the PFC region of the HfrST groups of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). This indicates that prolonged feeding of the high fructose diet appears to cause disruption of various signaling pathways in PFC resulting in the loss of cellular homeostasis, thus inducing the onset of neuropsychiatric disorders such as depression, anxiety, and cognitive loss in the animals' on high fructose diet.\u003c/p\u003e \u003cp\u003eWe validated a number of altered genes in the HfrST versus HfrUST group; these genes belong to the pathways that control not only inflammation and apoptosis but also angiogenesis and neurotrophic function, which several groups have reported to be associated with the etiopathology of the neuropsychiatric disorders (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Vascular endothelial growth factor (\u003cem\u003eVegf)\u003c/em\u003e is a potential angiogenic factor responsible for angiogenesis and has a neurotrophic effect too. Vegf expression in the brain is linked to neuroprotection, cognitive, and aging phenotype through the PI3K/AKT pathway (W. Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our finding of its attenuated expression in the prefrontal cortical region of the HfrST group (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) suggests the reduction in angiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), which might have caused perturbation of the cerebrovascular physiology leading to the neuropsychiatric disorder phenotype.\u003c/p\u003e \u003cp\u003eMetabolic disorders including diabetes, hypertension, and perturbed lipid profiles are known to activate various cellular processes such as oxidative stress, insulin resistance, and inflammatory pathways. Autophagy is a lysosomal-mediated degradation process that plays an important role in maintaining the homeostasis of the cellular metabolic process (Moulis and Vindis, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eBax\u003c/em\u003e is a BCL2-associated apoptotic regulator involved in regulating various cellular functions (L. Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). We observed that the expression of the \u003cem\u003eBax\u003c/em\u003e gene was reduced in the stressed group of animals (CoST) in comparison to the Control Unstressed (CoUST)ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), and HfrST in comparison to the HFrUST, thus suggesting stress-induced apoptotic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eHigh fructose and CUMS modulate the expression of transcription/ epigenetic factors\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eDysregulation of transcription factors\u003c/strong\u003e \u003cp\u003eDysregulation in the transcription regulatory network has been reported in some of the transcriptomic studies done on brain samples from rodent models of fructose-induced MeDs (Meng, 2016) (Table S11). Our data also corroborates this; several transcription factors (TFs) (Table S9) and epigenetic regulators (Table S10) were found altered in the Hfr diet and/or stress-induced cerebrovascular and neural changes. Many transcription factors are getting altered; some of these have been earlier reported, such as the ones belonging to the cAMP-CREB pathway involved in neuroplasticity regulation, cognition, and cognitive disorder, in addition to depression and related mood disorders (for reference see Table S8). One of the interesting families of transcription factors that were found altered in the stressed groups with or without the Hfr diet was the Tcf family; we found 3 members of this family getting dysregulated in the prefrontal cortex region, compared between different treatment groups (Table S8). It will be interesting in the future to investigate these family members of TFs as many members are affected. A few of these family members such as \u003cem\u003eTcf7l2\u003c/em\u003e, which is the strongest and most reliable signal for T2D found in human GWAS (Voight, 2010) (Table S8).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOne more highlighting feature of our study is the discovery of alternatively spliced variants, and dysregulation of some of the key splicing regulators and splice regulator binding proteins. The splicing regulators appear to be one such class. In one of the recent studies (Meng et al 2016) it was found that 20% of the genes altered in the hippocampus were at the isoform or alternative splicing levels rather than in the overall expression of genes. The authors report that fructose reprograms the rat brain network inducing cognitive disorder phenotype by engaging core TFs, epigenetic regulators like DNA methyltransferases, and splicing factors such as \u003cem\u003eBicc1, Prpf31\u003c/em\u003e, and \u003cem\u003eRbpms\u003c/em\u003e (Meng, 2016). Our data also showed dysregulation in one of the important Splicing factors \u003cem\u003eSrsf5\u003c/em\u003e, as shown by another group in fructose-induced changes in the brain as shown in Table S11 (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Another class of regulators we found altered in PFC in the Hfr group and also the Hfr Stressed group are the ones involved in epigenetic mechanisms of gene regulation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEpigenetic modulation\u003c/strong\u003e \u003cp\u003eEpigenetic changes are reversible and work via alterations in DNA and histone modifications and chromatin remodeling mechanisms (Table S9\u0026amp;S10). The dysregulation of epigenetic regulators and the epigenetic and transcription regulatory mechanism have been shown in recent years to play an important role in diverse stress-induced changes in the brain and in the etiopathogenesis of neurological and psychiatric disorders (Tsankova et al 2009). Unlike that shown in some previous reports and in our \u003cem\u003eLepr\u003c/em\u003e\u003csup\u003e\u003cem\u003edb/db\u003c/em\u003e\u003c/sup\u003emouse model, where many Sirtuins belonging to the epigenetic regulators of class III HDACs were found altered in the brain (unpublished finding from our lab), we could not find an alteration in any of the seven Situins in the RNA-Seq data in the mouse Hfr diet model that developed neuropsychiatric disorder like phenotype upon chronic stress exposure. However, we went ahead and mapped a few of the Sirtuins and found \u003cem\u003eSirt 6\u003c/em\u003e only to be dysregulated (downregulated), which is known for its neuroprotective role (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Since \u003cem\u003eSirt6\u003c/em\u003e negatively regulates genes that code for the pro-inflammatory cytokines and inflammasomes, we suggest that the downregulated \u003cem\u003eSirt 6\u003c/em\u003e might have driven the transcription level of these Sirt6 targets resulting in heightened neuroinflammatory condition. This might have affected the prefrontal cortical circuitry, leading to alteration in neuroglial response, neurogenesis, and neuroplasticity (as deduced from the corresponding pathway genes dysregulated in our data sets. This, in turn, led to the neuropsychiatric phenotype in mice on Hfr diet\u0026thinsp;+\u0026thinsp;stress, i.e. cognitive impairment and depressive disorder. In the \u003cem\u003eLepr\u003c/em\u003e\u003csup\u003e\u003cem\u003edb/db\u003c/em\u003e\u003c/sup\u003e mouse model studied by us, we found that \u003cem\u003eNlrp3\u003c/em\u003e and \u003cem\u003eNlrp4\u003c/em\u003e, the key inflammasomes, were highly upregulated (unpublished finding from our lab). However, in the transcriptomic data from our HfrUST and HfrSt groups, we could not find these genes regulated. Using the leftover RNA after making the library for RNA-Seq, we prepared the cDNA and ran qPCRs. The analysis revealed even the Nlrp3 was upregulated in our samples following the prolonged Hfr diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSince epigenetic mechanisms play an important role in gene-environment interaction and a number of these are found dysregulated in the brain regions of chronic stress-induced neuropsychiatric disorders models (Chakravarty S et al 2014 Int Rev Neurobiol), it is pertinent to look for the dysregulation in epigenetic regulators in PFC of animals from HfrST vs HfrUST and HFrUST vs CoUST groups. The ones found downregulated in the groups on Hfr diet and Hfr diet\u0026thinsp;+\u0026thinsp;Stress mice were histone lysine methyltransferases \u003cem\u003eEzh2, Dot1l\u003c/em\u003e, that target H3K27 di/tri methylation and K3K79 mono/di/tri methylation, respectively), and histone lysine demethylase \u003cem\u003eKdm6b\u003c/em\u003e that targets H3K27 di/tri methylation) (Table S9). Downregulation of \u003cem\u003eEzh2 (transcriptional repressor) and Dot1l\u003c/em\u003e (transcriptional activator) might have altered a large number of gene targets in the brain regions affected by MetDs and induced neuropsychiatric disorders (Table S9). These epigenetic regulators are known to regulate transcription and thus control processes as diverse as neuroglial response, development, cell cycle progression, somatic reprogramming, neural stem cell proliferation, differentiation, neurogenesis, and DNA damage repair.\u003c/p\u003e \u003cp\u003eThe involvement of the DNA methylation-based epigenetic mechanisms in the hypothalamus region of rats after the fructose diet-induced behavioural change, i.e. cognitive impairment, has recently been demonstrated that can regulate the transcriptome. Additionally, transcriptional regulators such as Atf3, Junb, Zbtb16, and Parp9, as well as microRNAs like rno-miR-421 and rno-miR-143, were found to co-occur with DNMTs, suggesting a transregulation mechanism (Meng, 2016) (Table S11). An increasing amount of evidence suggests that neurological and psychiatric disorders are influenced by epigenetics (reviewed in Tsankova, 2007).In particular, disruption in cell metabolism appears to be a key factor in epigenetic changes related to cognitive function (Tyagi, 2015).\u003c/p\u003e \u003cp\u003eFurthermore, Sirtuins (Sirt 1\u0026ndash;7) NAD+-dependent histone deacetylases of class III, are involved in regulating many important biological processes such as cell metabolism, cell senescence, proliferation, apoptosis, DNA repair and calorie restriction (Song and Kim, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eSirt 1\u003c/em\u003e and \u003cem\u003eSirt 6\u003c/em\u003e are reported to be involved in regulating the metabolism (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), apart from that expression of the \u003cem\u003eSirt 7\u003c/em\u003e was also attenuated in the striatum of the rat that developed metabolic disorder-induced psychiatric disorder (Reddy, 2016). In the current study, we found a decrease in \u003cem\u003eSirt 6\u003c/em\u003e expression in the prefrontal cortex region of mice on the Hfr diet exposed to CUMS, as shown. There were no significant changes in \u003cem\u003eSirt 1\u003c/em\u003e and \u003cem\u003eSirt 7\u003c/em\u003e expressions.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHfr diet and CUMS together induced neuropschyiatric disorder-like phenotype through various mechanisms\u003c/strong\u003e \u003cp\u003eAdditionally, the expression of the cytokine IL 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) appears to be involved in MetDs-induced neuroinflammation but no change was observed in the expression of the IL18 between the groups. Adamts19 plays a role in synaptic plasticity, neurodegenerative and neurological disorders; Apold, Mmp17, involved in angiogenesis, invasion, metastasis, and avoidance of immune surveillance, were found attenuated in the animals on the Hfr diet exposed to CUMS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).To sum up, our study concludes that the MetDs by Hfr diet for a prolonged period itself can bring about changes in brain reactions affecting neuroglial functions and neuroplasticity, by affecting inflammatory, neurotrophic, neurogenic, and vasculogenic Pathways.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eChronic stress on top of it appears to make these changes more severe and, additional alterations in some other genes and pathways thus cause more severe neurological and psychiatric phenotypes. So unlike previous studies, where metabolic disorder-induced vascular disorders or changes were focused on the cardiovascular system mostly, for the first time our study reflects on cerebrovascular changes, the change in the endothelium lining, endothelial cell biology, its maintenance by altering several genes involved in the cerebrovascular physiology and blood-brain barrier function. The leaky barrier thus allows the movement of some of the peripheral immune and/or inflammatory molecules to the brain parenchyma. Some of these molecules are involved in cerebrovascular and downstream pathophysiology, which we uncovered here, shown in the tables listing the differentially expressed genes (DEGs).\u003c/p\u003e \u003cp\u003eEarlier studies have also shown some of these changes, but for the first time, we are showing many other molecules dysregulated in the pathway.Ours is the first report of the prolonged Hfr diet +/- chronic stress-induced transcriptomic changes in the prefrontal cortex, one of the brain regions implicated in the rodent models of neuropsychiatric disorder-like phenotype. This investigation led us to get better molecular insight into the MetDs-induced changes in the brain.The outcome provides molecular evidence supporting the ability of fructose, or/and stress to disrupt the critical neural genes and the fundamental physiological processes; some of these genes are insulin, IL6, Nlrp3, Tlr4, CxCl12, Tnfrs8, Vegf, Crebp, Gsk3β, and Gad1, which are involved in various pathways including neurometabolism, neuroinflammatory signaling, vasculogenesis, neural cell proliferation, differentiation, etc. The majority of these pathways have been implicated in metabolic disorders and/ or neuropsychiatric disorders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eProlonged HFr diet intake induced not only the MetDs-like phenotype in mice, but also the aging-like phenotype. Further lifestyle changes such as chronic stress not only induced mild insulin tolerance but also induced neuropsychiatric disorder-like phenotype like cognitive decline and depression. The exposure to CUMS affected the expression of a number of critical genes in various neural and neurovascular pathways, including synaptic signaling, cytokine signaling, vasculogenesis, synaptic transmission, myelination, NFκB signaling, Toll-like receptor signaling, etc. in the prefrontal cortex region of the Hfr diet mice; that, in turn, might have triggered the severe neuropsychiatric disorder like phenotype. The outcome led us to better molecular insight into the MetDs-induced molecular changes in the brain. The outcome provides evidence supporting the ability of fructose, or/and stress to disrupt the critical neural genes and the fundamental physiological processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCUMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Unpredictable Mild Stress\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHfr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Fructose Diet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHfrUST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Fructose Unstressed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHfrST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Fructose stressed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCoUST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl Unstressed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCoST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl Stressed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Glucose tolerance test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA-Seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Glucose tolerance test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFinancial interests\u003c/h2\u003e \u003cp\u003eThe authors declare they have no conflict and financial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by funding from the Council for Scientific and Industrial Research (NCP/MLP0139).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSS: Experimental design, Animal experiments, Data acquisition, Analysis and Interpretation of Results, Manuscript writing; NKS: Transcriptomics Data Analysis; SVK: Transcriptomic Data analysis; UAB: Experiment, Pathway analysis; DTS: Transcriptomics data acquisition and supervision of analysis; SC: Animal Experiments, Analysis, and Interpretation of Results; AK: Conceptualization and Experimental design, Reagents, Analysis and Interpretation of Results, Manuscript writing, Supervision of the overall project work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported under the Council of Scientific and Industrial Research (CSIR) Major lab projects [NCP/MLP0139 to A.K.). The authors acknowledge Shashikant Patel for providing critical comments to improve the final manuscript. In addition, the authors would like to specially acknowledge N. Sai Ram of the Centre for Cellular and Molecular Biology (CCMB), Hyderabad, for the maintenance and care of animals throughout the study period; Mahesh Anumalla and Dr. V. Venugopal Rao for the technical assistance in biochemical assays.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data generated as well as analyzed in this study are included in this published article [and its supplementary information files]. Additionally, The reviewers may view the data GSE272983 at:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE272983\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkram, M., \u0026amp; Hamid, A. (2013). 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MiR-154 Functions as a Tumor Suppressor in Glioblastoma by Targeting Wnt5a. \u003cem\u003eMol Neurobiol\u003c/em\u003e, 54(4), 2823-2830. doi:10.1007/s12035-016-9867-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"metabolic-brain-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebr","sideBox":"Learn more about [Metabolic Brain Disease](https://www.springer.com/journal/11011)","snPcode":"11011","submissionUrl":"https://submission.nature.com/new-submission/11011/3","title":"Metabolic Brain Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metabolic disorders, chronic stress, Differentially Expressed Genes, Neuropsychiatric disorders","lastPublishedDoi":"10.21203/rs.3.rs-5373067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5373067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic disorders (MetDs)are growing at an alarming rate because of lifestyle changes and have cardiovascular and cerebrovascular consequences, in the long run resulting in neuropsychiatric disorders. However, there is a dearth of molecular studies that deal with the underlying neural mechanisms using relevant animal models of MetDs-induced neurological and psychiatric disorders. We modeled MetDs-like condition in C57BL/6 Ncrl mice by feeding a 60% high fructose diet (Hfr) for 56 weeks. Significant changes were observed in various MetD-related physiological parameters between the Hfr diet and the control group except for glucose intolerance. Prolong Hfr diet induced some of the metabolic disorder like phenotype including aging except type-2 diabetes. But 10 days of chronic unpredictable mild stress (CUMS) paradigm induced mild insulin intolerance in oral glucose tolerance test. Further the animals were found to develop neurological and cognitive impairment and major depressive disorder like phenotype. Transcriptomic analysis led to uncover underlying molecular changes into the prefrontal cortex region of mice. The pattern of differentially expressed genes (DEGs) was strikingly different in the Hfr group compared to the Ctrl group, thus correlating the phenotype, i.e. MetD-induced mood and cognitive disorders. Pathway analysis of the DEGs indicated perturbations in cellular metabolism, inflammation, innate immunity, neurogenesis, vasculogenesis, ion channels, and neuronal signaling. In addition, altered epigenetic regulators appear to mediate the stress-induced precipitation of metabolic and neuropsychiatric disorders. The outcome of our study supports the hypothesis of disease susceptibility due to lifestyle changes involving a high-calorie diet and chronic stress.\u003c/p\u003e","manuscriptTitle":"Elucidating altered neural molecular mechanisms in mice using transcriptomics underlying metabolic disorders induced cognitive and depressive disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 04:46:28","doi":"10.21203/rs.3.rs-5373067/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-26T16:34:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-26T03:21:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T08:49:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289306671977300132666838361180395488585","date":"2024-11-18T02:47:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173972017029779009651291560503382888304","date":"2024-11-18T01:51:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-17T22:38:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-15T18:23:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-15T18:23:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolic Brain Disease","date":"2024-11-01T11:44:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolic-brain-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebr","sideBox":"Learn more about [Metabolic Brain Disease](https://www.springer.com/journal/11011)","snPcode":"11011","submissionUrl":"https://submission.nature.com/new-submission/11011/3","title":"Metabolic Brain Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d1bc8b5-75d8-4047-a394-5364bc2e2f68","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:02:10+00:00","versionOfRecord":{"articleIdentity":"rs-5373067","link":"https://doi.org/10.1007/s11011-025-01648-0","journal":{"identity":"metabolic-brain-disease","isVorOnly":false,"title":"Metabolic Brain Disease"},"publishedOn":"2025-06-17 15:57:46","publishedOnDateReadable":"June 17th, 2025"},"versionCreatedAt":"2024-12-02 04:46:28","video":"","vorDoi":"10.1007/s11011-025-01648-0","vorDoiUrl":"https://doi.org/10.1007/s11011-025-01648-0","workflowStages":[]},"version":"v1","identity":"rs-5373067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5373067","identity":"rs-5373067","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}