Male Macaca fascicularis of about 18 years old were bought from Huazhen Biosciences Co., Ltd. (Guangzhou, China). All macaques were born from 16^th August 2001 to 11^th February 2004 (Supplementary Table S1) and grown in the animal rooms maintained at 16 ∼ 26°C and 40–70% room humidity on a 12-h/12-h light-dark cycle. All monkeys were housed individually in standard stainless steel cages (80 cm × 80 cm × 85 cm) and the assigned diets were supplemented twice a day at 8:00 (100 g) and 16:00 (150 g), plus 100 g of apple at 12:00. The animals were allowed to access water freely. The macaques of both NC and sDM groups were fed with the normal food (19.26% protein, 5% fat, no sugar, and no cholesterol), whereas the HFHS monkeys were grown up on the same diet until switching to the high-fat and high-sugar (HFHS) food (≧ 30% sugar, ≧ 15% fat, ≧ 10.5% protein, and ≧ 0.5% cholesterol) on 1^st March 2019 (Figure 1A). After 15 months of food change, blood and urine samples were collected from all macaques after 12–14 h of fasting for examination and the intravenous glucose tolerance test (IVGTT) was conducted. The routine hematological examination was performed on the Hematology Analyzer pocH-100iV (Sysmex, Kobe, Japan). Insulin was quantified using the Cobas E411 Analyzer (Roche, Basel, Switzerland), and other blood biochemical analyses, including the measurement of blood sugar, were done by the Cobas C311 Analyzer (Roche, Basel, Switzerland). Urinary analysis was done in the local hospital Guangzhou Conghua District Hospital of Traditional Chinese Medicine. IVGTT was done in the morning after 12–14 h of fasting. In IVGTT, 50% (w/v) glucose solution was injected into the limb vein of the anesthetized monkeys (1 ml/kg body weight) immediately after blood collection (0 min) from another limb. Then the blood samples were collected after 1, 3, 5, 10, 20, 40, and 60 min for both sugar and insulin analysis.

All macaques were then sacrificed via overdose anesthesia at about 18 years old, or 1.25 years after the switch of foods (Figure 1A; Supplementary Table S1). In brief, the animals were fasted for more than 12 h before euthanasia and transferred to the operating room with no other experimental animals present. Ketamine hydrochloride was injected intramuscularly at a dose of 10–25 mg/kg, followed by the intravenous injection of 4% sodium pentobarbital solution (100 mg/kg) after animal stabilization. Two veterinarians checked and confirmed the death of the monkey independently before the dissection. The liver, PB, and HPVB samples were collected by veterinarians. Both blood samples were centrifuged at 3,000 rpm for 10 min at 4°C immediately to separate sera. All samples were frozen in liquid nitrogen and stored at −80°C until use. The study protocol received prior approval (license number 2020025) from the Institutional Animal Care and Use Committee of Guangzhou Huazhen Biosciences.

The hydrophilic and hydrophobic compounds were extracted using methanol/water and MTBE/methanol/water solvent systems, respectively. Samples were first thawed on ice. To extract hydrophilic metabolites from the tissue samples, 1 ml of methanol/water (7:3, v/v) was added to 50 mg of the liver, and homogenized with steel balls for 3 min at 30 Hz, followed by 1 min of a vortex. The homogenate was then centrifuged at 12,000 rpm for 10 min at 4°C to collect the supernatant. Hydrophobic compounds were extracted from another 50 mg using a slightly modified protocol. Briefly, homogenization was done with 1 ml of MTBE/methanol (10:3, v/v) and 100 µL of water was mixed with the homogenate to extract before centrifugation. For the sera of PB and HPVB, 3 volumes (v/v) of methanol and a mixture of MTBE and methanol (10:3, v/v) were whirled with the serum samples for 3 min, followed by centrifugation at 12,000 rpm for 10 min at 4°C. All collected supernatants were dried and store at −80°C until LC-MS/MS analysis. Internal standards were dissolved in the solvents before extraction.

The tissue sample was ground into a powder in liquid nitrogen and mixed with 4 volumes (v/w) of lysis buffer (8 M urea and 1% Protease Inhibitor Cocktail). The mixture was sonicated three times (30 s each time, 2 s gap) on ice using a high-intensity ultrasonic processor (Scientz, Ningbo, China) and centrifuged at 12,000 ×g for 10 min at 4°C. The supernatant was collected and the protein concentration was determined with a BCA kit following the manufacturer’s protocol.

After that, trichloroacetic acid (TCA) was added to the same volume of each sample to a final concentration of 20%. After 1 min vortex, the samples were placed still at 4°C for 2 h, followed by centrifugation at 4,500 ×g for 5 min. The protein pellets were washed with pre-cooled acetone three times and completed dried with a nitrogen stream. The dried pellets were subsequently resuspended in 200 ml of TEAB with ultrasonication. Trypsin was then added at a 1:50 (trypsin:protein, w/w) ratio for digestion overnight. The tryptic peptide solution was reduced with 5 mM dithiothreitol (DTT) for 30 min at 56°C, followed by 15 min of alkylation with 11 mM iodoacetamide (IAA) at room temperature in darkness. The peptides were then separated with C18 cartridges (Waters, Milford, MA, United States) and subjected to LC-MS/MS analysis.

Total RNA was extracted using Trizol (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s instructions. In Brief, about 60 mg of tissues were ground into a powder in liquid nitrogen, and homogenized in the RNA extraction buffer for 2 min, followed by resting horizontally for 5 min. The mix was then centrifuged for 5 min with 12,000 ×g at 4°C, and the supernatant was transferred into a new tube with 0.3 ml chloroform/isoamyl alcohol (24:1). The mix was shacked vigorously for 15 s and centrifuged at 12,000 ×g for 10 min at 4°C. The upper aqueous phase was transferred into a new tube and mixed with an equal volume of isopropyl alcohol, followed by centrifugation at 13,600 rpm for 20 min at 4°C. Then the RNA pellet was washed twice with 1 ml 75% ethanol and centrifuged at 13,600 rpm for 3 min at 4°C to desert the residual ethanol. After 5–10 min of air dry in a biosafety cabinet, the RNA pellet was dissolved in 25–100 µL of DEPC-treated water, followed by qualification and quantification using a NanoDrop and Agilent 2100 bioanalyzer (Thermo Fisher Scientific, MA, United States), respectively.

Both hydrophilic and hydrophobic extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, Shim-pack UFLC SHIMADZU CBM A system, SHIMADZU, Japan; MS, QTRAP® System, Sciex, Washington, United States). ACQUITY UPLC HSS T3 C18 column (1.8 µm, 2.1 mm*100 mm, Waters, Milford, MA, United States) was used for UPLC, working with the following parameters: the Column temperature of 40°C, a flow rate of 0.4 ml/min, and injection volume of 2 μL. The analysis of hydrophilic metabolites used water containing 0.1% formic acid (A) and acetonitrile containing 0.1% formic acid (B) as mobile phases, with a gradient program (V/V) as follows: 95:5 at 0 min, 10:90 at 11.0 min, 10:90 at 12.0 min, 95:5 at 12.1 min, and 95:5 at 14.0 min. For lipidomic analysis, the samples were injected onto a Thermo C30 column (2.6 μm, 2.1 mm × 100 mm). Mobile phase was composed of acetonitrile/water (60/40, v/v) containing 0.04% acetic acid and 5 mmol/L ammonium formate (A) and acetonitrile/isopropanol (10/90, v/v) containing 0.04% acetic acid and 5 mmol/L ammonium formate (B). The gradient program (V/V) was as follows: 80:20 at 0 min, 50:50 at 3.0 min, 35:65 at 5.0 min, 25:75 at 9.0 min, 10:90 at 15.5 min, and 80:20 at 16.0 min. The metabolome and lipidome were measured on a mass spectrometric system with triple quadrupole (QqQ) scans combined with LIT scans in both positive and negative ion modes under the control of Analyst 1.6.3 software (Sciex, Washington, United States). The ion spray voltage (IS) of the ESI source was 5500 and −4500 V for positive and negative modes, respectively. Source temperature was set at 500°C, ion source gas I (GSI), gas II (GSII), curtain gas (CUR) at 55, 60, and 25.0 psi, respectively, and collision gas (CAD) high. Instrument tuning and mass calibration in QqQ and LIT modes were performed with 10 and 100 μmol/L polypropylene glycol solutions, respectively. Specific sets of multiple reaction monitoring (MRM) transitions of various periods of retention time were monitored according to an in-house library of metabolites.

NanoLC-MS/MS was performed by coupling a NanoElute and timsTOF Pro (Bruker, Billerica, MA, United States). The tryptic peptide sample was dissolved in 0.1% formic acid and 2% acetonitrile (solvent A) and directly loaded onto the NanoLC-MS/MS platform. A constant flow rate of 450 nL/min was used for the LC system and the solvent B of mobile phase was 0.1% formic acid in acetonitrile, with a gradient from 4 to 22% in 0–70 min, 22–30% in 70–84 min, 30–80% in 84–87 min, and holding at 80% for the last 3 min. The peptides were subjected to a glass capillary ion source with an electrospray voltage of 2.0 kV for tandem mass spectrometry analysis. The data were collected in parallel accumulation–serial fragmentation (PASEF) mode in which one MS full scan followed by 10 MS/MS scans with 30 s dynamic exclusion was used. Both the precursor ions and fragment ions were analyzed with high-resolution TOF. The peptide precursors of changes from +1 to +5 were detected in a scan range from m/z 100 to 1700.

Oligo (dT)-attached magnetic beads were applied to purify mRNA from the total RNA. Purified mRNA was fragmented into small pieces with fragment buffer. Then the first-strand cDNA was generated using random hexamer-primed reverse transcription, and the second-strand cDNA was synthesized. A-Tailing Mix and RNA Index Adapters were then added by incubation. The cDNA fragments were amplified by PCR, followed by purification by Ampure XP Beads, and then dissolved in EB solution. The product was validated on an Agilent Technologies 2100 bioanalyzer for quality control. The double-stranded PCR products were heated to denature and circularized by the splint oligo sequence to construct the final library. The final library was amplified with phi29 to make a DNA nanoball (DNB) which had more than 300 copies of one molecular. After that, DNBs were loaded into the patterned nanoarray and single end 50 base reads were generated on the BGIseq500 platform (BGI, Shenzhen, China).

Integration and correction of the peak areas corresponding to the targeted metabolites from the LC-MRM-MS/MS data were done with MulitQuant (version 3.0, Sciex, Washington, United States), followed by normalization against the total peak areas measured from each sample. The Automatic method of MultiQuent was used with the parameters specified as following: Gaussian smooth width: 0 points; RT half window: 30 s; min peak width: 2 points; min peak height: 800; noise percentage: 70.0%; baseline sub window: 2 min; peak splitting: 2 points; RT tolerance: 0.2 min.

The LC-MS/MS data for proteomics were identified and quantified using the MaxQuant search engine (Ver 1.6.6.0). MS2 were searched against the Macaca fascicularis (UP000233100_9541, 46,259 sequences) from the Uniprot database (https://www.uniprot.org/) concatenating with the contaminants database. Target-decay search strategy was applied with decoy databases of reversed sequences. The search parameters were specified as following: cleavage enzyme, Trypsin/P; missing cleavages, up to 2; the mass tolerances for precursor ions, 20 ppm in both the first search and the main search; the mass tolerance for fragment ions, 20 ppm; fixed modification, Carbamidomethyl on cysteine; variable modifications, acetylation at protein N-terminus, oxidation on methionine, and deamidation on both N-terminus and glutamine. False discovery rate, or say FDR, for the peptide-spectrum match (PSM) and protein identification were both 1%. The quantification method was set as LFQ.

After filtering the low-quality reads with SOAPnuke (Ver 1.5.2), sequencing alignment and mapping to the Macaca fascicularis genome (GCF_000364345.1) from NCBI (https://www.ncbi.nlm.nih.gov) were done by Bowtie (Ver 2.3.4.3) using the --sensitive preset and HISAT2 (Ver 2.1.0) with the default setting, respectively. The program RSEM (Ver 1.3.1) was then used to quantify the reads and the gene expression level was calculated by the FPKM method. The differentially expressed genes were given by DESeq2 according to q-value = 0.05.

Student’s t-test and calculation of Pearson correlation coefficients were done in R using the STATS package (v3.6.2). Orthogonal partial least squares discriminant analysis (OPLS-DA) was implemented using the R package ROPLS (v1.20.0), and the variables of log2 ratio outside ±0.6 and variables importance in projection (VIP) > 1 were considered as significantly changed variables. Principal component analysis (PCA) was performed by MetaboAnalyst5.0 (https://www.metaboanalyst.ca) without filtering. The data were first normalized by sum across each sample, then z-scored for each metabolite (i.e., centralized by mean and divided by the standard deviation) before PCA. Gene Ontology (GO) and pathway enrichment were analyzed through the GO Consortium (http://geneontology.org) and KEGG websites (https://www.genome.jp/kegg/), respectively.

Type 2 DM symptom was found in three macaques when they were about 15 years old. We conducted IVGTT on the monkeys and found their ability to secret insulin in response to glucose was significantly reduced compared to age-matched normal monkeys. In addition to the sDM group consisting of these three, other six carb-eating macaques of a similar age and housed at the same farm were recruited as the control in the study. A random half (three ones) of these control macaques were kept growing under the unchanged conditions, termed as normal control (NC). The other three have been fed with the HFHS diet for 1.25 years before sacrifice (Figure 1A), which was named the HFHS group. To profile the health conditions of each individual in detail, we have comprehensively performed clinical tests on each crab-eating macaque (Macaca fascicularis) ( Supplementary Table S1). The diabetes of the sDM monkey was characterized by the highest blood sugar, urine sugar, and glycated hemoglobin (HbA1c) levels (Table 1). As compared with the NC group, the sDM monkeys had dramatically increased urine sugar (677 fold, Figure 1B), and higher levels of both fasting blood sugar (4.6 fold, Figure 1C) and HbA1c (2.7 fold, Figure 1D). The average values of these three indicators of the HFHS ones were also higher than that of the NC group (2.8, 1.5, and 1.2 fold rise in urine sugar, blood sugar, and HbA1c, respectively) although only the increase of HbA1c in the HFHS group showed significantly (Figure 1D). The HFHS-caused alterations in the blood (p = 0.06) and urine (p = 0.1) sugars had larger p-values (Table 1), partially due to the limited number of subjects in each group. According to the definition from the American Diabetes Association, the criteria for diabetic humans are fasting blood glucose ≥7 mM and HbA1c ≥ 6.5% (American Diabetes Association, 2018). The two values of the sDM macaques (17.9 ± 1.0 mM fasting blood glucose and 10.4 ± 1.6% HbA1c) were much higher than these standards although both levels of NHPs are typically lower than humans (Lei et al., 2020). The HFHS group, on the other hand, had a fasting blood glucose of 5.8 ± 1.2 mM and an HbA1c level of 4.7 ± 0.2%, lower than but very close to the boundaries. Furthermore, the HFHS group showed a significant increase in body weight but that of sDM macaques were similar to the normal ones (Figure 1E). Over 1 year of the HFHS feeding seemed to result in a level of insulin significantly higher than that observed in the sDM monkeys (Figure 1F). IVGTT was applied before sacrifice to measure the response of the monkeys to blood glucose. As a result, escalated circulating glucose failed to induce the production of insulin in all sDM macaques, resulting in a prolonged last of high blood glucose (Figures 1G,H). These results indicated that the sDM individuals were at the later stages of type 2 DM with significantly reduced insulin secretion. The HFHS macaques showed enhanced secretion of insulin, but glucose metabolism was still slower than that of the normal ones, suggesting insulin resistance in these monkeys. The Oil Red O Staining of the liver tissues demonstrated the accumulation of lipids in the liver of the HFHS-fed monkeys. Although much less than that of the HFHS group, more lipid droplets were formatted in the liver of sDM macaques, as compared with that of the NC group (Figure 1I). These test results indicated that the three monkeys in the sDM group were suffering from diabetes mellitus, whereas in the HFHS group, the subjects who had developed some lesions similar to that of DM symptoms were prediabetes. We, therefore, profiled both hydrophilic and hydrophobic metabolites in the liver, PB, and HPVB of all nine individuals (Figure 1A). In parallel, we also extracted RNA and protein from all liver samples and measured transcriptomic and proteomic abundance profiles using RNA-seq and shotgun proteomics approaches, respectively (Figure 1A).

Using the UPLC-MS/MS workflow, we identified 1,082 metabolites from all 27 samples (Supplementary Table S2). Slight higher numbers of metabolites were detected from the serum samples. Principal component analysis (PCA) of all samples illustrated a significant difference between the circulating metabolome and that from the liver tissues (Figure 2A). Also, the blood metabolomes of all groups had specific fingerprints but relatively small variants between PB and HPVB samples were observed (Figure 2A). Consistently, higher levels of correlations were observed between blood samples and between liver samples, but not between any blood and the liver sample, indicating that the profiles of detected metabolites captured the signature of the whole metabolome in different samples (Supplementary Figure S1). The inter-group difference in serum metabolome was much larger than that between the PB and HPVB of the same group of macaques (Supplementary Figure S2). In the NC monkeys, the PB and HPVB metabolomes had a difference of only 92 metabolites (Figure 2B; Supplementary Figure S3). More metabolites varied between PB and HPVB in the HFHS (223) and sDM (139) groups (Figure 2B; Supplementary Figure S2). On the other hand, over 400 metabolites in either PB or HPVB varied with the disease condition and/or the switch of diet (Figures 2C–E; Supplementary Figure S4; Supplementary Table S2), indicating the significant difference in the metabolism of these three groups. The PB and HPVB metabolomes shared a majority of altered metabolites. Although the metabolites changed in the liver had relative unique profiles, over half of them also overlapped those altered in sera (Figures 2C–E; Supplementary Figure S4; Supplementary Table S2). The altered metabolites are mainly enriched in the various classes of lipids as well as amino acids and peptides (Figure 2F). The spontaneously developed diabetes mellitus caused significant changes in the liver, characterized by the altered lipids of diacylglycerols, monoacylglycerols, fatty acyls, sphingomyelins, glycerophosphocholines, glycerophosphoethanolamines, glycerophospholipids, and glycerophosphoserines. Intriguingly, only polyunsaturated fatty acids increased in the liver as well as the blood of the sDM monkeys, whereas elevated levels of saturated, mono- and polyunsaturated fatty acids were observed in the HFHS groups compared with the NC group (Supplementary Figure S5). Consistent with the lipid droplets found from the HFHS livers (Figure 1I), lots of di- and triacylglycerol species increased in the liver of HFHS groups (Figure 2G). Instead of accumulation in the liver, di- and triacylglycerols elevated in the blood of the sDM monkeys (Figures 2H,I), further evidenced the diabetes of these macaques. Accordingly, enhanced levels of bile acids were measured from the HPVB of both the HFHS and sDM groups (Supplementary Figure S6). However, as compared with the HFHS group, the sDM monkeys failed to increase some bile acid species, including hyodeoxycholic acid, a bile acid of importance in regulating glucose homeostasis (Zheng et al., 2021b). In contrast, the sDM subjects had higher levels of taurocholic acid and taurodeoxycholic acid in both the liver and HPVB, which was also observed in the plasma of human patients (Mantovani et al., 2021). These observations suggest that distinct metabolic signatures of sDM and HFHS monkeys can be revealed by metabolomics and lipidomics, although some tendencies of bile acid changes in the prediabetic models were similar to those in the monkeys of diabetes.

The transcriptomics has profiled the expression of 18,841 genes, which were collapsed from over 46,000 transcripts, in the liver tissues of these monkeys (Supplementary Table S3). The detected transcriptome from each individual was similar in terms of both the total number of transcripts and their abundance distribution (Supplementary Figure S7). As compared with the NC group, the HFHS monkeys altered the expression of 146 (80 decreased and 66 increased) genes (Figure 3A). The numbers of down- and upregulated genes in the sDM group were 59 and 80, respectively (i.e., 139 in total; Figure 3B). Between the HFHS and sDM groups, 160 genes were different, out of which 89 and 71 showed higher levels in HFHS and sDM subjects, respectively (Figure 3C). Interestingly, the HFHS and sDM groups showed the largest difference in terms of the number of altered genes. (Figure 3D). The cross-comparison among the three groups demonstrated that these two groups shared only 33 altered genes (Figure 3D). The altered genes between HFHS versus NC groups and those between HFHS versus sDM groups showed a higher number in common, similar to the pattern observed from the comparison of the liver metabolome (Figure 2E). The genes altered in the HFHS groups consisted also of the ones participating in the metabolic and signaling processes of lipoprotein particles, cholesterol, and fatty acids; meanwhile, very-low-density lipoprotein particle remodeling was observed in sDM monkeys rather than HFHS group (Supplementary Figure S8). Focusing on the spontaneous DM, we further compared the genes that commonly changed in both the HFHS and sDM groups, and those regulated only in the sDM macaques (Figure 3E). The only genes differentially expressed in all three groups were PRAP1 (Figures 3D,E), which was recently reported as a lipid-binding protein promoting lipid absorption (Peng et al., 2021). As expected, many genes merely changed in the sDM group, such as SULF2, GFAP (Pang et al., 2017), ABCG1 (Daffu et al., 2015), GCK (Haeusler et al., 2015), ISM1 (Liu et al., 2019), and CORIN (Pang et al., 2015) (Figure 3E), have been associated with diabetes. The significant alterations of these genes observed only from the sDM groups but not the HFHS groups were consistent with the mild DM-like symptoms found from these monkeys (Figure 1). We performed a Gene Ontology (GO) enrichment analysis of the genes commonly regulated in both HFHS and sDM groups (Figure 3F). In accordance with the alterations in the metabolome, these regulated genes enriched in the cellular processes related to lipid metabolism. Additionally, the genes regulated in both HFHS and sDM groups included also the ones participating in the development of the extracellular matrix. The genes commonly regulated in both HFHS and sDM groups included a bunch of genes encoding proteins functioning in the lipid metabolism, such as LIPG, APOA5, ACAA2, and FABP4 (Figure 3E). Taken together, these results demonstrated the similarity in lipid metabolism between the sDM and HFHS groups, as those observed from the metabolomes (Figure 2). However, the expression of some known marker genes in the prediabetic macaques was still more similar to that in the healthy monkeys after 15 months of intake of extra fat and sugar.

Surprisingly, structural proteins and related regulators accounted for nearly a quarter (8 out of 33) of the genes altered in both the sDM and HFHS groups. These eight genes included keratins (KRT 5/6C/14/16/17), cell-matrix adhesion protein Collagen alpha-1 (XVII) chain (COL17A1), and cytoskeleton-binding regulatory proteins Tensin-4 (TNS4) and Cornifin-B (SPRR1B) (Figures 3E,F). In addition, 14 commonly regulated genes were known for their functions related to cell morphology and cell movement. They were upregulated genes HAND1, OSR1, FABP4, PLAT, and PRAP1, and the downregulated ones PTK2B, S100A2, SFN, TNS4, SCG2, VIP, KRT16, VGF, and COL17A1. These results suggested that the morphological changes in the liver caused by the HFHS diet were likely similar to that in the diabetes liver, which might be related to the pathogenesis of DM.

Proteomics identified approximately 5000 proteins from the liver tissues (Supplementary Table S3). The average sequence coverage was 28.6% and about 99% of them (4,900 out of 4,954) were identified with at least one unique peptide (Figure 4A; Supplementary Table S4). Similar to that observed in the transcriptome, these proteomics data also showed that the highest alteration between the HFHS and sDM macaques, and the largest number of regulated proteins in common between the cross-comparisons of HFHS versus NC as well as HFHS versus sDM (Figure 4B). Individually, the HFHS diet triggered the alterations of 192 proteins, out of which 71 and 121 were down- and upregulated, respectively (Supplementary Figure S9A). Also, the spontaneous DM resulted in the changes in 153 proteins, 51 decreased and 102 increased, in the liver (Supplementary Figure S9B). Between the HFHS and sDM groups, we observed 298 proteins of various abundance, consisting of 169 down- and 129 upregulated ones (Supplementary Figure S9C). When comparing the differential proteins from these two pair-wise contrasts, it was found that the HFHS-enhanced metabolic pathways were largely overlapped when referred to the NC (Figure 4C) and sDM groups (Figure 4D). The commonly upregulated glycan degradation pathways, PPAR signaling pathway, fatty acid metabolism, sugar metabolism, and cholesterol metabolism indicated the significant changes in the hepatic proteome caused by the food composition. However, the proteins in the sDM livers that were differentially expressed in comparison to the proteins in the NC and the HFHS groups enriched in the distinct pathways (Figure 4E). In contrast to the NC macaques, the altered proteins in the sDM livers functioning in various singling pathways, whereas the comparison between the HFHS and sDM groups mainly highlighted the enrichment of changed proteins in biosynthetic pathways (Figure 4D). Four proteins were differentially expressed in all three groups (Figures 4B,F). Fatty acid binding protein 4 (FABP4) increased dramatically in both HFHS and sDM groups and showed the highest level in the former (Figure 4F). FABP4 has been reported as a marker protein for type 2 diabetes and a protein target for inflammatory diseases. CNTFR, the receptor for ciliary neurotrophic factor (CNTF), was reduced in both the HFHS and sDM groups. Preview studies have also proposed CNTF as a candidate agent for the therapy of diabetes complications (Ma et al., 2018). Both histidine ammonia-lyase (HAL) and mevalonate kinase (MVK) decreased in the HFHS group but elevated in the sDM groups (Figure 4F). Mevalonic acid is the precursor for cholesterol synthesis, in which MVK is a key enzyme. Epidemiological studies have demonstrated that inhibiting the production of mevalonic acid increases the risk of developing type 2 diabetes.

To find the common features of HFHS and sDM, we performed cross-omics analyses and provided insights into pathological changes in macaque livers. For the genes whose transcripts and their encoded proteins were both detected from transcriptomic and proteomic approaches, respectively, we calculated the Pearson correlation between the transcript and protein of each gene across all samples of each group and found positive correlations (Pearson’s correlation coefficient >0.5) for about a half of them (Figure 5A). For the significantly regulated genes, a strong correlation between the changes in transcripts and proteins was found when every two groups of macaques were compared (Figures 5B–D). The consistency showed the changed protein abundance caused by the pathogen of DM and/or the switch to HFHS food were predominately regulated via gene expression, which was captured in our data precisely. Both transcriptomic and proteomic data supported upregulation of FABP4 in both HFHS and sDM groups and showed the highest level in the former (Figures 4F, 5C,D). FABP4 is one of the biomarkers that has been associated with both type 1 and 2 DM (Rodríguez-Calvo et al., 2019; Xiao et al., 2021). The abundance of phosphotransferase (GCK) in the sDM livers was much lower than those in either the NC or HFHS groups (Figures 5B,D), evidencing the repression of hepatic glycolysis in DM. The HFHS diet triggered the downregulation of fructose-bisphosphate aldolase C (ALDOC), a key enzyme in both glycolysis and gluconeogenesis pathways (Figures 5B,C). Two enzymes that were involved in the mevalonate pathway, 3-hydroxy-3-methylglutaryl coenzyme A synthase (HMGCS1) and farnesyl-diphosphate farnesyltransferase 1 (FDFT1), were also increased in the HFHS livers. Together with the consistent changes in MVK (Figure 4F), these data indicated the suppression of cholesterol synthesis in the HFHS monkeys. Acyl-CoA synthetase short chain family member 2 (ACSS2) that promotes fat storage was also lower in the HFHS groups, suggesting the downregulation of this protein in response to the increased intake of fat. We then conducted the joint analysis of the metabolomics and proteomics data. As expected, all three groups had differentially regulated fat digestion and absorption pathway (Figure 5E). Also, switching to the HFHS diet resulted in significant changes in some mo lipid metabolism-related pathways, such as steroid biosynthesis, PPAR signaling, propanoate metabolism and cholesterol metabolism. As a disease group, the sDM group had significant changes in the regulatory and disease-related pathways, including the HIF-1 signaling pathway, prolactin signaling pathway, and growth hormone synthesis, secretion and action pathway. On the other hand, the divergence between the HFHS and sDM groups included both metabolic and regulatory pathways. Taken together, our data demonstrated that the HFHS diet-fed macaques showed many molecular alterations in common with the changes in the DM ones. However, the accumulated changes that can cause cellular responses in oxygen homeostasis and inflammation might be of importance in the development of diabetes.