A >7-Year Feeding Study on the Long-Term Effects of Genetically Modified Maize Containing cry1Ab/cry2Aj and EPSPS Genes on Immune Status and Serum Metabolites in Two Generations of Cynomolgus Macaques

Abstract

A >7-year study was conducted using cynomolgus macaques () to evaluate the long-term effects of genetically modified (GM) maize on the immune status and serum metabolic profile across two generations. The GM insect-resistant and herbicide-tolerant maize line used carried the cry1Ab/cry2Aj and G10evo-EPSPS genes. The macaques were maintained on GM maize-based, non-GM maize-based, or normal diet for >7 years, and their offspring received the corresponding diet postweaning. Multigenerational analyses encompassed immunoglobulin profiles, cytokine networks, and serum metabolome characteristics to assess the potential impact of GM maize on immune system development and metabolic homeostasis. No statistically significant differences were observed in the majority of parameters between the GM-fed and control (non-GM and normal diet) groups. This longitudinal investigation provides substantial evidence for the metabolic equivalence and immunological compatibility of the GM maize formulation in cynomolgus macaques.

1. Introduction

The global adoption of genetically modified (GM) crops has revolutionized agricultural practices, enabling yield enhancement, biotic stress resistance, and ecological sustainability. − GM maize, engineered with stacked insect-resistant traits (cry1Ab/cry2Aj) and herbicide-tolerant characteristics (G10evo-EPSPS), exemplifies this technological advancement. − Despite its beneficial prospects in agriculture, , persistent concerns regarding the long-term health implications of sustained consumption of GM crops demand rigorous investigation, − particularly concerning immune regulation , and metabolic homeostasis , in mammalian systems.

Current safety assessments predominantly rely on acute or subchronic exposure studies in rodents, − which serve as models with limited translational relevance due to interspecies divergence in immune pathways, xenobiotic metabolism, and lifespan. Hu et al.‘s research indicates that GM maize containing cry1Ab and epsps genes does not exert significant adverse effects on the reproductive system of third-generation rats. Liaqat et al. conducted a 90-day subchronic feeding study on Wistar rats fed with GM maize (CEMB-413) containing insect-resistant genes (cry1Ac and cry2Ab), and the results demonstrated that the GM diet did not have detrimental effects on the health of the animals. Nonhuman primates (NHPs), specifically cynomolgus macaques (), have emerged as a superior preclinical model because they share >93% genomic homology with humans and exhibit analogous physiological complexity of immune responses and metabolic networks. Compared to rodent species, the phylogenetic proximity of cynomolgus macaques provides unique insights into human dietary exposure scenarios, particularly helpful for evaluating multigenerational effectsan understudied dimension in the existing literature.

In this >7-year controlled feeding study spanning two cynomolgus macaque generations, we performed safety evaluation of GM maize. We employed a longitudinal, multigenerational design to systematically assess (1) cytokine networks associated with inflammation and immune tolerance and (2) serum metabolomic profile in macaques fed a GM maize-based diet. Our results indicate that long-term consumption of GM maize does not lead to immune modulation or metabolic disorders in cynomolgus macaques. The results are relevant for refining regulatory standards and informing public policy on genetically engineered food systems.

2. Materials and Methods

2.1. Experimental Materials

The GM maize variety Ruifeng 125 and its isogenic conventional counterpart Ruifeng 1 used in this study were provided by Hangzhou Ruifeng Biotechnology Co., Ltd. Ruifeng 125 integrates the dual insect-resistant genes cry1Ab/cry2Aj and the glyphosate-tolerant gene G10evo-EPSPS, with the protein content of Cry1Ab/Cry2Aj and G10evo-EPSPS in fresh kernels ranging from 1.2 to 1.4 μg/g and 3.6 to 5.4 μg/g, respectively. The experimental materials were cultivated in accordance with the “Regulations on the Safety Management of Genetically Modified Organisms (2017)” issued by the Ministry of Agriculture and Rural Affairs, implementing spatial isolation planting at the Inner Mongolia experimental base (isolation distance ≥ 300 m), and no glyphosate herbicide was applied throughout the cultivation period. After natural air-drying, the harvested grains were ground and stored as powder at −20 °C for future use.

Variety verification was conducted using the event-specific PCR method recommended in Announcement No. 2259 by the Ministry of Agriculture and Rural Affairs of China (Figure S1 and Table S1), confirming the sequence specificity of the inserted genes via duplex PCR amplification. Nutritional component analysis was commissioned to Kunming Fengmu Animal Food Technology Research and Development Co., Ltd., and was performed in accordance with the GB5009 series of national standards, with results indicating substantial equivalence in major nutritional components, such as crude protein, fat, and ash between the GM and non-GM maize (Table S2). The feed was formulated based on the full nutritional standards for experimental animals (GB14924.3), increasing the corn proportion to 70% in the basic diet (formulation composition has been detailed in Table S3). After processing into pellets by Beijing Keao Xie Li Co., Ltd. (Production License SCXK (Beijing) 2015-0013), the feed was sterilized via cobalt-60 irradiation and vacuum-packed for storage.

Nutritional parameters of the finished feed are detailed in Table S4. To ensure food safety, heavy metal (Table S5) and glyphosate residue (Table S6) testing was conducted on raw corn materials and finished feed by Qingdao Huace Testing Technology Co., Ltd., with all indicators meeting the requirements of the “Feed Hygiene Standard” GB13078 and the “Maximum Residue Limits for Pesticides in Foods” GB2763.

2.2. Feed Formulation

The daily theoretical protein exposure (P) for cynomolgus macaques is defined as

$$P=F\times \frac{{\mathrm{TS}}_{\mathrm{D}}}{{\mathrm{TS}}_{\mathrm{F}}}\times C\times R$$ 1

where P represents daily theoretical protein exposure (μg/day), F represents protein content in fresh maize (μg/g), TS_D represents total solid content of maize used in feed production (87%), TS_F represents total solid content of fresh maize (70%), C represents the proportion of maize in the feed (70%), R represents daily feed intake per monkey (∼270 g/day, calculated by weighing daily feed consumption against body mass [Table S7]). According to formula (1), the daily theoretical protein exposure of cynomolgus macaques to Cry1Ab/Cry2Aj and G10evo-EPSPS proteins was 281.9–328.9 and 845.6–1268.5 μg/day, respectively. This calculation represents the theoretical maximum prior to processing-induced protein degradation. Both experimental diets were vacuum-dried and subsequently stored at −20 °C. The feed for each group was produced every three months. A commercially produced normal diet that met the national standard requirements for primate nutrition was purchased from the same company.

2.3. Experimental Animals and Ethical Approval

2.3.1. Animal Housing and Ethical Approval

All animals were housed in ecologically designed cages (6 m × 3.9 m × 3.3 m) at the Medical Primate Center of the Institute of Medical Biology, Chinese Academy of Medical Sciences (Kunming, China; certification number SYXK (Dian) K2022-0006). Each cage included two compartments for the animals to rest and play. The cages were equipped with temperature-controlled perches (37 ± 1 °C), climbing facilities, and environmental enrichment toys. The newborn F1 generation were put on the feeding plan for the F0 generation until sexual maturity. Quantitative feed (supplemented with fresh fruits) was provided at regular times every day, and access to free drinking water was ensured to meet the hydration needs of the animals. All operations followed the “Guiding Opinions on Treating Laboratory Animals Humanely” issued by the Ministry of Science and Technology and the international 3R principle. The experimental protocol was approved by Institutional Animal Care and Use Committee of the Institute of Medical Biology, Chinese Academy of Medical Sciences (DWSP202202011) and met the requirements of the EU Directive 2010/63/EU and the NIH Guide for the Care and Use of Laboratory Animals in the United States.

2.3.2. Experimental Animals

Thirty-six cynomolgus macaques (F0 generation; 6 males aged 5–12 years and 30 females aged 5–10 years) meeting sexual maturity criteria were procured from Guangxi Guidong Primate Research Co., Ltd. (Experimental Animal Use Certificate: SCXK (Gui) 2011-0001, accredited by the Guangxi Science and Technology Commission). Following a 30-day quarantine period with daily health monitoring, the subjects were stratified by body weight and age into three experimental cohorts (n = 12/group, 2 males and 10 females) via computer-generated randomization:

• Group Z: GM maize-based diet (cry1Ab/cry2Aj + G10evo-EPSPS)

• Group Q: Non-GM maize-based diet

• Group D: Standard primate chow

Animals were individually housed in stainless steel single cages (0.7 × 0.8 × 0.9 m) with ad libitum access to water. After a 90-day dietary adaptation phase, each group was systematically allocated to two breeding units (1 male + 5 females/unit).

From October 13, 2018, to February 1, 2025, longitudinal monitoring yielded 52 F1 offspring through natural mating. Postweaning, F1 neonates (1.5–2-year-old) were transferred to individual housing. Among them, based on factors, such as age and pregnancy status, 33 F1 cynomolgus macaques (aged 1–6 years) were weaned and selected for immunophenotyping and serum metabolomic analyses (Table ). Additionally, 27 F0 cynomolgus macaques were selected based on the same criteria (Table ). The final analytical cohorts comprised six groups:

• F0 generation: DF0 group (n = 9), QF0 group (n = 10), ZF0 group (n = 8)

• F1 generation: DF1 group (n = 12), QF1 group (n = 9), ZF1 group (n = 12)

1. Group Assignments of Experimental Animals Consuming Different Diets.

	D	Q	Z	Total
F0	9	10	8	27
F1	12	9	12	33

2.4. Hematology and Blood Biochemistry Assays

Each animal underwent two hematology and blood biochemistry assays, with an interval of six months between two analyses. Fasting peripheral blood samples were collected from each monkey; 1 mL of blood was collected in heparinized tubes for complete blood count analysis, and the remaining blood was used for biochemical testing of serum. Routine blood tests were performed using an automatic blood analyzer (SYSMEX CORPORATION XN-1000 V, Sysmex Corporation). Biochemical parameters were measured using an automatic biochemical analyzer (Dimension, Siemens, USA).

2.5. Immunological Profiling

Biological specimens were collected under standardized phlebotomy protocols. For serum preparation, blood samples were collected in clot activator tubes, allowed to clot for 2 h at 4 °C, and centrifuged at 3000 × g for 20 min (4 °C). The supernatant was aliquoted and subjected to immunological profiling, including humoral immunity profiling (IgG, IgA, and IgM) and cytokine network analysis (IFN-γ, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, and TNF-α).

Immunoglobulin measurements (IgG, IgA, and IgM) were performed using ELISA kits (IgA LOT NO. 2412015; IgG LOT NO. 2412018; IgM LOT NO. 2412011) according to the manufacturer’s instructions. Briefly, serum samples were diluted 1:1000 in PBS-Tween 20 (0.05% v/v), loaded onto antimonkey IgG-precoated 96-well plates, and incubated at 37 °C for 60 min. After three washes with PBS (pH 7.4), horseradish peroxidase (HRP)-conjugated detection antibodies (1:5000 dilution) were added. The absorbance of the color developed after adding the HRF substrate 3,3′,5,5′-tetramethylbenzidine was quantified at 450 nm.

Luminex Performance was employed for cytokine profiling: Luminex Performance NHP XL Cytokine Kit (R&D Systems, Minneapolis, MN, USA; Cat# FCSTM21-09, Lot# L158400) was used to quantify nine cytokines (IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12, and TNF-α) and LabEx Express3 Cytokine Array (LabEx, Shanghai, China; Cat# LXRLDA03-1) was used to determine the concentrations of IL-1α, IL-9, and IL-3. Serum was 1:2 diluted in accordance with the manufacturer’s instructions, and a standard curve was generated using calibrated microspheres and quality control serum. Each sample was tested in duplicate, and the average value was determined. Data were analyzed using the Milliplex Analyst V.5.1 software, with concentrations expressed in pg/mL.

2.6. Serum Metabolomics Workflow

Cryopreserved serum samples (−80 °C) were transported on dry ice to the core facility (ambient exposure <2 min). Quality assurance protocols included: QC pool (equal-volume mixture of all experimental samples), process blanks (53% methanol–water solution processed in parallel), and instrument blanks (solvent-only injections between sample batches). The equipment used in the metabolomics workflow included the SCIEX QTRAP 6500+ mass spectrometer, SCIEX Exion LC chromatograph, and Waters Xselect HSS T3 chromatographic column. Liquid samples were processed using 80% methanol, followed by centrifugation and dilution, with the final supernatant employed for LC-MS analysis. Chromatographic conditions were established using gradient elution. Mass spectrometric parameters were configured separately for positive and negative ion modes to ensure efficient separation and detection of metabolites. Data processing was conducted using the SCIEX OS v1.4 software for peak integration and calibration, with qualitative and quantitative analyses performed based on the multiple reaction monitoring mode. Metabolite annotation was perfomed using databases, such as KEGG, HMDB, and LIPIDMaps. Statistical analyses were carried out using the MetaX software, which facilitated principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) to identify differential metabolites (variable importance in projection (VIP) > 1, p < 0.05, fold change (FC) ≥ 2 or FC ≤ 0.5), with data visualization achieved through volcano plots and bubble charts.

2.7. Statistical Analysis

All data are presented as mean ± SD. Statistical analyses were performed using GraphPad Prism v8.0.2. Independent Student’s t-test was used for two-group comparisons, with Šidák correction for multiple comparisons, where applicable. One-way ANOVA followed by Tukey’s honestly significant difference posthoc test was performed for multigroup comparisons. Significance levels were defined as p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).

3. Results

3.1. Hematology and Blood Biochemical Profiling

To evaluate the potential multigenerational effects of chronic GM diet exposure, systematic hematological analysis and serum biochemical profiling were performed at two longitudinal time points in both F0 and F1 generation macaques. No significant differences were observed for most of the parameters or significant differences were noted versus only one control group (Table S8). A few parameters were significantly different with respect to both the control groups (Table S8). The F1 transgenic group (ZF1, second measurement) demonstrated statistically significant reduction in three parameters compared with the control groups (DF1/QF1): mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT) and plateletcrit (PCT).

None of the blood biochemical parameters exhibited significant differences between the ZF0 and ZF1 groups compared with both the control groups (Table S9). Only the cholesterol (CHOL) levels in the ZF1 group and low-density lipoprotein (LDL) levels demonstrated extremely significant differences compared with the DF1 group (p < 0.001). These results indicated that long-term feeding of GM diets does not lead to significant negative effects on hematological parameters.

3.2. Immunological Evaluation

3.2.1. Humoral Immunity Profiling

Long-term feeding of GM maize did not significantly effect the serum levels of IgG, IgA, and IgM in cynomolgus macaques (Table ). The results indicated that genetically modified corn did not induce abnormal activation or suppression of humoral immunity.

2. Immunoglobulin (IgG, IgA, and IgM) Levels in Two Generations of Cynomolgus Macaques.

	F0			F1
Item (μg/mL)	DF0	QF0	ZF0	DF1	QF1	ZF1
IgG	110.5 ± 18.33	102.59 ± 13.35	104.91 ± 15.20	103.08 ± 16.81	110.77 ± 36.39	102.89 ± 12.07
IgA	20.36 ± 4.35	22.78 ± 3.60	19.99 ± 2.74	19.99 ± 3.81	22.38 ± 13.85	18.12 ± 3.66
IgM	19.84 ± 2.75	20.52 ± 4.79	19.39 ± 2.74	19.07 ± 4.14	22.21 ± 12.30	18.77 ± 4.60

3.2.2. Cytokine Network Analysis

Longitudinal cytokine monitoring revealed isolated perturbations (Table ). The intergroup differences for 11 cytokines (IFN-γ, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, and TNF-α) were not statistically significant (p > 0.05). IL-3 levels in the F0 GM diet group (ZF0) were reduced. Overlapping concentration distributions (90% confidence interval (CI) > 75%) across groups indicated biological equivalence of the cytokine networks.

3. Cytokine Levels in Two Generations of Cynomolgus Macaques .

	F0			F1
Cytokine (pg/mL)	DF0	QF0	ZF0	DF1	QF1	ZF1
IFN-γ	0.91 ± 0.53	6.52 ± 12.48	1.20 ± 0.95	1.23 ± 0.69	2.94 ± 2.15*	1.37 ± 1.11#
IL-1β	0.14 ± 0.09	0.11 ± 0.06	0.14 ± 0.16	0.15 ± 0.07	0.20 ± 0.13	0.14 ± 0.08
IL-10	9.62 ± 4.95	9.31 ± 4.09	8.17 ± 3.56	9.42 ± 2.92	13.1 ± 6.82	9.50 ± 4.40
IL-12	0.36 ± 0.20	0.33 ± 0.14	0.38 ± 0.29	0.37 ± 0.10	0.57 ± 0.35	0.42 ± 0.22
IL-5	0.34 ± 0.19	0.24 ± 0.15	0.27 ± 0.24	0.31 ± 0.10	0.40 ± 0.20	0.28 ± 0.14
IL-2	0.45 ± 0.48	0.29 ± 0.14	0.28 ± 0.19	0.42 ± 0.18	0.61 ± 0.48	0.32 ± 0.16
IL-4	0.06 ± 0.04	0.06 ± 0.06	0.05±0.02	0.06±0.02	0.07±0.04	0.05±0.02
IL-6	0.78 ± 0.46	57.44 ± 150.18	14.22 ± 35.29	1.01 ± 0.76	7.43 ± 23.94	1.00 ± 0.72
TNF-α	0.29 ± 0.11	0.30 ± 0.16	0.53 ± 0.77	0.32 ± 0.15	0.52 ± 0.24*	0.35 ± 0.22
IL-1α	1.60 ± 0.40	1.55 ± 0.26	1.40 ± 0.21	1.27 ± 0.27	1.40 ± 0.23	1.51 ± 0.29
IL-3	0.07 ± 0.02	0.07 ± 0.02	0.05 ± 0.01&#	0.05 ± 0.01	0.05 ± 0.01	0.05 ± 0.01
IL-9	8.42 ± 17.08	1.07 ± 0.89	2.78 ± 3.88	0.56 ± 0.17	2.10 ± 3.19	1.01 ± 0.32

^a#, p < 0.05; ##, p < 0.01; and ###, p < 0.001 for the comparison between the Z and Q groups. &, p < 0.05; &&, p < 0.01; and &&&, p < 0.001 for the comparison between the Z and D groups. *, p < 0.05; **, p < 0.01; and ***, p < 0.001 for the comparison between the Q and D groups.

3.3. Serum Metabolomic Investigation

3.3.1. Quality Assurance Protocol

The metabolomic workflow integrity was validated based on the following criteria: total ion chromatograms (TIC): >95% peak area overlap across QC injections (Figure S2); Pearson correlation: Inter-QC r > 0.99 (Figure S3); PCA clustering: QC samples clustered within 3 SD of centroid (Figure S4); orthogonal partial least-squares discriminant analysis validation: model robustness confirmed via permutation testing (Figure S5).

3.3.2. Differential Metabolite Screening

To investigate the effects of dietary interventions on serum metabolism, differential metabolites across experimental groups were systematically screened (Table ) based on the following criteria: VIP > 1.0, FC > 1.2 or FC < 0.833, and p < 0.05.

4. Differential Metabolite Screening Results.

	Compared samples	Num. of total ident	Num. of Total Sig.	Num. of Sig. Up	Num. of Sig. down
F0	ZF0 vs DF0	685	50	8	42
	ZF0 vs. QF0	685	44	22	22
	QF0 vs. DF0	685	93	11	82
F1	ZF1 vs. DF1	685	189	139	50
	ZF1 vs. QF1	685	120	108	12
	QF1 vs. DF1	685	119	49	70

Volcano plot analysis (Figure ) revealed that although a subset of metabolites exhibited statistically significant alterations, the majority clustered near the baseline (x = 0), indicating minimal fold changes or nonsignificant differences across groups. Matchstick diagrams (Figure S6) were used to visualized the top 10 differentially expressed metabolites between the compared groups. Notably, the ZF1 group displayed significant upregulation of tetrahydrocorticosterone and D-3-phenyllactic acid compared with the DF1 and QF1 groups. No other metabolite in the transgenic groups (ZF0/ZF1) demonstrated consistent differential expression relative to both the control groups (D/Q). Overall, these findings indicated that although limited metabolic variations existed between the groups, most metabolites remained unaffected by the dietary treatments.

1.

Volcano plots of differential metabolites identified via the (a) ZF0 vs DF0, (b) ZF0 vs QF0, (c) QF0 vs DF0, (d) ZF1 vs DF1, (e) ZF1 vs QF1, and (f) QF1 vs DF1 comparisons.

3.3.3. Pathway Enrichment Analysis

KEGG pathway enrichment analysis (Figure S7) revealed metabolic alterations under different dietary conditions. As evident from intergroup comparisons within the same generation, differentially enriched pathways primarily belonged to metabolic processes, with “Global and overview maps” constituting approximately 25% of all altered pathways.

The top 15 enriched pathways are illustrated in Figure , with specific group comparisons showing: 2 significantly altered pathways (e.g., sphingolipid metabolism, p = 0.003) in the ZF0 vs DF0 comparison; 9 differential pathways (e.g., serotonergic synapse, p = 0.0004) in the ZF0 vs QF0 comparison; 8 modified pathways (e.g., aldosterone-regulated sodium reabsorption, p = 0.018) in the QF0 vs DF0 comparison; 2 disrupted pathways (e.g., taurine and hypotaurine metabolism, p = 0.009) in the ZF1 vs DF1 comparison; 7 altered pathways (e.g., pentose phosphate pathway, p = 0.009) in the ZF1 vs QF1 comparison; and 12 perturbed pathways (e.g., biosynthesis of plant hormones, p = 0.002) in the QF1 vs DF1 comparison.

2.

Bubble charts of KEGG pathways enriched for the (a) ZF0 vs DF0, (b) ZF0 vs QF0, (c) QF0 vs DF0, (d) ZF1 vs DF1, (e) ZF1 vs QF1, and (f) QF1 vs DF1 comparisons.

The details of metabolite-pathway associations are presented in Table . These results indicated that intergroup differences predominantly involved core metabolic pathways related to endogenous compound regulation, although no treatment-specific pathway signatures were observed across all comparisons.

5. Differential Metabolites and Pathways Identified for the (A) ZF0 vs DF0, (B) ZF0 vs QF0, (C) QF0 vs DF0, (D) ZF1 vs DF1, (E) ZF1 vs QF1, and (F) QF1 vs DF1 Comparisons.

Name	KEGG pathway	p
ZF0 vs DF0
Sphinganine; 3-Ketodihydrosphingosine; Spingosine-1-phoshate	Sphingolipid metabolism	0.003
Sphinganine; Spingosine-1-phoshate	Sphingolipid signaling pathway	0.023
ZF0 vs QF0
Prostaglandin E2; Prostaglandin G2; Prostaglandin D2; Prostaglandin H2	Serotonergic synapse	0.0004
Prostaglandin G2; Prostaglandin I2; Prostaglandin H2	Platelet activation	0.0004
Prostaglandin E2; Prostaglandin G2; Prostaglandin I2; Prostaglandin D2; Prostaglandin H2	Arachidonic acid metabolism	0.0006
Hydrocortisone; Prostaglandin E2; Prostaglandin I2; Prostaglandin D2; L-Thyroxine	Neuroactive ligand–receptor interaction	0.002
Prostaglandin E2; Prostaglandin H2	Oxytocin signaling pathway	0.006
Prostaglandin G2; Prostaglandin H2	Eicosanoids	0.007
Prostaglandin E2; Prostaglandin D2	African trypanosomiasis	0.016
D-Xylulose 5-phosphate; L-Thyroxine	Thyroid hormone synthesis	0.031
Guanine; 1-Methylnicotinamide; N-Acetyl-l-glutamic acid; d-Ala-d-Ala; L-allo-Threonine; Hydrocortisone; Prostaglandin E2; l-Asparagine; dUMP; D-Xylulose 5-phosphate; orotidine-5-phosphate; Prostaglandin G2; Prostaglandin I2; Prostaglandin D2; Prostaglandin H2; Sorbitol; Inosine; Porphobilinogen; L-Thyroxine; 4-Aminobenzoate	Metabolic pathways	0.044
QF0 vs DF0
Cortisone; Hydrocortisone	Aldosterone-regulated sodium reabsorption	0.018
Cortisone; Hydrocortisone	Glucocorticoid and mineralocorticoid receptor agonists/antagonists	0.018
L-tryptophan; cis-Aconitic acid; Citric acid; D-Erythrose 4-phosphate; Isocitrate	Biosynthesis of phenylpropanoids	0.030
cis-Aconitic acid; Citric acid; Acetyl phosphate; Isocitrate	Carbon fixation pathways in prokaryotes	0.035
cis-gondoic acid; Arachidic acid; Linoleic acid	Biosynthesis of unsaturated fatty acids	0.036
L-tryptophan; 5-Hydroxyindoleacetate; Prostaglandin D2	Serotonergic synapse	0.036
L-tryptophan; cis-Aconitic acid; Citric acid; D-Erythrose 4-phosphate; Isocitrate	Biosynthesis of alkaloids derived from shikimate pathway	0.040
L-tryptophan; cis-Aconitic acid; Citric acid; D-Erythrose 4-phosphate; Isocitrate	Biosynthesis of plant hormones	0.040
ZF1 vs DF1
l-Alanine; Taurine; Acetyl phosphate; Sulfoacetic acid; l-Glutamate; Taurocholic acid	Taurine and hypotaurine metabolism	0.009
Taurine; Taurochenodeoxycholic acid; Chenodeoxycholic acid; Glycochenodeoxycholic acid; Taurocholic acid	Primary bile acid biosynthesis	0.048
ZF1 vs QF1
Gluconolactone; 6-Phospho-d-gluconate; d-Gluconic acid; d-Glucopyranose; D-Ribose 5-phosphate; 2-Keto-l-gluconate	Pentose phosphate pathway	0.009
cis-Aconitic acid; Fumaric acid; Citric acid; Isocitrate	Citrate cycle (TCA cycle)	0.020
Spermine; (5-l-Glutamyl)-L-Amino Acid; Glycine; L-Pyroglutamic acid	Glutathione metabolism	0.020
cis-Aconitic acid; Fumaric acid; Citric acid; l-Histidine; Isocitrate	Biosynthesis of alkaloids derived from histidine and purine	0.027
Glycine; D-Ribose 5-phosphate	Phosphonate and phosphinate metabolism	0.030
cis-Aconitic acid; Fumaric acid; Citric acid; Isocitrate	Biosynthesis of terpenoids and steroids	0.034
cis-Aconitic acid; Fumaric acid; Citric acid; Isocitrate	Biosynthesis of alkaloids derived from terpenoid and polyketide	0.034
QF1 vs DF1
1-Aminocyclopropane-1-carboxylic acid; cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; D-Erythrose 4-phosphate; Inosine 5′-Monophosphate; Isocitrate	Biosynthesis of plant hormones	0.002
Glycine; Citric acid; L-Malate; 6-Phospho-d-gluconate; Acetyl phosphate; D-Erythrose 4-phosphate; d-Gluconic acid; Hydroxypyruvic acid; Ribulose-5-phosphate; Isocitrate	Carbon metabolism	0.002
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; Isocitrate	Citrate cycle (TCA cycle)	0.003
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; Acetyl phosphate; Isocitrate	Carbon fixation pathways in prokaryotes	0.003
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; Isocitrate	Biosynthesis of terpenoids and steroids	0.006
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; Isocitrate	Biosynthesis of alkaloids derived from terpenoid and polyketide	0.006
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; Inosine 5′-Monophosphate; Isocitrate	Biosynthesis of alkaloids derived from histidine and purine	0.006
Isoursodeoxycholic acid; Hyodeoxycholate; Deoxycholic acid; Chenodeoxycholic acid; Ursodeoxycholic acid; Isolithocholic acid	Secondary bile acid biosynthesis	0.011
Glycine; cis-Aconitic acid; Citric acid; L-Malate; Hydroxypyruvic acid; Isocitrate	Glyoxylate and dicarboxylate metabolism	0.011
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; D-Erythrose 4-phosphate; Isocitrate	Biosynthesis of phenylpropanoids	0.025
cis-Aconitic acid; Fumaric acid; Citric acid; L-Malate; D-Erythrose 4-phosphate; Isocitrate	Biosynthesis of alkaloids derived from shikimate pathway	0.036
6-Phospho-d-gluconate; D-Erythrose 4-phosphate; d-Gluconic acid; Ribulose-5-phosphate; 2-Keto-l-gluconate	Pentose phosphate pathway	0.047

4. Discussion

To evaluate the safety of GM maize, we investigated the long-term effects of GM maize consumption on serum metabolites and immune parameters in cynomolgus macaques. The F0 generation was divided into three groups in 2017 and maintained on GM, non-GM, or normal diet for seven years. F1 offspring received the same diet postweaning. In 2024, a comprehensive health assessment, encompassing hematological profiling, serum biochemistry, immunoglobulin quantification, cytokine analysis, and serum metabolomics, was conducted across the two generations. Although minor parameter-specific differences were observed in group Z, no biologically significant alterations attributable to transgenic proteins were detected. Collectively, the data indicate that prolonged GM maize consumption does not induce clinically relevant adverse effects on immune function or metabolic homeostasis in cynomolgus macaques.

The transient reduction in MCHC, PLT, and PCT observed exclusively in the F1-ZF1 cohort during the second assessment likely reflected the natural biological variability rather than any toxicological effect. MCHC reflects the concentration of hemoglobin within red blood cells, and its decrease is typically associated with hypochromic anemia. However, in this study, the downregulation of MCHC in group Z was noted only upon second examination of the F1 generation, and the decrease was relatively small (a decrease of 2.44% and 3.03% compared with the DF1 and QF1 group, respectively). Decreased PLT counts may increase the risk of bleeding. PCT reflects the platelet mass measured in percentage. As for MCHC, the significant downregulation of PLT and PCT in the ZF1 group was only observed in the second examination of the F1 generation, and no abnormalities were detected in other platelet-related indicators (MPV, PDW, P-LCC, P-LCR, and IPF). Therefore, whether the decrease in PLT and PCT was attributable to the toxicity of GM maize remains unclear. Additionally, in blood biochemical profiling, the levels of CHOL and LDL in the ZF1 group were significantly lower than those in the other two groups (DF1/QF1). CHOL, a lipid present in the bloodstream, and LDL, one of its carriers, are indicators related to blood lipids. Elevated levels of CHOL and LDL are associated with an increased risk of cardiovascular diseases, potentially leading to severe conditions, such as angina and myocardial infarction. However, the lower levels of CHOL and LDL observed in the ZF1 group indicated that this difference was not toxicologically significant.

The immune system exhibits heightened sensitivity to environmental changes, with immune responses to xenobiotics often preceding overt pathological manifestations. Consequently, immunological safety assessment serves as a critical biomarker for evaluating the toxicity of compounds and is an essential component of GM crop safety evaluation. While prolonged dietary exposure to foreign proteins may theoretically disrupt immune tolerance and induce chronic inflammation or autoimmune responses, no such effects were observed in GM maize-fed cynomolgus macaque. The stability of serum IgG, IgA, and IgM levels indicated the absence of any abnormal humoral immune activation triggered by transgenic components. This finding is related to the immune mechanisms operative in the gut-associated lymphoid tissue (GALT) in primates. In this system, regulatory T cells (Tregs) are specifically responsible for suppressing excessive immune responses to dietary antigens. This provides evidence supporting the safety of Bt proteins expressed in genetically modified corn.

No significant differences were observed in the levels of 11 immune factors (IFN-γ, IL-1α/β, IL-2/4/5/6/9/10/12, and TNF-α) between experimental and control groups, except for a marginal decrease in IL-3 levels in the transgenic cohort (0.05–0.07 pg/mL, Δ = 0.02). Potential explanations for this isolated IL-3 variation include biological variability: compared to the DF0 and QF0 groups, the slight relative difference in IL-3 levels in the ZF0 group (0.02 pg/mL) may reflect natural fluctuations in this outbred primate model, and the limited sample size could amplify statistical significance without biological relevance; hypothetical immunomodulation: uncharacterized immune-regulatory properties of transgenic proteins (e.g., Bt toxins) might theoretically suppress IL-3 secretion via gut–immune interactions. However, the absence of coordinated changes in IL-3-associated cytokines (IL-4/5/9) negates this hypothesis.

Collectively, these data suggest that chronic GM maize consumption exerts negligible systemic immunotoxicity. The observed discrepancy for IL-3 probably originated from experimental variability rather than from specific immunotoxic effects, underscoring the need for multidimensional risk assessment frameworks.

Metabolome refers to a comprehensive collection of small molecules present in cells, tissues, or bodily fluids. Metabolomic analysis captures the metabolic state of an organism at a holistic level, reflecting its overall health status and responses to environmental stimuli. , When integrated with bioinformatics analysis, it provides a rapid, sensitive, and noninvasive detection approach for safety assessment of GM crops. Although in the present study, we observed differences in certain serum metabolites among the groups through blood biochemical indicators, the relatively low intake of animals in the safety assessment of GM crops renders traditional blood and urine biochemical indicators insufficient in meeting the sensitivity requirements for safety evaluation. In contrast, serum metabolomic analysis can yield extensive biochemical data, making it highly valuable in medical practice and safety assessments. , In this study, 685 metabolites were identified from serum samples of monkeys, with several differential metabolites observed among the groups. Quality control measures were implemented to ensure the reliability of the results. The analysis of differences in serum metabolites among groups revealed significant upregulation of tetrahydrocorticosterone and D-3-phenyllactic acid in the ZF1 group compared to the DF1 and QF1 groups. Tetrahydrocorticosterone is a metabolic product of cortisol, which is characterized as an inactive form of cortisol with no substantial biological activity. Tetrahydrocorticosterone levels significantly increase during all stages of pregnancy. This suggests that the upregulation of tetrahydrocorticosterone in the ZF1 group may be attributed to the presence of female monkeys at different physiological stages within the group, and therefore, it does not provide evidence for toxicity related to GM maize. D-3-Phenyllactic acid is an organic acid produced by certain lactic acid bacteria, such as Lactobacillus species. It is an ideal antimicrobial compound with broad and effective antibacterial activity against bacteria and fungi. Currently, there are no reports indicating any negative health impact of D-3-phenyllactic acid, and therefore, its significance in serum metabolism remains to be explored. Although some serum metabolites in the ZF1 group exhibited significant differences compared to the DF1 group, these differences were also noted in the QF1 group (e.g., for taurohyocholic acid), indicating the lack of biological significance attributable to GM maize-induced toxicity.

Furthermore, a comparative enrichment analysis of KEGG metabolic pathways among different groups from the same generation revealed 40 metabolic pathways with statistically significant differences across six groups. Notably, between ZF0 and DF0, two pathways exhibited significant differences, namely “Sphingolipid metabolism” and “Sphingolipid signaling pathway.” However, these two pathways were not significantly different between ZF0 and QF0, indicating that such differences do not provide evidence for the toxicity of GM maize. Similarly, the two pathways, “Taurine and hypotaurine metabolism” and “Primary bile acid biosynthesis,” which were significantly different between ZF1 and DF1, cannot be attributed to GM maize. For other metabolic pathways showing significant differences between Z and Q, no significant differences were observed between Z and D, thereby precluding the attribution of these differences to the GM protein.

This study encompassed two generations of macaques, focusing on the effects of a GM diet on the immune status and serum metabolomics. The results provide significant insights into the multigenerational impact of GM crops, particularly in the context of NHPs. Our study revealed no clinically significant differences in immune parameters and metabolic pathways between GM-fed and control groups. These findings demonstrate the absence of adverse immunomodulatory or metabolic derangements under chronic exposure to GM maize. A limitation of this study is the lack of in-depth analysis of mucosal immunity and cellular immunity. Future research should employ flow cytometry to assess T cell subsets (such as Th17 and Treg) in conjunction with intestinal tissue histopathology to comprehensively evaluate the immunological effects of GM maize. Nonetheless, by bridging the divide between agricultural innovation and toxicological rigor, this work establishes a methodological framework for longitudinal safety assessments while contributing to evidence-based discourse on GM crop risk-benefit analysis.

Glossary

Abbreviations

CHOL

cholesterol

CI

confidence interval

FC

fold change

GM

genetically modified

HRP

horseradish peroxidase

LDL

low-density lipoprotein

MCHC

mean corpuscular hemoglobin concentration

NHPs

nonhuman primates

PCA

principal component analysis

PCT

plateletcrit

PLS-DA

partial least-squares discriminant analysis

PLT

platelet count

QC

quality control

TIC

total ion chromatogram

VIP

variable importance in projection

The authors declare that the data supporting the findings of this study are available within this article and its Supporting Information file or from the corresponding authors upon request.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jafc.5c06423.

• Table S1: primers used in polymerase chain reaction (PCR); Table S2: nutritional analysis of genetically modified (GM) maize Ruifeng 125 and non-GM maize Ruifeng 1; Table S3: proportion of each ingredient in the nongenetically modified (GM) and GM diets; Table S4: nutrient composition of the non-genetically modified (GM), GM, and normal diets; Table S5: detection of heavy metals in genetically modified (GM) and non-GM maize; Table S6: glyphosate residues in maize and diets; Table S7: daily feed intake and average body weight of F0 and F1 macaques; Table S9: statistical analysis of serum biochemical parameters in cynomolgus macaques; Figure S1: verification of the maize strain; Figure S2: total ion chromatogram overlap diagram for quality control samples; Figure S3: analysis of quality control (QC) sample correlation; Figure S4: principal component analysis for all the samples; Figure S5: partial least-squares discriminant analysis score scatter plot and permutation validation plot; Figure S6: stick diagram of differential metabolites; Figure S7: KEGG classification of differential metabolites (PDF)

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M.L., Z.L., and W.L. contributed equally to this work. M.L.: Resources, Software, Validation, Visualization, WritingOriginal draft, Writingreview and editing. Z.L.: Formal analysis, Investigation, Resources, Validation. W.L.: Data curation, Formal analysis, Investigation, Validation. C.W.: Data curation, Investigation, Resources, Validation. Q.J.: Conceptualization, Methodology, Resources, Validation. Y.L.: Data curation, Investigation, Resources, Validation. C.L.: Investigation, Methodology, Resources. Z.L.: Conceptualization, Methodology, Resources, Validation. Y.D.: Data curation, Investigation, Resources, Validation. W.Y.: Data curation, Investigation, Validation. R.Y.: Data curation, Investigation, Validation. D.T.: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, WritingReview and Editing.

All animal-related protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of the Institute of Medical Biology, Chinese Academy of Medical Sciences, under approval number DWSP202202011.

The authors declare no competing financial interest.