Disruption of urine metabolome and its interaction with gut microbiome by low-dose exposure of glyphosate-based herbicide in a rodent model

Glyphosate-based herbicides (GBHs) have previously been considered safe to humans. However, emerging evidence indicates that GBHs can disrupt the host microbiota and influence human health. To build upon our previous findings of gut dysbiosis and other adverse health effects resulting from low-dose exposure of GBHs (glyphosate and Roundup) in Sprague-Dawley rats, in particular pups during early development, we explore potential effects of GBHs on urinary metabolites and their interactions with gut microbiome in the same animal model. Methods – We further used to evaluate between PND125. Overall results from the metabolite-microbial correlation analyses

firewood shavings as bedding. All animals were kept in a single room at 23 ± 3 °C and at 40-60 % relative humidity with light/dark cycles at 12 h each using artificial light. The animals were given the same standard "Corticella" pellet diet (Piccioni Laboratory, Milan, Italy) for both breeders and offspring; both feed and tap water were available ad libitum.
Feed and tap water were routinely analyzed to exclude biological and chemical contamination (mycotoxins, pesticides, arsenic, lead, mercury, selenium).

Treatment
The timeline of the experimental animal treatment and sample collection has been described previously 9,21 . As illustrated in Figure 1, we randomly selected 14 dams (N=5 controls, N=5 glyphosate and N=4 Roundup) and 30 F1 pups (15 female and 15 male). The F0 dams received the treatment through drinking water from gestation day (GD) 6 to the end of lactation (totally they were exposed for 49 ± 2 days). The F1 pups received the treatment from their dams starting from in utero (GD 6) and mainly through milk during lactation. After weaning, F1 pups were treated through drinking water until sacrifice (PND 70 or PND 125).

Urine and fecal sample collection
Urine samples were collected at the end of lactation for dams and at PND70 and PND125 for pups. The urine samples were centrifuged to remove any debris (50,000 × g at 10 °C for 15 minutes) then transferred to 1.5 ml cryovials. About 2-3 fecal droppings from each pup were collected as described previously 9 . Briefly, forceps used for collecting droppings were washed and cleaned using sterile water and 1 % sodium bicarbonate between each sampling to avoid cross contamination. The urine and fecal cryovials were stored at −20°C until shipment on dry ice to the testing laboratories at Icahn School of Medicine at Mount Sinai.

Metabolomics analysis
Urine samples were thawed on ice, vortexed and diluted with water down to a specific gravity of 1.002 for pre-acquisition normalization. A 20ul aliquot of the diluted sample was prepared and stored at -80°C until metabolomics analysis. Immediately prior to liquid chromatography -high resolution mass spectrometry (LC-HRMS) analysis, urine samples were combined with 180 ul of acetonitrile containing internal standards to remove proteins, and the supernatant transferred to LC vials.
Sample extracts were analyzed in ZIC HILIC positive (ZHP) and RP negative (RPN) modes separately using an ultra-high performance liquid chromatography (UHPLC) 1290 Infinity II system (including 0.3 µm inline filter, Agilent Technologies, Santa Clara, USA) with 1260 Infinity II isocratic pump (including 1:100 splitter) coupled to a 6550 iFunnel or 6545 quadrupole-time time of flight (Q-TOF) mass spectrometer with a dual AJS electrospray ionization source (Agilent Technologies, Santa Clara, USA). Samples were maintained at 5°C in the autosampler module. For polar metabolites separation, 2 uL of sample was injected onto a HILIC SeQuant® ZIC®-HILIC column (100 mm × 2.1 mm, 100 Å, 3.5 µm particle size, Merck, Darmstadt, Germany) maintained at 25°C. While for nonpolar metabolites separation, 2 uL of sample sandwiched between 10 uL of water was injected onto a Zorbax Eclipse Plus C18, RRHD column (50 mm × 2.1 mm, 1.8 µm particle size, Agilent Technologies, Santa Clara, USA) coupled to a guard column (5 mm × 2 mm, 1.8 µm Agilent Technologies, Santa Clara, USA) maintained at 50°C. Samples were analyzed in randomized order. To monitor system stability, a pooled QC sample prepared by combining aliquots of all samples was injected routinely throughout the run. See Supplemental Information for further details.

LC-MS pre-processing
Suspect screening was performed using an in-house database of over 600 authentic standards analyzed under the same conditions. For untargeted data analysis, the raw data files were first converted into mzxml 22 format and a peak table generated using [XCMS] 23 with parameters optimized by [IPO] 24 . Metabolite features with a CV < 30% in the pooled-QC injections and with a mean fold change > 3 or > 1.5 compared to blank extracts for untargeted analysis or suspect screening, respectively, were retained for further analysis.
The metabolomics data was further imputed by k-nearest neighbor imputation using [knn_impute] with a cutoff = 0.4 (40% missing values) and normalized by [normalize_met] with default settings using the R package [MetaboDiff] 25 .

Statistical analysis
Differential metabolomic analysis was performed using R package [MetaboDiff] 25 .
Unsupervised principal component analysis (PCA) was performed to compare overall metabolomic profiles by age, sex and exposure types. Tests for statistical significance were performed with the PERMANOVA test (adonis function in the vegan R package ) 26 .
Supervised partial least squares discriminant analysis (PLS-DA) was performed using the mixOmics R package 27 to select the major contributing metabolomic features that differentiate between study groups; A PLS-DA Variable Importance in Projection (VIP) score >2.0 was used as the cutoff value to identify top features contributing to metabolic differences. For each selected feature, we also calculated the fold change ratio and compared the mean by non-parametric test. Alternatively, we used the random forest algorithm, a supervised machine learning approach, using R package [Boruta] 28 to identify significant differential metabolomic features associated with exposures. The spearman correlation networks between microbiome and metabolites were constructed using R package [igrapgh] 29 and FDR adjusted p-values were obtained using R package [qvalue] 30 . The microbiome data of pups at PND125 was obtained from our previous study 9 .

Results
Metabolomic profile between sex and adulthood groups After filtering, untargeted metabolomic profiling resulted in 4637 peaks in RPN and 5346 peaks in ZHP for further statistical analysis. An unsupervised principal components analysis was initially employed to compare the overall metabolomic profiles and detect outliers. We found the overall metabolomic profiles differed significantly between dams and pups, and between male and female pups, but not by exposure group (Glyphosate, Roundup or control) (P-values=0.001, 0.001 and 0.17, respectively by multivariate PERMANOVA test, Figure 2A and supplementary Figure 1).

Metabolomic features by exposure group and sex
For the discrimination of the exposure types, the Partial Least Squares Discriminant Analysis (PLS-DA), a supervised clustering method was used ( Figure 2B) using the 154 metabolites identified through suspect screening. Metabolomic features identified in sets with different exposure groups (Glyphosate, Roundup or non-exposed control) in dams and female or male pups were selected by using the cutoff of PLS-DA Variable Importance in Projection (VIP) score > 2.0 and a P-value < 0.05 in the metabolite fold change level. The full list of metabolites with VIP scores was listed in Table S1 and the selected features were shown in Table 1-3. Distinct differential metabolites by exposure types were found in both dams and pups. In dams (Table 1), glyphosate-exposed animals had significantly reduced methionine levels (P-value=0.0079) compared to controls. Glyphosate-exposed dams also showed significant difference in 2-Methylglutarate, Pipecolate, Riboflavin, Dimethylglycine and Beta-alanine methyl ester compared to Roundup™. In female pups (Table 2), six metabolites (sebacic acid, N-methylglutamate, buberate, 10-hydroxydecanoate, dodecanedioic acid and aminocaproate) were significantly different in glyphosate-exposed compared to controls; whereas three metabolites (Adenine, Nacetylglycine, L-proline) were different in Roundup exposed animals compared to controls.
In addition, 5-Aminopentanoate was different between Glyphosate and Roundup exposed female pups. Of note, there was a trend of increased urinary adenine in both glyphosate and Roundup exposed female pups, however the VIP score was >2 only in the Roundup versus control comparison. In male pups (Table 3), four metabolites (1aminocyclopropanecarboxylate, homocysteine, mevalolactone and 2-oxobutanoate) were significantly associated with Glyphosate exposure, whereas Roundup™ exposure resulted in dysregulation of homocysteine, phenylethanolamine, 2-oxobutanoate and biopterin.
Importantly, compared to non-exposed male controls, both glyphosate and Roundup™ exposed animals displayed a significant increase in urinary homocysteine levels. Between glyphosate and Roundup™ exposed male pups, we found 7 metabolites, including Phenylacetate, 3-Hydroxybenzoate, Aminocaproate, 10-Hydroxydecanoate, Guanosine, Glycocholate and Adenine were differentially expressed. We did not observe overlapping features shared between dams and pups or between female and male pups.
Besides of PLS-DA, we also performed the metabolomic features selection using a random forest (RF) machine learning feature selection method with the Boruta algorithm. Among the features selected by RF (Table S2), we found many selected features were consistent with the results from the PLS-DA.

Correlations between metabolomics and microbiota
To test whether top metabolites selected by PLS-DA in this study are linked to gut microbiota, we performed a correlation based network analysis between differential metabolites (VIP>2) in pups at PND125 and paired gut microbial composition from the same animal at PND125. Overall results from the metabolite-microbial correlation analyses are presented in Figure 3A. We found the Prevotella genus was strongly correlated with 10-hydroxydecanoate (rho=0.57, FDR adjusted p-value=0.015), dodecanedioic acid (rho=-0.58, FDR adjusted p-value=0.012) and homocysteine (rho=-0.64, FDR adjusted p-value=0.0028). It should be noted that urinary homocysteine levels are also negatively correlated with not only Prevotella genus but also with its phylum, class, order and family.
Similarly, the level of 10-hydroxydecanoate is positively correlated with not only Prevotella genus but also its belonged Bacteroidetes phylum, Bacteroidia class, Bacteroidales order and Prevotellaceae family. In sex-stratified analyses ( Figure 3B), male pups showed a clear inverse relationship between the levels of homocysteine and relative abundance of Prevotella; female pups showed a similar trend; however the changes did not reach statistical significance.

Discussion
GBHs are the most applied herbicides worldwide and humans are commonly exposed to these environmental chemicals at various doses. Environmental GBHs are ubiquitous and GBHs residues can be found in food 31 , drinking-water 32 , crops 33 , animal feed 34 , groundwater 3 , even in air 35 . Although the effects of GBHs on human health are under intense public debate, evidence is emerging that they impact many health outcomes, including developmental and reproductive toxicity 36-38 , endocrine disruption 39,40 , host immunity 41-43 , obesity and diabetes 7,44 , gastrointestinal disorders 45 , cardiovascular disorders 46,47 and central nervous system dysfunction such as learning and memory impairment 48 , anxiety, depression 49 and autism 8 . These chronic health outcomes may occur even at doses lower than established risk safety guidelines, in particular during critical development windows as denoted in the DOHaD paradigm 50 .Environmental exposures may lead to changes in metabolism 18,51 . A comprehensive, unbiased metabolite profiling, so-called "metabolomics" is a promising approach to study the associations between environmental exposures and health effects. Although our sample size was small, our results showed that gestational and early-life low-dose exposure to glyphosate or Roundup, significantly altered urine metabolomics, in both dams and offspring.
The one-carbon metabolism is a metabolic process that serves to activate and transfer 1C units for biosynthetic processes including purine and thymidine synthesis and homocysteine remethylation 52 . Folate is the essential cofactor in the one-carbon cycle, animals and humans cannot biosynthesize folate, thus requiring dietary intake or absorption of biosynthesized by gut microbiota 53 . In this study, we observed that low-dose GBH exposure can influence multiple metabolites involved in one-carbon metabolism. One key metabolite induced by GBHs exposure in male pups is homocysteine, a nonproteinogenic α-amino acid, biosynthesized from methionine that can be remethylated back into methionine or converted into cysteine with the aid of certain B-vitamins.
Homocysteine metabolism of is highly dependent on vitamin derived cofactors; deficiencies in vitamin B12, folic acid and vitamin B6 are associated with higher levels of homocysteine in blood(hyperhomocysteinemia). In addition to homocysteine, we also observed GBH-induced deregulation of L-methionine and N-methylglutamate also involved in one carbon metabolism. Interestingly, probiotic bacteria, including Prevotella using products of the shikimate pathway, which is inhibited by GBHs, can biosynthesize B vitamins, including folate 54 . Thus, it is plausible that the increased urine homocysteine we observed in male pups exposed to low-dosage GBHs results from reduced production of folic acid by Prevotella bacteria, paralleling the increased in homocysteine in dietary vitamin deficiencies.
Although the potential mechanism is still not clear, studies found that children with autism spectrum disorder (ASD) lack microbial diversity and have a decreased abundance in probiotics including Prevotella, potentially leading to reduced folate production by microbiota in individuals with ASD 57 . As our results indicate the role of GBHs in the folate deficiencies, we hypothesize that the environmental exposure to GBHs during early development may contribute to the ASD or other neurodevelopmental disorders through the depletion of beneficial folate-synthesizing enteric bacteria leading to the accumulation of homocysteine, a known neurotoxin which plays a role in brain damage, cognitive and memory decline [58][59][60][61] .
To be noted is that distinct metabolomic features were not only found between exposed and non-exposed, but also found between Glyphosate and Roundup exposed animals.
Previous experimental evidence 62-64 supports that the Glyphosate formulations like Roundup are more toxic than Glyphosate alone, however the underlining mechanisms are still not clear. Our results suggested that metabolite profiling might be useful to identify possible metabolic pathways and to explain the excessive toxicity in those formulations.
This study has its limitations, mainly due to its small sample size, thus statistical power was limited subgroup analyses. Secondly, urinary creatinine concentrations are known to be different among age groups. Furthermore, the microbial survey using 16S rRNA gene amplicons-sequencing techniques in our study cannot capture the full metabolic activity of the microbial features correlated with host metabolomics. A more comprehensive whole genome metagenomic sequencing approach may be required for a full spectrum microbial metabolic function profiling to find underline mechanistic link between gut microbiome and the host metabolism.
In conclusion, to our knowledge, this is the first study on GBH-induced metabolomic     The urine homocysteine levels in pups were associated with gender and glyphosate or Roundup™ exposure and were strongly correlated with Prevotella abundance in gut microbiota. 2A. Correlation network between exposure associated metabolic features and gut microbiota. Prevotella and its belonged Bacteroidetes phylum to Prevotellaceae family all showed strong negative correlation with the urine Homocysteine. The links with FDR adjusted p-value<0.05 were colored with red (positive correlation) and blue (negative correlation). 2B and 2C. Boxplots showed that Prevotella abundances were lower in female pups than male pups. Prevotella was reduced in exposed male pups. In contrast, the female pups have higher Homocysteine than the male pups and the homocysteine levels were significantly increased by exposure in male pups.

Supplementary Files
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