All animal study procedures were performed at the Cesare Maltoni Cancer Research Centre/Ramazzini Institute (CMCRC/RI) (Bentivoglio, Italy), and the animal experiment conducted with strict adherence to the Italian law regulating the use and treatment of animals for scientific purposes (Decreto legislativo N. 26, 2014. Attuazione della direttiva n. 2010/63/UE in materia di protezione degli animali utilizzati a fini scientifici. - G.U. Serie Generale, n. 61 del 14 Marzo 2014). Before starting, the protocol was examined by the Internal Ethical Committee for approval. The protocol of the experiment was also approved and formally authorized by the ad hoc commission of the Italian Ministry of Health (ministerial approval n. 710/ 2015-PR). As previously described21, all the pups were housed with their dam until weaning; then, separated into treatment groups and identified by ear punch. The animals were randomized in the different groups of treatment in order to have minimal differences in body weight among them, with a standard deviation of no more than 10 % from the average. They were housed in Makrolon cages (cm 41 × 25 × 15) at two or three per cage, with a stainless-steel wire top and a shallow layer of white 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).
The timeline of the experimental animal treatment and sample collection has been described previously9,21. 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 previously9. 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.
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.
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 mzxml22 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.
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 package27 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 study9.