Chemicals
All solvents used for sample preparation were of optimal grade for mass spectrometry. Acetonitrile was purchased from Merck (Merck, 1499230-935), formic acid was purchased from Fluka (Fluka, 06450), while ammonium acetate and ammonium fluoride were supplied by Sigma-Aldrich. Ultra-high purity water was prepared by Millipore-Q SAS 67120MOLS HEIM (France).
Experimental design, animals, and management
All experimental procedures involving the use of animals were approved by the the Animal Management Committee (in charge of animal welfare issues) of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (IAS-CAAS, Beijing, China) and performed in accordance with the guidelines. Ethical approval on animal survival was given by the Animal Ethics Committee of IAS-CAAS, (Certification No.: IAS-2019-42).
One-day-old broiler chicks (Arbor Acres broilers) were reared in environmental chambers under continuous light for up to 3 weeks. At 21 days of age, 180 broilers with similar body weights (1.22 ± 0.03 kg) were randomly assigned to one of three treatments (35%, 60%, or 85% RH with an accuracy of ± 7%) including six replicate cages with 10 birds per cage. Birds were kept at 20 °C and 60% RH for 1 week to adapt to the chamber environment. Temperature was then gradually increased by 3 °C at 10:00 pm every 3 days from 20 °C to 32 °C (with an accuracy of ± 1 °C) over the course of 15 days. The experiment period ends at the 42 day of age. The experimental diet was designed according to the National Research Council (NRC, 1994) guidelines. The composition and nutrient levels of the basal diets are shown in Table 1. Feed and tap water were available ad libitum. Dead birds were recorded daily, and chick weight and feed intake per cage were measured weekly to calculate the average daily gain (ADG), average daily feed intake (ADFI), feed conversion rate (F/G) and mortality.
Sample collections and preparations
After the temperature was raised to 32 °C for 48 h, the skin temperature, rectal temperature and respiratory rate were measured.
The fecal were collected at 10:00 a. m ( 48 h after the temperature was raised to 32 °C). Then the fecal were immediately frozen in liquid nitrogen and then stored at −80 °C for metabolomics analysis. To facilitate individual sampling and quantitative collection of all voided feces without handling the animal, our method is similar to the one that be described by Touma et al (2003) [28]. Briefly, every ten broilers were housed individually in stainless steel wire cages (87×85×40 cm), which were placed in environment chambers of the same size. All excreta dropped through the bars of the steel wire cage and could be easily collected from the floor of the lower cage, which was completely covered with 1×1 m black garbage bags. During each sampling, the black garbage bags were renewed.
At 42 days of age, birds were individually weighed following a 12-h fast and six birds were chosen from each chamber (one bird per cage). Blood samples were collected into tubes without anticoagulant and centrifuged at 1 400 ´ g for 10 min at 4 °C for analysis of blood glucose, urea, AKP, CK, T3, T4 and CORT. Birds were then sacrificed by cervical dislocation. The hypothalamus was taken and frozen at -80 °C for analysis of HSP70. The right breast muscle was removed and frozen at -80 °C for analysis of glycogen levels and av UCP mRNA expression. Liver samples were also taken and frozen at -80 °C for analysis of glycogen levels.
Measurements of respiratory rate, core body temperature, skin temperature
The specific method of skin temperature measurement is: using the infrared thermal imager FLIR E4 (thermal resolution 0.07 °C, accuracy±2%) to shoot the side of the broiler head vertically, the shooting distance is 0.5 m, shooting once every 3 min, continuous Shoot 1 h and take 20 infrared photos per chicken. Through the FLIR Tools software analysis, the skin temperature of the leg, flipper, earlobe, comb, eyelids in each photograph was measured, and the average value of 20 data of the same chicken was taken as the true skin temperature value. Method for determining the core temperature: randomly select one chicken in each replicate of each group, and insert a digital thermometer (Model. JM 6200, resolution 0.01 °C) 5 cm long probe into the rectum almost, and record the value after stabilization. The core body temperature was recorded every 5 s, and a total of 4 times were recorded, and the average value was taken. The respiratory rate was measured once every 10 minutes, and the number of breaths in broilers within 1 min was measured. A total of 6 breaths were collected, and the respiratory rate was the average of 6 breaths.
Determination of glucose, glycogen, and urea
Blood glucose concentrations and urea, and muscle and liver glycogen levels were determined using commercial assay kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China), according to the manufacturer’s instructions.
Hormone concentrations of serum and hypothalamus HSP70 concentrations
Serum triiodothyronine (T3), thyroxine (T4), alkaline phosphatase (AKP), Creatine Kinase (CK), Corticosterone (CORT) and hypothalamus HSP70 were measured by commercial enzyme-linked immunosorbent assay (ELISA) kits specific for chicken (Nanjing Jiancheng Bioengineering Institute, Nanjing, China)), respectively, according to the manufacturer’s instructions.
RNA extraction and real-Time polymerase chain reaction (Real-Time PCR) assay
Av UCP expression was determined using the Quantitative Real-time PCR. Total mRNA from the chest muscle was isolated using TRIzol reagent (CW0581; ComWin Biotech, Beijing, China). Aliquots of the PCR products were sequenced (Takara Bio, Shiga, Japan) to verify authenticity. The quantification of target gene expression was evaluated using the 2−ΔΔCt method.
Extraction of fecal samples and quality control sample preparation
Take about 10 g frozen samples in a vacuum freeze drier fully lyophilized (LGJ-18, Beijing honored cologne instrument technology co., LTD). Then take about approximately 25 mg of each chicken lyophilized feces sample, add 1 mL of pre-chilled methanol/acetonitrile/water solution (2:2:1, v/v), vortex and mix, sonicate for 30 min/time, twice, and let stand at -20 °C for 60 minutes. Centrifuge at 14000 g for 20 min at 4 °C, take the supernatant, dry under vacuum, add 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) for re-dissolution during mass spectrometry, vortex, centrifuge at 14000 g at 4 °C for 15 min, take the supernatant for UHPLC-Q-TOF/MS analysis.
In parallel to the preparation of the test samples, we prepared a bulk quality control (QC) sample. The QC samples served two purposes. The first purpose was to act as a regular quality control sample to monitor the LC-MS response in real-time. Secondly, after the response had been characterized, the QC samples were used as standards of unknown composition to calibrate the data [29, 30]. The QC sample was made by mixing equal volumes (30 mL) from each of the samples being analyzed to create a pooled sample of sufficient volume to provide enough QC samples for the analytical run. Each aliquot of this sample was treated in the same way as the test samples.
UHPLC-MS analysis
Metabolomics analysis was performed with an Agilent 1290 Infinity LC ultra-high pressure liquid chromatograph (UHPLC) (Agilent, Palo Alto, USA) equipped with an electrospray ionization source operating in positive and negative ion modes.
For HILIC separation, samples were analyzed using a 2.1 mm ×100 mm ACQUIY UPLC BEH 1.7 µm column (waters, Ireland). In both ESI positive and negative modes, the mobile phase contained A=25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B=acetonitrile. The gradient was 85% B for 1 min and was linearly reduced to 65% in 11 min, and then was reduced to 40% in 0.1 min and kept for 4 min, and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed.
The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600℃, IonSpray Voltage Floating (ISVF) ±5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60-1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25-1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The collision energy (CE) was fixed at 35 V with ±15 eV. Declustering potential (DP) was set as ±60 V.
For RPLC separation, a 2.1 mm ×100 mm ACQUIY UPLC HSS T3 1.8 µm column (waters, Ireland) was used. In ESI positive mode, the mobile phase contained A= water with 0.1% formic acid and B= acetonitrile with 0.1% formic acid; and in ESI negative mode, the mobile phase contained A=0.5 mM ammonium fluoride in water and B= acetonitrile. The gradient was 1%B for 1.5 min and was linearly increased to 99% in 11.5 min and kept for 3.5 min. Then it was reduced to 1% in 0.1 min and a 3.4 min of re-equilibration period was employed. The gradients were at a flow rate of 0.3 mL/min, and the column temperatures were kept constant at 25℃. A 2 µL aliquot of each sample was injected.
The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 40, Ion Source Gas2 (Gas2) as 80, curtain gas (CUR) as 30, source temperature: 650℃, IonSpray Voltage Floating (ISVF) 5000 V in positive mode, and -4000 V in negative mode. In MS only acquisition, the instrument was set to acquire over the m/z range 60-1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25-1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The collision energy (CE) was fixed at 35 V with ±15 eV. Declustering potential (DP) was set as ±60 V.
Data deconvolution and processing and statistical analysis
For XCMS, the raw data files were first converted into the mzML format via ProteoWizard, and subsequently the converted files were imported into the XCMS software for nonlinear alignment in the time domain, automatic integration, and extraction of the peak intensities, with default parameter settings. The data were subsequently processed using XCMS for peak alignment and data filtering. MetaboAnalyst 2.0 (http://www.metaboanalyst.ca) was used for the statistical analysis. Principle component analysis (PCA) and hierarchical clustering were performed for the unsupervised multivariate statistical analysis. Partial-least squares discrimination analysis (PLS-DA) was performed as a supervised method to identify the important variables with discriminative power. PLS-DA models were validated based on the multiple correlation coefficient (R2) and cross-validated R2 (Q2) in cross-validation and permutation tests by applying 2000 iterations (P > 0.001). The significance of the biomarkers was ranked using the variable importance in projection (VIP) score (>1) from the PLS-DA model. For the univariate analysis, candidate specific biomarkers were determined using single-dimensional statistical analysis for one-way Anova analysis. P < 0.05 was considered to be statistically significant.
Metabolites identification and pathway analysis
The Metlin database was used to identify potential specific biomarker candidates based on their MS signature and tandem mass spectrometry (MS/MS) spectra, as well as eventual contaminants. Identification of potential biomarkers was carried out by searching METLIN (http://metlin.scripps.edu/), HMDB (http://www.hmdb.ca/), KEGG (http://www.genome.jp/kegg/), MassBank (http://www.mass bank.jp/), LIPIDMAPS (http://www.lipidmaps.org/) and Chemspider (http://www.chemspider.com) using the exact molecular weights or the MS/MS fragmentation pattern data, and a literature search was conducted to identify the affected metabolic pathways and to facilitate further biological interpretation. Mass accuracy tolerance within 25 ppm was used as the mass window for the database search. For confirmation of the metabolite identities using an authentic chemical standard, the MS/MS fragmentation pattern of the chemical standard was compared with that of the candidate metabolite under the same LC-MS conditions to reveal any matching. In the case of unknown metabolites, molecular formulae were generated using Mass Profiler Professional (Agilent Technologies).
Statistical analysis
Data on growth performance, respiratory rate, core body temperature, skin temperature of leg, flipper, earlobe, comb, eyelids, blood glucose, muscle glycogen, liver glycogen, T3, T4, CORT, AKP, CK, HSP 70, av UCP were analyzed using the one-way anova procedure in SAS version 9.2 (SAS Institue. Inc., Cary, NC, USA). Differences among means were tested using Duncan’s multiple range test. Replicate cage served as the experimental unit and P < 0.05 was considered to be statistically significant.