Method Development for Fecal Lipidomics Profiling

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Altered faecal lipidome reflects the gross outcome of nutrient intake, digestion, and absorption by both the gut microbiome and the animal gastrointestinal tract, providing means to study animal-microbiota interactions. Faecal lipidome profile also shows changes in lipid metabolism, a hallmark feature of diet-induced metabolic changes and hence the interest in its use as a biomarker to predict dietary composition. This study illustrates faecal lipidome components and their ratios as a biomarker for forage quality in beef cattle fed three contrasting temperate grassland forages.

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Abstract:

Faecal lipidome profiling has been proposed as a biomarker for various parameters that are difficult to measure (e.g. diet composition and feeding pattern of grassland and for wild animals). Here we present a database of faecal lipidome signatures from beef cattle based on nineteen faecal lipidome compounds (stanols, archaeol and fatty alcohols) and their ratios. Faecal lipidome profiling was done using gas chromatography-mass spectrometry (GC-MS) in beef cattle (n=10 per forage system) fed three typical grass silage diets representative of UK grassland system: 1) permanent pasture (mix of sown (perennial ryegrass) and unsown species; PP); 2) reseeded perennial ryegrass monoculture (PRG); and, 3) reseeded mixture of the same perennial ryegrass and white clover (ca.

70:30 fresh weight (FW): GWC). A significant dietary influence was observed for faecal lipidome compounds such as C18-OH, C22-OH, C24-OH, C26-OH, 24-ethyl coprostenol and 5?-stigmastanol (p0.05). The study also validated ratios of faecal steroids as faecal biomarkers since combinations of two or more steroids or their ratios reflected better dietary composition differences; individual steroids define independent sources of deviation in dietary composition and one steroid could balance for deficiencies in the other(s).

The faecal lipidome profiling was able to distinguish between the different perennial ryegrass cultivars in the present study (PP vs. PRG) but was unable to identify the presence of white clover within the same perennial ryegrass cultivar (PRG vs GWC). The findings are important for predicting diet composition as well as the environmental footprint of grassland livestock and wild herbivores.

Keywords: temperate grassland; herbivore, beef cattle; forage quality; biomarker; diet composition; faecal lipidome; GC-MS

1. Introduction

Lipidomics, a distinct branch of metabolomics, provides information about lipid metabolism in the animal's gastrointestinal tract and microbiome and their interactions [1-3]. Presently available techniques, e.g. gas chromatography-mass spectrometry (GC-MS), offer a comprehensive perception into the structure and function of particular lipid species [4,5]. Herbivore faeces comprise lipids from distinctive classes that are derived from host and microbial metabolism and these lipids can potentially interact; hence the concentration may change according to diet, animal and environment [6-8]. Understanding the relationship between the diet composition and faecal lipidome not only offers an improved understanding into lipid metabolism but also is important for the development of new biomarkers for parameters that are difficult to measure; e.g. diet preferences and feeding habits of grassland livestock and wild animals [9-11]. The faecal lipidome, including sterols, stanols, bile acids, archaeol, faecal long-chain fatty alcohols and acids has been investigated as a potential biomarker to identify the dietary composition, diet selection and species identification through perceived lipid metabolism changes [12-14]. The dietary composition is also used as a proxy to assess enteric methane emissions from ruminant livestock [13]. Moreover, the faecal analysis is particularly useful from the perspective of biomarker research, as it can be obtained more easily and less invasively than other biological samples [2]. Some of the faecal lipid compounds are not derived from the diet but released from the microbial degradation; e.g. archaeol is a unique membrane lipid component of methanogenic archaea; thus possible to comprehend complex host and microbiome metabolism and their interactions [3]. Use of multiple faecal lipidome compounds or their ratios predict the dietary composition and species specificity as individual lipidome compounds define independent sources of deviation in dietary composition and one compound could correct for insufficiencies in the other(s) [3,4]. Hence, identifying distinctive faecal lipidome compounds in a single assay is a promising method, giving a distinct steroid profile [6-8].

In UK temperate grassland systems the most common pastures are: 1) permanent pasture " defined as grassland which has not been reseeded for 10 years+ and will be a mixture of sown and unsown species; 2) monoculture re-sown grassland " mainly consisting of perennial ryegrass (Lolium perenne) species e.g. high sugar grasses, and 3) perennial ryegrass and white clover (Trifolium repens) mixtures " typically sown to reduce inorganic N inputs [15]. Previous studies have tested the relationship between faecal lipidome and diet composition, predominantly faecal fatty alcohols and acids on contrasting diets [5-7]. Since faecal steroid profile changes with diet and gut microbial activity, abundance and diversity, steroid ratios, rather than absolute individual steroid concentration enhance biomarker efficiency [2,4,12]. However, a detailed study on the effect of dietary composition on faecal lipidome and their ratios in beef cattle on more temperate grassland diets is lacking. We hypothesized that faecal lipidome profiling in beef cattle could differentiate between the three most common temperate grassland silage diets. We related the diagnostic signature obtained with nineteen faecal lipidome compounds and their ratios (the main faecal lipidome compounds and their ratios used in literature, see references [2-4]) with diet type and quality in terms of forage nutrient composition.

2. Materials and Methods

All experimental procedures were conducted in accordance with the requirements of the Animals (Scientific Procedures) Act 1986 and its amendments of 2013, issued by the Home Office of Her Majesty's Britannic Government.

2.1 Animals, experimental design and diet

Thirty Charolais Hereford-Friesian beef cattle were allocated evenly by sex across three forage-systems (n=10; 5 males and females) at the North Wyke Farm Platform (NWFP) in Devon, UK [15].

The NWFP consists of three 'farming systems'each occupying approximately 21 ha under the following pasture management strategies: 1) permanent pasture (PP), of predominately Lolium perenne; 2) reseeded pasture with a high-sugar perennial ryegrass cv. AberMagic; PRG, and 3) reseeded pasture with a mixture of perennial ryegrass (cv. AberMagic) and white clover (cv. AberHerald, GWC). Silage from these swards was fed during the winter housing period and animals grazed at pasture during summer months (average live weight = 337 ± 62.2 kg). A small quantity of concentrate was also fed (1 Kg animal-1day-1) during the winter housing period as a supplement in the GreenFeed emission monitoring system [15], (for nutrient composition of silage and concentrate see, Table 1).

2.2 Forage analysis

During the winter housing period, samples were collected randomly from each farmlet at weekly intervals (1 kg fresh weight (FW); bulked) as fed. The samples were dried, ground and stored at -20oC until analysis. Fibre and total N (as an estimator of crude protein, CP) were measured. In short, the various fractions of fibre composition were quantified using a FOSS Fibertec 8000 Auto Fiber Analysis System. Metabolisable energy (ME) and digestible organic matter (DOM) were also estimated. Dry matter (DM) was assessed as the difference between FW before and dry weight after freeze-drying. Ash was determined as the remaining mineral material following furnacing of plant-matter at 400 °C. Total N contents of feed were measured using an elemental analyser and isotope ratio mass spectrometer and crude protein were derived by applying a standard conversion coefficient of 6.25.

2.3 Faecal lipidome profiling

Faecal samples were collected from 5 males and 5 females in each farming system (n = 30) at the end of the winter housing period. Around 100 g FW of faecal matter was collected from each animal by anal faecal grab sampling and subsequently freeze-dried and milled to a 0.8 mm sieve size for lipidome profiling using GC-MS [16].

Total lipid extract (TLE) was obtained using a modified monophasic extraction procedure [16]. All solvents were of HPLC grade (Fisher Scientific, Loughborough, UK) and aqueous solutions were made using Milli-Q water, pre-extracted 3 times with dichloromethane (DCM). The monophasic solvent comprised buffered water, methanol, and chloroform in a ratio of 4:10:5. The buffered water was a potassium dihydrogen phosphate solution (0.05 M), adjusted to pH 7.2 using sodium hydroxide pellets, and then extracted 3 times using 50 mL of DCM. An internal standard of 100 µg 2-hexadecanol (Sigma Aldrich, Gillingham, UK) was added to approximately 300 mg of the sample before extraction. Monophasic solvent (2 mL) was added to the faeces, ultrasonicated for 15 min, centrifuged at 1268 g for 5 min, with the supernatant containing the TLE, decanted and retained. This was repeated 3 further times. The pooled extracts were then treated with 2 mL of buffered water and 2 mL of chloroform, vortexed, centrifuged at 1268 g for 5min and the organic (bottom) layer decanted and retained. The remaining aqueous layer was further extracted with 2 mL of chloroform 2 times, extracts pooled and evaporated under a moderate stream of N2.

For removal of polar head groups, acid methanolysis was used, where 5 mL of 5% hydrochloric acid in methanol was added to the TLE and heated in a tightly sealed boiling tube for 3 h at 100°C. When cool, the acidified sample had 2 mL of Milli-Q water and 2 mL of chloroform added, it was then vortexed and left to separate into the organic and aqueous phases. The organic (bottom) phase was retained and the aqueous phase was washed with chloroform further 2 times. The pooled extracts were evaporated under a moderate stream of N2. The TLE was fractionated to obtain the alcohol fraction using an activated silica column (0.5 g silica gel, 60 - particle size, activated at 125 °C, cooled to room temperature). The column was conditioned with 2 mL DCM and the sample applied to the column in 1 mL of DCM. An apolar fraction was then eluted with 4 mL DCM, followed by the alcohol fraction with 5 mL of DCM: methanol (1:1). The solvent was evaporated under a moderate stream of N2.

The alcohol fraction was reconstituted in 400 L DCM and 100 L was transferred to a vial evaporated under N2 and derivatised by adding 50 ?L of N, O-bis (trimethylsilyl) trifluoroacetamide containing 1% trimethylchlorosilane (BSTFA + 1% TCMS), capped and heated at 70 °C for 1 h. The BSTFA was then gently evaporated under a moderate stream of N2. The samples were dissolved in 100 ?L hexane before analysis by GC-MS (Agilent 6890N/5973N GCMS Agilent Technologies, Santa Clara USA) fitted with a capillary column HP-5MS (30 m x 0.25 mm x 0.25 ?m: Agilent Technologies). The GC oven programme was held at 50 °C for 2 min, increased to 320 °C at 10 °Cmin-1 and then held for 11 min. The instrument was operated using Agilent Chemstation software. Archaeol was identified based on its characteristic mass spectrum (key diagnostic ions being m/z 130, 278, 284, and 426 [16]) and retention time of archaeol standard. The alcohols and sterols were identified by their characteristic mass spectrum, retention times and with reference to the NIST library.

Quantification was undertaken by comparison of the target peak area with that of the internal standard peak area. The difference in response was taken into account by creating a calibration curve made by running 100 ng/?L of internal standard (2-hexadecanol; Mstd) along with 5, 25, 50, 150 and 250 ng/?L of archaeol standard (1,2-diO-phytanyl-sn-glycerol; Mx) using GC-MS. The peak areas of the internal standard (A std) and archaeol standard (Ax) was determined. The ratio of the standard in areas (Ax/Astd) was plotted against the ratio of the ng (Mx/Mstd), and the resulting regression equation for the slope was:

Mx/Mstd = (Ax/Astd*0.7716 +0.0658)

Internal standard (100 ?g) was added to each sample; therefore, Mx = (Ax/Astd*0.7716 + 0.0658) ?100.

A similar calibration curve to that above was used to quantify the sterols/stanols but using 5?-Cholestan-3?-ol to create the curve instead of archaeol:

Mx/Mstd = (Ax/Astd*0.9214 +0.0018)

Internal standard (100 ?g) was added to each sample; therefore, Mx = (Ax/Astd*0.9214 +0.0018) ? 100.

The alcohols were quantified by direct comparison of peak area with the internal standard, as the difference in response factor was negligible (see Figure S1).

2.4 Statistical analyses

The aim was to identify metabolic signatures or markers that explain the dietary differences and univariate and multivariate statistics were used to recognize the relative metabolic alterations between diet and sex groups. All analyses were performed in R v.3.3.2 [17]. Data were subjected to multivariate statistical analysis using principal component analysis (PCA) [2]. Variation in dietary composition in three diets was analysed using PCA with packages ggplot2, gg biplot and dev tools. To identify the differences between faecal lipidome fingerprints in faecal samples from different diets PCA was done using gg fortify and PCAmixdata packages. Interpretation of PCA was made using scores and loadings plot according to their influence in the division between groups. Additionally, nine lipidome compound ratios were also compared [2,4,12]. First PCA compared 19 faecal lipidome compounds and nine ratios and diet and sex impact. The second PCA was carried out eliminating the variables with low contributions, and finally, the third PCA assessed the dietary influence on faecal lipidome. Further, to quantify the statistical relationship between each faecal lipidome compound and their ratios, univariate analysis of variance was carried out and identified the relationship with diet quality, sex and diet quality x sex interaction. Between groups level of significance was measured using Tukey multiple comparisons of means at 95% family-wise confidence level.