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Article

First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study

by
Andri Grobbelaar
1,
Gernot Osthoff
2,
Ilse du Preez
3 and
Francois Deacon
1,*
1
Department of Animal Sciences, Faculty of Natural and Agricultural Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa
2
Department of Microbiology and Biochemistry, Faculty of Natural and Agricultural Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa
3
Centre for Human Metabolomics, North-West University, Potchefstroom 2531, South Africa
*
Author to whom correspondence should be addressed.
Metabolites 2024, 14(11), 586; https://doi.org/10.3390/metabo14110586
Submission received: 9 September 2024 / Revised: 21 October 2024 / Accepted: 23 October 2024 / Published: 28 October 2024
(This article belongs to the Section Animal Metabolism)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: This study provides the first insights to the fecal metabolome of the giraffe (Giraffa camelopardalis). By using untargeted metabolomics via gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS), this study primarily aims to provide results of the impact that external stimuli, such as supplemental feeding (SF) practices, seasonal variation and sex, might have on the fecal metabolome composition of healthy, free-roaming giraffes. Methods: Untargeted GCxGC/TOF-MS analysis was applied to the feces collected from thirteen giraffes (six males and seven females) from six different locations within the central Free State Province of South Africa over a period of two years. Statistical analysis of the generated data was used to identify the metabolites that were significantly different between the giraffes located in environments that provided SF and others where the giraffes only fed on the natural available vegetation. The same metabolomics analysis was used to investigate metabolite concentrations that were significantly different between the wet and dry seasons for a single giraffe male provided with SF over the two-year period, as well as for age and sex differences. Results: A total of 2042 features were detected from 26 giraffe fecal samples. Clear variations between fecal metabolome profiles were confirmed, with higher levels of amino acid-related and carbohydrate-related metabolites for giraffes receiving SF. In addition, a separation between the obtained profiles of samples collected from a single adult male giraffe during the wet and dry seasons was identified. Differences, such as higher levels of carbohydrate-related metabolites and organic compounds during the wet season were noted. Distinct variations in profiles were also identified for the metabolites from fecal samples collected from the six males and seven females, with higher concentrations in carbohydrate-related metabolites and alkanes for female giraffes comparatively. Conclusions: This is the first study to investigate the composition of the fecal metabolome of free-roaming giraffes, as well as the effects that external factors, such as environmental exposures, feeding practices, seasonal variations, age and sex, have on it. This novel use of fecal metabolomics assists in developing non-invasive techniques to determine giraffe populations’ health that do not require additional stressors such as capture, restraint and blood collection. Ultimately, such non-invasive advances are beneficial towards the conservation of wildlife species on a larger scale.

1. Introduction

The study of the key roles that gut microbiota and their associated metabolic processes have on host food digestion, energy supplementation, health and overall metabolism has become more evident in the last decade [1,2]. Fecal metabolites are effectively the end products of the complex digestive processes occurring inside the animal and events, exposures or phenomena occurring outside the animal (the environment). Feces are directly associated with the digestive system and health status of an individual and may reflect changes in the mammalian host’s diet and metabolism very early [3,4,5].
In comparison to human and crop health, the application of metabolomics to livestock research is, however, somewhat less widely used or appreciated [6]. Even less is known about the benefits that metabolomics research could have in the wildlife industry and conservation. The comprehensive measurement of metabolites (via metabolomics) could, however, provide better insights to determine the specific interactions between a biological specimen and the internal fluctuations and/or external disturbances to which it is exposed [7,8]. The systematic study of the small compounds left behind by specific cellular processes helps us to better understand the metabolic processes defining the phenotype of biological samples [3,9]. By using highly sensitive analytical equipment and complex statistical procedures, metabolomics provides a non-biased snapshot of all the detectable metabolites in a biological system [4,10,11]. The integrated impact of internal (e.g., genetics) and external (e.g., environment) stimuli on metabolism can also be defined by the study of composition, dynamics, relative abundance and interactions among metabolites in a particular organism [10,12].
Invasive methods, such as blood collections and necropsies, which are used to determine dietary requirements in domestic animals, are not appropriate for zoo or endangered animals [13]. Improved approaches are needed to investigate biological species conservation on a large scale [14,15]. Fecal metabolomics, as a non-invasive and insightful method, is a promising new candidate to assist with the evaluation of the digestive processes within wildlife. The world’s tallest ruminant, the giraffe (Giraffa camelopardalis), is currently listed as a “vulnerable” species on the International Union for Conservation of Nature (IUCN) Red List [16]. Its unique anatomy is also reflected in its intricate ruminant digestive properties. Being selective browsers, this species has evolved unique ways to effectively maximize the absorption of dietary nutrients and thereby maintain the critical energy balance needed for the upkeep of their metabolism. Fecal metabolomics could provide insight into the unique chemical fingerprints that specific cellular processes leave behind [9] within this vulnerable species. Especially in environments that are still novel in its use, untargeted metabolomic studies are key in discovering potential metabolome and biochemical changes underlying the response to internal and/or external stimuli [9]. Combined with the assessment of environmental matrices, metabolomics can be used as an effective tool for the assessment of animal welfare, diet, demography and behavior and for the management of protected areas globally [17].
By making use of non-invasive fecal collection, the current study investigated the fecal metabolome present in giraffes at the end of the ruminant browser digestive processes. The only other known study investigating the metabolome of giraffe excretion products was conducted on urine that was collected from giraffes housed in captivity [18]. By using untargeted metabolomics through the application of comprehensive gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS), this study primarily aims to provide the first-ever insights on the impact that external stimuli, such as supplemental feeding, seasonal variation and sex, have on the fecal metabolome composition of healthy, free-roaming giraffes.

2. Materials and Methods

2.1. Sample Collection

From July 2021 to June 2023, more than 300 fresh giraffe fecal samples were collected from six different locations and thirteen different giraffes in the central Free State, South Africa. To determine the differences between the metabolomes related to seasons and dietary supplements, 26 fecal samples (n = 26) were selected. All the populations were free-roaming and occurred within a 60 km radius from the city of Bloemfontein, which is located within the central Free State Province (South Africa). The location’s vegetation types include Bloemfontein Dry Grassland (Gh 5), Western Free State Clay Grassland (Gh 9) and Vaal-Vet Sandy Grassland (Gh 10) [19]. Supplemental feeding (SF) and natural available vegetation (NAV) feeding practices were followed at the different locations, which are listed in Table 1. Fecal collections occurred from 2021 to 2023 during the winter, dry (June–August), spring, dry (September–October), summer, wet (November–February) and autumn, wet (March–May) months. Metabolic analysis was also performed for the grouping of different seasons, such as dry (June to October) and wet (November to May), as listed in Table 1.
Fecal samples were handled similarly from collection to analysis. The collections were carried out directly after defecation to account for environmental factors that may affect the chemical and biological constituents of the feces. Fresh, wet samples were collected from the ground with a gloved hand and placed within plastic slider storage bags [20] and kept in a fridge (between 2 °C and 4 °C) for a maximum of three days, whereafter they were vacuum sealed and stored below freezing point until transportation to the North-West University (NWU), Centre for Human Metabolomics, South Africa, for metabolomics analysis. Immediate freezing of the fecal samples upon collection would be the best practice for preservation until analysis [21]. The lag time between collection and freezing is seen as one of the limitations of the current study but for logistical reasons, this condition was not feasible in the field [21].
Samples were given codes and were randomly analyzed in two batches. As with most free-roaming ungulate studies, our study design was unbalanced [22]. The results (n = 26) were pooled to differentiate between the habitats in which the management provides (SF) and does not provide supplemental feed (NAV), respectively, as listed in Table 1. The supplemental feed (SF) mainly comprised manufactured game pellets containing, in general, crude protein (minimum 100 g/kg), estimated energy (minimum 8 MJ/kg), moisture (maximum 120 g/kg), fat (minimum 30 g/kg), fiber (maximum 250 g/kg), calcium (maximum 10 g/kg) and phosphorous (minimum 4 g/kg) [23,24]. The provision of supplemental feed at Locations 1, 4 and 5 was conducted all year round. The samples were also pooled per month to compare the differences in the fecal metabolome during the dry (June to October) and wet (November to May) seasons (n = 9), as listed in Table 1.

2.2. Sample Preparation and Extraction

For sample preparation, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method used previously was applied, with slight variations [25]. The fecal sample (approximately 100 mg) was weighed off in a 5 mL Safelock microcentrifuge tube. The samples were ultrasonicated with 800 uL of methanol and 200 uL of 3-phenylbutyric acid (100ppm) in water (as internal standard) for 30 min and vortex mixed for 30 s. The sample mixtures were then centrifuged at 18,000 × g for 15 min at room temperature. The supernatant was transferred to a 2 mL glass vial and dried at 50 °C under a gentle stream of nitrogen gas. Toluene (100 uL) was added to the dry residue and evaporated at 50 °C under nitrogen gas to remove traces of water. The dried metabolic extract was then oximated with 50 μL of methoxyamine hydrochloride in pyridine (20 mg/mL) (Merck, Darmstadt, Germany) at 50 °C for 2 h. Following oximation, 100 uL of N, O-Bis (trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethyl chlorosilane (TMCS) (Sigma-Aldrich, St. Louis, MO, USA) was added, and the mixture was incubated at 50 °C for 45 min to form trimethylsilyl (TMS) derivatives. The derivatized sample extracts were transferred to 2 mL glass vials containing inserts. For quality control purposes, a fatty acid methyl ester standard mixture, system blank (no injection prior to the analytical run) and extraction blank (following the entire sample preparation procedure but using a vial containing only methanol and no sample matrix), respectively, were injected four times during each batch [26]. All organic solvents used were ultra-pure Burdick & Jackson brands (Honeywell International Inc., Muskegon, MI, USA).

2.3. Comprehensive Gas Chromatography Time-of-Flight Mass Spectrometry (GCxGC/TOFMS Analysis)

One microliter of each sample extract was injected (1:5 split ratio) into a Pegasus 4D GCxGC-TOFMS machine (Leco Corporation, St. Joseph, MI, USA), utilizing an Agilent 7890A gas chromatographer (Agilent, Atlanta, GA, USA) coupled to a time-of-flight mass spectrometer (TOFMS) (Leco Corporation, St. Joseph, MI, USA) equipped with a Gerstel Multipurpose Sampler (Gerstel GmbH & co. KG, Eberhard-Gerstel- Platz 1, D-45,473 Mülheim an der Ruhr).
First, dimensional separation was achieved with an Rxi-5Sil-MS primary column (28.2 m, 0.25 mm internal diameter and 0.25 μm film thickness) (Restch GmbH & Co. KG, Haan, Germany), and an Rxi-17 capillary column (1.320 m, 0.25 mm internal diameter and 0.25 μm film thickness) was fitted as the secondary column (Restch GmbH & co. KG, Haan, Germany). The front inlet temperature was held at a constant 270 °C for the entire run, ensuring rapid vaporization. For the primary oven, an initial GC oven temperature was set at 70 °C for 2 min followed by an increase in the oven temperature of 4 °C/min to a final temperature of 300 °C, which was held for 2 min. The secondary column oven temperature was set at 85 °C for 2 min and then increased by 4.5 °C/min until it reached a final temperature of 310 °C, at which it was maintained for an additional 4.5 min. The initial temperature of the modulator was 100 °C for 2 min, followed by a 4 °C/min increase to a final temperature of 310 °C that was held for 12 min. To control the effluent from the primary onto the secondary column, cryo-modulation and a hot pulse of nitrogen gas of 0.5 s every 3 s was used.
The acquisition delay for each run was 350 s, and the transfer line temperature was held at a constant 270 °C, with the ion source temperature at a constant 200 °C. The detector voltage was adjusted to 150 V and offset with a filament bias of −70 eV. Spectra were collected in scan mode from 50 to 950 m/z at an acquisition rate of 200 spectra per second. Mass spectral deconvolution, peak alignment and peak identification were performed using Leco Corporation’s ChromaTOF software (version 4.71). Mass spectral deconvolution was performed at a signal-to-noise ratio of 350, with a minimum of three apexing peaks. To eliminate the effect of retention time shifts and to create a data matrix containing the relative abundance of all compounds present in all samples, peaks with similar mass spectra and retention times were aligned using Statistical Compare, a package of ChromaTOF. Mass fragmentation patterns and their respective retention times were screened against commercially available National Institute of Standards and Technology (NIST) spectral libraries (mainlib, replib) for peak annotation, with a similarity setting of at least 80% [27].

2.4. Data Analysis

The raw GCxGC-MS data, including peak areas and annotations for each sample, were exported to MS Excel Version 2409 [28]. Compounds with the same name and unique mass were consolidated (summed). Contaminant compounds were identified by comparing the compounds detected in the extraction blank and system blank to those detected in the samples. Peaks detected in the system blanks were deleted from the dataset if their average concentration in the samples were 20 times or less than the average in the system blanks. Compounds present at concentrations 20 times or higher in the average of the samples, compared to the system blanks, were kept in the dataset and marked for future reference. Peaks detected in the extraction blanks were deleted from the dataset if the mean concentration in the samples was three times or less than that in the blanks [26]. If a compound was detected in both the blanks and samples but with an average concentration that was three times or more in the samples compared to the blanks, it was retained in the dataset and marked for future reference. The samples were quantified relative to the internal standard.
Statistical analysis was conducted using MetaboAnalyst (version 6.0) [29], a web server for metabolomics data analysis and interpretation based on the statistical program “R” Version 4.4.1 [30]. The data were normalized to the sample median, log transformed and auto-scaled prior to processing. To evaluate the cofounding effect of the available biological information, the processed data were analyzed by assigning the samples to different subgroups, including SF versus NAV, samples collected during the wet season versus the dry season and male versus female. Principal component analysis (PCA) was used to summarize the data based on the various subgroup comparisons: (a) giraffes provided with SF and only NAV, (b) wet and dry seasons for a single adult male giraffe, (c) fecal samples collected from six male and seven female giraffes from all locations, (d) fecal samples collected from five male and seven female giraffe from different feeding practices and (e) fecal samples collected from one adult male (I) and the sub-adult male (II) at Location 1. PCA is an unsupervised method that aims to find the directions that best explain the variance in a dataset (X) without referring to class labels (Y). The data are summarized into much fewer variables called scores, which are weighted average of the original variable. These scores can then be plotted in order to give a visual over-view of the samples and how they correlate with each other [11]. For univariate analyses, analysis of variance (ANOVA), including Student’s t-test and Fisher’s least significant difference (LSD), and fold-change (FC) analyses were performed [29]. Metabolites were identified as statistically significant differential compounds for the said group comparisons if they had a p < 0.05 and Log2 (FC) of > 4. Heatmaps (Pearson distance calculation and ward clustering algorithm) of the significant compounds were also created.

3. Results

3.1. Population

A total of 26 fecal samples from 13 different free-roaming giraffes were included in this study. The principal composition of the subgroups for analysis (feeding practice, season and sex) is described in Table 1.
The sample size was very small, and therefore, it should be noted that this is a pilot study to determine if metabolomics can be used as a tool to investigate the effects of age, sex, feeding practice and seasonal variation on the giraffe fecal composition.

3.2. Overview of the Metabolomic Data and Subgroup Comparisons

Clear variations between the data obtained from the SF and NAV giraffe samples were seen on the PCA scores plot, which is shown in Figure 1. Significantly higher concentrations of a number of compound classes (such as amino acid-related and carbohydrate-related metabolites), which are summarized in Table 2 and Figure 2, were noted for giraffes receiving SF compared to the NAV subgroup.
A clear separation between the data obtained from the wet and dry seasons, as identified from a single adult male giraffe (indicated as “I” in Table 1) was seen, as shown in Figure 3. Higher concentrations of a number of compound classes (carbohydrate-related metabolites and organic compounds) during the wet season were noted in Table 3. A heatmap of the compounds with an FC > 4 (wet versus dry comparison) identified from giraffe feces is visually summarized in Figure 4.
Variations in profiles were also identified for the metabolites from fecal samples collected from the six males and seven females, as shown in Figure 5. Differences, such as higher levels of carbohydrate-related metabolites, alkanes and phenols for female giraffes (compared to males) are noted in Table 4 and Figure 6.
Clear separate metabolite profiles were also identified for male and female giraffes provided with SF or NAV at different locations in the central Free State over a two-year period, as shown in Figure 7. Metabolomic data from the adult male giraffe (“I”) at Location 1 were excluded for this analysis in an attempt to balance the male and female representation. A heatmap of the of the significant annotated metabolites with an f-value > 13 (ANOVA; Fisher’s LSD) when comparing the male and female subgroups is visually summarized in Figure 8 and Table 5.
To investigate possible differences between adult and sub-adult individuals, a comparison was performed between the fecal samples collected from the one adult male (I) and the sub-adult male (II) at Location 1 (Table 1). A definite overlap in the data obtained from the adult male giraffe and the sub-adult was seen, as shown in Figure 9. Higher concentrations in male reproduction hormone metabolites, such as 3-hydroxyandrostan-17-one (androsterone) and testosterone, were noted for the adult male giraffe, as shown in Table 6. A heatmap of the compounds with an FC > 3 (adult male versus sub-adult male comparison) identified from giraffe feces is visually summarized in Figure 10.
The identified differentiating metabolites are hypothesis generating and can be used to guide the choice of future, more targeted analyses on giraffe health quantification. The following box–whisker plots were created and form part of the Supplementary Materials: Figure S1: Significant (p < 0.05) metabolites with an FC test (Log2 [FC] > 4) identified for giraffe receiving SF or only NAV; Figure S2: Significant (p < 0.05) metabolites with an FC test (Log2 [FC] > 4) identified for the wet and dry seasons from feces collected from a single adult male giraffe; Figure S3: Significant (p < 0.05) metabolites with an FC test (Log2 [FC] > 4) identified for male and female giraffes.

4. Discussion

The natural browsing diet of free-roaming giraffes includes material from trees and shrubs, such as leaves, shoots, flowers, fruits and twigs [31,32,33,34,35,36,37], and is rich in crude protein and lignin contents [38,39,40]. These materials are ingested, and in the rumen, the foregut fermentation process starts. Similar to other ruminants, giraffes depend on the symbiotic relationship with microorganisms in the rumen and the rest of the digestive system to digest and absorb energy such as volatile fatty acids (VFAs), which are locked in cellulose and hemicellulose [39,41,42,43,44,45,46,47], to nurture the animal, along with a great variety of excreted fecal metabolites as an end product [42,48,49,50]. As a result, feces are a mixture of undigested material, microbiota, enzymes and metabolites [51]. It is estimated that 40% of the daily energy is derived from VFAs and produced by microbial fermentation in the rumen [52] and that 60% is from the intestinal digestion of glucose [36].
Although several studies have been conducted on ruminant fecal metabolomes [6,53], most of the research focused on grazers (especially domestic livestock in animal production studies) and not browsers. Some fundamental differences exist between foraging and browsing materials, which are consumed by grazers and browsers, respectively, with regard to cell structure, plant chemistry and animal metabolism [54,55,56]. As a result, no direct relationships between the metabolomics of grazers and browsers can be drawn. Basic microbial fermentation and the created by-products do, however, remain the same for different types of ruminants. These would include short-chain fatty acids, aldehydes, alcohols, phenols, ketones, esters, carbohydrate-related metabolites and amino, propionic and butyric acids [57], only differing in concentrations and profiles based on the available diet and animal phenotype [58].
During the current study, clear variations in the metabolome profiles were noted for giraffe receiving SF and those only browsing on NAV, as illustrated in Figure 1. Comparatively, significantly higher concentrations of amino acid-related metabolites (threonine, oxoproline, serine, aspartic acid, alanine, glutamic acid and hydroxyphenylacetic acid) were identified for giraffes receiving SF, as shown in Table 2 and Figure 2. Amino acid-related metabolites are commonly used in the biosynthesis of proteins [29,59] and, in this case, can be indicative of the increased consumption of additional dietary supplements (SF) available from the commercially manufactured game pellets, which contain a mixture of forage, grain and roughage products, sugar by-products, proteins, minerals and trace mineral vitamins [23,24]. Significantly higher concentrations of organic compounds (pyrrolidine, pyrazine, gallic acid and pyrrolidinone) were, however, noted for giraffes with a diet of only NAV, as shown in Table 2 and Figure 2. Most likely, the greater availability and variety in natural vegetation options at Locations 2, 3 and 6 can be attributed to these significantly higher concentrations.
A significantly higher concentration in methylphosphonic acid (MPA) in the feces collected from SF giraffes (compared to NAV) was also noted. MPA is a breakdown product of organophosphorus compounds, which are found in some pesticides [60]. Its presence in feces could be due to environmental exposure or dietary sources [61]. Its increased detection as an end product from the digestive system of giraffes provided with SF raises questions about the safety of the SF or even the giraffes’ exposure to commercial farming practices at Locations 1, 4 and 5 (Table 1).
Fecal metabolome variations were identified in samples collected from a single adult male giraffe (Location 1, SF) during the wet and dry seasons, as shown in Figure 3. Differences, such as comparatively higher carbohydrate-related metabolites and organic compounds during the wet season were noted in Table 3 and Figure 4. This increase could be generally related to an increase in the available dietary energy during the spring and summer (wet) seasons [29,32,59], such as the growth of new plant shoots and flowers [36,62]. An increase in the sample size would most definitely aid in improving the statistical significance of a seasonal metabolome profile change and is noted as one of the limitations of the current study. A similar higher value for terthiophene, a pesticide [60], was noted from the feces of the adult male giraffe towards the end of the wet season. Its presence in feces could be due to environmental exposure or the dietary source [61]. Its increased detection as a digestion end product at the end of the wet season (M24, March 2023, Table 1) supports a possible increased environmental exposure or build-up coinciding with the continuous use of pesticides during the wet season, originating from surrounding agricultural practices.
Distinct variations in profiles were also identified for the metabolites from fecal samples collected from the six males and seven females, as shown in Figure 5. Differences, such as higher levels of carbohydrate-related metabolites, undecane and 3-ethylphenol for female giraffes (compared to males) were noted in Table 4 and Figure 6. Due to their ability to seek out the most nutritious plant parts, giraffes are known to be nonseasonal breeders [63]. Although the gestation time for the giraffe females were not determined for this study, it could be that the significantly higher concentrations of carbohydrate-related metabolites (xylose and arabinose) are indicative of an increased metabolism coinciding with the gestation period or milk production for lactation. Also of interest is the higher level of undecane. This organic compound (an alkane) has been found to be a mild sex attractant for various types of insects, such as moths and cockroaches [64], and an alert signal for a variety of ants [60,65]. During this study, undecane was seen to have the highest concentration in females with a lower concentration of xylose and arabinose metabolites in their feces. This observation leads to hypothesizing that undecane in the presence of female giraffe feces could be indicative of reproductive status, as previously found with fecal steroid analysis [63,66].
Excluding the metabolomic data from the adult male giraffe at Location 1 and comparing the profiles of the remaining five males and seven females still indicated clear separate metabolite profiles at the different locations, as summarized in Figure 7 and Figure 8 and Table 5. The concentration of Vinclozolin, again, a pesticide, was higher in the female (SF and NAV) subgroups when compared to the male subgroups. This compound has a antiandrogenic activity that has proven to result in delays in mammalian meiocyte differentiation and follicle growth [67]
To investigate possible differences between adult and sub-adult individuals, a comparison was conducted between the fecal samples collected from the single adult male (I) and the sub-adult male (II) at Location 1 (Table 1). A definite overlap in the data obtained from the adult male giraffe and the sub-adult from the same location was seen, as shown in Figure 9 and Figure 10. A notable difference was the higher concentrations in male reproduction hormone metabolites, such as 3-hydroxyandrostan-17-one (androsterone) and testosterone [60], as noted for the adult male giraffe in Table 6. The measurement of testosterone metabolites in male vertebrate feces has been previously described [68], with higher levels occurring during the breeding season and in sexually mature males [69], which would also explain the findings of the current study.

5. Conclusions

This is the first study of its kind to investigate the fecal metabolome of free-roaming giraffes, as well as the effects that external factors, such as environmental exposures, feeding practices, seasonal variations, age and sex, have on it. The variations in the fecal metabolome profiles that were identified for all subgroups using untargeted metabolomics analysis assist in the development of future improved study designs to further clarify its significance along with associated metabolic pathways. This novel use of fecal metabolomics adds to the non-invasive toolbox of analyses that do not require capture, restraint, blood collection and consequent influential stress factors to free-roaming giraffes. Such advances are beneficial towards the conservation of wildlife species on a larger scale.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/metabo14110586/s1. Figure S1: Significant (p < 0.05) metabolites with a fold-change test (Log2 [FC] > 4) identified for giraffe receiving SF or only NAV; Figure S2: Significant (p < 0.05) metabolites with a fold-change test (Log2 [FC] > 4) identified for the wet and dry seasons from feces collected from a single adult male giraffe; Figure S3: Significant (p < 0.05) metabolites with a fold-change test (Log2 [FC] > 4) identified for male and female giraffes. Other datasets can be made available by the authors on request.

Author Contributions

Conceptualization and methodology, A.G., I.d.P., G.O. and F.D.; formal analysis, A.G., I.d.P. and G.O.; investigation, A.G., I.d.P. and G.O.; resources, I.d.P., G.O. and F.D.; data curation, A.G.; writing—original draft preparation, A.G.; writing—review and editing, A.G., I.d.P., G.O. and F.D.; visualization, A.G., I.d.P., G.O. and F.D.; supervision, G.O. and F.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded, in part, by the National Research Foundation (NRF) of South Africa, grant number RA201126576714.

Institutional Review Board Statement

Ethical approval was obtained prior to the commencement of the study. The study was approved by the Animal Research Ethics Committee (AREC) of the University of the Free State (UFS) and Nature Conservation (reference numbers: UFS-AED2022/0006). The process was initiated with the formulation of a formal project proposal, which was submitted for review and approval by the scientific review panel, the official animal welfare committee (AREC) of the UFS and the State Veterinarian for Section 20 of the Animal Diseases Act, 1984 (Act No. 35 of 1984).

Informed Consent Statement

Not applicable.

Data Availability Statement

Dataset available on request from the authors.

Acknowledgments

The authors would like to thank the North-West University, Centre for Human Metabolomics, for the metabolomic GCxGC/TOFMS analysis. Appreciation is also extended to the landowners of the six locations within the central Free State who provided consent for fresh giraffe feces to be collected from their properties from 2021 to 2023.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chi, L.; Bian, X.; Gao, B.; Tu, P.; Lai, Y.; Ru, H.; Lu, K. Effects of the Artificial Sweetener Neotame on the Gut Microbiome and Fecal Metabolites in Mice. Molecules 2018, 23, 367. [Google Scholar] [CrossRef] [PubMed]
  2. Frankenfeld, C.L. Fecal Metabolome: New Addition to the Toolbox for Dietary Assessment? J. Nutr. 2022, 152, 2643–2644. [Google Scholar] [CrossRef]
  3. Erben, V.; Poschet, G.; Schrotz-King, P.; Brenner, H. Evaluation of Different Stool Extraction Methods for Metabolomics Measurements in Human Faecal Samples. BMJ Nutr. Prev. Health 2021, 4, 374–384. [Google Scholar] [CrossRef] [PubMed]
  4. Jain, A.; Li, X.H.; Chen, W.N. An Untargeted Fecal and Urine Metabolomics Analysis of the Interplay between the Gut Microbiome, Diet and Human Metabolism in Indian and Chinese Adults. Sci. Rep. 2019, 9, 9191. [Google Scholar] [CrossRef] [PubMed]
  5. Gregor, R.; Probst, M.; Eyal, S.; Aksenov, A.; Sasson, G.; Horovitz, I.; Dorrestein, P.C.; Meijler, M.M.; Mizrahi, I. Mammalian Gut Metabolomes Mirror Microbiome Composition and Host Phylogeny. ISME J. 2022, 16, 1262–1274. [Google Scholar] [CrossRef] [PubMed]
  6. Goldansaz, S.A.; Guo, A.C.; Sajed, T.; Steele, M.A.; Plastow, G.S.; Wishart, D.S. Livestock Metabolomics and the Livestock Metabolome: A Systematic Review. PLoS ONE 2017, 12, e0177675. [Google Scholar] [CrossRef]
  7. Osthoff, G.; Wiese, I.; Deacon, F. African Elephant Milk Short Saccharide and Metabolite Composition and Their Changes over Lactation. Animals 2023, 13, 544. [Google Scholar] [CrossRef]
  8. Van Zyl, C.D.W.; Van Reenen, M.; Osthoff, G.; Du Preez, I. Evaluation of BAYESIL for Automated Annotation of 1H NMR Data Using Limited Sample Volumes: Application to African Elephant Serum. Metabolomics 2023, 19, 31. [Google Scholar] [CrossRef]
  9. Di Minno, A.; Gelzo, M.; Stornaiuolo, M.; Ruoppolo, M.; Castaldo, G. The Evolving Landscape of Untargeted Metabolomics. Nutr. Metab. Cardiovasc. Dis. 2021, 31, 1645–1652. [Google Scholar] [CrossRef]
  10. Chen, L.; Lu, W.; Wang, L.; Xing, X.; Chen, Z.; Teng, X.; Zeng, X.; Muscarella, A.D.; Shen, Y.; Cowan, A.; et al. Metabolite Discovery through Global Annotation of Untargeted Metabolomics Data. Nat. Methods 2021, 18, 1377–1385. [Google Scholar] [CrossRef]
  11. Du Preez, I.; Loots, D.T. New Sputum Metabolite Markers Implicating Adaptations of the Host to Mycobacterium Tuberculosis, and Vice Versa. Tuberculosis 2013, 93, 330–337. [Google Scholar] [CrossRef] [PubMed]
  12. Garhwal, R.; Sangwan, K.; Mehra, R.; Bhardwaj, A.; Pal, Y.; Nayan, V.; Legha, R.A.; Tiwari, M.; Chauhan, M.S.; Iquebal, M.A.; et al. Comparative Metabolomics Analysis of Halari Donkey Colostrum and Mature Milk throughout Lactation Stages Using 1H Nuclear Magnetic Resonance. LWT 2023, 182, 114805. [Google Scholar] [CrossRef]
  13. Schmidt, D.A.; Ball, R.L.; Grobler, D.; Ellersieck, M.R.; Griffin, M.E.; Citino, S.B.; Bush, M. Serum Concentrations of Amino Acids, Fatty Acids, Lipoproteins, Vitamins A and E, and Minerals in Apparently Healthy, Free-ranging Southern Giraffe (Giraffa camelopardalis giraffe). Zoo. Biol. 2007, 26, 13–25. [Google Scholar] [CrossRef] [PubMed]
  14. Orellana, C.; Parraguez, V.H.; Arana, W.; Escanilla, J.; Zavaleta, C.; Castellaro, G. Use of Fecal Indices as a Non-Invasive Tool for Nutritional Evaluation in Extensive-Grazing Sheep. Animals 2020, 10, 46. [Google Scholar] [CrossRef] [PubMed]
  15. Pannoni, S.B.; Proffit, K.M.; Holben, W.E. Non-invasive Monitoring of Multiple Wildlife Health Factors by Fecal Microbiome Analysis. Ecol. Evol. 2022, 12, e8564. [Google Scholar] [CrossRef]
  16. Muller, Z. The IUCN Red List of Threatened Species: Giraffa Camelopardalis, Giraffe; Errata Version; 2016, Giraffa camelopardalis. The IUCN Red List of Threatened Species 2016: E.T9194A109326950. Available online: https://www.iucnredlist.org/species/9194/136266699 (accessed on 11 May 2024).
  17. Webster, A.B.; Ganswindt, A.; Small, C.; Rossouw, R. Optimised ICP-MS Quantification Method for Using Animal Faeces as a Measure of Protected Area Ecosystem Health. MethodsX 2021, 8, 101441. [Google Scholar] [CrossRef]
  18. Zhu, C.; Fasoli, S.; Isani, G.; Laghi, L. First Insights into the Urinary Metabolome of Captive Giraffes by Proton Nuclear Magnetic Resonance Spectroscopy. Metabolites 2020, 10, 157. [Google Scholar] [CrossRef]
  19. Mucina, L.; Rutherford, M.C. The Vegetation of South Africa, Lesotho and Swaziland; South African National Biodiversity Institute: Pretoria, South Africa, 2006; Volume Strelitzia 19, ISBN 978-1-919976-21-1. [Google Scholar]
  20. Wrench, J.M.; Meissner, H.H.; Grant, C.C.; Casey, N.H. Environmental Factors That Affect the Concentration of P and N in Faecal Samples Collected for the Determination of Nutritional Status. Koedoe 1996, 39, a291. [Google Scholar] [CrossRef]
  21. Carbillet, J.; Palme, R.; Maublanc, M.L.; Cebe, N.; Gilot-Fromont, E.; Verheyden, H.; Rey, B. Instability of Fecal Glucocorticoid Metabolites at 4 °C: Time to Freeze Matters. J. Exp. Zool. Part. A Ecol. Integr. Physiol. 2023, 339, 625–632. [Google Scholar] [CrossRef]
  22. Leslie, D.M.; Starkey, E.E. Fecal Indices to Dietary Quality of Cervids in Old-Growth Forests. J. Wildl. Manag. 1985, 49, 142. [Google Scholar] [CrossRef]
  23. Wild G Lubern Normal Game Pellet Composition 2024. Product Information Sheet. Available online: http://www.lubern.co.za/wp-content/uploads/2019/02/Lubern-Booklet2-2_Page_45.jpg (accessed on 22 October 2024).
  24. Voermol Game Pellet Composition 2024. Product Information Sheet. Available online: https://voermol.co.za/wp-content/uploads/2018/07/Game_Pellets_100.pdf (accessed on 11 May 2024).
  25. Phua, L.C.; Koh, P.K.; Cheah, P.Y.; Ho, H.K.; Chan, E.C.Y. Global Gas Chromatography/Time-of-Flight Mass Spectrometry (GC/TOFMS)-Based Metabonomic Profiling of Lyophilized Human Feces. J. Chromatogr. B 2013, 937, 103–113. [Google Scholar] [CrossRef] [PubMed]
  26. Broadhurst, D.; Goodacre, R.; Reinke, S.N.; Kuligowski, J.; Wilson, I.D.; Lewis, M.R.; Dunn, W.B. Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies. Metabolomics 2018, 14, 72. [Google Scholar] [CrossRef] [PubMed]
  27. Beukes, D.; Van Reenen, M.; Loots, D.T.; Du Preez, I. Tuberculosis Is Associated with Sputum Metabolome Variations, Irrespective of Patient Sex or HIV Status: An Untargeted GCxGC-TOFMS Study. Metabolomics 2023, 19, 55. [Google Scholar] [CrossRef]
  28. Excel Microsoft 365 Office Excel Data Analysis ToolPak 2016, Version 2409; Microsoft: Redmond, WA, USA.
  29. Pang, Z.; Lu, Y.; Zhou, G.; Hui, F.; Xu, L.; Viau, C.; Spigelman, A.F.; MacDonald, P.E.; Wishart, D.S.; Li, S.; et al. MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation. Nucleic Acids Res. 2024, 52, W398–W406. [Google Scholar] [CrossRef]
  30. R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2024. [Google Scholar]
  31. Leuthold, B.M.; Leuthold, W. Food Habits of Giraffe in Tsavo National Park, Kenya. East. Afr. Wildl. J. 1972, 10, 129–141. [Google Scholar] [CrossRef]
  32. Janecke, B.B.; Smit, G.N. Faecal Nitrogen of Browser and Mixed Feeder Game Species during Different Seasons. Afr. J. Range Forage Sci. 2015, 32, 203–212. [Google Scholar] [CrossRef]
  33. Paulse, J. The Behaviour and Feeding Ecology of Extralimital Giraffe within Albany Thicket Vegetation in the Little Karoo, South Africa. Master’s Thesis, University of the Western Cape, Bellville, South Africa, 2018. [Google Scholar]
  34. Deacon, F.; Smit, G.N.; Grobbelaar, A. Resources and Habitat Requirements for Giraffes’ (Giraffa Camelopardalis) Diet Selection in the Northwestern Kalahari, South Africa. Animals 2023, 13, 2188. [Google Scholar] [CrossRef]
  35. Dagg, A.I. Food Preferences of the Giraffe. Proc. Zool. Soc. Lond. 1960, 135, 640–642. [Google Scholar]
  36. Mitchell, G. How Giraffes Work; Oxford University Press: New York, NY, USA, 2021; ISBN 978-0-19-757119-4. [Google Scholar]
  37. Theron, M.E. Voedingsgedrag van kameelperde (Giraffa camelopardalis) in die Sentrale Vrystaat. Master’s Thesis, University of the Free State, Bloemfontein, South Africa, 2005. [Google Scholar]
  38. Caister, L.E.; Shields, W.M.; Gosser, A. Female Tannin Avoidance: A Possible Explanation for Habitat and Dietary Segregation of Giraffes (Giraffa camelopardalis peralta) in Niger. Afr. J. Ecol. 2003, 41, 201–210. [Google Scholar] [CrossRef]
  39. Roggenbuck, M.; Sauer, C.; Poulsen, M.; Bertelsen, M.F.; Sørensen, S.J. The Giraffe (Giraffa Camelopardalis) Rumen Microbiome. FEMS Microbiol. Ecol. 2014, 90, 237–246. [Google Scholar] [CrossRef] [PubMed]
  40. Shipley, L.A.; Blomquist, S.; Danell, K. Diet Choices Made by Free-Ranging Moose in Northern Sweden in Relation to Plant Distribution, Chemistry, and Morphology. Can. J. Zool. 1998, 76, 1722–1733. [Google Scholar] [CrossRef]
  41. Saengkerdsub, S.; Ricke, S.C. Ecology and Characteristics of Methanogenic Archaea in Animals and Humans. Crit. Rev. Microbiol. 2013, 40, 97–116. [Google Scholar] [CrossRef] [PubMed]
  42. Ley, R.E.; Hamady, M.; Lozupone, C.; Turnbaugh, P.J.; Ramey, R.R.; Bircher, J.S.; Schlegel, M.L.; Tucker, T.A.; Schrenzel, M.D.; Knight, R.; et al. Evolution of Mammals and Their Gut Microbes. Science 2008, 320, 1647–1651. [Google Scholar] [CrossRef]
  43. Dehority, B.A.; Odenyo, A.A. Influence of Diet on the Rumen Protozoal Fauna of Indigenous African Wild Ruminants. J. Eukaryot. Microbiol. 2003, 50, 220–223. [Google Scholar] [CrossRef]
  44. van der Walt, E. Characterisation of the African Elephant (Loxodonta Africana) Gut Microbiome for Bio-Energy Applications. Master’s Thesis, University of the Free State, Bloemfontein, South Africa, 2020. [Google Scholar]
  45. Youngblut, N.D.; Reischer, G.H.; Walters, W.; Schuster, N.; Walzer, C.; Stalder, G.; Ley, R.E.; Farnleitner, A.H. Host Diet and Evolutionary History Explain Different Aspects of Gut Microbiome Diversity among Vertebrate Clades. Nat. Commun. 2019, 10, 2200. [Google Scholar] [CrossRef]
  46. Zierer, J.; Jackson, M.A.; Kastenmüller, G.; Mangino, M.; Long, T.; Telenti, A.; Mohney, R.P.; Small, K.S.; Bell, J.T.; Steves, C.J.; et al. The Fecal Metabolome as a Functional Readout of the Gut Microbiome. Nat. Genet. 2018, 50, 790–795. [Google Scholar] [CrossRef]
  47. Zoelzer, F.; Burger, A.L.; Dierkes, P.W. Unraveling Differences in Fecal Microbiota Stability in Mammals: From High Variable Carnivores and Consistently Stable Herbivores. Anim. Microbiome 2021, 3, 77. [Google Scholar] [CrossRef]
  48. Leslie, D.M.; Bowyer, R.T.; Jenks, J.A. Facts From Feces: Nitrogen Still Measures Up as a Nutritional Index for Mammalian Herbivores. J. Wildl. Manag. 2008, 72, 1420–1433. [Google Scholar] [CrossRef]
  49. Codron, D.; Codron, J. Reliability of δ13C and δ15N in Faeces for Reconstructing Savanna Herbivore Diet. Mamm. Biol. 2009, 74, 36–48. [Google Scholar] [CrossRef]
  50. Ley, R.E.; Lozupone, C.A.; Hamady, M.; Knight, R.; Gordon, J.I. Worlds within Worlds: Evolution of the Vertebrate Gut Microbiota. Nat. Rev. Microbiol. 2008, 6, 776–788. [Google Scholar] [CrossRef] [PubMed]
  51. Giebelhaus, R.T.; Nguyen, G.; Schmidt, S.A.; Wang, S.; Mesfin, E.Y.; Nam, S.L.; De La Mata, A.P.; Harynuk, J.J. GC×GC-TOFMS Analysis of Fecal Metabolome Stabilized Using an At-Home Stool Collection Device. Appl. Biosci. 2024, 3, 348–359. [Google Scholar] [CrossRef]
  52. Dagg, A.I. Giraffe: Biology, Behaviour and Conservation; Cambridge University Press: Cambridge, UK, 2014; ISBN 978-1-107-03486-0. [Google Scholar]
  53. Malheiros, J.M.; Correia, B.S.B.; Ceribeli, C.; Cardoso, D.R.; Colnago, L.A.; Junior, S.B.; Reecy, J.M.; Mourão, G.B.; Coutinho, L.L.; Palhares, J.C.P.; et al. Comparative Untargeted Metabolome Analysis of Ruminal Fluid and Feces of Nelore Steers (Bos indicus). Sci. Rep. 2021, 11, 12752. [Google Scholar] [CrossRef] [PubMed]
  54. Shipley, L.A. Grazers and Browsers: How Digestive Morphology Affects Diet Selection. Ida. For. Wildl. Range Exp. Sta. Bull. 1999, 70, 20–27. [Google Scholar]
  55. Gordon, I.J. Browsing and Grazing Ruminants: Are They Different Beasts? For. Ecol. Manag. 2003, 181, 13–21. [Google Scholar] [CrossRef]
  56. Krizsan, S.J.; Mateos-Rivera, A.; Bertilsson, S.; Felton, A.; Anttila, A.; Ramin, M.; Vaga, M.; Gidlund, H.; Huhtanen, P. An in Vitro Evaluation of Browser and Grazer Fermentation Efficiency and Microbiota Using European Moose Spring and Summer Foods. Ecol. Evol. 2018, 8, 4183–4196. [Google Scholar] [CrossRef]
  57. Cordeiro, L.; Cabrita, A.R.J.; Oliveira, H.M.; Maia, M.R.G.; Rodrigues, J.A.; Fonseca, A.J.M.; Valente, I.M. A Novel Approach for Monitoring the Volatile Metabolome in Biological Samples from Ruminants through Miniaturized Liquid–Liquid Extraction and Multiclass Gas Chromatography Analysis. J. Agric. Food Chem. 2022, 70, 3886–3897. [Google Scholar] [CrossRef]
  58. Schrimpe-Rutledge, A.C.; Codreanu, S.G.; Sherrod, S.D.; McLean, J.A. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. J. Am. Soc. Mass. Spectrom. 2016, 27, 1897–1905. [Google Scholar] [CrossRef]
  59. Kim, S.; Chen, J.; Cheng, T. PubChem 2023 Update. Available online: https://pubchem.ncbi.nlm.nih.gov/docs/citation-guidelines (accessed on 15 August 2024).
  60. Wishart, D.S.; Guo, A.C.; Oler, E.; Wang, F.; Anjum, A.; Peters, H.; Dizon, R.; Sayeeda, Z.; Siyang, T. HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Res. 2022, 50, D622–D631. [Google Scholar] [CrossRef]
  61. Public Health Statement—Diisopropyl Methylphosphonate. 1998. Available online: https://www.atsdr.cdc.gov/ToxProfiles/tp119-c1-b.pdf (accessed on 11 May 2024).
  62. Dagg, A.I.; Foster, J.B. The Giraffe: Its Biology, Behavior and Ecology.; Van Nostrand Reinhold Co.: New York, NY, USA, 1976. [Google Scholar]
  63. Bercovitch, F.B.; Bashaw, M.J.; Del Castillo, S.M. Sociosexual Behavior, Male Mating Tactics, and the Reproductive Cycle of Giraffe Giraffa Camelopardalis. Horm. Behav. 2006, 50, 314–321. [Google Scholar] [CrossRef]
  64. Gundamalla, R.; Bantu, R.; Subba Reddy, B.V. Concise Approach for the Synthesis of 1,7-Dioxaspiro[5.5]Undecane, a Dacus Oleae Pheromone. Results Chem. 2023, 5, 100993. [Google Scholar] [CrossRef]
  65. Guo, X.; Lin, M.R.; Azizi, A.; Saldyt, L.P.; Kang, Y.; Pavlic, T.P.; Fewell, J.H. Decoding Alarm Signal Propagation of Seed-Harvester Ants Using Automated Movement Tracking and Supervised Machine Learning. Proc. R. Soc. B 2022, 289, 20212176. [Google Scholar] [CrossRef] [PubMed]
  66. del Castillo, S.M.; Bashaw, M.J.; Patton, M.L.; Rieches, R.R.; Bercovitch, F.B. Fecal Steroid Analysis of Female Giraffe (Giraffa camelopardalis) Reproductive Condition and the Impact of Endocrine Status on Daily Time Budgets. Gen. Comp. Endocrinol. 2005, 141, 271–281. [Google Scholar] [CrossRef] [PubMed]
  67. González-Sanz, S.; Barreñada, O.; Rial, E.; Brieño-Enriquez, M.A.; Del Mazo, J. The Antiandrogenic Vinclozolin Induces Differentiation Delay of Germ Cells and Changes in Energy Metabolism in 3D Cultures of Fetal Ovaries. Sci. Rep. 2020, 10, 18036. [Google Scholar] [CrossRef]
  68. Auer, K.E.; Kußmaul, M.; Möstl, E.; Hohlbaum, K.; Rülicke, T.; Palme, R. Measurement of Fecal Testosterone Metabolites in Mice: Replacement of Invasive Techniques. Animals 2020, 10, 165. [Google Scholar] [CrossRef]
  69. Otsuki, M.; Kohyama, K.; Goshima, W.; Kobayashi, M.; Hasegawa, Y.; Morita, Y.; Ijiri, S.; Mitani, Y. Non-Invasive Monitoring of Faecal Testosterone Metabolite Concentrations in a Northern Fur Seal (Callorhinus ursinus). Jpn. J. Zoo. Wildl. Med. 2020, 25, 29–34. [Google Scholar] [CrossRef]
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Figure 1. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles of giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV). PC: principal component.
Figure 1. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles of giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV). PC: principal component.
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Figure 2. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant (p < 0.05) annotated metabolites with a fold-change test (Log2 [FC]) of >4, when comparing data from giraffes receiving supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV).
Figure 2. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant (p < 0.05) annotated metabolites with a fold-change test (Log2 [FC]) of >4, when comparing data from giraffes receiving supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV).
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Figure 3. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles of a single giraffe during the wet and dry seasons over a two-year period. PC: principal component.
Figure 3. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles of a single giraffe during the wet and dry seasons over a two-year period. PC: principal component.
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Figure 4. Heatmap (Pearson distance calculation and ward clustering algorithm) of the annotated metabolites with a fold-change test (Log2 [FC]) of >4 when comparing seasonal variations (wet and dry seasons) from feces collected from a single adult male giraffe at a central Free State location over a two-year period.
Figure 4. Heatmap (Pearson distance calculation and ward clustering algorithm) of the annotated metabolites with a fold-change test (Log2 [FC]) of >4 when comparing seasonal variations (wet and dry seasons) from feces collected from a single adult male giraffe at a central Free State location over a two-year period.
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Figure 5. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for all male and female giraffes in free-roaming populations at six different locations in the central Free State over a two-year period. PC: principal component.
Figure 5. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for all male and female giraffes in free-roaming populations at six different locations in the central Free State over a two-year period. PC: principal component.
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Figure 6. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant (p < 0.05) annotated metabolites with a fold-change test (Log2 [FC]) of >4 when comparing male and female giraffes’ metabolite profiles.
Figure 6. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant (p < 0.05) annotated metabolites with a fold-change test (Log2 [FC]) of >4 when comparing male and female giraffes’ metabolite profiles.
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Figure 7. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded. PC: principal component.
Figure 7. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded. PC: principal component.
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Figure 8. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant annotated metabolites with an f-value > 13 (ANOVA; Fisher’s LSD) when comparing male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded.
Figure 8. Heatmap (Pearson distance calculation and ward clustering algorithm) of the significant annotated metabolites with an f-value > 13 (ANOVA; Fisher’s LSD) when comparing male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded.
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Figure 9. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for an adult male giraffe and sub-adult male giraffe at the same location in the central Free State over a two-year period. PC: principal component.
Figure 9. Principal component analysis scores plot (PC 1 and 2, with the variation explained in brackets) of fecal metabolite profiles identified for an adult male giraffe and sub-adult male giraffe at the same location in the central Free State over a two-year period. PC: principal component.
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Figure 10. Heatmap (Pearson distance calculation and ward clustering algorithm) of the annotated metabolites with a fold-change test (Log2 [FC]) of >3), when comparing the metabolite profiles of an adult male giraffe and sub-adult male giraffe at the same location.
Figure 10. Heatmap (Pearson distance calculation and ward clustering algorithm) of the annotated metabolites with a fold-change test (Log2 [FC]) of >3), when comparing the metabolite profiles of an adult male giraffe and sub-adult male giraffe at the same location.
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Table 1. Overview of the samples collected and analyzed for the fecal metabolomes of 13 different giraffes provided with supplemental feeding (SF) and those only fed with the natural available vegetation (NAV) at different locations within the central Free State over a two-year period. The samples collected and analyzed for the fecal metabolome from a single adult male giraffe at a Location 1, across the wet and dry seasons over the two-year period, are also indicated.
Table 1. Overview of the samples collected and analyzed for the fecal metabolomes of 13 different giraffes provided with supplemental feeding (SF) and those only fed with the natural available vegetation (NAV) at different locations within the central Free State over a two-year period. The samples collected and analyzed for the fecal metabolome from a single adult male giraffe at a Location 1, across the wet and dry seasons over the two-year period, are also indicated.
Sample CodeFeeding PracticeLocationAge Class, Sex and Individual NumberDate(s) of Fecal Collection 1Assigned Season 2
M01Supplemental feeding1Adult male (I)03, 18, 31 August 2021Winter, dry
M031Adult male (I)09, 18, 28 March 2022Autumn, wet
M051Adult male (I)01, 14, 22 June 2022Winter, dry
M071Adult male (I)04, 23 November 2022Summer, wet
M021Adult male (I)13 February 2022Summer, wet
M041Adult male (I)05 and 27 April 2022Autumn, wet
M061Adult male (I)11, 24, 25 August 2022Winter, dry
M221Adult male (I)07 April 2023Autumn, wet
M241Adult male (I)20 March 2023Autumn, wet
M091Sub-adult male (II)11 February 2022N/A 2
M111Sub-adult male (II)13, 19, 27 April 2022N/A
M191Sub-adult male (II)07 April 2023N/A
M081Sub-adult male (II)04, 09, 23 August 2021N/A
M101Sub-adult male (II)09, 18 March 2022N/A
M164Adult male (III)10 March 2023N/A
M184Adult female (IV)10 March 2023N/A
M285Adult female (V)13 September 2022N/A
M305Adult female (V)09 October 2022N/A
M15Natural available vegetation2Adult male (VI)10 March 2023N/A
M232Adult female (VII)22 April 2023N/A
M142Adult female (VIII)10 March 2023N/A
M253Adult male (IX)10 April 2023N/A
M273Adult female (X)10 April 2023N/A
M263Adult female (XI)10 April 2023N/A
M136Adult male (XII)16 February 2023N/A
M126Adult male (XIII)16 February 2023N/A
1 If collected on more than one date, the samples were pooled per month. 2 Wet versus dry season comparison was only performed for a single adult male giraffe (indicated as “I”).
Table 2. Statistically significant (p < 0.05) fecal metabolites identified when comparing giraffes provided with SF and giraffes only feeding on the NAV during a two-year period.
Table 2. Statistically significant (p < 0.05) fecal metabolites identified when comparing giraffes provided with SF and giraffes only feeding on the NAV during a two-year period.
ClassSignificant Differential Compounds (SF and NAV)FCLog2 (FC)Higher (↑) or Lower (↓) in SFp-Value
Amino acid relatedThreonine42.555.41↑p < 0.05
Amino acid related5-Oxoproline18.674.22↑p < 0.05
Amino acid relatedSerine18.494.21↑p < 0.05
Amino acid relatedAspartic acid17.684.14↑0.003
Amino acid relatedAlanine17.394.12↑0.0011
Amino acid relatedGlutamic acid17.384.12↑0.0002
Amino acid related3-Hydroxyphenylacetic acid17.324.11↑0.002
Organic compoundPyrrolidine0.029−5.10↓p < 0.05
Organic compoundPyrazine0.03−4.91↓p < 0.05
Organic compoundSqualene31.364.97↑p < 0.05
Organic compoundGallic acid0.05−4.30↓p < 0.05
Organic compoundNaphthalene17.494.13↑0.0002
Organic compound2-Pyrrolidinone0.058−4.31↓p < 0.05
Dicarboxylic acid2,3-Dimethylsuccinic acid22.764.51↑p < 0.05
Fatty alcohol2-Dodecanol44.955.49↑0.0002
Fatty nitrilesDodecanenitrile0.035−4.83↓p < 0.05
PhenolFerruginol35.695.16↑p < 0.05
Phenol5-Nonadecylresorcinol71.856.17↑0.0003
Inorganic compoundMethylphosphonic acid77.356.27↑0.0004
FC: fold change; NAV: natural available vegetation; SF: supplemental feeding.
Table 3. Significant differential fecal metabolites (with regards to fold change) identified from samples collected from a single adult male giraffe during the dry and wet seasons over a two-year period. No compounds were classified as significant with the t-test analyses (p < 0.05).
Table 3. Significant differential fecal metabolites (with regards to fold change) identified from samples collected from a single adult male giraffe during the dry and wet seasons over a two-year period. No compounds were classified as significant with the t-test analyses (p < 0.05).
ClassSignificant Differential Compounds
(Wet and Dry Seasons)		FCLog2 (FC)Higher (↑) or Lower (↓) in the Wet Season
Amino acid relatedN-Acetylglucosamine18.434.20↓
Carbohydrate relatedDeoxyglucose0.02−5.87↑
Carbohydrate relatedD-Fructose0.04−4.51↑
Carbohydrate relatedN-Acetyl glucosamine methoxime0.05−4.43↑
Carbohydrate relatedTalopyranose0.05−4.25↑
Carbohydrate relatedd-Glucose0.06−4.09↑
Carbohydrate relatedMethyl galactoside0.001−9.08↑
Carbohydrate relatedD-Gluconic acid0.06−4.12↑
Carbohydrate relatedEthyl à-D-glucopyranoside0.06−4.01↑
Organic compoundBenzyl alcohol0.03−5.18↑
Organic compoundTerthiophene0.03−4.92↑
SterolStigmastanol0.03−4.98↑
Inorganic compoundSilane0.06−4.19↑
FC: fold change.
Table 4. Statistically significant fecal metabolites identified from samples from male and female giraffes’ feces, collected from July 2021 to June 2023 from different locations in the central Free State.
Table 4. Statistically significant fecal metabolites identified from samples from male and female giraffes’ feces, collected from July 2021 to June 2023 from different locations in the central Free State.
ClassSignificant Differential Compounds (Males and Females)FCLog2 (FC)Higher (↑) or Lower (↓) in Femalesp-Value
Amino acid relatedL-Aspartic acid0.03−5.06↓0.0002
Carbohydrate relatedD-Arabinose1559813.93↑0.001
Carbohydrate relatedD-Xylose UM 1601587.510.63↑0.001
Carbohydrate relatedD-Xylose UM 10499.566.637↑0.001
Organic compoundPyrrolidine21.614.434↑0.0003
Organic compoundMethylphosphonic acid0.04−4.477↓NS
AlkanesUndecane18.504.21↑0.0002
Phenol3-Ethylphenol50.455.66↑0.001
Phenol5-Nonadecylresorcinol0.055−4.19↓0.001
FC: fold change; NS: not significant; UM: unique.
Table 5. Differential significant annotated metabolites with an f-value > 13 (ANOVA; Fisher’s LSD), when comparing male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded.
Table 5. Differential significant annotated metabolites with an f-value > 13 (ANOVA; Fisher’s LSD), when comparing male and female giraffes provided with supplemental feed (SF) and giraffes only feeding on the natural available vegetation (NAV) at different locations in the central Free State over a two-year period. Metabolomic data from the adult male giraffe at Location 1 were excluded.
ClassSignificant Differential Compounds
(SF Females, SF Males, NAV Females and NAV Males)		f-Valuep-ValueHigher (↑) or Lower (↓) in SF Females Compared to SF Males 1Higher (↑) or Lower (↓) in NAV Females Compared to NAV Males
Amino acid relatedSerine14.105<0.001↓↑
Carbohydrate relatedD-Talofuranose14.235<0.001↓↓
Fatty nitrilesDodecanenitrile27.559<0.001↑↑
Organic compoundBipyridine70.445<0.001↓↑
Organic compound2-Butyl-3-methylpyrazine27.936<0.001↑↑
Organic compoundPyrrolidinyl-2 propanone27.675<0.001↑↓
Organic compoundCaryophyllene23.22<0.001↓↑
Organic compoundVinclozolin21.978<0.001↑↑
Organic compoundHeptanedioic acid20.719<0.001↑↑
Organic compoundHumulene18.458<0.001↓↑
Organic compoundStigmasterol18.444<0.001↓↓
Organic compoundGallic acid14.032<0.001↑↑
Organic compoundNaphthalene13.417<0.001↓↑
1 ANOVA: analysis of variance; LSD: least significant difference; NAV: natural available vegetation; SF: supplemental feeding.
Table 6. Differential fecal metabolites (fold-change test (Log2 [FC]) of >3) identified from fecal samples collected from an adult male giraffe and sub-adult male from July 2021 to June 2023 at the same location in the central Free State.
Table 6. Differential fecal metabolites (fold-change test (Log2 [FC]) of >3) identified from fecal samples collected from an adult male giraffe and sub-adult male from July 2021 to June 2023 at the same location in the central Free State.
ClassSignificant Differential Compounds
(Adult and Sub-Adult Males)		FCLog2 (FC)Higher (↑) or Lower (↓) in the Adult Malep-Value
Amino acid relatedL-proline0.11−3.14↓NS
Carbohydrate relatedd-Mannose0.086−3.55↓NS
Carbohydrate relatedD-Talopyranose9.053.18↑NS
Organic compoundTriazole0.039−4.70↓NS
Organic compoundNonadecylresorcinol18.504.21↑NS
Organic compound3-hydroxyandrostan-17-one (androsterone)14.023.81↑<0.001
Organic compoundTestosterone13.773.78↑NS
Organic compoundEthene0.10−3.26↓NS
Organic compoundPentanoic acid10.873.44↑NS
Organic compoundHordenin0.11−3.14↓NS
NS: not significant.
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Grobbelaar, A.; Osthoff, G.; du Preez, I.; Deacon, F. First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study. Metabolites 2024, 14, 586. https://doi.org/10.3390/metabo14110586

AMA Style

Grobbelaar A, Osthoff G, du Preez I, Deacon F. First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study. Metabolites. 2024; 14(11):586. https://doi.org/10.3390/metabo14110586

Chicago/Turabian Style

Grobbelaar, Andri, Gernot Osthoff, Ilse du Preez, and Francois Deacon. 2024. "First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study" Metabolites 14, no. 11: 586. https://doi.org/10.3390/metabo14110586

APA Style

Grobbelaar, A., Osthoff, G., du Preez, I., & Deacon, F. (2024). First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study. Metabolites, 14(11), 586. https://doi.org/10.3390/metabo14110586

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