Exploration of urinary metabolite dynamicity for early detection of pregnancy in water buffaloes

doi:10.21203/rs.3.rs-1346568/v1

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Exploration of urinary metabolite dynamicity for early detection of pregnancy in water buffaloes

https://doi.org/10.21203/rs.3.rs-1346568/v1

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Early and precise pregnancy diagnosis can reduce the calving interval by minimizing postpartum barren period. The present study explored the differential urinary metabolites between pregnant and non-pregnant Murrah buffaloes (Bubalus bubalis) during early gestation to identify potential pregnancy detection biomarkers. Urine samples were collected on day 0, 10, 18, 35 and 42 of gestation from the pregnant and on day 0, 10 and 18 post-insemination from the non-pregnant animals. ¹H-NMR-based untargeted metabolomics followed by multivariate analysis initially identified twenty-four differentially expressed metabolites, among them 3-Hydroxykynurenine, Anthranilate, Tyrosine and 5-Hydroxytryptophan depicted consistent trends and matched the selection criteria of potential biomarkers. Predictive ability of these individual biomarkers through ROC curve analyses yielded AUC values 0.6-0.8. Subsequently, a logistic regression model was constructed using the most suitable metabolite combination to improve the diagnostic accuracy. The combination of Anthranilate, 3-Hydroxykynurenine, and Tyrosine yielded the best AUC value of 0.804. Aromatic amino acid biosynthesis, Tryptophan metabolism, Phenylalanine and Tyrosine metabolism were identified as potential pathway modulations during early gestation. The identified biomarkers were either precursor or products of these metabolic pathways, thus justifying their relevance. The study facilitates precise non-invassive urinary metabolite-based pen-side early pregnancy diagnostics in buffaloes, eminently before 21 days post-insemination.

Buffalo

Pregnancy diagnosis

Biomarkers

Urinary metabolomics

1H-NMR

Livestock is crucial for global food security and livelihoods. Although demand for animal products is anticipated to increase significantly in the near future, the ensuing climate change will intensify the competition for resources further demanding enhancement in productivity as well as efficiency from animal husbandry practices. The buffaloes, contributing about half of total milk production and 18.85% to meat production, are the backbone of Indian livestock sector [1]. Moreover, buffalo are gradually preferred as livestock species for dairy farming due to their inherent advantages such asimproved feed conversion efficiency and higher economic returns in terms of higher fat, milk and meat production. Considering 2003 as the base year, approximately 18% increase is expected in buffalo population by the year 2023 [2]. Among all buffalo breeds, Murrah is termed as ‘black gold' in India due tohigh lactation yields and efficient adaptation to the dry plane topography.

Production profile of livestock is flipside of their reproductive performances. Early pregnancy diagnosis alongside the factors regulating smooth progression of healthy pregnancy are important aspects for optimizing the production as it can reduce the calving interval by identifying the non-pregnant animals, their timely treatment and rebreeding to optimise the postpartum barren interval. It is presumed that monitoring of the sequential changes in metabolite profile from the day of estrus to successful conception and through progression of gestation, can lead to discovery of molecules, which will perhaps be novel and specific to the physiological stage of the animal. This have paved the way for identifying the metabolite signatures as early pregnancy diagnosis biomarkers by employing state-of-the art metabolomics intervention.

Pregnancy is a remarkable effectual stage; making an animal acclimatized to certain systemic changes anatomically, physiologically and metabolically to ensure apt fetal development [3]. Precise, timely and uninterrupted delivery of oxygen, nutrients, hormones, and biophysical cues from the mother to fetus is essential for optimum fetal growth [4]. Diligent introspection over the last three decades have vividly revealed that maternal nutrition perturbation (under/over) can affect the fetal growth and development in the intrauterine environment [5]. Further, early embryonic and foetal losses in high producing bovines has been enumerated to be around 56%. Subsequently, by day 42 of pregnancy, conception and implantation in bovine (cow and buffaloes) is successfully completed and the embryonic mortality falls below 5% afterwards [6]. This clearly urges in-depth introspection into the events of maternal metabolic reprogramming with more emphasis towards the early stage of pregnancy. Because the maternal metabolite trajectory can be valuable for evaluating foetal growth alongside feto-maternal associations, interactions and associated physio-pathological alterations [7, 8]. The unearthed knowledge can extend the scope for metabolite maneuvering through precise maternal management to curtail the early pregnancy loss. Despite such profound significance, knowledge database regarding the maternal metabolite dynamicity in early stage of pregnancy is inadequate in most of the bovine species including buffaloes. Urine is a preferred biofluid for metabolite analysis as it provides non-invasive collection method, serves as the repository of diverse metabolites released from the complex feto-maternal metabolic networkand already facilitated biomarker discovery in similar context [9]. Besides requiring minimal sample preparation, Nuclear magnetic resonance (NMR) spectoscopyis an ideal modality for metabolite analysis in biological fluids as it extends a perfect blend of ample robustness, considerable sensitivity along with proficient reliability for urinary metabolomics introspection [10] and demonstrating differential presence of biomolecules in relation to pathophysiological changes [11]. Thus, the current investigation has been carried out to explicate the maternal urinary metabolite dynamicity during early pregnancy in buffaloes as well as to identify the potential early pregnancy diagnostic metabolomics signatures through NMR analysis in a need-based and timely manner. This may satisfy the quest for metabolic biomarkers of early pregnancy diagnosis as well as deciphering the optimum metabolic profile of healthy pregnancy and elucidating key regulated metabolic pathways during early gestation.

Identification of the differentially expressed urinary metabolites

A total of twenty-four metabolites were identified through ¹H NMR analysis in the urine samples of pregnant and non-inseminated control anaimals at different days of estrous cycle/pregnancy (Table 1). Among them, twenty metabolites were consistently detected in the urine samples of pregnant as well as control animals at all the relevant experimental days. The four metabolites: 1,6-Anhydro-β-D-Glucose, Fumarate, Maleate and Tyramine that were not consistently detected in the urine samples of pregnant as well as control animals at all the different experimental days were excluded from univariate statistical analysis. However, Tyramine was exclusively detected in the pregnant animals only after 18 days of pregnancy with a consistently up-regulating trend during the subsequent experimental days. Among the twenty consistently detected urinary metabolites, 1-Methylhistidine, 3-Hydroxykynurenine, 3-Indoxysulfate, Anthranilate, Phenylalanine, Tryptophan, Tyrosine and 5-Hydroxytryptophan were depicted statistically significant (P < 0.05, FDR 0.05) differentiating pattern in ANOVA analysis at day 10 and day 18 between pregnant and control animals as well as with the respective metabolite concentrations in day 0 samples (Table 1). However, the filtering the criteria of the differentially expressed metabolites for potential early pregnancy biomarker identification were set as minimum 2-fold metabolite up/down-regulation at day 18 onwards in pregnant samples with respect to metabolite concentration of control animals at all day points except day 0 along with a consistent trend in metabolite up/down-regulation upto 42 days of pregnancy. The day 0 was escaped as it is the day of estrus showing typical receptive behavioural pattern of the animals to easily differentiate from pregnany. Four differentially expressed metabolites viz. 3-Hydroxykynurenine, Anthranilate, Tyrosine and 5-Hydroxytryptophan satisfied all the aforesaid criteria of the potential early pregnancy detection biomarker and subjected to Receiver Operating Characteristic (ROC) curve analyses. Among these four metabolites, anthranilate and 5-Hydroxytryptophan matched all the criteria as well as depicted prominent up-regulating trend as early as day 10 of pregnancy with over 10-fold increase with respect their respective day 0 level. Despite being differentially expressed, 1-Methylhistidine, 3-Indoxysulfate, Phenylalanine, and Tryptophan were excluded from ROC curve analysis as they were not consistently up/down-regulated upto 42 days of pregnancy.

Table 1

Details of urinary metabolites identified and quantified from pregnant groups of Murrah Buffalo heifers using NMR Spectrometry
Name of Metabolite	Status	Level in urine (mMol); day post insemination/estrus
Name of Metabolite	Status	0	10	18	35	42
1,6-Anhydro-β -D-Glucose	Preg.	27.05^a ± 5.17	ND	ND	1,741.6b ± 78.72	1,467.05^b ± 81.59
1,6-Anhydro-β -D-Glucose	NP	40.93 ± 6.01	95.40 ± 5.22	39.90 ± 5.13	-	-
1-Methylhistidine	Preg.	84.58^ax ± 0.95	757.68^bx ± 46.06	1,070.41^cx ± 48.52	248.10^d ± 9.68	779.48^b ± 121.35
1-Methylhistidine	NP	95.24^ay ± 1.79	426.35^by ± 20.29	753.28^cy ± 32.00	-	-
3-Hydroxykynurenine	Preg.	1,023.83^a ± 37.60	1,091.81^ax ± 93.65	1,481.46^bx ± 10.88	1,671.86^c ± 24.98	1,938.16^d ± 25.70
3-Hydroxykynurenine	NP	1,004.81^a ± 24.54	749.83^by ± 42.31	451.00^cy ± 12.61	-	-
3-Indoxysulfate	Preg.	176.78^a ± 22.81	1,083.88^bx ± 15.65	874.86^cx ± 35.94	651.06^d ± 102.71	248.36^a ± 16.20
3-Indoxysulfate	NP	152.93^a ± 21.86	347.0^ay ± 38.81	591.43^by ± 29.84	-	-
Acetate	Preg.	190.5 ± 2.40	682.58 ± 71.98	287.01 ± 23.47	509.93 ± 96.67	493.70 ± 80.28
Acetate	NP	522.81 ± 358.93	262.63 ± 32.16	162.91 ± 14.50	-	-
Anthranilate	Preg.	25.66^a ± 0.21	616.3^bx ± 18.08	909.86^cx ± 17.72	1,336.53^d ± 31.19	2,018.70^e ± 72.34
Anthranilate	NP	22.22^a ± 0.92	315.15^by ± 20.06	199.31^by ± 10.34	-	-
1,3-Dihydroxyacetone	Preg.	61.91^a ± 9.40	418.70^b ± 62.78	856.38^cx ± 77.67	278.63^ab ± 38.23	432.91^b ± 81.59
1,3-Dihydroxyacetone	NP	63.85^a ± 7.22	617.96^b ± 46.08	213.48^ay ± 12.10	-	-
Chlorogenate	Preg.	210.18^a ± 6.14	667.5^a ± 117.65	1,034.26^bx ± 13.60	190.20^ac ± 40.50	1,235.95^b ± 267.64
Chlorogenate	NP	206.15 ± 10.67	217.73 ± 14.93	334.41^y ± 32.59	-	-
EthyleneGlycol	Preg.	151.30^a ± 14.38	1,179.75^bx ± 193.32	516.56^c ± 18.03	337.20^ac ± 15.56	187.11^a ± 27.46
	NP	148.30^a ± 11.71	212.56^ay ± 14.91	611.06^b ± 18.52	-	-
Fumarate	Preg.	119.86^a ± 11.87	ND	ND	190.20b ± 12.99	80.75a ± 7.43
	NP	95.93^a ± 6.33	200.90^b ± 11.17	233.75^b ± 16.19	-	-
Glycolate	Preg.	104.56^a ± 5.76	7,203.95^bx ± 480.40	1,362.43^c ± 118.16	7,512.48^b ± 544.73	551.13^ac ± 55.43
Glycolate	NP	90.76 ± 9.65	184.71^y ± 24.16	601.05 ± 32.66	-	-
Leucine	Preg.	37.55^a ± 8.60	163.73^bx ± 33.26	93.48^ac ± 10.07	167.58^b ± 4.71	142.90^cb ± 18.00
	NP	30.30 ± 4.76	80.50^y ± 9.24	96.25 ± 9.31	-	-
Melatonin	Preg.	184.03^ab ± 20.10	202.73^ac ± 41.69	245.75^acx ± 32.39	165.48^ab ± 11.61	97.01^ab ± 7.06
	NP	164.68 ± 17.78	139.40 ± 12.20	70.48y ± 9.25	-	-
Maleate	Preg.	43.98^a ± 7.08	ND	ND	671.28^b ± 45.64	1,106.28^c ± 129.13
	NP	51.60^a ± 10.91	226.56^a ± 39.20	450.15^b ± 36.79	-	-
Phenylalanine	Preg.	1,623.46^a ± 25.38	2,185.03^bx ± 58.18	1,710.83^ax ± 17.29	284.18^c ± 16.89	195.91^c ± 8.91
	NP	1,624.55 ± 15.15	1,567.69^y ± 19.37	1,506.54^y ± 28.49	-	-
Protocatechuate	Preg.	531.38^a ± 17.65	438.53^abx ± 11.61	415.66^b ± 38.96	142.05^c ± 11.42	112.30^c ± 4.18
	NP	509.90^a ± 24.11	662.45^by ± 26.32	362.80^c ± 29.06	-	-
Quinolinate	Preg.	42.62^a ± 1.38	413.05^a ± 22.98	575.53^a ± 50.80	761.96^a ± 19.33	1,116.46^b ± 32.34
	NP	650.52 ± 1.07	499.55 ± 26.56	550.96 ± 35.25	-	-
Serotonin	Preg.	21.09^a ± 0.30	250.30^bx ± 18.52	432.80^c ± 25.19	859.50^d ± 37.15	1,045.96^e ± 54.59
	NP	18.32^a ± 0.21	581.96^by ± 25.92	309.80^c ± 34.36	-	-
Tryptophan	Preg.	290.08^a ± 9.68	338.96^abx ± 21.11	454.35^bx ± 43.67	176.90^a ± 21.15	213.41^a ± 21.09
	NP	270.25^a ± 17.68	640.30^by ± 37.88	303.03^ay ± 26.32	-	-
Tyrosine	Preg.	87.70^a ± 10.21	371.18^bx ± 25.29	314.73^bx ± 7.89	530.58^c ± 15.87	816.13^d ± 26.26
	NP	91.03^a ± 8.06	120.71^aby ± 5.73	164.71^by ± 15.74	-	-
5-Hydroxytryptophan	Preg.	37.06^a ± 8.97	796.35^bx ± 27.59	931.33^bx ± 30.78	1,804.15^c ± 92.91	2,174.05^d ± 44.60
	NP	42.11^a ± 3.97	416.48^by ± 19.21	219.95^ay ± 19.29	-	-
Histidine	Preg.	223.38^a ± 21.05	483.98^bx ± 41.54	181.93^ac ± 18.55	97.16^c ± 8.06	53.08^c ± 6.87
	NP	205.71^a ± 21.71	756.15^by ± 39.99	190.05^a ± 12.42	-	-
Valerate	Preg.	51.26^a ± 11.50	607.43^bx ± 94.14	453.00^c ± 35.48	597.76^bc ± 72.34	791.45^b ± 20.20
	NP	59.36^a ± 4.75	168.21^aby ± 20.62	372.70^b ± 30.35	-	-
Tyramine	Preg.	ND	ND	147.17 ^a±16.36	683.33 ^ab±18.60	714.83^b ± 21.05
	NP	ND	ND	ND	-	-
Mean with different superscripts (a, b, c and d) within the row i.e. between different days of pregnancy and (x, y) between the row i.e. between pregnant and non pregnant on same days for a particular group differ significantly (p < 0.05). All metatabolites listed have been arranged in alhabatical order.

Multivariate analysis of the urine metabolites

Principal component analysis (PCA) employed five principal components PC1 (43.3%), PC2 (19.3%), PC3 (12.9%), PC4 (9.6%) and PC5 (5.8%) to elucidate the overall metabolic differences between the pregnant and non-pregnant samples at different time-points (day 0, day 10, day 18, day 35 and day 42). In PC1 vs PC2 vs PC3 analysis, samples from the pregnant and non-pregnant animals overlaps only at day 0 (denoted as P-0 day and NP-0 day, respectively in Figure-1); while the pregnant animal samples at day 10, day 18, day 35 and day 42 (denoted as P-10 day, P-18 day, P-35 day, P-42 day, respectively in Figure-1) as well as non-pregnant animal samples at day 10 and day 18 (denoted as NP-10 day and NP-18 day, respectively in Figure-1) segregated as different clusters that clearly depicted inter-group as well as inter-day variations in metabolite profile in PCA synchronized 3D plot. The 2D score plot of PLS-DA analysis incorporating Component 1 (42.6%), Component 2 (13.4%) also presented similar pattern as in PCA analysis depicting prominent separate clusters of inter-group as well as inter-day variations except at day 0 (Figure-2). The hierarchical clustering of the differentially expressed metabolites depicted in the heat map also represented that metabolites only in the day 0 samples of pregnant and non-pregnant animals mingled with each other while the metabolites in the other group as well as the day-specific samples orient them in different distant clads in the dendrogram (Figure-3). Further, score plot of OPLS-DA analysis represented more prominent variation in metabolite profile between pregnant and the non-pregnant samples; while the superimposed area of both the ellipse indicated the overlapped metabolite profile of pregnant and the non-pregnant samples at the day 0 time-point (Figure-4). Permutation validation suggested no over fitting of OPLS-DA model with Q2 of 0.606 and R2Y of 0.691, p < 0.01. Variable Importance in Projection (VIP) score of the metabolites through OPLS-DA analysis elucidated that 5-hydroxytryptophan (1.28094), Tyrosine (1.2463) and Anthranilate (1.24374) were having VIP score above 1 and depicted high abundance ratio in pregnant samples (corresponding heat-map) among the four differentially expressed metabolites identified as the potential early pregnancy detection biomarker through ANOVA (Figure-5).

Further, correlation analyses among the four differentially expressed potential metabolite biomarkers depicted that 5-Hydroxytryptophan was positively correlated with Tyrosine (r = 0.80956; P < 0.001) and Anthranilate (r = 0.97457; P < 0.001) while inversely correlated with 3-Hydroxykynurenine (r = -0.33014; P = 0.021927). Evidently, 3-Hydroxykynurenine depicted non-significant correlation with Tyrosine (r = 0.00041668; P = 0.99776) and inverse correlation with Anthranilate (r = -0.29251; P = 0.043641) (Figure-6). Serotonin is a key metabolite of tryptophan metabolism was found to be positively correlated with Anthranilate (r = 0.67772; P < 0.001) and 5-Hydroxytryptophan (r = 0.70339; P < 0.001) while inversely correlated with 3-Hydroxykynurenine (r = -0.49279; P < 0.001). Quinolinate, another product of tryptophan metabolism depicted inverse correlation with 3-Hydroxykynurenine (r = -0.32987; P = 0.022042) and non-significant correlation with Anthranilate (r = 0.1152; P = 0.43558) and 5-Hydroxytryptophan (r = 0.095816; P = 0.5171). Whereas Tryptophan was depicted weak positive correlation with 3-Hydroxykynurenine (r = 0.42364; P = 0.0026965) and strong inverse correlation with Anthranilate (r = -0.60769; P < 0.001) and 5-Hydroxytryptophan (r = -0.62684; P < 0.001) (Figure-6).

Analysis of Predictive ability of the potential biomarkers

The predictive ability of the individual potential biomarkers as identified through ANOVA was analysed by calculating their Receiver Operating Characteristic (ROC) area under the curve (AUC) value through classical univariate ROC curve analysis. The ROC AUC values were 0.824 for Tyrosine with Sensitivity: 0.733 (0.583–0.867) and Specificity: 0.833 (0.638-1) at 95% confidence interval (CI); 0.82 for Anthranilate with Sensitivity: 0.667 (0.5–0.8) and Specificity: 0.889 (0.722-1) at 95% CI; 0.787 for 5-Hydroxytryptophan with Sensitivity: 0.667 (0.432–0.867) and Specificity: 0.889 (0.722-1) at 95% CI and 0.613 for 3-Hydroxykynurenine with Sensitivity: 0.6 (0.449–0.767) and Specificity: 0.722 (0.556–0.889) at 95% CI (Figure-7 and Table 2). The possibility of improvement in the prediction efficiency was also verified by employing a combination of more than one manually selected discriminatory metabolite via logistic regression analysis. However, combination of the four features viz. Anthranilate, 3-Hydroxykynurenine, Tyrosine, and 5-Hydroxytryptophan (frequency % in LASSO modeling were 100, 80, 40, and 20 respectively) did not improve the predictive ability yielding ROC-AUC value of 0.785, 95% CI: 0.61–0.912 (Figure-8a). The average predictive accuracy of the metabolite combination was found to be 0.691 based on 100 cross validations (Figure-8b). The best ROC-AUC value of 0.804, 95% CI: 0.685–0.922 was achieved by combining Anthranilate, 3-Hydroxykynurenine, and Tyrosine with average predictive accuracy of 0.703 based on 100 cross validations (Figure-9a & 9b). A logistic regression (LR) model was derieved with the three selected compounds by using the 10-fold Coss Validation. The equation of the LR model: logit(P) = log(P / (1 - P)) = 2.103 + 0.146 3-Hydroxykynurenine + 0.402 Tyrosine + 0.517 Anthranilate, where the numeric value of each named metabolite in the equation is the concentration after log transformation and auto-scaling. The performance of the LR Model in 10-fold Cross Validation yielded AUC value of 0.794, 95% CI: 0.667–0.922, Sensitivity: 0.733 (0.733 ~ 0.892) and Specificity: 0.778 (0.586 ~ 0.970) (Figure-10).

Table 2

Predictive ability of the individual potential biomarkers based on ROC curve analysis
Metabolite	P-value	Fold Change	log2 (FC)	ROC-AUC value
Tyrosine	0.0015266	0.2959	-1.7567	0.824
Anthranilate	0.000348184	0.1823	-2.4557	0.82
5-Hydroxytryptophan	0.00035073	0.1969	-2.3443	0.787
3-Hydroxykynurenine	0.73042	0.5101	-0.97126	0.613

Metabolic Pathway Impact and Functional Analysis

Based on the urinary metabolite profile of pregnant and non-pregnant samples, metabolic pathway analysis was performed using MetaboAnalyst 5.0 to elucidate the most relevant pathways modulated in response to pregnancy. The impact value of those pathways above 0.1 derieved from pathway topology analysis was identified as the most potent pregnancy-associated pathway modulations. According to the impact values, five metabolic pathways vizly Phenylalanine, tyrosine and tryptophan biosynthesis, Tryptophan metabolism, Phenylalanine metabolism and Tyrosine metabolism were identified as the most relevant pathways to be regulated in the early stage of pregnancy (Figure-11). Further the pathway analysis also depicted these five pathways encompass several key metabolites that were identified in the current study and also held different levels of significance in pathway modulation such as Phenylalanine, Tyrosine, Tryptophan, Anthranilate, 5-Hydroxytryptophan, Serotonin, Melatonin, Histidine, 1-Methylhistidine, Tyramine, Fumarate, etc. and significantly, the potential biomarkers predicted in the current study were also in accordance with the observation (Table 3) (Figure S1-S5).

Table 3

Results of pathway analysis with MetaboAnalyst 5.0 indicating potential metabolic pathways to be regulated in the early stage of pregnancy
Pathway Name	P value	Impact	FDR	Relevant metabolites
Phenylalanine, tyrosine and tryptophan biosynthesis	0.0010745	1.0	0.0017583	Phenylalanine, tyrosine, tryptophan,
Tryptophan metabolism	2.5426E-4	0.408	5.7207E-4	Melatonin, Serotonin, Quinolinate, Anthranilate, 3-hydroxykynurenine, 5-Hydroxytryptophan
Phenylalanine metabolism	0.0010745	0.357	0.0017583	Phenylalanine, tyrosine
Histidine metabolism	6.9527E-5	0.221	2.0858E-4	1-methylhistidine, histidine
Tyrosine metabolism	5.1469E-6	0.189	2.3161E-5	Phenylalanine, Tyrosine, Tyramine

Early and accurate pregnancy diagnosis is pivotal to avert the economic loss exerted through undesired extension of the open period by delayed insemination in non-pregnant animals and slaughtering of the pregnant animals resulting from improper pregnancy diagnosis [12–13]. The most commonly employed pregnancy diagnosis methods in buffaloes include per-rectal palpation that can detect pregnancy accurately but not earlier than 32 to 35 days post-insemination; while the other method transrectal ultrasonography serves the purpose only after 25 days of insemination [14–15]. As the estrous cycle repeats after 21 days and both the aforesaid methods are unable to detect pregnancy within 21 days of post-insemination, so escape of at least one estrous cycle in case of every unsuccessful conception stands inevitable. Further, the probability of escaping estrous cycle in buffaloes is higher than the other bovines as 15–73% of buffaloes present silent heat symptom, perticularly in the summer season [12, 16]. So, the necessity of an alternate pregnancy diagnostic method in buffaloes before 21 days of post-insemination is still well-perceived and vividly justified the objective of the current study. NMR-based urinary metabolite profiling has been depicted as one of the most basic, yet efficient technique to be extensively employed in biomarker discovery of diverse patho-physiological states across species [17–26]. In cattle, most notable change in maternal metabolite profile was recorded around day 14–19 of implantation, when the process of maternal recognition of pregnancy took place with attachment of the filamentous blastocyst to the placental surface along with increased utero-placental blood flow with marked changes in the level of associated metabolites in blood and urine [27, 28].

In the present study, untargeted ¹H NMR analysis of urinary metabolites of buffalo heifers during early pregnancy (0–42 days post-insemination) depicted prominent alterations in metabolite profile in comparison to the non-pregnant animals as an indication of maternal metabolic adaptations in response to the fetal growth. Univariate statistical analysis followed by PLS-DA, and VIP score plot of OPLS-DA analysis of the day-specific group-wise metabolite data elucidated the metabolites that were significantly contributing to the group difference between the pregnant and non-pregnant samples. According to the selection criteria and multivariate analyses output, Tyrosine, Anthranilate, 3-Hydroxykynurenine, and 5-Hydroxytryptophan were identified as the key pregnancy-associated differentially expressed metabolites depicting diagnostic potential. Evidance of strong relationship between these four metabolites was established in the current study through correlation analysis (Fig. 6). Moreover, these metabolites also depicted relevant relationship with the other key metabolites of tryptophan metabolic pathways such as Serotonin and Quinolinate indicating towards pregnancy-associated metabolic pathway modulation of certain aromatic amino acids (Fig. 6). In coherence with our finding, pregnancy-associated plasma metabolite alteration due to modulation in phenylalanine, tyrosine and tryptophan biosynthesis pathway was also reported in pregnant multiparous holestein cows during early gestation [29].

Enumeration of the predictive ability of these four potential metabolite biomarkers individually through ROC analyses depicted AUC values in the range of 0.6–0.8. Further, to improve the diagnostic potential, instead of using these metabolites individually, AUC values of different combinations of these selected metabolites were evaluated through ROC analysis. In accordance with the LASSO frequency percentage of these metabolites, the combination of Anthranilate, 3-Hydroxykynurenine, and Tyrosine yielded the best AUC value of 0.804, 95% CI: 0.685–0.922. An effective pregnancy diagnostic model was also constructed via logistic regression analysis by using this metabolite combination that depicted AUC value of 0.794, 95% CI: 0.667–0.922, Sensitivity: 0.733 and Specificity: 0.778 in 10-fold cross validation.

Further, the quest for potential pregnancy-associated metabolic pathway modulations based on their impact values elucidated five metabolic pathways: phenylalanine, tyrosine and tryptophan biosynthesis, tryptophan metabolism, phenylalanine metabolism and tyrosine metabolism as the most relevant pathways to be regulated during early pregnancy. This is also very well-correlated with the observations in correlation analysis of these potential metabolite biomarkers. Anthranilate, 3-Hydroxykynurenine, and 5-Hydroxytryptophan are the products of tryptophan metabolism which is keenly associated with successful completion of mammalian pregnancy because of (i) increased maternal demand, (ii) fetal growth and development, (iii) involvement in serotonin for signalling pathways, (iv) kynurenic acid (KA) for neuronal protection, (v) quinolinic acid for NAD⁺ synthesis, (vi) other kynurenines (Ks) for suppressing fetal rejection [30–32] (Fig. 12). The metabolic pathway modulations as well as urinary detection of phenylalanine, tyrosine and the products of the tryptophan metabolism such as 3-hydroxykynurenine, 5-hydroxytryptophan, anthranilate, quninolate, serotonin and melatonin depicted in the current study was also in consonance with the findings of metabolomics introspection in holestein cows during early gestation where tyrosine metabolism was reported to be modulated on day 17 and day 45 of pregnancy while phenylalanine, tyrosine and tryptophan biosynthesis was reported to be altered on day 45 of pregnancy [29]. Probably, elevation in tryptophan utilization takes place during pregnancy yielding several derivatives as well as certain organic acids through serotonin pathway and kynurenine pathway. Concentration of 5-hydroxytryptophan, a serotonin pathway intermediate was found to be increased in fetal cotyledons of buffaloes with advancement of pregnancy [33]. Further, tryptophan hydroxylase-1 expression was found to be induced by pregnancy through lactogenic signaling resulting into elevated synthesis of 5-hydroxytryptophan in pancreatic islets that promoted insulin producing beta cells proliferation (Fig. 12). Thus elevated production of 5-hydroxytryptophan and subsequent serotonin synthesis prevent maternal hyperglycemia and modulate energy metabolism to accommodate the foetal burden [34]. Melatonin, the downstream product of serotonin pathway was also observed to increase the expression of antioxidant enzymes in placenta [35], improves placental efficiency, birth weight of the foetus and reduces oxidative and hypoxic stress [36]. So, enhaced tryptophan utilization through serotonin pathway possibly exerted positive effect in pregnancy establishment and progression in buffaloes. Kynurenine pathway is another principal route of tryptophan metabolism which is associated with immune regulation and providing a tolerogenic environment in the placenta, inducing vasodilation, neovascularization at the feto-maternal surface and regulating oxygen homeostasis [37]. Therefore kynurenine pathway holds paramount importance to facilitate establishment and progression of healthy pregnancy, particularly during early gestation by preventing fetal rejection as well as facilitating nutrient supply to the fetus and providing anti-oxidant response by the pathway enzymes and metabolites (e.g. 3-hydroxykynurenine, xanthurenic acid, 3-hydroxyanthranilic acid, and kynurenic acid) in the placental microenvironment [37]. The degree of relevance of the pathway in pregnancy establishment and progression can be justified by the instance that blocking the first and rate-limiting enzyme of the pathway indoleamine 2,3-dioxygenase (IDO) by an IDO-inhibitor 1-methyltryptophan at the onset of pregnancy led to fetal loss in mice while the treatment after pregnancy establishment resulted various pregnancy complications [38–41]. Anthranilate and 3-Hydroxykynurenine, the two potential pregnancy diagnostic biomarkers depicted in the current study are kynurenine pathway metabolites; evidently elevated urinary concentration of these metabolites in pregnant samples might be obvious due to induction of kynurenine pathway influenced by strong placental IDO expression during pregnancy (Fig. 12) [40, 42–43]. Elevation in urinary concentration of tyrosine in pregnant samples that was depicted as another potential pregnancy diagnostic biomarker in the current study might be due to reduction in tyrosine metabolism and pregnancy associated selective aminoaciduria as reported during early gestation [44–45]. Decreased urinary tyrosine output was also reported to be associated with fetal growth restriction [46]. Further, reduced tyrosine level in maternal circulation was reported to be beneficial for pregnancy as high dose of tyrosine can lower serum progesterone level resulting into fetal loss in mice [47]. Reduction in circulatory tyrosine also prevents downstream catecholamine production and negates the probability of uterine contraction associated pregnancy loss [29].

Ethical approval statement

The experiments conducted for the present study were approved by the Institute Animal Ethics Committee (IAEC), Registration no. 406/GO/RBi/L/01/CPCSEA of the Central Institute for Research on Buffaloes, Hisar vide letter IAEC-CIRB/19-20/A/007. The committee works under the supervision of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), which is a statutory Committee of Department of Animal Husbandry and Dairying, Ministry of Fisheries, Animal Husbandry and Dairying, Government of India constituted under the Prevention of Cruelty to Animals (PCA) Act, 1960. All animals used in the experiment were maintained on scientific lines at the animal farm and were not subjected to any extra discomfort during sample collection for the purpose of this study.

Animal Selection

Apparently healthy normal cyclic Murrah Buffalo heifers (n=26), that were loose housed and maintained under uniform management conditions at the organised farm of the Central Institute for Research on Buffaloes (CIRB), Hisar, India were employed for the present study. We declare that all methods performed in the current study were in accordance with the ARRIVE guidelines (https://arriveguidelines.org). The heifers were confirmed to be in estrus through visual observations and aided by a teaser bull, were subsequently scanned with ‘B mode’ ultrasound scanner equipped with an intra-operative 7.0 MHz micro-convex multi-frequency transducer for the presenece of large dominant ovarian follicle (>12mm size). The animals also exhibited good uterine tone and cervical discharge to beselected for the experiment. Twenty (n=20) heifers were inseminated artificially (refereed as AI) using frozen thawed semen [48] and the remaining six (n=6) animals were maintined as control. The day of estrus / AI was designated as day 0. After insemination, all the animals were monitored for return to estrus as well as scanned ultrasonographically on day 35 and 42 for ascertaining pregnancy status. From the inseminated animals, six pregnant heifers (n=6) which sustained healthy pregnancy across day 42, were designated as ‘pregnant’ group while six non-inseminated normal cyclic animals (n=6) which repeated their estrus cyclicity after 21 days (day 0 of the next cycle) of the last estrus were selected as the ‘non-pregnant’ group. All the methods were performed in accordance with the relevant guidelines and regulations.

Sample collection and Processing

Approximately 250 ml naturally micturated urine samples were collected from all the inseminated animals at days 0, 10, 18, 35 and 42. The animals were retrospectively diagnosed as pregnant through rectal ultrasonography carried out day 35 onwards. Similarly, the urine samples were collected from the non-inseminated animals only at days 0, 10 and 18 as they returned to estrus after 21 days.

The urine samples were centrifuged and cleaned through 0.45 µM syringe filter (Sigma-Aldrich, USA). These samples were either processed afresh and/or stored at -80°C. Each urine sample (400 μl) was mixed witha buffer solution (230 μl) containing 0.2% sodium azide (NaN₃), 0.2 M disodium hydrogen phosphate (Na₂HPO₄₎, 0.2 Msodium dihydrogen phosphate (NaH₂PO₄) and 70 μl of 1 mg/ml sodium 3-trimethylsilyl-(2, 2, 3, 3-D₄) propionate (TSP) in heavy water (D₂O) and vortexed for 30sec. The mixture was allowed to stand for 5 min followed by centrifugation at 12,000 ×g for 5 min at 4°C to remove any precipitate. Aliquots of the supernatant (600 μl) were transferred into 5 mm NMR tubes.

Acquisition of ¹H NMR spectra

Briefly, ¹H NMR spectra of the urine samples were acquired using a Bruker Avance 400 spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 400.11 MHz and 298 K. The acquisition of ¹H NMR spectra of the urine samples were performed during both the recycle delay (1 seconds) and mixing time (tm, 100 milliseconds) using a standard 1D pulse sequence with water pre-saturation (recycle delay-90°-t1-90°-tm-90°-acquisition; XWIN–NMR 3.5). For each sample, 65536 Free induction decays (FIDs) were collected into 32 K data points using the 90° pulse length of 13.46μs with acquisition time of 4.08s to obtain 16 scans with spectral width of 8012.82 Hz. FIDs were zero-filled to twice the size and exponentially multiplied with a line broadening factor of 0.3 Hz before fourier transform. All the ¹H pulse frequency spectra were automatically phase and baseline corrected and calibrated to the peak of TSP (δ 0.00) using TopSpin software version 3.2 (Bruker Biospin, Germany).

Processing of ¹H NMR Data

All the NMR spectra (spectral region δ 10 to 0.5) were imported into MestReNova software 6.0.2-5475; referenced and corrected for phase and baseline distortion using Mestrelab Research: Analytical Chemistry Software Solutions (Santiago de Compostela, Spain). The spectral regions δ 4.0 to 5.4 were removed prior to median fold change normalisation as it was indicating residual water and urea resonances. Spectral assignments and metabolite quantification was performed using the ‘Profiler and Library Manager’ modules in Chenomx NMR Suite 8.40 (Chenomx Inc, Edmonton, Canada) [49]. Spectral signal from known concentration of TSP was considered as the reference for metabolite quantification. Additionally, spiking with aminoacids in some samples was also performed for further confirmation. Data normalization by sum and Pareto data scaling (mean-centering and division by the square root of standard deviation of each variable) was carried out to minimize the bias arising from samples of different days and replicate variability.

Univariate Statistical Analysis

All the identified metabolites which were consistently detected in the urine samples of 6 pregnant as well as 6 non-inseminated control animals at all the relevant experimental days were subjected to univariate analysis by two-way ANOVA using the SPSS software (version 20.0; SPSS, Inc., Chicago, IL, USA) [50]. Data were represented as metabolite concentration ± standard error (SE) and the level of significant differences in metabolite concentrations were considered at P < 0.05. The normalized data by sum and and Pareto data scaling were used for the statistical analysis to extend equal weightage to all the variables irrespective of their absolute value.

Multivariate Statistical Analysis

The metabolite data normalized by sum and Pareto data scaling (mean-centering and division by the square root of standard deviation of each variable) were subjected to principal component analysis (PCA) for examination of the intrinsic variation in the NMR data set and similarties in variables. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was performed to maximize the class discrimination using the MetaboAnalyst 5.0 program (https://www.metaboanalyst.ca) equipped with functions of the R software [51]. Score plots and heatmaps of the urinary metabolite data were also generated to enumerate the variations in pregnant and non-pregnant animals at different days of introspection. Further, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and enumeration of Variable Importance in Projection (VIP) score of the metabolites contributing to the group difference between the pregnant and non-pregnant samples was also performed. Receiver operating characteristic–Area Under Curve (ROC-AUC) analysis was carried out employing MetaboAnalyst software to analyze the cut-off values of the metabolites diagnostically relevant to determine the pregnancy or open status of the animals at different days of the experiment. Pathways related to the profoundly varied metabolites werealso subsequently analysed.

Metabolic pathway analysis

Metabolic pathways related to the profoundly varied metabolites were subsequently analysed through MetaboAnalyst 5.0. The Pathway Analysis program employed a combination of pathway enrichment analysis and pathway topology analysis to precisely identify the most relevant metabolic pathways involved in establishment and progression of healthy pregnancy. KEGG-metabolic pathway library of Bos taurus was used to elucidate the course of pregnancy-associated metabolite dynamicity.

The current study is the first to report promising urinary metabolite biomarker, or a set of biomarkers for non-invassive early detection of pregnancy in buffaloes with significant precision. The urinary metabolomics profile of pregnant and non-pregnant buffalo heifers during early gestation or post-insemination was explored through 1H-NMR analysis followed by identification of the consistent and significantly differing metabolite features between the groups. Initialy four differentially expressed metabolites viz. 3-Hydroxykynurenine, Anthranilate, Tyrosine and 5-Hydroxytryptophan fulfilled the selection criteria to be considered as potential pregnancy diagnosis biomarkers. Subsequently, the diagnostic potential of these individual biomarkers were analysed through ROC curve analyses. Finally, a logistic regression model was constructed using the combination of 3-Hydroxykynurenine, Anthranilate, and Tyrosine depicting the highest accuracy for pregnancy diagnosis in buffalo heifers during early gestation, most importantly before 21 days of post-insemination that was desirable to prevent the escape of next estrous cycle in case of unsuccessful conception. Elucidation of pregnancy-associated metabolic pathway modulations also identified aromatic amino acid biosynthesis, phenylalanine and tyrosine metabolism, and tryptophan metabolism as the most important pathways to be regulated during early gestation based on the impact values. The pregnancy diagnostic biomarkers deduced through the current introspection were also found to be precursor or metabolites of the aforesaid amino acid metabolic pathways, thus depicted ample coherence with the findings of pathway analysis. So, the outcomes of the present investigation provide the foundation for a novel and accurate urinary metabolite-based early pregnancy diagnosis paradigm in buffaloes to improve the reproductive and productive performances of these animals.

Conflict of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Author contributions

A. Sarangi worked on the topic as her Ph.D. thesis and prepared the initial draf of the manuscript. A.K. Balhara is the supervisor of A. Sarangi as his Ph.D. student, Investigation, Methodology, Supervision. M. Ghosh: Conceptualization, Data curation, Formal analysis, pathway analysis, Suman Sangwan: Writing – review & editing. Rajesh Kumar: Conceptualization, Data curation, Formal analysis, RK Sharma: Formal analysis & editing, SK Phulia: Formal analysis & editing, Sunesh Balhara: Statistical analysis, Sandeep Kumar: Conceptualization & pathway analysis, Subhasish Sahu: Formal analysis & editing, AK Mohanty: Formal analysis.

Funding

Authors are thankful to the Department of Biotechnology (DBT), Govt. of India and Director, ICAR-CIRB, Hisar for providing necessary funds for carrying out the study.

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Exploration of urinary metabolite dynamicity for early detection of pregnancy in water buffaloes

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Identification of the differentially expressed urinary metabolites

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