# Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation

P. Guarnido-Lopez, Isabelle Ortigues Marty, Sébastien Taussat, C. Fossaert, G. Renand, Gonzalo Cantalapiedra-Hijar

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## To cite this version:

P. Guarnido-Lopez, Isabelle Ortigues Marty, Sébastien Taussat, C. Fossaert, G. Renand, et al. Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation. Animal, 2021, 15 (8), pp.100318. 10.1016/j.animal.2021.100318. hal-03315912

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Distributed under a Creative Commons Attribution 4.0 International License
Plasma proteins δ¹⁵N vs plasma urea as candidate biomarkers of between-
animal variations of feed efficiency in beef cattle: phenotypic and genetic
evaluation

P. Guarnido-Lopez¹, I. Ortigues-Marty¹, S. Taussat², C. Fossaert³, G. Renand², G. Cantalapiedra-Hijar¹*

¹INRAE, VetAgro Sup, UMR Herbivores, Université Clermont Auvergne, F-63122
Saint-Genès-Champanelle, France;

² Université Paris-Saclay, INRAE, AgroParisTech, UMR Génétique Animale et Biologie Intégrative, 78350, Jouy-en-Josas, France

³ Institut de l'élevage, 75595 Paris, France

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24 *Corresponding author: Gonzalo Cantalapiedra-Hijar.

25 Email: gonzalo.cantalapiedra@inrae.fr
**Abstract**

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the ¹⁵N natural abundance in plasma proteins (plasma δ¹⁵N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ¹⁵N was significantly correlated with feed efficiency measured through the two criteria (44 and 45) and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ¹⁵N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance,
that influenced the relation between plasma δ¹⁵N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ¹⁵N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ¹⁵N and feed efficiency, that varied according to the efficiency index and the diet utilised.

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**Keywords:** feed conversion efficiency, biomarkers, individual variability, ¹⁵N natural abundance, ruminants

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## Implications

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Beef fattening cattle fed the same diet in the same contemporary group show individual variability in their ability to transform the feed into gain. We validated the potential of a new biomarker, the ¹⁵N natural abundance in plasma, to identify most efficient cattle within contemporary groups. The use of such a biomarker may help farmers and producers to make decisions in the context of precision feeding and thus improve the profitability and sustainability of the beef cattle industry.

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**Introduction**

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Feed efficiency (FE) is defined as the animal ability to transform feed resources into animal products. In a context of increasing demand for protein resources, of necessities for the industry to lower production costs and to improve the sustainability of livestock systems, FE should be improved (Cantalapiedra-Hijar., 2018a). Feed efficiency is usually characterised through different traits, described as ratios between animal inputs and outputs (feed conversion efficiency [FCE]), or as residual regression traits (residual feed intake [RFI]) (Berry and Crowley, 2013). In the case of ruminants, especially beef cattle, feed efficiency is low compared to other livestock species (Tolkamp, 2010), but also highly variable between animals raised in the same conditions (17% CV, Arthur et al., 2001). In addition, feed efficiency evaluated by either FCE or RFI is moderately heritable (Taussat et al., 2019), which gives rise to the possibility of improving this animal trait by genetic selection (Archer et al., 1999). However, a major limit for the genetic selection remains the phenotypic measurement of feed efficiency itself, which requires at least 70 days of individual DM intake (DMI) and BW gain recording and which, as a result, is expensive, laborious and not always feasible on a large number of animals or in extensive conditions. It is thus necessary to find alternative tools that are accurate, phenotypically and genetically related to feed efficiency traits and that can be used to predict feed efficiency in field conditions and to select animals for this trait.

In this regard, biomarkers of between-animal variation of feed efficiency constitute interesting alternative tools. In animals fed the same diet, feed efficiency is by definition mathematically associated to the nitrogen (N) use efficiency, which reflects the partition of N intake between N excretion and N accretion. Nitrogen use efficiency
(NUE) depends on both dietary conditions and individual animal protein metabolism (Dijkstra et al., 2013). Therefore, biomarkers of feed efficiency could be searched among proxies related to NUE (Jonker et al., 1998; Cantalapiedra-Hijar et al., 2018b).

A classical biomarker of N partitioning and NUE in ruminants is the urea concentration in milk or blood (Kohn et al., 2005b). Although its ability to discriminate dietary treatments in terms of N utilization is unquestionable (McNamara et al., 2003), no conclusive results have been obtained about its potential to reflect between-animal variations in NUE (Hof et al., 1997; Huhtanen et al., 2015). An alternative potential biomarker to predict NUE is based on the difference in ¹⁵N natural abundance between the diet and the animal proteins, the called isotopic N fractionation (Δ¹⁵N<sub>animal-diet</sub>), which has the potential to reflect both dietary and individual effects on NUE (Cantalapiedra-Hijar et al., 2018b). Nitrogen naturally exists as two stable isotopes, ¹⁵N and ¹⁴N, and δ¹⁵N notation reflects the relative abundance of the heaviest isotope. The δ¹⁵N in animal proteins results from the δ¹⁵N in the diet and the N isotopic fractionation occurring between N ingestion and N retention in animal tissues. The Δ¹⁵N<sub>animal-diet</sub> represents thus the difference between the isotopic signature of the product (animal proteins) and the substrate (feed consumed), and arises from rumen microbial and hepatic enzymatic activity (Silfer et al., 1992). Microbial and hepatic enzymes prefer substrates (ammonia and amino acids) containing the lighter N isotope (Macko et al., 1986; Wattiaux and Reed, 1995), which results in a greater excretion of ¹⁴N in urine and greater retention of ¹⁵N in animal proteins (Ganes et al., 1998). It is expected thus that efficient ruminants have lower Δ¹⁵N<sub>animal-diet</sub> values as a result of a lower amino acid catabolism (Cantalapiedra-Hijar et al., 2015) and higher ammonia uptake by rumen bacteria (Wattiaux and Reed, 1995). In this regard, Δ¹⁵N<sub>animal-diet</sub> was shown to be significantly
related to FCE (Wheadon et al., 2014; Cantalapiedra-Hijar et al., 2015) but not to RFI
(Wheadon et al., 2014; Meale et al., 2017) in beef cattle. It is noteworthy that if this
isotopic biomarker is used for discriminating individuals fed the same diet there is no
need to know the δ¹⁵N of diets (Wheadon et al., 2014) since all compared individuals
share the same dietary δ¹⁵N values. This may represent a considerable gain in the
applicability of this biomarker in field conditions, since only δ¹⁵N of animal proteins
(as plasma proteins) rather than Δ¹⁵N<sub>animal-diet</sub> could capture between-animal
differences in feed efficiency provided that animals are fed with identical diets.

Recent studies in beef cattle suggested that the relationships at the individual level
between FE and isotopic N signatures could be diet-dependent (Nasrollahi et al.,
2020), but this have not been evaluated experimentally on a large number of
animals. We assumed that the relationships at the individual level between δ¹⁵N in
plasma proteins (plasma δ¹⁵N) or plasma urea concentration and FE traits (FCE,
RFI) differ and that each relationship is affected by the diet fed to animals. Moreover,
to be used for genetic selection, genetic correlation between biomarkers and animal
traits should also be confirmed. Thus, the objectives of this study were to evaluate i)
the phenotypic and genetic relationships between plasma δ¹⁵N or plasma urea
concentration and the most common traits of feed efficiency (FCE and RFI), ii) to
which extent these relationships are diet-dependent, and iii) the applicability of these
candidate biomarkers in field conditions.

142 **Material and methods**

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144 *Experimental network design*

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This study was conceived as an experimental network, defined as a set of experiments sharing the same experimental protocol but conducted in a set of different environments (Makowski et al., 2019). The experimental network (Fig. 1) was constituted of 12 cohorts defined by the combination of four experimental farms located at different places in France (F.1, F.2, F.3, F.4) and several experimental periods within each experimental farm (2, 3 or 4 depending on the farm). The study started in 2015 and finished in 2019. Within each cohort, the same two contrasted diets (a grass silage-based diet rich in fibre vs a corn silage-based diet rich in starch) were tested. For each cohort, each animal (on average 55 individuals per cohort) was assigned to one of the two experimental diets (on average 27 animals per diet within a cohort). Within each cohort and diet, animals were grouped in pens (5 to 10 individuals/pen) according to their initial BW. As a result, half of the pens (n = 3-4) in each cohort was assigned to the grass silage diet while the other half (n = 3-4) was assigned to the corn silage diet. Only two cohorts out of 12 used only one pen per diet with a greater number of animals per pen. Therefore, each of the 72 individual pens represented a contemporary group (CG), defined as those animals that were reared in the same cohort, that had a similar initial body weight, and that were fed the same diet and housed in the same pen (Pereira et al., 2018). The experimental unit is the individual animal because treatments and measurements were applied at the animal level. The environmental factors contributing to the between-CG variability were therefore i) the cohort (n = 12), ii) the diet within each cohort (n = 2) and iii) the pen within diet and within cohort (1≤ n ≤4 depending on the cohort).

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169 *Animals and experimental diets*

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A total of 588 Charolais bulls (303 ± 25 days old and 393 ± 58 kg of BW at the onset of the experiment) were recorded for feed efficiency during a 200 ± 27 day test. The experimental animals were offspring of 70 known sires and were homogenously distributed across cohorts and diets according to their sire origin. The two experimental diets had a forage to concentrate ratio close to 60:40, were distributed as total-mixed rations and based on either corn or grass silage. The grass silage was mainly composed from grasses such as English ray grass or dactyl and legumes as violet clover. The concentrate was always composed of wheat grain and soybean meal, and beet pulp was added in the grass silage diet (Table 1). Both diets were iso-CP but differed slightly in their net energy levels (1.50 [grass silage diet] vs 1.63 [corn silage diet] Mcal/kg of DM; INRA, 2018). Diets differed by the nature of the carbohydrate, showing very contrasted NDF (48% vs 34%) and starch concentrations (6% vs 32%), for grass and corn silage diets, respectively, on a DM basis. Because of the different geographical locations and climatic conditions of farms, the chemical composition of silages changed slightly across cohorts. Therefore, in order to keep feed values as similar as possible for each type of diet, the proportions of feed ingredients varied slightly among cohorts (Table 1). Animals were fed *ad libitum* once a day between 0900 and 1030 and had free access to water throughout the experiment.

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## Measurements for feed efficiency

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Animals underwent an adaptation period of 4 weeks before the FE test to allow them to adapt to facilities and diets. Their age during the FE test varied slightly across cohorts, it ranged from 10 (minimum) to 17 (maximum) at the start of the test, but all

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FE tests always covered the period between 12 and 15 months of age. Animals were weighed on two consecutive days at the beginning and at the end of the test, and every 28 days in between, always at 1400h. Individual DMI was recorded daily with an automatic intake recording system based on mangers placed on weighing cells (Biocontrol, Rakkestad, Norway). Representative samples from each silo were collected between one and three times per cohort.

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## Chemical analyses of feeds and calculation of feed values

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Chemical composition of silages (n=24) was determined by four different laboratories in France (Départemental d'Analyses du Morbihan [www.morbihan.fr/Ida/laboratoire-departemental], CESAR laboratory [http://www.labo-cesar.com], Inovalys [www.inovalys.fr] and INRAE Site de Theix [www6.ara.inrae.fr]) using similar laboratory protocols. Dry matter and organic matter concentration were determined by oven-drying (103°C and 72h) and subsequent incineration in a muffle furnace at 550°C (NF V 18-101), respectively. CP concentration was analysed by the Dumas method (Ebeling, 1968), NDF according to Van Soest et al. (1991) and cellulose according to the Weende method (Nehring, 1966). In vitro organic matter digestibility was determined according to Aufrère et al. (2007) and starch was analysed by an enzymatic method (ISO 15914:2004). Feed values of silages, as defined by INRA (2018), were calculated from their chemical composition using the Prevalim® software (https://wwwdev.okteo.fr/).

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The chemical composition and feed values of concentrate ingredients were obtained from tabulated values (INRA, 2018). Thereafter, the feed values of the complete diets, integrating the digestive interactions, were estimated through the Inration V5®
software (https://wwwdev.okteo.fr/) from the measured (silages) or estimated (concentrates) chemical composition of ingredients, the ingredient composition of diets, and the observed average feeding level (DMI as %BW). Estimated dietary feed values included net energy (Mcal/kg DM), metabolisable protein (g/kg DM), rumen protein balance (g/kg DM), microbial protein synthesis (g/kg DM) and the rumen degradable protein (% CP). Finally, we estimated for each animal the fecal endogenous protein losses (FEPL) from the observed DMI and the non-digestible organic matter of the diet (INRA, 2018) as follows:

$$FEPL (g/d) = DMI (kg/d) \times (0.5 \times (5.7+0.074 \times \text{non-digestible organic matter (g/kg DM)})$$

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## Blood sampling and analyses

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Blood samples were obtained from each animal one month before the end of test at an animal age of 477±27 days. Sampling was done before meal distribution between 0800 and 1100 h. Blood was obtained by coccygeal venipuncture and collected into two tubes of 9 ml each (BD vacutainer, Playmouth, UK) containing either lithium heparin or ethylenediaminetetraacetic acid. Tubes were centrifuged at 2 500 × g during 10 min at room temperature. The tube containing lithium heparin was used for the determination δ¹⁵N in plasma proteins, while the other containing EDTA was used for plasma urea analysis. Plasma was stored at -80° C until laboratory analyses. Urea concentration was analysed in duplicate for each plasma sample (1.5 ml) by spectrophotometry with an automated analyser (Arene 20XT, Thermo Scientific, Vaanta, Finland). The accuracy profile (NF V03-110: 2010) of the method determined

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for concentrations ranging between 0.05 and 0.90 g/l yielded an average accuracy of 245 246 101% and an average CV for replicates of 8%.

The δ¹⁵N was determined in plasma proteins isolated by precipitation by adding 15 µL of a sulfosalicylic acid solution (1g/mL) into 300 µL of plasma. Supernatant and pellet were separated after 15 min of centrifugation (5 000 g at 4°C) followed by 1 h of storage at 4°C. The pellet was rinsed three times with MilliQ water and freeze-dried. The δ¹⁵N values were determined using an isotope-ratio mass spectrometer (Isoprime Vision; Elementar France) coupled to an elemental analyser (Vario cube; Elementar France) as described in Cantalapiedra-Hijar et al. (2020a). International standards (glutamic acid) were included in each run every 10 samples to correct for possible time-variations in the analysis. Results were expressed using the delta notation according to the following equation: 247 248 249 250 251 252 253 254 255 256

$$ \delta^{15}\text{N} = [(R_{\text{sample}} / R_{\text{standard}}) - 1] \times 1\,000, \qquad 257 $$

where Rsample is the N isotope ratio between the heavier isotope and the lighter isotope (¹⁵N/¹⁴N) for the sample being analysed, Rstandard the N isotope ratio between the heavier isotope and the internationally defined standard (atmospheric N2, Rstandard = 0.0036765), and δ is the delta notation in parts per 1 000 (‰) relative to the standard. Samples were analysed in duplicates and measurements errors for the analysed internal standard were lower than 1.1%CV. Through the manuscript, the δ¹⁵N of plasma proteins will be referred to as plasma δ¹⁵N. 258 259 260 261 262 263 264 265

Feed efficiency calculations 266

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The average daily gain (ADG) was calculated for each animal by regressing its BW over the time on test. Mid-test BW was calculated from the intercept and slope of the regression equation and using the mean time between the start and the end of the FE test. Mid-test BW was then expressed as mid-test metabolic BW by raising the former to the power of 0.75 (BW<sup>0.75</sup>). The individual daily DMI was calculated as the average daily DMI throughout the test period. The FCE was calculated as the ratio between ADG and average DMI.

RFI was determined as the difference between observed DMI and the DMI expected for a given mid-test metabolic BW (BW<sup>0.75</sup>) and ADG. To adjust RFI for the effect of CG, the RFI model included it as a fixed effect (Arthur et al., 2001) as follows:

$$Y = \beta_0 + CG + \beta_1 (BW^{0.75}) + \beta_2 (ADG) + e \quad (\text{Eq. 1})$$

where Y is the observed individual daily DMI, β₀ is the intercept, CG is the CG effect, β₁ is the regression coefficient for BW<sup>0.75</sup>, β₂ is the regression coefficient for ADG, and e is the residual of the model or RFI.

## Statistical analyses

Statistical analyses were performed in R (RStudio Core Team, version 1.1.463, 2018). All variables were tested for normality with the Lillie test and homoscedasticity within each environmental factor included in the CG effect (cohort, diet and pen) with the Levene test (Nortest package in R). Differences between treatments (diets) were tested through parametric (variables with normal distribution) or non-parametric (variables with non-normal distribution; Kruskal-Wallis test) analysis and declared significant when P ≤ 0.05.
293 *Analysis of sources of variation*

294 First, the between-animal variability expressed as CV (%) was calculated dividing the
295 SD observed within-CG (SDr) by the average values of recorded traits (Bender et al.,
296 1989). In the case of RFI, with an average value equal to 0, we divided SDr by the
297 average value of DMI.

298 We also determined the influence of each environmental factor (cohort, diet nested
299 within cohort, and pen nested within diet and cohort) on animal performances and
300 candidate plasma biomarkers values. For this, variance-estimates component
301 analyses were conducted with mixed-effect models to obtain the contribution of each
302 random environmental factor:

303 $Y = \beta_1 + E_c + E_d + E_p + \varepsilon$ (Eq.2)

304 $\beta_1$ is the mean value of the parameters in the population of individuals. The
305 deviations from the mean values are represented by the random effects of the cohort
306 ($E_c$), the diet within cohort ($E_d$) and the pen within diet within cohort ($E_p$). Finally, $\varepsilon$
307 represents the residual error of the model, which includes the between-animal
308 variation and the experimental error (Fisher et al., 2017).

309 The Intra-class correlation coefficient, which represents the percentage of variance
310 explained by each random environmental factor, was calculated as follows:

311 Intra-class correlation coefficient = $\sigma_i^2 / (\sigma_{c+}^2 + \sigma_{d+}^2 + \sigma_{p+}^2 + \sigma_{r+}^2)$ (Eq.3)

312 where, $\sigma_i^2$ refers to the between-class variability (i.e. variance ascribed to each
313 random experimental factor) and $\sigma_{c+}^2 + \sigma_{d+}^2 + \sigma_{p+}^2 + \sigma_{r+}^2$ represents the total variance
314 including the within-CG variability ($\sigma^2 r$).

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316 Relationships between feed efficiency traits and candidate plasma biomarkers

317 To evaluate the relationships between FE traits and candidate plasma biomarkers at
318 individual level, it was necessary to remove all environmental factors contributing to
319 the observed variability across-CG and only keep the observed variability within each
320 CG group (i.e between-animal variability). For this, two different statistical
321 approaches were applied.

322 i) Removing the between-contemporary group variability before linear regression
323 analysis

324 The random effect of the CG was removed from raw values of each variable (Phuong
325 et al., 2013; Cantalapiedra-Hijar et al., 2018b) by running the same model as shown
326 in Eq. 2 and storing the obtained residuals (ε). Eq. 3 was applied to both candidate
327 biomarkers and FCE. However, because RFI was already adjusted for the CG effect,
328 this initial step was not applied to this FE trait. Residuals of FE traits (FCE or RFI)
329 were then linearly regressed (as dependent Y variable) against the residuals of the
330 candidate plasma biomarkers (X) with the lm function in the R software.

331 In addition, we evaluated whether combining both candidate plasma biomarkers
332 (plasma δ¹⁵N and urea concentrations) improved the explanation of the FE variance.

333 For this, we first checked absence of multicolinearity through a variance inflation
334 factor test by the Car package, where values below 5 were indicative of non-
335 correlated variables (Vatcheva et al., 2016). Then, we tested the multivariable linear
336 model as follows:

337 $Y = (\beta_1 \times X) + (\beta_2 \times Z) + \varepsilon$ (Eq. 4)

338 where Y, X, Z are FE values (one equation for each FE trait), plasma δ¹⁵N values
339 and plasma urea values respectively, with β1 and β2 being the slopes of plasma δ¹⁵N
and plasma urea, respectively. Comparisons between models were conducted through Anova analysis.

ii) Accounting for the between-contemporary group variability through mixed effect models

The mixed-effect model analysis aimed to determine if biomarkers were significantly correlated to FE traits at individual level (within-CG) and to evaluate to what extent the two contrasted dietary conditions influenced this relationship. For this, we first attempted to consider the diet as a fixed effect. However, given the complex nested structure of our experimental network (Fig. 1), where the pen is nested within the diet, we decided to separate the database into two sub-databases, one for each diet (n = 294 for each one). This allowed the effect of pen to be considered as a random effect nested to the cohort. The plasma δ¹⁵N values were zero-centered by subtracting the overall mean to each individual value to avoid the expected correlation between intercept and slope occurring when values from the independent variable are far from zero (Pinheiro and Bates, 2002).

Different structures of random effect, from simple (with only the cohort effect) to nested factors (including the pen effect within the cohort) were tested on both intercept and slope as here detailed:

$$Y = (\beta0 + B0) + (\beta1 + B1) \times X + \varepsilon \quad (\text{Eq.5})$$

where Y and X are FCE and δ¹⁵N values, respectively, β0 and β1 are the fixed coefficients for the intercept and the slope respectively; B0 and B1 are the random coefficients of environmental factors (cohort and pen within cohort) and ε are the distributed within-groups errors, assumed to be independent of random effects. The best random structure was identified from AIC and BIC criteria and using the restricted maximum likelihood method in the nlme package.
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To evaluate the diet effect on the potential of the candidate biomarker to reflect FE
variations we compared the slope (β1) of both models through a t-test. For this, we
took into account the coefficient of slopes and their associated errors as well as the
degrees of freedom of both sub-databases (Andrade and Estévez-Pérez, 2014). We
also extracted the proportion of model's variance explained by random and fixed
effect through the package r.squaredGLMM in the library MuMin.

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Animal performances of individuals presenting extreme high-vs-low plasma proteins δ¹⁵N values

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We ranked animals within-CG according to their plasma δ¹⁵N values and then
selected those above the 90th percentile (High plasma δ¹⁵N values) and those below
the 10th percentile (Low plasma δ¹⁵N values). Differences in animal performances
and plasma biomarkers between these two groups (High vs Low) were conducted by
Anova by including the effect of diet, group and its interaction.

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Influence of dietary characteristics on the relationships at cohort level

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We aimed to evaluate whether some characteristics of the experimental diets could
influence the ability of candidate plasma biomarkers to reflect the between-animal
variation in FE. Therefore, we first obtained the 24 individual slopes (responses) from
the FE trait - biomarker relationships determined for each combination of diet and
cohort (i.e. 24 unique conditions sharing exactly the same diet x cohort). Half of these
individual slopes (12) were obtained with grass-diets and the other half with corn-
diets. The 24 slopes coefficients were regressed against their 24 corresponding feed
values and diet chemical composition. An ANCOVA model was used to evaluate

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these regressions, where the FE-biomarker slope represented the dependent variable (Y), the type of diet (n=2) was the categorical variable (X₁) and feed values or chemical composition were the quantitative variable (X₂). The *P*-values were obtained for both independent variables and their interaction. When significant relationships were obtained, the relevant feed characteristic was considered as having an impact on the response of the candidate plasma biomarker to FE variations.

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397 *Model's evaluation and cross-validation*

We first compared the quality of the different FE trait - biomarker relationships. The simple linear models based on residuals were compared based on their *P* value, R², AIC and RMSE to SD ratio (RSR), which allows comparing residual variables as RFI with mean intercept values equals to 0. The mixed effects models were compared based on their RSR and CV, calculated as the RMSE divided by the average of the variable Y. Fit was considered good when R² > 0.50 and RSR < 0.70 as recommended by Moriasi et al. (2007). Finally, normality of residuals was tested with the Shapiro Wilk's test (function shapiro.test in R) for every model.

We then evaluated the conditions of use of the δ¹⁵N biomarker relative to each FE trait using a Kfold cross-validation (James et al., 2013) performed from simple linear models based on residuals. The database was randomly split in 10 sub-database (package Caret) and each individual sub-database was treated as a validation set. The performance of the FE trait-δ¹⁵N relationships obtained from the whole database was defined as the average performance of the FE trait-δ¹⁵N relationships derived after the 10 random iterations from each sub-database for each diet, as in Benaouda et al. (2019). Parameters used to evaluate the FE trait - biomarker relationships were
the RMSE of predicted vs observed values, the correlation between observed and predicted values, the concordance correlation coefficient, as a parameter indicating precision and accuracy of obtained models (Tedeschi, 2006) and the percentage of error in central tendency, or mean bias as the average tendency to under or overestimate the predicted values according to the observed values (Gupta et al., 1999). Finally, we calculated the interval of model's prediction at 95% confidence, which reflects the prediction uncertainty around a single value, as ± 1.96 x RMSE of the model (Bruce and Bruce, 2017).

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423 Genetic parameters estimations

Heritability coefficients, environmental and genetic correlations were estimated for FE traits and candidate plasma biomarkers using the restricted estimation of maximum likelihood method of the Wombat software (Meyer, 2007). In addition to the random additive genetic effect, the model included the contemporary group (72 CG) as fixed effect, age at the start of the test as covariate and a farm origins × year random effect. The relationship matrix among the additive genetic effects included up to the 5th generation from bulls included in the study.

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432 Results

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All variables followed a non-normal distribution, except DMI and RFI (P > 0.05). Also, these two variables together with plasma urea concentrations did not fulfill homoscedasticity within CG (P < 0.05) (Supplementary Table S1).

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Animal performances, candidate biomarkers and their sources of variations

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The between-animal variability of DMI, ADG, FCE and RFI, expressed as CV, was always below 12% (Table 2). The between-animal variability of the candidate biomarkers however contrasted, 5.33% for plasma δ¹⁵N and 20.3% for plasma urea concentrations. Concerning the effect of diets (Table 2), animals fed the corn-based diets had higher ADG (+16%; *P* < 0.05), DMI (+4%; *P* < 0.05), FCE (+6%; *P* < 0.05) and plasma urea concentrations (+35%, *P* < 0.05) than animals fed the grass-based diets. On the other hand, animals fed corn-based diets had lower (-13%, *P* < 0.05) plasma δ¹⁵N values than animals fed grass-based diets.

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The cohort factor explained 29%, 19%, 13% and 7% of the total variance of FCE, plasma urea, ADG and DMI, respectively, but had only a minor contribution to the variability of plasma δ¹⁵N (Table 2). The diet effect (within cohort) strongly contributed to the variability of all variables explaining 70%, 46%, 45%, 33% and 21% of the total variance observed in plasma δ¹⁵N, plasma urea, ADG, FCE and DMI, respectively. Finally, the pen effect (within diet and within cohort) only explained less than 10% of the total variance of the variables except for DMI (16%). The residual part of the variance, i.e. the between-animal variation, represented a large and significant part of the total variance of DMI (55%), ADG (40%), FCE (33%), plasma δ¹⁵N (25%) and plasma urea (29%).

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Relationships between candidate plasma biomarkers and feed efficiency traits
at the individual level

460

461
All the simple linear models on residuals (*P* > 0.58) and the mixed effect models (*P* > 0.37) showed normal residuals. Models obtained for FCE were consistent, whatever the statistical methodology considered [removal (Table 3 and Fig. 2) or inclusion (Table 4 and Fig. 3) of the factors that significantly contributed to the GC in the model]. Plasma δ¹⁵N was negatively correlated to the between-animal variability of FCE with both approaches (*P* < 0.001; Table 3 [Eq. 1] and Table 4 [Eq. 10 and 11]). The negative slope of the FCE-plasma δ¹⁵N relationship was however greater (+35%, *P* < 0.05) with the corn than with the grass based diets in the mixed effects models analysis. Concerning plasma urea concentrations, they were non-significantly related to FCE whatever the approach (Table 3 [Eq. 2]; Supplementary Figure S1). In the case of RFI, the simple linear regression showed significant positive correlations with both plasma δ¹⁵N (*P* < 0.001; Table 3) and plasma urea concentrations (*P* = 0.04), despite the poor fitting (R² = 0.00) obtained with the latter biomarker.

When the variability due to contemporary group was removed before linear regression, the best models (based on R², AIC and RSR) were obtained for plasma δ¹⁵N whatever the FE trait. Combining both candidate biomarkers in the same model (Eq. 3 and 6 in Table 3) did not further improve the fit (*P* > 0.20). It had first been verified that plasma δ¹⁵N and urea presented no-multicolinearity (Variance inflation factor =1.02). The FCE- δ¹⁵N mixed-effect model (Table 4) was better in terms of RSR and error (CV) when animals were fed the corn as compared to the grass-based diets. In addition, the biomarker explained a higher percentage (17% vs 12%) of model's variance with the corn vs grass-based diets.

484

485

## Performances and candidate plasma biomarkers of extreme animals

486
Animals identified as having the 10% highest and lowest plasma δ¹⁵N values were selected as extreme individuals (Table 5), and showed differences in δ¹⁵N averaging 1.02‰ (18%CV). Compared to animals with the highest plasma δ¹⁵N values, those showing lowest values had lower DMI, FEPL and plasma urea concentrations (*P* < 0.001), but greater feed efficiency (*P* < 0.001). No interactions were observed between extreme δ¹⁵N groups (highest vs lowest) and diet (*P* > 0.05) on these analyzed parameters.

Influence of feed characteristics on the relationship between plasma proteins δ¹⁵N and feed efficiency

For this analysis, we removed two grass-based diets out of the 12 from our database because of 1) they were considered as outliers in most of the relationships (> 2.5 SD) and 2) biologically, the net energy concentrations of silages were low resulting in a lower average net energy intake (-10%) per average BW⁰.⁷⁵. Therefore, the number of observations used in this analysis was 22 rather than 24. In addition, because in the plasma urea-FCE relationship the diet effect was not significant (*P* > 0.05), the influence of diet characteristics was only analysed for the plasma δ¹⁵N-FE trait relationships. Graphs showing relationships at cohort level between plasma δ¹⁵N and FE traits are shown in Supplementary Figure S2 (FCE in panel A and RFI in panel B).

We found that the higher the metabolisable protein to net energy ratio of the diet the greater the slope of the plasma δ¹⁵N - FCE relationship (*P* = 0.002; Fig. 4, panel 1) and a similar trend for the rumen protein balance (*P* = 0.10; Fig. 4, panel 2). Concerning RFI, the higher the rumen N degradability of diets the greater the slope of the plasma δ¹⁵N -RFI relationship (*P* = 0.004; Supplementary table S2). Details of the
influence of the other dietary characteristics on the slope of plasma δ¹⁵N-FCE and
RFI relationships are presented in Supplementary Table S2.

513

## Ability of plasma proteins δ¹⁵N to discriminate between-animal variation of feed efficiency

The cross-validation was only carried out for the best-fitted plasma δ¹⁵N models (Eq: 1 and 4 from table 3). The plasma δ¹⁵N was found to capture the difference in FE between two individuals sharing the same diet as long as the difference in FE is higher than 0.049 g/g for FCE and 1.67 kg/d for RFI (interval of prediction in Table 6).

However, the concordance correlation coefficient of both models was low to moderate, accounting 0.16 and 0.33 for RFI and FCE, respectively. In addition the two FE traits- δ¹⁵N models presented a slightly under (4%) and overestimation (-3%), for FCE and RFI respectively, and the accuracy (r and concordance correlation coefficient) to predict FCE was better than to predict RFI from δ¹⁵N.

525

## Genetic and environmental correlations

527

We determined if phenotypic relationships between FE traits and candidate biomarkers involved genetic or environmental factors (Table 7). First, the heritability values were moderate for FE traits (h² = 0.18 for FCE and h² = 0.22 for RFI) and plasma biomarkers (h² = 0.27 for plasma urea and 0.28 for δ¹⁵N).

Concerning genetic correlations, RFI showed no-correlation with FCE (r_g < 0.05).
Both candidate plasma biomarkers showed no genetic correlations with FCE (r_g<0.20) and moderate genetic correlation with RFI (r_g =0.38 and 0.27 for plasma

534
δ¹⁵N and urea concentration, respectively), however the associated errors were large to consider significant these relationships. Concerning the environmental correlations, they were high between FCE and RFI (rₑ = -0.61) and between plasma δ¹⁵N and FCE (rₑ = -0.55), but moderate between δ¹⁵N and RFI (rₑ = 0.26), and low between plasma urea concentrations and both FE traits (rₑ < 0.15). Also, moderate genetic correlations (rₑ= 0.28) and low environmental correlations (rₑ=0.14) were found between both biomarkers.

542

## Discussion

544

Moderate between-animal variability exists for FE (Berry and Crowley, 2012). Genetic selection programs may exploit this moderate between-animal variability by selecting the FE superior animals for breeding, and biomarker-assisted genetic selection may contribute to accelerate the genetic progress through rapid identification of superior animals. Today, however, no validated biomarker, genetically linked to FE, has been proposed for beef cattle. Part of the difficulty to identify biomarkers of the between-animal variability of FE is due to a methodological limitation. Indeed, a non-negligible proportion of the measured between-animal variation, within a pen, is not ascribed to the true between-animal variability but to the experimental error, which can be as high as 50% (Fischer et al., 2018).

In this study, we tested two NUE biomarkers in ruminants (i.e plasma urea concentration [Kohn et al., 2005] and plasma proteins δ¹⁵N [Cantalapiedra-Hijar et al., 2018b]) and their ability to reflect FCE and RFI, using a large experimental setup of young bulls (n = 588) raised under similar conditions. As further discussed in the following sections our results highlight that plasma δ¹⁵N is significantly and
560 phenotypically correlated to the between-animal variations of FE, regardless of the
561 FE trait used, but with diet-dependent responses. Also these significant phenotypic
562 correlations might be mostly due to non-identified environmental factors rather than
563 to genetic ones. In contrast, plasma urea concentrations were only weakly, though
564 significantly, correlated with RFI and not significantly with FCE.

565

566 Plasma proteins δ<sup>15</sup>N vs plasma urea as candidate biomarkers

567

568 Both the plasma δ¹⁵N and plasma urea concentration are recognised biomarkers of
569 NUE. They are both strongly impacted by the balance between ammonia release
570 from dietary CP degradation and ammonia uptake by rumen bacteria (i.e. rumen
571 protein balance) and the hepatic amino acid catabolism (Wattiaux and Reed, 1995;
572 Cantalapiedra-Hijar et al., 2015; INRA, 2018). These two mechanisms determine
573 most of the variations in NUE in ruminants (INRA, 2018) and explain the relationship
574 between both candidate biomarkers and NUE in ruminants (Huhtanen et al., 2015;
575 Cantalapiedra-Hijar et al., 2018b). Also given the biological link that exists between
576 NUE and FE (Nasrollahi et al., 2020; Liu and VandeHaar, 2020), we anticipated
577 significant relationships between both candidate biomarkers and FE traits.

578 Our results confirm that both biomarkers are significantly related to between-animal
579 variations in FE, but not to the same extent. Plasma δ¹⁵N is a better biomarker than
580 plasma urea concentration. If the potential of blood or milk urea concentration to
581 predict the mean NUE is well established for groups of ruminants (Kohn et al., 2005),
582 reports highlighted its inability to phenotypically (Hof et al., 1997; Dechow et al.,
583 2017; Huhtanen et al., 2015) or genetically (Sebek et al., 2007; Vallimont et al., 2011;
584 Beatson et al., 2019) reflect the between-animal variation in NUE. In the present
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study, both plasma biomarkers were significantly and positively correlated, but only weakly (r = 0.21; *P* < 0.001). This suggests that their contrasted potential as biomarkers may originate from some specific metabolic features differing between them. We argue that the different ability of plasma δ¹⁵N and plasma urea concentration to reflect between-animal variations in FE is probably due to mechanisms such as post-absorptive kinetics, urea recycling and endogenous N as discussed below.

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First, the most likely and prominent factor that explains the within-CG variability of plasma urea concentration, is related to its postprandial variation. A considerable diurnal variation in plasma urea concentration has been widely reported in the literature associated to the daily pattern and frequency of feed intake (Gustafsson and Palmquist, 1993; Hwang et al., 2001). Because blood sampling lasted 3-4h per cohort, and because strictly speaking animals were not completely fasted (leftovers remained in the automatic feeder before sampling), we could argue that plasma urea was not determined exactly at the same postprandial moment for all animals. In contrast, isotopic signatures remain much more stable in plasma proteins because the short-term fluctuations of plasma δ¹⁵N values depend on the slow turnover of this protein pool (4.3%/d; Cantalapiedra-Hijar et al., 2019). This may explain why the within-CG variation was much higher for plasma urea vs plasma δ¹⁵N in our study (20% vs 5% of CV) and elsewhere (25-29% CV for urea [Jonker et al., 1998; Krogh et al., 2020] vs 13% CV for δ¹⁵N [Cantalapiedra-Hijar et al., 2018b]).

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Second, we considered whether renal urea reabsorption and clearance rate, hydration status and animal BW (reviewed by Spek et al. 2013) could explain why the between-animal variation are greater for plasma urea concentration than for NUE or δ¹⁵N values, but we had no data to support it. Instead and although somehow
speculative and counter-intuitive, we hypothesise that urea-N recycling and endogenous urinary N excretion (INRA, 2018) might have a greater impact on plasma δ¹⁵N than on plasma urea concentration, and thus may further explain the better ability of the plasma δ¹⁵N to detect the between-animal variability of FE. Urea recycling in beef cattle can be high and may contribute to increase the digestible N inflow from 43% to 85% (Lapierre and Lobley, 2001). But plasma urea is depleted in ¹⁵N relative to the diet (Sutoh et al., 1993), and thus, this extra N entering the rumen could, once degraded into ammonia and assimilated, decrease the naturally enriched rumen bacteria (Cantalapiedra-Hijar et al., 2016). This N conservation mechanism in ruminants would further deplete animal proteins in ¹⁵N and thus strengthen the negative relationship between NUE and plasma δ¹⁵N usually found in ruminants (Cantalapiedra-Hijar et al., 2018b). Conversely, plasma urea concentration is only weakly correlated to urea transfer into the rumen through the epithelium (i.e. portal drained viscera net flux) according to the review by Lapierre and Lobley (2001), which may partly explain the weak relationship between plasma urea concentration and FE.

Finally we speculate that animal variation in endogenous urinary N losses, which represent on average 20-30% of total urinary N excretion (INRA, 2018) and an estimated 13% of the total N intake in our database (INRA, 2018; data not shown), would mostly affect plasma δ¹⁵N and FE but not plasma urea concentration. While the urinary urea excretion is highly related to the hepatic urea synthesis and plasma urea concentration (Kohn et al., 2005), other non-ureic N compounds (e.g. creatine, creatinine, endogenous purine derivatives, methylhistidine and other free amino acids) are rather related to the endogenous protein renewal (Da Silva Braga et al., 2012). Because protein mobilization in ruminants highly affect both NUE and δ¹⁵N
values of animal proteins (Chen et al., 2020), it can be hypothesized that FE variations associated to protein turnover and metabolism would affect plasma δ¹⁵N in a higher extent than plasma urea concentration.

Taken together we consider that animal factors affecting NUE and thus FE variation are better signed by plasma δ¹⁵N than by plasma urea because the former is more stable in time and likely reflects some specific metabolic pathways related to N partitioning. Therefore, the following sections will only discuss the limits and potential of plasma δ¹⁵N to reflect the between-animal variation of FE.

643

644

The potential of plasma proteins δ¹⁵N depends on the feed efficiency trait

645

This is the first study showing by regression a significant relationship between plasma δ¹⁵N and RFI in contrast to previous studies (Wheadon et al., 2014; Meale et al., 2017). This relationship was however much weaker than that obtained between plasma δ¹⁵N and FCE. Although FCE and RFI were statistically related in this work (r = -0.29; *P* < 0.001) and share common biological processes related to FE, their biological determinants are not exactly the same (Taussat et al., 2020). While FCE favors the selection of growth traits with inconsistent intake responses (Arthur and Herd, 2012), selection based on RFI will downward feed intake without any impact on animal performances (Arthur et al., 2004). This would have implications in the way the energy and protein are allocated with both criteria (Rauw, 2012), and may explain why plasma δ¹⁵N correlated better with FCE than with RFI. Here, we discuss some statistical and biological reasons that may explain why the correlation between plasma δ¹⁵N and RFI found in the present study or previously reported is weak or null (Wheadon et al., 2014; Meale et al., 2017).

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First, from a statistical point of view, it can be expected that biomarkers correlate better with phenotypes presenting higher variability. In the present study, the within-CG variance was twofold higher for FCE (9.37% CV) than for RFI (4.54% CV) in agreement with previous studies (Wheadon et al., 2014; Meale et al., 2017). Thus, the difficulty of plasma δ¹⁵N to detect RFI variations could stem in part from the lower between-animal variability observed with RFI compared to FCE. Moreover, RFI being a residual it includes measurement errors (Fischer et al., 2018), which likely hampers the ability of plasma δ¹⁵N to detect the true between-animal variability in FE when measured as RFI.

Second, while from a biological point of view plasma δ¹⁵N is primarily a biomarker of NUE (Cantalapiedra-Hijar et al. 2018b), it can be simply argued that FCE and RFI just differ in the way they are related to NUE. At the individual level, FCE and NUE share a similar calculation principle based on the ratio between performances and intake, and thus correlations between both of them is mathematically expected and have been experimentally proven (Cantalapiedra-Hijar et al., 2015; Nasrollahi et al., 2020). Conversely, the relationship between NUE and RFI has not been experimentally observed (Lines et al., 2014; Carmona et al., 2020; Da Silva et al., 2020) despite the fact that efficient RFI animals are supposed to eat less CP while retaining an equal (or a greater) amount of body protein. More work is warranted to confirm that efficient RFI cattle upregulate N conservation mechanisms (Cantalapiedra-Hijar et al., 2020b) to the same extent as efficient FCE animals do (Arndt et al., 2015).

Third, because RFI is highly and consistently related to feed intake (Taussat et al., 2019), efficient RFI animals usually present higher rumen retention time and DM digestibility than inefficient RFI animals (Fitzsimons et al., 2014; Bonilha et al., 2017).
This would lead to a proportionally greater availability of substrates for rumen bacteria in efficient RFI animals, with the consequently greater N isotope fractionation occurring in the rumen (Wattiaux and Reeds, 1995) by unit intake. This can counterbalance the expected positive relationship between FE and plasma δ¹⁵N. We termed this phenomenon a “rumen interference” in the ability of plasma δ¹⁵N to detect FE variations. In this regard, a high contribution of the rumen efficiency to the overall FE was suggested as the reason for the weak relationship observed between milk δ¹⁵N and NUE in dairy cows (Cabrita et al., 2014). Likewise, Nasrollahi et al. (2020) identified in some conditions a yet unknown rumen effect lowering the ability of isotopic N discrimination to reflect between-animal variation in FE. Finally, FEPL could also contribute to the low correlation of plasma δ¹⁵N with RFI. Because the higher the DMI the greater the FEPL (INRA, 2018), the positive correlation between RFI and DMI found in our study (r = 0.57; *P* < 0.001) may suggest differences in FEPL between RFI efficient and non-efficient animals. Although FEPL represent a net N loss from the animal, and so may impact FE, these losses does not involve enzymatic reactions (i.e. desquamated epithelium and intestinal secretions) nor isotopic fractionation. Therefore, the estimated FEPL were positively correlated to RFI (r = 0.28; *P* < 0.001), which could have lowered FE of high RFI phenotypes without increasing plasma δ¹⁵N values. Because FCE, in contrast to RFI, presented no correlations with DMI in our study (r = -0.06; *P* > 0.05) no changes at the rumen or digestive level were expected across the FCE extreme cattle.

**The potential of plasma proteins δ¹⁵N to capture variations in feed conversion efficiency also depends on the type of diet**
The present study confirmed preliminary results obtained from a sub-set of animals of the present experimental setup (Nasrollahi et al., 2020), where the slope of the FCE-plasma δ¹⁵N relationship was significantly higher with diets high in starch vs high in fibre (Eq. 10 vs 11 in Table 4). It also supported our previous meta-analysis, which suggested that the relationship between the isotopic N discrimination and N use efficiency could be diet-dependent (Cantalapiedra-Hijar et al., 2018b). More specifically, the slope of the FCE-plasma δ¹⁵N relationship was affected by the farm-to-farm and year-to-year variations in feed composition (Supplementary Figure S2). To precisely assess which variables could explain the diet-to-diet variability, we characterised the experimental diets according to INRA (2018) (Supplementary Table S2).

The rumen protein balance, representing the difference between the protein intake and the protein flowing at duodenum (INRA, 2018), was an important dietary factor that explained the influence of dietary conditions on the ability of plasma δ¹⁵N to reflect FCE variations (Fig. 4B). The higher the rumen protein balance (i.e. higher rumen ammonia absorption compared to the urea recycled into the rumen), the smaller the slope of the FCE-plasma δ¹⁵N relationship. In other words, when rumen protein balance was above requirements (between 0 and -12 for 300 - 600 kg BW; INRA, 2018), the ability of plasma δ¹⁵N to reflect FCE variations decreased. This analysis reinforces our hypothesis about a "rumen interference" in the relationship between plasma δ¹⁵N and FE variation. In this sense, diets with high rumen degradable N concentration and leading to high rumen protein balance showed low or no correlations between δ¹⁵N and FE or NUE in several previous studies in ruminants (Cheng et al., 2013; Cabrita et al., 2014; Nasrollahi et al., 2020). Furthermore, in the present study, high-fibre diets presented a higher proportion of
metabolisable protein coming from microbial vs dietary origin (+7%; *P* = 0.02), which
may increase the N isotopic fractionation at rumen level (Wattiaux and Reed, 1995)
and consequently interfering in a greater extent on the expected relationship between
plasma δ¹⁵N and FE.

The metabolisable protein to net energy ratio, which represents the amount of
metabolisable protein for a given net energy level, was also identified as a dietary
factor explaining differences in the slope of the plasma δ¹⁵N - FCE relationship (Fig.
4A). Indeed, when this ratio was above requirements (between 48 and 53,
irrespective of the diet for 300 - 600 kg BW; INRA, 2018) the slope approached zero
and reflected the inability of plasma δ¹⁵N to catch FCE variations at high
metabolisable protein to net energy levels. It is known that amino acid catabolism
increases when the metabolisable protein to net energy ratio increases (Hanigan et
al., 1998), and thus our results may suggest that between-animal variability would be
lower when diets promoted greater amino acid catabolism.

749

750 Potential and limits of plasma proteins δ¹⁵N to predict feed efficiency

751

Unfortunately, the size of our experimental setup although important (n = 588) was
too restricted to detect any significant genetic correlations between plasma δ¹⁵N and
FE (FCE and RFI). This implies that the significant phenotypic relationships found
between the plasma δ¹⁵N and FE were mostly due to non-identified environmental
factors. Because diet conditions were controlled in our study, we speculate that other
uncontrolled environmental factors could be involved such as the previous
background of animals (pre-weaning and cow-calf period), feeding behavior
(preferential ingestion of specific low δ¹⁵N ingredients from the total-mixed ration
promoting higher FE) and mild subacute diseases not detected during fattening (lameness, rumen acidosis, liver abscess). We are aware that the size of our experimental setup may be a limitation for concluding about genetic correlation. In this regard, Lozano-Jaramillo et al. (2020) established that a population of 2 000 individuals minimum per environment was necessary to minimize the SE of genetic correlations and obtain robust genetic correlations. To the best of our knowledge, studies reporting genetic correlations between biomarkers and FE in beef cattle are very scarce in the literature and none has demonstrated a significant correlation. Nkrumah et al. (2007) reported a genetic correlation of -0.44 and -0.24 between serum leptin concentration and feed conversion ratio (the inverse of FCE) and RFI, respectively, from 813 steers. However, the large SE associated with their estimations (± 0.24 and ± 0.38 for feed conversion ratio and RFI, respectively) made it difficult to conclude to a true genetic association. Similarly, IGF-1 was shown to be genetically correlated to RFI but also with a large associated error (Johnston et al., 2002; Moore et al., 2005). Thus, more studies are warranted to establish genetic relations between FE and plasma δ¹⁵N with a greater population size.

Concerning the potential of this biomarker for phenotyping animals, our cross-validation model confirmed that plasma δ¹⁵N could be used as a biomarker to discriminate the FE of two animals from the same CG providing they differ by at least 0.049 g/g in FCE and 1.67 kg/d in RFI (Interval of model's prediction in Table 6). The number of animals within the same CG showing at least that difference was calculated to be 30% (198 out of 588) and 6% (35 out of 588) for FCE and RFI, respectively, highlighting once more the better ability of plasma δ¹⁵N to stablish between-animal difference in FCE than in RFI. These are the percentages of animals that can be discriminated in terms of FE when comparisons are done from two
animals randomly selected from the same CG. Logically, when comparisons are
done from a group of animals rather than from two randomly selected individuals the
statistical power increases. Indeed, animals with the 10% lowest plasma δ¹⁵N values
within-CG had on average significantly higher feed efficiency (+0.021 FCE and -0.47
RFI) than their counterparts having 10% highest plasma δ¹⁵N values (Table 5).
Because of the size of our experimental setup, the biomarker may still significantly
discriminate feed efficiency when all experimental animals (n = 588) were assigned
to either low or high plasma δ¹⁵N group (*P* < 0.001; data not shown).

Overall, we showed that plasma δ¹⁵N is capable of capture the between-animal
variations of FE in animals fed two contrasting diets. Nevertheless, better results may
be expected when using FCE than RFI, and when animals are fed with high-starch vs
high-fibre diets.

797

798 **Conclusion**

799

We demonstrated through an experimental network design and two different
statistical approaches, significant phenotypic correlations between plasma proteins
δ¹⁵N and the between-animal variation of FE (FCE and RFI) in fattening cattle. In our
conditions, we validated the capability of the isotopic biomarker to discriminate
animals in terms of FE when they differed by at least 0.049 g/g in FCE or 1.67 kg/d in
RFI. However, the size of our experimental setup (n = 588) was insufficient to show
significant genetic correlations between δ¹⁵N and FE. In addition, because δ¹⁵N is a
well established biomarker of N use efficiency in ruminants, we suggest that efficient
FCE and RFI animals may present better N utilization that their less efficient
counterparts. Finally, relationships between FE and biomarkers of N use efficiency
810 may depend on the diet and the feed efficiency trait since better statistical
811 correlations were obtained with high-energy diets and when using FCE over RFI.

812 Ethics approval

813 This study was carried out in compliance with the French legislation on animal care.
814 All procedures were approved by the regional ethics committee (Auvergne-Rhône-
815 Alpes, France) and subsequently validated by the French Ministry of Agriculture
816 under the authorization number APAFIS#2930-2015111814299194v3.

817

818 Data and model availability statement

819 The data was not deposited in an official repository. Data are confidential but
820 available to reviewers upon request.

821

822 Author ORCIDs

823 Pablo Guarnido: 0000-0002-5013-0888, Isabelle Ortigues-Marty: 0000-0002-0399-013X, Sebastien Taussat: 0000-0002-3101-9471, Gilles Renand: 0000-0002-0649-7957, Gonzalo Cantalapiedra-Hijar: 0000-0001-9486-8238

826

827 Author contributions

828 Gonzalo Cantalapiedra-Hijar, conceptualization, methodology, formal analysis,
829 investigation, validation, supervision, project administration, funding acquisition; G.
830 illes Renand, Sébastien Taussat and Clément Fossaert: conceptualisation, data
curation, formal analysis; Isabelle Ortigues-Marty: conceptualisation, supervision.
831
832 Pablo Guarnido-Lopez: data curation, formal analysis, writing-original draft. All
833 authors were involved in writing, reviewing & editing the final manuscript.

834 **Declaration of interest**

835 None

836

837 **Acknowledgements**

838 Authors wish to thank APIS-GENE for their financial support of this project, which
839 forms part of the larger national program BEEFALIM 2020. We also thank Marine
840 Gauthier and Céline Chantelauze (INRAE-UMRH) for their help with the laboratory
841 analyses and the "Chambres d'Agriculture" from Pays de Loire, Bretagne and Saône-
842 et-Loire (France) and the "Herbipôle" staff from INRAE experimental facilities at Theix
843 for their great support in conducting the feed efficiency tests, blood samplings and
844 measurements. In addition, we thank Mohammed Benaouda for its contribution and
845 support in the statistical analysis.

846 **Financial support statement**

847 The project was financially supported by APIS-GENE. The first author received a
848 PhD scholarship from APIS-GENE and INRAE-Phase.

849

850 **References**

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1074 Tables

1075

<table><caption>Table 1. Ingredients, chemical composition and feed values of the corn and grass based diets fed to Charolais bulls</caption><thead><tr><th></th><th colspan="2">Diet</th><th>1078</th><th>1079</th><th></th></tr><tr><th></th><th>Corn</th><th>Grass</th><th>1080</th><th>1081</th><th></th></tr></thead><tbody><tr><td>Ingredients<sup>1</sup> (% DM)</td><td></td><td></td><td>1082</td><td></td><td>r</td></tr><tr><td>Forages</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Corn silage</td><td>61.4 ± 2.2</td><td>-</td><td>1083</td><td></td><td>a</td></tr><tr><td>Grass silage</td><td>-</td><td>60.2 ± 6.0</td><td>1084</td><td></td><td>g</td></tr><tr><td>Wheat straw</td><td>5.8 ± 1.6</td><td>5.5 ± 1.2</td><td>1085</td><td></td><td>e</td></tr><tr><td>Concentrate</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wheat grain</td><td>20.4 ± 5.5</td><td>8.05 ± 1.2</td><td>1086</td><td></td><td></td></tr><tr><td>Beet pulp</td><td>-</td><td>20.9 ± 2.4</td><td>1087</td><td></td><td>v</td></tr><tr><td>Soybean meal</td><td>12.4 ± 2.0</td><td>5.25 ± 1.0</td><td>1088</td><td></td><td>a</td></tr><tr><td>Chemical composition (g/kg DM)</td><td></td><td></td><td>1088</td><td></td><td>a</td></tr><tr><td>Organic matter</td><td>958 ± 3.88</td><td>909 ± 1.09</td><td></td><td></td><td>l</td></tr><tr><td>CP</td><td>144 ± 9.69</td><td>142 ± 6.25</td><td></td><td></td><td>u</td></tr><tr><td>NDF</td><td>338 ± 25.3</td><td>479 ± 21.7</td><td></td><td></td><td>e</td></tr><tr><td>Starch</td><td>319 ± 36.2</td><td>56.4 ± 3.01</td><td></td><td></td><td>s</td></tr><tr><td>Starch/NDF (g/g)</td><td>0.95 ± 0.18</td><td>0.11 ± 0.07</td><td>1092</td><td></td><td></td></tr><tr><td>Feed Values<sup>2</sup></td><td></td><td></td><td>1093</td><td></td><td></td></tr><tr><td>Net Energy (Mcal/kg DM)</td><td>1.63 ± 0.04</td><td>1.50 ± 0.06</td><td></td><td></td><td>±</td></tr><tr><td>MP (g/kg DM)</td><td>85.6 ± 2.53</td><td>81.4 ± 3.72</td><td></td><td></td><td></td></tr><tr><td>MP/Net energy (g/Mcal)</td><td>52.5 ± 1.12</td><td>53.4 ± 1.85</td><td></td><td></td><td></td></tr><tr><td>RPB (g/kg DM)</td><td>7.75 ± 7.22</td><td>6.51 ± 6.81</td><td>1096</td><td></td><td>S</td></tr></tbody></table>

1097 D of the 12 experimental cohorts)

1098 Abbreviations: MP, metabolisable protein; RPB, rumen protein balance calculated as CP intake minus non-ammonia N at the duodenum (INRA, 2018).

1101 ¹ The whole ration was complemented (approximately 1% of total DM) with a vitamin-mineral supplement, non-reflected in the table

1104 ² Feed values of diets calculated from the measured chemical composition of diets and taking into account digestive interactions (INRA, 2018)
<table><caption>Table 2. Animal performances and candidate plasma biomarkers for Charolais bulls fed a corn or a grass based diet and their variance-component analysis</caption><thead><tr><th rowspan="3"></th><th colspan="2">Diet</th><th colspan="8">Variance-component analysis</th></tr><tr><th rowspan="2">Corn</th><th rowspan="2">Grass</th><th colspan="2">Cohort¹</th><th colspan="2">Diet within cohort</th><th colspan="2">Pen within diet and within cohort</th><th colspan="3">Residuals</th></tr><tr><th>SDc</th><th>ICC (%)</th><th>SDd</th><th>ICC (%)</th><th>SDp</th><th>ICC (%)</th><th>SDr</th><th>ICC (%)</th><th>CV² (%)</th></tr></thead><tbody><tr><td colspan="11"><strong>Animal performances</strong></td></tr><tr><td>DMI (kg/d)</td><td>9.82 ± 1.02<sup>a</sup></td><td>9.44 ± 1.19<sup>b</sup></td><td>0.30</td><td>7.19</td><td>0.52</td><td>21.6</td><td>0.45</td><td>16.2</td><td>0.83</td><td>55.1</td><td>8.64</td></tr><tr><td>ADG (kg/d)</td><td>1.62 ± 0.21<sup>a</sup></td><td>1.39 ± 0.30<sup>b</sup></td><td>0.10</td><td>12.5</td><td>0.19</td><td>45.1</td><td>0.04</td><td>2.00</td><td>0.18</td><td>40.4</td><td>11.8</td></tr><tr><td>FCE (g/g)</td><td>0.166 ± 0.02<sup>a</sup></td><td>0.147 ± 0.02<sup>b</sup></td><td>0.01</td><td>28.7</td><td>0.02</td><td>33.0</td><td>0.01</td><td>5.28</td><td>15.0</td><td>33</td><td>9.37</td></tr><tr><td>RFI (kg/d)</td><td>0.00 ± 0.44</td><td>0.00 ± 0.47</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.45</td><td>100</td><td>4.54</td></tr><tr><td colspan="11"><strong>Biomarkers</strong></td></tr><tr><td>Plasma proteins δ<sup>15</sup>N (‰)</td><td>5.13 ± 0.40<sup>b</sup></td><td>5.82 ± 0.52<sup>a</sup></td><td>0.01</td><td>0.03</td><td>0.49</td><td>70.4</td><td>0.13</td><td>4.95</td><td>0.29</td><td>24.6</td><td>5.33</td></tr><tr><td>Plasma urea (g/l)</td><td>0.195 ± 0.06<sup>a</sup></td><td>0.145 ± 0.05<sup>b</sup></td><td>0.03</td><td>18.7</td><td>0.04</td><td>46.1</td><td>0.016</td><td>6.09</td><td>0.04</td><td>29.2</td><td>20.3</td></tr></tbody></table>

Abbreviations: DMI, DM intake; ADG, average daily gain; FCE, feed conversion efficiency; ICC, intra-class correlation coefficient (calculated as the percentage of total variance explained by each environmental factor; RFI, residual feed intake; SDc, SDd, SDp and SDr, square root values of the cohort, diet, pen and residual variances respectively

a,b Averages with different letters within the same row are significantly different (P < 0.05)

¹ Cohort refers to the farm x period combination

² Coefficient of variation calculated as within-contemporary group SD divided by the average value of each variable (except for RFI where the DM intake was used as its average)
<table><caption>Table 3: Linear regression models between feed efficiency traits and candidate plasma biomarkers for Charolais bulls when variability across contemporary groups was previously removed.</caption><thead><tr><th>FE traits (Y)</th><th>N° Eq</th><th>Plasma biomarker (X)</th><th>Slope δ<sup>15</sup>N</th><th>Slope Urea</th><th>P-value</th><th>R<sup>2</sup></th><th>AIC</th><th>RSR</th></tr></thead><tbody><tr><td>FCE</td><td>1</td><td>Plasma proteins δ<sup>15</sup>N<sup>a</sup></td><td>-0.021 ±0.001***</td><td>-</td><td>&lt;0.001</td><td>0.20</td><td>-3280</td><td>0.47</td></tr><tr><td>FCE</td><td>2</td><td>Plasma urea<sup>b</sup></td><td>-</td><td>-0.037 ± 0.018*</td><td>0.053</td><td>0.00</td><td>-3044</td><td>0.55</td></tr><tr><td>FCE</td><td>3</td><td>Both<sup>a</sup></td><td>-0.023 ±0.002***</td><td>0.006 ± 0.017<sup>ns</sup></td><td>&lt;0.001</td><td>0.20</td><td>-3031</td><td>0.47</td></tr><tr><td>RFI</td><td>4</td><td>Plasma protein δ<sup>15</sup>N<sup>a</sup></td><td>0.468 ±0.064***</td><td>-</td><td>&lt;0.001</td><td>0.10</td><td>501</td><td>0.95</td></tr><tr><td>RFI</td><td>5</td><td>Plasma urea<sup>b</sup></td><td>-</td><td>1.330 ± 0.588*</td><td>0.04</td><td>0.00</td><td>669</td><td>1.00</td></tr><tr><td>RFI</td><td>6</td><td>Both<sup>a</sup></td><td>0.489 ±0.069***</td><td>0.419 ± 0.577<sup>ns</sup></td><td>&lt;0.001</td><td>0.09</td><td>584</td><td>0.94</td></tr></tbody></table>

Abbreviations: FE, feed efficiency; FCE, feed conversion efficiency; RFI, residual feed intake; AIC, akaike information criterion; Eq, N° of equation; RSR, RMSE-observations standard deviation ratio, calculated as the ratio of the RMSE and the SD of the efficiency trait.

Statistics: *** P < 0.001, * P < 0.05, ns = non-significant (P > 0.05).

a,b For a given feed efficiency trait, models with different letters are significantly different (P < 0.05).
<table><caption>Table 4: Mixed effect regression models between feed conversion efficiency (Y) and plasma proteins δ¹⁵N for Charolais bulls fed a corn or a grass based diet when the random</caption><thead><tr><th rowspan="2">Diet</th><th rowspan="2">N° Eq</th><th rowspan="2">Intercept δ¹⁵N</th><th rowspan="2">Slope δ¹⁵N (X)</th><th rowspan="2">P-value</th><th rowspan="2">CV%</th><th rowspan="2">RSR</th><th colspan="3">Explained variance¹ (%)</th></tr><tr><th>Ran</th><th>Biom</th><th>Res</th></tr></thead><tbody><tr><td>Corn based diets</td><td>10</td><td>0.159*** ± 0.008</td><td>-0.024*** ± 0.002<sup>a</sup></td><td>&lt;0.001</td><td>7.95</td><td>0.66</td><td>0.55</td><td>0.17</td><td>0.28</td></tr><tr><td>Grass based diets</td><td>11</td><td>0.153*** ± 0.009</td><td>-0.018*** ± 0.002<sup>b</sup></td><td>&lt;0.001</td><td>15.2</td><td>0.77</td><td>0.63</td><td>0.12</td><td>0.25</td></tr></tbody></table>

variability across contemporary groups was accounted

Abbreviations: FCE, feed conversion efficiency; CV, coefficient of variation calculated as the ratio between RMSE and the average value of FCE by diet; Eq, N° of equation; RSR, RMSE- observations standard deviation ratio calculated as the ratio of the RMSE and the SD of FCE by diet.

Statistics: *** P<0.001, ab Slopes with different letters are significantly different (P<0.05)

¹ Total variance (across-contemporary group variability) was partitioned into random (Ran), biomarker (Biom) and residual (Res).
<table><caption>Table 5: Animal performances and candidate plasma biomarkers from high and low extreme Charolais bulls in terms of plasma proteins δ¹⁵N values (90th and 10th percentile) and fed a corn or a grass based diet</caption><thead><tr><th rowspan="2">Diet</th><th colspan="2">Grass-based diets</th><th colspan="2">Corn-based diets</th><th rowspan="2">SEM</th><th colspan="3">P-value</th></tr><tr><th>High δ¹⁵N</th><th>Low δ¹⁵N</th><th>High δ¹⁵N</th><th>Low δ¹⁵N</th><th>Diet</th><th>δ¹⁵N</th><th>INT</th></tr></thead><tbody><tr><td>Extreme animals</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observed animal performances</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DMI (kg/d)</td><td>9.90</td><td>9.13</td><td>10.3</td><td>9.31</td><td>0.105</td><td>0.14</td><td>&lt;0.001</td><td>0.55</td></tr><tr><td>ADG (kg/d)</td><td>1.38</td><td>1.45</td><td>1.63</td><td>1.68</td><td>0.022</td><td>&lt;0.001</td><td>0.22</td><td>0.83</td></tr><tr><td>FCE (g/g)</td><td>0.139</td><td>0.160</td><td>0.159</td><td>0.181</td><td>0.0020</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.80</td></tr><tr><td>RFI (kg/d)</td><td>0.26</td><td>-0.19</td><td>0.27</td><td>-0.21</td><td>0.040</td><td>0.97</td><td>&lt;0.001</td><td>0.87</td></tr><tr><td>Candidate biomarkers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Plasm urea (g/L)</td><td>0.140</td><td>0.121</td><td>0.217</td><td>0.158</td><td>0.0058</td><td>&lt;0.001</td><td>0.001</td><td>0.12</td></tr><tr><td>Plasma proteins δ¹⁵N (‰)</td><td>6.38</td><td>5.40</td><td>5.65</td><td>4.60</td><td>0.042</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.71</td></tr><tr><td>FEPL (g/d)</td><td>171</td><td>162</td><td>182</td><td>165</td><td>1.6</td><td>0.03</td><td>&lt;0.001</td><td>0.26</td></tr></tbody></table>

Abbreviations: DMI, DM intake; ADG, average daily gain; FCE, feed conversion efficiency; RFI, residual feed intake; FEPL, Fecal endogenous protein losses; INT, effect of the interaction (Diet × δ¹⁵N)
<table><caption>Table 6: Statistical parameters from the K-fold cross validation with the best-fitted models for feed conversion efficiency (FCE) and residual feed intake (RFI) in Charolais bulls evaluated in the Table 3.</caption><thead><tr><th>Model<sup>1</sup></th><th>N°<br>Eq</th><th>RMSE</th><th>r</th><th>CCC</th><th>ECT</th><th>IP</th></tr></thead><tbody><tr><td>FCE ~ plasma proteins δ<sup>15</sup>N</td><td>1</td><td>0.012</td><td>0.44</td><td>0.33</td><td>4%</td><td>0.049 g/g</td></tr><tr><td>RFI ~ plasma proteins δ<sup>15</sup>N</td><td>4</td><td>0.422</td><td>0.29</td><td>0.16</td><td>-3%</td><td>1.67 kg/d</td></tr></tbody></table>

Abbreviations: CCC, concordance correlation coefficient; ECT, percentage of error in central tendency; IP, interval of model's prediction at 95%; Eq, N° of equation; r, correlation coefficient between observed and predicted values; RMSE, root mean squared error of predicted vs observed values (expressed in g/g for FCE and kg/d for RFI).

<footnote n="1">¹All statistical parameters presented are the average results of the 10 random iterations between observed and predicted values.</footnote>
<table><caption>Table 7: Heritability (Diagonal, ±SE), genetic (Above diagonal ±SE), and environmental (Below diagonal ±SE) correlations between animal performance</caption><thead><tr><th>Traits</th><th>FCE (g/g)</th><th>RFI (kg/d)</th><th>Plasma proteins δ<sup>15</sup>N (‰)</th><th>Plasma urea (g/L)</th></tr></thead><tbody><tr><td>FCE (g/g)</td><td>0.18 ± 0.10</td><td>-0.05 ± 0.36</td><td>0.00 ± 0.39</td><td>0.19 ± 0.37</td></tr><tr><td>RFI (kg/d)</td><td>-0.61 ± 0.09</td><td>0.22 ± 0.10</td><td>0.38 ± 0.32</td><td>0.27 ± 0.33</td></tr><tr><td>Plasma proteins δ<sup>15</sup>N (‰)</td><td>-0.55 ± 0.11</td><td>0.26 ± 0.12</td><td>0.28 ± 0.14</td><td>0.28 ± 0.33</td></tr><tr><td>Plasma urea (g/L)</td><td>-0.13 ± 0.11</td><td>0.07 ± 0.11</td><td>0.14 ± 0.13</td><td>0.27 ± 0.12</td></tr></tbody></table>

and candidate plasma biomarkers in Charolais bulls.

Abbreviations: FCE, Feed conversion efficiency; RFI, residual feed intake.
Figures

Figure 1. Experimental network design. Squares and circles below periods represent the experimental pens; half assigned to a corn-based diet (□) and half to a grass-based diet (○). Charolais bulls within each pen were considered the contemporary group (CG) unit. The number of cohorts (farm × period combination) and animals within cohort are shown at the bottom.

Figure 2. Relationships between the within-contemporary group (CG) variability of feed efficiency traits (feed conversion efficiency [FCE; Panels 1], residual feed intake [RFI; Panels 2]) and δ¹⁵N of plasma proteins (Panels A) or plasma urea concentration (Panels B) for Charolais bulls fed a corn or a grass based diet. Symbols represent individual values adjusted for the CG effect while lines represent the linear regression from models shown in Table 3.

Figure 3. Relationships between feed conversion efficiency (FCE) and plasma proteins δ¹⁵N (zero-centered values) in Charolais bulls. Panel A) Within-contemporary group regressions (n = 78) of FCE against plasma proteins δ¹⁵N obtained by simple linear model. Dotted grey lines represent corn-based diets and thick black line grass-based diets. Panel B) Overall within-diet regressions (n = 2) of FCE against plasma proteins δ¹⁵N obtained through mixed-effect model. Symbols are individual raw values for FCE and zero-centered values for plasma δ¹⁵N. Dotted grey
line and triangles represented animals fed corn-based diets whereas the thick
continuous line and black points represent animals fed grass-based diets.

Figure 4. Relationships between within-cohort slopes of plasma proteins δ¹⁵N vs. feed conversion efficiency (FCE) in Charolais bulls and significant feed characteristics. Panel 1; Metabolisable protein to Net energy ratio, Panel 2; Rumen protein balance. Dotted grey lines and continuous black lines represent relationship for corn and grass-based diets, respectively. Each observation within-diet correspond to one of the 12 cohorts.

Abbreviations: FC, feed characteristic; INT, interaction.

(Δ) Represent corn-based diets, while (●) represents grass-based diets.
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