Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms.

This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model's ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days.

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds.

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed-informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross-validation, 14 models had a global cross-validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares-discriminant analysis (PLS-DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS-DA method when selecting breed-informative SNPs.

Multiple Breeds and Countries' Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry.

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake.

We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models' performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models' performance with those achieved by the National Research Council's equation.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Signal Transduction at the Domain Interface of Prokaryotic Pentameric Ligand-Gated Ion Channels.

Pentameric ligand-gated ion channels are activated by the binding of agonists to a site distant from the ion conduction path. These membrane proteins consist of distinct ligand-binding and pore domains that interact via an extended interface. Here, we have investigated the role of residues at this interface for channel activation to define critical interactions that couple conformational changes between the two structural units. By characterizing point mutants of the prokaryotic channels ELIC and GLIC by electrophysiology, X-ray crystallography and isothermal titration calorimetry, we have identified conserved residues that, upon mutation, apparently prevent activation but not ligand binding. The positions of nonactivating mutants cluster at a loop within the extracellular domain connecting ß-strands 6 and 7 and at a loop joining the pore-forming helix M2 with M3 where they contribute to a densely packed core of the protein. An ionic interaction in the extracellular domain between the turn connecting ß-strands 1 and 2 and a residue at the end of ß-strand 10 stabilizes a state of the receptor with high affinity for agonists, whereas contacts of this turn to a conserved proline residue in the M2-M3 loop appear to be less important than previously anticipated. When mapping residues with strong functional phenotype on different channel structures, mutual distances are closer in conducting than in nonconducting conformations, consistent with a potential role of contacts in the stabilization of the open state. Our study has revealed a pattern of interactions that are crucial for the relay of conformational changes from the extracellular domain to the pore region of prokaryotic pentameric ligand-gated ion channels. Due to the strong conservation of the interface, these results are relevant for the entire family.

Stability of the octameric structure affects plasminogen-binding capacity of streptococcal enolase.

Group A Streptococcus (GAS) is a human pathogen that has the potential to cause invasive disease by binding and activating human plasmin(ogen). Streptococcal surface enolase (SEN) is an octameric α-enolase that is localized at the GAS cell surface. In addition to its glycolytic role inside the cell, SEN functions as a receptor for plasmin(ogen) on the bacterial surface, but the understanding of the molecular basis of plasmin(ogen) binding is limited. In this study, we determined the crystal and solution structures of GAS SEN and characterized the increased plasminogen binding by two SEN mutants. The plasminogen binding ability of SENK312A and SENK362A is ~2- and ~3.4-fold greater than for the wild-type protein. A combination of thermal stability assays, native mass spectrometry and X-ray crystallography approaches shows that increased plasminogen binding ability correlates with decreased stability of the octamer. We propose that decreased stability of the octameric structure facilitates the access of plasmin(ogen) to its binding sites, leading to more efficient plasmin(ogen) binding and activation.

Structural equation models to estimate risk of infection and tolerance to bovine mastitis.

BACKGROUND: One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. METHODS: We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples. RESULTS AND DISCUSSION: Structural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens. CONCLUSIONS: We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but not resistant to mastitis. Selection should aim at improved resistance to infection by mastitis pathogens, although further investigations are needed due to the limitations of the data used in this study.

Inhibition of the prokaryotic pentameric ligand-gated ion channel ELIC by divalent cations.

The modulation of pentameric ligand-gated ion channels (pLGICs) by divalent cations is believed to play an important role in their regulation in a physiological context. Ions such as calcium or zinc influence the activity of pLGIC neurotransmitter receptors by binding to their extracellular domain and either potentiate or inhibit channel activation. Here we have investigated by electrophysiology and X-ray crystallography the effect of divalent ions on ELIC, a close prokaryotic pLGIC homologue of known structure. We found that divalent cations inhibit the activation of ELIC by the agonist cysteamine, reducing both its potency and, at higher concentrations, its maximum response. Crystal structures of the channel in complex with barium reveal the presence of several distinct binding sites. By mutagenesis we confirmed that the site responsible for divalent inhibition is located at the outer rim of the extracellular domain, at the interface between adjacent subunits but at some distance from the agonist binding region. Here, divalent cations interact with the protein via carboxylate side-chains, and the site is similar in structure to calcium binding sites described in other proteins. There is evidence that other pLGICs may be regulated by divalent ions binding to a similar region, even though the interacting residues are not conserved within the family. Our study provides structural and functional insight into the allosteric regulation of ELIC and is of potential relevance for the entire family.

Structural basis of open channel block in a prokaryotic pentameric ligand-gated ion channel.

The flow of ions through cation-selective members of the pentameric ligand-gated ion channel family is inhibited by a structurally diverse class of molecules that bind to the transmembrane pore in the open state of the protein. To obtain insight into the mechanism of channel block, we have investigated the binding of positively charged inhibitors to the open channel of the bacterial homolog GLIC by using X-ray crystallography and electrophysiology. Our studies reveal the location of two regions for interactions, with larger blockers binding in the center of the membrane and divalent transition metal ions binding to the narrow intracellular pore entry. The results provide a structural foundation for understanding the interactions of the channel with inhibitors that is relevant for the entire family.