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ABSTRACT: At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (β-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-β-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Summary: Neurodegeneration in central nervous system disorders is linked to dysregulated neuronal calcium. Direct inhibition of glutamate-induced neuronal calcium influx, particularly via N-methyl-D-aspartate receptors (NMDAR), has led to adverse effects and clinical trial failures. A more feasible approach is to modulate NMDAR activity or calcium signaling indirectly. In this respect, the calcium-activated non-selective cation channel transient receptor potential melastatin 4 (TRPM4) has been identified as a promising target. However, high affinity and specific antagonists are lacking. Here, we conducted high-throughput screening of a compound library to identify high affinity TRPM4 antagonists. This yielded five lead compound series with nanomolar half-maximal inhibitory concentration values. Through medicinal chemistry optimization of two series, we established detailed structure-activity relationships and inhibition of excitotoxicity in neurons. Moreover, we identified their potential binding site supported by electrophysiological measurements. These potent TRPM4 antagonists are promising drugs for treating neurodegenerative disorders and TRPM4-related pathologies, potentially overcoming previous therapeutic challenges.

Abstract The transient receptor potential melastatin 4 (TRPM4) channel contributes to disease severity in the murine experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis and to neuronal cell death in models of excitotoxicity and traumatic brain injury. As TRPM4 is activated by intracellular calcium and conducts monovalent cations, we hypothesized that TRPM4 may contribute to and boost excitatory synaptic transmission in CA1 pyramidal neurons of the hippocampus. Using single-spine calcium imaging and electrophysiology, we found no effect of the TRPM4 antagonists 9-phenanthrol and glibenclamide on synaptic transmission in hippocampal slices from healthy mice. In contrast, glibenclamide but not 9-phenanthrol reduced excitatory synaptic potentials in slices from EAE mice, an effect that was absent in slices from EAE mice lacking TRPM4. We conclude that TRPM4 plays little role in basal hippocampal synaptic transmission, but a glibenclamide-sensitive TRPM4-mediated contribution to excitatory postsynaptic responses is upregulated at the acute phase of EAE.