Introduction: Saliva metabolites are suggested to reflect the health status of an individual in humans. The same could be true with the dog (Canis lupus familiaris), an important animal model of human disease, but its saliva metabolome is unknown. As a non-invasive sample, canine saliva could offer a new alternative material for research to reveal molecular mechanisms of different (patho)physiological stages, and for veterinary medicine to monitor dogs’ health trajectories. Objectives: To investigate and characterize the metabolite composition of dog and human saliva in a non-targeted manner. Methods: Stimulated saliva was collected from 13 privately-owned dogs and from 14 human individuals. We used a non-targeted ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) method to measure metabolite profiles from saliva samples. Results: We identified and classified a total of 211 endogenous and exogenous salivary metabolites. The compounds included amino acids, amino acid derivatives, biogenic amines, nucleic acid subunits, lipids, organic acids, small peptides as well as other metabolites, like metabolic waste molecules and other chemicals. Our results reveal a distinct metabolite profile of dog and human saliva as 25 lipid compounds were identified only in canine saliva and eight dipeptides only in human saliva. In addition, we observed large variation in ion abundance within and between the identified saliva metabolites in dog and human. Conclusion: The results suggest that non-targeted metabolomics approach utilizing UHPLC-qTOF-MS can detect a wide range of small compounds in dog and human saliva with partially overlapping metabolite composition. The identified metabolites indicate that canine saliva is potentially a versatile material for the discovery of biomarkers for dog welfare. However, this profile is not complete, and dog saliva needs to be investigated in the future with other analytical platforms to characterize the whole canine saliva metabolome. Furthermore, the detailed comparison of human and dog saliva composition needs to be conducted with harmonized study design.

Sterols, bile acids, and acylcarnitines are key players in human metabolism. Precise annotations of these metabolites with mass spectrometry analytics are challenging because of the presence of several isomers and stereoisomers, variability in ionization, and their relatively low concentrations in biological samples. Herein, we present a sensitive and simple qualitative LC–MS/MS (liquid chromatography with tandem mass spectrometry) method by utilizing a set of pure chemical standards to facilitate the identification and distribution of sterols, bile acids, and acylcarnitines in biological samples including human stool and plasma; mouse ileum, cecum, jejunum content, duodenum content, and liver; and pig bile, proximal colon, cecum, heart, stool, and liver. With this method, we detected 24 sterol, 32 bile acid, and 27 acylcarnitine standards in one analysis that were separated within 13 min by reversed-phase chromatography. Further, we observed different sterol, bile acid, and acylcarnitine profiles for the different biological samples across the different species. The simultaneous detection and annotation of sterols, bile acids, and acylcarnitines from reference standards and biological samples with high precision represents a valuable tool for screening these metabolites in routine scientific research.

Saliva is a complex oral fluid, and plays a major role in oral health. Primary Sjögren’s syndrome (pSS), as an autoimmune disease that typically causes hyposalivation. In the present study, salivary metabolites were studied from stimulated saliva samples (n = 15) of female patients with pSS in a group treated with low-dose doxycycline (LDD), saliva samples (n = 10) of non-treated female patients with pSS, and saliva samples (n = 14) of healthy age-matched females as controls. Saliva samples were analyzed with liquid chromatography mass spectrometry (LC-MS) based on the non-targeted metabolomics method. The saliva metabolite profile differed between pSS patients and the healthy control (HC). In the pSS patients, the LDD treatment normalized saliva levels of several metabolites, including tyrosine glutamine dipeptide, phenylalanine isoleucine dipeptide, valine leucine dipeptide, phenylalanine, pantothenic acid (vitamin B5), urocanic acid, and salivary lipid cholesteryl palmitic acid (CE 16:0), to levels seen in the saliva samples of the HC. In conclusion, the data showed that pSS is associated with an altered saliva metabolite profile compared to the HC and that the LLD treatment normalized levels of several metabolites associated with dysbiosis of oral microbiota in pSS patients. The role of the saliva metabolome in pSS pathology needs to be further studied to clarify if saliva metabolite levels can be used to predict or monitor the progress and treatment of pSS.

Dogs’ (Canis lupus familiaris) sense of smell is based on a unique anatomy and physiology that enables them to find and differentiate low concentrations of odor molecules. This ability is exploited when dogs are trained as search, rescue, or medical detection dogs. We performed a three-part study to explore the scent detection threshold of 15 dogs to an in-house-made Eucalyptus hydrolat. Here, decreasing concentrations of the hydrolat were tested using a three-alternative forced-choice method until the first incorrect response, which defined the limit of scent detection for each tested dog. Quantitative proton nuclear magnetic resonance spectroscopy was used to identify and measure the contents of ten commercial Eucalyptus hydrolats, which are used in a dog scent training sport called “nose work”. In this study, the dogs’ limit of detection initially ranged from 1:10⁴ to 1:10²³ but narrowed down to 1:10¹⁷–1:10²¹ after a training period. The results show that, with training, dogs learn to discriminate decreasing concentrations of a target scent, and that dogs can discriminate Eucalyptus hydrolat at very low concentrations. We also detected different concentrations of eucalyptol and lower alcohols in the hydrolat products and highlight the importance of using an identical source of a scent in training a dog for participation in canine scent sport competitions and in olfactory research.

Introduction: Blood microsampling (BµS) devices collect less than 100 µL of blood, offering a less invasive and more cost-effective alternative to venipuncture. However, its metabolomic comparability to conventional samples remains unclear, and standardized BµS metabolomic workflows are lacking. Objectives: This study evaluated the impact of using three BµS devices (Mitra ^® , Capitainer ^® , and Whatman ^™ 903) on the metabolomic interpretation of human biomonitoring samples. We compared them to conventional samples (plasma and whole blood) and evaluated the interplay of different analytical conditions. Methods: Venous blood from 10 adults (5 males, 5 females) was sampled onto the three devices. First, three agitation conditions (ultrasound, shaker, and homogenizer) were evaluated at three blood concentrations (1.5%, 5.5%, and 11%). The optimized method was then used to compare the metabolite profiles between BµS devices, whole blood, and plasma. Reverse-phase and hydrophilic-interaction chromatography, in positive and negative ionization modes, were combined for liquid chromatography–mass spectrometry (LC-MS) analysis. Results: All agitation conditions and concentrations proved suitable for BµS untargeted metabolomics. Combining different analytical modes and fragmentation ranges proved helpful for maximizing metabolite coverage. BµS-derived metabolite profiles aligned more closely with whole blood than plasma. Some metabolites were more characteristic of a sample type, whereas others were common across sample types. All sample types enabled sex-based differentiation, with metabolites such as amino acids, lipids, and acylcarnitines driving the separation. Conclusions: These findings enhance our understanding of BµS metabolite coverage and highlight its potential in human biomonitoring. The choice of device depends on the application and the metabolites of interest, offering flexibility for clinical use and research.

Understanding the interplay between host organisms and their microbiomes is central to the development of sustainable food systems. However, high dimensionality and spurious associations remain major obstacles to extracting meaningful biological insight from multi-omic host-associated microbiome data; a challenge further exacerbated when “holo-omic” analyses across the host-microbiome boundary are considered. Here, we show that a computational method designed for multi-omic analysis in eukaryotes can be leveraged to integrate and analyze five layers of holo-omic data from porcine hosts and their gut microbiomes. We collected caecal tissue and digesta samples during a feeding trial that tested the impact of microbiota-directed fibers (acetylated galactoglucomannan) at critical developmental stages. From 800,000 features including microbial and host genes, metagenome-assembled genomes, and metabolites from caecal tissue and digesta, we used multiset correlation and factor analysis to select the most relevant features for capturing coordinated patterns across omic layers. From these features, we predicted over 2,000 putative host-microbiome interactions based on co-occurrence. Some interactions reflected previously known relationships between animal and microbiome features, such as microbial genes for carbohydrate metabolism being linked to glycoside abundances in host tissue. Other predicted co-occurrences included features that were not detected in single-omic analysis and offer new hypotheses of host-microbiome interactions that warrant future investigation. Hence, we showcase an application of holo-omic analysis that avoids common pitfalls in high-dimensional data analysis, identifies known interactions as a form of validation, and most importantly, predicts new leads for understanding host-microbiome symbiosis. IMPORTANCE: While study systems involving mammalian hosts and their microbiomes are inherently complex, multi- and holo-omic analyses promise to provide interpretable results with translational value for the animal production industry. Unfortunately, computational methods capable of this kind of integration are currently scarce, as most existing multi-omics approaches have been developed for analysis of data layers within a single multicellular organism. We propose to adapt existing multi-omic methods for holo-omics by combining feature selection and interaction inference. This two-step analysis approach addresses common challenges in data-driven studies and can be implemented with a variety of tools for feature selection and interaction modeling. Through this holistic approach, we show that both known and novel relationships across the holobiont axis can be identified in a data-driven manner, offering new targets for the continued study and experimental validation of host-microbiome interactions and the effect of dietary interventions on production animals.