The current large spectrum of sheep phenotypic diversity results from the combined product of sheep selection for different production traits such as wool, milk and meat, and its natural adaptation to new environments. In this study, we scanned the genome of 25 Sasi Ardi and 75 Latxa sheep from the Western Pyrenees for three types of regions under selection: (1) regions underlying local adaptation of Sasi Ardi semi-feral sheep, (2) regions related to a long traditional dairy selection pressure in Latxa sheep, and (3) regions experiencing the specific effect of the modern genetic improvement program established for the Latxa breed during the last three decades. Thirty-two selected candidate regions including 147 annotated genes were detected by using three statistical parameters: pooled heterozygosity H, Tajima’s D, and Wright’s fixation index Fst. For Sasi Ardi sheep, chromosomes Ovis aries (OAR)4, 6, and 22 showed the strongest signals and harbored several candidate genes related to energy metabolism and morphology (BBS9, ELOVL3 and LDB1), immunity (NFKB2), and reproduction (H2AFZ). The major genomic difference between Sasi Ardi and Latxa sheep was on OAR6, which is known to affect milk production, with highly selected regions around the ABCG2, SPP1, LAP3, NCAPG, LCORL, and MEPE genes in Latxa sheep. The effect of the modern genetic improvement program on Latxa sheep was also evident on OAR15, on which several olfactory genes are located. We also detected several genes involved in reproduction such as ESR1 and ZNF366 that were affected by this selection program. Natural and artificial selection have shaped the genome of both Sasi Ardi and Latxa sheep. Our results suggest that Sasi Ardi traits related to energy metabolism, morphological, reproductive, and immunological features have been under positive selection to adapt this semi-feral sheep to its particular environment. The highly selected Latxa sheep for dairy production showed clear signatures of selection in genomic regions related to milk production. Furthermore, our data indicate that the selection criteria applied in the modern genetic improvement program affect immunity and reproduction traits.

Tench (Tinca tinca L.) has great economic potential due to its high rate of fecundity and long-life span. Population genetic studies based on allozymes, microsatellites, PCR-RFLP and sequence analysis of genes and DNA fragments have revealed the presence of Eastern and Western phylogroups. However, the lack of genomic resources for this species has complicated the development of genetic markers. In this study, the tench transcriptome and genome were sequenced by high-throughput sequencing. A total of 60,414 putative SNPs were identified in the tench transcriptome using a computational pipeline. A set of 96 SNPs was selected for validation and a total of 92 SNPs was validated, resulting in the highest conversion and validation rate for a non-model species obtained to date (95.83%). The validated SNPs were used to genotype 140 individuals belonging to two tench breeds (Tabor and Hungarian), showing low (F_ST = 0.0450) but significant (<0.0001) genetic differentiation between the two tench breeds. This implies that set of validated SNPs array can be used to distinguish the tench breeds and that it might be useful for studying a range of associations between DNA sequence and traits of importance. These genomic resources created for the tench will provide insight into population genetics, conservation fish stock management, and aquaculture.

Genome-resolved metagenomics, based on shotgun sequencing, has become a powerful strategy for investigating animal-associated bacterial communities, due its heightened capability for delivering detailed taxonomic, phylogenetic, and functional insights compared to amplicon sequencing-based approaches. While genome-resolved metagenomics holds promise across various non-lethal sample types, their effectiveness in yielding high-quality metagenome-assembled genomes remains largely unexplored. Our investigation of fecal and cloacal microbiota of the mesquite lizards (Sceloporus grammicus) using genome-resolved metagenomics revealed that fecal samples contributed 97% of the 127 reconstructed bacterial genomes, whereas only 3% were recovered from cloacal swabs, which were largely enriched with host DNA. Taxonomic, phylogenetic and functional alpha bacterial diversity was greater in fecal samples than in cloacal swabs. We also observed significant differences in bacterial community composition between sampling methods, and higher inter-individual variation in cloacal swabs. Bacteroides, Phocaeicola and Parabacteroides (all Bacteroidota) were more abundant in the feces, whereas Hafnia and Salmonella (both Pseudomonadota) increased in the cloaca. Functional analyses showed that metabolic capacities of the microbiota to degrade polysaccharides, sugars and nitrogen compounds were enriched in fecal samples, likely reflecting the role of intestinal bacteria in nutrient metabolism. Overall, our results indicate that fecal samples outperform cloacal swabs in characterizing bacterial assemblages within lizards using genome-resolved metagenomics.

European sardine or pilchard is a planktonic small pelagic fish present from the North Sea in Europe to the coast of Senegal in the North of Africa, and across the Mediterranean sea to the Black Sea. Ecologically, sardines are an intermediary link in the trophic network, preying on plankton and being predated by larger fishes, marine mammals, and seabirds. This species is of great nutritional and economic value as a cheap but rich source of protein and fat. It is either consumed directly by humans or fed as fishmeal for aquaculture and farm animals. Despite its importance in the food basket, little is known about the molecular mechanisms involved in protein and lipid synthesis in this species. We collected nine tissues of Sardina pilchardus and reconstructed the transcriptome. In all, 198,597 transcripts were obtained, from which 68,031 are protein-coding. Quality assessment of the transcriptome was performed by back-mapping reads to the transcriptome and by searching for Single Copy Orthologs. Additionally, Gene Ontology and KEGG annotations were retrieved for most of the protein-coding genes. Finally, each library was quantified in terms of Transcripts per Million to disclose their expression patterns.

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.