Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis.

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

A joint learning approach for genomic prediction in polyploid grasses.

Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

Do different probing depths exhibit striking differences in microbial profiles?

AIM: To perform a thorough characterization of the subgingival microbiota of shallow, moderate and deep sites in subjects with chronic periodontitis (ChP). MATERIAL AND METHODS: Subgingival samples were collected from subjects with ChP (n = 3/category of probing depth: ≤3, 4-6 and ≥7 mm) and periodontal health (PH). Individual samples were submitted to 16S rDNA high- throughput sequencing and the analysis was made using mothur and R packages. RESULTS: Nine subjects with ChP and seven with PH were included and 101 samples were evaluated. Thirteen phyla, 118 genera and 211 OTUs were detected. Taxa from Chloroflexi and Spirochaetes phyla were associated with initial stages of disease. Fretibacterium, Eubacterium[XI][G-6], Desulfobulbus, Peptostreptococcaceae[XI][G-1] and [G-3], Bacteroidetes[G-3], Bacteroidaceae[G-1] genera and Filifactor alocis, Fretibacterium fastidiosum, Johnsonella spHOT166, Peptostreptococcaceae[XIII][G-1]HOT113, Porphyromonas endodontalis and Treponema sp. HOT258, which are not conventionally associated with disease, increased with the deepening of the pockets and/or were elevated in ChP; while Streptococcus, Corynebacterium and Bergeyella genera were associated with PH (p < .05). CONCLUSION: Striking differences were observed between the microbiota of shallow and moderate/deep sites, but not between moderate and deep sites in ChP subjects. Differences between shallow sites in PH and ChP were also observed. The characterized microbiota included known oral microorganisms and newly identified periodontal taxa, some of them not-yet-cultured.