Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

LocoGSE, a sequence-based genome size estimator for plants.

Extensive research has focused on exploring the range of genome sizes in eukaryotes, with a particular emphasis on land plants, where significant variability has been observed. Accurate estimation of genome size is essential for various research purposes, but existing sequence-based methods have limitations, particularly for low-coverage datasets. In this study, we introduce LocoGSE, a novel genome size estimator designed specifically for low-coverage datasets generated by genome skimming approaches. LocoGSE relies on mapping the reads on single copy consensus proteins without the need for a reference genome assembly. We calibrated LocoGSE using 430 low-coverage Angiosperm genome skimming datasets and compared its performance against other estimators. Our results demonstrate that LocoGSE accurately predicts monoploid genome size even at very low depth of coverage (<1X) and on highly heterozygous samples. Additionally, LocoGSE provides stable estimates across individuals with varying ploidy levels. LocoGSE fills a gap in sequence-based plant genome size estimation by offering a user-friendly and reliable tool that does not rely on high coverage or reference assemblies. We anticipate that LocoGSE will facilitate plant genome size analysis and contribute to evolutionary and ecological studies in the field. Furthermore, at the cost of an initial calibration, LocoGSE can be used in other lineages.

Corrigendum: LocoGSE, a sequence-based genome size estimator for plants.

[This corrects the article DOI: 10.3389/fpls.2024.1328966.].

Deciphering microbial communities of three Savoyard raw milk cheeses along ripening and regarding the cheese process.

Different Savoyard cheeses are granted with PDO (Protected Designation or Origin) and PGI (Protected Geographical Indication) which guarantees consumers compliance with strict specifications. The use of raw milk is known to be crucial for specific flavor development. To unravel the factors influencing microbial ecosystems across cheese making steps, according to the seasonality (winter and summer) and the mode of production (farmhouse and dairy factory ones), gene targeting on bacteria and fungus was used to have a full picture of 3 cheese making technologies, from the raw milk to the end of the ripening. Our results revealed that Savoyard raw milks are a plenteous source of biodiversity together with the brines used during the process, that may support the development of specific features for each cheese. It was shown that rinds and curds have very contrasted ecosystem diversity, composition, and evolution. Ripening stage was selective for some bacterial species, whereas fungus were mainly ubiquitous in dairy samples. All ripening stages are impacted by the type of cheese technologies, with a higher impact on bacterial communities, except for fungal rind communities, for which the technology is the more discriminant. The specific microorganism's abundance for each technology allow to see a real bar-code, with more or less differences regarding bacterial or fungal communities. Bacterial structuration is shaped mainly by matrices, differently regarding technologies while the influence of technology is higher for fungi. Production types showed 10 differential bacterial species, farmhouses showed more ripening taxa, while dairy factory products showing more lactic acid bacteria. Meanwhile, seasonality looks to be a minor element for the comprehension of both microbial ecosystems, but the uniqueness of each dairy plant is a key explicative feature, more for bacteria than for fungus communities.