Introduction:
Sheep reproductive strategies serve as vital tools for studying evolutionary changes that occur within domestic livestock populations. The reproductive process of sheep depends on both natural genetic elements and environmental conditions which determine their evolutionary fitness. The research examines how sheep reproductive characteristics including fertility and sexual maturity age and litter numbers develop through natural selection and genetic diversity.

Methods:
The research team performed genomic studies on multiple sheep breeds to discover reproductive success-related genetic indicators. Researchers gathered information about sheep mating patterns and their reproductive patterns throughout different seasons and their ability to survive until birth. The research used quantitative genetics to determine reproductive trait heritability and the existing evolutionary selection forces. The research examined how human intervention through selective breeding has transformed reproductive performance in domesticated sheep.

Results:
The research discovered substantial genetic differences which affect reproductive characteristics between different sheep groups. The survival competition in certain environments selects for early sexual development and high reproductive capacity because these traits appear more strongly in such environments. Domesticated sheep breeds have shown better reproductive performance because of selective breeding programs. The study demonstrated that reproductive success differences between sheep populations result from both genetic drift and gene flow.

Conclusions:
The research demonstrates that evolutionary processes directly influence the reproductive patterns of sheep. The genetic structure of reproductive characteristics demonstrates how natural selection together with human intervention through breeding programs influence sheep fertility levels. The research findings enable scientists to create better breeding strategies which enhance animal productivity and enable effective reproductive health management in both natural and controlled settings.

Roads are essential infrastructure for human development. However, they generate strong ecological impacts and represent a growing threat to wildlife populations by causing the death of millions of wild animals each year. Snake mortality from roadkill is of particular concern because snakes play fundamental ecological roles and yet remain poorly studied from both ecological and evolutionary perspectives, especially in Neotropical countries. In this study, we evaluated the association between snake roadkill records in Colombia and certain functional traits, while accounting for their phylogenetic relationships. To do so, we compiled records from databases, collections, and literature, calculated values of evolutionary distinctiveness (ED) at both global and national scales, estimated the potential loss of phylogenetic diversity (PD), and assessed the presence of phylogenetic signal in the incidence and occurrence of roadkill. We employed Phylogenetic Generalized Least Squares (PGLS) and Phylogenetic Generalized Linear Models (PGLM) to evaluate the relationship between functional traits (e.g., body length, coloration, microhabitat, activity pattern, and distribution range) at two levels: species recorded as roadkilled and the entire set of snake species present in the country. We found 1,301 roadkill records for 94 species, mainly from the families Colubridae and Boidae, representing a PD = 2.007 Ma (41% of the national PD = 4.865 Ma). At the level of species recorded as roadkilled, no phylogenetic signal or association with the evaluated functional traits was detected, although the number of records increased with broader distribution ranges. On the other hand, considering all snake species present in the country, roadkill incidence was higher in those with conspicuous coloration, larger body size, and broader distribution ranges.

Reconstructing interspecies relationships from microbial dynamics in natural environments and linking them to phylogenetic associations remains challenging in ecological and evolutionary studies. As an alternative approach, studying model bacteria under precisely controlled laboratory conditions can reveal ecological niche patterns and evolutionary consequences under defined environments. In this study, we evaluated bacterial growth to examine how environmental factors differentially affect proliferation processes and to construct experimentally derived fitness landscapes that may reflect natural adaptive processes. Multiple representative bacterial strains of broad phylogenetic diversity and distinct ecological backgrounds were used in the present study. These bacteria were cultured separately in hundreds of different media, which were composed of both purified chemical compounds and natural materials. This approach generated thousands of growth-rate and carrying-capacity values under a wide range of nutritional conditions. Despite strain-specific differences in growth, significant correlations were observed across media conditions. Moreover, clustering patterns based on growth profiles closely matched known phylogenetic proximity and biogeographical traits. It revealed that bacterial growth in response to laboratory-defined conditions was as conserved as evolution in nature, despite the significant differentiations between culture media and wild nature. Together, these findings demonstrated that laboratory-reconstructed bacterial fitness landscapes could serve as an experimental reference for bacterial ecological niches in natural environments. It indicated that functional traits such as growth dynamics can bridge trait–phylogeny relationships, providing a conceptual framework for experimental ecology and insights for microbial community design in the future.

The western honey bee (Apis mellifera) is an essential pollinator that is facing global colony losses, caused by multiple factors, including pesticide exposure. Like other insects, honey bees rely on detoxification pathways to metabolize xenobiotics into less toxic or readily excretable forms. These pathways are central to insecticide resistance and are shaped by genetic variation. In honey bees, Cytochrome P450 monooxygenases (CYP) genes of clade 3 encode proteins are central to xenobiotic metabolism, but their allelic diversity across natural populations has never been systematically characterized.

We analyzed whole-genome sequencing data from 1,467 drones (haploid males) spanning 25 countries and 18 subspecies across all 4 major evolutionary lineages. Targeting all 28 CYP3 genes alongside 18 housekeeping controls, we identified 5247 single-nucleotide polymorphisms (SNPs) and calculated allele frequencies, haplotype diversity, and fixation index (F_ST). In addition, all variants were functionally annotated to evaluate their potential effects on protein function.

Our analysis revealed substantial heterogeneity in allelic diversity within the CYP3 clade. Genes implicated in xenobiotic detoxification—particularly CYP9Q3—displayed extensive allelic diversity, likely reflecting diversifying selection. Notably, we detected a previously reported CYP9Q3 haplotype conferring neonicotinoid sensitivity at low frequencies across Mediterranean populations, highlighting its potential utility for monitoring at-risk genotypes. Other genes mirrored the constrained mutational patterns of housekeeping genes, suggesting conserved physiological roles beyond detoxification. This functional specialization parallels observations in humans, where only a subset of CYPs bear the primary detoxification burden and are thus highly polymorphic.

Genetic diversity in A. mellifera is essential for colony resilience and adaptation. High CYP polymorphism likely enhances population-level tolerance to diverse environmental and dietary chemical exposures.

This work provides the first systematic assessment of CYP3 allelic diversity in A. mellifera, underscoring its fundamental importance for understanding pesticide responses and guiding protection efforts.

In the field of aging research, DNA methylation patterns have emerged as valuable epigenetic biomarkers for modeling the passage of time at the molecular level. Through so-called first-generation epigenetic clocks, it is possible to estimate an individual’s chronological age with remarkable accuracy using the β-values of multiple CpG sites. Traditionally, these clocks have been built using machine learning models based on regularized linear regression (ElasticNet) for both feature selection and prediction. However, simple linear regression presents certain limitations, as such models are unable to capture nonlinear interactions between CpG sites or to model local dependencies among them. To overcome these constraints, recent approaches have explored deep learning methods capable of addressing these nonlinear and spatial relationships, although further research is still needed in this area. In this study, we compiled a large catalog of DNA methylation data from various tissues of healthy individuals differing in age, sex, and geographic origin. Using this dataset, we propose an approach based on a biologically interpretable convolutional neural network (CNN), which has been trained with images derived from methylation maps, in which CpG sites have been spatially organised according to their genomic position. Our model aims to reduce systematic errors in chronological age estimation and to help identify new genomic regions involved in the epigenetic changes associated with aging.

The present investigation focused on formulating an effective biofungicide from the naturally occurring phylloplane fungus of Jasminum sambac (Jasmine) against the leaf blight pathogen, Alternaria alternata. The pathogen was isolated and identified using 18S rRNA analysis. The sequence was studied and submitted to NCBI (access number PV368447). A pathogenicity test was performed to confirm the causal organism’s pathogenicity. From the healthy leaves of Jasminum sambac, four different phyllosphere fungi, namely Aspergillus niger, Cladosporium sp, Fusarium sp, and Mucor sp, were isolated by the leaf impression method. The colony morphology of all the isolated fungi was studied and identified by lactophenol cotton blue (LPCB) staining. An antagonist activity test was performed using the four identified phylloplane fungi against the pathogen Alternaria alternata as a screening test before the formulation of the biofungicide. Aspergillus niger showed the highest activity among all the fungi. Based on the antagonistic activity results, Aspergillus niger was selected for the formulation of a biofungicide. Aspergillus niger spores were separated, and the spore suspension was mixed with carrier materials (coconut oil and glycerol) at different ratios. Coconut oil-based Aspergillus niger biofungicide was found to be better than glycerol-based Aspergillus niger biofungicide. The formulation was sprayed directly onto blight-affected Jasmine leaves for 10 days. It was observed that an Aspergillus niger spore-based coconut oil biofungicide significantly reduced leaf spots. Advantages of this formulation include cost-effectiveness, an alternative to chemical fertilizers, eco-friendliness, and improved efficacy.

Frailty syndrome (FS) is an age-related condition characterised by a loss of physiological reserves across multiple organs and systems, resulting in high vulnerability to even mild stressors [1]. This state of physiological deterioration and generalized loss of homeostasis has been shown to increase the risk of premature mortality, falls, fractures, hospitalization and institutionalization among the elderly [2]. An early and accurate diagnosis of FS is therefore critical for improving patient quality of life and guiding clinical decision-making.

FS is a complex phenotype influenced by multiple factors, with approximately 40% of its development attributable to genetic determinants. Genome-wide association studies have identified significant variants in genes involved in inflammation, neurotransmission, and aging pathways [3]. Concurrently, more evidence has emerged indicating a correlation between gut microbiota dysbiosis and the progression of FS [4].

In this study, a cohort comprising 936 genomic samples (whole-genome DNA microarrays) and 199 microbiome profiles obtained thro­ugh 16S rRNA sequencing was analysed. The cohort included both frail and healthy individuals aged ≥ 65 years. Supplementary clinical data provided additional context on participant health status. Predictive models were generated for each type of data: genomic, microbiome and clinical. Subsequently, an ensemble learning approach was implemented for the purpose of integrating all three model predictions, with a view to enhancing predictive accuracy.

The findings suggest that the combined ensemble model demonstrates superior performance in comparison to single-source predictors. The conclusions of the present study demonstrate the potential of omic data fusion and advanced machine learning techniques for FS diagnosis.

References:

1. Kim DH, Rockwood K. Frailty in Older Adults. N Engl J Med. 2024;391(6):538-548.

2. Khan KT, Hemati K, Donovan AL. Geriatric Physiology and the Frailty Syndrome. Anesthesiology clinics. 2019;37:453-474.

3. Weiss CO. Frailty and chronic diseases in older adults. Clinics in geriatric medicine. 2011;27:39-52.

4. Tongeren SP, Slaets JPJ, Harmsen HJM, Welling GW. Fecal microbiota composition and frailty. Applied and environmental microbiology. 2005;71:6438-6442.

We have several open lines of investigation with informational complexity metrics in different sets of organisms within our group and, we needed a centralised solution to fit our needs. Of these complexity metrics, the most used in our analysis are BioBit and GS.

Genomic Signature (GS) (1) is an informational index anchored to k-mers. It is calculated by identifying the k that yields the most significant separation between the observed distributions of k-mer classes and their theoretical equifrequent expectations. This approach relies on the relative abundances of short oligonucleotides alongside the use of the chaos game representation applied to the genome. Meanwhile, BioBit (2) is a k-mer based metric of complexity that quantifies the deficit of entropy: the difference between the maximal possible entropy for a k-mer (e.g., in a random genome of equivalent length) and the genome's own calculated entropy for that same k-mer.

Because of that, a web app is proposed integrated with a database of reference and representative genomes as well as some genomic parameters and the calculation of the mentioned metrics.

A workflow was designed to treat and filter the genomes available on RefSeq. Genomes with assembly level "Chromosome" or greater and those with RefSeq category of "Representative genome" or "Reference genome" were kept. Then, two informational complexity metrics were calculated, as well as several genomic parameters. An auto-updatable system was designed so that every two months, a new version of the database is created with the newest genomes available. All this workflow was implemented using Python, Bash and Django.

Introduction
Biochar is a promising soil amendment that can enhance carbon sequestration, improve nutrient availability, and increase crop yield. To evaluate its potential in mitigating ammonium (NH₄⁺) toxicity, we conducted a glasshouse trial and a soil incubation study using different rates of oil mallee and wheat chaff biochars that were fully characterised prior to the experiments.

Methods
Two experiments were conducted: a glasshouse trial and a soil incubation study. In the glasshouse experiment, we examined the effects of biochar type (oil mallee, wheat chaff), application rate (0, 5, 10, 20 t/ha), and NH₄⁺ supply (15, 60, 120 mg N/kg soil) on canola growth and soil pH.

In the soil incubation experiment, we evaluated the effects of biochar type, application rate (0, 5, 20 t/ha), and NH₄⁺ concentration (60, 120 mg N/kg soil) on soil NH₄⁺, NO₃⁻, total N, pH, and CO₂ emissions. Nitrogen was applied as ammonium chloride (NH₄Cl) in all treatments. Different NH₄⁺ levels were used to match the objectives of each experiment; the greenhouse study required a broader toxicity range, whereas the incubation focused on soil N transformations.

Results
In the glasshouse experiment, both biochar types improved canola shoot and root growth. Oil mallee biochar at 10–20 t/ha most effectively alleviated NH₄⁺ toxicity symptoms, and at 20 t/ha, increased soil pH (CaCl₂) significantly from 5.8 to 6.9.

In the incubation experiment, 20 t/ha oil mallee biochar consistently decreased soil NH₄⁺ concentration and promoted a greater shoot accumulation of NO₃⁻ and total N compared with wheat chaff biochar. The effects on soil pH and CO₂ emissions varied depending on biochar type, application rate, and NH₄⁺ concentration.

Conclusion
These results demonstrate that biochar application can substantially improve canola growth and soil chemical properties. Oil mallee biochar is highly effective in mitigating NH₄⁺ toxicity in canola.

Heat stress is one of the most critical environmental factors limiting plant growth and productivity, as it disrupts essential molecular processes that maintain cellular homeostasis. Among these, gene transcription is particularly vulnerable. During transcription, RNA Polymerase II (RNAPII) synthesizes RNA from DNA templates, but this process can be interrupted by several obstacles, such as compact chromatin structures, bound regulatory proteins, DNA lesions, or misincorporated nucleotides. These interruptions can cause RNAPII to pause or stall, resulting in incomplete or faulty RNA transcripts that compromise gene expression and stress tolerance.

In this study, we investigated how Arabidopsis thaliana and barley (Hordeum vulgare) respond to heat-induced transcriptional stress. Using a combination of genetic, molecular, and biochemical approaches, we analysed the roles of transcription elongation factors and RNA surveillance pathways that assist RNAPII in maintaining efficient transcription under elevated temperatures. Our findings indicate that plants deficient in key elongation or transcription recovery components show reduced thermotolerance, increased transcriptional arrest, and an accumulation of aberrant RNA species.

Furthermore, comparative analysis between Arabidopsis and barley revealed both conserved and species-specific mechanisms that support transcriptional integrity under heat stress. This highlights an evolutionarily adaptable network that safeguards transcriptome stability in diverse plant species. Overall, our results suggest that plants depend on a coordinated system of transcriptional support proteins and post-transcriptional quality control mechanisms to sustain accurate gene expression during thermal stress. By maintaining transcriptome integrity, these processes contribute to cellular resilience and enhance the plant’s overall adaptability to changing environmental conditions.