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Speciation events often occur in rapid bursts of diversification, but the ecological and genetic factors that promote these radiations are still much debated. Using whole transcriptomes from all 13 species in the ecologically and reproductively diverse wild tomato clade (Solanum sect. Lycopersicon), we infer the species phylogeny and patterns of genetic diversity in this group. Despite widespread phylogenetic discordance due to the sorting of ancestral variation, we date the origin of this radiation to approximately 2.5 million years ago and find evidence for at least three sources of adaptive genetic variation that fuel diversification. First, we detect introgression both historically between early-branching lineages and recently between individual populations, at specific loci whose functions indicate likely adaptive benefits. Second, we find evidence of lineage-specific de novo evolution for many genes, including loci involved in the production of red fruit color. Finally, using a \"PhyloGWAS\" approach, we detect environment-specific sorting of ancestral variation among populations that come from different species but share common environmental conditions. Estimated across the whole clade, small but substantial and approximately equal fractions of the euchromatic portion of the genome are inferred to contribute to each of these three sources of adaptive genetic variation. These results indicate that multiple genetic sources can promote rapid diversification and speciation in response to new ecological opportunity, in agreement with our emerging phylogenomic understanding of the complexity of both ancient and recent species radiations.

Simultaneous multiplex mutation of large gene families using Cas9 has the potential to revolutionize agriculture and plant sciences. The targeting of multiple genomic sites at once raises concerns about the efficiency and specificity in targeting. The model Arabidopsis thaliana is widely used in basic plant research. Previous work has suggested that the Cas9 off-target rate in Arabidopsis is undetectable. Here we use deep sequencing on pooled plants simultaneously targeting 14 distinct genomic loci to demonstrate that multiplex targeting in Arabidopsis is highly specific to on-target sites with no detectable off-target events. In addition, chromosomal translocations are extremely rare. The high specificity of Cas9 in Arabidopsis makes this a reliable method for clean mutant generation with no need to enhance specificity or adopt alternate Cas9 variants.

Science, technology, engineering, and mathematics instructors have been charged with improving the performance and retention of students from diverse backgrounds. To date, programs that close the achievement gap between students from disadvantaged versus nondisadvantaged educational backgrounds have required extensive extramural funding. We show that a highly structured course design, based on daily and weekly practice with problem-solving, data analysis, and other higher-order cognitive skills, improved the performance of all students in a college-level introductory biology class and reduced the achievement gap between disadvantaged and nondisadvantaged students—without increased expenditures. These results support the Carnegie Hall hypothesis: Intensive practice, via active-learning exercises, has a disproportionate benefit for capable but poorly prepared students.

The sessile lifestyle of plants requires them to cope with stresses . Plants overcome abiotic stresses by altering structure/morphology, and in some extreme conditions, by compressing the life cycle to survive the stresses in the form of seeds. Genetic and molecular studies have uncovered complex regulatory processes that coordinate stress adaptation and tolerance in plants, which are integrated at various levels. Investigating natural variation in stress responses has provided important insights into the evolutionary processes that shape the integrated regulation of adaptation and tolerance. This review primarily focuses on the current understanding of how transcriptional, post-transcriptional, post-translational, and epigenetic processes along with genetic variation orchestrate stress responses in plants. We also discuss the current and future development of computational tools to identify biologically meaningful factors from high dimensional, genome-scale data and construct the signaling networks consisting of these components.

Disruption of host-associated microbial communities can have detrimental impacts on host health. However, the capacity of individual host-associated microbial communities to resist disturbance has not been well defined. Using a novel fecal sampling method for honey bees ( Apis mellifera ), we examined the resistance of the honey bee gut microbiome to disruption from a low dose of the antibiotic, tetracycline (4.5 μg). Prior to the experiment, bacterial communities from fecal samples were compared to communities from dissected whole guts of the same individuals to ensure fecal samples accurately represented the gut microbiome. Fecal samples were collected from lab-caged honey bees prior to, and five days after, tetracycline exposure to assess how antibiotic disturbance affected the communities of individuals. We used metrics of alpha and beta diversity calculated from 16S rRNA gene amplicon sequences to compare gut community structure. Low dose tetracycline exposure did not consistently change honey bee gut microbiome structure, but there was individual variation in response to exposure and specific taxa (one ASV assigned to Lactobacillus kunkeei and one ASV in the genus Bombella ) were differentially abundant following tetracycline treatment. To assess whether individual variation could be influenced by the presence of tetracycline resistance genes, we quantified the abundance of tet(B) and tet(M) with qPCR. The abundance of tet(M) prior to tetracycline treatment was negatively correlated with change in community membership, assessed by difference in Jaccard dissimilarity over the five-day experiment. Our results suggest that the honey bee gut microbiome has some ability to resist or recover from antibiotic-induced change, specific taxa may vary in their susceptibility to tetracycline exposure, and antibiotic resistance genes may contribute to gut microbiome resistance.

Saccharomyces cerevisiae mutants have been used since the early 1980s as a tool for characterizing genes from other organisms by functional complementation. This approach has been extremely successful in cloning and studying transporters; for instance, plant amino acid, sugar, urea, ammonium, peptide, sodium, and potassium transporters were characterized using yeast mutants lacking these functions. Over the years, new strains lacking even more endogenous transporters have been developed, enabling the characterization of transport properties of heterologous proteins in a more precise way. Furthermore, these strains provide the added possibility of characterizing a transporter belonging to a family of proteins in isolation, and thus can be used to study the relative contribution of redundant transporters to the whole function. We focused on amino acid transport, starting with the yeast strain 22 ∆ 8AA, which was developed to clone plant amino acid transporters in the early 2000s. We recently deleted two additional amino acid permeases, Gnp1 and Agp1, creating 22 ∆ 10α. In the present work, five additional permeases (Bap3, Tat1, Tat2, Agp3, Bap2) were deleted from 22 ∆ 10α genome, in a combination of up to three at a time. Unexpectedly, the amino acid transport properties of the new strains were not very different from the parent, suggesting that these amino acid permeases play a minor role in amino acid uptake, at least in our conditions. Furthermore, the inability to utilize certain amino acids as sole nitrogen source did not correlate with reduced uptake activity, questioning the well-accepted relationship between lack of growth and loss of transport properties. Finally, in order to verify the mutations and the integrity of 22 ∆ 10α genome, we performed whole-genome sequencing of 22 ∆ 10α using long-read PacBio sequencing technology. We successfully assembled 22 ∆ 10α’s genome de novo , identified all expected mutations and precisely characterized the nature of the deletions of the ten amino acid transporters. The sequencing data and genome will serve as a valuable resource to researchers interested in using these strains as a tool for amino acid transport study.

The impact of anthropogenic climate change on terrestrial organisms is often predicted to increase with latitude, in parallel with the rate of warming. Yet the biological impact of rising temperatures also depends on the physiological sensitivity of organisms to temperature change. We integrate empirical fitness curves describing the thermal tolerance of terrestrial insects from around the world with the projected geographic distribution of climate change for the next century to estimate the direct impact of warming on insect fitness across latitude. The results show that warming in the tropics, although relatively small in magnitude, is likely to have the most deleterious consequences because tropical insects are relatively sensitive to temperature change and are currently living very close to their optimal temperature. In contrast, species at higher latitudes have broader thermal tolerance and are living in climates that are currently cooler than their physiological optima, so that warming may even enhance their fitness. Available thermal tolerance data for several vertebrate taxa exhibit similar patterns, suggesting that these results are general for terrestrial ectotherms. Our analyses imply that, in the absence of ameliorating factors such as migration and adaptation, the greatest extinction risks from global warming may be in the tropics, where biological diversity is also greatest.

Cannabis sativa has a complex history reflected in both selection on naturally occurring compounds and historical trade routes among humans. Iran is a rich resource of natural populationswhich hold the promise to characterize historical patterns of population structure and genetic diversity within Cannabis . Recent advances in high-throughput DNA sequencing technologies have dramatically increased our ability to produce information to the point that it is now feasible to inexpensively obtain population level genotype information at a large scale. In the present investigation, we have explored the use of Genotyping-By-Sequencing (GBS) in Iranian cannabis. We genotyped 98 cannabis samples 36 from Iranian locations and 26 accessions from two germplasm collections. In total, 24,710 high-quality Single Nucleotide Polymorphisms (SNP) were identified. Clustering analysis by Principal Component Analysis (PCA) identified two genetic clusters among Iranian populations and fineSTRUCTURE analysis identified 19 populations with some geographic partitioning. We defined Iranian cannabis in two main groups using the results of the PCA and discovered some strong signal to define some locations as population according to fineSTRUCTURE analyses. However, single nucleotide variant analysis uncovered a relatively moderate level of variation among Iranian cannabis.

Pseudomonas species are globally distributed bacteria that influence soil and plant health, functioning as both pathogens and plant growth-promoting rhizobacteria (PGPR). Genomic analyses have revealed the genetic basis for their ecological versatility, including the evolution of diverse nutrient acquisition and biocontrol traits. Here, we report the draft genome of ‘Candidatus Pseudomonas auctus’ JDE115, a novel soybean nodule–associated PGPR with biocontrol potential that may contribute to sustainable agriculture. The draft genome comprises 6.18 Mb with a GC content of 60.68%. Comparative genomic analyses with closely related Pseudomonas taxa revealed lineage-specific gene clusters associated with nutrient acquisition and biocontrol-related functions, including genes encoding extracellular serine proteases, siderophores, chitinases, hydrogen cyanide synthase, and non-ribosomal peptide synthetases. Functional annotation across KEGG, COG, Pfam, CAZy, and antiSMASH databases highlighted enriched pathways for phosphorus, sulfur, zinc, and potassium metabolism, as well as secondary metabolite biosynthesis. Several biosynthetic clusters, including arylpolyenes, cyclic lipopeptides, and β-lactones, appear to be expanded relative to other PGPR genomes, suggesting lineage-specific adaptations for rhizosphere persistence and pathogen suppression. Despite the agronomic importance of plant-associated Pseudomonas, the genomic basis of non-rhizobial soybean nodule endophytes with combined PGPR and biocontrol potential remains poorly understood. Highlights Draft genome of novel soybean endophyte ‘Candidatus Pseudomonas auctus’ JDE115. Comparative genomics reveals lineage-specific biosynthetic gene cluster expansion. Enriched cyclic lipopeptide, arylpolyene, and β-lactone clusters suggest biocontrol roles. Genome encodes versatile nutrient acquisition and stress adaptation pathways. Findings broaden PGPR genomic diversity and support sustainable agriculture potential.

Transcription factors (TFs) orchestrate gene expression programs by binding regulatory DNA sequences and modulating transcription of target genes. Identifying TF–target gene relationships is fundamental to understanding plant development, stress responses, and metabolic regulation. However, determining which genes a TF regulates remains technically challenging. This review provides a decision-oriented framework, that integrates experimental and computational plant TF–target identification. Placing emphasis on plant-specific constraints and practical method selection to guide researchers from initial TF discovery through comprehensive network characterization. We compare biochemical approaches (EMSA, Y1H), genome-wide mapping methods (ChIP-seq, DAP-seq, CUT&Tag), expression profiling techniques (RNA-seq on mutants and overexpression lines), and computational prediction tools (GENIE3, PTFSpot, ConnecTF). Critical trade-offs are discussed, between binding potential and functional regulation, throughput and resolution, and between different model and non-model plant systems. Finally, we highlight emerging technologies including high-throughput enhancer screening, single-cell approaches, and machine learning-based prediction platforms that promise to accelerate functional characterization of plant TFs and their regulatory networks.