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PubMed2026

Microbial partnerships and molecular mechanisms in plant stress physiology for climate-resilient and sustainable farming.

Plant-microbial partnerships and their underlying molecular mechanisms are indispensable, natural drivers of improved nutrient acquisition and stress tolerance in the face of climate-driven environmental challenges. Modern multi-omics tools, when coupled with artificial intelligence and synthetic biology, enable the precise design of targeted bioinoculants and synthetic microbial consortia. Translating these advanced microbiome-based strategies into scalable, field-level agricultural applications provides a sustainable path toward securing global food production while maintaining soil health. Global climate change imposes multifaceted abiotic and biotic stresses on crops, disrupting physiological and molecular processes and threatening agricultural productivity. Plant-associated microbes represent an underexplored yet powerful ally in enhancing crop resilience. This review presents current knowledge of plant-microbe interactions and the molecular mechanisms governing plant stress physiology, with an emphasis on climate-resilient and sustainable farming. Hence, ever-changing environmental cues pose a significant burden on agricultural productivity, and plant-associated microbial communities modulate a cascade of physiological and molecular responses, including production of phytohormones, signaling, regulation of reactive oxygen species homeostasis, and activation of plant immune responses to help plants withstand stress and enhance productivity. Moreover, root exudates, phytohormones, and quorum sensing mediate the central communication networks, facilitating plant-microbe cross talk. Additionally, the advances in OMICs approaches aid in disentangling the molecular underpinnings of these interactions by providing mechanistic insights and potential candidate gene targets for crop improvement and stress resilience. In the post-genomic era, integrating artificial intelligence and big data analysis to optimize microbiome-based strategies for sustainable agriculture is a new frontier for disentangling plant-microbe symbiosis to improve soil health, enhance crop yields, and improve stress tolerance. Thus, by integrating the ecological, physiological, and molecular perspectives, this review highlights the transformative potential of harnessing plant-microbe symbiosis for climate-resilient and sustainable agriculture.

Stress, Physiological↗
PubMed2026

Hormone priming and metabolic engineering of phytohormone crosstalk in rice under combined biotic and abiotic stresses: a multi-omics perspective for climate-resilient crop development.

Rice (Oryza sativa L.) is the caloric backbone for more than half of humanity, yet it remains one of the most vulnerable crops to the simultaneous biotic and abiotic stresses exacerbated by climate change. Phytohormone priming and the complex crosstalk networks governed by transcription factor hubs like WRKY, MYB, and NAC serve as the central adaptive mechanism for stress resilience. This review synthesizes how multi-omics integration, including spatial and single-cell transcriptomics, is resolving the molecular architecture of hormonal priming and epigenetic stress memory. We critically evaluate advanced metabolic engineering and genome-editing strategies such as CRISPR-Cas9, base/prime editing, and synthetic gene circuits that enable precision modifications to decouple stress tolerance from historical yield penalties. Furthermore, we discuss the emerging roles of microbiome-assisted priming via synthetic consortia and the application of artificial intelligence and digital twins (continuously updated computational models of crop physiology) for predictive stress management. By integrating these diverse technological pillars, we propose a systems-level roadmap for developing climate-resilient rice cultivars capable of maintaining yield stability across a volatile combinatorial stress landscape. This synthesis provides a framework for translating mechanistic hormonal insights into field-applicable cultivars to ensure global food security.

CRISPR↗
PubMed2025

Harnessing Landscape Genomics to Evaluate Genomic Vulnerability and Future Climate Resilience in an East Asia Perennial.

In this era of rapid climate change, understanding the adaptive potential of organisms is imperative for buffering biodiversity loss. Genomic forecasting provides invaluable insights into population vulnerability and adaptive potential under diverse climatic conditions, thereby facilitating management interventions and bolstering shaping species-specific germplasm conservation strategies. We primarily employed landscape genomics approaches, leveraging single-nucleotide polymorphisms obtained through whole-genome resequencing of 201 individuals across 43 Rheum palmatum complex populations, to pinpoint adaptive variation and its significance in the context of future climates, delineate seed zones, and establish guidelines for ex situ germplasm conservation. The species complex exhibited strong signatures of local adaptation and differential genomic vulnerabilities across its distribution range, with eastern lineage populations facing significant maladaptation risks under future climate scenarios. Using diverse datasets of putatively adaptive loci and climate change scenarios, we delineated three distinct seed zones within the species' range, estimated varying sample sizes per zone to capture most adaptive diversity, and predicted shifts in seed zone centroids ranging from 48.3 to 359.3 km from historical distributions to mitigate climate change impacts. Collectively, our findings underscore the importance of integrating genomic and environmental data to forecast the adaptive trajectory of an East Asian perennial under anticipated climate changes, guide seed zone delineation for germplasm conservation and enhance population resilience. These results provide a blueprint for designing targeted conservation strategies and restoration plans in other imperilled species.

Climate Change↗
PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗
PubMed2026

Holistic approaches for improvement of maize resistance against lodging stress: current status and future perspective.

Lodging is a major constraint in maize production, causing significant yield losses, reduced grain quality, and harvesting inefficiencies, thereby posing a serious challenge to global food security and climate-resilient agriculture. This review synthesizes current knowledge on the genetic, physiological, and agronomic determinants of maize lodging resistance and evaluates holistic strategies for improving tolerance to lodging stress. Recent advances in quantitative trait locus (QTL) mapping, genome-wide association studies (GWAS), functional gene characterization, genome editing, high-throughput phenotyping, and precision agronomy have provided powerful tools to enhance stalk biomechanics, root anchorage, and adaptive plant architecture. Integrating genomic discovery with advanced phenomics and optimized agronomic management offers a scalable framework for accelerating the development of high-yielding, lodging-resilient maize cultivars. However, critical gaps remain in understanding the genetic coordination between stalk strength and root system architecture, integrating multi-omics approaches to unravel regulatory networks, validating genome-editing interventions across diverse agro-ecologies, and developing environment-responsive predictive breeding models and cost-effective phenotyping tools, particularly for stress-prone regions. Addressing these challenges through coordinated multi-environment trials and integrative molecular-agronomic strategies will facilitate the translation of genomic discoveries into climate-resilient, high-performing maize cultivars. By consolidating molecular insights with applied breeding and management practices, this review provides a comprehensive framework that guides researchers in designing genome-informed and field-validated approaches to improve maize resistance to lodging stress and support sustainable crop production systems.

Zea mays↗
PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗
PubMed2026

Recurrent and niche-specific functional bacteriome of maize hybrid revealed by integrated metabarcoding and culturomics.

The plant microbiome plays a pivotal role in plant survival in natural habitats by facilitating nutrient acquisition, stress adaptation, and disease suppression, while also offering opportunities to enhance crop productivity and climate resilience. However, the distribution of persistent and culturable bacteriome across maize-associated niches and their functional potential remain poorly resolved. This study integrated metagenomic next-generation sequencing (mNGS-based metabarcoding) and culturomics to characterise the maize-associated bacteriome of bulk soil, rhizoplane, phylloplane, and cob of the maize hybrid PHM-1 under contrasting cropping and tillage systems, and to identify recurrent and agriculturally promising bacteriome components. The bacteriome exhibited pronounced niche-specific structuring, whereas overall bacterial community composition did not differ significantly across cropping and tillage treatments (ANOSIM, R = 0.038, p = 0.306). Proteobacteria predominated in the culturable bacteriome (69-84%; mean, 76.2%) but accounted for only 1% of the total bacteriome, whereas Patescibacteria and Firmicutes were relatively enriched. Niche-specific dominance was evident, with Pantoea accounting for 40.79% of the total and 56.27% of the culturable phylloplane bacteriome under cereal monocropping, while Serratia represented 31.59% and 59.40% of the total and culturable cob bacteriomes, respectively. Across niches, mNGS captured substantially greater bacteriome diversity, particularly uncultured and unidentified taxa in soil-associated compartments, whereas culturomics recovered a narrower but functionally accessible fraction. Culturomics yielded 99 isolates representing 32 species across 12 genera, including six genera shared with the mNGS-derived recurrent bacteriome: Bacillus, Enterobacter, Pantoea, Pseudomonas, Serratia, and Stenotrophomonas. Functional screening identified strong biocontrol and plant-beneficial traits among core-associated isolates. Pseudomonas oryzihabitans ZM-DL-PA10 inhibited Rhizoctonia solani, Macrophomina phaseolina, and Bipolaris maydis by up to 40.6%, 43.9%, and 45.2%, respectively, through secreted and volatile metabolites; exhibited P, K, and Zn solubilisation; and produced IAA and siderophores. It also recorded the lowest B. maydis disease index (ADI) of 1.00. Pantoea ananatis ZM-BH-EA4 showed 52.4% and 68.5% inhibition of R. solani and B. maydis, respectively, through volatile metabolites. Collectively, the integration of mNGS and culturomics revealed a strongly compartmentalised maize bacteriome and identified recurrent, culturable, and functionally promising bacterial taxa, providing a targeted resource for microbiome-based crop protection and climate-resilient maize production.

Zea mays↗
PubMed2026

Low-pass whole-genome sequencing reveals genomic diversity and ecotype-specific adaptation in indigenous Tigrayan chickens.

Indigenous chickens play a critical role in food security and climate resilience in smallholder systems, yet their genomic diversity and adaptive potential remain insufficiently characterised. This study employed low-pass whole-genome sequencing (LP-WGS; 0.2-1.99×) to investigate genomic diversity, population structure, inbreeding and candidate environment-associated genomic variation in 33 chickens from highland, midland, and lowland agroecologies in the Tigray region of northern Ethiopia. After imputation and stringent filtering, 23.4 million high-confidence SNPs were retained, including ~ 17% novel variants, indicating substantial uncharacterised genetic diversity in these populations. SNP density (13.8 ± 8.6 SNPs/kb) was comparable to values reported from high-coverage Ethiopian chicken datasets, demonstrating the suitability of LP-WGS for population genomics in resource-limited settings. Marked differences in genomic diversity were observed among ecotypes: midland chickens showed the highest nucleotide diversity (π = 0.00267), followed by lowland (π = 0.00233), whereas highland chickens showed the lowest diversity (π = 0.00203) and elevated genomic inbreeding (FROH and FHOM ≈ 0.18). Population structure analyses revealed clear genetic separation among ecotypes. PCA (13.91% variation explained) distinguished lowland chickens along PC1 and separated highland from midland along PC2, while ADMIXTURE and FST patterns supported three major ancestral genomic backgrounds. Functional annotation of private missense variants uncovered distinct adaptive signatures reflecting the contrasting agroecological conditions. Highland chickens showed enrichment of candidate genes potentially involved in physiological processes relevant to high-altitude environments, including cold response, angiogenesis, cardiovascular regulation and metabolic homeostasis (eg., PARP1, ACOX2, ITGB3, EDNRB, SOX8, and SOX10). Midland chickens exhibited candidate signals of selection in genes with known roles in innate antiviral immunity, bacterial defence and inflammatory regulation (eg., BAK1, CLSTN1, CYSLTR1, CYSLTR2, CXCR7, GIPR, DSCAM, GDAP1, TLR3, TLR4, TLR7, IFIH1, ADORA1, EPHB1, and TMPRSS2). Lowland chickens displayed candidate variants associated with heat-stress response, DNA damage repair, oxidative balance and cardiovascular support under extreme temperatures (e.g., MLH1, BDKRB1, GPR19, FLT1, CCL18, TGM2, and RAMP3). Overall, the results indicate substantial genomic differentiation among ecotypes and suggest candidate environment-associated genetic divergence across Tigray's diverse agroecological zones. These populations may represent important reservoirs of adaptive genetic variation for climate-resilient poultry breeding, warranting further functional validation and conservation-oriented management.

Animals↗
PubMed2026

Translating Flood-Tolerance Biology into Breeding: A 5D Framework for Next-Generation Rice Varieties.

Flooding is among the most devastating abiotic stresses limiting rice productivity. Although SUB1A introgression conferred submergence tolerance in several mega-varieties, this single-gene approach is insufficient for the diverse flood types-flash floods, stagnant floods, anaerobic germination, and deepwater inundation-progressively intensifying with climate change. Here, we review the physiological mechanisms and genetic architecture underlying tolerance to each flood type, emphasizing the dual role of reactive oxygen species (ROS) in signalling and damage, the management of elemental toxicities (Fe2+, Mn2+) under altered soil redox, and lessons from wetland species and lowland rice. We then examine why marker-assisted selection has failed for polygenic, multi-stress tolerance and identify persistent breeding bottlenecks. Building on this biological foundation, we outline an integrated 5D framework (Demand, Discovery, Design, Development, Deployment) that links gene-bank diversity, multi-omics discovery, predictive breeding and on-farm validation through continuous feedback. We discuss how connected breeding, the transition-from-trait-to-environment (TTE) strategy, and speed breeding can accelerate genetic gain, and we close with research priorities centred on the biology of multi-flood tolerance to develop climate-resilient rice.

5D breeding framework↗
PubMed2026

Brassinosteroids as Central Regulators of Plant Growth, Stress Tolerance, and Agricultural Resilience.

Brassinosteroids (BRs) are essential steroidal phytohormones that regulate plant growth, development, and responses to environmental stresses. Recent studies have demonstrated the important roles of BRs in enhancing plant tolerance to abiotic stresses, including drought, salinity, temperature extremes, heavy metal toxicity, and oxidative stress, as well as biotic stresses caused by pathogens and herbivores. This review summarizes current advances in BR biosynthesis, metabolism, transport, and signaling pathways, focusing on key components that mediate stress adaptation. We discuss the physiological and molecular mechanisms through which BRs improve stress tolerance, including regulation of antioxidant defense, ion homeostasis, osmotic adjustment, and stress-responsive gene expression. Particular attention is given to the extensive cross talk between BRs and other phytohormones, such as abscisic acid, jasmonic acid, salicylic acid, ethylene, auxin, and gibberellins, which enables plants to balance growth and defense under adverse conditions. Furthermore, we highlighted the potential applications of BRs in crop improvement through exogenous treatments, genetic engineering, and genome-editing approaches. However, the effectiveness of BR-based strategies is highly dependent on crop species, developmental stage, stress type, BR concentration, application method, and environmental conditions. In addition, excessive BR accumulation or application may result in undesirable growth responses, and further multi-location field validation is required before widespread agricultural implementation. Finally, we discuss emerging research trends, current knowledge gaps, and future perspectives for exploring BR signaling to develop climate-resilient crops. Overall, BRs represent promising targets for improving crop stress resilience; however, optimizing BR-mediated strategies and validating their long-term performance under diverse field conditions will be essential for their successful application in sustainable agriculture.

abiotic stress↗
PubMed2026

Engineering cold stress resilience in capsicum annuum through functional genomics and precision breeding.

This review synthesizes the molecular mechanisms of cold tolerance in pepper, integrating multi-omics data,genome editing, and precision breeding strategies to accelerate the development of cold-resilient cultivars. Cold stress is a significant environmental factor that affects the growth, productivity, and fruit quality of Capsicum annuum by impairing membrane integrity photosynthesis and cellular redox homeostasis. Although pepper has several endogenous cold-responsive regulators such as CaNAC035 and CabHLH035, along with antioxidant defense systems, its cold tolerance remains limited due to low transcriptional activation of key regulators, functional redundancy among cold-responsive genes, and the polygenicity of cold tolerance. These complexities, combined with low genetic diversity and linkage drag, have hindered the improvement of cold-resistant cultivars through conventional breeding. This review brings together the recent progress in understanding the molecular mechanisms of cold stress perception, signal transduction, transcriptional regulation, metabolic reprogramming, and phytohormone interactions in pepper. Precision Breeding 2.0 is a new innovation that combines the integration of multi-omics-based target identification with next-generation genome-editing techniques, allowing precise and multiplex engineering of complex and interconnected regulatory networks instead of single genes. We cover new approaches such as engineering the DREB/CBF pathway, allele-specific editing and targeted disruption of negative regulators to enhance the pathway(s) involved in cold response. Moreover, we propose a roadmap for integration of transcriptomics, proteomics, metabolomics, high-throughput phenomics, and speed breeding to accelerate the identification, validation, and deployment of superior alleles to boost cold tolerance. This review provides a foundation for developing climate-resilient pepper cultivars by connecting functional genomics with precision genome engineering approaches to maintain productivity under variable environmental conditions.

Capsicum↗
PubMed2026

Microbial diversity: the essential foundation for life on our planet.

The biological basis of life on Earth is microbial diversity that ensures human health, agricultural productivity, ecological balance, and ecosystem functioning. Microorganisms enable ecosystem restoration through bioremediation, maintain soil fertility, support plant growth, manage vital biogeochemical cycles, and contribute to climate resilience. Precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine are the examples of emerging microbiome-based therapies that offer promising therapeutic opportunities. In humans, the gut microbial community is essential for immune regulation, metabolism, and disease prevention. In terrestrial ecological systems, interactions between plants, fungi, bacteria, and other soil microorganisms improve carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in the given effects of climate change. Emerging uses in agriculture, environmental restoration, and medicine are made possible by advancements in multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence. Considering these developments, issues with ecological complexity, long-term validation, standardization, and field scale application still exist. Therefore, preserving microbial diversity is important for conserving ecological resilience and strengthening the One Health framework, which highlights the mutual dependance of health of animal, human, plant, and environment. This review summarizes what has been discovered about ecological and biomedical relevance of microbiome, identifies important research gaps, highlighting emerging technologies, and evaluates potential future directions for using microbiome to support planetary sustainability.

Bioremediation↗
PubMed2026

Landscape Genomics Reveals Divergent Adaptation Modes and Predicts Climate Vulnerability in Xinjiang Indigenous Sheep.

Climate change increasingly endangers precious indigenous sheep germplasm resources distributed across diverse Chinese landscapes, and systematically decoding their polygenic climate-adaptive genetic mechanisms is essential for targeted breed conservation and long-term sustainable pastoral production. Whole-genome resequencing data from 93 individuals covering six representative local sheep breeds were analyzed in this work. After filtering highly collinear climate variables, three mature landscape genomic approaches were jointly applied to identify environment-linked gene variants, while two predictive metrics across ten CMIP6 future climate scenarios quantified each breed's long-term adaptive risks. Six temperature- and water-related environmental factors jointly drove sheep population genetic differentiation, with temperature fluctuation indices showing markedly stronger explanatory power. Detected adaptive genes were significantly enriched in ion transport, energy metabolism and cellular stress response pathways. Future projections indicated western breeds (Bayinbuluke, Cele Black, Xiahe) face severe maladaptation risks under high-emission SSP370 scenarios by 2100, whereas central and eastern breeds possess much broader climate tolerance. This study systematically reveals the core genomic basis of ovine climate adaptation and quantifies distinct breed-specific climate vulnerability, providing solid reliable theoretical support for precision germplasm conservation and selective breeding of climate-resilient sheep varieties.

adaptive loci↗
PubMed2026

Seed shattering habit in millets and the secrets of the abscission layer - a comprehensive review.

Though seed shattering continues to be a significant barrier affecting yield stability and harvesting efficiency in millets and other grasses, millets are increasingly acknowledged as climate-resilient, nutrient-rich 2007cereal crops with the potential to strengthen global nutritional and food security under the combined pressures of climate change, population growth, and limited natural resources. Since strong artificial selection favoured non-shattering phenotypes during domestication, seed shattering, an adaptive trait in wild species that promotes seed dispersal through the formation and activation of specialised abscission layers, became a distinguishing feature of cultivated cereals. With a focus on the morphological, physiological, hormonal, and genetic modulation of the abscission zone, this article summarizes the state of the art regarding seed shattering in millets. Abscission layer morphology, location, and lignification vary greatly among grasses, from well-defined lignified zones in rice and sorghum to non-lignified and anatomically subtle zones in Setaria and Panicum species. Cell wall-modifying enzymes like polygalacturonases, cellulases, expansins, and pectin methylesterases that mediate middle lamella degradation are modulated by coordinated hormonal signalling involving auxin, ethylene, and abscisic acid, which controls the timing and progression of cell separation at the physiological level. Domestication-related genes, including SH1, qSH1, SH4, and LES1, demonstrate convergent evolutionary mechanisms controlling abscission layer development in a variety of grass lineages at the molecular level. Understanding these regulatory networks has been greatly enhanced by recent developments in transcriptomics, functional genomics, and genome sequencing in both model species and underused millets. The role of millets as climate-smart cereals for sustainable future agriculture is reinforced by the integration of anatomical, physiological, and genetic insights, which offer a solid basis for targeted breeding and genome-editing strategies intended to improve seed retention, enhance yield stability, and increase harvest efficiency.

Abscission Layer↗
PubMed2025

On the origin of the late-flowering ppd-H1 allele in barley.

To breed for climate resilient crops, an understanding of the genetic and environmental factors influencing adaptation is critical. Barley provides a model species to study adaptation to climate change. Here we present a detailed analysis of genetic variation at a major photoperiod response locus and relate this to the domestication history and dispersal of barley. The PPD-H1 locus (a PSEUDO-RESPONSE REGULATOR 7) promotes flowering under long-day conditions, and a natural mutation at this locus resulted in a recessive, late-flowering ppd-H1 allele. This mutation proved beneficial in high-latitude environments such as Northern Europe, where it allows extended vegetative growth during long spring days. We infer the origin of the mutated late-flowering ppd-H1 allele by re-sequencing a large geo-referenced collection of 942 Hordeum spontaneum, 5 Hordeum agriocrithon and 1110 domesticated (Hordeum vulgare) barleys. We demonstrate that the late-flowering phenotype originated from Desert-type wild barley in the Southern Levant and present evidence suggesting a post-domestication origin of the mutated ppd-H1 allele.

Hordeum↗
PubMed2026

Genome-wide variation analysis of two Salvia hispanica L. genotypes and implication for associations with metabolic and adaptive traits.

BACKGROUND: Advances in next-generation sequencing have accelerated genome-wide exploration of genetic diversity in underutilized oilseed crops. Salvia hispanica L. (chia), a high-nutrient pseudocereal rich in omega-3 fatty acids, is increasingly valued for its health benefits and commercial potential, yet it remains poorly characterized at the genomic level. Understanding the scale and nature of genomic variation is essential for improving complex traits such as oil yield, stress tolerance, and seed quality. METHODS: Two contrasting chia genotypes, Black-chia (CACH-B) and White- chia (CACH-W), were resequenced using the Bio-Resequencing Toolkit (BRT) pipeline. High-coverage sequencing, with a mapping rate exceeding 99% and an average depth of approximately 28×, facilitated the detection and annotation of single-nucleotide polymorphisms (SNPs), insertions and deletions (InDels), copy-number variations (CNVs), and structural variants (SVs). The functional classification of variant impacts enabled the identification of genes potentially linked to metabolic and adaptive traits. RESULTS: A total of 1.97 million SNPs, 401,493 InDels, 836 CNVs, and 15,288 SVs were identified across the chia genome. Notably, approximately 53% of exonic SNPs were non-synonymous (dN/dS ≈ 1.28), predominantly affecting lipid metabolism, transcriptional regulation, and stress response pathways, potentially altering key agronomic traits. In addition, CNV hotspots were concentrated in chromosomes 3 and 6, overlapping MYB, WRKY, and bZIP transcription factor loci, may potentially be involved in stress tolerance and yield. Furthermore, structural rearrangements, including inversions and duplications within the FAD2, FAD3, and CYP450 gene clusters, were potentially associated with seed pigmentation and omega-3 biosynthesis, pointing to their potential breeding relevance. Observed heterozygosity (Hₒ ≈ 0.71) and nucleotide diversity (π ≈ 7 × 10-3) indicated moderate to high allelic richness. In addition, the low FST value (0.038) indicates substantial genomic similarity between the two genotypes. CONCLUSION: This study presents the first comprehensive map integrating SNPs, CNVs, and SVs in S. hispanica L. The results reveal a structurally dynamic genome characterized by substantial sequence and structural variation, providing valuable insights into genomic diversity and potential adaptive mechanisms in chia. The coexistence of high SNP diversity and abundant structural variation underpins chia's nutritional specialization and environmental resilience. These results deliver a foundational genomic resource for marker-assisted breeding, genome-wide association studies, and the development of climate-resilient chia cultivars.

Copy-number variation, structural variation↗
PubMed2026

Whole-genome sequencing reveals divergent and shared selection signatures of heat stress adaptation in indigenous Ethiopian zebu cattle from dry-hot and humid-hot environments.

African zebu cattle (Bos indicus) exhibit remarkable adaptations to extreme thermal conditions, yet the genomic basis of this resilience remains incompletely characterized. Ethiopia provides a unique natural setting in which closely related zebu populations have adapted divergently to dry-hot (DHETZ) and humid-hot (HHETZ) climates. In this study, we reanalyzed publicly available whole-genome sequencing datasets from 46 Ethiopian zebu cattle from five populations and compared them with Asian zebu, Sudanese zebu, African taurine, and European taurine breeds. By integrating genome-wide SNP analysis, population genetic structure assessment, and multiple selection scans (iHS, Hp, XP-EHH, and XP-CLR), we identified distinct and shared selection signatures between DHETZ and HHETZ. We detected 33.7 million and 34.2 million biallelic autosomal SNPs in DHETZ and HHETZ, respectively. Ethiopian zebu clustered closely with Sudanese zebu but showed clear divergence from Asian zebu and taurine breeds. DHETZ and HHETZ exhibited very low genetic differentiation (FST = 0.0063), consistent with their shared ancestry; however, each group displayed unique selection signals. In DHETZ, iHS and Hp detected 298 and 113 candidate regions, respectively, whereas in HHETZ, they detected 244 and 138 regions, respectively. Cross-population XP-EHH and XP-CLR analyses identified 163 and 227 divergent regions between DHETZ and HHETZ, respectively. Integration of the four selection scans identified 19 high-confidence candidate regions in DHETZ and 13 in HHETZ. DHETZ showed strong selection in genes involved in oxidative stress regulation, protein folding, mitochondrial function, and vascular remodeling, including SESN2, DNAJC8, GRPEL2, ABLIM3, and AFAP1L1. In contrast, HHETZ displayed signatures in genes associated with immune responses, energy metabolism, and angiogenesis inhibition, including MYD88, PRKACA, PRKACB, and WIF1. Several genes, including VEGFC, TNIP3, and DMXL2, were under selection in both groups, suggesting conserved mechanisms of thermotolerance and reproductive adaptation. The shared VEGFC signal and the HHETZ-specific WIF1 signal may indicate a distinct vascular regulatory mechanism in the dry-hot and humid-hot environments. Our results reveal a dual pattern of genomic adaptation in Ethiopian zebu cattle and provide candidate loci for future validation and climate-resilient livestock breeding.

Animals↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗