Natural Variation Research Articles

A highly variable habitat selection in moose across diel and seasonal scales

PurposeHabitat selection in animals is a hierarchal process that operates across multiple temporal and spatial scales, adapting to changes in environmental conditions, human disturbances, and predation risks. Despite its significance, previous research often oversimplifies temporal dynamics by categorizing them into broad seasonal and diel patterns, overlooking the continuous nature of temporal variability and habitat specificity.MethodsWe investigated the temporal patterns in habitat selection of moose (Alces alces) in highly heterogenous landscapes at the southwestern edge of their European range using step-selection functions. Utilizing over 700,000 GPS locations from 34 adult moose, we aimed to assess seasonal and diel patterns in their selectivity for both natural and human-related habitats.ResultsOur findings revealed significant overall temporal variation in moose habitat selection at both seasonal and diel scales. Moose selectivity toward different habitats showed low repeatability over time, with 35% of cases displaying negative correlation between selectivity in different time windows. Diel changes were more pronounced, showing 5.6-fold difference in cumulative selectivity, compared to 1.4-fold difference in seasonal dynamics. Notably, moose exhibited lower selectivity during nighttime hours throughout the year compared to daytime hours. The study also highlighted distinct habitat selection patterns across different habitat types: natural habitats (deciduous forests, coniferous forests, wetlands) exhibited pronounced seasonal variation, while anthropogenic habitats (grasslands, arable land, roads and settlements) showed more diel variability. Moose generally avoided human-related habitats during daytime hours, but their preferences during nighttime varied depending on the habitat type and time of year.ConclusionThis research advances our understanding of the complex temporal patterns in habitat selection by large herbivores and underscores the importance of considering temporal dynamics in habitat selection modelling.

Natural variation in MdNAC5 contributes to fruit firmness and ripening divergence in apple

Abstract Fruit firmness is an important trait for characterizing the quality and value of apple. It is also serves as an indicator of fruit maturity, as it is a complex trait regulated by multiple genes. Re-sequencing techniques can be employed to elucidate variations in such complex fruit traits. Here, the whole genomes of 294 F1 hybrids of ‘Fuji’ and ‘Cripp's Pink’ were re-sequenced, and a high-density bin-map was constructed using 5,014 bin markers with a total map distance of 2,213.23 cM and an average map distance of 0.44 cM. Quantitative trait loci (QTLs) of traits related to fruit were mapped, and an A-T allele variant identified in the coding region of MdNAC5 was found to potentially regulate fruit firmness and ripening. The overexpression of MdNAC5A resulted in higher production of methionine and 1-aminocyclopropanecarboxylic acid compared to MdNAC5T, leading to reduced fruit firmness and accelerated ripening in apples and tomatoes. Furthermore, the activities of MdNAC5A and MdNAC5T were enhanced through their differential binding to the promoter regions of MdACS1 and MdERF3. Spatial variations in MdNAC5A and MdNAC5T caused changes in MdACS1 expression following their interaction with MdERF3. Ultimately, utilizing different MdNAC5 alleles offers a strategy to manipulate fruit firmness in apple breeding.

Distortion of Magnetic Fields Near a Steel Construction. Modeling

Natural variations of the Earth's magnetic field and its man-made distortions are considered. The rules and regulations in force in the Russian Federation for staying and living in places with a distortion of the Earth's magnetic field are analyzed. Existing methods for eliminating distorted magnetic fields inside residential and work premises are considered. Modeling of distortions of the Earth's magnetic field by building materials was carried out and the influence of their location in space on the overall picture of the resulting magnetic field was shown. To modeling the influence of reinforcement of reinforced concrete constructions on the resulting magnetic field, 6 models of the location of reinforcing bars and their groups were used: one reinforcing bar magnetized against the Earth’s magnetic field and along the Earth’s field; 4 reinforcing bars each, magnetized against the Earth’s magnetic field and magnetized in an alternating manner; the simplest model of a residential structure or public building with 36 reinforcing bars magnetized against the Earth’s magnetic field and with 36 magnetized bars in different directions. Near the model of a rod with magnetization along the Earth’s magnetic field, there are hypogeomagnetic fields. Near the models of a reinforcing bar and four reinforcing bars magnetized against the Earth’s magnetic field, there are no hypogeomagnetic zones and changes in the direction of the magnetic field strength vectors, but strong increases in the resulting magnetic field can be observed - 500 times or more. Near models with 4 reinforcing bars magnetized in an alternating manner, there are the most dangerous zero values of magnetic field strength at a distance of 1 m. Inside the model with 36 reinforcing bars there is a hypogeomagnetic field in the very center of the model and a change in the direction of the magnetic field strength vectors. Near the model with 36 magnetized rods in different directions, a more even resulting magnetic field is observed without sharp changes in its direction and absolute value. Methods have been proposed for eliminating magnetic pathogenic zones at the stages of installation and construction of building that have ferromagnetic materials in their design.

Can Monitoring Data of Forensically Important Blow Flies Be Transferred Between Cities?

ABSTRACTThe use of monitoring data to build prediction models for the abundance and activity of necrophagous blow flies is common practice in forensic entomology, but its advantages and disadvantages are still debated. A frequently asked question is the transferability of such species prediction models. So far no study has examined whether the assumption of low transferability of these data between cities and seasons holds true. In the present study, we evaluate whether models calibrated with a specific training data set from a specific place and time can be transferred to other data sets for different time periods and locations. We developed models using five different algorithms to predict the activity and abundance of four forensically relevant blow fly species (Calliphora vicina Robineau‐Desvoidy, Lucilia ampullacea Villeneuve, Lucilia caesar (Linnaeus), Lucilia sericata (Meigen)). The training data set was obtained from a single city, and the transferability of the models was evaluated using monitoring data from this and three other cities. The geographic transferability of the models was confirmed for all algorithms, but only for two species, C. vicina and L. sericata, and for two of the four cities. Lucilia caesar and L. ampullacea were rare in the test data set, and their species‐specific adaptation to environmental parameters was not captured by the models. Cities that did not work differed from the training data set in terms of climate and habitat features. To build generalised predictive models of blow fly abundance and activity, we need training data sets based on monitoring data from different regions, seasons and years to cover a wide range of environmental conditions. This is essential for describing and predicting natural variability.

Selective-Integrative Technology for the Separation of Colostrum Into Components and the Possibilities of Obtaining Protein Substances From Different Sources

Background. Obtaining biologically active natural compounds involved in the regulation of metabolism is an important goal in biotechnology. Colostrum is a unique natural source of various biologically active compounds. However, the extremely high natural variability of colostrum composition does not meet the existing requirements for standardization in pharmaceutical preparations. Objective. To develop a method for separating colostrum into its basic components (lipids, casein, and protein fractions), thereby reducing the variability of whole colostrum composition, obtaining several target products, and demonstrating the possibility of acquiring new protein substances from different sources. Methods. Colostrum separation was carried out through centrifugation and membrane filtration. Plant proteins (sunflower) and milk proteins were used to obtain protein substances from different sources. The composition of proteins, carbohydrates, and nucleic acids was determined using mass spectrometry, centrifugation, and membrane filtration. Results. The proposed method for obtaining basic substances from colostrum significantly reduced the vari­ability in composition compared to whole colostrum. The efficiency of protein sedimentation in concentrated protein solutions by centrifugation and ultrafiltration was shown to depend on protein concentration. Additionally, the formation of non-specific protein aggregates in the centrifugal field allowed the extraction of protein substances from various natural sources, which is relevant for functional nutrition. Conclusions. The proposed selective-integrative technology for obtaining different substances from colostrum significantly reduces the high variability of whole colostrum composition. It increases the efficiency of component separation into lipid, casein fractions, low molecular weight protein fractions, and ultrafiltrate, while also enabling the acquisition of protein substances from diverse sources.

Designing Learning Centres that Facilitate Knowledge Construction on Science Process Skills in Early Childhood Classrooms

Science education is perceived to have the potential to develop 21st century competencies. In Zimbabwe, the teaching of science in early childhood by using learning centres is considered as a way of empowering young learners with scientific knowledge and skills to become productive citizens in future. To strengthen the initiative, resources are being channelled and policies crafted to support the teaching of STEM (science, technology, engineering and mathematics). Despite considerable efforts to introduce STEM in early childhood development (ECD), research concerning the benefits of developing science skills using learning centres is sparse. This study adopted an interpretive paradigm and multiple case study design for three schools and three purposively selected ECD teachers. Data was solicited through semi-structured interviews, document analysis and classroom observations. The variable nature of learning centres in ECD classrooms is an indication of shortcomings in policy on teaching using learning centres. At learning centres, learners construct scientific knowledge, ideas and skills through interaction with peers and developmentally appropriate materials. Teachers should be creative and innovative and create learning centres that enable learners to explore the world and construct scientific knowledge through active participation and sharing ideas. A policy to guide the teachers on constructing appropriate learning centres for effective teaching of science skills should be drafted.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

Natural variation at the cotton HIC locus increases trichome density and enhances resistance to aphids.

Plant trichomes are an excellent model for studying cell differentiation and development, providing crucial defenses against biotic and abiotic stresses. There is a well-established inverse relationship between trichome density and aphid prevalence, indicating that higher trichome density leads to reduced aphid infestations. Here we present the cloning and characterization of a dominant quantitative trait locus, HIC (hirsute cotton), which significantly enhances cotton trichome density. This enhancement leads to markedly improved resistance against cotton aphids. The HIC encodes an HD-ZIP IV transcriptional activator, crucial for trichome initiation. Overexpression of HIC leads to a substantial increase in trichome density, while knockdown of HIC results in a marked decrease in density, confirming its role in trichome regulation. We identified a variant in the HIC promoter (-810 bp A to C) that increases transcription of HIC and trichome density in hirsute cotton compared with Gossypium hirsutum cultivars with fewer or no trichomes. Interestingly, although the -810 variant in the HIC promoter is the same in G. barbadense and hirsute cotton, the presence of a copia-like retrotransposon insertion in the coding region of HIC in G. barbadense causes premature transcription termination. Further analysis revealed that HIC positively regulates trichome density by directly targeting the EXPANSIN A2 gene, which is involved in cell wall development. Taken together, our results underscore the pivotal function of HIC as a primary regulator during the initial phases of trichome formation, and its prospective utility in enhancing aphid resistance in superior cotton cultivars via selective breeding.

Genome-wide Association Studies of Photosynthetic and Agronomic Traits in Cowpea Collection.

Exploring genomic regions linked with drought tolerance and photosynthesis in cowpea could accelerate breeding of climate-resilient cowpea varieties A Genome-wide association study (GWAS) was conducted to identify marker-trait associations for agronomic and photosynthetic traits measured under well-watered and water-stressed conditions. One hundred and twelve cowpea accessions from IITA were phenotyped for agronomic and photosynthetic traits across three locations in two years: Ibadan, Ikenne (2020 and 2021) and Kano (2021 and 2022). The accessions were genotyped using 19,000 DArT-Seq SNP markers from which 9,210 markers were utilized for GWAS analysis using BLINK and mixed linear model (MLM) in GAPIT. Results revealed significant accession × environment interactions for measured traits while ΦPSII, ΦNO and ΦNPQ had significant and consistent correlations with grain yield across conditions. GWAS identified five SNP markers having consistent associations with grain yield under well-watered and water-stressed conditions and three markers associated with ΦNPQ and ΦNO. Gene annotations revealed Vigun04g169000 and Vigun08g168900 genes linked with grain yield and highly expressed under water-stressed conditions have functional roles in regulating plant development and adaptive response to environmental stress. Vigun07g133400, Vigun07g132700 and Vigun07g258000 genes linked with ΦNPQ and ΦNO are involved in activities controlling photoprotection and stress-induced damage in plants. This study identified natural genetic variation in cowpea and correlations between photosynthetic traits and grain yield under real-field drought conditions. The identified SNP markers upon validation would be valuable in marker-assisted selection and useful for cowpea breeders to harness the role of photosynthesis in genetic enhancement of cowpea tolerance to drought.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion

BackgroundEpilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient’s work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals.MethodologyFirstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted.ResultsThe model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively.ConclusionThe findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

Exploring Novel Genomic Loci and Candidate Genes Associated with Plant Height in Bulgarian Bread Wheat via Multi-Model GWAS.

In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat.

Natural variation in the promoter of qRBG1/OsBZR5 underlies enhanced rice yield

Seed size, a key determinant of rice yield, is regulated by brassinosteroid (BR); however, the BR pathway in rice has not been fully elucidated. Here, we report the cloning and characterization of the quantitative trait locus Rice Big Grain 1 (qRBG1) from single-segment substitution line Z499. Our data show that qRBG1Z is an unselected rare promoter variation that reduces qRBG1 expression to increase cell number and size, resulting in larger grains, whereas qRBG1 overexpression causes smaller grains in recipient Nipponbare. We demonstrate that qRBG1 encodes a non-canonical BES1 (Bri1-EMS-Suppressor1)/BZR1(Brassinazole-Resistant1) family member, OsBZR5, that regulates grain size upon phosphorylation by OsGSK2 (GSK3-like Kinase2) and binding to D2 (DWARF2) and OFP1 (Ovate-Family-Protein1) promoters. qRBG1 interacts with OsBZR1 to synergistically repress D2, and to antagonistically mediate OFP1 for grain size. Our results reveal a regulatory network controlling grain size via OsGSK2–qRBG1–OsBZR1–D2–OFP1 module, providing a target for improving rice yield.

Global assessment of interannual variability in coastal urban areas and ecosystems

Abstract Both seasonal and extreme climate conditions are influenced by long-term natural internal variability. However, in general, long-term hazard variation has not been incorporated into coastal risk assessments. There are coastal regions of high interest, such as urban areas, where a large number of people are exposed to hydrometeorological hazards, and ecosystems, which provide protection, where long-term natural variability should be considered a design factor. In this study, we systematized climate analysis to identify high-interest regions where hazard long-term variability should be considered in risk assessment, disaster reduction, and future climate change adaptation and protection designs. To achieve this goal, we examined the effect of the leading modes of climate variability (Arctic Oscillation, Southern Annular Mode, and El Niño–Southern Oscillation) on the variation in the recurrence of extreme coastal hazard events, including as a first step sea surface temperature, winds, and waves. Neglecting long-term variability could potentially lead to the underperformance of solutions, or even irreversible damage that compromises the conditions of ecosystems for which nature-based solutions are designed.

Genomic variation of European beech reveals signals of local adaptation despite high levels of phenotypic plasticity

Local adaptation is key for ecotypic differentiation and species evolution. Understanding underlying genomic patterns can allow the prediction of future maladaptation and ecosystem stability. Here, we report the whole-genome resequencing of 874 individuals from 100 range-wide populations of European beech (Fagus sylvatica L.), an important forest tree species in Europe. We show that genetic variation closely mirrors geography with a clear pattern of isolation-by-distance. Genome-wide analyses for genotype-environment associations (GEAs) identify relatively few potentially adaptive variants after correcting for an overwhelming signal of statistically significant but non-causal GEAs. We characterize the single high confidence genomic region and pinpoint a candidate gene possibly involved in winter temperature adaptation via modulation of spring phenology. Surprisingly, allelic variation at this locus does not result in any apparent fitness differences in a common garden. More generally, reciprocal transplant experiments across large climate distances suggest extensive phenotypic plasticity. Nevertheless, we find indications of polygenic adaptation which may be essential in natural ecosystems. This polygenic signal exhibits broad- and fine-scale variation across the landscape, highlighting the relevance of spatial resolution. In summary, our results emphasize the importance, but also exemplify the complexity, of employing natural genetic variation for forest conservation under climate change.

A genome-wide association study reveals molecular mechanism underlying powdery mildew resistance in cucumber

BackgroundPowdery mildew is a disease with one of the most substantial impacts on cucumber production globally. The most efficient approach for controlling powdery mildew is the development of genetic resistance; however, few genes associated with inherent variations in cucumber powdery mildew resistance have been identified as of yet.ResultsIn this study, we re-sequence 299 cucumber accessions, which are divided into four geographical groups. A genome-wide association study identifies 50 sites significantly associated with natural variations in powdery mildew resistance. Linkage disequilibrium analysis further divides these 50 sites into 32 linkage disequilibrium blocks containing 41 putative genes. Virus-induced gene silencing and gene expression analysis implicate CsGy5G015960, which encodes a phosphate transporter, as the candidate gene regulating powdery mildew resistance. On the basis of the resequencing data, we generate five CsGy5G015960 haplotypes, identifying Hap.1 as the haplotype most likely associated with powdery mildew resistance. In addition, we determine that a 29-bp InDel in the 3′ untranslated region of CsGy5G015960 is responsible for mRNA stability. Overexpression of CsGy5G015960Hap.1 in the susceptible line enhances powdery mildew resistance and phosphorus accumulation. Further comparative RNA-seq analysis demonstrates that CsGy5G015960Hap.1 may regulate cucumber powdery mildew resistance by maintaining a higher H2O2 level through the depletion of multiple class III peroxidases.ConclusionsHere we identify a candidate powdery mildew-resistant gene in cucumber using GWAS. The identified gene may be a promising target for molecular breeding and genetic engineering in cucumber to enhance powdery mildew resistance.

Management and Treatment of Neurofibromatosis Type I

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumour suppressor syndrome associated with benign and malignant tumours, predominantly affecting the skin and nervous system. NF1, the most prevalent neurocutaneous syndrome, and the focus of this review, has a frequency of ~ 1/1,900-1/3,500 people worldwide. Disease manifestations can present at birth and emerge with age, negatively impacting multiple clinical domains and imparting a profound impact on a patient’s quality of life and life expectancy. Given its progressive nature and marked clinical variability, NF1 warrants a multidisciplinary approach to management and treatment.

Differential response of multiple stream ecosystem processes to basin- and reach-scale drivers

Stream ecosystems are inherently dependent on their surroundings and, thus, highly vulnerable to anthropogenic impacts, which alter both their structure and functioning. Anchored in biologically-mediated processes, the response of stream ecosystem functioning to environmental conditions exhibits intricate patterns, reflecting both natural dynamics and human-induced changes. Our study aimed at determining the natural and anthropogenic drivers influencing multiple stream ecosystems processes (nutrient uptake, biomass accrual, decomposition, and ecosystem metabolism) at a regional scale. By examining 38 natural and anthropogenic variables across 63 stream reaches in Gipuzkoa (northern Iberian Peninsula), we used structural equation modeling to unravel the cascading effect of basin- and reach-scale drivers onto ecosystem process. The results reveal significant variability in ecosystem processes, with contrasting spatial patterns, suggesting that studied processes respond differently to environmental factors. Urban land-use emerged as a primary basin-scale driver, whereas reach-scale variables reflected both natural and anthropogenic influence. Nutrient uptake rates were primarily driven by nutrient concentrations in stream water, but models for biomass accrual, decomposition, and ecosystem metabolism exhibited more complex cause-effect relationships. Our findings highlight the impact of urban areas on multiple ecosystem processes and services, disproportionate when considering their small land cover. The present study emphasizes the convenience of measuring multiple ecosystem functions simultaneously to get a comprehensive diagnosis of the functional status of rivers.

Comprehensive analysis of ankyrin repeat gene family revealed SbANK56 confers drought tolerance in sorghum

Ankyrin repeat (ANK) proteins are crucial for cell growth, development, and response to hormones and environmental stress. However, there has been little research done to clarify the roles of ANK proteins in sorghum. In this study, 142 ANK genes of sorghum were identified and classified into 12 subfamilies according to the conserved domains. The cis-elements analysis revealed a substantial presence of stress-responsive elements within the promoter region of SbANK genes. After treated with drought, salt, and abscisic acid, SbANK56 showed the highest expression levels among family members by using quantitative real-time PCR (qRT-PCR) analysis. The survival rate was significantly improved by the overexpression of SbANK56 compared to wild type (WT) under drought conditions. SbANK56 overexpressing plants displayed lower malondialdehyde and higher proline contents compared to WT plants under drought conditions. Additionally, the expression levels of drought-associated genes were significantly increased in SbANK56 transgenic plants. Importantly, the analysis of natural variation in SbANK56 revealed a significant positive correlation between SbANK56Hap4 and both its differential expression and drought stress tolerance. Taken together, our results provide some evidence for improving drought tolerance in sorghum through breeding initiatives while also advancing our knowledge of the evolutionary trends and functional mechanisms underlying ANK genes.

Widespread Impact of Natural Genetic Variations in CRISPR-Cas9 Outcomes.

The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) is a genome editing tool widely used in biological research and clinical therapeutics. Natural human genetic variations, through altering the sequence context of CRISPR-Cas9 target regions, can significantly affect its DNA repair outcomes and ultimately lead to different editing efficiencies. However, these effects have not been systematically studied, even as CRISPR-Cas9 is broadly applied to primary cells and patient samples that harbor such genetic diversity. Here, we present comprehensive investigations of natural genetic variations on CRISPR-Cas9 outcomes across the human genome. The utility of our analysis is illustrated in two case studies, on both preclinical discoveries of CD33 knockout in chimeric antigen receptor-T cell therapy and clinical applications of transthyretin (TTR) inactivation for treating TTR amyloidosis. We further expand our analysis to genome-scale, population-stratified common variants that may lead to gene editing disparity. Our analyses demonstrate pitfalls of failing to account for the widespread genetic variations in Cas9 target selection and how they can be effectively examined and avoided using our method. To facilitate broad access to our analysis, a web platform CROTONdb is developed, which provides predictions for all possible CRISPR-Cas9 target sites in the coding and noncoding regulatory regions, spanning over 5.38 million guide RNA targets and 90.82 million estimated variant effects. We anticipate CROTONdb having broad clinical utilities in gene and cellular therapies.