Genomic Regions Research Articles

Discover QTLs for the level of blood components in Shaoxing duck using GWAS and haplotype sharing analysis

Blood composition is indicative of health-related traits such as immunity and metabolism. The use of molecular genetics to investigate alterations in these attributes in laying ducks is a novel approach. Our objective was to employ genome − wide association studies (GWAS) and haplotype − sharing analysis to identify genomic regions and potential genes associated with 11 blood components in Shaoxing ducks. Our findings revealed 35 SNPs and 1 SNP associated with low-density lipoprotein cholesterol (LDL) and globulin (GLB), respectively. We identified 36 putative candidate genes for the LDL trait in close proximity to major QTLs and key loci. Based on their biochemical and physiological properties, TRA2A, NPY, ARHGEF26, DHX36, and AADAC are the strongest putative candidate genes. Through linkage disequilibrium analysis and haplotype sharing analysis, we identified three haplotypes and one haplotype, respectively, that were significantly linked with LDL and GLB. These haplotypes could be selected as potential candidate haplotypes for molecular breeding of Shaoxing ducks. Additionally, we utilized a bootstrap test to verify the reliability of GWAS with small experimental samples. The test can be accessed at https://github.com/xuwenwu24/Bootstrap-test.

Epigenetic Study of Cohort of Monozygotic Twins With Hypertrophic Cardiomyopathy Due to MYBPC3 (Cardiac Myosin-Binding Protein C).

Hypertrophic cardiomyopathy is an autosomal dominant cardiac disease. The mechanisms that determine its variable expressivity are poorly understood. Epigenetics could play a crucial role in bridging the gap between genotype and phenotype by orchestrating the interplay between the environment and the genome regulation. In this study we aimed to establish a possible correlation between the peripheral blood DNA methylation patterns and left ventricular hypertrophy severity in patients with hypertrophic cardiomyopathy, evaluating the potential impact of lifestyle variables and providing a biological context to the observed changes. Methylation data were obtained from peripheral blood samples (Infinium MethylationEPIC BeadChip arrays). We employed multiple pair-matched models to extract genomic positions whose methylation correlates with the degree of left ventricular hypertrophy in 3 monozygotic twin pairs carrying the same founder pathogenic variant (MYBPC3 p.Gly263Ter). This model enables the isolation of the environmental influence, beyond age, on DNA methylation changes by removing the genetic background. Our results revealed a more anxious personality among more severely affected individuals. We identified 56 differentially methylated positions that exhibited moderate, proportional changes in methylation associated with left ventricular hypertrophy. These differentially methylated positions were enriched in regions regulated by repressor histone marks and tended to cluster at genes involved in left ventricular hypertrophy development, such as HOXA5, TRPC3, UCN3, or PLSCR2, suggesting that changes in peripheral blood may reflect myocardial alterations. We present a unique pair-matched model, based on 3 monozygotic twin pairs carrying the same founder pathogenic variant and different phenotypes. This study provides further evidence of the pivotal role of epigenetics in hypertrophic cardiomyopathy variable expressivity.

Multi-omics evaluation of clinical-grade human umbilical cord-derived mesenchymal stem cells in synergistic improvement of aging related disorders in a senescence-accelerated mouse model

BackgroundThe prevalence of age-related disorders, particularly in neurological and cardiovascular systems, is an increasing global health concern. Mesenchymal stem cell (MSC) therapy, particularly using human umbilical cord-derived MSCs (HUCMSCs), has shown promise in mitigating these disorders. This study investigates the effects of HUCMSCs on aging-related conditions in a senescence-accelerated mouse model (SAMP8), with a focus on DNA damage, gut microbiota alterations, and metabolic changes.MethodsSAMP8 mice were treated with clinical-grade HUCMSCs via intraperitoneal injections. Behavioral and physical assessments were conducted to evaluate cognitive and motor functions. The Single-Strand Break Mapping at Nucleotide Genome Level (SSiNGLe) method was employed to assess DNA single-strand breaks (SSBs) across the genome, with particular attention to exonic regions and transcription start sites. Gut microbiota composition was analyzed using 16S rRNA sequencing, and carboxyl metabolomic profiling was performed to identify changes in circulating metabolites.ResultsHUCMSC treatment significantly improved motor coordination and reduced anxiety in SAMP8 mice. SSiNGLe analysis revealed a notable reduction in DNA SSBs in MSC-treated mice, especially in critical genomic regions, suggesting that HUCMSCs may mitigate age-related DNA damage. The functional annotation of the DNA breaktome indicated a potential link between reduced DNA damage and altered metabolic pathways. Additionally, beneficial alterations in gut microbiota were observed, including an increase in short-chain fatty acid (SCFA)-producing bacteria, which correlated with improved metabolic profiles.ConclusionThe administration of HUCMSCs in SAMP8 mice not only reduces DNA damage but also induces favorable changes in gut microbiota and metabolism. The observed alterations in DNA break patterns, along with specific changes in microbiota and metabolic profiles, suggest that these could serve as potential biomarkers for evaluating the efficacy of HUCMSCs in treating age-related disorders. This highlights a promising avenue for the development of new therapeutic strategies that leverage these biomarkers, to enhance the effectiveness of HUCMSC-based treatments for aging-associated diseases.

Micro-capture-C in vessel wall cell types identifies mechanisms underlying coronary artery disease susceptibility loci

Abstract Genetics is estimated to account for 50% of Coronary Artery Disease (CAD) risk. Genome-Wide Association Studies (GWAS) have revealed 100s of genomic loci which are associated with CAD (1). However, moving from GWAS signals to causal genes has proved challenging, slowing progress in developing interventions for CAD and other diseases. The greatest challenge in interpreting GWAS data is that only 10% of variants are in protein-coding regions and 90% are in "non-coding" genomic regions. Many of these non-coding variants are found in regulatory elements such as enhancers. Enhancers are distal regulatory elements that bind transcription factors and regulate genes up to 1 million base pairs away. This is achieved by DNA looping bringing enhancers into physical proximity with target genes. However, it is not possible to predict which gene(s) a given enhancer will regulate based on genomic location alone. We aimed to develop a generalisable workflow to solve CAD GWAS signals accurately and sensitively, by identifying causal enhancer SNPs and linking them mechanistically to output genes in a cell-type specific manner. We combined cell-specific epigenomic mapping with machine learning to prioritise 21,959 SNPs from 861 candidate CAD loci, in endothelial cells (HUVECs) and smooth muscle cells (HCASMCs). Micro-Capture-C (MCC) was used to resolve enhancer-promoter interactions at base-pair resolution (2). MNase footprinting determined differential transcription factor binding at specific enhancer SNPs. In HCASMCs, 20 putative enhancers containing CAD SNPs were found to interact with 32 genes, and in HUVECs 25 enhancers interacted with 48 genes. These genes were enriched for pathways including TGFb signalling, RUNX3 activity, and MAPK6/MAPK4 signalling. To prove causality at specific loci, heterozygous SNPs were identified in the individual primary cell lines. The interaction frequency at these elements was statistically compared between risk and non-risk alleles. For example, at the 9p21.3 locus, an enhancer containing a non-risk T allele was found to interact more strongly with the CDKN2B gene, compared to the risk G allele. Interestingly, this imbalance between allele interaction was potentiated by stimulation with TGFb, a key mediator in atherosclerosis. Furthermore, the transcription factor footprint at the SNP location was altered by the risk allele, indicating the mechanistic cause of the CAD-associated change in CDKN2B expression. We demonstrate a combined computational and experimental approach to identify causal non-coding variants in CAD risk loci, at scale. Among the key outputs of this study is the identification of the risk variant (rs1537373), output gene (CDKN2B), and specific cell type (HCASMC) underlying the 9p21.3 locus association with CAD risk. Using this approach to translate GWAS signals into cell-type specific outputs genes, will lead to a better understanding of the pathogenesis of CAD, and new therapeutic opportunities.

Correction: Gene expression and chromatin conformation of microglia in virally suppressed people with HIV.

"Despite ART, we detected occasional microglia containing cell-associated HIV RNA and HIV DNA integrated into open regions of the host's genome (∼0.005%)" should be corrected to: "Despite ART, we detected occasional microglia containing cell-associated HIV RNA and HIV DNA integrated into open regions of the host's genome (∼0.5%)."

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Abstract Background Conventional analysis of the electrocardiogram (ECG) is based on the extraction of human-defined, visually recognisable features. However, these are not optimised to capture all the information content in the ECG. The variational autoencoder (VAE), a form of unsupervised machine learning, can address this shortcoming by computationally extracting comprehensive and interpretable new ECG features, called latent factors. These latent factors provide a low-dimensional representation of the ECG that maximises capture of data content. This approach could expand our understanding of electrophysiology by identifying novel genetic and phenotypic traits associations with the ECG. Purpose This study aims to uncover genetic determinants of VAE latent factors, comparing them to determinants of conventional, human-derived ECG parameters to gain novel insights into cardiac electrophysiology and related diseases. Methods Over one million median beat ECGs derived from a United States (US) based secondary care centre were used to train a machine learning VAE model, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes and echocardiographic traits. Results Utilising median beat ECGs, the VAE model identified 20 features, with partial correlations to traditional ECG traits. A genome-wide association study (GWAS) identified 105 associated genomic regions, compared with 62 regions identified with conventional ECG traits on the same dataset. Most discovered loci have been previously associated with ECG features in larger GWAS, providing a positive control validation. We also discovered and validated five genomic regions not previously linked to the ECG (mapped to TRIOBP, EFEMP1, NEDD9, ATP10A and P2RX1). We found rare variant associations for seven genes (NEK6, IL17RA, NME7, MYBPC3, CCT8, ADAMTS6 and SCN5A). The latent factors uncovered associations with 158 phenotypic traits (2093 phenotypes tested) and 147 disease codes (2074 disease phecodes tested) which were not identified by ECG traits. Out of 38 echocardiographic traits, 35 showed higher correlations with the latent factors, while six exhibited significant associations exclusively with the latent factors. Conclusion Our study demonstrates that VAE-derived latent factors outperform conventional ECG parameters in uncovering novel genetic and phenotypic associations. Our findings underscore the opportunity for optimised data-driven feature extraction to enhance our understanding of cardiac electrophysiology.

Contrast and Genomic Characterisation of Ancient and Recent Interspecific Introgression Between Deeply Diverged Moustache Toads (Leptobrachium).

Recent genomic analyses have provided new insights into the process of interspecific introgression and its consequences on species evolution. Most recent studies, however, focused on hybridization between recently radiated species, with few examining the genomic outcomes of ancient hybridization across deeply diverged species. Using whole genome data of moustache toads (Leptobrachium), we identified signals of three hybridization events among nine species that diverged at the Eocene. An ancient introgression from L. leishanense to the ancestral branch (C1) of L. liui introduced adaptive variants. The highly introgressed regions include genes with important functions in odorant detection and immune responses. These genes are preserved in all three descendent populations of L. liui_C1, and these regions likely have been positively selected over a long filtering process. A recent introgression occurred from L. huashen to L. tengchongense, with the introgressed regions being mostly neutral. Furthermore, one F1 hybrid individual was detected between sympatric L. ailaonicum and L. promustache. The signals of introgression largely disappeared after removing the hybrid individual, indicating an occasional hybridization but minimal introgression. Further examination of highly divergent but low introgressed genomic regions revealed both pre-mating isolation and genetic incompatibility as potential mechanisms of resisting introgression and maintaining species boundaries. Additionally, no large X-effect was found in these introgression events. Hybridization between deeply diverged amphibian species may be common, but detectable introgressions are likely less so, with recent introgression being mostly neutral and the rare ancient one potentially adaptive. Our findings complement recent genomic work, and together they provide a better understanding of the genomic characteristics of interspecific introgression and its significance in species adaptation and evolution.

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Abstract Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.

Evidence of circulating recombinants between deformed wing virus and Varroa destructor virus-1 in honey bee colonies in Türkiye.

Deformed wing virus (DWV), which is an important honey bee virus transmitted by Varroa destructor (V. destructor), causes colony losses in honey bee colonies. This study aimed to investigate the prevalence and genetic diversity of DWV in honey bees in Türkiye and to determine the role of V. destructor in the transmission of the genetic variants of DWV. Honey bee samples were collected from 62 apiaries, by simple random sampling, during March 2022 and April 2023. The presence of V. destructor in collected bee samples was examined using a stereo microscope. Real-time RT-PCR was used for the detection of DWV-A and DWV-B (Varroa destructor virus-1 (VDV-1)) viruses. Genetic characterisation of the positive samples was conducted by sequencing polyprotein genomic region. Considering the V. destructor infestation rate of 3% as relevant, out of the 62 apiaries examined, 17 (27.4%) were positive. However, DWV-A and VDV-1 specific RNA was not detected in V. destructor samples. VDV-1 specific RNA was detected in 6.5% (4/62) of the apiaries, whereas DWV-A was not detected in the sampled apiaries. Phylogenetic analysis showed that isolates detected in this study were located in a separate cluster from previously characterised DWV-A and VDV-1 isolates. According to RDP4 and GARD analyses, DWV-VDV-1 recombination breakpoints were detected in field isolates. To the best our knowledge, this is the first report of the presence of VDV-1-DWV recombinants in Türkiye. Further studies are needed to determine the impact of VDV-1-DWV recombinants and their virological and antigenic properties.

Detection and quantification of introgression using bayesian inference based on conjugate priors.

Introgression (the flow of genes between species) is a major force structuring the evolution of genomes, potentially providing raw material for adaptation. Here, we present a versatile Bayesian model selection approach for detecting and quantifying introgression, df0BF, that builds upon the recently published distance-based df statistic. Unlike df, df0BF accounts for the number of variant sites within a genomic region. The underlying model parameter of our df0BF method, here denoted as dfθ, accurately quantifies introgression, and the corresponding Bayes Factors (df0BF) enables weighing the strength of evidence for introgression. To ensure fast computation we use conjugate priors with no need for computationally demanding MCMC iterations. We compare our method with other approaches including df, fd, Dp, and Patterson's D using a wide range of coalescent simulations. Furthermore, we showcase the applicability of df0BF and dfθ using whole-genome mosquito data. Finally, we integrate the new method into the powerful genomics R-package PopGenome. The presented methods are implemented within the R-package PopGenome (https://github.com/pievos101/PopGenome) and the simulation as the application results can be reproduced from the source code available from a dedicated GitHub repository (https://github.com/pievos101/Introgression-Simulation). Supplementary data are available at Bioinformatics online.

Genotyping single nucleotide polymorphisms in homologous regions using multiplex kb level amplicon capture sequencing.

Single nucleotide polymorphisms (SNPs) in homologous regions play a critical role in the field of genetics. However, genotyping these SNPs is challenging due to the presence of repetitive sequences within genome, which demand specific method. We introduce a new, mid-throughput method that simplifies SNP genotyping in homologous DNA sequences by utilizing a combination of multiplex kb level PCR (PCR size 2.5k-3.5kb) for capturing targeted regions and multiplex nested PCR library construction for next-generation sequencing (Multi-kb level capture-seq). First of all, we randomly selected 7 SNPs in homologous regions and successfully captured 6-plex kb level amplicons (one of segments contains 2 SNPs, while the remaining segments each have only one SNP) in a single tube. And then, the amplification products were subjected to multiplex nested PCR for library construction and sequenced on Illumina platform. We tested this strategy using 600 amplicons from 100 samples and accurately genotyped 96.8% of target SNPs with a coverage depth of ≥ 15×. For the uniformity within the samples, over 66.7% (4/6) of the amplicons had a coverage depth above 0.2-fold of average sequencing depth. To validate the accuracy of this approach, we performed Ligase detection reaction PCR for genotyping the 100 samples, and found that the genotyping data was 97.71% consistent with our NGS results. In conclusion, we have developed a highly efficient and accurate method for SNP genotyping in homologous regions, which offers researchers a new strategy to explore the complex regions of genome.

Clonal hematopoiesis in cardiovascular aging: Insights from the verona heart study.

Clonal hematopoiesis of indeterminate potential (CHIP), marked by the accumulation of somatic mutations in hematopoietic stem cells, significantly elevates the risk of all-cause mortality, mainly due to cardiovascular events. Therefore, investigating this pathophysiological phenomenon is crucial for understanding cardiovascular aging and enhancing both health span and lifespan. In the present study, we examined samples of subjects enrolled within the angiographically controlled Verona Heart Study (VHS), which provides a robust model for cardiovascular aging, particularly regarding coronary artery disease (CAD). We analyzed 44 older subjects diagnosed with coronary artery disease (CAD) and 42 healthy, sex- and age-matched controls (CAD-FREE). Employing deep sequencing and an amplicon-based approach, we focused on 11 key genetic regions in ASXL1, DNMT3A, IDH1, IDH2, JAK2, PPM1D, SF3B1, SRSF2, TET2, TP53, and U2AF1 genes to investigate clonal hematopoiesis. Subjects in the CAD group exhibited a significantly higher variant burden than those in the CAD-FREE group, both in terms of the total number of somatic variants and disruptive variants affecting protein function. This increased mutational load was notably influenced by six specific genetic regions: ASXL1, DNMT3A, IDH2, JAK2, TET2, and U2AF1, which displayed elevated variant rates in the CAD subjects. Moreover, ASXL1, DNMT3A, IDH2, JAK2, SF3B1, TET2, and TP53 exhibited substantially higher levels of disruptive variants in the CAD group. In summary, our findings highlight a correlation between clonal hematopoiesis and the accumulation of disruptive variants in specific genomic regions in the VHS cohort, thereby shedding light on their potential role in cardiovascular aging.

Diabetes Mellitus Disrupts LncRNA Malat1 Regulation of Cardiac Mitochondrial Genome-Encoded Protein Expression.

Understanding the cellular mechanisms behind diabetes-related cardiomyopathy is crucial as it is a common and deadly complication of diabetes mellitus. Dysregulation of the mitochondrial genome has been linked to diabetic cardiomyopathy, and can be ameliorated by altering microRNA (miRNA) availability in the mitochondrion. Long non-coding RNAs (lncRNAs) have been identified to downregulate miRNAs. This study aimed to determine if diabetes mellitus impacts the mitochondrial localization of lncRNAs, their interaction with miRNAs, and how this influences mitochondrial and cardiac function. In mouse and human non-diabetic and type 2 diabetic cardiac tissue, RNA was isolated from purified mitochondria and sequenced (Ilumina HiSeq). Malat1 was significantly downregulated in both human and mouse cardiac mitochondria. Use of a mouse model with an insertional deletion of Malat1 transcript expression resulted in exacerbated systolic and diastolic dysfunction when evaluated in conjunction with a high-fat diet. The cardiac effects of a high-fat diet were countered in a mouse model with transgenic overexpression of Malat1. MiR-320a, a miRNA that binds to both mitochondrial genome-encoded gene NADH-ubiquinone oxidoreductase chain 1 (MT-ND1) as well as Malat1, was upregulated in human and mouse diabetic mitochondria. Conversely, MT-ND1 was downregulated in human and mouse diabetic mitochondria. Mice with an insertional inactivation of Malat1 displayed increased recruitment of both miR-320a and MT-ND1 to the RNA-induced silencing complex (RISC). In vitro pulldown assays of Malat1 fragments with conserved secondary structure confirmed binding capacity for miR-320a. In vitro Seahorse assays indicated that Malat1 knockdown and miR-320a overexpression impaired overall mitochondrial bioenergetics and Complex I functionality. In summary, the disruption of Malat1 presence in mitochondria as observed in diabetic cardiomyopathy is linked to cardiac dysfunction and mitochondrial genome regulation.

Regulation of genome edited organismsin Australia.

Whether organisms developed with the use of genome editing techniques, or food derived from such organisms, are, or should be, regulated as genetically modified organisms (GMOs) or genetically modified (GM) food, respectively, remains a subject of debate globally. Much of the discussion has been scientific and focussed on the similar genetic outcomes of some genome editing techniques and 'conventional' or natural mutagenesis. Many jurisdictions, including Australia, have considered, or are considering, how their regulatory frameworks will deal with such organisms and products. In Australia, organisms developed with site directed nuclease 1 (SDN-1, with no added template to guide homology-directed repair) are not regulated as GMOs, pursuant to exclusions in the Gene Technology Regulations 2001. The exclusion of SDN-1 organismsfrom regulation in Australia is sometimes misrepresented, including in scientific peer reviewed publications, as extending to all genome edited organisms. This highlights the importance for researchers, developers and other stakeholders to understand that whether genome edited organisms are, or are not, subject to regulation as GMOs in a particular jurisdiction may quintessentially be a legal question, not a scientific one.

Genome-wide association studies for milk production traits and persistency of first calving Holstein cattle in Türkiye

The study presents a comprehensive investigation into the genetic determinants of 100-day milk yield (100DMY), 305-day milk yield (305DMY), total milk yield (TMY), and persistency using first lactation records of 374 Holstein heifers reared in a private farm at Çanakkale province of Türkiye, employing a genome-wide association study (GWAS) approach. The research underscores the substantial genetic component underlying these economically important traits through detailed descriptive statistics and heritability estimations. The estimated moderate to high heritabilities (0.32–0.54) for milk production traits suggest the feasibility of targeted genetic improvement strategies. By leveraging GWAS, the study identifies many significant and suggestively significant single nucleotide polymorphisms (SNP) associated with studied traits. Noteworthy genes have identified in this analysis include BCAS3, MALRD1, CTNND2, DOCK1, TMEM132C, NRP1, CNTNAP2, GPRIN2, PLEKHA5, GLRA1, SCN7A, HHEX, KTM2C, RAB40C, RAB11FIP3, and FXYD6. These findings provide valuable understandings of the genetic background of milk production and persistency in Holstein cattle, shedding light on specific genomic regions and candidate genes playing pivotal roles in these traits. This research contributes valuable knowledge to the field of dairy cattle genetics and informs future breeding efforts to improve milk production sustainability and efficiency in Holstein cattle populations.

Luteibacter sahnii sp. nov., A Novel Yellow-Colored Xanthomonadin Pigment Producing Probiotic Bacterium from Healthy Rice Seed Microbiome.

To explore the rice seed microbiome, our objective was to isolate novel strains of Xanthomonas, a plant-associated bacterium with diverse lifestyles. Four isolates, anticipated to be Xanthomonas based on morphological features of yellow colonies, were obtained from healthy rice seeds. Phylo-taxono-genomic analysis revealed that these isolates formed monophyletic lineages belonging to a novel species within the genus Luteibacter. Pairwise ortho Average Nucleotide Identity and digital DNA-DNA hybridization confirmed their distinct species status. We propose Luteibacter sahnii sp. nov. as a novel species, with PPL193T = MTCC 13290T = ICMP 24807T = CFBP 9144T as the type strain and PPL201, PPL552, and PPL554 as other constituent members. The fatty acid profile of the type strain is dominated by branched fatty acids like Iso-C15:0, consistent with other members of the genus. The novel species displays non-pathogenic attributes and exhibits plant probiotic properties, protecting rice plants from the leaf blight pathogen X. oryzae pv. oryzae. Production of Indole-3-Acetic Acid (IAA) and genomic regions encoding anti-microbial peptides emphasize its potential contributions to plant hosts. This study underscores the importance of employing a combination of phenotypic and genotypic methods in culturomics to enhance our understanding of rice seed microbiome diversity.

Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize.

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.

Genome-wide association study (GWAS) analyses of early anatomical changes in rose adventitious root formation

Adventitious root (AR) formation is a genetically complex trait with high genotypic variability. Therefore, only a limited range of cultivars are currently propagated by cuttings in rose. In this study, we analysed the anatomy of in vitro shoots, the early formation of root primordia (RP) and the formation of ARs in a diverse set of 106 rose genotypes. Correlation analysis indicated that the growth in shoot diameter and the vasculature dimensions after 1 week of rooting contributed to successful AR formation. Using phenotypic data for genome-wide association studies (GWAS) analyses, nine significantly associated single nucleotide polymorphisms (SNPs) and genomic regions contributing to various RP and AR formation traits were identified. The contribution of genomic regions to trait variation was notably greater for traits associated with earlier processes than for traits associated with later developmental stages. The combination of RP and AR data allowed the detection of regions by GWAS that contain factors that potentially limit RP emergence. Homologues of 47 genes known to be involved in AR formation from the literature could be assigned to the identified peaks. Further studies are needed to investigate the suitability of SNPs exhibiting strong effects as allele-specific PCR markers for use in breeding.

Novel genetic variants of banana streak MY virus and banana streak IM virus naturally infecting banana in Northeast India.

Divergent banana streak viruses (BSV) were characterized from banana plants exhibiting diverse symptoms in the Northeast region (NER) of India. Using rolling circle amplification (RCA), the complete genome sequences of seven episomal banana streak MY virus (BSMYV) isolates, including two novel variants, and two new banana streak IM virus (BSIMV) isolates were characterized. The novel BSMYV genetic variants were associated with conspicuous necrosis on newly emerged leaves, peduncle distortion, pseudostem internal necrosis, in addition to common streak symptoms. For complete genome nucleotide sequences, BSMYV-IN4 and IN5 shared 77-79% identity with other BSMYVs, while BSMYV-IN7 and IN8 exhibited identities of 77-97%. This study reports for the first time, the complete genomes of two banana streak IM virus (BSIMV-IN1 and -IN2) infecting triploid banana hybrids exhibiting leaf distortion, stunted rosette-like growth, and necrosis, sharing 87% sequence identity with reference BSIMV genome (GenBank accession no. HQ593112). Phylogenetic inference based on complete genomes revealed the distinct and congruent placement of BSMYV-IN4 and IN5 within the BSMYV cluster. Pairwise sequence comparisons of the conserved RT/RNase H nucleotide (nt) sequences revealed that the BSMYV-IN7 and IN4 isolates showed 85% and 97% identity to BSMYV (AY805074), respectively, which shared highest nt identity with BSMYV-IN6, IN9, and IN10, at 100%. The RT/RNase H nt sequences of BSIMV-IN1 and IN2 had 98% identity with the BSIMV (HQ593112), but were characterized as novel variants of BSIMV based on complete genomes. An analysis of relative synonymous codon usage (RSCU) pattern in the ORFIII polyprotein of BSMYV and BSIMV isolates revealed AGA and AGG (arginine) as the most frequently overrepresented codons (>1.5), evolutionary conserved in the genome of both species. A total of 14 recombination events were detected among the 36 BSV genomes, with recombination breakpoints mainly located in the ORFI, III, and IGR genomic regions. A novel phylogenetic cluster, comprised of BSMYV-IN4 and IN5 within the clade I was probably derived from heterologous recombination between parents resembling banana streak VN virus (BSVNV; AY750155) and banana streak GF virus (BSGFV; KJ013507) isolates. The present study conclusively reports the infection of genetically and symptomatically distinct variants of BSMYV and BSIMV infecting banana hybrids in NER India.