Genomic Regions Research Articles

Discarded sequencing reads uncover natural variation in pest resistance in Thlaspi arvense.

Understanding the genomic basis of natural variation in plant pest resistance is an important goal in plant science, but it usually requires large and labor-intensive phenotyping experiments. Here, we explored the possibility that non-target reads from plant DNA sequencing can serve as phenotyping proxies for addressing such questions. We used data from a whole-genome and -epigenome sequencing study of 207 natural lines of field pennycress (Thlaspi arvense) that were grown in a common environment and spontaneously colonized by aphids, mildew, and other microbes. We found that the numbers of non-target reads assigned to the pest species differed between populations, had significant SNP-based heritability, and were associated with climate of origin and baseline glucosinolate contents. Specifically, pennycress lines from cold and thermally fluctuating habitats, presumably less favorable to aphids, showed higher aphid DNA load, i.e., decreased aphid resistance. Genome-wide association analyses identified genetic variants at known defense genes but also novel genomic regions associated with variation in aphid and mildew DNA load. Moreover, we found several differentially methylated regions associated with pathogen loads, in particular differential methylation at transposons and hypomethylation in the promoter of a gene involved in stomatal closure, likely induced by pathogens. Our study provides first insights into the defense mechanisms of Thlaspi arvense, a rising crop and model species, and demonstrates that non-target whole-genome sequencing reads, usually discarded, can be leveraged to estimate intensities of plant biotic interactions. With rapidly increasing numbers of large sequencing datasets worldwide, this approach should have broad application in fundamental and applied research.

Transcriptional regulatory network reveals key transcription factors for regulating agronomic traits in soybean

BackgroundTranscription factors (TFs) bind regulatory genomic regions to orchestrate spatio-temporal expression of target genes. Global dissection of the cistrome is critical for elucidating transcriptional networks underlying complex agronomic traits in crops.ResultsHere, we generate a comprehensive genome-wide binding map for 148 TFs using DNA affinity purification sequencing in soybean. We find TF binding sites (TFBSs) exhibit elevated chromatin accessibility and contain more rare alleles than other genomic regions. Intriguingly, the methylation variations at TFBSs partially contribute to expression bias among whole genome duplication paralogs. Furthermore, we construct a soybean gene regulatory network (SoyGRN) by integrating TF-target interactions with diverse datasets encompassing gene expression, TFBS motifs, chromatin accessibility, and evolutionarily conserved regulation. SoyGRN comprises 2.44 million genome-wide interactions among 3188 TFs and 51,665 target genes. We successfully identify key TFs governing seed coat color and oil content and prioritize candidate genes within quantitative trait loci associated with various agronomic traits using SoyGRN. To accelerate utilization of SoyGRN, we develop an interactive webserver (www.soytfbase.cn) for soybean community to explore functional TFs involved in trait regulation.ConclusionsOverall, our study unravels intricate landscape of TF-target interactions in soybean and provides a valuable resource for dissecting key regulators for control of agronomic traits to accelerate soybean improvement.

Comparison of targeted next generation sequencing assays in non-small cell lung cancer patients

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer the mutational spectrum of which has been extensively characterized. Treatment of patients with NSCLC based on their molecular profile is now part of the standard clinical care. The aim of this study was firstly to investigate two different NGS-based tumor profile genetic tests and secondly to assess the clinical actionability of the mutations and their association with survival and clinicopathological characteristics. Overall, 52 mutations were identified in 31 patients by either one or both assays. The most frequently mutated genes were TP53 (40.4%), KRAS (13.46%) and EGFR (9.62%). TP53 and KRAS mutations were associated with worst overall survival while KRAS was positively correlated with adenocarcinoma. The two methods showed a high concordance for the commonly covered genomic regions (97.14%). Ten mutations were identified in a genomic region exclusively covered by the MEDICOVER Genetics custom tumor profile assay. Likewise, one MET mutation was identified by the Ion Amliseq assay in a genomic region exclusively covered by Ion Amliseq. In conclusion both assays showed highly similar results in the commonly covered genomic areas, however, the MEDICOVER Genetics assay identified additional clinically actionable mutations that can be applied in clinical practice for personalized treatment decision making for patients with NSCLC.

Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso

BackgroundThe recent success of a population control gene drive targeting the doublesex gene in Anopheles gambiae paved the way for developing self-sustaining and self-limiting genetic control strategies targeting the sex determination pathway to reduce and/or distort the reproductive capacity of insect vectors. However, targeting these genes for genetic control requires a better understanding of their genetic variation in natural populations to ensure effective gene drive spread. Using whole genome sequencing (WGS) data from the Ag1000G project (Ag3.0, 3.4 and 3.8), and Illumina pooled amplicon sequencing, we investigated the genetic polymorphism of the intron-4–exon-5 boundary of the doublesex gene in the natural populations of An. gambiae sensu lato (s.l.).ResultsThe analyses showed a very low variant density at the gRNA target sequence of the Ag(QFS)1 gene drive (previously called dsxFCRISPRh) within the populations of West and East Africa. However, populations from the forest area in Central Africa exhibited four SNP at frequencies ranging from 0.011 to 0.26. The SNP (2R:48714641[C > T]) at high frequencies, i.e. 0.26 is identified within the An. coluzzii population from Angola. The analyses also identified 90 low frequency (1 − 5%) SNPs in the genomic region around the gRNA target sequence (intron-4–exon-5 boundary). Three of these SNPs (2R:48714472 A > T; 2R:48714486 C > A; 2R:48714516 C > T) were observed at frequencies higher than 5% in the UTR region of the doublesex gene. The results also showed a very low variant density and constant nucleotide diversity over a five-year survey in natural An. gambiae s.l. populations of Burkina Faso.ConclusionThese findings will guide the implementation of doublesex-targeted gene drives to support the current control tools in malaria elimination efforts. Our methods can be applied to efficiently monitor the evolution of any sequence of interest in a natural population via pooled amplicon sequencing, surpassing the need for WGS.

Genomic regions associated with flag leaf and panicle architecture in rice (Oryza sativa L.)

BackgroundFlag leaf (FL) and panicle architecture (PA) are critical for increasing rice grain yield as well as production. A genome-wide association study (GWAS) can better understand the genetic pathways behind complex traits like FL and PA.ResultsIn this study, 208 diverse rice germplasms were grown in the field at the Texas A&M AgriLife Research Center at Beaumont, TX, during 2022 and 2023 following Augmented Randomized Complete Block Design. After heading, eight different flag leaf and panicle architecture (FLPA) related traits were measured. GWAS analyses were performed to identify potential genomic regions associated with FLPA traits. A total of 97 quantitative trait loci (QTLs) (48 in 2022 and 49 in 2023) were distributed across all 12 chromosomes. GWAS revealed four QTLs (qSBPP4-2, qFLW6-2, qGNPP9, and qGWPP2-3) with phenotypic variation ranging from 11.7 to 22.3%. Two genetic loci were identified as multi-trait QTLs, i.e., S04_32100268 (qFLL4-1 and qFLA4-1) and S04_11552936 (qFLW4 and qFLA4-2) during 2022 and 2023, respectively. Additionally, these loci were further utilized to analyze candidate genes, and 65 genes were predicted in the 100-kb genomic region upstream and downstream. In silico expression analysis revealed 15 genes were expressed during the reproductive stage. These genes were associated with protein kinase, heat shock transcription factor family, sugar transporter conserved site and transcription factor bHLH95- like basic helix-loop-helix domain protein, as well as those that regulate the FLPA-related traits. Os04g0631100 was identified as a potential candidate gene that is highly expressed during the endosperm development stage, and it is associated with an important sugar transporter protein that will be helpful in grain improvement.ConclusionGWAS results revealed four major and two multi-trait QTLs. Expanding their candidate genes, and expression analysis provide the genetic information for molecular improvement of the FLPA-related trait in rice breeding programs.

Genome-wide association studies and functional annotation of pre-weaning calf mortality and reproductive traits in Nellore cattle from experimental selection lines

BackgroundReproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle. In this study, 3,728 cows with 17,094 reproductive records and 11,785 calves were evaluated. A total of 3,351 genotyped animals and 383,739 SNP markers were considered for GWAS analyses. SNP effects were estimated using the weighted single-step GWAS (WssGWAS), which considered two iterations. The top ten genomic windows with the highest contribution to the additive genetic variance of the traits were selected for gene annotation. Candidate genes were then analyzed for Gene Ontology terms (GO) and metabolic pathways.ResultsThe top ten genomic windows that explained the largest proportion of the direct additive genetic variance () for CS, PL, SB, and PWM accounted for 17.03% (overlapping with 79 genes), 16.76% (57 genes), 11.71% (73 genes), and 12.03% (65 genes) of the total , respectively. For CS, significant GO terms included Somitogenesis (GO:0001756), Somite Development (GO:0061053), and Chromosome Segregation (GO:0007059). Considering PL, the processes annotated were the Regulation of Hormone Secretion (GO:0046883), and Hormone Transport (GO:0009914), along with the Glucagon Signaling Pathway (bta04922). Embryonic Development (GO:0045995), and Cerebellum Development (GO:0021549) were the main biological processes found in the gene enrichment analysis for SB. For PWM, the Regulation of Glucose metabolic processes (GO:0010906), Zinc Ion Homeostasis (GO:0055069), Lactation (GO:0007595), and Regulation of Insulin Secretion (GO:0050796) were the most significant GO terms observed.ConclusionsThese findings provide valuable information on genomic regions, candidate genes, biological processes, and metabolic pathways that may significantly influence the expression of complex reproductive traits in Nellore cattle, offering potential contributions to breeding strategies and future genomic selection strategies.

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning

BackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs.ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges.ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

Near-complete telomere-to-telomere de novo genome assembly in Egyptian clover (Trifolium alexandrinum).

Egyptian clover (Trifolium alexandrinum L.), also known as berseem clover, is an important forage crop to Semi-arid conditions that was domesticated in ancient Egypt since 6,000 years BC and introduced and well adapted to numerous countries including India, Pakistan, Turkey, and Mediterranean region. Despite its agricultural importance, genomic research on Egyptian clover has been limited to developing efficient modern breeding programs. In the present study, we constructed near-complete telomere-to-telomere-level genome assemblies for two Egyptian clover cultivars, Helaly and Fahl. Initial assemblies were established by using highly-fidelity long-read technology. To extend sequence contiguity, we developed a gap-targeted sequencing (GAP-Seq) method, in which contig ends are targeted for sequencing to obtain long reads bridging two contigs. The total length of the resultant chromosome-level assemblies was 547.7Mb for Helaly and 536.3Mb for Fahl. These differences in sequence length can be attributed to the expansion of DNA transposons. Population genomic analysis using single-nucleotide polymorphisms revealed genomic regions highly differentiated between two cultivars and increased genetic uniformity within each cultivar. Gene ontologies associated with metabolic and biosynthetic processes and developmental processes were enriched in these genomic regions, indicating that these genes may determine the unique characteristics of each cultivar. Comprehensive genomic resources can provide valuable insights into genetic improvements in Egyptian clover and legume genomics.

The N6-methyladenosine RNA epigenetic modification modulates the amplification of coxsackievirus B1 in human pancreatic beta cells

Type 1 diabetes (T1D) is characterized by a prolonged autoimmune attack resulting in the massive loss of insulin-producing beta cells. The initiation and progression of T1D depends on a complex interaction between genetic, immunological and environmental factors. Epidemiological, experimental and clinical evidence suggest a link between viral infections, particularly Coxsackievirus type B (CVB), and T1D development. Specifically, infections by the CVB serotype 1 (CVB1) contribute to the triggering of autoimmunity against beta cells in genetically predisposed individuals, and prolonged and probably non-lytic infections by CVB are associated with the development of T1D. However, the molecular mechanisms underlying CVB1 replication and establishing persistent infections in human pancreatic beta cells remain poorly understood. Here we show that the N6-methyladenosine (m6A) RNA epigenetic modification machinery regulates CVB1 amplification in the human beta cells. Using small interfering RNA (siRNA) targeting m6A writers and erasers, we observed that downregulation of m6A writers increases CVB1 amplification, while the downregulation of m6A erasers decreases it. Notably, the inhibition of Fat Mass and Obesity-associated protein (FTO), a key m6A eraser, reduced by 95% the production of infectious CVB1 in both human insulin-producing EndoC-βH1 cells and in induced pluripotent stem cell (iPSC)-derived islets. The FTO inhibitor reduced CVB1 expression within 6 h post-infection, suggesting a direct regulation of the CVB1 genome by m6A modification. Furthermore, in the absence of viral replication, FTO inhibition also decreased the translation of the incoming CVB1 genome, indicating that m6A plays a critical role in the initial stages of viral RNA translation. In addition, modulation of the m6A machinery affected the type I interferon response after poly-IC transfection, a mimic of RNA virus replication, but did not affect the cellular antiviral response in CVB1-infected cells. Altogether, these observations suggest that m6A directly affects CVB1 production. Our study provides the first evidence that the m6A epigenetic modification machinery controls CVB amplification in human pancreatic beta cells. This suggests that the m6A machinery is a potential target to control CVB infection in T1D and raises the possibility of an epigenetic control in the establishment of persistent CVB infections observed in the pancreas in individuals with type 1 diabetes.

Advancing diagnosis and early risk assessment of preeclampsia through noninvasive cell-free DNA methylation profiling

BackgroundAberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach.Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy to diagnose preeclampsia accurately. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from pregnant individuals experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies.A significant strength of this study is its enzymatic digest, which enriches CpG-rich regions across the genome without the need for proprietary reagents or prior selection of regions of interest. This makes it useful for the cost-effective discovery of novel markers.ResultsInvestigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.ConclusionsThis study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of noninvasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this noninvasive approach into routine prenatal care.

Identifying candidate genes for sugar accumulation in sugarcane: an integrative approach

BackgroundElucidating the intricacies of the sugarcane genome is essential for breeding superior cultivars. This economically important crop originates from hybridizations of highly polyploid Saccharum species. However, the large size (10 Gb), high degree of polyploidy, and aneuploidy of the sugarcane genome pose significant challenges to complete genome sequencing, assembly, and annotation. One successful strategy for identifying candidate genes linked to agronomic traits, particularly those associated with sugar accumulation, leverages synteny and potential collinearity with related species.ResultsIn this study, we explored synteny between sorghum and sugarcane. Genes from a sorghum Brix QTL were used to screen bacterial artificial chromosome (BAC) libraries from two Brazilian sugarcane varieties (IACSP93-3046 and SP80-3280). The entire region was successfully recovered, confirming synteny and collinearity between the species. Manual annotation identified 51 genes in the hybrid varieties that were subsequently confirmed to be present in Saccharum spontaneum. This study employed a multifaceted approach to identify candidate genes for sugar accumulation, including retrieving the genomic region of interest, performing a gene-by-gene analysis, analyzing RNA-seq data for internodes from Saccharumofficinarum and S. spontaneum accessions, constructing a coexpression network to examine the expression patterns of genes within the studied region and their neighbors, and finally identifying differentially expressed genes (DEGs).ConclusionsThis comprehensive approach led to the discovery of three candidate genes potentially involved in sugar accumulation: an ethylene-responsive transcription factor (ERF), an ABA 8’-hydroxylase, and a prolyl oligopeptidase (POP). These findings could be valuable for identifying additional candidate genes for other important agricultural traits and directly targeting candidate genes for further work in molecular breeding.

Dissecting the shared genetic architecture between nonalcoholic fatty liver disease and type 2 diabetes.

Observational studies have reported a bidirectional correlation between nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D), but the shared genetic basis between the two conditions remains unclear. Using genome-wide association study (GWAS) summary data from European-ancestry populations, we examined the cross-trait genetic correlation and identified genomic overlaps and shared risk loci. We employed a latent causal variable model and Mendelian randomization (MR) analysis to infer causal relationships. Colocalization analysis and conditional/conjunctional false discovery rate (condFDR/conjFDR) were used to identify genomic overlaps and shared risk loci. Two-step MR analysis was utilized to identify potential mediators. We observed a strong positive genomic correlation between NAFLD and T2D (rg = 0.652, P = 5.67 × 10-6) and identified tissue-specific transcriptomic correlations in the pancreas, liver, skeletal muscle, subcutaneous adipose, and blood. Genetic enrichment was observed in NAFLD conditional on associations with T2D and vice versa, indicating significant polygenic overlaps. We found robust evidence for the causal effect of NAFLD on T2D, particularly insulin-related T2D, rather than vice versa. Colocalization analysis identified shared genomic regions between NAFLD and T2D, including GCKR, FTO, MAU2-TM6SF2, and PNPLA3-SAMM50. High-density lipoprotein cholesterol and insulin were partly mediated the association between NAFLD and T2D. These findings unveil a close genetic link between NAFLD and T2D, shedding light on the biological mechanisms connecting NAFLD progression to T2D.

Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay

BackgroundThe COVID-19 has put emphasis on pivotal needs for diagnosis and surveillance worldwide, with the subsequent shortage of diagnostic reagents and kits. Therefore, it has become strategic for the countries to access diagnostics, expand testing capacity, and develop their own diagnostic capabilities and alternative rapid accurate nucleic acid diagnostics that are at lower costs. Here, we propose a visual SARS-CoV-2 detection using a one-step fast multiplex reverse transcription-PCR (RT-PCR) amplification coupled to lateral flow immunoassay detection on a PCRD device (Abingdon Health, UK).MethodsWe developed various simplex fast-PCRs for screening sets of primer pairs newly designed or selected from literature or from validated WHO diagnostics, targeting S, N, E, RdRp or ORF1ab genes. We retained primers showing specific and stable amplification to assess for their suitability for detection on PCRD. Thus, fast RT-PCR amplifications were performed using the retained primers. They were doubly labeled with Fam and Biotin or Dig and Biotin to allow visual detection of the labeled amplicons on the lateral flow immunoassay PCR Detection (PCRD) device, looking at lack of interaction of the labeled primers (or primer dimers) with the test-lines in negative or no RNA controls. We set up all the assays using RNAs isolated from patients’ nasopharyngeal swabs. We used two simplex assays, targeting two different viral genomic regions (N and E) and showing specific detection on PCRD, to set up a one-step fast multiplex RT-PCR assay (where both differently labeled primer pairs were engaged) coupled to amplicons’ detection on a PCRD device. We evaluated this novel assay on 50 SARS-CoV-2 positive and 50 SARS-CoV-2 negative samples and compared its performance to the results of the quantitative RT-PCR (RT-qPCR) assays used for diagnosing the patients, here considered as the standard tests.ResultsThe new assay achieved a sensitivity of 88% (44/50) and a specificity of 98% (49/50). All patients who presented Ct values lower than 33 were positive for our assay. Except for one patient, those with Ct values above 33 returned negative results.ConclusionOur results have brought proof of principle on the usefulness of the one-step fast multiplex RT- PCR assay coupled to PCRD as a new assay for specific, sensitive, and rapid detection of SARS-CoV-2 without requiring costly laboratory equipment, and thus, at reduced costs in a format prone to be deployed when resources are limited. This assay offers a viable alternative for COVID-19 diagnosis or screening at points of need.

Assessment of Parental Polymorphism between Swarna / Oryza rufipogon IC309814 Derived Mapping Population

Detection of molecular markers with parental polymorphism is a pre-requisite for QTL mapping and further identification of genomic regions. PCR based InDel markers tend to have a greater degree to show polymorphism among the parental genotypes for any specific cross. Polymorphic studies were carried out between the two parents Swarna (cultivated rice) and Oryza rufipogon IC309814 (Wild rice) using 192 randomly selected InDel markers and 40 gene specific markers for Iron and Zinc covering the entire 12 chromosomes. Results revealed that InDel markers were polymorphic between both the parents and highest percentage of polymorphism observed on chromosome 10 (75%) followed by chromosome 5 (60%) and chromosome 9 (55.6%) and lowest polymorphism percentage was shown in chromosome 3 (8.3%) with an overall 22.4 % polymorphism percentage among all the markers used in this study. Detected polymorphic (SSR) markers were for genotyping the mapping population derived from the cross between Swarna cultivar and Oryza rufipogon to map Quantitative Trait Loci (QTLs) associated with yield and grain quality traits.

Genomic characterization and histologic analysis of uterine leiomyosarcoma arising from leiomyoma with bizarre nuclei.

Leiomyoma with bizarre nuclei (LM-BN) is a rare variant of leiomyoma with a benign clinical course. In contrast, leiomyosarcoma (LMS) is a high-grade, malignant neoplasm characterized by high recurrence rates and poor survival. While LM-BN and LMS show distinct morphologies, they share similar immunoprofiles and molecular alterations, with both considered 'genomically unstable'. Rare cases of LM-BN associated with LMS have been reported; however, the histogenesis and molecular relationship between these two tumors remains unclear. In this study, we assessed 11 cases of LMS arising in conjunction with LM-BN confirmed by histology and immunohistochemistry (IHC), further analyzed by clinical, histologic, and molecular characteristics of these distinct components. LM-BN and LMS had similar p16 and p53 IHC patterns, but LMS had a higher Ki-67 index and lower estrogen and progesterone eceptor expression. Digital image analysis based on cytologic features revealed spatial relationships between LMS and LM-BN. Genomic copy number alterations (CNAs) demonstrated the same clonal origin of LMS arising from existing LM-BN through conserved CNAs. LMS harbored highly complex CNAs and more frequent losses of the TP53, RB1, and PTEN genomic regions than LM-BN (p = 0.0031), with CDKN2A/B deletion identified in LMS only. Mutational profiling revealed many shared oncogenic alterations in both LM-BN and LMS; however, additional mutations were present within LMS, indicative of tumor progression through progressive genomic instability. Analysis of spatial transcriptomes defined uniquely expressed gene signatures that matched the geographic distribution of LM-BN, LMS, and other cell types. Our findings indicate for the first time that a subset of LMS arises from an existing LM-BN, and highly complex genomic alterations could be potential high risks associated with disease progression in LM-BN. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant ‘local’ genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson’s disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

Genetic Composition of Polish Hucul Mare Families mtDNA Diversity

Backround: The Hucul horse breed formed in the region of the Eastern Carpathians, likely through the natural crossbreeding of oriental horses. After World War II, their population significantly decreased, leading to the breeding being based on only 14 female lines, whose founders often had unknown origins. To preserve the breed’s unique characteristics, it is now part of a Genetic Resources Conservation Program, which prioritizes the maintenance of genetic diversity. This study aims to clarify the maternal relatedness of founder mares and assess genetic diversity using mitochondrial DNA (mtDNA). Methods: The hyper-variable region of the mitochondrial genome was analyzed in 57 horses. Pedigree records were used to trace genealogical lines, and molecular analysis focused on identifying maternal relationships between founder mares. Results: The analysis revealed close maternal kinships between the lines of Jagoda and Bajkałka, as well as Sekunda and Sroczka. In the Hucul population, seventeen mitochondrial haplotypes were identified, with three that did not match any established lines. The findings reveal discrepancies between pedigree records and mitochondrial DNA data, suggesting potential inaccuracies in the Hucul horse studbook. Conclusions: The findings highlight the importance of combining pedigree and molecular data to refine strategies to preserving genetic diversity, minimizing inbreeding, and improving the management the Genetic Resources Conservation Program.

Genomic island-encoded LmiA regulates acid resistance and biofilm formation in enterohemorrhagic Escherichia coli O157:H7

ABSTRACT Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is an important intestinal pathogen that causes severe foodborne diseases. We previously demonstrated that the genomic island-encoded regulator LmiA activates the locus of enterocyte effacement (LEE) genes to promote EHEC O157:H7 adherence and colonization in the host intestine. However, whether LmiA is involved in the regulation of any other biological processes in EHEC O157:H7 remains largely unexplored. Here, we compared global gene expression differences between the EHEC O157:H7 wild-type strain and an lmiA mutant strain using RNA-seq technology. Genes whose expression was affected by LmiA were identified and classified using the Cluster of Orthologous Groups (COG) database. Specifically, the expression of acid resistance genes (including gadA, gadB, and gadC) was significantly downregulated, whereas the transcript levels of biofilm-related genes (including Z_RS00105, yadN, Z_RS03020, and fdeC) were increased, in the ΔlmiA mutant compared to the EHEC O157:H7 wild-type strain. Further investigation revealed that LmiA enhanced the acid resistance of EHEC O157:H7 by directly activating the transcription of gadA and gadBC. In contrast, LmiA reduced EHEC O157:H7 biofilm formation by indirectly repressing the expression of biofilm-related genes. Furthermore, LmiA-mediated regulation of acid resistance and biofilm formation is highly conserved and widespread among EHEC and enteropathogenic E. coli (EPEC). Our findings provide essential insight into the regulatory function of LmiA in EHEC O157:H7, particularly its role in regulating acid resistance and biofilm formation.

Histone methyltransferase SETDB1 safeguards mouse fetal hematopoiesis by suppressing activation of cryptic enhancers

The H3K9me3-specific histone methyltransferase SETDB1 is critical for proper regulation of developmental processes, but the underlying mechanisms are only partially understood. Here, we show that deletion of Setdb1 in mouse fetal liver hematopoietic stem and progenitor cells (HSPCs) results in compromised stem cell function, enhanced myeloerythroid differentiation, and impaired lymphoid development. Notably, Setdb1-deficient HSPCs exhibit reduced quiescence and increased proliferation, accompanied by the acquisition of a lineage-biased transcriptional program. In Setdb1-deficient HSPCs, we identify genomic regions that are characterized by loss of H3K9me3 and increased chromatin accessibility, suggesting enhanced transcription factor (TF) activity. Interestingly, hematopoietic TFs like PU.1 bind these cryptic enhancers in wild-type HSPCs, despite the H3K9me3 status. Thus, our data indicate that SETDB1 restricts activation of nonphysiological TF binding sites which helps to ensure proper maintenance and differentiation of fetal liver HSPCs.

Meta-QTL mapping for wheat thousand kernel weight

Wheat domestication and subsequent genetic improvement have yielded cultivated species with larger seeds compared to wild ancestors. Increasing thousand kernel weight (TKW) remains a crucial goal in many wheat breeding programs. To identify genomic regions influencing TKW across diverse genetic populations, we performed a comprehensive meta-analysis of quantitative trait loci (MQTL), integrating 993 initial QTL from 120 independent mapping studies over recent decades. We refined 242 loci into 66 MQTL, with an average confidence interval (CI) 3.06 times smaller than that of the original QTL. In these 66 MQTL regions, a total of 4,913 candidate genes related to TKW were identified, involved in ubiquitination, phytohormones, G-proteins, photosynthesis, and microRNAs. Expression analysis of the candidate genes showed that 95 were specific to grain and might potentially affect TKW at different seed development stages. These findings enhance our understanding of the genetic factors associated with TKW in wheat, providing reliable MQTL and potential candidate genes for genetic improvement of this trait.