Genome Biology最新文献

Background: Transposable elements (TEs) occupy nearly half of the human genome and play diverse biological roles. Despite their abundance, the extent to which TEs contribute to three-dimensional (3D) genome structure remains unclear.

Results: To investigate this, we generate a modified Hi-C analysis pipeline to probe TE-associated chromatin interactions. Our analysis reveals that TE sequences are responsible for 3D genome structure in interphase nuclei. This phenomenon is mediated by the recruitment of specific epigenetic/transcription factors to TEs, which both promote and impair chromatin contacts. We computationally identified known factors positively associated with chromatin contacts (CTCF, RAD21, SMC3) and chromatin contact impairing proteins (RNF2). Additionally, we identiy potential novel factors (SMARCA4, MAFK), which, when knocked down, lead to decreased chromatin contacts and loops at and between TEs. Notably, SMARCA4 knockdown selectively reduce short-range contacts, highlighting its role in maintaining 3D genome structure through TE binding.

Conclusions: Overall, our findings demonstrate that TEs are crucial determinants of 3D genome organization in mammalian cells.

Background: While height is a highly heritable trait with strong polygenic prediction, previous studies have postulated that minimal variation of its individual differences can be captured by DNA methylation (DNAm). We investigated the role of blood-based genome-wide DNAm in capturing the variance in adult height in a large population-based cohort of 7,654 unrelated individuals from Generation Scotland using DNAm profiled on the Illumina EPIC array. The posterior DNAm probe effects were used to construct a DNAm profile score (Methylation Profile Score-MPS) which was evaluated in three independent cohorts.

Results: Genome-wide DNAm captures 25.0% (95% credible interval (CrI) 17.2-31.9) of the phenotypic variation in height when applying Bayesian penalised regression using BayesR + conditional on genetic effects. The total variation captured jointly by DNAm and genetic effects (80.3%, 95% CrI 70.1-87.2) is larger than the marginal estimate based on genetic effects only (56.3%, 95% CrI 45.8-66.8). Out-of-sample prediction shows that the MPS is weakly correlated with measured height (Pearson correlation ranging from 0.14-0.26), as well as being associated with several health and lifestyle factors in the LBC1936 that are established correlates of height.

Conclusion: With the advent of larger sample sizes in epigenomics anticipated to improve the power to detect associations between DNAm and complex traits, we urge caution when making assumptions around "null traits" based solely on methylome-wide association study results and encourage the use of whole-genome methods to assess the proportion of variation in a trait that may be captured by DNAm.

Background: Drought is a major abiotic stress that affects the growth and yield of maize. Alternative transcripts are crucial in abiotic stress responses in plants. However, the genetic basis of alternative transcripts mediated drought response in maize remains largely unknown.

Results: We characterize thousands of drought-responsive genes based on the transcriptomic dataset of 197 maize association population under well-watered and drought-stressed conditions. We perform mRNA profiling of the seedlings at six-leaf stage under drought stress. Through co-expression analysis and experimental validation, we identify a splicing associated factor ZmMBF1, which positively regulates drought response in maize. We also detect thousands of alternative transcript QTLs (atQTLs) and expression QTLs (eQTLs), some of them are linked with stress responsive genes under well-watered and drought-stressed conditions, respectively. Co-localization analysis demonstrates that most of the natural variations in alternative transcripts and gene expression levels are regulated independently by different sequence variations. Variations in transposons, inverted repeats, and UA-rich sequences are significantly associated with atQTLs, suggesting important roles of these variations in regulating alternative transcripts and drought response. As proof of concept, we demonstrate that variations in UA-rich sequence of ZmPYL8 intron regulate drought resistance by affecting ZmPYL8 alternative transcripts, generating two transcripts that function antagonistically in regulating ABA signaling and drought response.

Conclusions: This study reveals the response of maize alternative transcripts to drought at the population level, illustrating the pivotal roles of intron variations in regulating maize alternative transcripts and drought response. It also provides genetic resources and theoretical basis for breeding maize with drought-resistance.

Emerging DNA language models provide powerful tools to address the challenge of accurately predicting chromatin loops, fundamental structures governing 3D genome organization and gene regulation. Here we present CLAMP, which utilizes a deep language model pre-trained on broad cross-species chromatin accessibility data. CLAMP achieves superior performance compared to existing methods in predicting specific protein-mediated loops across 10 species, 18 proteins, and 24 cell types. CLAMP incorporates a novel CoVE explainer that reveals context-dependent genomic feature contributions, providing insights into the features driving predictions. CLAMP predictions effectively identify functionally significant chromatin loops and associated biological pathways.

RNA editing by adenosine deaminases acting on RNA (ADARs) is an essential cellular process performed by three enzymes in mammals: ADAR1-p150, ADAR1-p110, and ADAR2, demonstrating different target specificity and selectivity. Here we describe TSniffer, a novel tool to analyze RNA editing in RNA-sequencing datasets. TSniffer uses a rolling window approach to identify editing sites and operates in two modes allowing identification and quantification in single samples, and quantification in predefined regions across multiple datasets. Using wild type and ADAR-deficient datasets, we provide strategies for identification of ADAR editing sites and verify the accuracy and biological relevance of our findings.

Background: The human gut microbiota exhibits significant diversity across populations, influenced by factors such as geography, diet, and lifestyle, particularly between the Han Chinese and non-Chinese populations. While previous studies have predominantly focused on the taxonomic abundance of the gut microbiome, the impact of single nucleotide polymorphisms (SNPs) in driving population-specific differences remains largely underexplored.

Results: In this study, we systematically investigated gut microbial differences between the Han Chinese and non-Chinese populations using the Human Gut Microbiome Reference Genome Catalog (HGMRGC). We observed geography was the primary driver of microbial variation of abundance and SNPs. We identified 689 population-specific genome clusters from the Collinsella genus with functional differences in carbohydrate utilization and 108 species exhibiting distinct prevalence related to vitamin biosynthesis, antibiotic resistance, and carbohydrate metabolism. Beta diversity analysis highlighted significant inter-population differences in both microbial abundance and SNPs, while alpha diversity analysis revealed that non-Chinese populations exhibited higher diversity in microbial abundance, and Han Chinese populations displayed greater diversity in SNPs.

Conclusions: This study offers a comprehensive analysis of gut microbial differences between Han Chinese and non-Chinese populations, highlighting the profound influence of population-specific traits on microbial diversity and function. We also provide a comprehensive human gut microbial reference genome catalog, with a particular focus on the Han Chinese population, laying a foundation for future research on gut microbiota genomic variations.

Background: Northwestern Xinjiang is situated at the confluence of the central Eurasian Steppe, the Inner Asian Mountain Corridor and the Tianshan mountains, and is home to rich archaeological, cultural and genetic diversity. However, the local population dynamics remain poorly understood due to the lack of time-series ancient DNA data.

Results: We analyze DNA from ten individuals from the Narensu site in northwestern Xinjiang spanning the Chalcolithic to the Iron Age. Our findings reveal that the earliest inhabitants of northwestern Xinjiang were formed by a genetic admixture of Ancient North Eurasians and Altai hunter-gatherers around 6000 years ago. The simultaneous arrival of ancestry related to the Bactria Margiana Archaeological Complex from Central Asia and Afanasievo-related populations from the Steppe in the early Bronze Age was detected, thereby highlighting the important role of the Inner Asian Mountain Corridor as a migration route between southern Central Asia and Xinjiang. This may also have involved the formation of the Chemurchek population in Altai, northern Xinjiang bordering Russia. Eurasian steppe ancestry identified in Narensu has changed to the late Bronze Age Sintashta populations, and eastern Eurasian ancestry from Baikal turns prominent since the Iron Age.

Conclusions: Here, by reconstructing the population dynamics from the Chalcolithic to the Iron Age, our study reveals that the Narensu inhabitants have continuously accumulated with multiple waves of gene influx from surrounding regions. Altogether, these findings provide a comprehensive picture into the population fusion history of northwestern Xinjiang as well as across the Eurasian continent.

Background: The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions.

Results: Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein-protein dynamics.

Conclusions: The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Background: Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae) that reached an epidemic scale in the Middle Ages. Nowadays, the disease is absent in Europe and host genetic influences have been considered as a contributing factor to leprosy disappearance. In this study, we perform a case-control association analysis between multiple human leukocyte antigen (HLA) alleles and leprosy in a medieval European population. The sample comprises 302 individuals from 18 archaeological sites in Denmark (N = 16) and Germany (N = 2).

Results: Our results indicate that HLA-B*38 is associated with leprosy risk. Furthermore, we detect three novel variants that were possibly involved in leprosy risk or protection: HLA-A*23, DRB1*04, and DRB1*13. We also note a subtle temporal change in frequency for several alleles previously associated with infectious diseases, inflammatory disorders, and cancer in present-day populations.

Conclusions: This study demonstrates the potential of ancient DNA in the identification of genetic variants involved in predisposition to diseases that are no longer present in Europe but remain endemic elsewhere. Although it is difficult to pinpoint the reason behind the temporal frequency shift, past epidemics of infectious diseases have likely influenced the HLA pool in present-day Europe.