Genome Biology最新文献

Background: Metagenomics combined with High-throughput Chromosome Conformation Capture (Hi-C) provides a powerful approach to study microbial communities by linking genomic content with spatial interactions. Hi-C complements shotgun sequencing by revealing taxonomic composition, functional interactions, and genomic organization within a single sample. However, aligning Hi-C reads to metagenomic contigs is challenging due to variable insert sizes of Hi-C paired-end reads, multi-species complexity, and gaps in assemblies. Although several benchmark studies have evaluated general alignment tools and Hi-C data alignment, none have specifically focused on metagenomic Hi-C data.

Results: We evaluated seven alignment strategies commonly used in Hi-C analyses: BWA MEM -5SP, BWA MEM default, BWA aln default, Bowtie2 default, Bowtie2 -very-sensitive-local, Minimap2 default, and Chromap Hi-C default. We benchmarked these tools on one synthetic dataset and seven real-world environments. Performance was assessed based on the number of inter-contig Hi-C read pairs and their impact on downstream tasks, such as binning quality.

Conclusions: We show that BWA MEM -5SPgenerally outperformed all other tools across most environments in terms of inter-contig read pairs and binning quality, followed by BWA MEM default. Chromap and Minimap2, while less effective in these metrics, demonstrated the highest computational efficiency.

Phase separation is an important process in biology associated with formation of membraneless organelles but possibly related to the emergence of solid inclusions. TDP-43 is a largely studied paradigmatic case, as it forms neuronal cytoplasmic inclusions in neurodegenerative diseases and is an essential component of many membraneless organelles. Here, we review the physicochemical fundamentals of liquid-liquid phase separation (LLPS) of TDP-43 and its fragments in vitro, showing that full-length TDP-43 requires RNA or chaperones to form stable liquid droplets. We describe TDP-43-containing membraneless organelles and the debate on whether these assemblies represent reservoirs for pathological solid inclusion formation.

Background: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize.

Results: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies.

Conclusion: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.

Background: Genomic alterations are a hallmark of cancer, and extrachromosomal DNA (ecDNA) has emerged as a key source of oncogene selection, tumor growth, and drug resistance. The intratumor heterogeneity and clonal selection of ecDNA is, however, poorly understood.

Results: In this study, we pursue a computational approach that leverages allelic imbalance and outlier expression from standard single-cell RNA sequencing (scRNA-seq) to deconvolve the tumor heterogeneity of ecDNA at the single-cell level (ecSingle). Using this approach, we identify oncogene-carrying ecDNAs in tumor samples at the single-cell level, which we validate using genome sequencing. Moreover, we show the superiority of using single-molecule long-read sequencing in resolving ecDNA. ecDNAs displayed extensive intratumor heterogeneity, including subclonal oncogene-carrying ecDNA in primary tumor cells that segregate with distinct transcriptional cell states. Importantly, we show that a rare ecDNA+ clone in the primary tumor can expand to form dominant clones in relapse tumors.

Conclusions: Our study introduces a novel approach to studying ecDNA at the single-cell level, enabling both clonal evolution and transcription cell state analysis. We apply this approach to cancer samples to gain deeper insights into the role of ecDNA in intratumor heterogeneity and cellular plasticity.

Background: In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance.

Results: Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin.

Conclusions: Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.

Background: Largemouth bass (Micropterus salmoides) is among the most economically important freshwater fish species. High temperature is a major abiotic stressor, leading to increased mortality and significant economic losses. However, research on the regulatory mechanisms of heat stress response in largemouth bass is limited. This study aims to elucidate the mechanisms of adaptability in largemouth bass during heat stress and subsequent recovery.

Results: The morphobiochemical alterations and adaptive mechanisms induced by high water temperature in the gill, brain and liver tissues of largemouth bass are investigated through biochemical blood analysis, haematoxylin and eosin staining, transmission electron microscopy and transcriptomic and proteomic profiles. The results reveal that heat stress-induced oxidative stress causes severe damage to the gill, brain and liver tissues, as well as to the mitochondria, endoplasmic reticulum and Golgi apparatus of these tissues. Such damage is alleviated during the recovery stage, which is closely associated with the PPAR signalling pathway, focal adhesion, ErbB signalling pathway, retinoid metabolism, and cytochrome P450 pathways. These pathways contribute to damage repair, functional recovery, and maintenance of homeostasis after heat stress. Furthermore, experimental validation reveals the pivotal role of Hspa9 in the heat stress response.

Conclusions: These findings reveal that oxidative stress induced by heat stress can severely damage critical tissues in largemouth bass, but the tissues are heterogeneous and have complex and flexible heat stress response regulatory mechanisms. Furthermore, Hspa9 plays an important protective role in the process of heat stress.

Background: As the ancestor of CRISPR-Cas12 nucleases, TnpB represents the most compact gene editing tool currently available. Recent studies have identified multiple TnpB systems with gene editing activity in mammalian cells, and the potential of TnpB in treating diseases has been demonstrated in animal models. However, the editing characteristics of various TnpB systems, comparable to CRISPR tools, require more extensive investigation.

Results: Using a standardized evaluation framework, we conduct a thorough analysis of the editing properties of four TnpB variants alongside representative Cas12 and Cas9 tools applications. Overall, TnpBs exhibit intermediate editing activity and safety profiles among all tested systems, with ISYmu1 TnpB demonstrating a good performance in both editing activity and specificity. Considering its compact size, potent editing efficiency and high specificity, ISYmu1 TnpB represents a promising candidate for gene therapy.

Conclusions: By comprehensively analyzing genome editing outcomes, we characterize TnpB systems for genome editing and identify ISYmu1 TnpB as an optimal miniature RNA-guided genome editors with balanced performance, highlighting its potential for therapeutic applications.

Single-cell three-dimensional genome sequencing (sc3DG-seq) reveals genome regulation and heterogeneity in various biological processes, but a universal analysis tool is lacking. Here we present STARK, a versatile toolkit for processing, quality control, and analysis of diverse sc3DG-seq data. Utilizing STARK, we benchmark 15 technologies, quantitatively comparing their strengths and limitations. We also develop EmptyCells to filter empty barcodes and introduce Spatial Structure Capture Efficiency (SSCE) to assess chromatin structure capture quality. Additionally, we establish scNucleome, a uniformly processed repository of sc3DG-seq datasets, to serve as a foundational resource for future 3D genome research.

Background: Deconvolution of bulk RNA-expression data unlocks the cellular complexity of cancer, yet traditional pseudobulk benchmarks may not always be reliable in real-world settings where absolute cell proportions are unknown.

Results: Here, we introduce a novel real-data framework, leveraging 18 real bulk RNA-expression cohorts (5,891 samples) across nine cancer types to evaluate five deconvolution methods based on differentially proportioned (DP) and prognosis-related (PR) cell types. Across three innovative benchmark scenarios-consistency with scRNA-seq, reproducibility across cohorts, and reproducibility of prognostic relevance-ReCIDE and BayesPrism stand out as two robust deconvolution methods. Application of a pan-cancer analysis based on the deconvolution of TCGA cohorts identifies matrix cancer-associated fibroblasts (mCAF) as a prognostic marker with consistent effects across multiple cancers. Building on this finding, we find a prognostic indicator combining classical monocytes and mCAF cell proportions to be significant in five TCGA cohorts, which we further validate in five independent GEO cohorts.

Conclusions: This study broadens deconvolution benchmarking, offering actionable tools for precision oncology and guiding method selection for translational research.

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.