Briefings in Functional Genomics最新文献

A standard model that is able to generalize data on myriad involvement of the immune system in organismal physio-pathology and to provide a unified evolutionary teleology for immune functions in multicellular organisms remains elusive. A number of such 'general theories of immunity' have been proposed based on contemporaneously available data, starting with the usual description of self-nonself discrimination, followed by the 'danger model' and the more recent 'discontinuity theory.' More recent data deluge on involvement of immune mechanisms in a wide variety of clinical contexts, a number of which fail to get readily accommodated into the available teleologic standard models, makes deriving a standard model of immunity more challenging. But technological advances enabling multi-omics investigations into an ongoing immune response, covering genome, epigenome, coding and regulatory transcriptome, proteome, metabolome and tissue-resident microbiome, bring newer opportunities for developing a more integrative insight into immunocellular mechanisms within different clinical contexts. The new ability to map the heterogeneity of composition, trajectory and endpoints of immune responses, in both health and disease, also necessitates incorporation into the potential standard model of immune functions, which again can only be achieved through multi-omics probing of immune responses and integrated analyses of the multi-dimensional data.

Biomineralisation is the process by which living organisms produce hard structures such as shells and bone. There are multiple independent origins of biomineralised skeletons across the tree of life. This review gives a glimpse into the diversity of spiralian biominerals and what they can teach us about the evolution of novelty. It discusses different levels of biological organisation that may be informative to understand the evolution of biomineralisation and considers the relationship between skeletal and non-skeletal biominerals. More specifically, this review explores if cell type and gene regulatory network approaches could enhance our understanding of the evolutionary origins of biomineralisation.

Our understanding of the mechanisms that modulate gene expression in animals is strongly biased by studying a handful of model species that mainly belong to three groups: Insecta, Nematoda and Vertebrata. However, over half of the animal phyla belong to Spiralia, a morphologically and ecologically diverse animal clade with many species of economic and biomedical importance. Therefore, investigating genome regulation in this group is central to uncovering ancestral and derived features in genome functioning in animals, which can also be of significant societal impact. Here, we focus on five aspects of gene expression regulation to review our current knowledge of functional genomics in Spiralia. Although some fields, such as single-cell transcriptomics, are becoming more common, the study of chromatin accessibility, DNA methylation, histone post-translational modifications and genome architecture are still in their infancy. Recent efforts to generate chromosome-scale reference genome assemblies for greater species diversity and optimise state-of-the-art approaches for emerging spiralian research systems will address the existing knowledge gaps in functional genomics in this animal group.

Recent developments in sequencing technologies have greatly improved our knowledge of phylogenetic relationships and genomic architectures throughout the tree of life. Spiralia, a diverse clade within Protostomia, is essential for understanding the evolutionary history of parasitism, gene conversion, nervous systems and animal body plans. In this review, we focus on the current hypotheses of spiralian phylogeny and investigate the impact of long-read sequencing on the quality of genome assemblies. We examine chromosome-level assemblies to highlight key genomic features that have driven spiralian evolution, including karyotype, synteny and the Hox gene organization. In addition, we show how chromosome rearrangement has influenced spiralian genomic structures. Although spiralian genomes have undergone substantial changes, they exhibit both conserved and lineage-specific features. We recommend increasing sequencing efforts and expanding functional genomics research to deepen insights into spiralian biology.

Information on how 3D genome topology emerged in animal evolution, how stable it is during development, its role in the evolution of phenotypic novelties and how exactly it affects gene expression is highly debated. So far, data to address these questions are lacking with the exception of a few key model species. Several gene regulatory mechanisms have been proposed, including scenarios where genome topology has little to no impact on gene expression, and vice versa. The ancient and diverse clade of spiralians may provide a crucial testing ground for such mechanisms. Sprialians have followed distinct evolutionary trajectories, with some clades experiencing genome expansions and/or large-scale genome rearrangements, and others undergoing genome contraction, substantially impacting their size and organisation. These changes have been associated with many phenotypic innovations in this clade. In this review, we describe how emerging genome topology data, along with functional tools, allow for testing these scenarios and discuss their predicted outcomes.

Spiralians represent the least studied superclade of bilaterian animals, despite exhibiting the widest diversity of organisms. Although spiralians include iconic organisms, such as octopus, earthworms and clams, a lot remains to be discovered regarding their phylogeny and biology. Here, we review recent attempts to apply single-cell transcriptomics, a new pioneering technology enabling the classification of cell types and the characterisation of their gene expression profiles, to several spiralian taxa. We discuss the methodological challenges and requirements for applying this approach to marine organisms and explore the insights that can be brought by such studies, both from a biomedical and evolutionary perspective. For instance, we show that single-cell sequencing might help solve the riddle of the homology of larval forms across spiralians, but also to better characterise and compare the processes of regeneration across taxa. We highlight the capacity of single-cell to investigate the origin of evolutionary novelties, as the mollusc shell or the cephalopod visual system, but also to interrogate the conservation of the molecular fingerprint of cell types at long evolutionary distances. We hope that single-cell sequencing will open a new window in understanding the biology of spiralians, and help renew the interest for these overlooked but captivating organisms.

The chromatin loops in the three-dimensional (3D) structure of chromosomes are essential for the regulation of gene expression. Despite the fact that high-throughput chromatin capture techniques can identify the 3D structure of chromosomes, chromatin loop detection utilizing biological experiments is arduous and time-consuming. Therefore, a computational method is required to detect chromatin loops. Deep neural networks can form complex representations of Hi-C data and provide the possibility of processing biological datasets. Therefore, we propose a bagging ensemble one-dimensional convolutional neural network (Be-1DCNN) to detect chromatin loops from genome-wide Hi-C maps. First, to obtain accurate and reliable chromatin loops in genome-wide contact maps, the bagging ensemble learning method is utilized to synthesize the prediction results of multiple 1DCNN models. Second, each 1DCNN model consists of three 1D convolutional layers for extracting high-dimensional features from input samples and one dense layer for producing the prediction results. Finally, the prediction results of Be-1DCNN are compared to those of the existing models. The experimental results indicate that Be-1DCNN predicts high-quality chromatin loops and outperforms the state-of-the-art methods using the same evaluation metrics. The source code of Be-1DCNN is available for free at https://github.com/HaoWuLab-Bioinformatics/Be1DCNN.

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

Background: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations.

Results: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs.

Conclusion: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.