Review Articles in 2022

Alternative splicing of pre-mRNAs is key for cellular function and underpins the aetiology of numerous diseases. Here, we review major advances in understanding the structures and functions of the splicing machinery and its regulation, and in harnessing this knowledge for the design of novel therapies.

In this Review, the authors discuss our latest understanding of the spatial aspects of cancer evolution, including the roles of cancer subclonal structure, tissue architecture, and interactions between cancer cells and diverse cell types of the microenvironment at local and distant sites.

In this Review, Berger and Yu discuss how the sheer amounts of sequence data create bottlenecks in downstream analytical pipelines that must be overcome by new analysis strategies, each with their own trade-offs for properties such as speed, accuracy and applicability.

This Review surveys the known mechanisms of communication between RBPs and their effectors and their roles in reducing the complexity of RNA networks. The authors review the emerging roles of RBP–effector interactions in the control of RNA processing and regulation of biological outcomes, and their contribution to human health and disease.

In this Review, Spitale and Incarnato discuss how the application of sequencing-based RNA structure mapping methods to entire transcriptomes in living cells is providing insight into the RNA structurome, the dynamics of RNA ensembles and how RNA structure regulates cellular processes.

In this Review, Chen and Liu discuss the latest developments in prime editing systems, including improvements to their editing efficiency and capabilities, as well as diverse emerging applications in research and preclinical therapeutic studies.

Non-B DNA secondary structures, such as G quadruplexes, H-DNA or Z-DNA, have key roles in genetic instability and disease aetiology. The authors review the impact of non-B DNA on transcription, replication, recombination and DNA damage and repair, the mechanisms of non-B DNA-induced mutagenesis and the role of non-B DNA sequences in human disease.

Boulias and Greer review the functions of N⁶-methyladenosine (m⁶A) in RNA at the molecular, genomic and organismal level. They describe the impact of m⁶A deposition on RNA substrates, chromatin architecture, epigenetic regulation of gene expression and genome stability, as well as key roles of m⁶A in stem-cell differentiation, neurogenesis and immunity.

Microbiome epidemiology associates microbial community features with health outcomes, traits or exposures in human host populations. In this Review, the authors discuss ways in which various microbiome features at varying levels of resolution (community, strain, pathway or gene) influence human health using established examples of microbiome-associated changes linked with host outcomes.

In this Review, the authors describe advances in deep learning approaches in genomics, whereby researchers are moving beyond the typical ‘black box’ nature of models to obtain biological insights through explainable artificial intelligence (xAI).

Inducible protein degradation technologies enable the depletion of loop extrusion factors within short time frames, leading to the rapid reconfiguration of the 3D genome. Nora and de Wit review insights from degron approaches into the molecular factors controlling genome folding and how these findings have changed our understanding of genome organization, including its role in transcription.

Mendelian defects in genes encoding factors that regulate telomere length, structure and function cause telomeropathies, or telomere biology disorders (TBDs). The authors review confirmed as well as potential TBD-causing genes and their main functions in telomere biology. They also discuss genetic features that underlie the complex nature of these diseases.

This Review discusses how chromosome tracing has deepened our understanding of the role of 3D chromatin topology in transcriptional regulation by helping to resolve open questions and opposing models rising from data generated by sequencing-based approaches, such as 3C and HiC.

The circadian system and sleep physiology are linked to myriad biological processes, the disruption of which is detrimental to human health. Here, the authors review insights from genetic studies of human circadian and sleep phenotypes and disorders, with a focus on those with causal contributions to other complex diseases.

Sex-specific morphs exhibit phenotypes that differ between the sexes and typically include many different traits. Here, the author reviews recent genomic and transcriptomic studies that are yielding new insights into the evolutionary origin and development of sex-specific morphs in a wide range of animal species.

In this Review, the authors discuss our latest understanding of extrachromosomal DNA (ecDNA), a type of circular DNA element commonly found in cancers. They discuss ecDNA properties, including oncogene amplifications and transcriptional hub formation, as well as opportunities for therapeutic interventions.

Spatial omics methods enable the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. The authors review the developing landscape of in situ spatial transcriptome, genome and proteome technologies and highlight their impact on basic and translational research.

In this Review, Preissl, Gaulton and Ren discuss single-cell epigenomic methods and data analysis tools, their readiness for profiling cis-regulatory elements in human tissues and the insight they can provide into dynamic, context-specific gene regulation.

In this Review, Isbel et al. describe our current understanding of how transcription factors navigate features of chromatin — particularly DNA methylation and nucleosomes — and how this contributes to specificity of genomic binding and, ultimately, transcriptional regulation.

Molecular measures of biological ageing based on high-throughput omics technologies are enabling the quantitative characterization of ageing. The authors review how epigenomic, transcriptomic, proteomic, metabolomic and other omics data can be harnessed using machine learning to build ‘ageing clocks’.