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While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. We validate TOBIAS using paired ATAC-seq and ChIP-seq data, and find that TOBIAS outperforms existing methods for bias correction and footprinting. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. Footprinting analysis allows genome-wide investigation of transcription factor (TF) binding on chromatin. Here the authors developed a framework termed TOBIAS aimed at identifying footprints of chromatin-associated proteins from ATAC-seq accessibility profiles and apply it to zygotic development datasets.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis. Single-cell multi-omics and deep learning could lead to the inference of biological networks across specific cell types. Here, the authors develop DeepMAPS, a deep learning, graph-based approach for cell-type specific network inference from single-cell multi-omics data that is tested on healthy and tumour tissue datasets.

Plants monitor seasonal cues to optimize reproductive success by tuning onset of reproduction and inflorescence architecture. TERMINAL FLOWER 1 (TFL1) and FLOWERING LOCUS T (FT) and their orthologs antagonistically regulate these life history traits, yet their mechanism of action, antagonism and targets remain poorly understood. Here, we show that TFL1 is recruited to thousands of loci by the bZIP transcription factor FD. We identify the master regulator of floral fate, LEAFY ( LFY ) as a target under dual opposite regulation by TFL1 and FT and uncover a pivotal role of FT in promoting flower fate via LFY upregulation. We provide evidence that the antagonism between FT and TFL1 relies on competition for chromatin-bound FD at shared target loci. Direct TFL1-FD regulated target genes identify this complex as a hub for repressing both master regulators of reproductive development and endogenous signalling pathways. Our data provide mechanistic insight into how TFL1-FD sculpt inflorescence architecture, a trait important for reproductive success, plant architecture and yield. The flowering regulators TFL1 and FT act antagonistically to synchronize reproductive transition in flowering plants. Here Zhu et al. show that TFL1 is recruited by the bZIP transcription factor FD to thousands of target loci and reveal that FT competes with TFL1 for chromatin bound-FD at shared targets including LFY , upregulation of which by FT promotes floral fate.

Although more studies are demonstrating that a father’s environment can influence child health and disease, the molecular mechanisms underlying non-genetic inheritance remain unclear. It was previously thought that sperm exclusively contributed its genome to the egg. More recently, association studies have shown that various environmental exposures including poor diet, toxicants, and stress, perturbed epigenetic marks in sperm at important reproductive and developmental loci that were associated with offspring phenotypes. The molecular and cellular routes that underlie how epigenetic marks are transmitted at fertilization, to resist epigenetic reprogramming in the embryo, and drive phenotypic changes are only now beginning to be unraveled. Here, we provide an overview of the state of the field of intergenerational paternal epigenetic inheritance in mammals and present new insights into the relationship between embryo development and the three pillars of epigenetic inheritance: chromatin, DNA methylation, and non-coding RNAs. We evaluate compelling evidence of sperm-mediated transmission and retention of paternal epigenetic marks in the embryo. Using landmark examples, we discuss how sperm-inherited regions may escape reprogramming to impact development via mechanisms that implicate transcription factors, chromatin organization, and transposable elements. Finally, we link paternally transmitted epigenetic marks to functional changes in the pre- and post-implantation embryo. Understanding how sperm-inherited epigenetic factors influence embryo development will permit a greater understanding related to the developmental origins of health and disease. Increasing evidence suggests parental environmental stressors can influence offspring health and disease outcomes. In this perspective, the authors provide an update of the paternal epigenetic inheritance field and propose links between paternally transmitted epigenetic influences and functional changes to the embryo.

The mechanisms underlying gene repression and silencers are poorly understood. Here we investigate the hypothesis that H3K27me3-rich regions of the genome, defined from clusters of H3K27me3 peaks, may be used to identify silencers that can regulate gene expression via proximity or looping. We find that H3K27me3-rich regions are associated with chromatin interactions and interact preferentially with each other. H3K27me3-rich regions component removal at interaction anchors by CRISPR leads to upregulation of interacting target genes, altered H3K27me3 and H3K27ac levels at interacting regions, and altered chromatin interactions. Chromatin interactions did not change at regions with high H3K27me3, but regions with low H3K27me3 and high H3K27ac levels showed changes in chromatin interactions. Cells with H3K27me3-rich regions knockout also show changes in phenotype associated with cell identity, and altered xenograft tumor growth. Finally, we observe that H3K27me3-rich regions-associated genes and long-range chromatin interactions are susceptible to H3K27me3 depletion. Our results characterize H3K27me3-rich regions and their mechanisms of functioning via looping. Mechanisms underlying gene repression and silencers remain poorly understood. Here the authors investigate the role of H3K27me3-rich regions in the genome, as defined from clusters of H3K27me3 peaks, in regulating gene expression via looping.

Targeting ferroptosis, an iron-dependent form of regulated cell death triggered by the lethal overload of lipid peroxides, in cancer therapy is impeded by our limited understanding of the intersection of tumour’s metabolic feature and ferroptosis vulnerability. In the present study, arginine is identified as a ferroptotic promoter using a metabolites library. This effect is mainly achieved through arginine’s conversion to polyamines, which exerts their potent ferroptosis-promoting property in an H 2 O 2 -dependent manner. Notably, the expression of ornithine decarboxylase 1 (ODC1), the critical enzyme catalysing polyamine synthesis, is significantly activated by the ferroptosis signal——iron overload——through WNT/MYC signalling, as well as the subsequent elevated polyamine synthesis, thus forming a ferroptosis-iron overload-WNT/MYC-ODC1-polyamine-H 2 O 2 positive feedback loop that amplifies ferroptosis. Meanwhile, we notice that ferroptotic cells release enhanced polyamine-containing extracellular vesicles into the microenvironment, thereby further sensitizing neighbouring cells to ferroptosis and accelerating the “spread” of ferroptosis in the tumour region. Besides, polyamine supplementation also sensitizes cancer cells or xenograft tumours to radiotherapy or chemotherapy through inducing ferroptosis. Considering that cancer cells are often characterized by elevated intracellular polyamine pools, our results indicate that polyamine metabolism exposes a targetable vulnerability to ferroptosis and represents an exciting opportunity for therapeutic strategies for cancer. Ferroptosis plays an important role in response to radiotherapy and chemotherapy, however, the sensitivity of cancer cell to ferroptosis varies. Here, the authors show that ODC1-mediated polyamine synthesis induces ferroptosis and demonstrate the potential of targeting this axis by combining polyamine supplements with radiotherapy or chemotherapy in preclinical lung cancer models.

N6-methyladenosine (m 6 A) is the most prevalent RNA modification at the posttranscriptional level and involved in various diseases and cellular processes. However, the underlying mechanism of m 6 A regulation in intervertebral disc degeneration (IVDD) remains elusive. Here, we show that methylation of the lncRNA NORAD significantly increases in senescent nucleus pulposus cells (NPCs) by m 6 A sequencing. Subsequent loss- and gain-of-function experiments reveal WTAP is increased in senescent NPCs due to an epigenetic increase in H3K4me3 of the promoter mediated by KDM5a, and significantly promotes NORAD m 6 A modification. Furthermore, YTHDF2-mediated decay of NORAD is enhanced in senescent NPCs, and then deficiency of NORAD results in less sequestraion of PUMILIO proteins, contributing to the augmented activity of PUM1/2, thus repressing the expression of target E2F3 mRNAs and promoting the cellular senescence. Here, we show interruption of NORAD m 6 A modification or the NORAD/PUMILIO/E2F3 axis could serve as a potential therapeutic target to inhibit the senescence of NPCs and development of IVDD. Intervertebral disc degeneration (IVDD) is the leading cause of low back pain and Nucleus pulposus cell senescence contributes a lot to its progression. Here, the authors reveal WTAP-mediated m6A modification of lncRNA NORAD promotes IVDD.

The genome is partitioned into topologically associated domains and genomic compartments with shared chromatin valence. This architecture is constrained by the DNA polymer, which precludes interactions between genes on different chromosomes. Here we report a marked divergence from this pattern of nuclear organization that occurs in mouse olfactory sensory neurons. Chromatin conformation capture using in situ Hi-C on fluorescence-activated cell-sorted olfactory sensory neurons and their progenitors shows that olfactory receptor gene clusters from 18 chromosomes make specific and robust interchromosomal contacts that increase with differentiation of the cells. These contacts are orchestrated by intergenic olfactory receptor enhancers, the ‘Greek islands’, which first contribute to the formation of olfactory receptor compartments and then form a multi-chromosomal super-enhancer that associates with the single active olfactory receptor gene. The Greek-island-bound transcription factor LHX2 and adaptor protein LDB1 regulate the assembly and maintenance of olfactory receptor compartments, Greek island hubs and olfactory receptor transcription, providing mechanistic insights into and functional support for the role of trans interactions in gene expression. Specific interchromosomal contacts in olfactory sensory neurons form a super-enhancer that controls the expression of a single olfactory receptor in each neuron.

To explore the origins and consequences of tetraploidy in the African clawed frog, we sequenced the Xenopus laevis genome and compared it to the related diploid X. tropicalis genome. We characterize the allotetraploid origin of X. laevis by partitioning its genome into two homoeologous subgenomes, marked by distinct families of ‘fossil’ transposable elements. On the basis of the activity of these elements and the age of hundreds of unitary pseudogenes, we estimate that the two diploid progenitor species diverged around 34 million years ago (Ma) and combined to form an allotetraploid around 17–18 Ma. More than 56% of all genes were retained in two homoeologous copies. Protein function, gene expression, and the amount of conserved flanking sequence all correlate with retention rates. The subgenomes have evolved asymmetrically, with one chromosome set more often preserving the ancestral state and the other experiencing more gene loss, deletion, rearrangement, and reduced gene expression. The two homoeologous subgenomes in the allotetraploid frog Xenopus laevis evolved asymmetrically; one often retained the ancestral state, whereas the other experienced gene loss, deletion, rearrangement and reduced gene expression. Genomic evolution in Xenopus laevis Xenopus laevis , also known as the African clawed frog or platanna, is an important model organism that is used in the study of vertebrate cell and developmental biology. It is a palaeotetraploid—the product of genome duplications that occurred many millions of years ago. This makes X. laevis ideal for the study of polyploidy, but has greatly complicated genome sequencing. Here an international research collaboration reports the X. laevis genome sequence and compares it to that of the related X. tropicalis . Their analyses confirm that X. laevis is an allotetraploid and distinguishes two subgenomes that evolved asymmetrically—one often retained the ancestral state and the other was subject to gene loss, deletion, rearrangement and reduced expression. The two diploid progenitor species diverged about 34 million years ago, combining to form an allotetraploid about 18 million years ago.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome. Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.