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Cis -regulatory elements (CREs), such as enhancers and promoters, are fundamental regulators of gene expression and, across different cell types, the MYC locus utilizes a diverse regulatory architecture driven by multiple CREs. To better understand differences in CRE function, we perform pooled CRISPR inhibition (CRISPRi) screens to comprehensively probe the 2.8 Mb topologically-associated domain containing MYC in 6 human cancer cell lines with nucleotide resolution. We map 32 CREs where inhibition leads to changes in cell growth, including 8 that overlap previously identified enhancers. Targeting specific CREs decreases MYC expression by as much as 60%, and cell growth by as much as 50%. Using 3-D enhancer contact mapping, we find that these CREs almost always contact MYC but less than 10% of total MYC contacts impact growth when silenced, highlighting the utility of our approach to identify phenotypically-relevant CREs. We also detect an enrichment of lineage-specific transcription factors (TFs) at MYC CREs and, for some of these TFs, find a strong, tumor-specific correlation between TF and MYC expression not found in normal tissue. Taken together, these CREs represent systematically identified, functional regulatory regions and demonstrate how the same region of the human genome can give rise to complex, tissue-specific gene regulation. The oncogene MYC plays a key role in cancer initiation and progression. Using thousands of CRISPR perturbations, the authors investigate regulators of MYC in six different cancers. These tumor-specific regulators suggest potential therapeutic targets for this oncogene.

Higher-order chromatin structure is important for the regulation of genes by distal regulatory sequences 1 , 2 . Structural variants (SVs) that alter three-dimensional (3D) genome organization can lead to enhancer–promoter rewiring and human disease, particularly in the context of cancer 3 . However, only a small minority of SVs are associated with altered gene expression 4 , 5 , and it remains unclear why certain SVs lead to changes in distal gene expression and others do not. To address these questions, we used a combination of genomic profiling and genome engineering to identify sites of recurrent changes in 3D genome structure in cancer and determine the effects of specific rearrangements on oncogene activation. By analysing Hi-C data from 92 cancer cell lines and patient samples, we identified loci affected by recurrent alterations to 3D genome structure, including oncogenes such as MYC , TERT and CCND1 . By using CRISPR–Cas9 genome engineering to generate de novo SVs, we show that oncogene activity can be predicted by using ‘activity-by-contact’ models that consider partner region chromatin contacts and enhancer activity. However, activity-by-contact models are only predictive of specific subsets of genes in the genome, suggesting that different classes of genes engage in distinct modes of regulation by distal regulatory elements. These results indicate that SVs that alter 3D genome organization are widespread in cancer genomes and begin to illustrate predictive rules for the consequences of SVs on oncogene activation. Results are presented that indicate that alterations to gene regulatory three-dimensional architecture are a critical mechanism that enables structural variant-based oncogene activation in cancer genomes and sheds light on the essential elements for such gene activation events.

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes. The authors present an integrative framework for identifying structural variants (SVs) in cancer that applies optical mapping, Hi-C, and whole-genome sequencing. They find SVs affecting distal regulatory sequences, DNA replication, and three-dimensional chromatin structure.

Somatic mutations in driver genes may ultimately lead to the development of cancer. Understanding how somatic mutations accumulate in cancer genomes and the underlying factors that generate somatic mutations is therefore crucial for developing novel therapeutic strategies. To understand the interplay between spatial genome organization and specific mutational processes, we studied 3,000 tumor–normal-pair whole-genome datasets from 42 different human cancer types. Our analyses reveal that the change in somatic mutational load in cancer genomes is co-localized with topologically-associating-domain boundaries. Domain boundaries constitute a better proxy to track mutational load change than replication timing measurements. We show that different mutational processes lead to distinct somatic mutation distributions where certain processes generate mutations in active domains, and others generate mutations in inactive domains. Overall, the interplay between three-dimensional genome organization and active mutational processes has a substantial influence on the large-scale mutation-rate variations observed in human cancers. Somatic mutational loads in cancer genomes change with chromatin domain boundaries. Different mutational processes lead to distinct somatic mutation distributions in active versus inactive domains, including during tumor evolution.

Somatic mutations arise during the life history of a cell. Mutations occurring in cancer driver genes may ultimately lead to the development of clinically detectable disease. Nascent cancer lineages continue to acquire somatic mutations throughout the neoplastic process and during cancer evolution. Extrinsic and endogenous mutagenic factors contribute to the accumulation of these somatic mutations. Understanding the underlying factors generating somatic mutations is crucial for developing potential preventive, therapeutic and clinical decisions. Earlier studies have revealed that DNA replication timing and chromatin modifications are associated with variations in mutational density. What is unclear from these early studies, however, is whether all extrinsic and exogenous factors that drive somatic mutational processes share a similar relationship with chromatin state and structure. In order to understand the interplay between spatial genome organization and specific individual mutational processes, we report here a study of 3000 tumor-normal pair whole genome datasets from more than 40 different human cancer types. Our analyses revealed that different mutational processes lead to distinct somatic mutation distributions between chromatin folding domains. APOBEC- or MSI-related mutations are enriched in transcriptionally-active domains while mutations occurring due to tobacco-smoke, ultraviolet (UV) light exposure or a signature of unknown aetiology (signature 17) enrich predominantly in transcriptionally-inactive domains. Active mutational processes dictate the mutation distributions in cancer genomes, and we show that mutational distributions shift during cancer evolution upon mutational processes switch. Moreover, a dramatic instance of extreme chromatin structure in humans, that of the unique folding pattern of the inactive X-chromosome leads to distinct somatic mutation distribution on X chromosome in females compared to males in various cancer types. Overall, the interplay between three-dimensional genome organization and active mutational processes has a substantial influence on the large-scale mutation rate variations observed in human cancer.

Structural variants can contribute to oncogenesis through a variety of mechanisms, yet, despite their importance, the identification of structural variants in cancer genomes remains challenging. Here, we present an integrative framework for comprehensively identifying structural variation in cancer genomes. For the first time, we apply next-generation optical mapping, high-throughput chromosome conformation capture (Hi-C) techniques, and whole genome sequencing to systematically detect SVs in a variety of cancer cells. Using this approach, we identify and characterize structural variants in up to 29 commonly used normal and cancer cell lines. We find that each method has unique strengths in identifying different classes of structural variants and at different scales, suggesting that integrative approaches are likely the only way to comprehensively identify structural variants in the genome. Studying the impact of the structural variants in cancer cell lines, we identify widespread structural variation events affecting replication timing and the functions of non-coding sequences in the genome, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel 3D chromatin structural domains. These results underscore the importance of comprehensive structural variant identification and indicate that non-coding structural variation may be an underappreciated mutational process in cancer genomes.

No systematic review and narrative synthesis on personal recovery in mental illness has been undertaken. To synthesise published descriptions and models of personal recovery into an empirically based conceptual framework. Systematic review and modified narrative synthesis. Out of 5208 papers that were identified and 366 that were reviewed, a total of 97 papers were included in this review. The emergent conceptual framework consists of: (a) 13 characteristics of the recovery journey; (b) five recovery processes comprising: connectedness; hope and optimism about the future; identity; meaning in life; and empowerment (giving the acronym CHIME); and (c) recovery stage descriptions which mapped onto the transtheoretical model of change. Studies that focused on recovery for individuals of Black and minority ethnic (BME) origin showed a greater emphasis on spirituality and stigma and also identified two additional themes: culturally specific facilitating factors and collectivist notions of recovery. The conceptual framework is a theoretically defensible and robust synthesis of people's experiences of recovery in mental illness. This provides an empirical basis for future recovery-oriented research and practice.

Hypoxia has been linked to elevated instances of therapeutic resistance in breast cancer. The exposure of proliferating cancer cells to hypoxia has been shown to induce an aggressive phenotype conducive to invasion and metastasis. Regions of the primary tumors in the breast may be exposed to different types of hypoxia including acute, chronic or intermittent. Intermittent hypoxia (IH), also called cyclic hypoxia, is caused by exposure to cycles of hypoxia and reoxygenation (H-R cycles). Importantly, there is currently no consensus amongst the scientific community on the total duration of hypoxia, the oxygen level, and the possible presence of H-R cycles. In this review, we discuss current methods of hypoxia research, to explore how exposure regimes used in experiments are connected to signaling by different hypoxia inducible factors (HIFs) and to distinct cellular responses in the context of the hallmarks of cancer. We highlight discrepancies in the existing literature on hypoxia research within the field of breast cancer in particular and propose a clear definition of acute, chronic, and intermittent hypoxia based on HIF activation and cellular responses: (i) acute hypoxia is when the cells are exposed for no more than 24 h to an environment with 1% O 2 or less; (ii) chronic hypoxia is when the cells are exposed for more than 48 h to an environment with 1% O 2 or less and (iii) intermittent hypoxia is when the cells are exposed to at least two rounds of hypoxia (1% O 2 or less) separated by at least one period of reoxygenation by exposure to normoxia (8.5% O 2 or higher). Our review provides for the first time a guideline for definition of hypoxia related terms and a clear foundation for hypoxia related in vitro (breast) cancer research.

Most research on nutritional effects on aging has focussed on the impact of manipulating single dietary factors such as total calorie intake or each of the macronutrients individually. More recent studies using a nutritional geometric approach called the Geometric Framework have facilitated an understanding of how aging is influenced across a landscape of diets that vary orthogonally in macronutrient and total energy content. Such studies have been performed using ad libitum feeding regimes, thus taking into account compensatory feeding responses that are inevitable in a non-constrained environment. Geometric Framework studies on insects and mice have revealed that diets low in protein and high in carbohydrates generate longest lifespans in ad libitum-fed animals while low total energy intake (caloric restriction by dietary dilution) has minimal effect. These conclusions are supported indirectly by observational studies in humans and a heterogeneous group of other types of interventional studies in insects and rodents. Due to compensatory feeding for protein dilution, low-protein, high-carbohydrate diets are often associated with increased food intake and body fat, a phenomenon called protein leverage. This could potentially be mitigated by supplementing these diets with interventions that influence body weight through physical activity and ambient temperature.

In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex with a deep self-supervised generative model, β -VAE, which disentangles sensory data into interpretable latent factors, such as gender or age. Our results demonstrate a strong correspondence between the generative factors discovered by β -VAE and those coded by single IT neurons, beyond that found for the baselines, including the handcrafted state-of-the-art model of face perception, the Active Appearance Model, and deep classifiers. Moreover, β -VAE is able to reconstruct novel face images using signals from just a handful of cells. Together our results imply that optimising the disentangling objective leads to representations that closely resemble those in the IT at the single unit level. This points at disentangling as a plausible learning objective for the visual brain. Little is known about the brain’s computations that enable the recognition of faces. Here, the authors use unsupervised deep learning to show that the brain disentangles faces into semantically meaningful factors, like age or the presence of a smile, at the single neuron level.