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Chronic inflammation is accompanied by recurring cycles of tissue destruction and repair and is associated with an increased risk of cancer(1-3). However, how such cycles affect the clonal composition of tissues, particularly in terms of cancer development, remains unknown. Here we show that in patients with ulcerative colitis, the inflamed intestine undergoes widespread remodelling by pervasive clones, many of which are positively selected by acquiring mutations that commonly involve the NFKBIZ, TRAF3IP2, ZC3H12A, PIGR and HNRNPF genes and are implicated in the downregulation of IL-17 and other pro-inflammatory signals. Mutational profiles vary substantially between colitis-associated cancer and non-dysplastic tissues in ulcerative colitis, which indicates that there are distinct mechanisms of positive selection in both tissues. In particular, mutations in NFKBIZ are highly prevalent in the epithelium of patients with ulcerative colitis but rarely found in both sporadic and colitis-associated cancer, indicating that NFKBIZ-mutant cells are selected against during colorectal carcinogenesis. In further support of this negative selection, we found that tumour formation was significantly attenuated in Nfkbiz-mutant mice and cell competition was compromised by disruption of NFKBIZ in human colorectal cancer cells. Our results highlight common and discrete mechanisms of clonal selection in inflammatory tissues, which reveal unexpected cancer vulnerabilities that could potentially be exploited for therapeutics in colorectal cancer.

Genetic variations underlying susceptibility to complex autoimmune and allergic diseases are concentrated within noncoding regulatory elements termed enhancers(1). The functions of a large majority of disease-associated enhancers are unknown, in part owing to their distance from the genes they regulate, a lack of understanding of the cell types in which they operate, and our inability to recapitulate the biology of immune diseases in vitro. Here, using shared synteny to guide loss-of-function analysis of homologues of human enhancers in mice, we show that the prominent autoimmune and allergic disease risk locus at chromosome 11q13.5(2-7) contains a distal enhancer that is functional in CD4(+) regulatory T (T-reg) cells and required for T-reg-mediated suppression of colitis. The enhancer recruits the transcription factors STAT5 and NF-kappa B to mediate signal-driven expression of Lrrc32, which encodes the protein glycoprotein A repetitions predominant (GARP). Whereas disruption of the Lrrc32 gene results in early lethality, mice lacking the enhancer are viable but lack GARP expression in Foxp3(+) T-reg cells, which are unable to control colitis in a cell-transfer model of the disease. In human T-reg cells, the enhancer forms conformational interactions with the promoter of LRRC32 and enhancer risk variants are associated with reduced histone acetylation and GARP expression. Finally, functional fine-mapping of 11q13.5 using CRISPR-activation (CRISPRa) identifies a CRISPRa-responsive element in the vicinity of risk variant rs11236797 capable of driving GARP expression. These findings provide a mechanistic basis for association of the 11q13.5 risk locus with immune-mediated diseases and identify GARP as a potential target in their therapy.

Shared synteny guides loss-of-function analysis of human enhancer homologues in mice, identifying a distal enhancer at the autoimmune and allergic disease risk locus at chromosome 11q13.5 whose function in regulatory T cells provides a mechanistic basis for its role in disease.

Breakdown of the blood-brain barrier in individuals carrying the epsilon 4 allele of the APOE gene, but not the epsilon 3 allele, increases with and predicts cognitive impairment and is independent of amyloid beta or tau pathology.

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

The lysosomal polyamine transporter ATP13A2 controls the cellular polyamine content, and impaired lysosomal polyamine export represents a lysosome-dependent cell death pathway that may be implicated in ATP13A2-associated neurodegeneration.