A TGFß-ECM-integrin signaling axis drives structural reconfiguration of the bile duct to promote polycystic liver disease.

The formation of multiple cysts in the liver occurs in a number of isolated monogenic diseases or multisystemic syndromes, during which bile ducts develop into fluid-filled biliary cysts. For patients with polycystic liver disease (PCLD), nonsurgical treatments are limited, and managing life-long abdominal swelling, pain, and increasing risk of cyst rupture and infection is common. We demonstrate here that loss of the primary cilium on postnatal biliary epithelial cells (via the deletion of the cilia gene Wdr35) drives ongoing pathological remodeling of the biliary tree, resulting in progressive cyst formation and growth. The development of cystic tissue requires the activation of transforming growth factor-ß (TGFß) signaling, which promotes the expression of a procystic, fibronectin-rich extracellular matrix and which itself is perceived by a changing profile of integrin receptors on the cystic epithelium. This signaling axis is conserved in liver cysts from patients with either autosomal dominant polycystic kidney disease or autosomal dominant polycystic liver disease, indicating that there are common cellular mechanisms for liver cyst growth regardless of the underlying genetic cause. Cyst number and size can be reduced by inhibiting TGFß signaling or integrin signaling in vivo. We suggest that our findings represent a therapeutic route for patients with polycystic liver disease, most of whom would not be amenable to surgery.

Comparative transcriptome in large-scale human and cattle populations.

BACKGROUND: Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. RESULTS: Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. CONCLUSIONS: In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.

Higher-order interactions in statistical physics and machine learning: A model-independent solution to the inverse problem at equilibrium.

The problem of inferring pairwise and higher-order interactions in complex systems involving large numbers of interacting variables, from observational data, is fundamental to many fields. Known to the statistical physics community as the inverse problem, it has become accessible in recent years due to real and simulated big data being generated. Current approaches to the inverse problem rely on parametric assumptions, physical approximations, e.g., mean-field theory, and ignoring higher-order interactions which may lead to biased or incorrect estimates. We bypass these shortcomings using a cross-disciplinary approach and demonstrate that none of these assumptions and approximations are necessary: We introduce a universal, model-independent, and fundamentally unbiased estimator of all-order symmetric interactions, via the nonparametric framework of targeted learning, a subfield of mathematical statistics. Due to its universality, our definition is readily applicable to any system at equilibrium with binary and categorical variables, be it magnetic spins, nodes in a neural network, or protein networks in biology. Our approach is targeted, not requiring fitting unnecessary parameters. Instead, it expends all data on estimating interactions, hence substantially increasing accuracy. We demonstrate the generality of our technique both analytically and numerically on (i) the two-dimensional Ising model, (ii) an Ising-type model with four-point interactions, (iii) the restricted Boltzmann machine, and (iv) simulated individual-level human DNA variants and representative traits. The latter demonstrates the applicability of this approach to discover epistatic interactions causal of disease in population biomedicine.

Pervasive lesion segregation shapes cancer genome evolution.

Cancers arise through the acquisition of oncogenic mutations and grow by clonal expansion1,2. Here we reveal that most mutagenic DNA lesions are not resolved into a mutated DNA base pair within a single cell cycle. Instead, DNA lesions segregate, unrepaired, into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterize this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can produce multiple alternative alleles in successive cell divisions, thereby generating both multiallelic and combinatorial genetic diversity. The phasing of lesions enables accurate measurement of strand-biased repair processes, quantification of oncogenic selection and fine mapping of sister-chromatid-exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

Zebrafish MITF-Low Melanoma Subtype Models Reveal Transcriptional Subclusters and MITF-Independent Residual Disease.

The melanocyte-inducing transcription factor (MITF)-low melanoma transcriptional signature is predictive of poor outcomes for patients, but little is known about its biological significance, and animal models are lacking. Here, we used zebrafish genetic models with low activity of Mitfa (MITF-low) and established that the MITF-low state is causal of melanoma progression and a predictor of melanoma biological subtype. MITF-low zebrafish melanomas resembled human MITF-low melanomas and were enriched for stem and invasive (mesenchymal) gene signatures. MITF-low activity coupled with a p53 mutation was sufficient to promote superficial growth melanomas, whereas BRAFV600E accelerated MITF-low melanoma onset and further promoted the development of MITF-high nodular growth melanomas. Genetic inhibition of MITF activity led to rapid regression; recurrence occurred following reactivation of MITF. At the regression site, there was minimal residual disease that was resistant to loss of MITF activity (termed MITF-independent cells) with very low-to-no MITF activity or protein. Transcriptomic analysis of MITF-independent residual disease showed enrichment of mesenchymal and neural crest stem cell signatures similar to human therapy-resistant melanomas. Single-cell RNA sequencing revealed MITF-independent residual disease was heterogeneous depending on melanoma subtype. Further, there was a shared subpopulation of residual disease cells that was enriched for a neural crest G0-like state that preexisted in the primary tumor and remained present in recurring melanomas. These findings suggest that invasive and stem-like programs coupled with cellular heterogeneity contribute to poor outcomes for MITF-low melanoma patients and that MITF-independent subpopulations are an important therapeutic target to achieve long-term survival outcomes. SIGNIFICANCE: This study provides a useful model for MITF-low melanomas and MITF-independent cell populations that can be used to study the mechanisms that drive these tumors as well as identify potential therapeutic options.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/22/5769/F1.large.jpg.