Christopher R. S. Banerji

Cellular network entropy as the energy potential in Waddington's differentiation landscape

Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular samples position in the global differentiation hierarchy. Here we adopt a systems view and consider the samples network entropy, a measure of signaling pathway promiscuity, computable from a samples genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a samples differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cells undifferentiated state, defining its elevation in Waddingtons landscape.

Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers.

Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a samples gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology.

DUX4 induces a transcriptome more characteristic of a less-differentiated cell state and inhibits myogenesis

ABSTRACT Skeletal muscle wasting in facioscapulohumeral muscular dystrophy (FSHD) results in substantial morbidity. On a disease-permissive chromosome 4qA haplotype, genomic and/or epigenetic changes at the D4Z4 macrosatellite repeat allows transcription of the DUX4 retrogene. Analysing transgenic mice carrying a human D4Z4 genomic locus from an FSHD-affected individual showed that DUX4 was transiently induced in myoblasts during skeletal muscle regeneration. Centromeric to the D4Z4 repeats is an inverted D4Z4 unit encoding DUX4c. Expression of DUX4, DUX4c and DUX4 constructs, including constitutively active, dominant-negative and truncated versions, revealed that DUX4 activates target genes to inhibit proliferation and differentiation of satellite cells, but that it also downregulates target genes to suppress myogenic differentiation. These transcriptional changes elicited by DUX4 in mouse have significant overlap with genes regulated by DUX4 in man. Comparison of DUX4 and DUX4c transcriptional perturbations revealed that DUX4 regulates genes involved in cell proliferation, whereas DUX4c regulates genes engaged in angiogenesis and muscle development, with both DUX4 and DUX4c modifing genes involved in urogenital development. Transcriptomic analysis showed that DUX4 operates through both target gene activation and repression to orchestrate a transcriptome characteristic of a less-differentiated cell state. Summary: DUX4 underlies pathogenesis in facioscapulohumeral muscular dystrophy. DUX4 acts mainly as a transcriptional activator that inhibits myogenesis by orchestrating a gene expression profile representative of a more stem-cell-like state.

β-Catenin is central to DUX4-driven network rewiring in facioscapulohumeral muscular dystrophy.

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable disease, characterized by skeletal muscle weakness and wasting. Genetically, FSHD is characterized by contraction or hypomethylation of repeat D4Z4 units on chromosome 4, which causes aberrant expression of the transcription factor DUX4 from the last repeat. Many genes have been implicated in FSHD pathophysiology, but an integrated molecular model is currently lacking. We developed a novel differential network methodology, Interactome Sparsification and Rewiring (InSpiRe), which detects network rewiring between phenotypes by integrating gene expression data with known protein interactions. Using InSpiRe, we performed a meta-analysis of multiple microarray datasets from FSHD muscle biopsies, then removed secondary rewiring using non-FSHD datasets, to construct a unified network of rewired interactions. Our analysis identified β-catenin as the main coordinator of FSHD-associated protein interaction signalling, with pathways including canonical Wnt, HIF1-α and TNF-α clearly perturbed. To detect transcriptional changes directly elicited by DUX4, gene expression profiling was performed using microarrays on murine myoblasts. This revealed that DUX4 significantly modified expression of the genes in our FSHD network. Furthermore, we experimentally confirmed that Wnt/β-catenin signalling is affected by DUX4 in murine myoblasts. Thus, we provide the first unified molecular map of FSHD signalling, capable of uncovering pathomechanisms and guiding therapeutic development.

Ret function in muscle stem cells points to tyrosine kinase inhibitor therapy for facioscapulohumeral muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) involves sporadic expression of DUX4, which inhibits myogenesis and is pro-apoptotic. To identify target genes, we over-expressed DUX4 in myoblasts and found that the receptor tyrosine kinase Ret was significantly up-regulated, suggesting a role in FSHD. RET is dynamically expressed during myogenic progression in mouse and human myoblasts. Constitutive expression of either RET9 or RET51 increased myoblast proliferation, whereas siRNA-mediated knockdown of Ret induced myogenic differentiation. Suppressing RET activity using Sunitinib, a clinically-approved tyrosine kinase inhibitor, rescued differentiation in both DUX4-expressing murine myoblasts and in FSHD patient-derived myoblasts. Importantly, Sunitinib also increased engraftment and differentiation of FSHD myoblasts in regenerating mouse muscle. Thus, DUX4-mediated activation of Ret prevents myogenic differentiation and could contribute to FSHD pathology by preventing satellite cell-mediated repair. Rescue of DUX4-induced pathology by Sunitinib highlights the therapeutic potential of tyrosine kinase inhibitors for treatment of FSHD. DOI: http://dx.doi.org/10.7554/eLife.11405.001

Network transfer entropy and metric space for causality inference

A measure is derived to quantify directed information transfer between pairs of vertices in a weighted network, over paths of a specified maximal length. Our approach employs a general, probabilistic model of network traffic, from which the informational distance between dynamics on two weighted networks can be naturally expressed as a Jensen Shannon divergence. Our network transfer entropy measure is shown to be able to distinguish and quantify causal relationships between network elements, in applications to simple synthetic networks and a biological signaling network. We conclude with a theoretical extension of our framework, in which the square root of the Jensen Shannon Divergence induces a metric on the space of dynamics on weighted networks. We prove a convergence criterion, demonstrating that a form of convergence in the structure of weighted networks in a family of matrix metric spaces implies convergence of their dynamics with respect to the square root Jensen Shannon divergence metric.

PAX7 target genes are globally repressed in facioscapulohumeral muscular dystrophy skeletal muscle

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable myopathy, linked to hypomethylation of D4Z4 repeats on chromosome 4q causing expression of the DUX4 transcription factor. However, DUX4 is difficult to detect in FSHD muscle biopsies and it is debatable how robust changes in DUX4 target gene expression are as an FSHD biomarker. PAX7 is a master regulator of myogenesis that rescues DUX4-mediated apoptosis. Here, we show that suppression of PAX7 target genes is a hallmark of FSHD, and that it is as major a signature of FSHD muscle as DUX4 target gene expression. This is shown using meta-analysis of over six FSHD muscle biopsy gene expression studies, and validated by RNA-sequencing on FSHD patient-derived myoblasts. DUX4 also inhibits PAX7 from activating its transcriptional target genes and vice versa. Furthermore, PAX7 target gene repression can explain oxidative stress sensitivity and epigenetic changes in FSHD. Thus, PAX7 target gene repression is a hallmark of FSHD that should be considered in the investigation of FSHD pathology and therapy.Facioscapulohumeral muscular dystrophy is a myopathy linked to ectopic expression of the DUX4 transcription factor. The authors show that the suppression of targets genes of the myogenesis regulator PAX7 is a signature of FSHD, and might explain oxidative stress sensitivity and epigenetic changes.

A notion of graph likelihood and an infinite monkey theorem

We play with a graph-theoretic analogue of the folklore infinite monkey theorem. We define a notion of graph likelihood as the probability that a given graph is constructed by a monkey in a number of time steps equal to the number of vertices. We present an algorithm to compute this graph invariant and closed formulas for some infinite classes. We have to leave the computational complexity of the likelihood as an open problem.