Punctured polygons and polyominoes on the square lattice

We use the finite lattice method to count the number of punctured staircase and self-avoiding polygons with up to three holes on the square lattice. New or radically extended series have been derived for both the perimeter and area generating functions. We show that the critical point is unchanged by a finite number of punctures, and that the critical exponent increases by a fixed amount for each puncture. The increase is 1.5 per puncture when enumerating by perimeter and 1.0 when enumerating by area. A refined estimate of the connective constant for polygons by area is given. A similar set of results is obtained for finitely punctured polyominoes. The exponent increase is proved to be 1.0 per puncture for polyominoes.Comment: 36 pages, 11 figure

Metabolic investigations prevent liver transplantation in two young children with citrullinemia type I

Acute liver failure may be caused by a variety of disorders including inborn errors of metabolism. In those cases, rapid metabolic investigations and adequate treatment may avoid the need for liver transplantation. We report two patients who presented with acute liver failure and were referred to our center for liver transplantation work-up. Urgent metabolic investigations revealed citrullinemia type I. Treatment for citrullinemia type I avoided the need for liver transplantation. Acute liver failure as a presentation of citrullinemia type I has not previously been reported in young children. Although acute liver failure has occasionally been described in other urea cycle disorders, these disorders may be underestimated as a cause. Timely diagnosis and treatment of these disorders may avoid liver transplantation and improve clinical outcome. Therefore, urea cycle disorders should be included in the differential diagnosis in young children presenting with acute liver failure

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision

TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer.

PURPOSE: To infer the prognostic value of simultaneous androgen receptor (AR) and TP53 profiling in liquid biopsies from metastatic castration-resistant prostate cancer (mCRPC) patients starting a new line of AR signalling inhibitors (ARSi). EXPERIMENTAL DESIGN: Between March 2014 and April 2017, we recruited mCRPC patients (n=168) prior to ARSi in a cohort study encompassing 10 European centres. Blood samples were collected for comprehensive profiling of CellSearch-enriched circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA). Targeted CTC RNA-seq allowed the detection of eight AR splice variants (ARVs). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss-of-heterozygosity, mutations and structural rearrangements in ctDNA. Clinical or radiological progression-free survival (PFS) was estimated by Kaplan-Meier analysis, and independent associations were determined using multivariable Cox-regression models. RESULTS: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumour burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value (HR 1.88, 95%CI 1.18-3.00, p = 0.008), and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs 7.51 vs 2.62 months, p < 0.0001), which was validated in an independent mCRPC cohort (n=202) starting first-line ARSi (median, 14.3 vs 6.39 vs 2.23 months, p < 0.0001). CONCLUSIONS: In an all-comer cohort, tumour burden estimates and TP53 outperform any AR perturbation to infer prognosis

Loregic: A Method to Characterize the Cooperative Logic of Regulatory Factors

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible twoinput- one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a generalpurpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcriptionfactor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy