Hippo Pathway Activity Influences Liver Cell Fate

SummaryThe Hippo-signaling pathway is an important regulator of cellular proliferation and organ size. However, little is known about the role of this cascade in the control of cell fate. Employing a combination of lineage tracing, clonal analysis, and organoid culture approaches, we demonstrate that Hippo pathway activity is essential for the maintenance of the differentiated hepatocyte state. Remarkably, acute inactivation of Hippo pathway signaling in vivo is sufficient to dedifferentiate, at very high efficiencies, adult hepatocytes into cells bearing progenitor characteristics. These hepatocyte-derived progenitor cells demonstrate self-renewal and engraftment capacity at the single-cell level. We also identify the NOTCH-signaling pathway as a functional important effector downstream of the Hippo transducer YAP. Our findings uncover a potent role for Hippo/YAP signaling in controlling liver cell fate and reveal an unprecedented level of phenotypic plasticity in mature hepatocytes, which has implications for the understanding and manipulation of liver regeneration

Copy number variant detection in inbred strains from short read sequence data

Summary: We have developed an algorithm to detect copy number variants (CNVs) in homozygous organisms, such as inbred laboratory strains of mice, from short read sequence data. Our novel approach exploits the fact that inbred mice are homozygous at virtually every position in the genome to detect CNVs using a hidden Markov model (HMM). This HMM uses both the density of sequence reads mapped to the genome, and the rate of apparent heterozygous single nucleotide polymorphisms, to determine genomic copy number. We tested our algorithm on short read sequence data generated from re-sequencing chromosome 17 of the mouse strains A/J and CAST/EiJ with the Illumina platform. In total, we identified 118 copy number variants (43 for A/J and 75 for CAST/EiJ). We investigated the performance of our algorithm through comparison to CNVs previously identified by array-comparative genomic hybridization (array CGH). We performed quantitative-PCR validation on a subset of the calls that differed from the array CGH data sets

Lock-in detection using a cryogenic low noise looped current preamplifier for the readout of resistive bolometers

We implemented a low noise current preamplifier for the readout of resistive bolometers. We tested the apparatus on thermometer resistances ranging from 10 Ohm to 500 Mohm. The use of current preamplifier overcomes constraints introduced by the readout time constant due to the thermometer resistance and the input capacitance. Using cold JFETs, this preamplifier board is shown to have very low noise: the Johnson noise of the source resistor (1 fA/Hz1/2) dominated in our noise measurements. We also implemented a lock-in chain using this preamplifier. Because of fast risetime, compensation of the phase shift may be unnecessary. If implemented, no tuning is necessary when the sensor impedance changes. Transients are very short, and thus low-passing or sampling of the signal is simplified. In case of spurious noise, the modulation frequency can be chosen in a much wider frequency range, without requiring a new calibration of the apparatus.Comment: 18 pages, 7 figures, Accepted in NIM

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area

High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses

<p>Abstract</p> <p>Background</p> <p>Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.</p> <p>Methods</p> <p>We present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses.</p> <p>Results</p> <p>13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data.</p> <p>Conclusions</p> <p>Normalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.</p> <p>Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.</p

A High-Resolution Map of Segmental DNA Copy Number Variation in the Mouse Genome

Submicroscopic (less than 2 Mb) segmental DNA copy number changes are a recently recognized source of genetic variability between individuals. The biological consequences of copy number variants (CNVs) are largely undefined. In some cases, CNVs that cause gene dosage effects have been implicated in phenotypic variation. CNVs have been detected in diverse species, including mice and humans. Published studies in mice have been limited by resolution and strain selection. We chose to study 21 well-characterized inbred mouse strains that are the focus of an international effort to measure, catalog, and disseminate phenotype data. We performed comparative genomic hybridization using long oligomer arrays to characterize CNVs in these strains. This technique increased the resolution of CNV detection by more than an order of magnitude over previous methodologies. The CNVs range in size from 21 to 2,002 kb. Clustering strains by CNV profile recapitulates aspects of the known ancestry of these strains. Most of the CNVs (77.5%) contain annotated genes, and many (47.5%) colocalize with previously mapped segmental duplications in the mouse genome. We demonstrate that this technique can identify copy number differences associated with known polymorphic traits. The phenotype of previously uncharacterized strains can be predicted based on their copy number at these loci. Annotation of CNVs in the mouse genome combined with sequence-based analysis provides an important resource that will help define the genetic basis of complex traits

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Clinical Outcomes and Quality of Life in Recipients of Livers Donated after Cardiac Death

Donation after cardiac death (DCD) has expanded in the last decade in the US; however, DCD liver utilization has flattened in recent years due to poor outcomes. We examined clinical and quality of life (QOL) outcomes of DCD recipients by conducting a retrospective and cross-sectional review of patients from 2003 to 2010. We compared clinical outcomes of DCD recipients (n=60) to those of donation after brain death (DBD) liver recipients (n=669) during the same time period. DCD recipients had significantly lower rates of 5-year graft survival (P<0.001) and a trend toward lower rates of 5-year patient survival (P=0.064) when compared to the DBD cohort. In order to examine QOL outcomes in our cohorts, we administered the Short Form Liver Disease Quality of Life questionnaire to 30 DCD and 60 DBD recipients. The DCD recipients reported lower generic and liver-specific QOL. We further stratified the DCD cohort by the presence of ischemic cholangiopathy (IC). Patients with IC reported lower QOL when compared to DBD recipients and those DCD recipients without IC (P<0.05). While the results are consistent with clinical experience, this is the first report of QOL in DCD recipients using standardized measures. These data can be used to guide future comparative effectiveness studies