Pathway-Based Genomics Prediction using Generalized Elastic Net.

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach

Induced pluripotent stem cells (iPSC) created from skin fibroblasts of patients with Prader-Willi syndrome (PWS) retain the molecular signature of PWS

AbstractPrader-Willi syndrome (PWS) is a syndromic obesity caused by loss of paternal gene expression in an imprinted interval on 15q11.2-q13. Induced pluripotent stem cells were generated from skin cells of three large deletion PWS patients and one unique microdeletion PWS patient. We found that genes within the PWS region, including SNRPN and NDN, showed persistence of DNA methylation after iPSC reprogramming and differentiation to neurons. Genes within the PWS minimum critical deletion region remain silenced in both PWS large deletion and microdeletion iPSC following reprogramming. PWS iPSC and their relevant differentiated cell types could provide in vitro models of PWS

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Figure S9. Screenshot of the interactive Tumor Map visualization, showing HOCUS applied to the TCGA Pancan-12 mutation data. Each point is one tumor sample, which we have color-coded by tissue type. A dotted box highlights the cluster of samples that have both PIK3CA and TP53 mutations, which are usually mutually exclusive. (EPS 751 kb

Detailed monitoring reveals the nature of submarine turbidity currents

Seafloor sediment flows, called turbidity currents, form the largest sediment accumulations, deepest canyons, and longest channels on Earth. It was once thought that turbidity currents were impractical to measure in action, especially due to their ability to damage sensors in their path, but direct monitoring since the mid 2010s has measured them in detail. In this Review, we summarise knowledge of turbidity currents gleaned from this direct monitoring. Monitoring identifies triggering mechanisms from dilute river-plumes, and shows how rapid sediment accumulation can precondition slope failure, but the final triggers can be delayed and subtle. Turbidity currents are consistently more frequent than predicted by past sequence stratigraphic models, including at sites >300 km from any coast. Faster (>~1.5 m s–1) flows are driven by a dense near-bed layer at their front, whereas slower flows are entirely dilute. This frontal layer sometimes erodes large (>2.5 km3) volumes of sediment, yet maintains a near-uniform speed, leading to a travelling wave model. Monitoring shows that flows sculpt canyons and channels through fast-moving knickpoints, and how deposits originate. Emerging technologies with reduced cost and risk can lead to widespread monitoring of turbidity currents, so their sediment and carbon fluxes can be compared with other major global transport processes

The Sixth Annual Translational Stem Cell Research Conference of the New York Stem Cell Foundation

The New York Stem Cell Foundation's "Sixth Annual Translational Stem Cell Research Conference" convened on October 11-12, 2011 at the Rockefeller University in New York City. Over 450 scientists, patient advocates, and stem cell research supporters from 14 countries registered for the conference. In addition to poster and platform presentations, the conference featured panels entitled "Road to the Clinic" and "The Future of Regenerative Medicine". © 2012 New York Academy of Sciences

Fluid venting in the eastern Aleutian subduction zone

Fluid venting has been observed along 800 km of the Alaska convergent margin. The fluid venting sites are located near the deformation front, are controlled by subsurface structures, and exhibit the characteristics of cold seeps seen in other convergent margins. The more important characteristics include (1) methane plumes in the lower water column with maxima above the seafloor which are traceable to the initial deformation ridges; (2) prolific colonies of vent biota aligned and distributed in patches controlled by fault scarps, over‐steepened folds or outcrops of bedding planes; (3) calcium carbonate and barite precipitates at the surface and subsurface of vents; and (4) carbon isotope evidence from tissue and skeletal hard parts of biota, as well as from carbonate precipitates, that vents expel either methane‐ or sulfide‐dominated fluids. A biogeochemical approach toward estimating fluid flow rates from individual vents based on oxygen flux measurements and vent fluid analysis indicates a mean value of 5.5±0.7 L m−2 d−1 for tectonics‐induced water flow [Wallmann et al., 1997b]. A geophysical estimate of dewatering from the same area [von Huene et al., 1997] based on sediment porosity reduction shows a fluid loss of 0.02 L m−2 d−1 for a 5.5 km wide converged segment near the deformation front. Our video‐guided surveys have documented vent biota across a minimum of 0.1% of the area of the convergent segment off Kodiak Island; hence an average rate of 0.006 L m−2 d−1 is estimated from the biogeochemical approach. The two estimates for tectonics‐induced water flow from the accretionary prism are in surprisingly good agreement

Generation of iPSC lines from archived non-cryoprotected biobanked dura mater

Background: Induced pluripotent stem cells (iPSCs) derived from patients with neurodegenerative disease generally lack neuropathological confirmation, the gold standard for disease classification and grading of severity. The use of tissue with a definitive neuropathological diagnosis would be an ideal source for iPSCs. The challenge to this approach is that the majority of biobanked brain tissue was not meant for growing live cells, and thus was not frozen in the presence of cryoprotectants such as DMSO. Results: We report the generation of iPSCs from frozen non-cryoprotected dural tissue stored at −80°C for up to 11 years. This autopsy cohort included subjects with Alzheimer’s disease and four other neurodegenerative diseases. Conclusions: Disease-specific iPSCs can be generated from readily available, archival biobanked tissue. This allows for rapid expansion of generating iPSCs with confirmed pathology as well as allowing access to rare patient variants that have been banked

iPSC-Derived Dopamine Neurons Reveal Differences between Monozygotic Twins Discordant for Parkinson’s Disease

SummaryParkinson’s disease (PD) has been attributed to a combination of genetic and nongenetic factors. We studied a set of monozygotic twins harboring the heterozygous glucocerebrosidase mutation (GBA N370S) but clinically discordant for PD. We applied induced pluripotent stem cell (iPSC) technology for PD disease modeling using the twins’ fibroblasts to evaluate and dissect the genetic and nongenetic contributions. Utilizing fluorescence-activated cell sorting, we obtained a homogenous population of “footprint-free” iPSC-derived midbrain dopaminergic (mDA) neurons. The mDA neurons from both twins had ∼50% GBA enzymatic activity, ∼3-fold elevated α-synuclein protein levels, and a reduced capacity to synthesize and release dopamine. Interestingly, the affected twin’s neurons showed an even lower dopamine level, increased monoamine oxidase B (MAO-B) expression, and impaired intrinsic network activity. Overexpression of wild-type GBA and treatment with MAO-B inhibitors normalized α-synuclein and dopamine levels, suggesting a combination therapy for the affected twin