Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease

Variational Methods for Biomolecular Modeling

Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrostatics and solvation that includes the curvature energy of the molecular surface, the formation of microdomains on lipid membrane due to the geometric and molecular mechanics at the lipid interface, and the mean curvature driven protein localization on membrane surfaces. By further implicitly representing the interface using a phase field function over the entire domain, one can simulate the dynamics of the interface and the corresponding energy variation by evolving the phase field function, achieving significant reduction of the number of degrees of freedom and computational complexity. Strategies for improving the efficiency of computational implementations and for extending applications to coarse-graining or multiscale molecular simulations are outlined.Comment: 36 page

Transcriptional Profiling of Plasmodium falciparum Parasites from Patients with Severe Malaria Identifies Distinct Low vs. High Parasitemic Clusters

Background: In the past decade, estimates of malaria infections have dropped from 500 million to 225 million per year; likewise, mortality rates have dropped from 3 million to 791,000 per year. However, approximately 90% of these deaths continue to occur in sub-Saharan Africa, and 85% involve children less than 5 years of age. Malaria mortality in children generally results from one or more of the following clinical syndromes: severe anemia, acidosis, and cerebral malaria. Although much is known about the clinical and pathological manifestations of CM, insights into the biology of the malaria parasite, specifically transcription during this manifestation of severe infection, are lacking. Methods and Findings: We collected peripheral blood from children meeting the clinical case definition of cerebral malaria from a cohort in Malawi, examined the patients for the presence or absence of malaria retinopathy, and performed whole genome transcriptional profiling for Plasmodium falciparum using a custom designed Affymetrix array. We identified two distinct physiological states that showed highly significant association with the level of parasitemia. We compared both groups of Malawi expression profiles with our previously acquired ex vivo expression profiles of parasites derived from infected patients with mild disease; a large collection of in vitro Plasmodium falciparum life cycle gene expression profiles; and an extensively annotated compendium of expression data from Saccharomyces cerevisiae. The high parasitemia patient group demonstrated a unique biology with elevated expression of Hrd1, a member of endoplasmic reticulum-associated protein degradation system. Conclusions: The presence of a unique high parasitemia state may be indicative of the parasite biology of the clinically recognized hyperparasitemic severe disease syndrome

Aortic dissection at the University hospital of the West Indies: A 20-year clinicopathological study of autopsy cases

<p>Abstract</p> <p>Background</p> <p>An autopsy study of aortic dissection (AD) at our institution was previously reported. In the approximately 20 years since then, however, many aspects of diagnosis and treatment of this disease have changed, with a fall in mortality reported in many centers around the world. An impression amongst our pathologists that, there might be an increase in the prevalence of AD in the autopsy service at our hospital, since that earlier report, led to this repeated study, in an attempt to validate that notion. We also sought to identify any changes in clinicopathological features between the two series or any occurring during this study period itself.</p> <p>Findings</p> <p>All cases of AD identified at autopsy, during the 20-year period since the conclusion of the last study, were collected and pertinent clinical and pathological data were analyzed and compared, both within the two decades of this study period and against the results of the last study.</p> <p>Fifty-six cases comprised this study group including 36 males and 20 females, with a mean age of 63.9 years. There were, more patients in the second decade (n = 33; 59%) compared with the first decade (n = 23; 41%). Hypertension as a risk factor was identified in 52 (93%) cases and rupture occurred in 49 (88%) cases. A clinical diagnosis of AD was considered prior to surgery or autopsy in 25 (45%) cases overall, more during the second decade. Surgery was attempted in 25% of all cases with an increase in the second decade compared with the first.</p> <p>Conclusions</p> <p>Compared with the earlier review, a variety of changes in the profile of patients with AD in the autopsy service has been noted, including a reversal in the female predominance seen previously. Other observations include an increase in cases where the correct clinical diagnosis was considered and in which surgical treatment was attempted, changes also evident when the second decade of the present study was compared with the earlier decade. Overall, there were many positive trends. However, areas that could still be improved include an increased index of suspicion for the diagnosis of AD and perhaps in the initiation of treatment, earlier, in those cases where the correct diagnosis was considered.</p

Metabolic Effects of Acute Thiamine Depletion Are Reversed by Rapamycin in Breast and Leukemia Cells

Thiamine-dependent enzymes (TDEs) control metabolic pathways that are frequently altered in cancer and therefore present cancer-relevant targets. We have previously shown that the recombinant enzyme thiaminase cleaves and depletes intracellular thiamine, has growth inhibitory activity against leukemia and breast cancer cell lines, and that its growth inhibitory effects were reversed in leukemia cell lines by rapamycin. Now, we first show further evidence of thiaminase therapeutic potential by demonstrating its activity against breast and leukemia xenografts, and against a primary leukemia xenograft. We therefore further explored the metabolic effects of thiaminase in combination with rapamycin in leukemia and breast cell lines. Thiaminase decreased oxygen consumption rate and increased extracellular acidification rate, consistent with the inhibitory effect of acute thiamine depletion on the activity of the TDEs pyruvate dehydrogenase and 2-oxoglutarate dehydrogenase complexes; these effects were reversed by rapamycin. Metabolomic studies demonstrated intracellular thiamine depletion and the presence of the thiazole cleavage product in thiaminase-treated cells, providing validation of the experimental procedures. Accumulation of ribose and ribulose in both cell lines support the thiaminase-mediated suppression of the TDE transketolase. Interestingly, thiaminase suppression of another TDE, branched chain amino ketoacid dehydrogenase (BCKDH), showed very different patterns in the two cell lines: in RS4 leukemia cells it led to an increase in BCKDH substrates, and in MCF-7 breast cancer cells it led to a decrease in BCKDH products. Immunoblot analyses showed corresponding differences in expression of BCKDH pathway enzymes, and partial protection of thiaminase growth inhibition by gabapentin indicated that BCKDH inhibition may be a mechanism of thiaminase-mediated toxicity. Surprisingly, most of thiaminase-mediated metabolomic effects were also reversed by rapamycin. Thus, these studies demonstrate that acute intracellular thiamine depletion by recombinant thiaminase results in metabolic changes in thiamine-dependent metabolism, and demonstrate a previously unrecognized role of mTOR signaling in the regulation of thiamine-dependent metabolism

Ecosystem Services in Conservation Planning: Targeted Benefits vs. Co-Benefits or Costs?

There is growing support for characterizing ecosystem services in order to link conservation and human well-being. However, few studies have explicitly included ecosystem services within systematic conservation planning, and those that have follow two fundamentally different approaches: ecosystem services as intrinsically-important targeted benefits vs. substitutable co-benefits. We present a first comparison of these two approaches in a case study in the Central Interior of British Columbia. We calculated and mapped economic values for carbon storage, timber production, and recreational angling using a geographical information system (GIS). These ‘marginal’ values represent the difference in service-provision between conservation and managed forestry as land uses. We compared two approaches to including ecosystem services in the site-selection software Marxan: as Targeted Benefits, and as Co-Benefits/Costs (in Marxan's cost function); we also compared these approaches with a Hybrid approach (carbon and angling as targeted benefits, timber as an opportunity cost). For this analysis, the Co-Benefit/Cost approach yielded a less costly reserve network than the Hybrid approach (1.6% cheaper). Including timber harvest as an opportunity cost in the cost function resulted in a reserve network that achieved targets equivalently, but at 15% lower total cost. We found counter-intuitive results for conservation: conservation-compatible services (carbon, angling) were positively correlated with each other and biodiversity, whereas the conservation-incompatible service (timber) was negatively correlated with all other networks. Our findings suggest that including ecosystem services within a conservation plan may be most cost-effective when they are represented as substitutable co-benefits/costs, rather than as targeted benefits. By explicitly valuing the costs and benefits associated with services, we may be able to achieve meaningful biodiversity conservation at lower cost and with greater co-benefits

Exploiting protein flexibility to predict the location of allosteric sites

Background: Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. Results: By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. Conclusions: We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Selection of Reserves for Woodland Caribou Using an Optimization Approach

Habitat protection has been identified as an important strategy for the conservation of woodland caribou (Rangifer tarandus). However, because of the economic opportunity costs associated with protection it is unlikely that all caribou ranges can be protected in their entirety. We used an optimization approach to identify reserve designs for caribou in Alberta, Canada, across a range of potential protection targets. Our designs minimized costs as well as three demographic risk factors: current industrial footprint, presence of white-tailed deer (Odocoileus virginianus), and climate change. We found that, using optimization, 60% of current caribou range can be protected (including 17% in existing parks) while maintaining access to over 98% of the value of resources on public lands. The trade-off between minimizing cost and minimizing demographic risk factors was minimal because the spatial distributions of cost and risk were similar. The prospects for protection are much reduced if protection is directed towards the herds that are most at risk of near-term extirpation

Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction