Stem cell-derived astrocytes:are they physiologically credible?

Astrocytes are now increasingly acknowledged as having fundamental and sophisticated roles in brain function and dysfunction. Unravelling the complex mechanisms that underlie human brain astrocyte-neuron interactions is therefore an essential step on the way to understanding how the brain operates. Insights into astrocyte function to date, have almost exclusively been derived from studies conducted using murine or rodent models. Whilst these have led to significant discoveries, preliminary work with human astrocytes has revealed a hitherto unknown range of astrocyte types with potentially greater functional complexity and increased neuronal interaction with respect to animal astrocytes. It is becoming apparent, therefore, that many important functions of astrocytes will only be discovered by direct physiological interrogation of human astrocytes. Recent advancements in the field of stem cell biology have provided a source of human based models. These will provide a platform to facilitate our understanding of normal astrocyte functions as well as their role in CNS pathology. A number of recent studies have demonstrated that stem cell derived astrocytes exhibit a range of properties, suggesting that they may be functionally equivalent to their in vivo counterparts. Further validation against in vivo models will ultimately confirm the future utility of these stem-cell based approaches in fulfilling the need for human- based cellular models for basic and clinical research. In this review we discuss the roles of astrocytes in the brain and highlight the extent to which human stem cell derived astrocytes have demonstrated functional activities that are equivalent to that observed in vivo

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data

Ostm1 from Mouse to Human: Insights into Osteoclast Maturation

The maintenance of bone mass is a dynamic process that requires a strict balance between bone formation and resorption. Bone formation is controlled by osteoblasts, while osteoclasts are responsible for resorption of the bone matrix. The opposite functions of these cell types have to be tightly regulated not only during normal bone development, but also during adult life, to maintain serum calcium homeostasis and sustain bone integrity to prevent bone fractures. Disruption of the control of bone synthesis or resorption can lead to an over accumulation of bone tissue in osteopetrosis or conversely to a net depletion of the bone mass in osteoporosis. Moreover, high levels of bone resorption with focal bone formation can cause Paget&rsquo;s disease. Here, we summarize the steps toward isolation and characterization of the osteopetrosis associated trans-membrane protein 1 (Ostm1) gene and protein, essential for proper osteoclast maturation, and responsible when mutated for the most severe form of osteopetrosis in mice and humans

Unzipping Natural Products: Improved Natural Product Structure Predictions by Ensemble Modeling and Fingerprint Matching

This pre-print explores ensemble modeling of natural product targets to match chemical structures to precursors found in large open-source gene cluster repository antiSMASH. Commentary on method, effectiveness, and limitations are enclosed. All structures are public domain molecules and have been reviewed for release

Valhidepsin Lipopeptides from Chromobacterium vaccinii: Structures, Biosynthesis, and Coregulation with FR900359 Production.

Microbial secondary metabolites continue to provide a valuable source of both chemical matter and inspiration for drug discovery in a broad range of therapeutic areas. Beyond this, the corresponding microorganisms represent a sustainable modality for biotechnological production of structurally complex molecules at the quantities required for drug development or even commercial manufacturing. Chromobacterium vaccinii, which has recently been reported as a producer of the pharmacologically highly important Gq inhibitor FR900359 (FR), represents such an example. The characterization of an orphan biosynthetic gene cluster (BGC) located directly downstream of the frs BCG led to the discovery of eight new lipopeptides, valhidepsins A-H (1-8), produced by C. vaccinii. Their chemical structures were elucidated through analysis of 1D and 2D NMR data and high-resolution MS/MS fragmentation methods. The valhidepsins did not display significant antibiotic nor cytotoxic activities but showed surfactant properties. The cluster-compound correlation was demonstrated by generation of a knockout mutant, which abolished production of valhidepsins. This knockout mutant yielded a significantly increased isolated yield of FR

Unzipping Natural Products: Improved Natural Product Structure Predictions by Ensemble Modeling and Fingerprint Matching

This pre-print explores ensemble modeling of natural product targets to match chemical structures to precursors found in large open-source gene cluster repository antiSMASH. Commentary on method, effectiveness, and limitations are enclosed. All structures are public domain molecules and have been reviewed for release