Stem cell-based therapeutic applications in retinal degenerative diseases

Retinal degenerative diseases that target photoreceptors or the adjacent retinal pigment epithelium (RPE) affect millions of people worldwide. Retinal degeneration (RD) is found in many different forms of retinal diseases including retinitis pigmentosa (RP), age-related macular degeneration (AMD), diabetic retinopathy, cataracts, and glaucoma. Effective treatment for retinal degeneration has been widely investigated. Gene-replacement therapy has been shown to improve visual function in inherited retinal disease. However, this treatment was less effective with advanced disease. Stem cell-based therapy is being pursued as a potential alternative approach in the treatment of retinal degenerative diseases. In this review, we will focus on stem cell-based therapies in the pipeline and summarize progress in treatment of retinal degenerative disease.

Embedding Languages Without Breaking Tools

Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Time-lapse microscopy and classification of 2D human mesenchymal stem cells based on cell shape picks up myogenic from osteogenic and adipogenic differentiation

Current methods to characterize mesenchymal stem cells (MSCs) are limited to CD marker expression, plastic adherence and their ability to differentiate into adipogenic, osteogenic and chondrogenic precursors. It seems evident that stem cells undergoing differentiation should differ in many aspects, such as morphology and possibly also behaviour; however, such a correlation has not yet been exploited for fate prediction of MSCs. Primary human MSCs from bone marrow were expanded and pelleted to form high-density cultures and were then randomly divided into four groups to differentiate into adipogenic, osteogenic chondrogenic and myogenic progenitor cells. The cells were expanded as heterogeneous and tracked with time-lapse microscopy to record cell shape, using phase-contrast microscopy. The cells were segmented using a custom-made image-processing pipeline. Seven morphological features were extracted for each of the segmented cells. Statistical analysis was performed on the seven-dimensional feature vectors, using a tree-like classification method. Differentiation of cells was monitored with key marker genes and histology. Cells in differentiation media were expressing the key genes for each of the three pathways after 21 days, i.e. adipogenic, osteogenic and chondrogenic, which was also confirmed by histological staining. Time-lapse microscopy data were obtained and contained new evidence that two cell shape features, eccentricity and filopodia (= 'fingers') are highly informative to classify myogenic differentiation from all others. However, no robust classifiers could be identified for the other cell differentiation paths. The results suggest that non-invasive automated time-lapse microscopy could potentially be used to predict the stem cell fate of hMSCs for clinical application, based on morphology for earlier time-points. The classification is challenged by cell density, proliferation and possible unknown donor-specific factors, which affect the performance of morphology-based approaches. Copyright © 2012 John Wiley & Sons, Ltd.

Intervertebral disc regeneration or repair with biomaterials and stem cell therapy--feasible or fiction?

The "gold standard" for treatment of intervertebral disc herniations and degenerated discs is still spinal fusion, corresponding to the saying "no disc - no pain". Mechanical prostheses, which are currently implanted, do only have medium outcome success and have relatively high re-operation rates. Here, we discuss some of the biological intervertebral disc replacement approaches, which can be subdivided into at least two classes in accordance to the two different tissue types, the nucleus pulposus (NP) and the annulus fibrosus (AF). On the side of NP replacement hydrogels have been extensively tested in vitro and in vivo. However, these gels are usually a trade-off between cell biocompatibility and load-bearing capacity, hydrogels which fulfill both are still lacking. On the side of AF repair much less is known and the question of the anchoring of implants is still to be addressed. New hope for cell therapy comes from developmental biology investigations on the existence of intervertebral disc progenitor cells, which would be an ideal cell source for cell therapy. Also notochordal cells (remnants of the embryonic notochord) have been recently pushed back into focus since these cells have regenerative potential and can activate disc cells. Growth factor treatment and molecular therapies could be less problematic. The biological solutions for NP and AF replacement are still more fiction than fact. However, tissue engineering just scratched the tip of the iceberg, more satisfying solutions are yet to be added to the biomedical pipeline.

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

Gene expression in cortex and hippocampus during acute pneumococcal meningitis

BACKGROUND: Pneumococcal meningitis is associated with high mortality (approximately 30%) and morbidity. Up to 50% of survivors are affected by neurological sequelae due to a wide spectrum of brain injury mainly affecting the cortex and hippocampus. Despite this significant disease burden, the genetic program that regulates the host response leading to brain damage as a consequence of bacterial meningitis is largely unknown.We used an infant rat model of pneumococcal meningitis to assess gene expression profiles in cortex and hippocampus at 22 and 44 hours after infection and in controls at 22 h after mock-infection with saline. To analyze the biological significance of the data generated by Affymetrix DNA microarrays, a bioinformatics pipeline was used combining (i) a literature-profiling algorithm to cluster genes based on the vocabulary of abstracts indexed in MEDLINE (NCBI) and (ii) the self-organizing map (SOM), a clustering technique based on covariance in gene expression kinetics. RESULTS: Among 598 genes differentially regulated (change factor > or = 1.5; p < or = 0.05), 77% were automatically assigned to one of 11 functional groups with 94% accuracy. SOM disclosed six patterns of expression kinetics. Genes associated with growth control/neuroplasticity, signal transduction, cell death/survival, cytoskeleton, and immunity were generally upregulated. In contrast, genes related to neurotransmission and lipid metabolism were transiently downregulated on the whole. The majority of the genes associated with ionic homeostasis, neurotransmission, signal transduction and lipid metabolism were differentially regulated specifically in the hippocampus. Of the cell death/survival genes found to be continuously upregulated only in hippocampus, the majority are pro-apoptotic, while those continuously upregulated only in cortex are anti-apoptotic. CONCLUSION: Temporal and spatial analysis of gene expression in experimental pneumococcal meningitis identified potential targets for therapy.

The Mycoplasma conjunctivae genome sequencing, annotation and analysis

BACKGROUND: The mollicute Mycoplasma conjunctivae is the etiological agent leading to infectious keratoconjunctivitis (IKC) in domestic sheep and wild caprinae. Although this pathogen is relatively benign for domestic animals treated by antibiotics, it can lead wild animals to blindness and death. This is a major cause of death in the protected species in the Alps (e.g., Capra ibex, Rupicapra rupicapra). METHODS: The genome was sequenced using a combined technique of GS-FLX (454) and Sanger sequencing, and annotated by an automatic pipeline that we designed using several tools interconnected via PERL scripts. The resulting annotations are stored in a MySQL database. RESULTS: The annotated sequence is deposited in the EMBL database (FM864216) and uploaded into the mollicutes database MolliGen http://cbi.labri.fr/outils/molligen/ allowing for comparative genomics. CONCLUSION: We show that our automatic pipeline allows for annotating a complete mycoplasma genome and present several examples of analysis in search for biological targets (e.g., pathogenic proteins).

[Mastitis management in Swiss dairy farms with udder health problems]

The objective of this study was to describe the udder health management in Swiss dairy herds with udder health problems. One hundred dairy herds with a yield-corrected somatic cell count of 200'000 to 300'000 cells/ml during 2010 were selected. Data concerning farm structure, housing system, milking technique, milking procedures, dry-cow and mastitis management were collected during farm visits between September and December 2011. In addition, quarter milk samples were collected for bacteriological culturing from cows with a composite somatic cell count ≥ 150'000 cells/ml. The highest quarter level prevalence was 12.3 % for C. bovis. Eighty-two percent of the pipeline milking machines in tie-stalls and 88 % of the milking parlours fulfilled the criteria for the vacuum drop, and only 74 % of the pipeline milking machines met the criteria of the 10-l-water test. Eighty-five percent of the farms changed their milk liners too late. The correct order of teat preparation before cluster attachment was carried out by 37 % of the farmers only. With these results, Swiss dairy farmers and herd health veterinarians can be directed to common mistakes in mastitis management. The data will be used for future information campaigns to improve udder health in Swiss dairy farms.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Scaleable Code Clone Detection

Code clone detection helps connect developers across projects, if we do it on a large scale. The cornerstones that allow clone detection to work on a large scale are: (1) bad hashing (2) lightweight parsing using regular expressions and (3) MapReduce pipelines. Bad hashing means to determine whether or not two artifacts are similar by checking whether their hashes are identical. We show a bad hashing scheme that works well on source code. Lightweight parsing using regular expressions is our technique of obtaining entire parse trees from regular expressions, robustly and efficiently. We detail the algorithm and implementation of one such regular expression engine. MapReduce pipelines are a way of expressing a computation such that it can automatically and simply be parallelized. We detail the design and implementation of one such MapReduce pipeline that is efficient and debuggable. We show a clone detector that combines these cornerstones to detect code clones across all projects, across all versions of each project.

Genome profiling of sterol synthesis shows convergent evolution in parasites and guides chemotherapeutic attack

Sterols are an essential class of lipids in eukaryotes, where they serve as structural components of membranes and play important roles as signaling molecules. Sterols are also of high pharmacological significance: cholesterol-lowering drugs are blockbusters in human health, and inhibitors of ergosterol biosynthesis are widely used as antifungals. Inhibitors of ergosterol synthesis are also being developed for Chagas's disease, caused by Trypanosoma cruzi. Here we develop an in silico pipeline to globally evaluate sterol metabolism and perform comparative genomics. We generate a library of hidden Markov model-based profiles for 42 sterol biosynthetic enzymes, which allows expressing the genomic makeup of a given species as a numerical vector. Hierarchical clustering of these vectors functionally groups eukaryote proteomes and reveals convergent evolution, in particular metabolic reduction in obligate endoparasites. We experimentally explore sterol metabolism by testing a set of sterol biosynthesis inhibitors against trypanosomatids, Plasmodium falciparum, Giardia, and mammalian cells, and by quantifying the expression levels of sterol biosynthetic genes during the different life stages of T. cruzi and Trypanosoma brucei. The phenotypic data correlate with genomic makeup for simvastatin, which showed activity against trypanosomatids. Other findings, such as the activity of terbinafine against Giardia, are not in agreement with the genotypic profile.

Selective resistance to the PARP inhibitor olaparib in a mouse model for BRCA1-deficient metaplastic breast cancer

Metaplastic breast carcinoma (MBC) is a rare histological breast cancer subtype characterized by mesenchymal elements and poor clinical outcome. A large fraction of MBCs harbor defects in breast cancer 1 (BRCA1). As BRCA1 deficiency sensitizes tumors to DNA cross-linking agents and poly(ADP-ribose) polymerase (PARP) inhibitors, we sought to investigate the response of BRCA1-deficient MBCs to the PARP inhibitor olaparib. To this end, we established a genetically engineered mouse model (GEMM) for BRCA1-deficient MBC by introducing the MET proto-oncogene into a BRCA1-associated breast cancer model, using our novel female GEMM ES cell (ESC) pipeline. In contrast to carcinomas, BRCA1-deficient mouse carcinosarcomas resembling MBC show intrinsic resistance to olaparib caused by increased P-glycoprotein (Pgp) drug efflux transporter expression. Indeed, resistance could be circumvented by using another PARP inhibitor, AZD2461, which is a poor Pgp substrate. These preclinical findings suggest that patients with BRCA1-associated MBC may show poor response to olaparib and illustrate the value of GEMM-ESC models of human cancer for evaluation of novel therapeutics.

Two-Dimensional Crystallisation of Membrane Proteins and Structural Assessment

Two-dimensional (2D) crystallisation of Membrane proteins reconstitutes them into their native environment, the lipid bilayer. Electron crystallography allows the structural analysis of these regular protein–lipid arrays up to atomic resolution. The crystal quality depends on the protein purity, ist stability and on the crystallisation conditions. The basics of 2D crystallisation and different recent advances are reviewed and electron crystallography approaches summarised. Progress in 2D crystallisation, sample preparation, image detectors and automation of the data acquisition and processing pipeline makes 2D electron crystallography particularly attractive for the structural analysis of membrane proteins that are too small for single-particle analyses and too unstable to form three-dimensional (3D) crystals.