Poloxomer 188 Has a Deleterious Effect on Dystrophic Skeletal Muscle Function

Duchenne muscular dystrophy (DMD) is an X-linked, fatal muscle wasting disease for which there is currently no cure and limited palliative treatments. Poloxomer 188 (P188) is a tri-block copolymer that has been proposed as a potential treatment for cardiomyopathy in DMD patients. Despite the reported beneficial effects of P188 on dystrophic cardiac muscle function, the effects of P188 on dystrophic skeletal muscle function are relatively unknown. Mdx mice were injected intraperitoneally with 460 mg/kg or 30 mg/kg P188 dissolved in saline, or saline alone (control). The effect of single-dose and 2-week daily treatment was assessed using a muscle function test on the Tibialis Anterior (TA) muscle in situ in anaesthetised mice. The test comprises a warm up, measurement of the force-frequency relationship and a series of eccentric contractions with a 10% stretch that have previously been shown to cause a drop in maximum force in mdx mice. After 2 weeks of P188 treatment at either 30 or 460 mg/kg/day the drop in maximum force produced following eccentric contractions was significantly greater than that seen in saline treated control mice (P = 0.0001). Two week P188 treatment at either dose did not significantly change the force-frequency relationship or maximum isometric specific force produced by the TA muscle. In conclusion P188 treatment increases susceptibility to contraction-induced injury following eccentric contractions in dystrophic skeletal muscle and hence its suitability as a potential therapeutic for DMD should be reconsidered

Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

<p>Abstract</p> <p>Background</p> <p>An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context.</p> <p>Methods</p> <p>Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System.</p> <p>Results</p> <p>Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics.</p> <p>Conclusions</p> <p>The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.</p

Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia.

N6-methyladenosine (m6A) is an abundant internal RNA modification1,2 that is catalysed predominantly by the METTL3-METTL14 methyltransferase complex3,4. The m6A methyltransferase METTL3 has been linked to the initiation and maintenance of acute myeloid leukaemia (AML), but the potential of therapeutic applications targeting this enzyme remains unknown5-7. Here we present the identification and characterization of STM2457, a highly potent and selective first-in-class catalytic inhibitor of METTL3, and a crystal structure of STM2457 in complex with METTL3-METTL14. Treatment of tumours with STM2457 leads to reduced AML growth and an increase in differentiation and apoptosis. These cellular effects are accompanied by selective reduction of m6A levels on known leukaemogenic mRNAs and a decrease in their expression consistent with a translational defect. We demonstrate that pharmacological inhibition of METTL3 in vivo leads to impaired engraftment and prolonged survival in various mouse models of AML, specifically targeting key stem cell subpopulations of AML. Collectively, these results reveal the inhibition of METTL3 as a potential therapeutic strategy against AML, and provide proof of concept that the targeting of RNA-modifying enzymes represents a promising avenue for anticancer therapy

Automatic structure classification of small proteins using random forest

<p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p

A Novel Protein LZTFL1 Regulates Ciliary Trafficking of the BBSome and Smoothened

Many signaling proteins including G protein-coupled receptors localize to primary cilia, regulating cellular processes including differentiation, proliferation, organogenesis, and tumorigenesis. Bardet-Biedl Syndrome (BBS) proteins are involved in maintaining ciliary function by mediating protein trafficking to the cilia. However, the mechanisms governing ciliary trafficking by BBS proteins are not well understood. Here, we show that a novel protein, Leucine-zipper transcription factor-like 1 (LZTFL1), interacts with a BBS protein complex known as the BBSome and regulates ciliary trafficking of this complex. We also show that all BBSome subunits and BBS3 (also known as ARL6) are required for BBSome ciliary entry and that reduction of LZTFL1 restores BBSome trafficking to cilia in BBS3 and BBS5 depleted cells. Finally, we found that BBS proteins and LZTFL1 regulate ciliary trafficking of hedgehog signal transducer, Smoothened. Our findings suggest that LZTFL1 is an important regulator of BBSome ciliary trafficking and hedgehog signaling

Combinatorial Binding in Human and Mouse Embryonic Stem Cells Identifies Conserved Enhancers Active in Early Embryonic Development

Transcription factors are proteins that regulate gene expression by binding to cis-regulatory sequences such as promoters and enhancers. In embryonic stem (ES) cells, binding of the transcription factors OCT4, SOX2 and NANOG is essential to maintain the capacity of the cells to differentiate into any cell type of the developing embryo. It is known that transcription factors interact to regulate gene expression. In this study we show that combinatorial binding is strongly associated with co-localization of the transcriptional co-activator Mediator, H3K27ac and increased expression of nearby genes in embryonic stem cells. We observe that the same loci bound by Oct4, Nanog and Sox2 in ES cells frequently drive expression in early embryonic development. Comparison of mouse and human ES cells shows that less than 5% of individual binding events for OCT4, SOX2 and NANOG are shared between species. In contrast, about 15% of combinatorial binding events and even between 53% and 63% of combinatorial binding events at enhancers active in early development are conserved. Our analysis suggests that the combination of OCT4, SOX2 and NANOG binding is critical for transcription in ES cells and likely plays an important role for embryogenesis by binding at conserved early developmental enhancers. Our data suggests that the fast evolutionary rewiring of regulatory networks mainly affects individual binding events, whereas “gene regulatory hotspots” which are bound by multiple factors and active in multiple tissues throughout early development are under stronger evolutionary constraints

Global assessment of genomic variation in cattle by genome resequencing and high-throughput genotyping

<p>Abstract</p> <p>Background</p> <p>Integration of genomic variation with phenotypic information is an effective approach for uncovering genotype-phenotype associations. This requires an accurate identification of the different types of variation in individual genomes.</p> <p>Results</p> <p>We report the integration of the whole genome sequence of a single Holstein Friesian bull with data from single nucleotide polymorphism (SNP) and comparative genomic hybridization (CGH) array technologies to determine a comprehensive spectrum of genomic variation. The performance of resequencing SNP detection was assessed by combining SNPs that were identified to be either in identity by descent (IBD) or in copy number variation (CNV) with results from SNP array genotyping. Coding insertions and deletions (indels) were found to be enriched for size in multiples of 3 and were located near the N- and C-termini of proteins. For larger indels, a combination of split-read and read-pair approaches proved to be complementary in finding different signatures. CNVs were identified on the basis of the depth of sequenced reads, and by using SNP and CGH arrays.</p> <p>Conclusions</p> <p>Our results provide high resolution mapping of diverse classes of genomic variation in an individual bovine genome and demonstrate that structural variation surpasses sequence variation as the main component of genomic variability. Better accuracy of SNP detection was achieved with little loss of sensitivity when algorithms that implemented mapping quality were used. IBD regions were found to be instrumental for calculating resequencing SNP accuracy, while SNP detection within CNVs tended to be less reliable. CNV discovery was affected dramatically by platform resolution and coverage biases. The combined data for this study showed that at a moderate level of sequencing coverage, an ensemble of platforms and tools can be applied together to maximize the accurate detection of sequence and structural variants.</p

Response of Sunflower (Helianthus annuus L.) Leaf Surface Defenses to Exogenous Methyl Jasmonate

Helianthus annuus, the common sunflower, produces a complex array of secondary compounds that are secreted into glandular trichomes, specialized structures found on leaf surfaces and anther appendages of flowers. The primary components of these trichome secretions are sesquiterpene lactones (STL), a diverse class of compounds produced abundantly by the plant family Compositae and believed to contribute to plant defense against herbivory. We treated wild and cultivated H. annuus accessions with exogenous methyl jasmonate, a plant hormone that mediates plant defense against insect herbivores and certain classes of fungal pathogens. The wild sunflower produced a higher density of glandular trichomes on its leaves than the cultivar. Comparison of the profiles of glandular trichome extracts obtained by liquid chromatography–mass spectroscopy (LC-MS) showed that wild and cultivated H. annuus were qualitatively similar in surface chemistry, although differing in the relative size and proportion of various compounds detected. Despite observing consistent transcriptional responses to methyl jasmonate treatment, we detected no significant effect on glandular trichome density or LC-MS profile in cultivated or wild sunflower, with wild sunflower exhibiting a declining trend in overall STL production and foliar glandular trichome density of jasmonate-treated plants. These results suggest that glandular trichomes and associated compounds may act as constitutive defenses or require greater levels of stimulus for induction than the observed transcriptional responses to exogenous jasmonate. Reduced defense investment in domesticated lines is consistent with predicted tradeoffs caused by selection for increased yield; future research will focus on the development of genetic resources to explicitly test the ecological roles of glandular trichomes and associated effects on plant growth and fitness

Accurate molecular classification of cancer using simple rules

<p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p

Tandemly repeated DNA families in the mouse genome

<p>Abstract</p> <p>Background</p> <p>Functional and morphological studies of tandem DNA repeats, that combine high portion of most genomes, are mostly limited due to the incomplete characterization of these genome elements. We report here a genome wide analysis of the large tandem repeats (TR) found in the mouse genome assemblies.</p> <p>Results</p> <p>Using a bioinformatics approach, we identified large TR with array size more than 3 kb in two mouse whole genome shotgun (WGS) assemblies. Large TR were classified based on sequence similarity, chromosome position, monomer length, array variability, and GC content; we identified four superfamilies, eight families, and 62 subfamilies - including 60 not previously described. 1) The superfamily of centromeric minor satellite is only found in the unassembled part of the reference genome. 2) The pericentromeric major satellite is the most abundant superfamily and reveals high order repeat structure. 3) Transposable elements related superfamily contains two families. 4) The superfamily of heterogeneous tandem repeats includes four families. One family is found only in the WGS, while two families represent tandem repeats with either single or multi locus location. Despite multi locus location, TRPC-21A-MM is placed into a separated family due to its abundance, strictly pericentromeric location, and resemblance to big human satellites.</p> <p>To confirm our data, we next performed <it>in situ </it>hybridization with three repeats from distinct families. TRPC-21A-MM probe hybridized to chromosomes 3 and 17, multi locus TR-22A-MM probe hybridized to ten chromosomes, and single locus TR-54B-MM probe hybridized with the long loops that emerge from chromosome ends. In addition to <it>in silico </it>predicted several extra-chromosomes were positive for TR by <it>in situ </it>analysis, potentially indicating inaccurate genome assembly of the heterochromatic genome regions.</p> <p>Conclusions</p> <p>Chromosome-specific TR had been predicted for mouse but no reliable cytogenetic probes were available before. We report new analysis that identified <it>in silico </it>and confirmed <it>in situ </it>3/17 chromosome-specific probe TRPC-21-MM. Thus, the new classification had proven to be useful tool for continuation of genome study, while annotated TR can be the valuable source of cytogenetic probes for chromosome recognition.</p