Indigenous Student Success in Public Schools: A “We” Approach for Educators

What does Indigenous student success look like in public school boards? Seven urban Indigenous educators’ interview responses to this question were interpreted and reported by the lead author, a teacher and researcher of English, Irish, and Scottish heritage—a Settler Canadian. The “Connected Beads Model” is the result of these educator-to-educator interviews. It shows how Indigenous students’ success can be promoted when Settler and Indigenous educators take a “We” stance alongside students, families, and communities through honoring story, relationship, and holism in school. The concepts embedded in the model and its practical applications are explored through participants’ quotations and considered alongside related literature on Indigenous education. À quoi ressemble la réussite des élèves autochtones dans les conseils scolaires publics ? Les réponses en entrevues des sept éducateurs autochtones en milieu urbain ont été interprétées et dévoilées par l’auteur principal, un enseignant et chercheur d’origine anglaise, irlandaise et écossaise—un Canadien « de souche ». De ces entrevues entre enseignants découle le modèle dit des « perles liées » qui démontre l’effet positif sur la réussite des élèves autochtones qui se crée lorsque les éducateurs « canadiens de souche » et les éducateurs autochtones adoptent une attitude de solidarité avec les élèves, les familles et les communautés et qu’ils rendent hommage aux récits, aux relations et à l’holisme à l’école. Les concepts incorporés au modèle et les applications pratiques de celui-ci sont explorés par le biais des commentaires des participants et dans le contexte de la littérature connexe portant sur l’éducation autochtone. 

International guidelines for the management and treatment of Morquio A syndrome.

Morquio A syndrome (mucopolysaccharidosis IVA) is a lysosomal storage disorder associated with skeletal and joint abnormalities and significant non-skeletal manifestations including respiratory disease, spinal cord compression, cardiac disease, impaired vision, hearing loss, and dental problems. The clinical presentation, onset, severity and progression rate of clinical manifestations of Morquio A syndrome vary widely between patients. Because of the heterogeneous and progressive nature of the disease, the management of patients with Morquio A syndrome is challenging and requires a multidisciplinary approach, involving an array of specialists. The current paper presents international guidelines for the evaluation, treatment and symptom-based management of Morquio A syndrome. These guidelines were developed during two expert meetings by an international panel of specialists in pediatrics, genetics, orthopedics, pulmonology, cardiology, and anesthesia with extensive experience in managing Morquio A syndrome

Inferring condition-specific transcription factor function from DNA binding and gene expression data

Numerous genomic and proteomic datasets are permitting the elucidation of transcriptional regulatory networks in the yeast Saccharomyces cerevisiae. However, predicting the condition dependence of regulatory network interactions has been challenging, because most protein–DNA interactions identified in vivo are from assays performed in one or a few cellular states. Here, we present a novel method to predict the condition-specific functions of S. cerevisiae transcription factors (TFs) by integrating 1327 microarray gene expression data sets and either comprehensive TF binding site data from protein binding microarrays (PBMs) or in silico motif data. Importantly, our method does not impose arbitrary thresholds for calling target regions ‘bound' or genes ‘differentially expressed', but rather allows all the information derived from a TF binding or gene expression experiment to be considered. We show that this method can identify environmental, physical, and genetic interactions, as well as distinct sets of genes that might be activated or repressed by a single TF under particular conditions. This approach can be used to suggest conditions for directed in vivo experimentation and to predict TF function

Mining, visualizing and comparing multidimensional biomolecular data using the Genomics Data Miner (GMine) web-server

Genomics Data Miner (GMine) is a user-friendly online software that allows non-experts to mine, cluster and compare multidimensional biomolecular datasets. Various powerful visualization techniques are provided, generating high quality figures that can be directly incorporated into scientific publications. Robust and comprehensive analyses are provided via a broad range of data-mining techniques, including univariate and multivariate statistical analysis, supervised learning, correlation networks, clustering and multivariable regression. The software has a focus on multivariate techniques, which can attribute variance in the measurements to multiple explanatory variables and confounders. Various normalization methods are provided. Extensive help pages and a tutorial are available via a wiki server. Using GMine we reanalyzed proteome microarray data of host antibody response against Plasmodium falciparum. Our results support the hypothesis that immunity to malaria is a higher-order phenomenon related to a pattern of responses and not attributable to any single antigen. We also analyzed gene expression across resting and activated T cells, identifying many immune-related genes with differential expression. This highlights both the plasticity of T cells and the operation of a hardwired activation program. These application examples demonstrate that GMine facilitates an accurate and in-depth analysis of complex molecular datasets, including genomics, transcriptomics and proteomics data

Notch and MAML-1 Complexation Do Not Detectably Alter the DNA Binding Specificity of the Transcription Factor CSL

Canonical Notch signaling is initiated when ligand binding induces proteolytic release of the intracellular part of Notch (ICN) from the cell membrane. ICN then travels into the nucleus where it drives the assembly of a transcriptional activation complex containing the DNA-binding transcription factor CSL, ICN, and a specialized co-activator of the Mastermind family. A consensus DNA binding site motif for the CSL protein was previously defined using selection-based methods, but whether subsequent association of Notch and Mastermind-like proteins affects the DNA binding preferences of CSL has not previously been examined.Here, we utilized protein-binding microarrays (PBMs) to compare the binding site preferences of isolated CSL with the preferred binding sites of CSL when bound to the CSL-binding domains of all four different human Notch receptors. Measurements were taken both in the absence and in the presence of Mastermind-like-1 (MAML1). Our data show no detectable difference in the DNA binding site preferences of CSL before and after loading of Notch and MAML1 proteins.These findings support the conclusion that accrual of Notch and MAML1 promote transcriptional activation without dramatically altering the preferred sites of DNA binding, and illustrate the potential of PBMs to analyze the binding site preferences of multiprotein-DNA complexes

Using a structural and logics systems approach to infer bHLH–DNA binding specificity determinants

Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein–DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the in vitro DNA binding specificities of 19 Caenorhabditis elegans basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH–DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans.National Institute of General Medical Sciences (U.S.) (DK068429)National Institute of General Medical Sciences (U.S.) (HG003985)European Union (PROSPECTS HEALTH-F4-2008-201648

Using protein design algorithms to understand the molecular basis of disease caused by protein–DNA interactions: the Pax6 example

Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein’s function is not trivial. Protein design tools are commonly used to explain mutations that affect protein stability, or protein–protein interaction, but not for mutations that could affect protein–DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several diseases such as aniridia. The validity of FoldX to deal with protein–DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos. We conclude that 88% of the Pax6 mutations can be linked to changes in intrinsic stability (77%) and/or to its capabilities to bind DNA (30%). Our study emphasizes the importance of structure-based analysis to understand the molecular basis of diseases and shows that protein–DNA interactions can be analyzed to the same level of accuracy as protein stability, or protein–protein interactions