Stromal control of oncogenic traits expressed in response to the overexpression of GLI2, a pleiotropic oncogene.

Hedgehog signaling is often activated in tumors, yet it remains unclear how GLI2, a transcription factor activated by this pathway, acts as an oncogene. We show that GLI2 is a pleiotropic oncogene. The overexpression induces genomic instability and blocks differentiation, likely mediated in part by enhanced expression of the stem cell gene SOX2. GLI2 also induces transforming growth factor (TGF)B1-dependent transdifferentiation of foreskin and tongue, but not gingival fibroblasts into myofibroblasts, creating an environment permissive for invasion by keratinocytes, which are in various stages of differentiation having downregulated GLI2. Thus, upregulated GLI2 expression is sufficient to induce a number of the acquired characteristics of tumor cells; however, the stroma, in a tissue-specific manner, determines whether certain GLI2 oncogenic traits are expressed

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Parenting a child with phenylketonuria or galactosemia: implications for health-related quality of life

Parents of children with chronic disorders have an impaired health-related quality of life (HRQoL) compared to parents of healthy children. Remarkably, parents of children with a metabolic disorder reported an even lower HRQoL than parents of children with other chronic disorders. Possibly, the uncertainty about the course of the disease and the limited life expectancy in many metabolic disorders are important factors in the low parental HRQoL. Therefore, we performed a cross-sectional study in parents of children with phenylketonuria (PKU, OMIM #261600) and galactosemia (OMIM #230400), metabolic disorders not affecting life expectancy, in order to investigate their HRQoL compared to parents of healthy children and to parents of children with other metabolic disorders. A total of 185 parents of children with PKU and galactosemia aged 1-19 years completed two questionnaires. Parents of children with PKU or galactosemia reported a HRQoL comparable to parents of healthy children and a significantly better HRQoL than parents of children with other metabolic disorders. Important predictors for parental mental HRQoL were the psychosocial factors emotional support and loss of friendship. As parental mental functioning influences the health, development and adjustment of their children, it is important that treating physicians also pay attention to the wellbeing of the parents. The insight that emotional support and loss of friendship influence the HRQoL of the parents enables treating physicians to provide better support for these parents

Structural basis for CRISPR RNA-guided DNA recognition by Cascade

The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

A large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression

Background Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique "important" gene. Methods We carried out a systematic analysis of more than 140,000 observations of CNAs in cancers and searched by enrichments in gene functional modules associated to high frequencies of loss or gains. Results The analysis of CNAs in cancers clearly demonstrates the existence of a significant pattern of loss of gene modules functionally related to cancer initiation and progression along with the amplification of modules of genes related to unspecific defense against xenobiotics (probably chemotherapeutical agents). With the extension of this analysis to an Array-CGH dataset (glioblastomas) from The Cancer Genome Atlas we demonstrate the validity of this approach to investigate the functional impact of CNAs. Conclusions The presented results indicate promising clinical and therapeutic implications. Our findings also directly point out to the necessity of adopting a function-centric, rather a gene-centric, view in the understanding of phenotypes or diseases harboring CNAs.Spanish Ministry of Science and Innovation (grant BIO2008-04212)Spanish Ministry of Science and Innovation (grant FIS PI 08/0440)GVA-FEDER (PROMETEO/2010/001)Red Temática de Investigación Cooperativa en Cáncer (RTICC) (grant RD06/0020/1019)Instituto de Salud Carlos III (ISCIII)Spanish Ministry of Science and InnovationSpanish Ministry of Health (FI06/00027

Multilevel analysis in CSCL Research

Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2011). Multilevel analysis in CSCL research. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 187-205). New York: Springer. doi:10.1007/978-1-4419-7710-6_9CSCL researchers are often interested in the processes that unfold between learners in online learning environments and the outcomes that stem from these interactions. However, studying collaborative learning processes is not an easy task. Researchers have to make quite a few methodological decisions such as how to study the collaborative process itself (e.g., develop a coding scheme or a questionnaire), on the appropriate unit of analysis (e.g., the individual or the group), and which statistical technique to use (e.g., descriptive statistics, analysis of variance, correlation analysis). Recently, several researchers have turned to multilevel analysis (MLA) to answer their research questions (e.g., Cress, 2008; De Wever, Van Keer, Schellens, & Valcke, 2007; Dewiyanti, Brand-Gruwel, Jochems, & Broers, 2007; Schellens, Van Keer, & Valcke, 2005; Strijbos, Martens, Jochems, & Broers, 2004; Stylianou-Georgiou, Papanastasiou, & Puntambekar, chapter #). However, CSCL studies that apply MLA analysis still remain relatively scarce. Instead, many CSCL researchers continue to use ‘traditional’ statistical techniques (e.g., analysis of variance, regression analysis), although these techniques may not be appropriate for what is being studied. An important aim of this chapter is therefore to explain why MLA is often necessary to correctly answer the questions CSCL researchers address. Furthermore, we wish to highlight the consequences of failing to use MLA when this is called for, using data from our own studies

Current understanding of the human microbiome

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Medicine 24 (2018): 392–400, doi:10.1038/nm.4517.Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.Many of the studies described here in our laboratories were supported by the NIH, NSF, DOE, and the Alfred P. Sloan Foundation.2018-10-1