Interaction of reelin signaling and Lis1 in brain development.

Loss-of-function mutations in RELN (encoding reelin) or PAFAH1B1 (encoding LIS1) cause lissencephaly, a human neuronal migration disorder. In the mouse, homozygous mutations in Reln result in the reeler phenotype, characterized by ataxia and disrupted cortical layers. Pafah1b1(+/-) mice have hippocampal layering defects, whereas homozygous mutants are embryonic lethal. Reln encodes an extracellular protein that regulates layer formation by interacting with VLDLR and ApoER2 (Lrp8) receptors, thereby phosphorylating the Dab1 signaling molecule. Lis1 associates with microtubules and modulates neuronal migration. We investigated interactions between the reelin signaling pathway and Lis1 in brain development. Compound mutant mice with disruptions in the Reln pathway and heterozygous Pafah1b1 mutations had a higher incidence of hydrocephalus and enhanced cortical and hippocampal layering defects. Dab1 and Lis1 bound in a reelin-induced phosphorylation-dependent manner. These data indicate genetic and biochemical interaction between the reelin signaling pathway and Lis1.

Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy.

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.

Whole-Body Movement during Videogame Play Distinguishes Youth with Autism from Youth with Typical Development.

Individuals with autism spectrum disorder struggle with motor difficulties throughout the life span, and these motor difficulties may affect independent living skills and quality of life. Yet, we know little about how whole-body movement may distinguish individuals with autism spectrum disorder from individuals with typical development. In this study, kinematic and postural sway data were collected during multiple sessions of videogame play in 39 youth with autism spectrum disorder and 23 age-matched youth with typical development (ages 7-17 years). The youth on the autism spectrum exhibited more variability and more entropy in their movements. Machine learning analysis of the youths' motor patterns distinguished between the autism spectrum and typically developing groups with high aggregate accuracy (up to 89%), with no single region of the body seeming to drive group differences. Moreover, the machine learning results corresponded to individual differences in performance on standardized motor tasks and measures of autism symptom severity. The machine learning algorithm was also sensitive to age, suggesting that motor challenges in autism may be best characterized as a developmental motor delay rather than an autism-distinct motor profile. Overall, these results reveal that whole-body movement is a distinguishing feature in autism spectrum disorder and that movement atypicalities in autism are present across the body.

Pafah1b2 mutations suppress the development of hydrocephalus in compound Pafah1b1; Reln and Pafah1b1; Dab1 mutant mice.

Reelin, an extracellular protein that signals through the Dab1 adapter protein, and Lis1 regulate neuronal migration and cellular layer formation in the brain. Loss of Reelin and reduction in Lis1 activity in mice or humans results in the disorganization of cortical structures. Lis1, the product of the Pafah1b1 gene associates with Alpha1 (the product of the Pafah1b3 gene) and Alpha2 (the product of the Pafah1b2 gene) to form the Pafah1b heterotrimeric complex. This complex interacts biochemically and genetically with the Reelin pathway, however, the role of Alpha1 and Alpha2 in brain development is poorly understood. We previously demonstrated that compound mutations of Pafah1b1 with genes in Reelin pathway result in layering defects and the appearance of hydrocephalus in double mutant mice. Here, we generate triple mouse mutants to investigate the effect of individual Pafah1b Alpha subunits on cellular layer formation and hydrocephalus. We found that Pafah1b3 mutations exacerbate the layering defects, whereas Pafah1b2 mutations strongly suppress the hydrocephalus phenotype of compound mutant mice. The data indicate that the two Pafah1b Alpha subunits have profoundly different effects on brain development and interact in a significantly different manner with the Reelin signaling pathway.

Classification of patients with pain based on neuropathic pain symptoms: comparison of an artificial neural network against an established scoring system.

Wider use of pain assessment tools that are specifically designed for certain types of pain--such as neuropathic pain--contribute an increasing amount of information which in turn offers the opportunity to employ advanced methods of data analysis. In this manuscript, we present the results of a study where we employed artificial neural networks (ANNs) in an analysis of pain descriptors with the goal of determining how an approach that uses a specific symptoms-based tool would perform with data from the real world of clinical practice. We also used traditional statistics approaches in the form of established scoring systems as well as logistic regression analysis for the purpose of comparison. Our results confirm the clinical experience that groups of pain descriptors rather than single items differentiate between patients with neuropathic and non-neuropathic pain. The accuracy obtained by ANN analysis was only slightly higher than that of the traditional approaches, indicating the absence of nonlinear relationships in this dataset. Data analysis with ANNs provides a framework that extends what current approaches offer, especially for dynamic data, such as the rating of pain descriptors over time.

Detection of a gravitropism phenotype in glutamate receptor-like 3.3 mutants of Arabidopsis thaliana using machine vision and computation.

Gene disruption frequently produces no phenotype in the model plant Arabidopsis thaliana, complicating studies of gene function. Functional redundancy between gene family members is one common explanation but inadequate detection methods could also be responsible. Here, newly developed methods for automated capture and processing of time series of images, followed by computational analysis employing modified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study of mutants lacking the Glutamate Receptor-Like 3.3 gene. Root gravitropism was selected as the process to study with high spatiotemporal resolution because the ligand-gated Ca(2+)-permeable channel encoded by GLR3.3 may contribute to the ion fluxes associated with gravity signal transduction in roots. Time series of root tip angles were collected from wild type and two different glr3.3 mutants across a grid of seed-size and seedling-age conditions previously found to be important to gravitropism. Statistical tests of average responses detected no significant difference between populations, but LDA separated both mutant alleles from the wild type. After projecting the data onto LDA solution vectors, glr3.3 mutants displayed greater population variance than the wild type in all four conditions. In three conditions the projection means also differed significantly between mutant and wild type. Wavelet analysis of the raw response curves showed that the LDA-detected phenotypes related to an early deceleration and subsequent slower-bending phase in glr3.3 mutants. These statistically significant, heritable, computation-based phenotypes generated insight into functions of GLR3.3 in gravitropism. The methods could be generally applicable to the study of phenotypes and therefore gene function.

Differential interaction of the Pafah1b alpha subunits with the Reelin transducer Dab1.

The Reelin signaling pathway controls radial neuronal migration and maturation in the developing brain. The platelet activating factor (PAF) acetyl hydrolase 1b (Pafah1b) complex is also involved in multiple aspects of brain development. We previously showed that the Reelin pathway and the Pafah1b complex interact genetically and biochemically. Lis1, the regulatory subunit of Pafah1b interacts with phosphoDab1, an essential mediator of Reelin signaling. Compound mutants carrying mutations in both, the Reelin pathway and Lis1 exhibit hydrocephalus, a phenotype that is suppressed by mutations in the gene encoding the Alpha2 subunit of Pafah1b. This subunit, like the Alpha1 catalytic subunit of Pafah1b also binds the Reelin receptor VLDLR. Here we investigated the molecular interactions of the Pafah1b catalytic subunits with Dab1. We found that Alpha2 coprecipitates with Dab1 from brain extracts of normal and reeler mutant mice lacking Reelin, and from cell-free extracts containing normal or a phosphorylation mutant form of Dab1, suggesting that Dab1 phosphorylation is not necessary for binding to Alpha2. This interaction is specific for Alpha2 and not Alpha1, and depends on a unique tyrosine residue of Alpha2. Biochemical assays using mutant mice lacking Alpha2 further demonstrated that this subunit is not required for Reelin-induced Dab1 phosphorylation. However, increasing amounts of Alpha2 in a cell-free system disrupted the formation of Dab1-Lis1 complexes without affecting the association of Dab1 with VLDLR. Our data suggest that the Alpha2 subunit may play a modulatory role in the formation of protein complexes that affect brain development and hydrocephalus.

HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis.

Analysis of time series of images can quantify plant growth and development, including the effects of genetic mutations (phenotypes) that give information about gene function. Here is demonstrated a software application named HYPOTrace that automatically extracts growth and shape information from electronic gray-scale images of Arabidopsis (Arabidopsis thaliana) seedlings. Key to the method is the iterative application of adaptive local principal components analysis to extract a set of ordered midline points (medial axis) from images of the seedling hypocotyl. Pixel intensity is weighted to avoid the medial axis being diverted by the cotyledons in areas where the two come in contact. An intensity feature useful for terminating the midline at the hypocotyl apex was isolated in each image by subtracting the baseline with a robust local regression algorithm. Applying the algorithm to time series of images of Arabidopsis seedlings responding to light resulted in automatic quantification of hypocotyl growth rate, apical hook opening, and phototropic bending with high spatiotemporal resolution. These functions are demonstrated here on wild-type, cryptochrome1, and phototropin1 seedlings for the purpose of showing that HYPOTrace generated expected results and to show how much richer the machine-vision description is compared to methods more typical in plant biology. HYPOTrace is expected to benefit seedling development research, particularly in the photomorphogenesis field, by replacing many tedious, error-prone manual measurements with a precise, largely automated computational tool.

The Pafah1b complex interacts with the reelin receptor VLDLR.

Reelin is an extracellular protein that directs the organization of cortical structures of the brain through the activation of two receptors, the very low-density lipoprotein receptor (VLDLR) and the apolipoprotein E receptor 2 (ApoER2), and the phosphorylation of Disabled-1 (Dab1). Lis1, the product of the Pafah1b1 gene, is a component of the brain platelet-activating factor acetylhydrolase 1b (Pafah1b) complex, and binds to phosphorylated Dab1 in response to Reelin. Here we investigated the involvement of the whole Pafah1b complex in Reelin signaling and cortical layer formation and found that catalytic subunits of the Pafah1b complex, Pafah1b2 and Pafah1b3, specifically bind to the NPxYL sequence of VLDLR, but not to ApoER2. Compound Pafah1b1(+/-);Apoer2(-/-) mutant mice exhibit a reeler-like phenotype in the forebrain consisting of the inversion of cortical layers and hippocampal disorganization, whereas double Pafah1b1(+/-);Vldlr(-/-) mutants do not. These results suggest that a cross-talk between the Pafah1b complex and Reelin occurs downstream of the VLDLR receptor.

Previously uncharacterized roles of platelet-activating factor acetylhydrolase 1b complex in mouse spermatogenesis.

Platelet-activating factor (PAF) has been shown to affect sperm motility and acrosomal function, thereby altering fertility. PAF acetylhydrolase 1b (PAFAH1B) hydrolyzes PAF and is composed of three subunits [the lissencephaly (LIS1) protein and alpha1 and alpha2 subunits] and structurally resembles a GTP-hydrolyzing protein. Besides the brain, transcripts for Lis1, alpha1, and alpha2 are localized to meiotic and early haploid germ cells. Here, we report disruptions of the alpha2 (Pafah1b2) and alpha1 (Pafah1b3) genes in mice. Male mice homozygous null for alpha2(alpha2-/-) are infertile, and spermatogenesis is disrupted at mid- or late pachytene stages of meiosis or early spermiogenesis. Whereas mice homozygous mutant for alpha1(alpha1-/-) have normal fertility and normal spermatogenesis, those with disruptions of both alpha1 and alpha2 (alpha1-/-alpha2-/-) manifest an earlier disturbance of spermatogenesis with an onset at preleptotene or leptotene stages of meiosis. Testicular Lis1 protein levels are up-regulated in the alpha2-/- and alpha1-/-alpha2-/- mice. Lowering Lis1 levels by inactivating one allele of Lis1 in alpha2 null or alpha1/alpha 2 null genetic backgrounds (i.e., alpha2-/-Lis1+/- or alpha1-/-alpha2-/-Lis1+/- mice) restored spermatogenesis and male fertility. Our data provide evidence for unique roles of the PAFAH1B complex and, particularly, the lissencephaly protein Lis1 in spermatogenesis.