Alain Desgagné, Philippe Gagnon.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 205--221.

Abstract:
Whole robustness is a nice property to have for statistical models. It implies that the impact of outliers gradually vanishes as they approach plus or minus infinity. So far, the Bayesian literature provides results that ensure whole robustness for the location-scale model. In this paper, we make two contributions. First, we generalise the results to attain whole robustness in simple linear regression through the origin, which is a necessary step towards results for general linear regression models. We allow the variance of the error term to depend on the explanatory variable. This flexibility leads to the second contribution: we provide a simple Bayesian approach to robustly estimate finite population means and ratios. The strategy to attain whole robustness is simple since it lies in replacing the traditional normal assumption on the error term by a super heavy-tailed distribution assumption. As a result, users can estimate the parameters as usual, using the posterior distribution.

In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging.

We propose a neural network-based framework, emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.

In causal settings, such as instrumental variable settings, it is well known that estimators based on ordinary least squares (OLS) can yield biased and non-consistent estimates of the causal parameters. This is partially overcome by two-stage least squares (TSLS) estimators. These are, under weak assumptions, consistent but do not have desirable finite sample properties: in many models, for example, they do not have finite moments. The set of K-class estimators can be seen as a non-linear interpolation between OLS and TSLS and are known to have improved finite sample properties. Recently, in causal discovery, invariance properties such as the moment criterion which TSLS estimators leverage have been exploited for causal structure learning: e.g., in cases, where the causal parameter is not identifiable, some structure of the non-zero components may be identified, and coverage guarantees are available. Subsequently, anchor regression has been proposed to trade-off invariance and predictability. The resulting estimator is shown to have optimal predictive performance under bounded shift interventions. In this paper, we show that the concepts of anchor regression and K-class estimators are closely related. Establishing this connection comes with two benefits: (1) It enables us to prove robustness properties for existing K-class estimators when considering distributional shifts. And, (2), we propose a novel estimator in instrumental variable settings by minimizing the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal parameter. We call this estimator PULSE (p-uncorrelated least squares estimator) and show that it can be computed efficiently, even though the underlying optimization problem is non-convex. We further prove that it is consistent.

Gregory Cox.

Source: The Annals of Statistics, Volume 48, Number 1, 584--606.

Abstract:
This paper establishes the argmin of a random objective function to be unique almost surely. This paper first formulates a general result that proves almost sure uniqueness without convexity of the objective function. The general result is then applied to a variety of applications in statistics. Four applications are discussed, including uniqueness of M-estimators, both classical likelihood and penalized likelihood estimators, and two applications of the argmin theorem, threshold regression and weak identification.

Ningshan Zhang, Kyle Schmaus, Patrick O. Perry.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2260--2288.

Abstract:
We consider a particular instance of a common problem in recommender systems, using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and subgenres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational. The data sizes are large and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnitude faster than standard maximum likelihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply nested hierarchical generalized linear mixed models.

Ian L. Dryden, Kwang-Rae Kim, Charles A. Laughton, Huiling Le.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2213--2234.

Abstract:
Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dimensional representation. However, Euclidean techniques such as principal components analysis (PCA) can be problematic as the data may lie far from in a flat manifold. Principal nested spheres gives a fundamentally different decomposition of data from the usual Euclidean subspace based PCA [ Biometrika 99 (2012) 551–568]. Subspaces of successively lower dimension are fitted to the data in a backwards manner with the aim of retaining signal and dispensing with noise at each stage. We adapt the methodology to 3D subshape spaces and provide some practical fitting algorithms. The methodology is applied to cluster analysis of peptides, where different states of the molecules can be identified. Also, the temporal transitions between cluster states are explored.

Lin Liu, Yuqi Qiu, Loki Natarajan, Karen Messer.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1370--1396.

Abstract:
It is common to encounter missing data among the potential predictor variables in the setting of model selection. For example, in a recent study we attempted to improve the US guidelines for risk stratification after screening colonoscopy ( Cancer Causes Control 27 (2016) 1175–1185), with the aim to help reduce both overuse and underuse of follow-on surveillance colonoscopy. The goal was to incorporate selected additional informative variables into a neoplasia risk-prediction model, going beyond the three currently established risk factors, using a large dataset pooled from seven different prospective studies in North America. Unfortunately, not all candidate variables were collected in all studies, so that one or more important potential predictors were missing on over half of the subjects. Thus, while variable selection was a main focus of the study, it was necessary to address the substantial amount of missing data. Multiple imputation can effectively address missing data, and there are also good approaches to incorporate the variable selection process into model-based confidence intervals. However, there is not consensus on appropriate methods of inference which address both issues simultaneously. Our goal here is to study the properties of model-based confidence intervals in the setting of imputation for missing data followed by variable selection. We use both simulation and theory to compare three approaches to such post-imputation-selection inference: a multiple-imputation approach based on Rubin’s Rules for variance estimation ( Comput. Statist. Data Anal. 71 (2014) 758–770); a single imputation-selection followed by bootstrap percentile confidence intervals; and a new bootstrap model-averaging approach presented here, following Efron ( J. Amer. Statist. Assoc. 109 (2014) 991–1007). We investigate relative strengths and weaknesses of each method. The “Rubin’s Rules” multiple imputation estimator can have severe undercoverage, and is not recommended. The imputation-selection estimator with bootstrap percentile confidence intervals works well. The bootstrap-model-averaged estimator, with the “Efron’s Rules” estimated variance, may be preferred if the true effect sizes are moderate. We apply these results to the colorectal neoplasia risk-prediction problem which motivated the present work.

Arun K. Kuchibhotla, Rohit K. Patra.

Source: Bernoulli, Volume 26, Number 2, 1587--1618.

Abstract:
We consider estimation and inference in a single index regression model with an unknown but smooth link function. In contrast to the standard approach of using kernels or regression splines, we use smoothing splines to estimate the smooth link function. We develop a method to compute the penalized least squares estimators (PLSEs) of the parametric and the nonparametric components given independent and identically distributed (i.i.d.) data. We prove the consistency and find the rates of convergence of the estimators. We establish asymptotic normality under mild assumption and prove asymptotic efficiency of the parametric component under homoscedastic errors. A finite sample simulation corroborates our asymptotic theory. We also analyze a car mileage data set and a Ozone concentration data set. The identifiability and existence of the PLSEs are also investigated.

Martin Kolb, Mladen Savov.

Source: Bernoulli, Volume 26, Number 2, 1453--1472.

Abstract:
We derive a criterium for the almost sure finiteness of perpetual integrals of Lévy processes for a class of real functions including all continuous functions and for general one-dimensional Lévy processes that drifts to plus infinity. This generalizes previous work of Döring and Kyprianou, who considered Lévy processes having a local time, leaving the general case as an open problem. It turns out, that the criterium in the general situation simplifies significantly in the situation, where the process has a local time, but we also demonstrate that in general our criterium can not be reduced. This answers an open problem posed in ( J. Theoret. Probab. 29 (2016) 1192–1198).

Moore, Amy, 1908-2005 -- Family.

Indian authorities used drones and fire engines to disinfect the pandemic-hit city of Ahmedabad on Saturday, as virus cases surged and police clashed with migrant workers protesting against a reinforced lockdown. The western city of 5.5 million people in Prime Minister Narendra Modi's home state has become a major concern for authorities as they battle an uptick in coronavirus deaths and cases across India.

Boeing CEO Dave Calhoun said the company was aiming to resume production this month, despite the ongoing grounding and coronavirus pandemic.

China says it will improve public health systems after criticism of its early response to the virus.

Philippe Gagnon, Alain Desgagné, Mylène Bédard.

Source: Bayesian Analysis, Volume 15, Number 2, 389--414.

Abstract:
Linear regression is ubiquitous in statistical analysis. It is well understood that conflicting sources of information may contaminate the inference when the classical normality of errors is assumed. The contamination caused by the light normal tails follows from an undesirable effect: the posterior concentrates in an area in between the different sources with a large enough scaling to incorporate them all. The theory of conflict resolution in Bayesian statistics (O’Hagan and Pericchi (2012)) recommends to address this problem by limiting the impact of outliers to obtain conclusions consistent with the bulk of the data. In this paper, we propose a model with super heavy-tailed errors to achieve this. We prove that it is wholly robust, meaning that the impact of outliers gradually vanishes as they move further and further away from the general trend. The super heavy-tailed density is similar to the normal outside of the tails, which gives rise to an efficient estimation procedure. In addition, estimates are easily computed. This is highlighted via a detailed user guide, where all steps are explained through a simulated case study. The performance is shown using simulation. All required code is given.

Federico Camerlenghi, David B. Dunson, Antonio Lijoi, Igor Prünster, Abel Rodríguez.

Source: Bayesian Analysis, Volume 14, Number 4, 1303--1356.

Abstract:
Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and prediction, among others. In this paper, we consider nested processes and study the dependence structures they induce. Dependence ranges between homogeneity, corresponding to full exchangeability, and maximum heterogeneity, corresponding to (unconditional) independence across samples. The popular nested Dirichlet process is shown to degenerate to the fully exchangeable case when there are ties across samples at the observed or latent level. To overcome this drawback, inherent to nesting general discrete random measures, we introduce a novel class of latent nested processes. These are obtained by adding common and group-specific completely random measures and, then, normalizing to yield dependent random probability measures. We provide results on the partition distributions induced by latent nested processes, and develop a Markov Chain Monte Carlo sampler for Bayesian inferences. A test for distributional homogeneity across groups is obtained as a by-product. The results and their inferential implications are showcased on synthetic and real data.

T. Tony Cai.

Source: Statistical Science, Volume 34, Number 4, 635--656.

Abstract:
A wide range of nonparametric function estimation models have been studied individually in the literature. Among them the homoscedastic nonparametric Gaussian regression is arguably the best known and understood. Inspired by the asymptotic equivalence theory, Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) developed a unified approach to turn a collection of non-Gaussian function estimation models into a standard Gaussian regression and any good Gaussian nonparametric regression method can then be used. These Gaussianization Machines have two key components, binning and transformation. When combined with BlockJS, a wavelet thresholding procedure for Gaussian regression, the procedures are computationally efficient with strong theoretical guarantees. Technical analysis given in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) shows that the estimators attain the optimal rate of convergence adaptively over a large set of Besov spaces and across a collection of non-Gaussian function estimation models, including robust nonparametric regression, density estimation, and nonparametric regression in exponential families. The estimators are also spatially adaptive. The Gaussianization Machines significantly extend the flexibility and scope of the theories and methodologies originally developed for the conventional nonparametric Gaussian regression. This article aims to provide a concise account of the Gaussianization Machines developed in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046), Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433).

Jared W. Young
Mar 4, 2009; 29:2663-2665
Journal Club

Karim Fifel
Jan 24, 2018; 38:784-786
Journal Club

Irune Diaz-Aparicio
Feb 12, 2020; 40:1453-1482
Development Plasticity Repair

Stefan Klöppel
Mar 3, 2010; 30:3271-3275
BRIEF COMMUNICATION

KM Harris
Jul 1, 1992; 12:2685-2705
Articles

KM Harris
Aug 1, 1989; 9:2982-2997
Articles

HG Kuhn
Mar 15, 1996; 16:2027-2033
Articles

Spanish translation of the BIS press release about the BIS Quarterly Review, March 2018

To view more press releases, please visit http://www.snl.com/irweblinkx/news.aspx?iid=1024452.

Hepatocyte growth factor (HGF) is a multifunctional protein that signals through the MET receptor. HGF stimulates cell proliferation, cell dispersion, neuronal survival, and wound healing. In the inner ear, levels of HGF must be fine-tuned for normal hearing. In mice, a deficiency of HGF expression limited to the auditory system, or an overexpression of HGF, causes neurosensory deafness. In humans, noncoding variants in HGF are associated with nonsyndromic deafness DFNB39. However, the mechanism by which these noncoding variants causes deafness was unknown. Here, we reveal the cause of this deafness using a mouse model engineered with a noncoding intronic 10 bp deletion (del10) in Hgf. Male and female mice homozygous for del10 exhibit moderate-to-profound hearing loss at 4 weeks of age as measured by tone burst auditory brainstem responses. The wild type (WT) 80 mV endocochlear potential was significantly reduced in homozygous del10 mice compared with WT littermates. In normal cochlea, endocochlear potentials are dependent on ion homeostasis mediated by the stria vascularis (SV). Previous studies showed that developmental incorporation of neural crest cells into the SV depends on signaling from HGF/MET. We show by immunohistochemistry that, in del10 homozygotes, neural crest cells fail to infiltrate the developing SV intermediate layer. Phenotyping and RNAseq analyses reveal no other significant abnormalities in other tissues. We conclude that, in the inner ear, the noncoding del10 mutation in Hgf leads to developmental defects of the SV and consequently dysfunctional ion homeostasis and a reduction in the EP, recapitulating human DFNB39 nonsyndromic deafness.

SIGNIFICANCE STATEMENT Hereditary deafness is a common, clinically and genetically heterogeneous neurosensory disorder. Previously, we reported that human deafness DFNB39 is associated with noncoding variants in the 3'UTR of a short isoform of HGF encoding hepatocyte growth factor. For normal hearing, HGF levels must be fine-tuned as an excess or deficiency of HGF cause deafness in mouse. Using a Hgf mutant mouse with a small 10 bp deletion recapitulating a human DFNB39 noncoding variant, we demonstrate that neural crest cells fail to migrate into the stria vascularis intermediate layer, resulting in a significantly reduced endocochlear potential, the driving force for sound transduction by inner ear hair cells. HGF-associated deafness is a neurocristopathy but, unlike many other neurocristopathies, it is not syndromic.

Synaptic dysfunction provoking dysregulated cortical neural circuits is currently hypothesized as a key pathophysiological process underlying clinical manifestations in Alzheimer's disease and related neurodegenerative tauopathies. Here, we conducted PET along with postmortem assays to investigate time course changes of excitatory and inhibitory synaptic constituents in an rTg4510 mouse model of tauopathy, which develops tau pathologies leading to noticeable brain atrophy at 5-6 months of age. Both male and female mice were analyzed in this study. We observed that radiosignals derived from [¹¹C]flumazenil, a tracer for benzodiazepine receptor, in rTg4510 mice were significantly lower than the levels in nontransgenic littermates at 2-3 months of age. In contrast, retentions of (E)-[¹¹C]ABP688, a tracer for mGluR5, were unaltered relative to controls at 2 months of age but then gradually declined with aging in parallel with progressive brain atrophy. Biochemical and immunohistochemical assessment of postmortem brain tissues demonstrated that inhibitory, but not excitatory, synaptic constituents selectively diminished without overt loss of somas of GABAergic interneurons in the neocortex and hippocampus of rTg4510 mice at 2 months of age, which was concurrent with enhanced immunoreactivity of cFos, a well-characterized immediate early gene, suggesting that impaired inhibitory neurotransmission may cause hyperexcitability of cortical circuits. Our findings indicate that tau-induced disruption of the inhibitory synapse may be a critical trigger of progressive neurodegeneration, resulting in massive neuronal loss, and PET assessments of inhibitory versus excitatory synapses potentially offer in vivo indices for hyperexcitability and excitotoxicity early in the etiologic pathway of neurodegenerative tauopathies.

SIGNIFICANCE STATEMENT In this study, we examined the in vivo status of excitatory and inhibitory synapses in the brain of the rTg4510 tauopathy mouse model by PET imaging with (E)-[¹¹C]ABP688 and [¹¹C]flumazenil, respectively. We identified inhibitory synapse as being significantly dysregulated before brain atrophy at 2 months of age, while excitatory synapse stayed relatively intact at this stage. In line with this observation, postmortem assessment of brain tissues demonstrated selective attenuation of inhibitory synaptic constituents accompanied by the upregulation of cFos before the formation of tau pathology in the forebrain at young ages. Our findings indicate that selective degeneration of inhibitory synapse with hyperexcitability in the cortical circuit constitutes the critical early pathophysiology of tauopathy.

The transcription factor Nurr1 has been identified to be ectopically induced in the striatum of rodents expressing l-DOPA-induced dyskinesia (LID). In the present study, we sought to characterize Nurr1 as a causative factor in LID expression. We used rAAV2/5 to overexpress Nurr1 or GFP in the parkinsonian striatum of LID-resistant Lewis or LID-prone Fischer-344 (F344) male rats. In a second cohort, rats received the Nurr1 agonist amodiaquine (AQ) together with l-DOPA or ropinirole. All rats received a chronic DA agonist and were evaluated for LID severity. Finally, we performed single-unit recordings and dendritic spine analyses on striatal medium spiny neurons (MSNs) in drug-naïve rAAV-injected male parkinsonian rats. rAAV-GFP injected LID-resistant hemi-parkinsonian Lewis rats displayed mild LID and no induction of striatal Nurr1 despite receiving a high dose of l-DOPA. However, Lewis rats overexpressing Nurr1 developed severe LID. Nurr1 agonism with AQ exacerbated LID in F344 rats. We additionally determined that in l-DOPA-naïve rats striatal rAAV-Nurr1 overexpression (1) increased cortically-evoked firing in a subpopulation of identified striatonigral MSNs, and (2) altered spine density and thin-spine morphology on striatal MSNs; both phenomena mimicking changes seen in dyskinetic rats. Finally, we provide postmortem evidence of Nurr1 expression in striatal neurons of l-DOPA-treated PD patients. Our data demonstrate that ectopic induction of striatal Nurr1 is capable of inducing LID behavior and associated neuropathology, even in resistant subjects. These data support a direct role of Nurr1 in aberrant neuronal plasticity and LID induction, providing a potential novel target for therapeutic development.

SIGNIFICANCE STATEMENT The transcription factor Nurr1 is ectopically induced in striatal neurons of rats exhibiting levodopa-induced dyskinesia [LID; a side-effect to dopamine replacement strategies in Parkinson's disease (PD)]. Here we asked whether Nurr1 is causing LID. Indeed, rAAV-mediated expression of Nurr1 in striatal neurons was sufficient to overcome LID-resistance, and Nurr1 agonism exacerbated LID severity in dyskinetic rats. Moreover, we found that expression of Nurr1 in l-DOPA naïve hemi-parkinsonian rats resulted in the formation of morphologic and electrophysiological signatures of maladaptive neuronal plasticity; a phenomenon associated with LID. Finally, we determined that ectopic Nurr1 expression can be found in the putamen of l-DOPA-treated PD patients. These data suggest that striatal Nurr1 is an important mediator of the formation of LID.

Theoretical models and experimental evidence have suggested that connections from the dentate gyrus (DG) to CA3 play important roles in representing orthogonal information (i.e., pattern separation) in the hippocampus. However, the effects of eliminating the DG on neural firing patterns in the CA3 have rarely been tested in a goal-directed memory task that requires both the DG and CA3. In this study, selective lesions in the DG were made using colchicine in male Long–Evans rats, and single units from the CA3 were recorded as the rats performed visual scene memory tasks. The original scenes used in training were altered during testing by blurring to varying degrees or by using visual masks, resulting in maximal recruitment of the DG–CA3 circuits. Compared with controls, the performance of rats with DG lesions was particularly impaired when blurred scenes were used in the task. In addition, the firing rate modulation associated with visual scenes in these rats was significantly reduced in the single units recorded from the CA3 when ambiguous scenes were presented, largely because DG-deprived CA3 cells did not show stepwise, categorical rate changes across varying degrees of scene ambiguity compared with controls. These findings suggest that the DG plays key roles not only during the acquisition of scene memories but also during retrieval when modified visual scenes are processed in conjunction with the CA3 by making the CA3 network respond orthogonally to ambiguous scenes.

SIGNIFICANCE STATEMENT Despite the behavioral evidence supporting the role of the dentate gyrus in pattern separation in the hippocampus, the underlying neural mechanisms are largely unknown. By recording single units from the CA3 in DG-lesioned rats performing a visual scene memory task, we report that the scene-related modulation of neural firing was significantly reduced in the DG-lesion rats compared with controls, especially when the original scene stimuli were ambiguously altered. Our findings suggest that the dentate gyrus plays an essential role during memory retrieval and performs a critical computation to make categorical rate modulation occur in the CA3 between different scenes, especially when ambiguity is present in the environment.

The tuberal hypothalamus is comprised of the dorsomedial, ventromedial, and arcuate nuclei, as well as parts of the lateral hypothalamic area, and it governs a wide range of physiologies. During neurogenesis, tuberal hypothalamic neurons are thought to be born in a dorsal-to-ventral and outside-in pattern, although the accuracy of this description has been questioned over the years. Moreover, the intrinsic factors that control the timing of neurogenesis in this region are poorly characterized. Proneural genes, including Achate-scute-like 1 (Ascl1) and Neurogenin 3 (Neurog3) are widely expressed in hypothalamic progenitors and contribute to lineage commitment and subtype-specific neuronal identifies, but the potential role of Neurogenin 2 (Neurog2) remains unexplored. Birthdating in male and female mice showed that tuberal hypothalamic neurogenesis begins as early as E9.5 in the lateral hypothalamic and arcuate and rapidly expands to dorsomedial and ventromedial neurons by E10.5, peaking throughout the region by E11.5. We confirmed an outside-in trend, except for neurons born at E9.5, and uncovered a rostrocaudal progression but did not confirm a dorsal-ventral patterning to tuberal hypothalamic neuronal birth. In the absence of Neurog2, neurogenesis stalls, with a significant reduction in early-born BrdU⁺ cells but no change at later time points. Further, the loss of Ascl1 yielded a similar delay in neuronal birth, suggesting that Ascl1 cannot rescue the loss of Neurog2 and that these proneural genes act independently in the tuberal hypothalamus. Together, our findings show that Neurog2 functions as a classical proneural gene to regulate the temporal progression of tuberal hypothalamic neurogenesis.

SIGNIFICANCE STATEMENT Here, we investigated the general timing and pattern of neurogenesis within the tuberal hypothalamus. Our results confirmed an outside-in trend of neurogenesis and uncovered a rostrocaudal progression. We also showed that Neurog2 acts as a classical proneural gene and is responsible for regulating the birth of early-born neurons within the ventromedial hypothalamus, acting independently of Ascl1. In addition, we revealed a role for Neurog2 in cell fate specification and differentiation of ventromedial -specific neurons. Last, Neurog2 does not have cross-inhibitory effects on Neurog1, Neurog3, and Ascl1. These findings are the first to reveal a role for Neurog2 in hypothalamic development.

Nestin, an intermediate filament protein widely used as a marker of neural progenitors, was recently found to be expressed transiently in developing cortical neurons in culture and in developing mouse cortex. In young cortical cultures, nestin regulates axonal growth cone morphology. In addition, nestin, which is known to bind the neuronal cdk5/p35 kinase, affects responses to axon guidance cues upstream of cdk5, specifically, to Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, and changes in microtubules and actin filaments are well studied. In contrast, the roles of intermediate filament proteins in this process are poorly understood, even in cultured neurons. Here, we investigate the molecular mechanism by which nestin affects growth cone morphology and Sema3a sensitivity. We find that nestin selectively facilitates the phosphorylation of the lissencephaly-linked protein doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected by nestin. We uncover that this substrate selectivity is based on the ability of nestin to interact with DCX, but not with other cdk5 substrates. Nestin thus creates a selective scaffold for DCX with activated cdk5/p35. Last, we use cortical cultures derived from Dcx KO mice to show that the effects of nestin on growth cone morphology and on Sema3a sensitivity are DCX-dependent, thus suggesting a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating the intracellular kinase signaling environment in developing neurons. The sex of animal subjects is unknown.

SIGNIFICANCE STATEMENT Nestin, an intermediate filament protein highly expressed in neural progenitors, was recently identified in developing neurons where it regulates growth cone morphology and responsiveness to the guidance cue Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, but the roles of intermediate filaments in this process are poorly understood. We now report that nestin selectively facilitates phosphorylation of the lissencephaly-linked doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected. This substrate selectivity is based on preferential scaffolding of DCX, cdk5, and p35 by nestin. Additionally, we demonstrate a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating intracellular kinase signaling in developing neurons.

FAO is working to help typhoon-affected farmers to ensure the next harvests in 2014 – You can help as well. Philippine farmers need urgent assistance  to avoid a double tragedy befalling rural survivors of Typhoon Haiyan. The typhoon hit just as farmers were beginning a new planting season, and FAO estimates that over one million farmers have been affected and hundreds of [...]

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