Faculty Bibliography

Skeletal myogenesis is regulated by signal transduction, but the factors and mechanisms involved are not well understood. The group I Paks Pak1 and Pak2 are related protein kinases and direct effectors of Cdc42 and Rac1. Group I Paks are ubiquitously expressed and specifically required for myoblast fusion in Drosophila We report that both Pak1 and Pak2 are activated during mammalian myoblast differentiation. One pathway of activation is initiated by N-cadherin ligation and involves the cadherin coreceptor Cdo with its downstream effector, Cdc42. Individual genetic deletion of Pak1 and Pak2 in mice has no overt effect on skeletal muscle development or regeneration. However, combined muscle-specific deletion of Pak1 and Pak2 results in reduced muscle mass and a higher proportion of myofibers with a smaller cross-sectional area. This phenotype is exacerbated after repair to acute injury. Furthermore, primary myoblasts lacking Pak1 and Pak2 display delayed expression of myogenic differentiation markers and myotube formation. These results identify Pak1 and Pak2 as redundant regulators of myoblast differentiation in vitro and in vivo and as components of the promyogenic Ncad/Cdo/Cdc42 signaling pathway.

Neogenin-1 (NEO1) is a transmembrane receptor involved in axonal guidance, angiogenesis, neuronal cell migration and cell death, during both embryonic development and adult homeostasis. It has been described as a dependence receptor, because it promotes cell death in the absence of its ligands (Netrin and Repulsive Guidance Molecule (RGM) families) and cell survival when they are present. Although NEO1 and its ligands are involved in tumor progression, their precise role in tumor cell survival and migration remain unclear. Public databases contain extensive information regarding the expression of NEO1 and its ligands Netrin-1 (NTN1) and Netrin-4 (NTN4) in primary neuroblastoma (NB) tumors. Analysis of this data revealed that patients with high expression levels of both NEO1 and NTN4 have a poor survival rate. Accordingly, our analyses in NB cell lines with different genetic backgrounds revealed that knocking-down NEO1 reduces cell migration, whereas silencing of endogenous NTN4 induced cell death. Conversely, overexpression of NEO1 resulted in higher cell migration in the presence of NTN4, and increased apoptosis in the absence of ligand. Increased apoptosis was prevented when utilizing physiological concentrations of exogenous Netrin-4. Likewise, cell death induced after NTN4 knock-down was rescued when NEO1 was transiently silenced, thus revealing an important role for NEO1 in NB cell survival. In vivo analysis, using the chicken embryo chorioallantoic membrane (CAM) model, showed that NEO1 and endogenous NTN4 are involved in tumor extravasation and metastasis. Our data collectively demonstrate that endogenous NTN4/NEO1 maintain NB growth via both pro-survival and pro-migratory molecular signaling.

Receptor tyrosine kinase (RTK) signaling is tightly regulated by protein allostery within the intracellular tyrosine kinase domains. Yet the molecular determinants of allosteric connectivity in tyrosine kinase domain are incompletely understood. By means of structural (X-ray and NMR) and functional characterization of pathogenic gain-of-function mutations affecting the FGF receptor (FGFR) tyrosine kinase domain, we elucidated a long-distance allosteric network composed of four interconnected sites termed the 'molecular brake', 'DFG latch', 'A-loop plug', and 'alphaC tether'. The first three sites repress the kinase from adopting an active conformation, whereas the alphaC tether promotes the active conformation. The skewed design of this four-site allosteric network imposes tight autoinhibition and accounts for the incomplete mimicry of the activated conformation by pathogenic mutations targeting a single site. Based on the structural similarity shared among RTKs, we propose that this allosteric model for FGFR kinases is applicable to other RTKs.

Serotonin is an evolutionarily ancient molecule that functions in generating and modulating many behavioral states. Although much is known about how serotonin acts on its cellular targets, how serotonin release is regulated in vivo remains poorly understood. In the nematode C. elegans, serotonin neurons that drive female reproductive behavior are directly modulated by inhibitory neuropeptides. Here, we report the isolation of mutants in which inhibitory neuropeptides fail to properly modulate serotonin neurons and the behavior they mediate. The corresponding mutations affect the T-type calcium channel CCA-1 and symmetrically re-tune its voltage-dependencies of activation and inactivation towards more hyperpolarized potentials. This shift in voltage dependency strongly and specifically bypasses the behavioral and cell physiological effects of peptidergic inhibition on serotonin neurons. Our results indicate that T-type calcium channels are critical regulators of a C. elegans serotonergic circuit and demonstrate a mechanism in which T-type channels functionally gate inhibitory modulation in vivo.

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments requires a series of computational steps that identify and quantify LC-MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC-tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts' expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.

Mutations in mtDNA lead to muscular and neurological diseases and are linked to aging. The most frequent aberrancy is the "common deletion" that involves a 4,977-bp region flanked by 13-bp repeats. To investigate the basis of this deletion, we developed a single-molecule mtDNA combing method. The analysis of replicating mtDNA molecules provided in vivo evidence in support of the asymmetric mode of replication. Furthermore, we observed frequent fork stalling at the junction of the common deletion, suggesting that impaired replication triggers the formation of this toxic lesion. In parallel experiments, we employed mito-TALENs to induce breaks in distinct loci of the mitochondrial genome and found that breaks adjacent to the 5' repeat trigger the common deletion. Interestingly, this process was mediated by the mitochondrial replisome independent of canonical DSB repair. Altogether, our data underscore a unique replication-dependent repair pathway that leads to the mitochondrial common deletion.

Discrimination discovery from data is an important data mining task, whose goal is to identify patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities. While any legally valid proof of discrimination requires evidence of causality, the state-of-the-art methods are essentially correlation based, albeit, as it is well known, correlation does not imply causation. In this paper, we take a principled causal approach to discrimination detection following Suppes"™ probabilistic causation theory. In particular, we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data. The result is a type of constrained Bayesian network, which we dub Suppes-Bayes causal network (SBCN). Next, we develop a toolkit of methods based on random walks on top of the SBCN, addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism. Our experiments on real-world datasets confirm the inferential power of our approach in all these different tasks.