Proteomic Analysis of Dynein-Interacting Proteins in Amyotrophic Lateral Sclerosis Synaptosomes Reveals Alterations in the RNA-Binding Protein Staufen1.

Synapse disruption takes place in many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the mechanistic understanding of this process is still limited. We set out to study a possible role for dynein in synapse integrity. Cytoplasmic dynein is a multisubunit intracellular molecule responsible for diverse cellular functions, including long-distance transport of vesicles, organelles, and signaling factors toward the cell center. A less well-characterized role dynein may play is the spatial clustering and anchoring of various factors including mRNAs in distinct cellular domains such as the neuronal synapse. Here, in order to gain insight into dynein functions in synapse integrity and disruption, we performed a screen for novel dynein interactors at the synapse. Dynein immunoprecipitation from synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, followed by mass spectrometry analysis on synaptic fractions of the ALS model mSOD1(G93A) and wild-type controls, was performed. Using advanced network analysis, we identified Staufen1, an RNA-binding protein required for the transport and localization of neuronal RNAs, as a major mediator of dynein interactions via its interaction with protein phosphatase 1-beta (PP1B). Both in vitro and in vivo validation assays demonstrate the interactions of Staufen1 and PP1B with dynein, and their colocalization with synaptic markers was altered as a result of two separate ALS-linked mutations: mSOD1(G93A) and TDP43(A315T). Taken together, we suggest a model in which dynein's interaction with Staufen1 regulates mRNA localization along the axon and the synapses, and alterations in this process may correlate with synapse disruption and ALS toxicity.

Regulation of Sec16 levels and dynamics links proliferation and secretion.

We currently lack a broader mechanistic understanding of the integration of the early secretory pathway with other homeostatic processes such as cell growth. Here, we explore the possibility that Sec16A, a major constituent of endoplasmic reticulum exit sites (ERES), acts as an integrator of growth factor signaling. Surprisingly, we find that Sec16A is a short-lived protein that is regulated by growth factors in a manner dependent on Egr family transcription factors. We hypothesize that Sec16A acts as a central node in a coherent feed-forward loop that detects persistent growth factor stimuli to increase ERES number. Consistent with this notion, Sec16A is also regulated by short-term growth factor treatment that leads to increased turnover of Sec16A at ERES. Finally, we demonstrate that Sec16A depletion reduces proliferation, whereas its overexpression increases proliferation. Together with our finding that growth factors regulate Sec16A levels and its dynamics on ERES, we propose that this protein acts as an integrator linking growth factor signaling and secretion. This provides a mechanistic basis for the previously proposed link between secretion and proliferation.

An integer programming framework for inferring disease complexes from network data.

MOTIVATION: Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process. RESULTS: Here, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge. AVAILABILITY AND IMPLEMENTATION: The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip CONTACT: roded@post.tau.ac.il.

Interactome Mapping Provides a Network of Neurodegenerative Disease Proteins and Uncovers Widespread Protein Aggregation in Affected Brains.

Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP100 that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer's disease patients, suggesting widespread protein aggregation in NDs.

Functional Alignment of Metabolic Networks.

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Elucidating influenza inhibition pathways via network reconstruction.

Viruses evade detection by the host immune system through the suppression of antiviral pathways. These pathways are thus obscured when measuring the host response to viral infection and cannot be inferred by current network reconstruction methodology. Here we aim to close this gap by providing a novel computational framework for the inference of such inhibited pathways as well as the proteins targeted by the virus to achieve this inhibition. We demonstrate the power of our method by testing it on the response to influenza infection in humans, with and without the viral inhibitory protein NS1, revealing its direct targets and their inhibitory effects.

A minimum-labeling approach for reconstructing protein networks across multiple conditions.

BACKGROUND: The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. RESULTS: We propose a novel formulation for the problem of network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection and ER export regulation in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.