ProbeSelect: selecting differentially expressed probes in transcriptional profile data.

SUMMARY: Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a simple statistical technique that identifies differentially expressed probes in heterogeneous populations as compared with controls. AVAILABILITY AND IMPLEMENTATION: The algorithm has been implemented in Java and available at www.sourceforge.net/projects/probeselect.

Structure-based prediction reveals capping motifs that inhibit ß-helix aggregation.

The parallel ß-helix is a geometrically regular fold commonly found in the proteomes of bacteria, viruses, fungi, archaea, and some vertebrates. ß-helix structure has been observed in monomeric units of some aggregated amyloid fibers. In contrast, soluble ß-helices, both right- and left-handed, are usually "capped" on each end by one or more secondary structures. Here, an in-depth classification of the diverse range of ß-helix cap structures reveals subtle commonalities in structural components and in interactions with the ß-helix core. Based on these uncovered commonalities, a toolkit of automated predictors was developed for the two distinct types of cap structures. In vitro deletion of the toolkit-predicted C-terminal cap from the pertactin ß-helix resulted in increased aggregation and the formation of soluble oligomeric species. These results suggest that ß-helix cap motifs can prevent specific, ß-sheet-mediated oligomeric interactions, similar to those observed in amyloid formation.

SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone.

MOTIVATION: One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif. RESULTS: We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile-profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions. Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/

Struct2Net: a web service to predict protein-protein interactions using a structure-based approach.

Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein-protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the first community-wide resource to provide structure-based PPI predictions that go beyond homology modeling. Also, most web-resources for predicting PPIs currently rely on functional genomic data (e.g. GO annotation, gene expression, cellular localization, etc.). Our structure-based approach is independent of such methods and only requires the sequence information of the proteins being queried. The web service allows multiple querying options, aimed at maximizing flexibility. For the most commonly studied organisms (fly, human and yeast), predictions have been pre-computed and can be retrieved almost instantaneously. For proteins from other species, users have the option of getting a quick-but-approximate result (using orthology over pre-computed results) or having a full-blown computation performed. The web service is freely available at http://struct2net.csail.mit.edu.

Cell-Selective Adeno-Associated Virus-Mediated SCN1A Gene Regulation Therapy Rescues Mortality and Seizure Phenotypes in a Dravet Syndrome Mouse Model and Is Well Tolerated in Nonhuman Primates.

Dravet syndrome (DS) is a developmental and epileptic encephalopathy caused by monoallelic loss-of-function variants in the SCN1A gene. SCN1A encodes for the alpha subunit of the voltage-gated type I sodium channel (NaV1.1), the primary voltage-gated sodium channel responsible for generation of action potentials in GABAergic inhibitory interneurons. In these studies, we tested the efficacy of an adeno-associated virus serotype 9 (AAV9) SCN1A gene regulation therapy, AAV9-REGABA-eTFSCN1A, designed to target transgene expression to GABAergic inhibitory neurons and reduce off-target expression within excitatory cells, in the Scn1a+/- mouse model of DS. Biodistribution and preliminary safety were evaluated in nonhuman primates (NHPs). AAV9-REGABA-eTFSCN1A was engineered to upregulate SCN1A expression levels within GABAergic inhibitory interneurons to correct the underlying haploinsufficiency and circuit dysfunction. A single bilateral intracerebroventricular (ICV) injection of AAV9-REGABA-eTFSCN1A in Scn1a+/- postnatal day 1 mice led to increased SCN1A mRNA transcripts, specifically within GABAergic inhibitory interneurons, and NaV1.1 protein levels in the brain. This was associated with a significant decrease in the occurrence of spontaneous and hyperthermia-induced seizures, and prolonged survival for over a year. In NHPs, delivery of AAV9-REGABA-eTFSCN1A by unilateral ICV injection led to widespread vector biodistribution and transgene expression throughout the brain, including key structures involved in epilepsy and cognitive behaviors, such as hippocampus and cortex. AAV9-REGABA-eTFSCN1A was well tolerated, with no adverse events during administration, no detectable changes in clinical observations, no adverse findings in histopathology, and no dorsal root ganglion-related toxicity. Our results support the clinical development of AAV9-REGABA-eTFSCN1A (ETX101) as an effective and targeted disease-modifying approach to SCN1A+ DS.

Cx3cr1-deficient microglia exhibit a premature aging transcriptome.

CX3CR1, one of the highest expressed genes in microglia in mice and humans, is implicated in numerous microglial functions. However, the molecular mechanisms underlying Cx3cr1 signaling are not well understood. Here, we analyzed transcriptomes of Cx3cr1-deficient microglia under varying conditions by RNA-sequencing (RNA-seq). In 2-mo-old mice, Cx3cr1 deletion resulted in the down-regulation of a subset of immune-related genes, without substantial epigenetic changes in markers of active chromatin. Surprisingly, Cx3cr1-deficient microglia from young mice exhibited a transcriptome consistent with that of aged Cx3cr1-sufficient animals, suggesting a premature aging transcriptomic signature. Immunohistochemical analysis of microglia in young and aged mice revealed that loss of Cx3cr1 modulates microglial morphology in a comparable fashion. Our results suggest that CX3CR1 may regulate microglial function in part by modulating the expression levels of a subset of inflammatory genes during chronological aging, making Cx3cr1-deficient mice useful for studying aged microglia.

Protein Prenylation Constitutes an Endogenous Brake on Axonal Growth.

Suboptimal axonal regeneration contributes to the consequences of nervous system trauma and neurodegenerative disease, but the intrinsic mechanisms that regulate axon growth remain unclear. We screened 50,400 small molecules for their ability to promote axon outgrowth on inhibitory substrata. The most potent hits were the statins, which stimulated growth of all mouse- and human-patient-derived neurons tested, both in vitro and in vivo, as did combined inhibition of the protein prenylation enzymes farnesyltransferase (PFT) and geranylgeranyl transferase I (PGGT-1). Compensatory sprouting of motor axons may delay clinical onset of amyotrophic lateral sclerosis (ALS). Accordingly, elevated levels of PGGT1B, which would be predicted to reduce sprouting, were found in motor neurons of early- versus late-onset ALS patients postmortem. The mevalonate-prenylation pathway therefore constitutes an endogenous brake on axonal growth, and its inhibition provides a potential therapeutic approach to accelerate neuronal regeneration in humans.

A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.

Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu.

Sparse estimation for structural variability.

BACKGROUND: Proteins are dynamic molecules that exhibit a wide range of motions; often these conformational changes are important for protein function. Determining biologically relevant conformational changes, or true variability, efficiently is challenging due to the noise present in structure data. RESULTS: In this paper we present a novel approach to elucidate conformational variability in structures solved using X-ray crystallography. We first infer an ensemble to represent the experimental data and then formulate the identification of truly variable members of the ensemble (as opposed to those that vary only due to noise) as a sparse estimation problem. Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise. We validate our predictions for structures in the Protein Data Bank by comparing with NMR experiments, as well as on synthetic data. In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data. In the case of Human Ubiquitin-conjugating enzyme Ubc9, variability identified by the algorithm corresponds to functionally important residues implicated by mutagenesis experiments. Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.

Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve a coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer-related genes involved in chromatin remodeling, nucleosome organization, and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.