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.

ANAT 2.0: reconstructing functional protein subnetworks.

Background: ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human.

Results: Here we report on a new version of the tool, ANAT 2.

Metabolic Network Prediction of Drug Side Effects.

Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.

A network-based analysis of colon cancer splicing changes reveals a tumorigenesis-favoring regulatory pathway emanating from ELK1.

Splicing aberrations are prominent drivers of cancer, yet the regulatory pathways controlling them are mostly unknown. Here we develop a method that integrates physical interaction, gene expression, and alternative splicing data to construct the largest map of transcriptomic and proteomic interactions leading to cancerous splicing aberrations defined to date, and identify driver pathways therein. We apply our method to colon adenocarcinoma and non-small-cell lung carcinoma.

Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae.

The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ∼6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome.

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.

Experimental design schemes for learning Boolean network models.

Motivation: A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein-protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human.

Pathway-based analysis of genomic variation data.

A holy grail of genetics is to decipher the mapping from genotype to phenotype. Recent advances in sequencing technologies allow the efficient genotyping of thousands of individuals carrying a particular phenotype in an effort to reveal its genetic determinants. However, the interpretation of these data entails tackling significant statistical and computational problems that stem from the complexity of human phenotypes and the huge genotypic search space.

Alternative splicing regulates biogenesis of miRNAs located across exon-intron junctions.

The initial step in microRNA (miRNA) biogenesis requires processing of the precursor miRNA (pre-miRNA) from a longer primary transcript. Many pre-miRNAs originate from introns, and both a mature miRNA and a spliced RNA can be generated from the same transcription unit. We have identified a mechanism in which RNA splicing negatively regulates the processing of pre-miRNAs that overlap exon-intron junctions.

A genetic screen for high copy number suppressors of the synthetic lethality between elg1Δ and srs2Δ in yeast.

Elg1 and Srs2 are two proteins involved in maintaining genome stability in yeast. After DNA damage, the homotrimeric clamp PCNA, which provides stability and processivity to DNA polymerases and serves as a docking platform for DNA repair enzymes, undergoes modification by the ubiquitin-like molecule SUMO. PCNA SUMOylation helps recruit Srs2 and Elg1 to the replication fork.

iPoint: an integer programming based algorithm for inferring protein subnetworks.

Large scale screening experiments have become the workhorse of molecular biology, producing data at an ever increasing scale. The interpretation of such data, particularly in the context of a protein interaction network, has the potential to shed light on the molecular pathways underlying the phenotype or the process in question. A host of approaches have been developed in recent years to tackle this reconstruction challenge.

ANAT: a tool for constructing and analyzing functional protein networks.

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins.

An algorithmic framework for predicting side effects of drugs.

One of the critical stages in drug development is the identification of potential side effects for promising drug leads. Large-scale clinical experiments aimed at discovering such side effects are very costly and may miss subtle or rare side effects. Previous attempts to systematically predict side effects are sparse and consider each side effect independently.

Combining drug and gene similarity measures for drug-target elucidation.

Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction.