The insulin-like growth factor 1 receptor (IGF-1R) plays crucial roles in developmental and cancer biology. Most of its biological effects have been ascribed to its tyrosine kinase activity, which propagates signaling through the phosphatidylinositol 3-kinase and mitogen-activated protein kinase pathways. Here, we report that IGF-1 promotes the modification of IGF-1R by small ubiquitin-like modifier protein-1 (SUMO-1) and its translocation to the nucleus. Nuclear IGF-1R associated with enhancer-like elements and increased transcription in reporter assays. The SUMOylation sites of IGF-1R were identified as three evolutionarily conserved lysine residues-Lys(1025), Lys(1100), and Lys(1120)-in the beta subunit of the receptor. Mutation of these SUMO-1 sites abolished the ability of IGF-1R to translocate to the nucleus and activate transcription but did not alter its kinase-dependent signaling. Thus, we demonstrate a SUMOylation-mediated mechanism of IGF-1R signaling that has potential implications for gene regulation.

The incongruency between a gene tree and a corresponding species tree can be attributed to evolutionary events such as gene duplication and gene loss. This paper describes a combinatorial model where a so-called DTL-scenario is used to explain the differences between a gene tree and a corresponding species tree taking into account gene duplications, gene losses, and lateral gene transfers (also known as horizontal gene transfers). The reasonable biological constraint that a lateral gene transfer may only occur between contemporary species leads to the notion of acyclic DTLscenarios. Parsimony methods are introduced by defining appropriate optimization problems. We show that finding most parsimonious acyclic DTL-scenarios is NP-complete. However, by dropping the condition of acyclicity, the problem becomes tractable, and we provide a dynamic programming algorithm as well as a fixed-parameter-tractable algorithm for finding most parsimonious DTLscenarios.

Breast carcinoma (BC) has been extensively profiled by high-throughput technologies for over a decade, and broadly speaking, these studies can be grouped into those that seek to identify patient subtypes (studies of heterogeneity) or those that seek to identify gene signatures with prognostic or predictive capacity. The sheer number of reported signatures has led to speculation that everything is prognostic in BC. Here, we show that this ubiquity is an apparition caused by a poor understanding of the interrelatedness between subtype and the molecular determinants of prognosis. Our approach constructively shows how to avoid confounding due to a patient's subtype, clinicopathological profile, or treatment profile. The approach identifies patients who are predicted to have good outcome at time of diagnosis by all available clinical and molecular markers but who experience a distant metastasis within 5 years. These inherently difficult patients (~7% of BC) are prioritized for investigations of intratumoral heterogeneity.

Lateral gene transfer (LGT)--which transfers DNA between two non-vertically related individuals belonging to the same or different species--is recognized as a major force in prokaryotic evolution, and evidence of its impact on eukaryotic evolution is ever increasing. LGT has attracted much public attention for its potential to transfer pathogenic elements and antibiotic resistance in bacteria, and to transfer pesticide resistance from genetically modified crops to other plants. In a wider perspective, there is a growing body of studies highlighting the role of LGT in enabling organisms to occupy new niches or adapt to environmental changes. The challenge LGT poses to the standard tree-based conception of evolution is also being debated. Studies of LGT have, however, been severely limited by a lack of computational tools. The best currently available LGT algorithms are parsimony-based phylogenetic methods, which require a pre-computed gene tree and cannot choose between sometimes wildly differing most parsimonious solutions. Moreover, in many studies, simple heuristics are applied that can only handle putative orthologs and completely disregard gene duplications (GDs). Consequently, proposed LGT among specific gene families, and the rate of LGT in general, remain debated. We present a Bayesian Markov-chain Monte Carlo-based method that integrates GD, gene loss, LGT, and sequence evolution, and apply the method in a genome-wide analysis of two groups of bacteria: Mollicutes and Cyanobacteria. Our analyses show that although the LGT rate between distant species is high, the net combined rate of duplication and close-species LGT is on average higher. We also show that the common practice of disregarding reconcilability in gene tree inference overestimates the number of LGT and duplication events.