Olfactory receptors likely constitute the largest gene superfamily in the vertebrate genome. Here we present the nearly complete human olfactory subgenome elucidated by mining the genome draft with gene discovery algorithms. Over 900 olfactory receptor genes and pseudogenes (ORs) were identified, two-thirds of which were not annotated previously. The number of extrapolated ORs is in good agreement with previous theoretical predictions. The sequence of at least 63% of the ORs is disrupted by what appears to be a random process of pseudogene formation. ORs constitute 17 gene families, 4 of which contain more than 100 members each. “Fish-like” Class I ORs, previously considered a relic in higher tetrapods, constitute as much as 10% of the human repertoire, all in one large cluster on chromosome 11. Their lower pseudogene fraction suggests a functional significance. ORs are disposed on all human chromosomes except 20 and Y, and nearly 80% are found in clusters of 6–138 genes. A novel comparative cluster analysis was used to trace the evolutionary path that may have led to OR proliferation and diversification throughout the genome. The results of this analysis suggest the following genome expansion history: first, the generation of a “tetrapod-specific” Class II OR cluster on chromosome 11 by local duplication, then a single-step duplication of this cluster to chromosome 1, and finally an avalanche of duplication events out of chromosome 1 to most other chromosomes. The results of the data mining and characterization of ORs can be accessed at the Human Olfactory Receptor Data Exploratorium Web site (http://bioinfo.weizmann.ac.il/HORDE).

Human epidermal growth factor receptors (HER/erbB) constitute a family of four cell surface receptors involved in transmission of signals controlling normal cell growth and differentiation. A range of growth factors serve as ligands, but none is specific for the HER2 receptor. HER receptors exist as both monomers and dimers, either homo- or heterodimers. Ligand binding to HERI, HER3 or HER4 induces rapid receptor dimerization, with a marked preference for HER2 as a dimer partner. Moreover, HER2-containing heterodimers generate intracellular signals that are significantly stronger than signals emanating from other HER combinations. In normal cells, few HER2 molecules exist at the cell surface, so few heterodimers are formed and growth signals are relatively weak and controllable. When HER2 is overexpressed multiple HER2 heterodimers are formed and cell signaling is stronger, resulting in enhanced responsiveness to growth factors and malignant growth. This explains why HER2 overexpression is an indicator of poor prognosis in breast tumors and may be predictive of response to treatment. HER2 is a highly specific and promising target for new breast cancer treatments. The recombinant human anti-HER2 monoclonal antibody (rhuMAb-HER2, trastuzumab, Herceptin) induces rapid removal of HER2 from the cell surface, thereby reducing its availability to heterodimers and reducing oncogenicity.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

During a cell state transition, cells travel along trajectories in a gene expression state space. This dynamical systems framework complements the traditional concept of molecular pathways that drive cell phenotype switching. To expose the structure that hinders cancer cells from exiting robust proliferative state, we assessed the perturbation capacity of a drug library and identified 16 non-cytotoxic compounds that stimulate MCF7 breast cancer cells to exit from proliferative state to differentiated state. The transcriptome trajectories triggered by these drugs diverged, then converged. Chemical structures and drug targets of these compounds overlapped minimally. However, a network analysis of targeted pathways identified a core signaling pathway - indicating common stress-response and down-regulation of STAT1 before differentiation. This multi-trajectory analysis explores the cells' state transition with a multitude of perturbations in combination with traditional pathway analysis, leading to an encompassing picture of the dynamics of a therapeutically desired cell-state switching.

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized exponential growth. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture the departure from the exponential growth model in the initial growth phase. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth kinetics and the presence of subpopulations with different growth rates that endured for many generations. Based on the hypothesis of existence of multiple inter-converting subpopulations, we developed a branching process model that captures the experimental observations.