Hereditary chronic neutrophilic leukemia in a four-generation family without transformation to acute leukemia.

Chronic neutrophilic leukemia (CNL) is a rare myeloproliferative neoplasm (MPN) characterized by peripheral blood neutrophilia, marrow granulocyte hyperplasia, hepatosplenomegaly, and driver mutations in the colony-stimulating factor 3 receptor (CSF3R). Designation of activating CSF3R mutations as a defining genomic abnormality for CNL has led to increased recognition of the disease. However, the natural history of CNL remains poorly understood with most patients reported being of older age, lacking germline data, and having poor survival, in part due to transformation to acute leukemia. CSF3R driver mutations in most patients with CNL have been reported to be acquired, although rare cases of germline mutations have been described. Here, we report the largest pedigree to date with familial CNL, spanning four generations with affected family members ranging in age from 4 to 53 years, none of whom have transformed to acute leukemia. A heterozygous T618I CSF3R mutation was identified in peripheral blood and mesenchymal stromal cells from the proband and in all affected living family members, while the unaffected family members tested were homozygous wild type. We show that the T618I mutation also confers a survival advantage to neutrophils in an MCL1-dependent manner. Collectively, these data provide additional insights into the natural history of familial CNL arising from T618I CSF3R mutations and suggest that enhanced neutrophil survival also contributes to the high neutrophil count observed in patients with CNL.

Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer.

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman's correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score ( S g ) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)-KRAS (mutant gene) pair and CD82-NRAS pair in the DLD-1 colon cancer cell line (Spearman's correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.

Precision oncology: lessons learned and challenges for the future.

The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these "first generation" early adopters.

Literature-based discovery of new candidates for drug repurposing.

Drug development is an expensive and time-consuming process; these could be reduced if the existing resources could be used to identify candidates for drug repurposing. This study sought to do this by text mining a large-scale literature repository to curate repurposed drug lists for different cancers. We devised a pattern-based relationship extraction method to extract disease-gene and gene-drug direct relationships from the literature. These direct relationships are used to infer indirect relationships using the ABC model. A gene-shared ranking method based on drug target similarity was then proposed to prioritize the indirect relationships. Our method of assessing drug target similarity correlated to existing anatomical therapeutic chemical code-based methods with a Pearson correlation coefficient of 0.9311. The indirect relationships ranking method achieved a significant mean average precision score of top 100 most common diseases. We also confirmed the suitability of candidates identified for repurposing as anticancer drugs by conducting a manual review of the literature and the clinical trials. Eventually, for visualization and enrichment of huge amount of repurposed drug information, a chord diagram was demonstrated to rapidly identify two novel indications for further biological evaluations.

Translating genomic sequences into antibody efficacy and safety against influenza toward clinical trial outcomes: a case study.

Antibodies (Abs) are regarded as a newly emerging form of therapeutics that can provide passive protection against influenza. Although the application of genomics in clinics has increased dramatically, the number of therapeutics available for the treatment of many diseases remains insufficient. To translate genomics into medicines, we established a computational workflow to reconstruct 3D structures of hemagglutinin [HA, antigen (Ag)] and Ab for modeling Ab-HA interactions, based on their protein sequences. This platform was capable of testing the validity of bioinformatics predictions against viral neutralization titers for four Abs: CH65, CR8020, C05, and 5J8. By considering off-target effects, CR8020, the only successful candidate in clinical trials, was prospectively identified. Our approach could facilitate the discovery of Ab drugs against infectious diseases.

Systematic repression of transcription factors reveals limited patterns of gene expression changes in ES cells.

Networks of transcription factors (TFs) are thought to determine and maintain the identity of cells. Here we systematically repressed each of 100 TFs with shRNA and carried out global gene expression profiling in mouse embryonic stem (ES) cells. Unexpectedly, only the repression of a handful of TFs significantly affected transcriptomes, which changed in two directions/trajectories: one trajectory by the repression of either Pou5f1 or Sox2; the other trajectory by the repression of either Esrrb, Sall4, Nanog, or Tcfap4. The data suggest that the trajectories of gene expression change are already preconfigured by the gene regulatory network and roughly correspond to extraembryonic and embryonic fates of cell differentiation, respectively. These data also indicate the robustness of the pluripotency gene network, as the transient repression of most TFs did not alter the transcriptomes.

SHAVE: shrinkage estimator measured for multiple visits increases power in GWAS of quantitative traits.

Measurement error and biological variability generate distortions in quantitative phenotypic data. In longitudinal studies with repeated measurements, the multiple measurements provide a route to reduce noise and correspondingly increase the strength of signals in genome-wide association studies (GWAS).To optimize noise correction, we have developed Shrunken Average (SHAVE), an approach using a Bayesian Shrinkage estimator. This estimator uses regression toward the mean for every individual as a function of (1) their average across visits; (2) their number of visits; and (3) the correlation between visits. Computer simulations support an increase in power, with results very similar to those expected by the assumptions of the model. The method was applied to a real data set for 14 anthropomorphic traits in ∼6000 individuals enrolled in the SardiNIA project, with up to three visits (measurements) for each participant. Results show that additional measurements have a large impact on the strength of GWAS signals, especially when participants have different number of visits, with SHAVE showing a clear increase in power relative to single visits. In addition, we have derived a relation to assess the improvement in power as a function of number of visits and correlation between visits. It can also be applied in the optimization of experimental designs or usage of measuring devices. SHAVE is fast and easy to run, written in R and freely available online.

Stochastic modeling for the expression of a gene regulated by competing transcription factors.

It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

Zscan4 regulates telomere elongation and genomic stability in ES cells.

Exceptional genomic stability is one of the hallmarks of mouse embryonic stem (ES) cells. However, the genes contributing to this stability remain obscure. We previously identified Zscan4 as a specific marker for two-cell embryo and ES cells. Here we show that Zscan4 is involved in telomere maintenance and long-term genomic stability in ES cells. Only 5% of ES cells express Zscan4 at a given time, but nearly all ES cells activate Zscan4 at least once during nine passages. The transient Zscan4-positive state is associated with rapid telomere extension by telomere recombination and upregulation of meiosis-specific homologous recombination genes, which encode proteins that are colocalized with ZSCAN4 on telomeres. Furthermore, Zscan4 knockdown shortens telomeres, increases karyotype abnormalities and spontaneous sister chromatid exchange, and slows down cell proliferation until reaching crisis by passage eight. Together, our data show a unique mode of genome maintenance in ES cells.

Uncovering early response of gene regulatory networks in ESCs by systematic induction of transcription factors.

To examine transcription factor (TF) network(s), we created mouse ESC lines, in each of which 1 of 50 TFs tagged with a FLAG moiety is inserted into a ubiquitously controllable tetracycline-repressible locus. Of the 50 TFs, Cdx2 provoked the most extensive transcriptome perturbation in ESCs, followed by Esx1, Sox9, Tcf3, Klf4, and Gata3. ChIP-Seq revealed that CDX2 binds to promoters of upregulated target genes. By contrast, genes downregulated by CDX2 did not show CDX2 binding but were enriched with binding sites for POU5F1, SOX2, and NANOG. Genes with binding sites for these core TFs were also downregulated by the induction of at least 15 other TFs, suggesting a common initial step for ESC differentiation mediated by interference with the binding of core TFs to their target genes. These ESC lines provide a fundamental resource to study biological networks in ESCs and mice.

An analytical rate expression for the kinetics of gene transcription mediated by dimeric transcription factors.

To model gene transcription kinetics, empirical fitting with the Hill function or S-system is often used. In this study, we derived an analytical expression for gene transcription rates in a manner similar to that developed for enzyme kinetics to describe the kinetics of gene transcription mediated by dimeric transcription factors (TFs) such as Gcn4p, a Saccharomyces cerevisiae master gene regulator. We showed that the analytical rate expression and its parameters estimated from several sets of experimental data could accurately reproduce the experimentally measured promoter-binding activity of Gcn4p. Furthermore, the analytical rate expression allowed us to derive analytically, rather than fit empirically, the parameters of the Hill function and S-system for use in modelling transcription kinetics. We found that a plot of gene transcription rate against Gcn4p concentration gave a sigmoidal dose-response curve with a positive co-operativity Hill coefficient (approximately 1.25), in accordance with previous experimental findings on the promoter binding of dimeric TFs. The characteristics of the dose-response curve around the estimated cellular Gcn4p concentration suggest that transcription regulation is efficiently controlled under physiological conditions. This work is a useful initial step towards analytically modelling and simulating complicated gene transcription networks.

The role of microRNA in the delayed negative feedback regulation of gene expression.

Oscillatory gene expression plays an important role in somite segmentation during the early developmental stages of vertebrates. Recent experimental studies have shown that microRNA can regulate gene expression by stimulating degradation of mRNA and/or repression of translation. In this communication, we incorporate miRNA into a previous mathematical model of gene expression with delayed negative feedback and demonstrate how this modified model can elucidate the possible effect of miRNA on the oscillatory gene expression. Our finding suggests that miRNA maybe a destabilizing or stabilizing factor in the dynamics of gene expression depending on the severity of its effect on mRNA degradation. Our finding provides testable hypothesis for experimental biologists to further investigate miRNA's increasing functional roles in regulating cellular processes and development.

A network perspective on the topological importance of enzymes and their phylogenetic conservation.

BACKGROUND: A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes. RESULTS: Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance. CONCLUSION: Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network.