Allegra A. Petti

Automated modelling of signal transduction networks

BackgroundIntracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved.ResultsWe have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles.ConclusionWe show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

TP53 and Decitabine in Acute Myeloid Leukemia and Myelodysplastic Syndromes

BACKGROUND The molecular determinants of clinical responses to decitabine therapy in patients with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS) are unclear. METHODS We enrolled 84 adult patients with AML or MDS in a single-institution trial of decitabine to identify somatic mutations and their relationships to clinical responses. Decitabine was administered at a dose of 20 mg per square meter of body-surface area per day for 10 consecutive days in monthly cycles. We performed enhanced exome or gene-panel sequencing in 67 of these patients and serial sequencing at multiple time points to evaluate patterns of mutation clearance in 54 patients. An extension cohort included 32 additional patients who received decitabine in different protocols. RESULTS Of the 116 patients, 53 (46%) had bone marrow blast clearance (<5% blasts). Response rates were higher among patients with an unfavorable-risk cytogenetic profile than among patients with an intermediate-risk or favorable-risk cytogenetic profile (29 of 43 patients [67%] vs. 24 of 71 patients [34%], P<0.001) and among patients with TP53 mutations than among patients with wild-type TP53 (21 of 21 [100%] vs. 32 of 78 [41%], P<0.001). Previous studies have consistently shown that patients with an unfavorable-risk cytogenetic profile and TP53 mutations who receive conventional chemotherapy have poor outcomes. However, in this study of 10-day courses of decitabine, neither of these risk factors was associated with a lower rate of overall survival than the rate of survival among study patients with intermediate-risk cytogenetic profiles. CONCLUSIONS Patients with AML and MDS who had cytogenetic abnormalities associated with unfavorable risk, TP53 mutations, or both had favorable clinical responses and robust (but incomplete) mutation clearance after receiving serial 10-day courses of decitabine. Although these responses were not durable, they resulted in rates of overall survival that were similar to those among patients with AML who had an intermediate-risk cytogenetic profile and who also received serial 10-day courses of decitabine. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT01687400 .).

Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia

IMPORTANCE Tests that predict outcomes for patients with acute myeloid leukemia (AML) are imprecise, especially for those with intermediate risk AML. OBJECTIVES To determine whether genomic approaches can provide novel prognostic information for adult patients with de novo AML. DESIGN, SETTING, AND PARTICIPANTS Whole-genome or exome sequencing was performed on samples obtained at disease presentation from 71 patients with AML (mean age, 50.8 years) treated with standard induction chemotherapy at a single site starting in March 2002, with follow-up through January 2015. In addition, deep digital sequencing was performed on paired diagnosis and remission samples from 50 patients (including 32 with intermediate-risk AML), approximately 30 days after successful induction therapy. Twenty-five of the 50 were from the cohort of 71 patients, and 25 were new, additional cases. EXPOSURES Whole-genome or exome sequencing and targeted deep sequencing. Risk of identification based on genetic data. MAIN OUTCOMES AND MEASURES Mutation patterns (including clearance of leukemia-associated variants after chemotherapy) and their association with event-free survival and overall survival. RESULTS Analysis of comprehensive genomic data from the 71 patients did not improve outcome assessment over current standard-of-care metrics. In an analysis of 50 patients with both presentation and documented remission samples, 24 (48%) had persistent leukemia-associated mutations in at least 5% of bone marrow cells at remission. The 24 with persistent mutations had significantly reduced event-free and overall survival vs the 26 who cleared all mutations. Patients with intermediate cytogenetic risk profiles had similar findings. [table: see text]. CONCLUSIONS AND RELEVANCE The detection of persistent leukemia-associated mutations in at least 5% of bone marrow cells in day 30 remission samples was associated with a significantly increased risk of relapse, and reduced overall survival. These data suggest that this genomic approach may improve risk stratification for patients with AML.

RNA splicing is a primary link between genetic variation and disease.

RNA splicing links genetics to disease Many genetic variants associated with disease have no apparent effect on any specific protein coding sequence. Li et al. systematically analyzed the effects of DNA variants on the main steps of gene regulation, from the chromatin state through protein function. One-third of expression quantitative train loci (QTLs) are mediated through transcriptional processes, not chromatin. Splice QTLs and expression QTLs are about comparable in their complex disease risk. Posttranscriptional mechanisms therefore play a large role in translating genotype to phenotype. Science, this issue p. 600 Phenotype is most affected by genetic variants that influence gene expression and transcript splicing. Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate

Oscillations in patterns of expression of a large fraction of yeast genes are associated with the “metabolic cycle,” usually seen only in prestarved, continuous cultures of yeast. We used FISH of mRNA in individual cells to test the hypothesis that these oscillations happen in single cells drawn from unsynchronized cultures growing exponentially in chemostats. Gene-expression data from synchronized cultures were used to predict coincident appearance of mRNAs from pairs of genes in the unsynchronized cells. Quantitative analysis of the FISH results shows that individual unsynchronized cells growing slowly because of glucose limitation or phosphate limitation show the predicted oscillations. We conclude that the yeast metabolic cycle is an intrinsic property of yeast metabolism and does not depend on either synchronization or external limitation of growth by the carbon source.

pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens

Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq.

Survival of starving yeast is correlated with oxidative stress response and nonrespiratory mitochondrial function

Survival of yeast during starvation has been shown to depend on the nature of the missing nutrient(s). In general, starvation for “natural” nutrients such as sources of carbon, phosphate, nitrogen, or sulfate results in low death rates, whereas starvation for amino acids or other metabolites in auxotrophic mutants results in rapid loss of viability. Here we characterized phenotype, gene expression, and metabolite abundance during starvation for methionine. Some methionine auxotrophs (those with blocks in the biosynthetic pathway) respond to methionine starvation like yeast starving for natural nutrients such as phosphate or sulfate: they undergo a uniform cell cycle arrest, conserve glucose, and survive. In contrast, methionine auxotrophs with defects in the transcription factors Met31p and Met32p respond poorly, like other auxotrophs. We combined physiological and gene expression data from a variety of nutrient starvations (in both respiratory competent and incompetent cells) to show that successful starvation response is correlated with expression of genes encoding oxidative stress response and nonrespiratory mitochondrial functions, but not respiration per se.

Fast-acting and nearly gratuitous induction of gene expression and protein depletion in Saccharomyces cerevisiae

We developed systems to rapidly express any yeast gene or to specifically degrade any protein, each with minimal untargeted disturbance of cell physiology. We illustrate applications of these new tools for elucidating the architecture and dynamics of genetic regulatory networks.

Coordinated regulation of sulfur and phospholipid metabolism reflects the importance of methylation in the growth of yeast.

The sulfur assimilation and phospholipid biosynthesis pathways interact metabolically and transcriptionally. Genetic analysis, genome-wide sequencing, and expression microarrays show that regulators of these pathways, Met4p and Opi1p, control cellular methylation capacity that can limit the growth rate.

Combinatorial control of diverse metabolic and physiological functions by transcriptional regulators of the yeast sulfur assimilation pathway

The sulfur assimilation pathway is used to understand how combinatorial transcription coordinates cellular processes. Global gene expression was measured in yeast lacking different combinations of transcription factors in order to determine how these factors coordinate sulfur assimilation with diverse metabolic and physiological processes.