Association between ovarian hormones and smoking behavior in women.

Studies examining the association between menstrual cycle phases and smoking behavior in women have yielded mixed results. The purpose of this study was to elucidate the associations between ovarian hormones and smoking by directly measuring ovarian hormone levels and obtaining a laboratory assessment of smoking behaviors. Four hypotheses were tested: increased smoking will be associated with 1) low absolute levels of estradiol and progesterone; 2) decreasing (i.e., dynamic changes in) estradiol and progesterone; 3) lower ratios of progesterone to estradiol, and 4) higher ratios of estradiol to progesterone. Female smokers (≥10 cigarettes/day) with regular menstrual cycles were recruited as part of a larger, ongoing study examining the influence of ovarian hormones on smoking cessation treatment. Participants completed two study visits, including a one-hour adlib smoking topography session, which provided a detailed assessment of smoking behavior. Both the change in hormone levels over time and the relative ratios of ovarian hormones were associated with smoking behavior, but each to a limited extent. Decreases in estradiol (r=−.21, p=.048), and decreases in progesterone (r=−.23, p=.03) were associated with increased puff intensity. Lower ratios of progesterone to estradiol were associated with a greater number of puffs (r=−.26, p=.01) and weight of cigarettes smoked (r=−.29, p=.005). The best predictors of smoking behavior were the ratio of progesterone to estradiol (z=−2.7, p=.004) and the change in estradiol and progesterone over time (z=−2.1, p=.02). This pattern of results may help to explain inconsistent findings in previous studies and suggest potential mechanisms by which hormones influence nicotine addiction

Prioritization of HCV treatment in the direct-acting antiviral era: an economic evaluation

BACKGROUND & AIMS: We determined the optimal HCV treatment prioritization strategy for interferon-free (IFN-free) HCV direct-acting antivirals (DAAs) by disease stage and risk status incorporating treatment of people who inject drugs (PWID). METHODS: A dynamic HCV transmission and progression model compared the cost-effectiveness of treating patients early vs. delaying until cirrhosis for patients with mild or moderate fibrosis, where PWID chronic HCV prevalence was 20, 40 or 60%. Treatment duration was 12weeks at £3300/wk, to achieve a 95% sustained viral response and was varied by genotype/stage in alternative scenarios. We estimated long-term health costs (in £UK=€1.3=$1.5) and outcomes as quality adjusted life-years (QALYs) gained using a £20,000 willingness to pay per QALY threshold. We ranked strategies with net monetary benefit (NMB); negative NMB implies delay treatment. RESULTS: The most cost-effective group to treat were PWID with moderate fibrosis (mean NMB per early treatment £60,640/£23,968 at 20/40% chronic prevalence, respectively), followed by PWID with mild fibrosis (NMB £59,258 and £19,421, respectively) then ex-PWID/non-PWID with moderate fibrosis (NMB £9,404). Treatment of ex-PWID/non-PWID with mild fibrosis could be delayed (NMB -£3,650). In populations with 60% chronic HCV among PWID it was only cost-effective to prioritize DAAs to ex-PWID/non-PWID with moderate fibrosis. For every one PWID in the 20% chronic HCV setting, 2 new HCV infections were averted. One extra HCV-related death was averted per 13 people with moderate disease treated. Rankings were unchanged with reduced drug costs or varied sustained virological response/duration by genotype/fibrosis stage. CONCLUSIONS: Treating PWID with moderate or mild HCV with IFN-free DAAs is cost-effective compared to delay until cirrhosis, except when chronic HCV prevalence and reinfection risk is very high

The Role of Cargo Proteins in GGA Recruitment

Coat proteins are recruited onto membranes to form vesicles that transport cargo from one compartment to another, but the extent to which the cargo helps to recruit the coat proteins is still unclear. Here we have examined the role of cargo in the recruitment of Golgi-localized, γ-ear-containing, ADP ribosylation factor (ARF)-binding proteins (GGAs) onto membranes in HeLa cells. Moderate overexpression of CD8 chimeras with cytoplasmic tails containing DXXLL-sorting signals, which bind to GGAs, increased the localization of all three GGAs to perinuclear membranes, as observed by immunofluorescence. GGA2 was also expressed at approximately twofold higher levels in these cells because it was degraded more slowly. However, this difference only partially accounted for the increase in membrane localization because there was a approximately fivefold increase in GGA2 associated with crude membranes and a ∼12-fold increase in GGA2 associated with clathrin-coated vesicles (CCVs) in cells expressing CD8-DXXLL chimeras. The effect of cargo proteins on GGA recruitment was reconstituted in vitro using permeabilized control and CD8-DXXLL-expressing cells incubated with cytosol containing recombinant GGA2 constructs. Together, these results demonstrate that cargo proteins contribute to the recruitment of GGAs onto membranes and to the formation of GGA-positive CCVs

An Early Biomarker Algorithm Predicts Lethal Graft-Vs-Host Disease and Survival after Allogeneic Hematopoietic Cell Transplantation

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Mutability and mutational spectrum of chromosome transmission fidelity genes

It has been more than two decades since the original chromosome transmission fidelity (Ctf) screen of Saccharomyces cerevisiae was published. Since that time the spectrum of mutations known to cause Ctf and, more generally, chromosome instability (CIN) has expanded dramatically as a result of systematic screens across yeast mutant arrays. Here we describe a comprehensive summary of the original Ctf genetic screen and the cloning of the remaining complementation groups as efforts to expand our knowledge of the CIN gene repertoire and its mutability in a model eukaryote. At the time of the original screen, it was impossible to predict either the genes and processes that would be overrepresented in a pool of random mutants displaying a Ctf phenotype or what the entire set of genes potentially mutable to Ctf would be. We show that in a collection of 136 randomly selected Ctf mutants, >65% of mutants map to 13 genes, 12 of which are involved in sister chromatid cohesion and/or kinetochore function. Extensive screening of systematic mutant collections has shown that ~350 genes with functions as diverse as RNA processing and proteasomal activity mutate to cause a Ctf phenotype and at least 692 genes are required for faithful chromosome segregation. The enrichment of random Ctf alleles in only 13 of ~350 possible Ctf genes suggests that these genes are more easily mutable to cause genome instability than the others. These observations inform our understanding of recurring CIN mutations in human cancers where presumably random mutations are responsible for initiating the frequently observed CIN phenotype of tumors

Biomarkers Predict Graft-Vs-Host Disease Outcomes Better Than Clinical Response after One Week of Treatment

Abstract Graft-versus-host disease (GVHD), the primary cause of non-relapse mortality (NRM) following allogeneic hematopoietic stem cell transplantation, does not always respond to treatment with high dose systemic corticosteroids. We have recently shown that a combination of three biomarkers (TNFR1, ST2, and REG3α) measured at onset of GVHD can predict day 28 response to treatment and 6-month NRM (Levine, Lancet Haem, 2015). Our goal in the current study was to determine if the same biomarker-based Ann Arbor GVHD algorithm can alsopredict treatment response andmortality whenapplied after one week of systemic corticosteroid treatment. The study population consisted of 378 patients (pts) with acute GVHD from 11 centers in the Mount Sinai Acute GVHD International Consortium. All pts were treated with systemic steroids and provided a plasma or serum sample obtained after one week of treatment (±3 days). The median starting dose of systemic steroids for Grade II-IV GVHD was 2.0 mg/kg/day and for Grade I was 1.0 mg/kg/day, after which treatment varied. Patients were divided into test (n=236) and validation (n=142) cohorts. We applied the Ann Arbor GVHD algorithm to concentrations of TNFR1, ST2, and REG3α measured after one week of treatment to generate a predicted probability of 6-month NRM, which we term the treatment score (TS). We employed unsupervised k-medoidclustering to partition TS values from the test cohort into two groups (high and low). This unbiased approach identified a high score group made up of 25% of pts (n=58) in the test cohort. We observed that the day 28 response rate (complete, CR + partial, PR) was significantly lower in pts with high scores compared to low scores in the test cohort (24% vs 65%, p<0.0001) (Fig 1A). Analysis of the validation cohort using the same TS definitions showed similar differences in response rates (22% vs 61%, p<0.0001) (Fig 1B). Further, nearly four times as many pts with high scores in both cohorts died within 6 months from non-relapse causes compared to pts with low scores (test: 57% vs 17%, p<0.0001; validation: 57% vs 14%, p<0.0001) (Fig 1C/D). As expected, the majority of non-relapse deaths in pts treated for GVHD were directly attributable to GVHD (test: 95%; validation: 89%). Relapse rates for high and low score pts were similar (data not shown), and thus pts with a high TS experienced significantly worse overall survival in both cohorts (test: 37% vs 72%, p<0.0001; validation: 38% vs 79%, p<0.0001) (Fig 1E/F). Approximately half of the pts in each cohort (test: 48%; validation: 44%) responded (CR+PR) to the first week of steroids and these ptshad significantly lower 6-month NRM than non-responders (NR) (test: 17% vs 36%, p=0.0002; validation: 13% vs 36%, p=0.0014). Yet the TS continued to stratify mortality risk independently of clinical response. In the test cohort, pts with a high score comprised 16% of all early responders and experienced more than twice the NRM of early responders with a low score (33% vs 13%, p=0.022) (Fig 2A). Conversely, test cohort pts who did not respond by day 7, but had a low score, fared much better than non-responders with a high score (NRM 21% vs 68%, p<0.0001) (Fig 2B). Two thirds of early non-responders comprised this more favorable group. These highly significant results reproduced in the independent validation cohort in similar proportions (CR+PR: 45% vs 6%, p=0.0003; NR: 61% vs 22%, p=0.0001) (Fig 2C/D). Finally, a subset analysis revealed that pts classified as NR after one week of steroids due to isolated, yet persistent, grade I skin GVHD (24/378, 6%) overwhelmingly had low treatment scores (22/24, 92%) and experienced rates of NRM (9%) comparable to responders with low scores, thus forming a distinct, albeit small, subset of pts with non-responsive GVHD that fares particularly well (Fig 3). In conclusion, a treatment score based on three GVHD biomarkers measured after one week of steroids stratifies pts into two groups with distinct risks for treatment failure and 6-month NRM. It is particularly noteworthy that the TS identifies two subsets of pts with steroid refractory (SR) GVHD who have highly different outcomes (Fig 2B/D). The much larger group, approximately two thirds of all SR pts, may not need the same degree of treatment escalation as is traditional for clinical non-response, and thus overtreatment might be avoided. Because the TSis measured at a common decision making time point, it may prove useful to guide risk-adapted therapy. Disclosures Mielke: Novartis: Consultancy; MSD: Consultancy, Other: Travel grants; Celgene: Other: Travel grants, Speakers Bureau; Gilead: Other: Travel grants; JAZZ Pharma: Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Ferrara:Viracor: Patents & Royalties: GVHD biomarker patent. Levine:Viracor: Patents & Royalties: GVHD biomarker patent

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS

Analysis of genetic systems using experimental evolution and whole-genome sequencing

The application of whole-genome sequencing to the study of microbial evolution promises to reveal the complex functional networks of mutations that underlie adaptation. A recent study of parallel evolution in populations of Escherichia coli shows how adaptation involves both functional changes to specific proteins as well as global changes in regulation

Ribosome-Dependent ATPase Interacts with Conserved Membrane Protein in Escherichia coli to Modulate Protein Synthesis and Oxidative Phosphorylation

Elongation factor RbbA is required for ATP-dependent deacyl-tRNA release presumably after each peptide bond formation; however, there is no information about the cellular role. Proteomic analysis in Escherichia coli revealed that RbbA reciprocally co-purified with a conserved inner membrane protein of unknown function, YhjD. Both proteins are also physically associated with the 30S ribosome and with members of the lipopolysaccharide transport machinery. Genome-wide genetic screens of rbbA and yhjD deletion mutants revealed aggravating genetic interactions with mutants deficient in the electron transport chain. Cells lacking both rbbA and yhjD exhibited reduced cell division, respiration and global protein synthesis as well as increased sensitivity to antibiotics targeting the ETC and the accuracy of protein synthesis. Our results suggest that RbbA appears to function together with YhjD as part of a regulatory network that impacts bacterial oxidative phosphorylation and translation efficiency

Zds2p Regulates Swe1p-dependent Polarized Cell Growth in Saccharomyces cerevisiae via a Novel Cdc55p Interaction Domain

A C-terminal region in Zds2p (ZH4) is required for regulation of Swe1p-dependent polarized cell growth and this region is necessary and sufficient for interaction with protein phosphatase 2A regulatory subunit, Cdc55p. Our results indicate that the Zds proteins regulate the Swe1p-dependent G2/M checkpoint in a CDC55-dependent manner