Biology subjects

Smith, N.

Publications and source records attributed to Smith, N..

4 recordsLinked to original sources

bioRxiv2018

Thermal acclimation of leaf respiration consistent with optimal plant function

Main Main References Introduction Terrestrial plant respiration is a major component of the global carbon cycle, releasing ca. 60 Pg C yr-1 to the atmosphere: six times more than anthropogenic CO2 emissions from all sources combined (Ciais et al. 2014). About half this flux is leaf maintenance respiration in darkness (Rd) (Atkin et al. 2007). Rd is highly temperature-dependent, following a near-exponential relationship over short time-scales (Heskel et al. 2016). It has been predicted that global warming will increase Rd and accelerate future climate change via a carbon-climate feedback (Cox et al. 2000; Huntingford et al. 2013). However, large uncertainties in future ...

ecology↗

bioRxiv2018

Genome-wide Meta-analysis of 158,000 Individuals of European Ancestry Identifies Three Loci Associated with Chronic Back Pain

OBJECTIVESTo conduct a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP).\n\nMETHODSAdults of European ancestry were included from 16 cohorts in Europe and North America. CBP cases were defined as those reporting back pain present for >3-6 months; non-cases were included as comparisons (\"controls\"). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5x10-8. Suggestive (p<5x10-7) and genome-wide significant (p<5x10-8) variants were carried forward for replication or further investigation in an independent sample.\n\nRESULTSThe discovery sample was comprised of 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p=7.2x10-10). This was subsequently replicated in an independent sample of 283,752 subjects, including 50,915 cases (OR 1.06, p=5.3x10-11), and exceeded genome-wide significance in joint meta-analysis (0R=1.07, p=4.5x10-19). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p=4.4x10-13), and an intronic variant, rs4384683, in DCC (OR 0.97, p=2.4x10-10).\n\nDISCUSSIONIn this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC).

genomics↗

bioRxiv2017

Stress granule-inducing eukaryotic translation initiation factor 4A inhibitors block influenza A virus replication

Eukaryotic translation initiation factor 4A (eIF4A) is a helicase that facilitates assembly of the translation preinitiation complex by unwinding structured mRNA 5 untranslated regions. Pateamine A (PatA) and silvestrol are natural products that disrupt eIF4A function and arrest translation, thereby triggering the formation of cytoplasmic aggregates of stalled preinitiation complexes known as stress granules (SGs). Here we examined the effects of eIF4A inhibition by PatA and silvestrol on influenza A virus (IAV) protein synthesis and replication in cell culture. Treatment of infected cells with either PatA or silvestrol at early times post-infection results in SG formation, arrest of viral protein synthesis and failure to replicate the viral genome. PatA, which irreversibly binds to eIF4A, sustained long-term blockade of IAV replication following drug withdrawal, and inhibited IAV replication at concentrations that had minimal cytotoxicity. By contrast, the antiviral effects of silvestrol were fully reversible; drug withdrawal caused rapid SG dissolution and resumption of viral protein synthesis. IAV inhibition by silvestrol was invariably associated with cytotoxicity. PatA blocked replication of genetically divergent IAV strains, suggesting common dependence on host eIF4A activity. This study demonstrates the feasibility of targeting core host protein synthesis machinery to prevent viral replication.\n\nIMPORTANCEInfluenza A virus (IAV) relies on cellular protein synthesis to decode viral messenger RNAs. Pateamine A and silvestrol are natural products that inactivate an essential protein synthesis protein known as eIF4A. Here we show that IAV is sensitive to these eIF4A inhibitor drugs. Treatment of infected cells with pateamine A or silvestrol prevented synthesis of viral proteins, viral genome replication and release of infectious virions. The irreversible eIF4A inhibitor pateamine A sustained long-term blockade of viral replication, whereas viral protein synthesis quickly resumed after silvestrol was removed from infected cells. Prolonged incubation of either infected or uninfected cells with these drugs induced the programmed cell death cascade called apoptosis. Our findings suggest that core components of the host protein synthesis machinery are viable targets for antiviral drug discovery. The most promising drug candidates should selectively block protein synthesis in infected cells without perturbing bystander uninfected cells.

microbiology↗

bioRxiv2017

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.\n\nAuthor SummaryCell cycle - the process in which a parent cell replicates its DNA and divides into two daughter cells - is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the \"edge of chaos\" is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

systems biology↗