Biology subjects

Johansson, M.

Publications and source records attributed to Johansson, M..

7 recordsLinked to original sources

bioRxiv2018

Elimination of ribosome inactivating factors improves the efficiency of Bacillus subtilis and Saccharomyces cerevisiae cell-free translational systems

Cell-free translational systems based on cellular lysates optimized for in vitro protein synthesis have multiple applications both in basic and applied science, ranging from studies of translation regulation to cell-free production of proteins and ribosome-nascent chain complexes. In order to achieve both high activity and reproducibility in a translational system, it is essential that the ribosomes in the cellular lysate are enzymatically active. Here we demonstrate that genomic disruption of genes encoding ribosome inactivating factors - HPF in Bacillus subtilis and Stm1 in Saccharomyces cerevisiae - robustly improve the activities of bacterial and yeast translational systems. A possible next step in developing strains for production of even more efficient cell-free translation systems could be achieved by combining a genomic disruption of the ribosome hibernation machinery with inactivation of the genes responsible for proteolysis and RNA degradation.

biochemistry↗

bioRxiv2018

Direct observation of rotation-coupled protein diffusion along DNA on the microsecond timescale

Many proteins that bind specific DNA sequences search the genome by combining three dimensional (3D) diffusion in the cytoplasm with one dimensional (1D) sliding on non-specific regions of the DNA1-5. It is however not known how sliding proteins are oriented with respect to DNA in order to recognize specific sequences. Here we measure the polarization of fluorescence emission from single fluorescently labeled lac repressor (LacI) molecules sliding on stretched DNA. Real-time feedback-coupled confocal single-particle tracking allows us to measure fluorescence correlation of the sliding molecules. We find that the fluctuations in the fluorescence signal on the s timescale are accurately described by rotation-coupled sliding on DNA. On average, LacI moves [~]50 base pairs per revolution, which is significantly longer than the 10.5 bp helical periodicity of DNA. Our data support a facilitated diffusion model1 where the transcription factor (TF) scans the DNA grooves for hydrogen bonding opportunities in a pre-aligned orientation with occasional slippage out of the groove.

biophysics↗

bioRxiv2018

DTK-Dengue: A new agent-based model of dengue virus transmission dynamics

Dengue virus (DENV) is a pathogen spread by Aedes mosquitoes that has a considerable impact on global health. Agent-based models can be used to explicitly represent factors that are difficult to measure empirically, by focusing on specific aspects of DENV transmission dynamics that influence spread in a particular location. We present a new agent-based model for DENV dynamics, DTK-Dengue, that can be readily applied to new locations and to a diverse set of goals. It extends the vector-borne disease module in the Institute for Disease Modellings Epidemiological Modeling Disease Transmission Kernel (EMOD-DTK) to model DENV dynamics. There are three key modifications present in DTK-Dengue: 1) modifications to how climatic variables influence vector development for Aedes mosquitoes, 2) updates to adult vector behavior to make them more similar to Aedes, and 3) the inclusion of four DENV serotypes, including their effects on human immunity and symptoms. We demonstrate DTK-Dengues capabilities by fitting the model to four interrelated datasets: total and serotype-specific dengue incidences between January 2007 and December 2008 from San Juan, Puerto Rico; the age distribution of reported dengue cases in Puerto Rico during 2007; and the number of adult female Ae. aegypti trapped in two neighborhoods of San Juan between November 2007 and December 2008. The model replicated broad patterns in the reference data, including a correlation between vector population dynamics and rainfall, appropriate seasonality in the reported incidence, greater circulation of DENV-3 than any other serotype, and an inverse relationship between age and the proportion of cases associated with each age group over 20 years old. This exercise demonstrates the potential for DTK-Dengue to assimilate multiple types of epidemiologic data into a realistic portrayal of DENV transmission dynamics. Due to the open availability of the DTK-Dengue software and the availability of numerous other modules for modeling disease transmission and control from EMOD-DTK, this new model has potential for a diverse range of future applications in a wide variety of settings.

epidemiology↗

bioRxiv2018

Inferring rates of metastatic dissemination using stochastic network models

The formation of metastases is driven by the ability of cancer cells to disseminate from the site of the primary tumour to target organs. The process of dissemination is constrained by anatomical features such as the flow of blood and lymph in the circulatory system. We exploit this fact in a stochastic network model of metastasis formation, in which only anatomically feasible routes of dissemination are considered. By fitting this model to two different clinical datasets (tongue & ovarian cancer) we show that incidence data can be modelled using a small number of biologically meaningful parameters. The fitted models reveal site specific relative rates of dissemination and also allow for patient-specific predictions of metastatic involvement based on primary tumour location and stage. Applied to other data sets this type of model could yield insight about seed-soil effects, and could also be used in a clinical setting to provide personalised predictions about the extent of metastatic spread. AUTHOR SUMMARYFor most cancer patients the occurrence of metastases equals incurable disease. Despite this fact our quantitative knowledge about the process of metastatic dissemination is limited. In this manuscript we improve on a previously published mathematical model by incorporating known biological facts about metastatic spread and also consider the temporal dimension of dissemination. The model is fit to two different cancer types with very different patterns of spread, which highlights the versatility of our framework. Properly parametrised this type of model can be used for making personalised predictions about metastatic burden.

cancer biology↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2018

What lies beneath: a spatial mosaic of Zika virus transmission in the 2015-2016 epidemic in Colombia

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

epidemiology↗

bioRxiv2017

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

epidemiology↗