Biology subjects

Bohlin, J.

Publications and source records attributed to Bohlin, J..

6 recordsLinked to original sources

bioRxiv2026

Exploring the influence of mutation rates with stochastic perturbations on genome evolution

We present a stochastic version of the classical Luria-Delbruck mutation rate model using Ito calculus. Our model is used to explore implications of population growth with randomly varying mutation rates. By comparing the It&ocirc version of the Luria-Delbruck mutation rate model to the deterministic we find that stochastic effects can have profound implications for genome evolution. In particular, as the growth rate decreases below the population's carrying capacity, or another given threshold, we show that stochastic effects can dominate the evolution of a population in a feedback-based manner. Moreover, we find that the influence of stochastic effects increase as the population reaches its carrying capacity if not accounted for. Our model also suggests strategies for a population to overcome increasing mutational load and evolve 'robustness' to genetic drift. We discuss our methodology in light of the SARS-CoV-2 virus and implications for its molecular evolution. Finally, we conclude that Ito calculus incorporates stochastic effects in mathematical models in a way that is very natural to biological systems.

evolutionary biology↗

bioRxiv2024

The evolutionary and molecular history of a chikungunya virus outbreak lineage

In 2018-2019, Thailand experienced a nationwide spread of chikungunya virus (CHIKV), with approximately 15,000 confirmed cases of disease reported. Here, we investigated the evolutionary and molecular history of the East/Central/South African (ECSA) genotype to determine the origins of the 2018-2019 CHIKV outbreak in Thailand. This was done using newly sequenced clinical samples from travellers returning to Sweden from Thailand in late 2018 and early 2019 and previously published genome sequences. Our phylogeographic analysis showed that before the outbreak in Thailand, the Indian Ocean lineage (IOL) found within the ESCA, had evolved and circulated in East Africa, South Asia, and Southeast Asia for about 15 years. In the first half of 2017, an introduction occurred into Thailand from another South Asian country, most likely Bangladesh, which subsequently developed into a large outbreak in Thailand with export to neighbouring countries. Based on comparative phylogenetic analyses of the complete CHIKV genome and protein modelling, we also identified amino acid substitutions that may be associated with immune evasion, increased spread, and virulence. We identified several mutations in the E1/E2 spike complex, such as E1 K211E and E2 V264A, which are highly relevant as they may lead to changes in vector competence, transmission efficiency and pathogenicity of the virus. A number of mutations (E2 G205S, Nsp3 D372E, Nsp2 V793A), that emerged shortly before the outbreak of the virus in Thailand in 2018 may have altered antibody binding and recognition due to their position. This study not only improves our understanding of the factors contributing to the epidemic in Southeast Asia, but also has implications for the development of effective response strategies and the potential development of new vaccines. Author SummaryWe investigated the evolutionary and molecular history of the East/Central/South African (ECSA) genotype to determine the origins of the 2018-2019 chikungunya virus (CHIKV) outbreak in Thailand. We used newly sequenced clinical samples from travellers returning to Sweden from Thailand in late 2018 and early 2019 together with previously published genome sequences. Our phylogeographic analysis shows that the Indian Ocean lineage (IOL), found within ECSA, evolved in Eastern Africa, Southern Asia, and Southeast Asia for about 15 years before the outbreak in Thailand in 2018. We have also identified amino acid substitutions that may be associated with immune evasion, increased spread, and higher virulence that occurred prior to the outbreak and may have played a critical role in the rapid spread of the virus. Our study concludes that monitoring and understanding CHIKV dynamics remains critical for an effective response to the previously unpredictable outbreaks of the virus.

microbiology↗

bioRxiv2022

The X-factor in ART: does the use of Assisted Reproductive Technologies influence DNA methylation on the X chromosome?

BackgroundAssisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate DNAm differences on the X chromosome. Materials and MethodsThe discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). The replication cohort consisted of 149 ART and 58 non-ART neonates from the Australian "Clinical review of the Health of adults conceived following Assisted Reproductive Technologies" (CHART) study. The Illumina EPIC array was used to measure DNA methylation (DNAm) in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ("XWASs" hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. For replication purposes, we ran an analogous model to our main model on the CHART dataset. Results and conclusionsIn the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted even after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental infertility as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. Overall, genes that co-localized with the significant CpGs and DMRs are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism. These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to naturally-conceived.

genetics↗

bioRxiv2021

Cross-fitted instrument: a blueprint for one-sample Mendelian Randomization

Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a simple solution for handling weak instrument bias by introducing a new type of instrumental variable called cross-fitted instrument (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. The estimates are then used to perform an IVR on each partition. We adapt CFI to Mendelian randomization (MR) and term this adaptation Cross-Fitting for Mendelian Randomization (CFMR). A major advantage of CFMR is its use of all the available data to select genetic instruments, as opposed to traditional two-sample MR where a large part of the data is only used for instrument selection. Consequently, CFMR has the potential to enhance the power of MR in a meta-analysis setting by enabling an unbiased one-sample MR to be performed in each cohort prior to meta-analyzing the results across all the cohorts. In a similar fashion, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in consortia-led meta-analyses where the participating cohorts might be of different ethnicities. To our knowledge, there are currently no MR approach that can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting. Key messagesO_LIWe develop a new method to enable an unbiased one-sample Mendelian Randomization. C_LIO_LIThe new method provides the same power as the standard two-sample Mendelian Randomization approach and does not require summary statistics from a genome-wide association study in an independent cohort. C_LIO_LIOur approach enables a cross-ethnic instrumental variable regression to account for heterogeneity in a sample consisting of multiple ethnicities. C_LI

bioinformatics↗

bioRxiv2021

Amplification of the epigenetic (gestational) age acceleration signal

BackgroundEpigenetic (gestational) age acceleration (E(G)AA) is associated with environmental exposures and health outcomes in humans. However, E(G)AA is the residual term from a regression of epigenetic age (outcome) on chronological (gestational) age (predictor) and therefore strongly obscured by noise from multiple sources. Here, we propose a simple procedure, based on regression, principal component analysis (PCA), and the Lasso, that amplifies E(G)AA signals. More specifically, we first regress given (gestational) age against each CpG used for epigenetic (gestational) age prediction. The CpGs are typically taken from one of several epigenetic clocks available. PCA is subsequently performed on the resulting matrix of residual vectors for each CpG as it projects the E(G)AA signal onto perpendicular principal components (PCs), thereby separating signal from noise. Finally, we use the Lasso to select PCs associated with an outcome of interest. We apply our method to previous studies: EAA in patients with Downs syndrome and Werners syndrome and EGAA of newborns exposed to prenatal smoking as well as associations with maternal BMI. ResultsThe extracted EAA components computed using our proposed procedure revealed a significant association with Downs syndrome (PB<0.05, Bonferroni adjusted for multiple testing) as well as for Werners Syndrome (PB<0.05). For EGAA we find a significant association with maternal prenatal smoking (PB<0.05, also Bonferroni adjusted) and maternal BMI (PB<0.05). Additionally, by examining the loadings of the PCs of interest, and contrary to residual EGAA, our method can identify implicated CpGs. ConclusionsOur findings suggest that our proposed procedure leads to a remarkable amplification of the E(G)AA signal. Furthermore, our method reveals that E(G)AA is a composite signal that can be driven by multiple independent factors.

bioinformatics↗

bioRxiv2020

Design, optimization, and analysis of large DNA and RNA nanostructures through interactive visualization, editing, and molecular simulation

This work seeks to remedy two deficiencies in the current nucleic acid nanotechnology software environment: the lack of both a fast and user-friendly visualization tool and a standard for common structural analyses of simulated systems. We introduce here oxView, a web browser-based visualizer that can load structures with over 1 million nucleotides, create videos from simulation trajectories, and allow users to perform basic edits to DNA and RNA designs. We additionally introduce open-source software tools for extracting common structural parameters to characterize large DNA/RNA nanostructures simulated using the coarse-grained modeling tool, oxDNA, which has grown in popularity in recent years and is frequently used to prototype new nucleic acid nanostructural designs, model biophysics of DNA/RNA processes, and rationalize experimental results. The newly introduced software tools facilitate the computational characterization of DNA/RNA designs by providing multiple analysis scripts, including mean structures and structure flexibility characterization, hydrogen bond fraying, and interduplex angles. The output of these tools can be loaded into oxView, allowing users to interact with the simulated structure in a 3D graphical environment and modify the structures to achieve the required properties. We demonstrate these newly developed tools by applying them to in silico design, optimization and analysis of a range of DNA and RNA nanostructures.

biophysics↗