Evaluation of Epigenetic Age Based on DNA Methylation Analysis of Several CpG Sites in Ukrainian Population

Epigenetic clocks are the models, which use CpG methylation levels for the age prediction of an organism. Although there were several epigenetic clocks developed there is a demand for development and evaluation of the relatively accurate and sensitive epigenetic clocks that can be used for routine research purposes. In this study, we evaluated two epigenetic clock models based on the 4 CpG sites and 2 CpG sites in the human genome using the pyrosequencing method for their methylation level estimation. The study sample included 153 people from the Ukrainian population with the age from 0 to 101. Both models showed a high correlation with the chronological age in our study sample (R2 = 0.85 for the 2 CpG model and R2 = 0.92 for the 4 CpG model). We also estimated the accuracy metrics of the age prediction in our study sample. For the age group from 18 to 80 MAD was 5.1 years for the 2 CpG model and 4.1 years for the 4 CpG model. In this regard, we can conclude, that the models evaluated in the study have good age predictive accuracy, and can be used for the epigenetic age evaluation due to the relative simplicity and time-effectiveness.

Selecting and averaging relaxed clock models in Bayesian tip dating of Mesozoic birds

Relaxed clock models are fundamental in Bayesian clock dating, but a single distribution characterizing the clock variation is typically selected. Hence, I developed a new reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm for drawing posterior samples between the independent lognormal (ILN) and independent gamma rates (IGR) clock models. The ability of the rjMCMC algorithm to infer the true model was verified through simulations. I then applied the algorithm to the Mesozoic bird data previously analyzed under the white noise (WN) clock model. In comparison, averaging over the ILN and IGR models provided more reliable estimates of the divergence times and evolutionary rates. The ILN model showed slightly better fit than the IGR model and much better fit than the autocorrelated lognormal (ALN) clock model. When the data were partitioned, different partitions showed heterogeneous model fit for ILN and IGR clocks. The implementation provides a general framework for selecting and averaging relaxed clock models in Bayesian dating analyses.

Genetic and Phenotypic Evidence for the Causal Relationship Between Aging and COVID-19

Epidemiological studies revealed that the elderly and those with comorbidities are most susceptible to COVID-19. To understand how genetics affects the risk of COVID-19, we conducted a multi-instrument Mendelian Randomization (MR) analysis and found that the genetic variation that supports a longer life is significantly associated with the lower risk of COVID-19 infection, as well as being hospitalized after infected. The odds ratio is 0.31 (P = 9.7e-6) and 0.46 (P = 3.3e-4), respectively, per additional 10 years of life. We further applied aging clock models and detected an association between biological age acceleration and future incidence and severity of COVID-19 infection for all subjects and individuals free of chronic disease. Biological age acceleration was also significantly associated with the risk of death in COVID-19 patients. A bivariate genomic scan for age-related COVID-19 infection identified a key contribution of the Notch signaling pathway and immune system. Finally, we performed MR using 389 immune cell traits as exposure and observed a significant negative correlation between their effect on lifespan and COVID-19 risk, especially for B cell-related traits. More specifically, we discovered the lower CD19 level on B cells indicates an increased risk of COVID-19 and potentially decreases the lifespan expectancy, which is further validated in clinical data from COVID-19 patients. Our analysis suggests that the factors that accelerate aging and limit lifespan cause an increased COVID-19 risk. Thus, the interventions target these factors (e.g., reduce biological age), after further validation, may have the opportunity to reduce the risk of COVID-19.

Multi-omic rejuvenation and lifespan extension upon exposure to youthful circulation

Heterochronic parabiosis (HPB) is known for its functional rejuvenation effects across several mouse tissues. However, its impact on the biological age of organisms and their long-term health remains unknown. Here, we performed extended (3-month) HPB, followed by a 2-month detachment period of anastomosed pairs. Old detached mice exhibited improved physiological parameters and lived longer than control isochronic mice. HPB drastically reduced the biological age of blood and liver based on epigenetic analyses across several clock models on two independent platforms; remarkably, this rejuvenation effect persisted even after 2 months of detachment. Transcriptomic and epigenomic profiles of anastomosed mice showed an intermediate phenotype between old and young, suggesting a comprehensive multi-omic rejuvenation effect. In addition, old HPB mice showed transcriptome changes opposite to aging, but akin to several lifespan-extending interventions. Altogether, we reveal that long-term HPB can decrease the biological age of mice, in part through long-lasting epigenetic and transcriptome remodeling, culminating in the extension of lifespan and healthspan.

Bayesian phylogenetic analysis of linguistic data using BEAST

Bayesian phylogenetic methods provide a set of tools to efficiently evaluate large linguistic datasets by reconstructing phylogenies—family trees—that represent the history of language families. These methods provide a powerful way to test hypotheses about prehistory, regarding the subgrouping, origins, expansion, and timing of the languages and their speakers. Through phylogenetics, we gain insights into the process of language evolution in general and into how fast individual features change in particular. This article introduces Bayesian phylogenetics as applied to languages. We describe substitution models for cognate evolution, molecular clock models for the evolutionary rate along the branches of a tree, and tree generating processes suitable for linguistic data. We explain how to find the best-suited model using path sampling or nested sampling. The theoretical background of these models is supplemented by a practical tutorial describing how to set up a Bayesian phylogenetic analysis using the software tool BEAST2.

The emergence of SARS-CoV-2 variants of concern is driven by acceleration of the evolutionary rate

The ongoing SARS-CoV-2 pandemic has seen an unprecedented amount of rapidly generated genome data. These data have revealed the emergence of lineages with mutations associated to transmissibility and antigenicity, known as variants of concern (VOCs). A striking aspect of VOCs is that many of them involve an unusually large number of defining mutations. Current phylogenetic estimates of the evolutionary rate of SARS-CoV-2 suggest that its genome accrues around 2 mutations per month. However, VOCs can have around 15 defining mutations and it is hypothesised that they emerged over the course of a few months, implying that they must have evolved faster for a period of time. We analysed genome sequence data from the GISAID database to assess whether the emergence of VOCs can be attributed to changes in the evolutionary rate of the virus and whether this pattern can be detected at a phylogenetic level using genome data. We fit a range of molecular clock models and assessed their statistical fit. Our analyses indicate that the emergence of VOCs is driven by an episodic increase in the evolutionary rate of around 4-fold the background phylogenetic rate estimate that may have lasted several weeks or months. These results underscore the importance of monitoring the molecular evolution of the virus as a means of understanding the circumstances under which VOCs may emerge.

Testing the inferred transcription rates of a dynamic, gene network model in absolute units

The circadian clock coordinates plant physiology and development. Mathematical clock models have provided a rigorous framework to understand how the observed rhythms emerge from disparate, molecular processes. However, models of the plant clock have largely been built and tested against RNA timeseries data in arbitrary, relative units. This limits model transferability, refinement from biochemical data and applications in synthetic biology. Here, we incorporate absolute mass units into a detailed model of the clock gene network in Arabidopsis thaliana. We re-interpret the established P2011 model, highlighting a transcriptional activator that overlaps the function of REVEILLE 8/LHY-CCA1-LIKE 5. The U2020 model incorporates the repressive regulation of PRR genes, a key feature of the most detailed clock model KF2014, without greatly increasing model complexity. We tested the experimental error distributions of qRT-PCR data calibrated for units of RNA transcripts/cell and of circadian period estimates, in order to link the models to data more appropriately. U2019 and U2020 models were constrained using these data types, recreating previously-described circadian behaviours with RNA metabolic processes in absolute units. To test their inferred rates, we estimated a distribution of observed, transcriptome-wide transcription rates (Plant Empirical Transcription Rates, PETR) in units of transcripts/cell/hour. The PETR distribution and the equivalent degradation rates indicated that the models’ predicted rates are biologically plausible, with individual exceptions. In addition to updated clock models, FAIR data resources and a software environment in Docker, this validation process represents an advance in biochemical realism for models of plant gene regulation.

Integrating Oscillator-Based Circadian Clocks with Crop Growth Simulations

Circadian rhythms play critical roles in plant physiology, growth, development, and survival, and their inclusion in crop growth models is essential for high fidelity results, especially when considering climate change. Commonly used circadian clock models are often inflexible or result in complex outputs, limiting their use in general simulations. Here we present a new circadian clock model based on mathematical oscillators that easily adapts to different environmental conditions and produces intuitive outputs. We then demonstrate its utility as an input to Glycine max development models. This oscillator clock model has the power to simplify the inclusion of circadian cycles and photoperiodic effects in crop growth models and to unify experimental data from field and controlled environment observations.

Clock modelling techniques for an enhanced GNSS orbit determination

<p>The determination of GNSS orbits is generally based on the processing of pseudorange and carrier phase measurements from a station network, with an Orbit Determination and Time Synchronization (ODTS) process. This process involves the satellite and ground station clocks as part of the GNSS measurement reconstruction. The clocks are generally estimated as a snapshot parameter, without assuming any correlation between epochs. However, the stability of satellite and some station clocks, based on technologies of hydrogen, cesium or rubidium, allows for a significant predictability. Taking advantage of this predictability the ODTS process can be improved, especially in those cases where the station network is limited or does not provide a good coverage for certain areas.</p><p>The clock modelling can be directly done by estimating additional parameters in the filter. A quadratic model is generally estimated for each clock, keeping a small snapshot contribution to account for the stochastic part and for potential deviations with respect to the theoretical behavior of the clock. The detection of this kind of deviations in the satellite and station clocks becomes a major factor for achieving a good performance with these techniques. In case the clock experiences feared events like phase or frequency jumps, the estimated clock model stops being valid and the estimation of model parameters needs to be reset.</p><p>In case a composite clock algorithm is used to provide the reference timescale for the ODTS, the estimation of clock models can rely on this algorithm. Algorithms of composite clock are generally based on a Kalman filter that estimates as part of the state vector the differences between each contributing clock and the composite timescale. These differences can be used not only to define the reference timescale of the ODTS, but also to remove the deterministic part of the clocks in the measurement reconstruction. As for the case of clock modelling, for algorithms of composite clock the detection and correction of anomalies in the contributing clocks becomes a critical point.</p><p>In this work, the integration of orbit determination, clock modelling and composite clock algorithms will be described. The impact of clock modeling techniques on the GNSS orbit determination accuracy will be presented, both considering a direct estimation of clock models in the ODTS and the estimation provided by the composite clock algorithm. These analyses will be based on NEODIS, the orbit determination software developed by Thales Alenia Space, which integrates with a Kalman filter approach GNSS orbit determination and composite clock algorithms.</p><p> </p>