HIV Protease and Integrase Empirical Substitution Models of Evolution: Protein-Specific Models Outperform Generalist Models

Diverse phylogenetic methods require a substitution model of evolution that should mimic, as accurately as possible, the real substitution process. At the protein level, empirical substitution models have traditionally been based on a large number of different proteins from particular taxonomic levels. However, these models assume that all of the proteins of a taxonomic level evolve under the same substitution patterns. We believe that this assumption is highly unrealistic and should be relaxed by considering protein-specific substitution models that account for protein-specific selection processes. In order to test this hypothesis, we inferred and evaluated four new empirical substitution models for the protease and integrase of HIV and other viruses. We found that these models more accurately fit, compared with any of the currently available empirical substitution models, the evolutionary process of these proteins. We conclude that evolutionary inferences from protein sequences are more accurate if they are based on protein-specific substitution models rather than taxonomic-specific (generalist) substitution models. We also present four new empirical substitution models of protein evolution that could be useful for phylogenetic inferences of viral protease and integrase.

nQMaker: estimating time non-reversible amino acid substitution models

Amino acid substitution models are a key component in phylogenetic analyses of protein sequences. All amino acid models available to date are time-reversible, an assumption designed for computational convenience but not for biological reality. Another significant downside to time-reversible models is that they do not allow inference of rooted trees without outgroups. In this paper, we introduce a maximum likelihood approach nQMaker, an extension of the recently published QMaker method, that allows the estimation of time non-reversible amino acid substitution models and rooted phylogenetic trees from a set of protein sequence alignments. We show that the non-reversible models estimated with nQMaker are a much better fit to empirical alignments than pre-existing reversible models, across a wide range of datasets including mammals, birds, plants, fungi, and other taxa, and that the improvements in model fit scale with the size of the dataset. Notably, for the recently published plant and bird trees, these non-reversible models correctly recovered the commonly known root placements with very high statistical support without the need to use an outgroup. We provide nQMaker as an easy-to-use feature in the IQ-TREE software (http://www.iqtree.org), allowing users to estimate non-reversible models and rooted phylogenies from their own protein datasets.

MODELING AMINO ACID SUBSTITUTIONS FOR WHOLE GENOMES

Modeling amino acid substitution process is a core task in bioinformatics. New advanced sequencing technologies have generated huge datasets including whole genomes from various species. Estimating amino acid substitution models from whole genome datasets provides us unprecedented opportunities to accurately investigate relationships among species. In this paper, we review state-of-the-art computational methods to estimate amino acid substitution models from large datasets. We also describe a comprehensive pipeline to practically estimate amino acid models from whole genome datasets. Finally, we apply amino acid substitution models to build phylogenomic trees from bird and plant genome datasets. We compare our newly reconstructed phylogenomic trees and published ones and discuss new findings.

Can Fluid-Substitution Models Continue to Ignore the Complex Physico-Chemical Properties of Crude Oil?

The mechanical nature of fluid-substitution models has always been recognized as a major cause of their limited predictive power. Saturants, for instance, are typically treated as simple fluids characterized only by their densities, viscosities, and moduli of elasticity; their chemistry is just ignored, even when that fluid is crude oil. However, crude oil is a complex mixture of several thousands of organic compounds characterized by a variety of molecular weights, polarities, and polarizabilities, and the response of its rheological behavior to acoustic wave propagation is difficult to predict, especially when it resides inthe pore space of rocks. Here, we report ultrasonic-velocity measurements performed on carbonate core plugs saturated with brine and with a light crude oil that are mechanically similar (i.e., having comparable densities, viscosities, and moduli of elasticity) and that show a significant and consistent excess of hardening when the saturant is oil. Dispersion and wettability are excluded as explanations for the data. We hypothesize that asphaltene aggregation and adsorption, as well as paraffin-wax crystallization (and possibly volumetric expansion), combine to cause crude oil to exhibit a dilatant-like behavior within the pore space of carbonates at ultrasonic frequencies. Roughly speaking, the observed effect would be similar to the hardening of oobleck at high deformation rates. This hypothesis could betested in the future by an adequate combination of high-resolution imaging and microfluidic setups. This and similar studies would be beneficial in developing physical fluid-substitution models with a more consistent predictive power.

A time irreversible model of nucleotide substitution for the coronavirus evolution

SARS-CoV-2 is the cause of the worldwide epidemic of severe acute respiratory syndrome. Evolutionary studies of the virus genome will provide a predictor of the fate of COVID-19 in the near future. Recent studies of the virus genomes have shown that C to U substitutions are overrepresented in the genome sequences of SARS-CoV-2. Traditional time-reversible substitution models cannot be applied to the evolution of SARS-CoV-2 sequences. Therefore, in this study, I propose a new time-irreversible model and a new method for estimating the nucleotide substitution rate of SARS-CoV-2. Computer simulations showed that that the new method gives good estimates. I applied the new method to estimate nucleotide substitution rates of SARS-CoV-2 sequences. The result suggests that the rate of C to U substitution of SARS-Cov-2 is ten times higher than other types of substitutions.

Physical Activity Intensity and Type 2 Diabetes: Isotemporal Substitution Models in the “Seguimiento Universidad de Navarra” (SUN) Cohort

Which intensity of physical activity (PA) is associated with type 2 diabetes (T2D) prevention remains unclear. Isotemporal substitution models assess the relationship of replacing the amount of time spent in one activity for another. We aimed to assess T2D incidence associated with light-to-moderate physical activity (LMPA) and vigorous physical activity (VPA) using isotemporal substitution models of one hour (1 h) sitting by 1 h of LMPA or VPA. Furthermore, we evaluated the effect on T2D of an isotemporal substitution of 1 h sitting by 1 h of slow (light physical activity) or brisk–very brisk walking (moderate physical activity). In total, 20,060 participants (both sexes) of the SUN cohort (Spain) initially free of T2D followed-up during a median of 12 years were included. Cox regression models were fitted to assess the association between the substitution of 1 h LMPA, VPA, slow and brisk–very brisk pace by 1 h sitting and T2D. The replacement of 1 h sitting time by 1 h of VPA was associated with an adjusted HR of 0.52 (95% CI: 0.34–0.80), not observed for the substitution by 1 h of LMPA (HR 0.93; 95% CI: 0.73–1.20). An apparent inverse association was observed for the replacement of 1 h sitting time by 1 h of brisk/very brisk walking (HR: 0.69; 95% CI: 0.46–1.04), not observed by 1 h of slow pace. From equal conditions of duration and frequency of PA, the higher the intensity of PA, the greater the T2D prevention.

Disentangling sources of interference in the N-back task

The N-back task is a common working memory paradigm: subjects judge whether the current item in a sequence (the probe) matches the one presented N items ago (the target). Performance in the N-back task is susceptible to interference from intervening items (distractors), especially when the probe and the distractor are similar. However, the categorical stimuli usually used in the classic N-back task are not suitable for distinguishing possible causes of interference. Here, we instead used parametric stimuli (varying in orientation or color) to measure the amount of interference induced in a 2-back task by the 1-back distractor. As expected, we find that interference was larger when the feature distance between the probe and the distractor was smaller. To investigate the source of the interference, we fitted four mathematical models to the data. In the No interference model, the observer utilizes an optimal decision strategy. In the Early pooling model, the ob- server has access only to a mixed measurement of the target and distractor, but employs an optimal de-mixing strategy. In the Late pooling model, the observer mixes decision variables that are optimal for the target and distractor separately. In the Substitution model, the observer swaps the target with the distractor on some proportion of trials, and decides optimally otherwise. The No interference and the Early pooling models described the data substantially worse than the Late pooling and the Substitution models. However, we were not able to distinguish the Late pooling and Substitution models. Our modeling results suggest that interference in the 2-back task results either from the pooling of decision variables based the “target” and “distractor” separately, or from the substitution of the distractor for the target. Our study opens the door to parametrically examining the sources of interference in similar cognitive tasks.