Biology subjects

Theobald, D.

Publications and source records attributed to Theobald, D..

8 recordsLinked to original sources

bioRxiv2026

High Intensity Interval Training in Aged Female Mice Preserves Physical, Cognitive, and Cardiovascular Function

BACKGROUNDAlong with advancing age comes declines in physical, cognitive, and cardiovascular function. This diminished capacity may lead to decreased ability to perform activities of daily living, disability onset, and loss of independence. Exercise is a regenerative medicine therapy that can mitigate this loss of function. High intensity interval training (HIIT) is an aerobic exercise paradigm consisting of intense activity periods interspersed with bouts of active recovery. Previously we demonstrated that HIIT preserved physical function in adult, middle-aged, and older male mice. However, whether HIIT preserves physical, cognitive, and cardiovascular function, mitigates frailty, and improves brain and heart health in older adult female mice remains unknown. HYPOTHESISCognitive, physical, and cardiovascular function in older adult female C57BL/6 will be preserved in exercised mice (HIIT) versus sedentary control (SED). METHODSMice (HIIT and SED, both n=9, 24m at end) were tested pre/post-intervention for physical (rotarod, treadmill, grip meter, inverted cling, voluntary wheel running, activity monitor), cognitive (open field, novel object recognition, puzzle box, y-maze), and cardiovascular (blood pressure, echocardiogram) function, body composition, and whole body calorimetry. The mice underwent 14-weeks of HIIT training with progressive volume and intensity. RESULTSHIIT significantly (p<0.05) increased or preserved function in many tests including: aerobic capacity (+71% HIIT versus, vs, no change, NC, in SED), four limb strength/endurance (-67% SED vs -28% HIIT), forelimb strength (-16% SED vs NC HIIT), overall motor function (NC SED vs +39% HIIT), executive function (NC SED vs +73% HIIT), and exploratory behavior, which improved across multiple tests with HIIT while remaining unchanged in SED. HIIT also reduced both systolic blood pressure by 12% (-17 mmHg) and mean arterial pressure by -16 mmHg. In addition, HIIT significantly reduced cardiac fibrosis, increased muscle fiber type 2a percentage, reduced IL-1{beta} expression in the hypothalamus, and mitigated frailty onset. CONCLUSIONHIIT significantly reduced age-related functional loss in all three domains assessed while preventing frailty onset in older adult females and improving markers of brain and heart health.

physiology↗

bioRxiv2024

Crystal Structure of Caryolan-1-ol Synthase, a Sesquiterpene Synthase Catalyzing an Initial Anti-Markovnikov Cyclization Reaction

In a continuing effort to understand reaction mechanisms of terpene synthases catalyzing initial anti-Markovnikov cyclization reactions, we solved the X-ray crystal structure of (+)-caryolan-1-ol synthase (CS) from Streptomyces griseus, with and without an inactive analog of the FPP substrate, 2-fluorofarnesyl diphosphate (2FFPP), bound in the active site of the enzyme. The CS-2FFPP complex was solved to 2.65 [A] resolution and showed the ligand in a linear, elongated orientation, incapable of undergoing the initial cyclization event to form a bond between carbons C1 and C11. Intriguingly, the apo CS structure (2.2 [A]) also had electron density in the active site, in this case density that was well fit with a curled-up tetraethylene glycol molecule presumably recruited from the crystallization medium. The density was also well fit by a molecule of farnesene suggesting that the structure may mimic an intermediate along the reaction coordinate. The curled-up conformation of tetraethylene glycol was accompanied by dramatic rotamer shifts among active-site residues. Most notably, W56 was observed to undergo a 90{degrees} rotation between the 2FFPP complex and apo-enzyme structures, suggesting that it contributes to steric interactions that help curl the tetraethylene glycol molecule in the active site, and by extension perhaps also a derivative of the FPP substrate in the normal course of the cyclization reaction. In support of this proposal, the CS W56L variant lost the ability to cyclize the FPP substrate and produced only the linear terpene products farnesol and - and {beta}-farnesene.

biochemistry↗

bioRxiv2023

The most probable ancestral sequence reconstruction yields proteins without systematic bias in thermal stability or activity

Ancestral sequence resurrection (ASR) is the inference of extinct biological sequences from extant sequences, the most popular of which are based on probabilistic models of evolution. ASR is becoming a popular method for studying the evolution of enzyme characteristics. The properties of ancestral enzymes are biochemically and biophysically characterized to gain some knowledge regarding the origin of some enzyme property. Current methodology relies on resurrection of the single most probable (SMP) sequence and is systematically biased. Previous theoretical work suggests this will result in a thermostability bias in resurrected SMP sequences, and even the activity, calling into question inferences derived from ancestral protein properties. We experimentally test the potential stability bias hypothesis by resurrecting 40 malate and lactate dehydrogenases. Despite the methodological bias in resurrecting an SMP protein, the measured biophysical and biochemical properties of the SMP protein are not biased in comparison to other, less probable, resurrections. In addition, the SMP protein property seems to be representative of the ancestral probability distribution. Therefore, the conclusions and inferences drawn from the SMP protein are likely not a source of bias. SignificanceAncestral sequence resurrection (ASR) is a powerful tool for: determining how new protein functions evolve; inferring the properties of an environment in which species existed; and protein engineering applications. We demonstrate, using lactate and malate dehydrogenases (L/MDHs), that resurrecting the single most probable sequence (SMP) from a maximum likelihood phylogeny does not result in biased activity and stability relative to sequences sampled from the posterior probability distribution. Previous studies using experimentally measured phenotypes of SMP sequences to make inferences about the environmental conditions and the path of evolution are likely not biased in their conclusions. Serendipitously, we discover ASR is also a valid tool for protein engineering because sampled reconstructions are both highly active and stable.

biochemistry↗

bioRxiv2022

Ancestral sequence reconstructions evaluated by extant sequence cross-validation

Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term "extant sequence reconstruction" (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein.

evolutionary biology↗

bioRxiv2021

Bayesian machine learning analysis of single-molecule fluorescence colocalization images

Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non-specific surface binding of the fluorescent molecules, and analysis methods that require subjective inputs to achieve accurate results. Here, we use Bayesian probabilistic programming to implement Tapqir, an unsupervised machine learning method that incorporates a holistic, physics-based causal model of CoSMoS data. This method accounts for uncertainties in image analysis due to photon and camera noise, optical non-uniformities, non-specific binding, and spot detection. Rather than merely producing a binary "spot/no spot" classification of unspecified reliability, Tapqir objectively assigns spot classification probabilities that allow accurate downstream analysis of molecular dynamics, thermodynamics, and kinetics. We both quantitatively validate Tapqir performance against simulated CoSMoS image data with known properties and also demonstrate that it implements fully objective, automated analysis of experiment-derived data sets with a wide range of signal, noise, and non-specific binding characteristics.

biophysics↗

bioRxiv2020

Bimodal evolution of Src and Abl kinase substrate specificity revealed using mammalian cell extract as substrate pool

The specificity of phosphorylation by protein kinases is essential to the integrity of biological signal transduction. While peptide sequence specificity for individual kinases has been examined previously, here we explore the evolutionary progression that has led to the modern substrate specificity of two non-receptor tyrosine kinases, Abl and Src. To efficiently determine the substrate specificity of modern and reconstructed ancestral kinases, we developed a method using mammalian cell lysate as the substrate pool, thereby representing the naturally occurring substrate proteins. We find that the oldest tyrosine kinase ancestor was a promiscuous enzyme that evolved through a more specific last common ancestor into a specific human Abl. In contrast, the parallel pathway to human Src involved a loss of substrate specificity, leading to general promiscuity. These results add a new facet to our understanding of the evolution of signaling pathways, with both subfunctionalization and neofunctionalization along the evolutionary trajectories.

biochemistry↗

bioRxiv2019

A Probabilistic Programming Approach to Protein Structure Superposition

Optimal superposition of protein structures is crucial for understanding their structure, function, dynamics and evolution. We investigate the use of probabilistic programming to superimpose protein structures guided by a Bayesian model. Our model THESEUS-PP is based on the THESEUS model, a probabilistic model of protein superposition based on rotation, translation and perturbation of an underlying, latent mean structure. The model was implemented in the deep probabilistic programming language Pyro. Unlike conventional methods that minimize the sum of the squared distances, THESEUS takes into account correlated atom positions and heteroscedasticity (i.e., atom positions can feature different variances). THESEUS performs maximum likelihood estimation using iterative expectation-maximization. In contrast, THESEUS-PP allows automated maximum a-posteriori (MAP) estimation using suitable priors over rotation, translation, variances and latent mean structure. The results indicate that probabilistic programming is a powerful new paradigm for the formulation of Bayesian probabilistic models concerning biomolecular structure. Specifically, we envision the use of the THESEUS-PP model as a suitable error model or likelihood in Bayesian protein structure prediction using deep probabilistic programming.

bioinformatics↗

bioRxiv2018

Functional and structural resilience of the active site loop in the evolution of Plasmodium lactate dehydrogenase.

The malarial pathogen Plasmodium falciparum (Pf) is a member of the Apicomplexa, which independently evolved a highly specific lactate dehydrogenase (LDH) from an ancestral malate dehydrogenase (MDH) via a five-residue insertion in a key active site loop. PfLDH is widely considered an attractive drug target due to its unique active site. Apicomplexan loop conservation suggests that a particular insertion sequence was required to evolve LDH specificity, and we previously showed (Boucher 2014) that a tryptophan in the insertion, W107f, is essential for activity and specificity. However, the roles of other residues in the loop are currently unknown. Here we show that PfLDH activity is remarkably resilient to radical perturbations of both loop identity and length. Thus, alternative insertions could have evolved LDH specificity as long as they contained a tryptophan in the proper location. PfLDH therefore has high potential to develop resistance to drugs that target its distinctive active site.

biochemistry↗