bioRxiv Science⌕ Search

Biology subjects

Orlandi, M.

Publications and source records attributed to Orlandi, M..

2 recordsLinked to original sources

Older adults do not have a higher metabolic cost than younger adults in outdoor overground walking.

The metabolic cost of walking is widely used to evaluate human performance and effectiveness of clinical interventions. Decades of laboratory research, largely based on treadmill experiments, have established a canonical relationship between walking speed and metabolic cost, and suggested that ageing shifts this relationship upward, implying reduced efficiency in older adults. However, this relationship has not been well tested during overground walking across matched speeds. We compared healthy younger (n=16; 26{+/-}2yr) and older (n=11; 74{+/-}3yr) adults across eight outdoor overground walking trials at different speeds: preferred walking speed (PWS), three fixed speeds (0.8, 1.2, 1.6 m{middle dot}s-{superscript 1}), and four speeds at {+/-}5% and {+/-}10% of PWS. Contrary to our hypothesis, older adults did not show higher gross or net metabolic cost of walking (GCOW and NCOW) than younger adults at any speed; rather, both trended consistently lower in older adults, reaching significance for GCOW at 0.8 m{middle dot}s-{superscript 1} only. Comparisons of resting metabolic rate and respiratory exchange ratio to prior reference groups did not indicate that our older cohort was unusually fit. Independent of age, GCOW was significantly higher at 0.8 m{middle dot}s-{superscript 1} than at the remaining speeds (1.2-1.6 m{middle dot}s-{superscript 1}), confirming that walking at slower speeds increases GCOW. These findings challenge the view that ageing intrinsically increases the energetic cost of walking, suggesting instead that previously reported upward shifts in cost may reflect treadmill-specific constraints or speed effects. Future work is needed to explore direct comparisons of outdoor, overground walking with treadmill walking at fixed speeds in both younger and older adults.

bioengineering↗

RosettaDDGPrediction for high-throughput mutational scans: from stability to binding

Reliable prediction of free energy changes upon amino acidic substitutions ({Delta}{Delta}Gs) is crucial to investigate their impact on protein stability and protein-protein interaction. Moreover, advances in experimental mutational scans allow high-throughput studies thanks to sophisticated multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput calculations of {Delta}{Delta}Gs. In this context, the Rosetta modeling suite implements effective approaches to predict the change in the folding free energy in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. Their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for {Delta}{Delta}G prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. RosettaDDGPrediction assists with checking whether the runs are completed successfully aggregates raw data for multiple variants, and generates publication-ready graphics. We showed the potential of the tool in selected case studies, including variants of unknown significance found in children who developed cancer, proteins with known experimental unfolding {Delta}{Delta}Gs values, interactions between target proteins and a disordered functional motif, and phospho-mimetic variants. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

bioinformatics↗