Exercise and ageing impact the kynurenine/tryptophan pathway and acylcarnitine metabolite pools in skeletal muscle of older adults.

Exercise-induced perturbation of skeletal muscle metabolites is a probable mediator of long-term health benefits in older adults. Although specific metabolites have been identified to be impacted by age, physical activity and exercise, the depth of coverage of the muscle metabolome is still limited. Here, we investigated resting and exercise-induced metabolite distribution in muscle from well-phenotyped older adults who were active or sedentary, and a group of active young adults. Percutaneous biopsies of the vastus lateralis were obtained before, immediately after and 3 h following a bout of endurance cycling. Metabolite profile in muscle biopsies was determined by tandem mass spectrometry. Mitochondrial energetics in permeabilized fibre bundles was assessed by high resolution respirometry and fibre type proportion was assessed by immunohistology. We found that metabolites of the kynurenine/tryptophan pathway were impacted by age and activity. Specifically, kynurenine was elevated in muscle from older adults, whereas downstream metabolites of kynurenine (kynurenic acid and NAD+ ) were elevated in muscle from active adults and associated with cardiorespiratory fitness and muscle oxidative capacity. Acylcarnitines, a potential marker of impaired metabolic health, were elevated in muscle from physically active participants. Surprisingly, despite baseline group difference, acute exercise-induced alterations in whole-body substrate utilization, as well as muscle acylcarnitines and ketone bodies, were remarkably similar between groups. Our data identified novel muscle metabolite signatures that associate with the healthy ageing phenotype provoked by physical activity and reveal that the metabolic responsiveness of muscle to acute endurance exercise is retained [NB]:AUTHOR: Please ensure that the appropriate material has been provide for Table S2, as well as for Figures S1 to S7, as also cited in the text with age regardless of activity levels. KEY POINTS: Kynurenine/tryptophan pathway metabolites were impacted by age and physical activity in human muscle, with kynurenine elevated in older muscle, whereas downstream products kynurenic acid and NAD+ were elevated in exercise-trained muscle regardless of age. Acylcarnitines, a marker of impaired metabolic health when heightened in circulation, were elevated in exercise-trained muscle of young and older adults, suggesting that muscle act as a metabolic sink to reduce the circulating acylcarnitines observed with unhealthy ageing. Despite the phenotypic differences, the exercise-induced response of various muscle metabolite pools, including acylcarnitine and ketone bodies, was similar amongst the groups, suggesting that older adults can achieve the metabolic benefits of exercise seen in young counterparts.

An evolution-based analysis scheme to identify CO2/O2 specificity-determining factors for ribulose 1,5-bisphosphate carboxylase/oxygenase.

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCo) catalyzes a rate-limiting step in photosynthetic carbon assimilation (reacting with CO2) and its competitive photo-respiratory carbon oxidation (reacting with O2). RuBisCo enzyme with an enhanced CO2/O2 specificity would boost the ability to make great progress in agricultural production and environmental management. RuBisCos in marine non-green algae, resulting from an earlier endo-symbiotic event, diverge greatly from those in green plants and cyanobacteria and, further, have the highest CO2/O2 specificity whereas RuBisCos in cyanobacteria have the lowest. We assumed that there exist different levels of CO2/O2 specificity-determining factors, corresponding to different evolutionary events and specificity levels. Based on this assumption, we devised a scheme to identify these substrate-determining factors. From this analysis, we are able to discover different categories of the CO2/O2 specificity-determining factors that show which residue substitutions account for (relatively) small specificity changes, as happened in green plants, or a tremendous enhancement, as observed in marine non-green algae. Therefore, the analysis can improve our understanding of molecular mechanisms in the substrate specificity development and prioritize candidate specificity-determining surface residues for site-directed mutagenesis.

In silico discovery of enzyme-substrate specificity-determining residue clusters.

The binding between an enzyme and its substrate is highly specific, despite the fact that many different enzymes show significant sequence and structure similarity. There must be, then, substrate specificity-determining residues that enable different enzymes to recognize their unique substrates. We reason that a coordinated, not independent, action of both conserved and non-conserved residues determine enzymatic activity and specificity. Here, we present a surface patch ranking (SPR) method for in silico discovery of substrate specificity-determining residue clusters by exploring both sequence conservation and correlated mutations. As case studies we apply SPR to several highly homologous enzymatic protein pairs, such as guanylyl versus adenylyl cyclases, lactate versus malate dehydrogenases, and trypsin versus chymotrypsin. Without using experimental data, we predict several single and multi-residue clusters that are consistent with previous mutagenesis experimental results. Most single-residue clusters are directly involved in enzyme-substrate interactions, whereas multi-residue clusters are vital for domain-domain and regulator-enzyme interactions, indicating their complementary role in specificity determination. These results demonstrate that SPR may help the selection of target residues for mutagenesis experiments and, thus, focus rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.

Ruleminer: a knowledge system for supporting high-throughput protein function annotations.

In this paper, we present RuleMiner, a knowledge system to facilitate a seamless integration of multi-sequence analysis tools and define profile-based rules for supporting high-throughput protein function annotations. This system consists of three essential components, Protein Function Groups (PFGs), PFG profiles and rules. The PFGs, established from an integrated analysis of current knowledge of protein functions from Swiss-Prot database and protein family-based sequence classifications, cover all possible cellular functions available in the database. The PFG profiles illustrate detailed protein features in the PFGs as in sequence conservations, the occurrences of sequence-based motifs, domains and species distributions. The rules, extracted from the PFG profiles, describe the clear relationships between these PFGs and all possible features. As a result, the RuleMiner is able to provide an enhanced capability for protein function analysis, such as results from the integrated sequence analysis tools for given proteins can be comparatively analyzed due to the clear feature-PFG relationships. Also, much needed guidance is readily available for such analysis. If the rules describe one-to-one (unique) relationships between the protein features and the PFGs, then these features can be utilized as unique functional identifiers and cellular functions of unknown proteins can be reliably determined. Otherwise, additional information has to be provided.

An SVM-based algorithm for identification of photosynthesis-specific genome features.

This paper presents a novel algorithm for identification and functional characterization of "key" genome features responsible for a particular biochemical process of interest. The central idea is that individual genome features are identified as "key" features if the discrimination accuracy between two classes of genomes with respect to a given biochemical process is sufficiently affected by the inclusion or exclusion of these features. In this paper, genome features are defined by high-resolution gene functions. The discrimination procedure utilizes the Support Vector Machine classification technique. The application to the oxygenic photosynthetic process resulted in 126 highly confident candidate genome features. While many of these features are well-known components in the oxygenic photosynthetic process, others are completely unknown, even including some hypothetical proteins. It is obvious that our algorithm is capable of discovering features related to a targeted biochemical process.