Sergi Garcia-Retortillo, Óscar Abenza, Yaopeng J X Ma, Ladda Thiamwong, Hugo Posada-Quintero, Javier Pinzon-Arenas, Carla Leinbach, David H Fukuda, Plamen Ch Ivanov
The isolate showed dose-dependent mortality in mice, with an LD50 of approximately 6.3 × 106 CFU. Histopathological examination revealed lesions consistent with acute suppurative mastitis. Whole-genome sequencing and Flye assembly via EPI2ME produced a de novo genome assembly of 2,771,674 bp, with 77× mean coverage and a GC content of 32.83%. MLST assigned the isolate to ST151, and cgMLST identified cgST-32654 as the closest profile, with 1,654 of 1,716 loci matched and 62 allelic differences. Virulence profiling detected genes encoding exoenzymes, hemolysins, leukocidins, and egc-associated enterotoxin-like proteins. CARD RGI detected mainly efflux-associated and regulatory antimicrobial resistance-associated loci, whereas ResFinder/PointFinder did not detect acquired antimicrobial resistance genes or known resistance-associated point mutations.
PURPOSE: Human function emerges from dynamic interactions among physiological systems. While decades of research have provided understanding of individual systems, how musculoskeletal, cardiovascular, and respiratory systems coordinate as an integrated network during exercise remains unclear. We characterized multisystem coordination during cardiopulmonary exercise test (CPET) of incremental cycling.
METHODS: Twenty-six young adults performed a graded cycling test until exhaustion (25 W/min). Continuous synchronized recordings included electromyography from bilateral vastus lateralis (Leg) and erector spinae (Back), three-lead electrocardiography, and respiratory waveform via chest belt. Multisystem coordination was assessed using Amplitude-Amplitude Cross-frequency Coupling (ACFC), which quantifies the dynamic co-modulation of signal amplitudes. ACFC yielded three network-based markers: inter-muscular, cardio-muscular, and respiratory-muscular coupling. Analyses compared the Beginning (first third) and End (last third) of the test.
RESULTS: At the Beginning, inter-muscular coupling was strongest within the Leg-Leg sub-network. At the End, Leg-Leg coupling decreased by ~30% (p < 0.05), whereas Leg-Back and Back-Back coupling increased by ~100-300% (p < 0.05). Cardio- and respiratory-muscular coupling also increased, by ~30% in Heart-Leg and ~50-75% in Respiration-Leg, with larger and significant increments in the Heart-Back and Respiration-Back sub-networks (p < 0.05).
CONCLUSION: Incremental cycling reorganizes multisystem coordination, shifting from leg-dominant toward a distributed muscle-heart-lung network with fatigue. Exercise responses thus arise not only from individual systems, but also from their dynamic coupling as an integrated network. These network markers provide a novel, complementary dimension for assessing integrative physiological function during exercise.