ABSTRACT
Objective: Saccadic Intrusions (SIs) are abnormal eye movements during gaze fixation. Studies have indicated the clinical relevance of SIs, especially of square wave jerks (SWJ) in ALS. We used a software-based platform to extract SIs as a part of an interventional drug trial. The objective was to examine SIs' change over time as a potential biomarker of ALS disease progression. Methods: 28 ALS patients (61.95 ± 8.6 years) were assessed with the revised ALS Functional Rating Scale (ALSFRS-R) and with an oculometric test. Changes of SIs over time and correlations with ALSFRS-R and its bulbar subscale were calculated. A power calculation was conducted to understand the practical implications of results. Results: A significant increase of SWJ over trial duration was observed, with an increase in frequency (mean rise of 0.14 ± 0.28, p < 0.01), amplitude (0.001 ± 0.0016 degrees, p < 0.005), overall duration of SWJ (0.13 ± 0.25, in %, p < 0.01), and in their relative part out of all intrusions (0.18 ± 0.32, in %, p < 0.005). Negative correlations were found with the bulbar subscale (R=-0.43, -0.41, -0.39 and -0.47, respectively, p < 0.001). The required sample size for observing a 40% reduction in bulbar aspects when using the oculometric test (α = 0.05 and ß = 0.8), was found to be 150 patients per arm, compared with 200 patients using the bulbar subscale. Conclusions: Evaluation of saccadic intrusions during fixation was able to detect disease progression over time, correlated with ALSFRS-R bulbar subscale. Eye movements can potentially serve as an objective biomarker in ALS clinical trials and reduce the required sample size to show clinical effect of therapies.
ABSTRACT
Predicting the metabolic state of an organism after a gene knockout is a challenging task, because the regulatory system governs a series of transient metabolic changes that converge to a steady-state condition. Regulatory on/off minimization (ROOM) is a constraint-based algorithm for predicting the metabolic steady state after gene knockouts. It aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type. ROOM is shown to accurately predict steady-state metabolic fluxes that maintain flux linearity, in agreement with experimental flux measurements, and to correctly identify short alternative pathways used for rerouting metabolic flux in response to gene knockouts. ROOM's growth rate and flux predictions are compared with previously suggested algorithms, minimization of metabolic adjustment, and flux balance analysis (FBA). We find that minimization of metabolic adjustment provides accurate predictions for the initial transient growth rates observed during the early postperturbation state, whereas ROOM and FBA more successfully predict final higher steady-state growth rates. Although FBA explicitly maximizes the growth rate, ROOM does not, and only implicitly favors flux distributions having high growth rates. This indicates that, even though the cell has not evolved to cope with specific mutations, regulatory mechanisms aiming to minimize flux changes after genetic perturbations may indeed work to this effect. Further work is needed to identify metrics that characterize the complete trajectory from the initial to the final metabolic steady states after genetic perturbations.
