Moore, L. D. | bioRxiv Science

bioRxiv2023

Collaborative metabolic curation of an emerging model marine bacterium, Alteromonas macleodii ATCC 27126

Inferring the metabolic capabilities of an organism from its genome is a challenging process, relying on computationally-derived or manually curated metabolic networks. Manual curation can correct mistakes in the draft network and add missing reactions based on the literature, but requires significant expertise and is often the bottleneck for high-quality metabolic reconstructions. Here, we present a synopsis of a community curation workshop for the emerging model marine bacterium Alteromonas macleodii ATCC 27126 and its genome database in BioCyc, focusing on pathways for utilizing organic carbon and nitrogen sources. Due to the scarcity of biochemical information or gene knock-outs, the curation process relied primarily on published growth phenotypes and bioinformatic analyses, including comparisons with related Alteromonas strains. We report full pathways for the utilization of the algal polysaccharides alginate and pectin in contrast to inconclusive evidence for one carbon metabolism and mixed acid fermentation, in accordance with the lack of growth on methanol and formate. Pathways for amino acid degradation are ubiquitous across Alteromonas macleodii strains, yet enzymes in the pathways for the degradation of threonine, tryptophan and tyrosine were not identified. Nucleotide degradation pathways are also partial in ATCC 27126. We postulate that demonstrated growth on nitrate as sole N source proceeds via a nitrate reductase pathway that is a hybrid of known pathways. Our evidence highlights the value of joint and interactive curation efforts, but also shows major knowledge gaps regarding Alteromonas metabolism. The manually-curated metabolic reconstruction is available as a "Tier-2" database on BioCyc. ImportanceMetabolic reconstructions are vital for the systemic understanding of an organisms ecology. Here, we report the outcome of a collaborative, interactive curation workshop to build a curated "metabolic encyclopedia" for Alteromonas macleodii ATCC 27126, a marine heterotrophic bacterium with widespread occurrence. Curating pathways for polysaccharide degradation, one-carbon metabolism, and others closed major knowledge gaps, and identified further avenues of research. Our study highlights how the combination of bioinformatic, genomic and physiological evidence can be harvested into a detailed metabolic model, but also identifies challenges if little experimental data is available for support. Overall, we show how an interactive get-together by a diverse group of scientists can advance the ecological understanding of emerging model bacteria, with relevance for the entire scientific community.

microbiology↗

bioRxiv2018

Predicting the effects of epidural stimulation to improve hand function in patients with spinal cord injury: An active learning-based solution using dynamic sample weighting

In patients with chronic spinal cord injury (SCI), few therapies are available to improve neurological function. Neuromodulation of the spinal cord with epidural stimulation (EDS) has shown promise enabling the voluntary activation of motor pools caudal to the level of the injury. EDS is performed with multiple electrode arrays in which several stimulation variables such as the frequency, amplitude, and location of the stimulation significantly affect the type and amplitude of motor responses. This paper presents a novel technique to predict the final functionality of a patient with SCI after cervical EDS within a deep learning framework. Additionally, we suggest a committee-based active learning method to reduce the number of clinical experiments required to optimize EDS stimulation variables by exploring the stimulation configuration space more efficiently. We also developed a novel method to dynamically weight the results of different experiments using neural networks to create an optimal estimate of the quantity of interest. The essence of our approach was to use machine learning methods to predict the hand contraction force in a patient with chronic SCI based on different EDS parameters. The accuracy of the prediction of stimulation outcomes was evaluated based on three measurements: mean absolute error, standard deviation, and correlation coefficient. The results show that the proposed method can be used to reliably predict the outcome of cervical EDS on maximum voluntary contraction force of the hand with a prediction error of approximately 15%. This model could allow scientists to establish stimulation parameters more efficiently for SCI patients to produce enhanced motor responses in this novel application.\n\nAuthor SummarySpinal cord injury (SCI) can lead to permanent sensorimotor deficits that have a major impact on quality of life. In patients with a motor complete injury, there is no therapy available to reliably improve motor function. Recently, neuromodulation of the spinal cord with epidural stimulation (EDS) has allowed patients with motor-complete SCI regain voluntary movement below the level of injury in the cervical and thoracic spine. EDS is performed using multi-electrode arrays placed in the dorsal epidural space spanning several spinal segments. There are numerous stimulation parameters that can be modified to produce different effects on motor function. Previously, defining these parameters was based on observation and empiric testing, which are time-consuming and inefficient processes. There is a need for an automated method to predict motor and sensory function based on a given combination of EDS settings. We developed a novel method to predict the gripping function of a patient with SCI undergoing cervical EDS based on a set of stimulation parameters within a deep learning framework. We also addressed a limiting factor in machine learning methods in EDS, which is a general lack of training measurements for the learning model. We proposed a novel active learning method to minimize the number of training measurements required. The model for predicting responses to EDS could be used by scientists and clinicians to efficiently determine a set of stimulation parameters that produce a desired effect on motor function.

neuroscience↗