Current and Emerging Tools of Computational Biology To Improve the Detoxification of Mycotoxins.

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we focus on mycotoxins-fungus-produced toxins that contaminate food and feed-and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

Partner-assisted artificial selection of a secondary function for efficient bioremediation.

Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.

Using Corneal Confocal Microscopy to Identify Therapeutic Agents for Diabetic Neuropathy.

Corneal confocal microscopy (CCM) is emerging as a tool for identifying small fiber neuropathy in both peripheral neuropathies and neurodegenerative disease of the central nervous system (CNS). The value of corneal nerves as biomarkers for efficacy of clinical interventions against small fiber neuropathy and neurodegenerative disease is less clear but may be supported by preclinical studies of investigational agents. We, therefore, used diverse investigational agents to assess concordance of efficacy against corneal nerve loss and peripheral neuropathy in a mouse model of diabetes. Ocular delivery of the peptides ciliary neurotrophic factor (CNTF) or the glucagon-like peptide (GLP) analog exendin-4, both of which prevent diabetic neuropathy when given systemically, restored corneal nerve density within 2 weeks. Similarly, ocular delivery of the muscarinic receptor antagonist cyclopentolate protected corneal nerve density while concurrently reversing indices of systemic peripheral neuropathy. Conversely, systemic delivery of the muscarinic antagonist glycopyrrolate, but not gallamine, prevented multiple indices of systemic peripheral neuropathy and concurrently protected against corneal nerve loss. These data highlight the potential for use of corneal nerve quantification by confocal microscopy as a bridging assay between in vitro and whole animal assays in drug development programs for neuroprotectants and support its use as a biomarker of efficacy against peripheral neuropathy.