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Lanthanides (Ln) are a new group of life metals, and many questions remain regarding how they are acquired and used in biology. Methylotrophic bacteria can acquire, transport, biomineralize, and use Ln as part of a cofactor complex with pyrroloquinoline quinone (PQQ) in alcohol dehydrogenases. For most methylotrophic bacteria use is restricted to the light Ln, which range from lanthanum to samarium (atomic numbers 57–62). Understanding how the cell differentiates between light and heavy Ln, and the impacts of these metals on the metabolic network, will advance the field of Ln biochemistry and give insights into enzyme catalysis, stress homeostasis, and metal biomineralization and compartmentalization. We report robust methanol growth with the heavy Ln gadolinium by a genetic variant of the model methylotrophic bacterium Methylorubrum extorquens AM1, named evo -HLn, for “ evo lved for H eavy L antha n ides.” A non-synonymous single nucleotide polymorphism in a cytosolic hybrid histidine kinase/response regulator allowed for sweeping transcriptional alterations to heavy metal stress response, methanol oxidation, and central metabolism. Increased expression of genes for Ln acquisition and uptake, production of the Ln-chelating lanthanophore, PQQ biosynthesis, and phosphate transport and metabolism resulted in gadolinium hyperaccumulation of 36-fold with a trade-off for light Ln accumulation. Gadolinium was hyperaccumulated in an enlarged acidocalcisome-like compartment. This is the first evidence of a bacterial intracellular Ln-containing compartment that we name the “lanthasome.” Carotenoid and toblerol biosynthesis were also upregulated. Due to its unique capabilities, evo -HLn can be used to further magnetic resonance imaging (MRI) and bioremediation technologies. In this regard, we show that gadolinium hyperaccumulation was sufficient to produce MRI contrast in whole cells, and that evo -HLn was able to readily acquire the metal from the MRI contrast agent gadopentetic acid. Finally, hyperaccumulation of gadolinium, differential uptake of light and heavy Ln, increased PQQ levels, and phosphate transport provide new insights into strategies for Ln recovery.
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A Multistage Backward Differentiable Method for Constructing Light Convolutional Neural Networks
We propose a multistage differentiable method to select convolutional channels and construct light neural networks from a heavy network for inference on a subset of a big data set. The selection proceeds backward in layers and utilizes sparse penalty to diversify channel scores. The resulting light network gains sizable accuracy over the baseline heavy network.
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- PAR ID:
- 10158890
- Date Published:
- Journal Name:
- 2019 Second International Conference on Artificial Intelligence for Industries
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/DOI 10.1109/AI4I.2019.00029
