Characterization Of Arsd: An Arsenic Chaperone For The Arsab As(iii)-Translocating Atpase

Arsenic is a metalloid toxicant that is widely distributed throughout the earth\u27s crust and causes a variety of health and environment problems. As an adaptation to arsenic-contaminated environments, organisms have developed resistance systems. In bacteria and archaea various ars operons encode ArsAB ATPases that pump the trivalent metalloids As(III) or Sb(III) out of cells. In these operons, an arsD gene is almost always adjacent to the arsA gene, suggesting a related function. ArsA is the catalytic subunit of the pump that hydrolyzes ATP in the presence of arsenite or antimonite. ArsB is a membrane protein which containing arsenite-conducting pathway. ArsA forms complex with ArsB, therefore ATP hydrolysis is coupled to extrusion of As(III) or Sb(III) through ArsB. Most transition and heavy metal ions do not exist as free ions in the cytosol but are sequestered by a variety of proteins called metal ion chaperones, scaffolds or intracellular carriers. ArsD was recently shown to be a chaperone for transfer of cytosolic As(III) to the 583-residue ArsA ATPase, the catalytic subunit of the efflux pump. ArsD is a 120-residue protein with three conserved cysteine residues, Cys12, Cys13 and Cys18 required for chaperone activity. ArsA exhibits a low, basal rate of ATPase activity in the absence of As(III) or Sb(III) and a higher, activated rate in their presence. ArsA has a high affinity metalloid binding site composed of Cys113 and Cys422 and a third residue, Cys172, which participates in high affinity binding and activation of ATP hydrolysis. By directly transferring As(III) to ArsA, ArsD also increased ArsA ATPase activity at environmental concentrations of arsenic. Therefore, ArsAB pump efficiency is increased and less As(III) will be accumulated in the cells. In analogy with the mechanism of copper transfer from chaperones to copper pumps or enzymes, a step-wise transfer of As(III) from the cysteines of ArsD to the cysteines of ArsA, was proposed. The properties of As(III) binding by ArsD and subsequent transfer to ArsA were examined. X-ray absorption spectroscopy was used to show that As(III) is coordinated with three sulfur atoms, consistent with Cys12, Cys13 and Cys18 forming the As(III) binding site. An assay using intrinsic protein fluorescence was developed as a probe of metalloid binding to ArsD. Two single tryptophan derivatives of ArsD were constructed by changing either Thr15 or Val17 to tryptophan in a tryptophan-free background. Both exhibited quenching of fluorescence upon binding of As(III) or Sb(III), from which the apparent affinity for metalloid could be estimated. Since it is likely that cytosolic As(III) is bound to reduced glutathione (GSH), the effect of GSH on binding to ArsD was examined. GSH greatly increased the rate of binding As(III) to ArsD, suggesting that ArsD accepts metalloid from the As(GS)3 complex. In contrast, GSH did not affect the As(III)-stimulated ArsA ATPase activity, suggesting that As(III) is directly transferred from ArsD to ArsA, as opposed to release from ArsD, binding to GSH and then interaction of ArsA with the As(GS)3 complex. To differentiate between these two possibilities, the effect of the As(III) chelator dimercaptosuccinic acid (DMSA) was examined. The chelator did not affect transfer, indicating channeling of As(III) from ArsD to ArsA. Transfer occurs only under conditions where ArsA hydrolyzes ATP, suggesting that ArsD transfer As(III) to an ArsA conformation transiently formed during catalysis and not simply to the closed conformation that ArsA adopts when As(III) and MgATP are bound. R773 ArsD was shown to be a dimer in crystal structure. Whether the dimerization form is a physiological one existing in the solution, was studied by mutagenesis. Residues, Ser68, Arg87 and Arg96, involved in dimerization were mutated to alanine. ArsD dimerization equilibrium was shifted to the monomer direction by mutating these residues to alanine, but not totally a monomeric form. One mutant ArsDG86E was selected from reverse yeast two-hybrid analysis, showing no dimerization with wild-type ArsD. Gel-filtration chromatography confirmed mutation G86E shifts ArsD dimerization equilibrium to the monomer direction, but not totally change ArsD to a monomeric form. Since Gly86 sits on the dimerization interface in the crystal structure, it is most likely the crystallographic ArsD dimer forms in the solution. All these mutants still retain the ability to stimulate ArsA ATPase activity, suggesting dimerization is not strictly required for ArsD metallochaperone function. ArsA and ArsD crystal structure have been solved individually. But little is known about ArsA-ArsD interaction interface. Yeast two-hybrid and reverse yeast two-hybrid are combined to select for totally 14 ArsD mutants with weaker or stronger interaction with ArsA. Additionally, Lys37 and Lys62 were shown to be important for ArsD function by site-directed mutagenesis. ArsD loses function when Lys37 and Lys62 were mutated to alanine as well as acetylated by Sulfo-NHS acetate. The charge carried by Lys37 and Lys62 was shown to be important since protein is still active when they are mutated to arginine. Yeast two-hybrid confirmed mutating Lys37 and Lys62 to alanine has effect on ArsA-ArsD interaction. Mapping all the mutations on ArsD structure gives us information on ArsA-ArsD interaction interface. Four residues, Ser14, Val17, Thr20 and Val22, are in the loop containing the important metal binding site Cys12-Cys13-Cys18. This suggests the metal binding site may be directly involved in the interaction with ArsA. Seven residues, Gln24, Val27, Asp28, Thr31, Gln34, Lys37 and Gln38 are located on helix Α1. They are aligned at one side of helix Α1 and solvent exposed, suggesting this region might be directly involved in interaction. A structure model of ArsA-ArsD complex was generated by docking. The model suggested an extensive interaction interface at multiple directions, consistent with most of the yeast two-hybrid results

Radical scavenging activity of crude polysaccharides from Camellia sinensis

A preparation of crude polysaccharides (TPS) was isolated from Camellia sinensis by precipitation and ultrafiltration. TPS1, TPS2, and TPS3 had molecular weights of 240, 21.4, and 2.46 kDa, respectively. The radical scavenging activities of TPS were evaluated by DPPH free radical, hydroxyl radical and superoxide radical scavenging. These results revealed that TPS exhibited strong radical scavenging activity in a concentration-dependent manner. TPS3 with lowest molecular weight showed a higher radical scavenging activity

Brain Decodes Deep Nets

We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, FactorTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (literally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides insight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchically organized network suffer less from catastrophic forgetting after fine-tuned.Comment: Website: see https://huzeyann.github.io/brain-decodes-deep-nets . Code: see https://github.com/huzeyann/BrainDecodesDeepNet

Memory Encoding Model

We explore a new class of brain encoding model by adding memory-related information as input. Memory is an essential brain mechanism that works alongside visual stimuli. During a vision-memory cognitive task, we found the non-visual brain is largely predictable using previously seen images. Our Memory Encoding Model (Mem) won the Algonauts 2023 visual brain competition even without model ensemble (single model score 66.8, ensemble score 70.8). Our ensemble model without memory input (61.4) can also stand a 3rd place. Furthermore, we observe periodic delayed brain response correlated to 6th-7th prior image, and hippocampus also showed correlated activity timed with this periodicity. We conjuncture that the periodic replay could be related to memory mechanism to enhance the working memory

Feature Selection and Model Selection for Supervised Learning Algorithms

Ph.DDOCTOR OF PHILOSOPH