One of the pathological hallmarks of AD is insoluble deposits of neurofibrillary tangles (NFTs) that consist of hyperphosphorylated tau. The human DYRK1A gene is mapped to chromosome 21, and the protein is associated with the formation of inclusion bodies in AD. For example, DYRK1A directly phosphorylates multiple serine and threonine residues of tau, including Thr212. However, the mechanism underpinning DYRK1A involvement in Trisomy 21-related pathological tau aggregation remains unknown. Here, we explored a novel regulatory mechanism of DYRK1A and subsequent tau pathology through a phosphatase. Using LC-MS/MS technology, we analyzed multiple DYRK1A-binding proteins, including PPM1B, a member of the PP2C family of Ser/Thr protein phosphatases, in HEK293 cells. We found that PPM1B dephosphorylates DYRK1A at Ser258, contributing to the inhibition of DYRK1A activity. Moreover, PPM1B-mediated dephosphorylation of DYRK1A reduced tau phosphorylation at Thr212, leading to inhibition of toxic tau oligomerization and aggregation. In conclusion, our study demonstrates that DYRK1A autophosphorylates Ser258, the dephosphorylation target of PPM1B, and PPM1B negatively regulates DYRK1A activity. This finding also suggests that PPM1B reduces the toxic formation of phospho-tau protein via DYRK1A modulation, possibly providing a novel cellular protective mechanism to regulate toxic tau-mediated neuropathology in AD of DS.Down syndrome (DS) is mainly caused by an extra copy of chromosome 21 (trisomy 21), and patients display a variety of developmental symptoms, including characteristic facial features, physical growth delay, intellectual disability, and neurodegeneration ( Down syndrome (DS) is caused by the presence of an extra whole or partial copy of human chromosome 21 (trisomy 21) -phosphorylation-regulated kinases (DYRKs), which includes DYRK1A, DYRK1B, DYRK2, DYRK3, and DYRK4 . DYRK1A Moreover, DYRK1A phosphorylates several neurodegenerative disease (NDD)-associated proteins, including tau, amyloid precursor protein (APP), and α-synuclein , affecti2+ or Mn2+ for catalytic activity  family are mostly monomeric Ser/Thr phosphatases and require the presence of Mgactivity . PPM1A iactivity . PPM1B pactivity . PPM1B aactivity .As the name implies, DYRK1A kinase possesses dual specificity due to its capability to autophosphorylate three amino acids  as well as to phosphorylate its substrates at serine or threonine residues . WhereasA). Analyses of anti-DYRK1A complexes using the LC-MS/MS technique identified a previously known DYRK1A-binding protein, DDB1- and CUL4-associated factor 7  and its novel binding partner, Mg2+/Mn2+-dependent protein phosphatase 1B . In addition, P1 band was identified as the bait protein DYRK1A and P2 was an unnamed protein product .To reveal the molecular mechanism underlying regulation of DYRK1A activity, the novel DYRK1A-interacting proteins were firstly analyzed in HEK 293 cells stably overexpressing epitope FLAG-tagged wild-type DYRK1A A. AnalysB). Next, immunoblotting of anti-PPM1B immunocomplexes with anti-DYRK1A antibody demonstrated that endogenous DYRK1A interacts with endogenous PPM1B in HEK293 cells (C). When the coimmunoprecipitation assays were performed in a reverse order, we observed the same result (D). To determine whether DYRK1A colocalizes with PPM1B, HEK293 cells were transfected with Myc-PPM1B and HA-DYRK1A. Immunostaining of cells with anti-Myc (red) and anti-HA (green) antibodies revealed both proteins are commonly expressed in the cytosol (E).We next examined whether DYRK1A binds to two PPM family proteins (PPM1A and PPM1B) in other mammalian cells, such as dopaminergic neuroblastoma SH-SY5Y cells. After transiently transfecting cells with Xpress-DYRK1A, Myc-PPM1A, and/or Myc-PPM1B, cell lysates were immunoprecipitated with anti-Myc antibody. Immunoblot analysis of anti-Myc immunocomplexes with anti-Xpress antibody revealed that DYRK1A binds to both PPM1A and PPM1B B. Next, 93 cells C. When te result D. To det cytosol E.These data suggest that the DYRK1A interacts with PPM1B in mammalian cells.in vitro kinase and phosphatase assays (A and B). Wild-type PPM1B (WT) protein and its phosphatase-inactive mutant having the substitution R179G (PPM1B-MT) were expressed in Escherichia coli and purified. For DYRK1A, HEK293 cells were transfected with Myc-DYRK1A, and the anti-Myc immunocomplexes were prepared. After anti-Myc-DYRK1A immunoprecipitates were incubated with [γ-32P]ATP alone or with either bacterially purified recombinant PPM1B-WT or PPM1B-MT, in vitro assays for protein phosphorylation and dephosphorylation revealed that DYRK1A efficiently autophosphorylates in the absence of PPM1B (A and B). However, PPM1B was not measurably phosphorylated by DYRK1A. Interestingly, the amount of autophosphorylated DYRK1A was markedly reduced in the presence of wild-type PPM1B, whereas this effect was not seen with PPM1B-MT (A and B). These results suggest that PPM1B dephosphorylates phospho-DYRK1A.To gain further insight as to how DYRK1A and PPM1B are linked, we addressed whether DYRK1A directly phosphorylates PPM1B and/or whether PPM1B dephosphorylates DYRK1A. To do this, we employed e assays , A and Bof PPM1B , A and BPPM1B-MT , A and BC–H). Immunoblotting of the samples with anti-Xpress antibody revealed that the amount of phosphorylated DYRK1A at Tyr or Thr residue was not reduced with PPM1B (C–F). However, cotransfection of PPM1B and DYRK1A greatly reduced the level of phosphorylated DYRK1A at Ser residue(s), compared with the sample transfected with DYRK1A alone (G and H).As DYRK1A has dual substrate specificity, DYRK1A autophosphorylates on Ser/Thr as well as Tyr residues for self-activation , 9. Baseth PPM1B , C–F. Ho1A alone , G and HTaken together, these results indicate that PPM1B dephosphorylates DYRK1A at the serine residue(s), but not tyrosine or threonine site.http://www.phosphosite.org), two novel Ser sites within DYRK1A were predicted to serve as putative phosphorylation targets of PPM1B: Ser-310 and -324, respectively. X-ray analysis of DYRK1A protein structure indicated that the S310 is located close to the previously reported autophosphorylation site Y321 and facing outward . According to the previous report, phosphorylation of DYRK1A at S529 also increases its binding affinity for 14-3-3β protein . Lysates were then immunoprecipitated with anti-HA antibody. Immunoblotting of the samples with anti-pSer antibody revealed that DYRK1A-S310/S529A still displayed a considerable pSer band. In addition, DYRK1A-S310/529A displayed greatly reduced phosphorylation in the presence of PPM1B, similar to the pattern of DYRK1A-S529A. Moreover, comparing with the effect of DYRK1A-WT on tau phosphorylation, three DYRK1A point mutants  exhibit similar activity (F and G). However, it was not seen with the kinase-inactive DYRK1A-K188R mutant, as expected.We next examined whether S310 could be a target site of PPM1B. Cells were transfected for 24 h with HA-DYRK1A-S529A, HA-DYRK1A-S310/529A, or Myc-PPM1B alone or in combination , D and Eactivity , F and GTaken together, these results suggest that neither S310 nor S529 of DYRK1A is the target site of PPM1B.i.e., DYRK1A159–763, DYRK1A317–736, and DYRK1A417–736 were generated  in the absence of PPM1B to an extent similar to the full-length DYRK1A. However, DYRK1A317–736 and DYRK1A417–736 failed to be phosphorylated at Ser residue(s) (A and B). These results indicated that the target serine residue is present between amino acid 159 and 316.To precisely locate the region of DYRK1A dephosphorylated by PPM1B, three DYRK1A deletion constructs, enerated . To exclsidue(s) , A and B159–316 region contains a total of eight serine residues, i.e., S163, S169, S242, S258, S282, S301, S310, and S311, two DYRK1A mutants having the mutations of these sites were generated. The first DYRK1A-I-5SA mutant (DYRK1A-5SA) has five substitutions of Ser with Ala at S163, S169, S242, S258, and S310, and the second DYRK1A-II-4SA (DYRK1A-4SA) has four similar substitutions at S282, S301, S310, and S311 (A). After cells were transfected with one of these HA-tagged mutants alone or together with Myc-PPM1B, we measured the phosphorylation status of DYRK1A-5SA and -4SA mutants and compared with that of wild-type DYRK1A (C and D). Immunoblotting of anti-HA immunocomplexes with anti-pSer IgG demonstrated that the phosphorylation levels of wild-type DYRK1A and DYRK1A-4SA were greatly reduced by PPM1B, but the amount of phospho-DYRK1A-5SA was unaffected by PPM1B (C and D). These results suggest that PPM1B targets and dephosphorylates the serine residue(s) substituted within the DYRK1A-5SA mutant, which include S163, S169, S242, and S258.We next tried to identify the exact PPM1B-targeting site(s) on DYRK1A. As the DYRK1Aand S311 A. After e DYRK1A , C and Dby PPM1B , C and DE, three DYRK1A mutants having no S258A mutation were still efficiently dephosphorylated by PPM1B. However, comparing with other mutants, the phosphorylation level of the DYRK1A-S163A/S169A/S242A/S258A mutant was greatly reduced, and this level was not significantly altered in the presence of PPM1B (E). In vitro kinase assay further revealed that the S258A mutant displayed a reduction in DYRK1A phosphorylation by 75%, compared with wild-type DYRK1A (F and G). In addition, the phosphorylation level of the DYRK1A-S258A mutant was not significantly affected by PPM1B. Moreover, the phosphorylation level of phospho-mimetic DYRK1A-S258D mutant was unaffected by PPM1B (F and G).To identify the exact PPM1B-targeted pSer residue(s) among these four residues, we next generated four DYRK1A mutants having the mutation of S163A or multiple mutations at S163A/S169A, S163A/S169A/S242A, or S163A/S169A/S242A/S258A and examined whether they were dephosphorylated. As shown in Figure 4of PPM1B E. In vite DYRK1A , F and Gby PPM1B , F and GTaken together, these data indicated that DYRK1A is constitutively autophosphorylated at Ser258, which serves as the target residue of PPM1B.Except for autophosphorylation at residues Tyr321 and Ser529, the identification of Ser258 as an autophosphorylation site and its modulatory effect on DYRK1A activity were not assessed at all. Therefore, we next examined whether and how the phosphorylation–dephosphorylation of DYRK1A at Ser258 affects its kinase activity. As described previously, DYRK1A phosphorylates a number of nuclear and cytosolic substrate proteins, including several NDD-associated targets , 20. BasA). We then determined how PPM1B and PPM1A affect the kinase activity of DYRK1A by measuring the change in DYRK1A-mediated tau phosphorylation at T212. After HEK293 cells were transfected with plasmids encoding Xpress-DYRK1A, V5-tau, Myc-PPM1A, or Myc-PPM1B alone or together, immunoblot analysis of cell lysates with anti-p-tau antibody (T212) showed that tau was not phosphorylated at Thr212 in the absence of DYRK1A (B). In addition, DYRK1A considerably promoted tau phosphorylation at T212, which was consistent with the previous report (B). However, the increase of tau phosphorylation in HEK293 cells overexpressing DYRK1A was significantly blocked by coexpression of PPM1B, whereas this effect was not observed with PPM1A (B). These results indicate that PPM1B decreases tau phosphorylation via DYRK1A activity. They also suggest that PPM1B inhibits DYRK1A activity.Before determining the presumed regulatory effect of PPM1B on DYRK1A-mediated tau phosphorylation, we examined whether PPM1B is biochemically associated with tau as a control. HEK293 cells were transfected with plasmids encoding V5-Tau, Myc-PPM1B, and/or Myc-PPM1A, and cell lysates were immunoprecipitated with anti-Myc antibody. Immunoblot analysis of anti-Myc immunocomplexes with anti-V5 antibody revealed that tau does not bind to either PPM1B or PPM1A A. We thef DYRK1A B. In adds report B. Howeveth PPM1A B. These C and D). Cells were transfected with V5-tau alone or together with HA-tagged DYRK1A-S310A, DYRK1A-4SA, or DYRK1A-5SA, and immunoblot analysis of cell lysates with anti-p-Tau IgG (T212) showed that wild-type DYRK1A and two other mutants (DYRK1A-S310A and DYRK1A-4SA) efficiently execute the phosphorylation of tau (C and D). However, the DYRK1A-5SA mutant with the substitution S258A did not promote tau phosphorylation (C and D). In addition, by using the same method employing three other DYRK1A point mutants, i.e., the phosphorylation-resistant (DYRK1A-S258A) and phosphorylation-mimetic (DYRK1A-S258D) mutants, we measured their effect on tau phosphorylation. As shown in Figure 5E, the DYRK1A-S258A mutant failed to mediate tau phosphorylation at T212, compared with wild-type DYRK1A. However, it was rescued and restored to DYRK1A wild-type level by the DYRK1A-S258D mutant (E and F). In addition, much reduced level of tau phosphorylation was not considerably affected by coexpression of PPM1B and DYRK1A-S258A (G and H).To confirm this hypothesis, we tested the effect of several DYRK1A point mutants on tau phosphorylation , C and Dn of tau , C and Drylation , C and DD mutant , E and F1A-S258A , G and Hin vitro kinase assay with the anti-HA immunocomplex as the kinase, recombinant His-tagged tau as the substrate, and [γ-32p]ATP. Autoradiographic analysis of reaction products demonstrated that the DYRK1A-S258A mutant caused much less tau phosphorylation than DYRK1A-WT. On the contrary, phospho-mimetic mutant DYRK1A-S258D promoted tau phosphorylation to a level comparable with or slightly greater than DYRK1A-WT (I and J).To verify that PPM1B-mediated dephosphorylation of DYRK1A at S258 reduces tau phosphorylation, HEK293 cells were transfected with plasmids expressing HA-DYRK1A-WT, HA-DYRK1A-S258A, or HA-DYRK1A-S258D, and cell lysates were immunoprecipitated with anti-HA antibody. Then, we performed an YRK1A-WT , I and JTaken together, these data suggest that the autophosphorylation of DYRK1A at S258 appears to be essential for the kinase activity, but not enough by itself to enhance its activity. In addition, dephosphorylation of DYRK1A-S258 by PPM1B decreases its kinase activity, subsequently downregulating tau phosphorylation at T212.A and B, there was no interaction between V5-tau and GFP-tau in the absence of DYRK1A or PPM1B. Overexpression of DYRK1A markedly induced the binding of V5-tau to GFP-tau. These data suggest that DYRK1A stimulates tau oligomerization possibly via tau phosphorylation, which is consistent with the previous finding (A and B).Excessive phosphorylation of tau protein can result in the self-assembly of oligomers and aggregation into paired helical filaments, which are involved in the pathogenesis of AD, frontotemporal dementia, and other tauopathies . Lastly, finding . Moreove finding , A and BC and D). However, the presence of PPM1B reduced DYRK1A-induced tau aggregation to the control level (C and D).Similarly, the results of filter trap assay revealed that DYRK1A increased the amount of 1% SDS-insoluble tau aggregates by approximately twofold , C and Dol level , C and DE and F, expression of V5-tau alone produced a significant amount of Triton X-100-insoluble tau aggregates. Similar to other results, DYRK1A also promoted the level of tau aggregates by 1.8-fold, which was then rescued and restored to the basal level by PPM1B (E and F).The effect of PPM1B on the formation of tau aggregates was further examined by separating the Triton X-100-insoluble fraction from the cell lysates, followed by immunoblotting of the insoluble and soluble samples with anti-V5 (tau-tagging) antibody. As shown in by PPM1B , E and FThe overall results suggest that PPM1B decreases tau phosphorylation and oligomerization through the reduction of DYRK1A activity.A). Next, we examined whether the presence of PPM1B affects serum deprivation-induced cell death in H19-7/DYRK1A cells. As shown in Figure 7B, the presence of PPM1B considerably reduced the cytotoxicity of H19-7/DYRK1A under serum deprivation. These results might be explained by the previous and present finding that PPM1B dephosphorylates pDYRK1A at S258 and downregulates DYRK1A kinase activity, consequently decreasing the toxic modification of two AD-pathogenic proteins, such as tau and APP (C–E).We have previously reported that overexpression of DYRK1A in hippocampal progenitor H19-7 (H19-7/DYRK1A) cells caused an increase in tau phosphorylation and inclusion formation . FurtherFig. 7 and APP . To valiTaken together, these data demonstrated that PPM1B inhibits serum deprivation-induced toxic tau aggregation and reduces cellular toxicity in stable DYRK1A-overexpressing H19-7 cells.via its intramolecular interaction, three amino acids have been documented for autophosphorylation—S97, Y321, and S529  revealed that residue Y321 is not located inside or in close proximity to the active site of DYRK1A, which strongly binds to the β-carboline alkaloid harmine, the potent and specific inhibitor of DYRK1A , fetal bovine serum (FBS), Alexa Fluor 488-conjugated mouse IgG, Alexa Fluor 546-conjugated rabbit IgG, ProLong Gold Antifade Mountant with 4′,6-diamidino-2-phenylindole (DAPI), rabbit polyclonal anti-p-tau (Thr212) antibodies (cat. #: 44-740G), mouse monoclonal anti-Xpress (R910-25), and anti-V5 (R960-25) antiserum were purchased from Invitrogen. Protein A-Sepharose beads and Ni-NTA agarose beads were purchased from GE Healthcare Life Sciences. Enhanced chemiluminescence (ECL) reagents were purchased from AbClon. Mouse monoclonal anti-FLAG (cat. #: F3165-1MG) and polyethylenimine (PEI) were purchased from Sigma-Aldrich. HRP-conjugated anti-rabbit (cat. #: AP132P) and anti-mouse (AP124P) secondary antibodies were purchased from EMD Millipore. Mouse anti-pTyr (cat. #: 05-321) and anti-pSer (05-1000) antibodies were purchased from Millipore. Rabbit anti-pThr antibodies (cat. #: ab9337) were purchased from Abcam. Monoclonal mouse anti-DYRK1A (cat. #: PAB10345) and anti-Myc (H00001859-M01) antibodies were purchased from Abnova. Rabbit polyclonal anti-FLAG (cat. #: F7425-2MG), polyclonal anti-Myc (cat. #: sc-789), anti-actin (sc-47778), anti-Hsp90 (sc-13119), mouse anti-GFP (sc-9996), and anti-tubulin (sc-5286) antibodies were purchased from Santa Cruz Biotechnology. Polyclonal anti-HA (cat. #: PAB0801) and monoclonal anti-PPM1B (H00005495-M01) IgGs were obtained from Abnova. Monoclonal anti-HA (cat. #: 901502) and rabbit polyclonal anti-V5 (cat. #: ab9116) antibodies were purchased from Covance and Abcam, respectively. Alexa Fluor 488 conjugated anti-mouse (cat. #: A-11029) and Alexa Fluor 594 conjugated anti-rabbit (A-11012) secondary antibodies were purchased from Invitrogen.159–763, DYRK1A317–763, or DYRK1A417–763, were prepared as described previously  were inserted into vector pcDNA3.1-Myc or pET28a-His. Bacterial plasmid encoding His-fused PPM1B was amplified by PCR using the forward primer 5′-GAAGAATTCATGGGTGCATTTTT GGATAAACCC-3′ and the reverse primer 5′-GAAGCGGCCGCTATTTTTTCACCACTCATCTTTGTCCC-3′, and the product was digested by EcoR1 and Not1. To make a variety of DYRK1A constructs encoding its deletion or point-mutants, site-directed mutagenesis was performed using the QuikChange XL site-directed mutagenesis kit (Agilent Technologies). All cDNA constructs were confirmed by DNA sequencing (COSMO Genetech).Mammalian constructs encoding rat wild-type Dyrk1a N-terminally tagged with Xpress- and 6 × His (pXpress-His6-DYRK1A-WT), its kinase-inactive mutant having the substitution of K188R (pXpress-His6-DYRK1A-KD), and FLAG-tagged wild-type Dyrk1a (pRK5-FLAG-DYRK1A) were prepared as described previously . Plasmideviously . Plasmid2. All DNA transfections were performed using PEI and Lipofectamine 2000 reagents, according to the manufacturer’s protocol. The H19-7 cells stably overexpressing DYRK1A (H19-7/DYRK1A) and its control cell line carrying vector pTK-Hyg (H19-7/pTK) were prepared as described previously  cells were maintained in DMEM containing 10% FBS and 100 U/ml penicillin-streptomycin. Cells were grown at 37 °C in 5% COeviously .HEK293 cells were cotransfected with pTK-Hyg and pcDNA3.1-FLAG-DYRK1A and screened with 250 μg/ml of hygromycin B. To detect levels of DYRK1A in each cell line, 75 μg of each cell lysate was analyzed by immunoblotting with anti-DYRK1A, anti-FLAG, or anti-Hsp90 antibody. To map DYRK1A binding partners from FLAG-DYRK1A-overexpressing HEK293 cells (Flag-DYRK1A), ESI-MS/MS was performed with parental HEK293 vector cells (Vector) as a control, as previously described . OverexpE. coli BL21 (DE3)-Codon Plus competent cells (Invitrogen) were transformed with pET28a-His-Tau plasmid. A single colony was inoculated into 10 ml selective LB broth containing 50 μg/ml kanamycin and grown overnight at 37 °C. Cultured cells (10 ml) were inoculated into 100 ml selective LB broth containing 50 μg/ml kanamycin and grown at 37 °C with gentle agitation. Cell cultures were grown until OD at 600 nm reached 0.6 and then incubated with 0.05 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG) for 2 h at 25 °C, pelleted by centrifugation at 3000g for 15 min at 4 °C. The cells were sonicated in lysis buffer . After centrifuging the cell lysates at 6000g for 15 min at 4 °C, the supernatant was incubated with Ni-NTA agarose beads overnight at 4 °C. The beads were then washed three times with washing buffer , and the recombinant tau proteins were eluted by adding elution buffer containing 500 mM imidazole. Bacterial recombinant PPM1B protein was produced and purified with the same protocol.3VO4, 10 mM NaF, and 1 × protease inhibitor cocktail ). Cells were then scraped, and supernatants were collected after centrifugation at 15,700g for 15 min at 4 °C. For immunoprecipitation, 1 μg of appropriate antibody was incubated with 500 μg cell lysate overnight at 4 °C with gentle rotation. The mixture was then incubated with 30 μl of a 1:1 mixture of protein A-Sepharose bead suspension for 2 h at 4 °C. Beads were pelleted by centrifugation at 9300g for 1 min and washed three times with 1% Nonidet P-40 lysis buffer. The immunocomplexes were dissociated by boiling in SDS-PAGE sample buffer, separated on SDS-PAGE, and transferred to nitrocellulose membranes (Millipore). Membranes were blocked for 1 h at room temperature using TRIS-buffered saline with Tween  containing 5% nonfat dry milk and then incubated overnight at 4 °C in TBST containing the appropriate antibody. Membranes were then washed three times in TBST and incubated with secondary IgG-coupled horseradish peroxidase for 2 h. The blots were washed three times with TBST, and the bands were visualized using ECL reagents (Abclon), following the manufacturer’s instructions.Cell lysates were prepared by rinsing cells with ice-cold phosphate-buffered saline (PBS) and lysed with 1% Nonidet P-40 lysis buffer . Complexes immunoprecipitated with Myc- or HA-tagged DYRK1A were mixed with 1 μg recombinant tau protein, the kinase reaction buffer, 0.2 mM Na3VO4, and 10 μM ATP. In vitro kinase reaction was initiated by the addition of 10 μCi [γ-32P]ATP. The reaction was allowed to proceed for 30 min at 30 °C and terminated by the addition of SDS-PAGE sample buffer. The samples were then resolved by SDS-PAGE and incorporated [γ-32P]ATP radioisotope was detected by autoradiography.After cell lysis in lysis buffer, approximately 1000 μg of protein was incubated with anti-Myc or anti-HA antibodies for overnight at 4 °C. The immunocomplexes were mixed with protein A-Sepharose beads for 2 h at 4 °C. Beads were centrifuged at 60002, 0.03% β-mercaptoethanol) and mixed with 1 μg of recombinant PPM1B-WT or PPM1B-MT protein for 30 min at 30 °C. The reactions were stopped by addition of 2× SDS sample buffer, and the mixtures were analyzed by SDS-PAGE and visualized by autoradiography.Complexes immunoprecipitated with Myc- or HA-tagged DYRK1A were washed twice times with 1× PPM1B buffer . After brief sonication, equal amounts of cell lysates were passed through a nitrocellulose filter with pore size of 0.2 μm. The resultant membranes were washed three times with lysis buffer containing 1% SDSand blocked in 5% nonfat milk for 1 h, followed by immunoblotting with anti-V5 antibody. For the assay of tau aggregates, cells were lysed with oligomerization buffer , and the supernatants were collected after centrifugation at 15,700Cellular cytotoxicity was measured using the LDH Cytotoxicity Detection Kit (Takara Korea), according to the manufacturer protocol. To assess the serum deprivation-induced cytotoxicity in H19-7 cells, N2 medium was added to the cells before reaching 70∼90% confluence, and cells were cultured at 37 °C for 0 to 72 h. The supernatant (100 μl) was then transferred to a new 96-well plate and 100 μl of LDH substrate mix was added. After 20-min incubation at room temperature, the absorbance of the sample was measured at 490∼600 nm.H19-7/DYRK1A and control H19-7/pTK cells were seeded onto poly-L-lysine-coated coverslips in 6-well plates to approximately 50∼70% confluence at 37 °C. To induce toxic tau aggregation followed by apoptotic cell death, cells were incubated with N2 medium for 48 h at 37 °C, washed twice with PBS (pH 7.4), and immediately fixed with 3.7% formaldehyde for 30 min. After fixation, cells were permeabilized with 0.2% Triton X-100 for 30 min and blocked with 1% bovine serum albumin (BSA) for 30 min at room temperature, followed by immunostaining with anti-tau or anti-Myc antibody. FITC-conjugated secondary antibody was then used to detect the primary antibodies. The samples were counterstained with DAPI, mounted, and analyzed by LSM 880 confocal microscopy (Carl Zeiss). The data were processed by Zeiss LSM Image Browser (Carl Zeiss).t-test and the IBM SPSS statistical analysis software (version 23.0), and p < 0.05 was considered statistically significant. Values are reported as mean ± SD of at least three independent experiments. The densities of western blot bands were measured by GelQuant.NET software (version 1.8.2), according to the protocol provided by the manufacturer (http://www.biochemlabsolutions.com).The S-Group means of samples were compared using Student’s All data that support the findings of this study are contained within the article and are available from the corresponding author upon reasonable request.The authors declare that they have no conflicts of interest with the contents of this article.


In addition, we found the conserved S92 was phosphorylated by the PKC isoforms. To reveal the structural effect of MCU S92 phosphorylation (S92p), we determined crystal structures of the MCU NTD of S92E and D119A mutants and analysed the molecular dynamics simulation of WT and S92p. We observed conformational changes of the conserved loop2-loop4 (L2-L4 loops) in MCU NTDS92E, NTDD119A, and NTDS92p due to the breakage of the S92-D119 hydrogen bond. The results suggest that the phosphorylation of S92 induces conformational changes as well as enhancements of the negative charges at the L2-L4 loops, which may affect the dimerization of two MCU-EMRE tetramers.The mitochondrial calcium uniporter (MCU) plays essential roles in mitochondrial calcium homeostasis and regulates cellular functions, such as energy synthesis, cell growth, and development. Thus, MCU activity is tightly controlled by its regulators as well as post-translational modification, including phosphorylation by protein kinases such as proline-rich tyrosine kinase 2 (Pyk2) and AMP-activated protein kinase (AMPK). In our  However, prolonged overload of mitochondrial Ca2+ uptake can trigger the production of large amounts of reactive oxygen species (ROS), induce opening of the mitochondrial permeability transition pore, cause disruption of mitochondrial membrane potential, and eventually lead to apoptotic and necrotic cell death1. The malfunction of mitochondrial Ca2+ homeostasis causes pathological diseases, including ischemia reperfusion, myocardial infarction, and epilepsy4.Under physiological conditions, mitochondria, which uptake and sequester Ca2+ uptake across the inner mitochondrial membrane (IMM) is through the mitochondrial calcium uniporter (MCU) complex, which facilitates Ca2+ entry into the IMM through the electrochemical potential gradient driven by the mitochondrial membrane potential (Δψ = approximately −180 mV)6. The MCU is the pore-forming subunit of the MCU complex and acts as a selective Ca2+ channel. The MCU complex consists of MCU, along with its regulatory proteins, MCU paralog (MCUb), mitochondrial calcium uptake 1, 2 & 3 , essential MCU regulator (EMRE), and mitochondrial calcium uniporter regulator 1 (MCUR1). The interaction of these regulatory proteins with MCU controls MCU Ca2+ uptake activity under different mitochondrial conditions and in different cell types8.A key pathway for mitochondrial Ca10. The mitochondrial Ca2+ conductance measurements using whole-mitoplast current recording suggest that the level of Ca2+ entry mediated by MCU differs between mouse tissues, including skeletal muscle, heart, brown fat, kidney, and liver9. In addition, uncoupling proteins 2 and 3 (UCP2 and UCP3) interact with methylated MICU1 and can differentially modulate the mitochondrial calcium uptake in different cells, including HeLa, Ea.hy926, HUVEC, and PAEC cells10.Mitochondrial calcium uptake greatly varies depending on the cell types and tissues due to the differential expression levels of MCU complex components2+ channel for mitochondrial Ca2+ uptake is supported by functional studies of the MCU in human cells and mouse models16. Inhibition of Ca2+ uptake in the matrix has been previously demonstrated by blocking the MCU pore with ruthenium red (Ru360)16, and by genetic ablation of MCU11. Previous research has also shown that the microRNA miR-25 can reduce the mRNA level of MCU and directly down-regulate MCU expression, thus inhibiting the mitochondrial Ca2+ uptake12. In the MCU pore-forming region, mutations of negatively charged acidic residues  in human MCU, have also been shown to inhibit the MCU activity16. Although the MCU knock-out mouse was reported as the mild phenotype, unexpected compensatory changes that affect cytosolic Ca2+ homeostasis or modulate mitochondrial Ca2+-dependent metabolism impair the short-term mitochondrial Ca2+ uptake at a “fight-or-flight” response15.The role of the MCU as an essential Ca2+ binding selectivity, redox sensor, and regulation of MCU Ca2+ uptake activity19. In addition, the MCU NTD can be altered by post-translational modifications in the mitochondrial matrix space21. Under inflammatory and hypoxic conditions, MCU undergoes S-glutathionylation in a highly conserved C97 residue and functions as a mitochondrial ROS sensor in the mitochondrial matrix7. Phosphorylation of the MCU  by proline-rich tyrosine kinase 2 (Pyk2) induces an increase in the mitochondrial Ca2+ uptake by facilitating formation of the MCU channel via MCU oligomerization20. S57 phosphorylation in the MCU by AMP-activated protein kinase (AMPK) facilitates mitochondrial Ca2+ entry during mitosis and boosts mitochondrial respiration to maintain energy homeostasis21. Regulatory functions of MCU Ca2+ uptake by Ca2+/calmodulin-dependent protein kinase II (CaMKII) still remain controversial26, although Nguyen et al. suggest S92 phosphorylation (S92p) of MCU by CaMKII in vivo25. Since our study focuses on the S92p of MCU by protein kinase C (PKC), we exclude an argument about the functional role of MCU driven by CaMKII.The N-terminal domain (NTD) of the MCU (MCU NTD) plays essential roles in the dimerization of two MCU-EMRE tetramers, MCUR1 interaction, MCUb NTD interaction, Mg27, and PKCβII (one of the splice variants of PKCβ), PKCδ, and PKCε are localized in the mitochondrial matrix in response to ROS29. PKC directly phosphorylates a wide range of cellular substrates and regulates various cellular functions, such as cell migration, differentiation, proliferation, senescence, and apoptosis29.PKC isoforms, a heterogeneous family of serine/threonine (Ser/Thr) kinases, are encoded by nine genes  in humanβII, PKCδ, and PKCε, via in vitro kinase assay. To uncover the structural effects of phosphorylated S92 (S92p), we determined two crystal structures of MCU NTDS92E, an S92p mimic, and NTDD119A mutants at a resolution of 2.50 Å and 2.85 Å, respectively, and analysed the molecular dynamics simulation for NTDWT and NTDS92p. We propose that phosphorylation at S92 induces conformational and electrostatic changes in the L2-L4 loops of the MCU NTDWT due to the breakage of S92-D119 hydrogen bonds. As a result, it may affect the dimerization of the two MCU-EMRE tetramers.In this study, we observed that the MCU S92 was phosphorylated by mitochondrial PKC isoforms, including PKCMCU gene, was highly conserved based on 230 MCU NTD homologous protein sequences in the ConSurf server  and four threonines . Among these, the highly conserved S92 in the 89-RLPS-92 sequences  was determined to be a putative recognition site for phosphorylation by Ser/Thr kinases containing CaMKII, cAMP-dependent protein kinases (PKA), and PKC, using the KinasePhos 2.0 server and Group-based Prediction System (GPS) 2.0 softwares  and detected the S92p of MCU by CaMKII using specific MCU S92p antibodies in an immunoblotting25. To further investigate whether S92 is phosphorylated by other Ser/Thr kinases such as PKA and PKC localized in the mitochondrial matrix37, we performed in vitro kinase assays with myelin basic protein , MCU NTDWT, MCU NTDAAS92 , and [γ-32P]ATP. In control experiments, MBP, a multiple phosphorylation target by Ser/Thr kinases40, was phosphorylated by PKA and PKC isoforms   formation in the matrix29. In additional in vitro kinase assays, we observed that PKCβII, PKCδ, and PKCε phosphorylated S92, and that S92 phosphorylation activities by PKCβII and PKCδ were stronger than that of PKCε  of the ver Fig. 30. The MPKA Fig.  and MCU PKC Fig. . In all KCε Fig. . Thus, wS92E) to improve protein solubility for crystallographic studies6. We determined the structure of T4-MCU NTDS92E at a resolution of 2.50 Å by molecular replacement using the MCU NTDWT (PDB ID: 4XSJ) and T4 lysozyme (PDB ID: 2LZM) structures as templates  with root-mean-square deviation (RMSD) of 0.57 Å for 87 Cα atoms, and consisted of two helices (α1 and α2) and six β-strands , and the side chain of R93 moved up to the position of S92 and formed a new hydrogen bond with E92 and D119. The MCU NTDS92E L90 in the hydrophobic interior, which also contained V88, L115, I122, V125, and I153 in MCU NTDS92E moved away from that of MCU NTDWT at a distance of 2.2 Å  at 2.85 Å resolution. Overall, the structures of MCU NTDWT and the MCU NTDD119A mutant were similar, with an RMSD of 0.61 Å for 86 Cα atoms 42. Overall structures of two mutants showed similar RMSF scores in dynamics to that of the MCU NTDWT, while dramatic RMSF changes were observed in the L2-L4 loops of the MCU NTD mutants compared to the MCU NTDWT  (Å) from the ensemble refinement results of the MCU NTDDWT Fig. .Figure 344. Negative charges in the mutant S92E (pKa ~4.3) and S92p  by deprotonation of the hydroxyl group were increased by −1.0 and −2.0, respectively, whereas the negative charge of −1.0 in the D119A mutant (pKa ~3.9) was reduced in comparison with the MCU NTDWT. In agreement with the changes of the negative charge, electrostatic surface charge was enhanced in the L2-L4 loops of the MCU NTDS92E and the S92p model structures, whereas the positive surface charge of MCU NTDD119A was increased compared to the MCU NTDWT  simulations were performed on the NTDion Fig. . The fluDWT Fig. . The aveWT, the S92 and R93 in the L2 loop were hydrogen-bonded with D119 in the L4 loop  was higher than that of WT , suggesting that the conformational changes and enhancement of negative charges by S92 phosphorylation may affect the dimerization of two MCU-EMRE channels , the three key monomer pair distances  were monitored during 10 ns simulations times  away. Overall, our MD simulation studies suggest that the S92 phosphorylation can weaken dimerization of the MCU-EMRE tetramer.To confirm this hypothesis, we performed MD simulations for the NTDmes Fig.  and the mes Fig.  show tha21. In addition, the MCU NTD plays pivotal roles in MCUR1 interaction, MCUb NTD interaction, Mg2+ binding selectivity, phosphorylation, redox sensor, oligomerization of MCU-EMRE channel complexes, and regulation of MCU Ca2+ uptake activity19. Thus, we believe functional roles of MCU NTD for its Ca2+ uptake activity warrants further investigation, although recent studies of NTD deletion of MCU appears to be functionally dispensable in mitochondrial Ca2+ uptake46MCU activity is modulated by its regulatory proteins, including MICU1, MICU2, MCUb, EMRE, and MCUR1, as well as post-translational modifications such as phosphorylation2+ channels by phosphorylation. Phosphorylation of Ca2+ channels containing ryanodine receptor 2 and inositol 1,4,5-trisphosphate receptors regulates Ca2+ release in the SR through PKA and CaMKII51. The MCU is directly phosphorylated by Pyk2 and AMPK and phosphorylated MCU facilitates Ca2+ entry into the mitochondria21. The conserved S92 in the MCU is a putative recognition site  for phosphorylation by Ser/Thr kinases such as CaMKII, PKA, and PKC on the basis of prediction of KinasePhos 2.0 server and GPS 2.0 software35. Nguyen et al. observed S92 phosphorylation of MCU by CaMKII using MCU S92 phospho-specific antibody in vivo25, although the regulatory functions of MCU activity by CaMKII still remain controversial26. Instead, our in vitro kinase assay results indicated MCU S92 was phosphorylated by PKC isoforms  localized in the mitochondrial matrix, but was not phosphorylated by PKA  and mitochondria, and modulate function of CaPKA Fig. 27,28. Fu53. Under physiological conditions, the MCU uptakes Ca2+ ions into the matrix; Ca2+ ions play an essential role modulating ATP synthesis through TCA cycle and the electron transfer chain, and finally induce ROS production as by-products in the mitochondria54. However, continuous overload of mitochondrial Ca2+ entry can produce large amounts of ROS and eventually lead to apoptotic or necrotic cell death54. Upon production of ROS, PKCβII, PKCδ, and PKCε translocate to the mitochondria and modulate functions of enzymes and Ca2+ channels by Ser/Thr phosphorylation, as well as ROS production29. In our studies, we observed that PKCβII, PKCδ, and PKCε phosphorylated the MCUS92in vitro. We speculate that regulation of the S92 phosphorylation by the PKC isoforms under physiological conditions play important roles in ROS homeostasis or programmed cell death by excessive ROS, driven by the MCU Ca2+ uptake. Additional experimental evidence will be required to clarify the functional roles of PKC isoforms in the MCU31.Free radicals, such as ROS and reactive nitrogen species (RNS), generate in a well-modulated manner to maintain cellular homeostasis as signalling second messengers, and play critical roles in the activation of enzymes and alteration of lipids, protein, and DNAβII, PKCδ, and PKCε, are capable of phosphorylating S92 in the MCU NTD. We also characterized local conformational changes in our structural determination of MCU NTDS92E and NTDD119A as well as in MD simulation analysis of the WT and S92p. The conformational changes and enhancement of negative charge of the L2-L4 loops in the MCU NTD by S92 phosphorylation may be essential for regulating MCU activity, despite there lacks of functional data for the MCU activity modulation by S92 phosphorylation. Further studies are required to reveal the functional effects of MCU S92 phosphorylation by the PKC. The results provide a framework for further studies investigating the functional and structural roles of MCU phosphorylation by PKC.In conclusion, we identified that the PKC isoforms, PKC6-bacteriophage T4 lysozyme 56 in the modified pET21a vector (Novagen), was constructed as previously described6. Point mutagenesis using polymerase chain reaction (PCR) was performed to construct the S92E or D119A mutants. For the in vitro kinase assay of the MCU NTD S92, we designed all Ser/Thr to Ala mutants . PCR was used to synthesize the MCU NTDAA construct using 12 oligonucleotides; then, MCU NTDAAS92 was generated using the A92S mutation from the MCU NTDAA.For structural studies, human MCU NTD (residues 75 − 165), including N-terminal HisS92E or T4 lysozyme-MCU NTDD119A was purified using the same method for T4 lysozyme-MCU NTDWT as previously described6. The proteins were expressed in the Escherichia coli strain BL21-CodonPlus (DE3), followed by purification using Ni-NTA affinity and size exclusion chromatography (SEC) on a HiLoad 16/60 Superdex 75 column . The samples were concentrated by centrifugation using Amicon Ultra-15 10 K filter units (Millipore) to 5 mg mL−1 in final buffer containing 20 mM Tris-HCl (pH 8.0), 50 mM NaCl, 5% (v/v) glycerol, and 1 mM DTT.The T4 lysozyme-MCU NTDin vitro kinase assays, MCU NTDWT and MCU NTDAAS92 were purified by a similar procedure using the following buffers: lysis buffer , wash buffer , and elution buffer . The samples were then purified using SEC on a HiLoad 16/60 Superdex 75 column  pre-equilibrated with a buffer [50 mM Tris-HCl (pH 7.5) and 150 mM NaCl]. Then, the fractions containing human MCU NTDWT and MCU NTDAAS92 were collected. The protein was concentrated using an Amicon Ultra-15 10 K filter unit (Millipore) at a concentration of 0.3 mg mL−1. Final human MCU NTD proteins were stored at −80 °C.For WT, 20 μg of purified MCU NTDAAS92, and 32 μg of commercially obtained MBP  were phosphorylated by PKA , PKC mixtures , PKCβII , PKCδ , and PKCε . In vitro phosphorylation of PKA was performed in 25 mM Tris-HCl (pH 7.5), 10 mM MgCl2, 2 mM DTT, 5 mM β-glycerophosphate, 0.1 mM Na3VO4, 0.2 mM Mg-ATP, and 3 pmol of [γ-32P]ATP (3000 Ci/mmol) with 20 ng PKA for MBP and 100 ng PKA for MCU NTDWT and MCU NTDAAS92. In vitro phosphorylation assays of PKC mixtures, PKCβII, PKCδ, and PKCε were performed in 1 × reaction buffer A  , 2 mM DTT , 1 × PKC lipid activator , 0.2 mM Mg-ATP, and 3 pmol of [γ-32P]ATP (3000 Ci/mmol) with 100 ng PKC mixtures, PKCβII, PKCδ, and PKCε for MBP and 100 ng PKC, 50 ng PKCβII, 200 ng PKCδ, and 200 ng PKCε for MCU NTDWT, and MCU NTDAAS92. All in vitro kinase assays were performed at 30 °C for 60 min. The reaction was halted by the addition of SDS-PAGE sample buffer. Then, reaction samples were resolved by SDS-PAGE and visualized by autoradiography.Six micrograms (6 μg) of purified MCU NTDS92E or T4 lysozyme-MCU NTDD119A was crystalized using the same method for T4 lysozyme-MCU NTDS92A as previously described6. Crystals of T4 lysozyme-MCU NTDS92E or T4 lysozyme-MCU NTDD119A were produced using the hanging drop vapour diffusion and microseeding method, using T4 lysozyme-MCU NTDWT crystals as seeds in the reservoir solution containing 20% (w/v) polyethylene glycol (PEG) 3350, 5% (v/v) glycerol, 0.3 M (NH4)2SO4, and 0.1 M Bis-Tris-HCl (pH 5.5). Once the microcrystals (<0.01 − 0.02 mm) of the T4 lysozyme-MCU NTDWT grew at 20 °C, 2 μL of T4 lysozyme-MCU NTDS92E or T4 lysozyme-MCU NTDD119A proteins and 2 μL of the reservoir solution were added directly to the 1 μL drop containing T4 lysozyme-MCU NTDWT seed crystals. The final T4 lysozyme-MCU NTDS92E or NTDD119A crystals were grown at 20 °C in 5 μL mixtures containing the WT and the S92E or D119A mutant at a 1:4 molar ratio. The crystals were directly flash-frozen in liquid nitrogen.The T4 lysozyme-MCU NTDS92E or T4 lysozyme-MCU NTDD119A crystals were collected at 100 K using synchrotron X-ray sources on beamlines 5 C at the Pohang Acceleratory Laboratory (PAL) . We finally collected diffraction data for T4 lysozyme-MCU NTDS92E at a resolution of 2.50 Å and for T4 lysozyme-MCU NTDD119A at 2.85 Å using a single wavelength, 0.9795 Å. The diffraction data were processed using the HKL2000 suite57. Molecular replacement was carried out using Phaser in the CCP4 suite58, using the structures of the bacteriophage T4 lysozyme (PDB ID: 2LZM) and MCU NTD (PDB ID: 4XTB) as templates. The obtained models were subjected to iterative rounds of model building and refinement using programs Coot59 and REFMAC5 in CCP4 suite58. The details of data collection and refinement statistics are provided in Table Diffraction data of T4 lysozyme-MCU NTD30. The CCP4 program LSQKAB was used to superimpose the structures of MCU NTDWT (PDB ID: 4XTB), MCU NTDS92E, and MCU NTDD119A and to estimate RMSD (Å) scores of Cα atoms58. The electrostatic surface charges of MCU NTDs  were analysed using the PDB2PQR server60 and visualized using PyMOL version 1.5.0.4 (Schrödinger LLC).All structural figures were generated using PyMOL version 1.5.0.4 (Schrödinger LLC). The amino acid sequence and protein surface conservation of the MCU NTD were calculated using the ConSurf serverWT (PDB ID: 4XSJ), T4 lysozyme-MCU NTDS92E, and T4 lysozyme-MCU NTDD119A was performed using structures and structural factors by phenix.ensemble_refinement41. Default parameters were used in the phenix.ensemble_refinement, including pTLS = 0.8 and Tbath = 5 K, and solvent updated every 25 cycles. The simulations have an equilibration phase (10τx) in which the temperature, X-ray weight and averaged structure factors stabilize, followed by an acquisition phase (10τx). The output structures by ensemble refinement were visualized using PyMOL version 1.5.0.4 (Schrödinger LLC) with a script ‘ens_tool.py’. The RMSF difference histogram for the MCU NTDWT and mutants (S92E and D119A) was plotted using SigmaPlot 12.Ensemble refinement for T4 lysozyme-MCU NTDWT and NTDS92p monomer and octamer structures were carried out using GROMACS  2018.4 package63 with amber99sb-star-ILDNP force field64. Molecular topologies for phosphorylated S92 were generated by AnteChamber Python Parser interface (ACPYPE) with generalized AMBER force field 2 (GAFF2)66. All four systems were solvated with TIP3P water molecules67 in a dodecahedron box and Na+ counter ions were added to neutralize the net changes of the systems by replacing water molecules. In all cases, bond lengths were constrained with LINCS68 and long-range electrostatics were calculated using the smooth particle mesh Ewald (PME) method with a cut-off of 1.0 nm70. A cut-off of short-range non-bonded interactions, van der Waals (vdW), were truncated at 1.0 nm. All MD simulations were conducted energy minimization using the steepest descent method. Equilibration was then performed in two phases, during which position restraints applied to all heavy atoms of the protein. First, the simulations were run under NVT conditions at 300 K, using Berendsen’s coupling algorithm71 for 100 ps. The second phase of equilibration was carried out an NPT ensemble for 100 ps, using the Nose-Hoover thermostat73 and the Parrinello-Rahman barostat75 with coupling time constants of 2.0 ps and 5.0 ps to maintain 300 K and 1 bar, respectively. Production MD was then conducted for 10 ns without any restraint and under the same conditions as the NPT ensemble. All analyses of MD simulation results were performed using the analysis tools in the GROMACS package.Four molecular dynamics (MD) simulations of NTDS92E and T4 lysozyme-MCU NTDD119A have been deposited in the PDB with the accession numbers, 6JG0 and 6KVX, respectively.Atomic coordinates and structure factors of T4 lysozyme-MCU NTDSupplementary information.


I) that is based on the mean of the absolute value of the standardized differences in means on the site characteristics.Pragmatic trials often consist of cluster-randomized controlled trials (C-RCTs), where staff of existing clinics or sites deliver interventions and randomization occurs at the site level. Covariate-constrained randomization (CCR) methods are often recommended to minimize imbalance on important site characteristics across intervention and control arms because sizable imbalances can occur by chance in simple randomizations when the number of units to be randomized is relatively small. CCR methods involve multiple random assignments initially, an assessment of balance achieved on site-level covariates from each randomization, and the final selection of an allocation that produces acceptable balance. However, no clear consensus exists on how to assess imbalance or identify allocations with sufficient balance. In this article, we describe an overall imbalance index (I, then conduct simulation studies to examine its empirical properties under the varying covariate distributions and inter-correlations.We derive the theoretical distribution of I has an expected value of 0.798 and, assuming independent site characteristics, a variance of 0.363/k, where k is the number of site characteristics being balanced. Simulations indicated that the properties of I are robust under varying covariate circumstances as long as k is greater than 3 and the covariates are not too highly inter-correlated.I below the 10th percentile indicate sufficient overall site balance in CCRs. Definitions of acceptable randomizations might also include individual covariate criteria specified in advance, in addition to overall balance criteria.We recommend that values of  Pragmatic trials are increasingly proposed as a way to implement and evaluate new intervention or treatment programs directly into clinical practice settings , 2. ManyIn many C-RCTs, the number of sites to be randomized can be relatively small. A simple randomization of experimental units to two or more intervention conditions is assured of achieving relative balance across conditions on measured and unmeasured potential confounding variables only if the number of units to be randomized is relatively large. When the number of clusters or sites to be randomized is relatively small, problematic imbalances on potentially important and confounding site characteristics are likely to arise purely by chance. Optimized randomization procedures are often recommended to ensure balance on cluster characteristics across treatment conditions in C-RCTs . PragmatOptimized randomization procedures for achieving site balance in C-RCTs have been categorized into four general types . These ip values from nonparametric analyses of individual site characteristics by assigned treatment condition to be 0.30 or higher. Although this recommendation ensures that a retained randomization will not have any significant or marginally significant imbalances on any site characteristic, p values are somewhat dependent on the number of sites being randomized, and notable but non-significant imbalances may still remain if the number of sites being randomized is relatively small. In addition, as the number of balancing site variables increases, a smaller and smaller proportion of all randomizations will be identified as satisfactory using this criterion.Moulton  developeIn addition to defining balance based on individual site characteristic criteria, overall balance across all site characteristics collectively can be considered. Raab and Butcher  proposedI) index and compare it to Raab and Butcher’s [B index as an overall site balance index for use in future C-RCTs with two treatment conditions.In this paper, we develop and describe an overall imbalance index that is based on the absolute value of standardized mean differences calculated from multiple site variables. We point out that the frequency distribution of this imbalance metric is based on the folded normal or half-normal distribution , 14. We utcher’s  B index I:For each site characteristic for which balance is desired, calculate the observed difference in means between treatment arms  after each random allocation to those conditions.Calculate the standard deviation of the mean difference between treatment arms, then divide the observed mean differences by their respective standard deviations to standardize those mean differences.Take the absolute value of each of those standardized mean differences.I index.Calculate the mean of those absolute values from step 3 across the multiple site balancing variables. This mean is the The following procedures are used to determine μ = 0) and becomes the positively-skewed, half-normal distribution [absolute value of this difference in means (AVDM) is:For one site variable and one simple random assignment to two conditions, the expected value under the null hypothesis of a raw mean difference between the two treatment arms is 0. However, the absolute value of this mean difference will not have an expected value of 0. When the absolute value of a normally distributed variable with a mean of 0 is taken, the normal distribution is folded at that population mean , and the central limit theorem applies to the distribution of this average. This results in a distribution that approximates the normal distribution as k becomes sufficiently large. As k increases, the median approaches the mean of 0.798, and if the covariates are assumed to be independent, then the variance of I becomes 0.363/k  is known, the degree of overall imbalance can be calculated using either standard deviation units or percentile rankings.When calculating I in a standard 2-arm C-RCT, we conducted multiple simulation studies under a variety of conditions that might occur in practice. Although there is no gold standard to evaluate overall covariate imbalance, we compared the I index with the B index of Raab and Butcher [I and its distributional properties under multiple varying conditions.In order to examine the statistical properties of  Butcher  and with Butcher . We simuI) index. We assumed that the site balancing variables come from a multivariate normal distribution. We varied the number of variables from 2 to 4 and examined a range of 4 equally spaced correlations from 0 to 0.75 among these variables . Descriptive statistics of I were computed from the simulated data and compared with statistics theoretically derived based on central limit theorem and with the simulated B index.Simulation 1 assessed the effect of varying number of site balancing variables and correlation among these variables on the statistical properties of the imbalance , where μ = ’ and Σ = IX1 ~ Bernoulli (0.3), X2 ~ Bernoulli (0.5), X3, X4 ~ Normal X1 ~ lognormal , X2 ~ lognormal , X3, X4 ~ Normal Simulation 2 examined the dependence of the We assumed the variables were drawn independently in each of these scenarios.I index and that it was not necessary to conduct all possible randomizations. For 12 sites, we conducted all possible randomizations into the two treatment arms, which involved 462 randomizations for the 1:1 allocation ratio and 495 for the 1:2 allocation ratio. Random assignments for each site were determined by random numbers generated by the SAS statistical software package .The simulation studies focused on two treatment arms. To evaluate the dependence on the number of sites, we considered 12, 18, or 60 sites. Besides the 1:1 design, which is most commonly implemented in C-RCTs, we also examined a 1:2 allocation ratio as well. For 18 and 60 sites, we simulated 10,000 unique randomizations without duplicate by assigning the 18 or 60 sites into two treatment arms with 1:1 or 1:2 allocation ratios. For the 1:1 allocation ratio and 18 sites, there are 24,310 unique distinct splits of 9 vs. 9 sites, and there are over 1,000,000 distinct randomizations when the number of sites is 24 or larger. Earlier simulations indicated that 10,000 unique randomizations were sufficient for examining the statistical properties and distributions of the I and B) and examined individual site characteristic p values for differences by the two groups using the Kruskal-Wallis (KW) statistic as recommended by Ciolino and colleagues [I and B, we chose the lowest 10% to signify sufficient balance. For KW, we specified a min  > 0.30 as the threshold of sufficient balance [For each randomization, we calculated the overall imbalance metrics . This results in each site variable being equally weighted in the calculation of B. If the k site-level variables are independent of each other, then this weighting results in B being distributed as a chi-square statistic with k degrees of freedom.We calculated the  Butcher , namely: Butcher , we usedI, we first calculated absolute values of standardized mean differences for each site characteristic and then averaged across multiple balancing variables.SDMi is the standard deviation of the difference in means for the ith site-level variable. As stated previously, if the k site-level variables are assumed to be independent of each other, then I ~ N  were minimal. Thus, because the 1:1 allocation ratio is the most commonly used approach in practice, only the results from that allocation ratio are further presented here.I index from 1:1 allocation ratio simulations using 2, 3, and 4 multivariate normal distributed cluster-level variables with correlations ranging from 0 to 0.75 (simulation 1) are illustrated in Figs. r = 0.50) or highly correlated (r = 0.75) site variables, the observed distributions tended to more closely approximate the normal distribution as the number of simulation sites increased to 60 and as the number of site variables increased to 4, but some moderate deviations from normality were still evident. The more extreme the correlation, the greater the deviation of the distribution from normal distribution due to the violation of independent covariate assumption in central limit theorem. Because the empirical I index could never be less than 0, deviations between the observed distributions and the overlaid normal density curves tended to be greatest in the lower tails of those distributions.The distributions of the I index from simulations that included 4 uncorrelated site-level variables with different combinations of normal, Bernoulli, and lognormal distributions (simulation 2). There were minimal differences in the distribution of the I index regardless of the distributions of the site-level variables, suggesting that the I index of imbalance can be confidently used with a mix of different variable types as long as those site variables are minimally correlated.Figure 3) from the four variable simulations and examined the distributions of group differences of X3 separately for sufficiently and insufficiently balanced allocations as determined by the 10th percentile of the I index. These results are displayed in Fig. We anticipate that the mean difference of site-level variables between two arms equals zero, while the standard deviation of the group mean difference for allocations deemed as sufficiently balanced should be markedly reduced compared with insufficiently balanced allocations. To examine this, we selected one site-level variable (XI and B) and for the criterion that each individual characteristic have a KW p value > 0.30. As the number of site variables increased, the mean for I stays relatively constant and the SD decreases per the central limit theorem, whereas both the mean and SD of B increase, consistent with the increasing degrees of freedom of the chi-square distribution. Additional analyses examined the associations between I and B from the multivariate normally distributed variables. The two metrics were closely correlated with Spearman correlation coefficients ranging from 0.96 to 0.99. Across all simulated scenarios, I and B were in agreement for over 96% of the simulations in terms of whether those indices were in the bottom 10% of their respective distributions, with kappa agreement statistics ranging from 0.84 to 0.91. For the KW p value criterion, the proportion of acceptable randomizations is much larger when the number of site characteristics is small but steadily decreases as the number of site characteristics increases. Kappa statistics of agreement between the KW p value criterion and both I and B were small to medium in magnitude, ranging from 0.21 with both I and B when only 2 site characteristics were examined and increasing steadily as the number of site characteristics increased. Kappa was 0.44 in relation to I and 0.47 in relation to B for 4 uncorrelated, multivariate normal site characteristics.Tables I for up to 10 independent site characteristics. Theoretical percentile cutpoints are provided for thresholds of both highly balanced (10th percentile) and moderately balanced (25th percentile) randomizations. The theoretical percentile cutpoints for 2, 3, and 4 site variables in Table I index was acceptable in comparison to the empirical thresholds but not acceptable in relation to the theoretical cutpoint. The 10th percentile theoretical cutpoints in Table Table This paper builds on previous efforts , 9 to deI index is an advance over previous work in that it provides a standardized metric that can be used for multiple purposes. Like the B index described by Raab and Butcher [I index can be used to guide the selection of random allocations in C-CRTs that demonstrate sufficient overall balance on a set of covariates or site characteristics collectively. Due to its standardization and the distributional properties illustrated here, the I index also provides a metric of the overall degree of imbalance across all site characteristics that can be interpreted, at least approximately, into percentile ranks from the theoretical normal distribution. Percentile ranks can also be obtained from the chi-square distribution for the B index and used similarly. The metrics could be used to conduct comparisons of the level of imbalance obtained in other trials that might use a different number of sites or site characteristics. Both I and B can be calculated for quantitative or categorical variables; for categorical variables with j different categories, j – 1 dummy-coded (1 vs. 0) variables can be created and the method otherwise proceeds as outlined here.The  Butcher , the I iI index, a few hundred randomizations will typically be adequate to identify a sufficient number of acceptable randomization allocations from which one might be randomly selected for implementation in the trial. In cases where the number of sites is relatively small  or the correlations among some site characteristics are high , the theoretical cutpoints from the normal approximation may be misleading. We recommend that empirical percentile cutpoints using either I for B from a sufficiently large number of randomizations be used to identify suitable randomizations in these situations.Our simulations generally indicate that 3 or more minimally or only moderately correlated site characteristics are sufficient to assume normality in most cases, especially if 18 or more sites are available to be randomized. The theoretical percentile ranks and cutpoints should be particularly useful to investigators in these cases because they allow an estimation the degree of imbalance in a particular randomization without conducting all possible randomizations and calculating empirical cutpoints. As the number of sites increases, it becomes unnecessary and perhaps even cumbersome to conduct all possible randomizations, which quickly can expand to hundreds of thousands or even millions of distinct random allocations. Using the I would continue to even more closely approximate the normal distribution as the number of site characteristics increases or as the number of sites to be randomized increases. Future simulations might examine modifications to the I index when randomizations to more than two treatment conditions are desired. Future work is also needed to examine additional mixtures of site characteristic distributions and inter-correlation patterns.Limitations to our work include the many restrictions we implemented in our simulations. We recognize that only a small and finite number of variable conditions were simulated, including no more than 4 site characteristics and only two treatment conditions. We are confident that the distributions of I index is a standardized, comparative metric of reduced imbalance, it may be useful as a metric for future simulation studies that examine associations between the degree of balance achieved and the alterations in observed type I or type II error rates.Another important area where future research is needed is on the implications of optimized randomization procedures for the statistical analysis of treatment effects on outcomes. In most cases, the increased balance achieved on covariates from optimized randomization procedures is expected to result in more precise estimates of treatment effects and greater power, potentially rendering traditional parametric analyses overly conservative , 15. BecAs pragmatic trials continue to evolve and become more dominant sources of evidence for both the efficacy and effectiveness of interventions , it willI as provided in Table p value for all statistical comparisons of those individual site characteristics. In general, as investigators hope to optimize research designs and maintain rigor in their pragmatic trials, making these decisions carefully before the trial is initiated should ensure sufficient balance across treatment conditions and eliminate any opportunities for biased treatment assignments.We recommend that randomization method decisions and balance acceptability thresholds be established before individual allocations to treatment conditions are reviewed. Moulton  has prev


THs have the lowest energy C–X*-type unoccupied orbitals and overlap with the Se lp donor leads to high donor-acceptor energies and the greatest activation of the C–X bond. The higher energy C–Br* and C–Cl* orbitals similarly result in weaker donor-acceptor complexes and less activation of the C–X bond. Comparison of the I···Se interactions for the TH group suggest that a threshold XB strength may be required for dehalogenation. Only highly brominated PBDEs have binding energies in the same range as THs, suggesting that these compounds may inhibit Dio and undergo debromination. While these small models provide insight on the I···Se XB interaction itself, interactions with other active site residues are governed by regioselective preferences observed in Dios.Iodothyronine deiodinases (Dios) are involved in the regioselective removal of iodine from thyroid hormones (THs). Deiodination is essential to maintain TH homeostasis, and disruption can have detrimental effects. Halogen bonding (XB) to the selenium of the selenocysteine (Sec) residue in the Dio active site has been proposed to contribute to the mechanism for iodine removal. Polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) are known disruptors of various pathways of the endocrine system. Experimental evidence shows PBDEs and their hydroxylated metabolites (OH-BDEs) can inhibit Dio, while data regarding PCB inhibition are limited. These xenobiotics could inhibit Dio activity by competitively binding to the active site Sec through XB to prevent deiodination. XB interactions calculated using density functional theory (DFT) of THs, PBDEs, and PCBs to a methyl selenolate (MeSe Dei. Dei3 (1T3 (rT3) . Dio3 alive site .Disruption of TH homeostasis by xenobiotics can have long-term negative health effects such as structural abnormalities, cardiovascular diseases, and hypo/hyperthyroidism ,8. OrganPBDEs are used in commercial products to increase flame resistance a 28,29],2928,29]PCBs, like PBDEs, are industrial flame retardants with high chemical stability b 42,43],4342,43]Halogen bonding (XB) has gained importance in drug design and crystal engineering ,68,69,70lp) and the antibonding orbital (σR-X*) on the acceptor fragment  stronger Lewis bases due to destabilization of the lp donor MOs. In Dio, a partial explanation for the preference of Sec of Cys is the greater Lewis basicity of the selenolate over the thiolate [D→A) as the extent to which donation into the σR-X* acceptor stabilizes of the lp donor MO  and ΔED→A)  ad ΔED→A)  28]..lp) and R-X* MO has a greater contribution from the halide AOs. Decreasing the electronegativity from F to I causes the X AOs to destabilize relative to the R fragment, resulting in orbitals with more ‘R’-like character in σR-X while increasing the X contribution (%X) to σR-X* . Th. ThZPE) yronine) . For exa 3,3′-T2  [28]. ThC–I bond . For exa0.158 Å) . A minime of Dio .− at each unique halogen position   with MeSeor para)  21]. In. In− at s of Dio .D→A = 44.77 kcal mol−1, Δd(C–Br) = +0.206 Å) is in the same range of THs and could be susceptible to debromination  = +0.102 Å) and may be less likely to undergo debromination  compared to para . However, upon hydroxylation, XB at the para position of 5-HO-BDE-47  is enhanced by the proximal OH group for a more favorable interaction than ortho  (4 was held in the Dio3 active site by a His202-Arg275 clamp through which His202 forms a hydrogen bond to the T4 4′-OH group (Some of the highly brominated PBDEs/OH-BDE (≥5 Br) complexes have donor-acceptor interaction energies in the same range as THs. For example, in 6-HO-BDE-47, the strong interaction at the 2′ position (ΔEmination . Howevermination . In DFT l mol−1) . In OH-BOH group  [7].ZPE ≈ −13.00 to −23.00 kcal mol−1) showed a positive correlation between concentration and free T3 levels, suggesting Dio1 inhibition. However, other mechanisms may be disrupted, such as displacement of THs from transport proteins . Me. MeC-Cl*eptor MO .3/rT3 ratio, indicating Dio3 inhibition [Experimental evidence for Dio inhibition by PCBs is limited and shows conflicting results ,58,114. hibition . This nehibition .− model provide insight into potential mechanisms of inhibition. Xenobiotics such as PBDEs and PCBs may inhibit Dio by forming an X···Se XB interaction to the catalytic Sec in the active site. XB favorability in the order of THs > PBDEs > PCBs agrees with the expected trends (I > Br > Cl). THs generally undergo deiodination by Dios, an exception being 3-T1 which has the weakest I···Se XB interaction of the series. This observation suggests that XB interaction strength with the active site Sec must exceed an energy threshold for deiodination. While some of the highly substituted PBDEs/OH-BDEs have similar interaction energies to THs and may undergo debromination, PCBs have less favorable interactions, suggesting dechlorination by Dio would not be observed.Modeling the XB interactions of halogenated endocrine disruptors with the Dio active site model SeMe3 as its preferred substrate, while Dio2 and Dio3 prefer ORD (meta-) and IRD (ortho-) with T4 and T3, respectively. A PCB, PBDE or related compound with a structure containing solely meta halogens (such as PCB-80 or BDE-80), or its strongest XB interactions at the meta position, may preferentially inhibit Dio2. Likewise, a PCB, PBDE or related compound containing solely ortho chlorines (such as PCB-54 or BDE-54), or its strongest XB interactions at the ortho position, may target Dio3 for inhibition. These preferences will be subject to other interactions within the Dio active site. Understanding the regioselectivity of these preferences may aid in the drug design to target specific Dios.XB interaction strengths vary by position. The preference for XB at the meta and ortho positions of diphenyl ethers suggests that the substitution pattern of THs may have been selected to facilitate enzymatic deiodination. The position dependence of XB interactions of PBDEs (ortho and meta) and PCBs (meta and para) suggest that these compounds may target Dio types with substitution patterns similar to their preferred substrate . For exaThe conformational preferences of THs and halogenated aromatics will also affect Dio binding. For instance, since PBDEs have the same diphenyl ether core as THs, they may bind to Dio in a similar fashion. Xenobiotics with large halogens, such as THs and PBDEs, may be less able to adapt their most favorable conformations to the active site due to steric interactions. PCBs lack the ether linkage connecting the phenyl rings and are classified as dioxin or non-dioxin-like based on ortho-substitutions. These two conformations differ in terms of toxicity—non-dioxin-like PCBs are only toxic at higher concentrations (>1000 nM), while dioxin-like PCBs are highly toxic and mimic the structure of TCDD. Addition of halogens to ortho positions restrain the PCB to a noncoplanar conformation due to steric clashes, leading to lower flexibility around the central C-C bond . TherefoWhile these small models provide insight on the XB interactions in the active site of Dio, the simple model itself is insufficient for describing various factors that may influence overall XB favorability. Interactions with active site residues will control regioselective binding and activation of THs and the ability of inhibitors to block the active site. For example, OH-substituted inhibitors may be accommodated by the His-Arg clamp proposed for Dio3 . The X··From the modeling perspective, simulations of full proteins will be needed to understand how these factors affect the XB interaction and substrate binding. Force fields have been developed to account for XB interactions through the use of dummy atoms to represent the anisotropic density at the halogen ,120,121.


After 10 days, the snow cover was reversed (either removed or added), resulting in a rapid change in irradiance at the ice-water interface. In treatments acclimated to low light, the subsequent exposure to high irradiance resulted in a ~400× increase in the production of hydrogen peroxide (H2O2) and a 10× increase in nitric oxide (NO) concentration after 24 h. The observed increase in oxidative free radicals also resulted in significant changes in photosynthetic electron flow, RNA-oxidative damage, and community structural dynamics. In contrast, there was no significant response in sea-ice algae acclimated to high light and then exposed to a significantly lower irradiance at either 24 or 72 h. Our results demonstrate that microsensors can be used to track real-time in-situ stress in sea-ice microbial communities. Extrapolating to ecologically relevant spatiotemporal scales remains a significant challenge, but this approach offers a fundamentally enhanced level of resolution for quantifying the microbial response to global change.Sea ice supports a unique assemblage of microorganisms that underpin Antarctic coastal food-webs, but reduced ice thickness coupled with increased snow cover will modify energy flow and could lead to photodamage in ice-associated microalgae. In this study, microsensors were used to examine the influence of rapid shifts in irradiance on extracellular oxidative free radicals produced by sea-ice algae. Bottom-ice algal communities were exposed to one of three levels of incident light for 10 days: low (0.5 μmol photons m When lioduction . In landoduction  and bottoduction . Active  m−2 s−1 , 2000. H m−2 s−1 . The ext m−2 s−1 . However m−2 s−1  and the  m−2 s−1  in this  m−2 s−1 . The prein-situ primary production over timescales ranging from days  free-radicals that can be converted into H2O2 and hydroxyl radicals (OH−). Under normal photosynthetic conditions, the level of these radicals is reduced to sustainable levels by efficient antioxidation mechanisms that limit damage. However, when exposed to excessive irradiance, ROS detoxification mechanisms can be insufficient, leading to ROS-induced cellular damage and the release of oxidative free radicals. Recently, Environmental perturbation can influence multiple aspects of phototrophic metabolism and cause significant stress that manifests first in the photosynthetic apparatus e.g., . While pies ROS; . This lein-situ physiological status of under-ice algae communities is needed particularly during events that trigger environmental flux. In this study, microsensors were used to examine the influence of rapid shifts in irradiance on extracellular oxidative free radicals produced by sea-ice algae. Specifically, we aim to determine if (a) the photosynthetically coupled oxidative metabolites H2O2 and NO are produced following controlled manipulation of PAR at the ice-water interface and (b) whether these changes influence ice-algal biomass, community structure, photophysiology, and intracellular integrity.A non-destructive method that aims to measure the n = 3; −2 s−1 in low light treatments (LL – 30 cm snow cover) to 13 μmol photons m−2 s−1 (HL – no snow) when snow was removed. Similarly, in high light treatments (HL – no snow), irradiance decreased from 13 to 0.5 μmol photons m−2 s−1 (LL – 30 cm snow) when snow was added. Control treatments (mid-range light – 5 μmol photons m−2 s−1 – 10 cm of snow) were not “transitioned”; snow cover was kept constant at 10 cm depth for the duration of the experiment. Plots allocated a HL (no snow) treatment were cleared of any settled snow throughout the day, to ensure maximum light penetration. Samples were extracted immediately before the transition and 24 and 72 h following the rapid alteration in irradiance.Sea-ice algae concentrated in the bottom 300 mm of 1.8 m thick fast-ice at Cape Evans, McMurdo Sound, Antarctica  were collected between November 11 and December 7, 2018. An area measuring ~20 × 20 m was selected in which nine 5 × 5 m plots were established . These dIce cores were extracted from the center of each quadrat, three cores were used for microsensor experimentation, and one for species identification and intracellular assays. Cores were obtained by drilling to within 300–500 mm of the ice-water interface with a powered Jiffy ice auger, and cores 300–500 mm long were then extracted using a Mark V SIPRE ice corer . The bottom section of the core was collected for analysis, the remaining section of the core  and ice shavings were returned to the hole of extraction to limit light transmission. To minimize light shock, all cores were extracted under a black cover, wrapped in black plastic, and immediately transferred to a field laboratory . For I-Pa fluorescence was measured using a Maxi imaging pulse amplitude modulated fluorometer  using the methods described by Chlorophyll a (chla) concentration was determined by collecting microalgal cells from within a cylinder embedded on the ice-surface of inverted ice-cores. Samples were filtered onto 25 mm GF/F filters and extracted in 10 ml of methanol in the dark at 4°C for 12 h. Chla extracts were subsequently measured on a Turner fluorometer . Chla concentration was determined in triplicate using the acidification protocol as described by Chlorophyll For species composition, a 2 ml sample was taken randomly from the bottom of an ice core collected from the center of each treatment and fixed in 0.5% glutaraldehyde. A 0.5 ml aliquot from each sample was prepared using a settling chamber and cells were examined using a Nikon eclipse Ci . Whole-cell microalgae were counted from 10 randomly selected fields of view and taxonomically identified using the descriptions in in-situ under-ice irradiance , pre-chilled (−1.8°C) seawater as per the manufacturer’s instructions. The sensors were left to equilibrate for 1 min before logging the electrochemical trace over a total of 6 min. H2O2 (μmol), NO (nmol), and O2 (μmol) concentrations were determined by extracting the last 5 min of each electrochemical trace and calculating the mean; the results were then normalized to the chla concentration of each sample. Chla is the primary photosynthetic pigment utilized by Antarctic sea-ice algae and is the accepted proxy for estimating algal biomass  in collecting microsensor data. Cut cores were left to equilibrate in the water bath in the dark for 3 min. Samples were then exposed to their respective light regime for 6 min during which time, H biomass , 2018. B2 and NO were measured with Clarke-type electrodes  connected to a field multimeter .Hydrogen peroxide concentration was measured using electrodes obtained from World Precision Instruments , these were connected to a WPI free radical analyzer. OTo determine if oxidative free radicals can cause damage to intracellular molecules, an oxidation RNA damage ELISA kit was used . This assay detects the formation of 8-hydroxyguanosine (8-OHG), which is a biomarker for oxidative damage. Calculation of 8-OHG concentration was conducted fluorometrically on a microplate reader  using the manufacturer’s instructions. 8-OHG levels were normalized to the total RNA concentration of each sample. RNA was initially extracted using a RNeasy Mini Kit  supplemented with molecular grinding resin  to maximize RNA yield.post-hoc pairwise t-tests were conducted on Bonferroni adjusted values of p (p < 0.05) to determine the influence of the main effect (snow loading) had on oxidative free-radical release over time (n = 3). Data are expressed as mean ± 1 SD unless otherwise stated.Where appropriate, data were analyzed using two-way repeated measures Analysis of Variance (ANOVA) to examine variation among treatments (snow loading) over time using the software package R . Data were checked for normality by the Shapiro-Wilk test and sphericity using Mauchly’s test using the R function anova_test. If significant differences were detected, n = 3) was used for this analysis. Means of the traces were used to determine the significance of correlations.Pearson correlations were performed to observe the relationship between different extracellular oxidative species. The final 60 s of each electrochemical trace . After 72 h, H2O2 production in LL → HL plots reduced to a mean of 13.23 μmol H2O2 mg chla L−1 h−1, which was not significantly different compared to the other treatments (2O2 production in treatments transitioning from high light (no snow) to low light  compared to the control (mid-range – 10 cm snow). In the control treatments, no significant increase in H2O2 was observed during the experiment.Hydrogen peroxide production was successfully measured for the first time in sea-ice cores. Following the removal of snow from low light plots (LL → HL), a significant increase in Heatments . There wa L−1 h−1 24 h after the increase in irradiance  when compared to the control , there was a significant increase in the concentration of NO from 2.97 ± 1.22 SD to 10.32 ± 3.66 nmol mg chl2 concentration was observed in treatments that transitioned from low to high light  although this change was not significant (p > 0.05). There was also no significant difference in O2 concentration when comparing the control (mid-range light) and high to low light treatment , and 72 h after  and high to low light treatments.To determine if the increase in Hfficient . A stati < 0.001 . There wNitzschia stellata dominated communities exposed to low irradiances and was marginally higher in the control. In treatments exposed to HL (no snow) for 10-days, the tube-dwelling diatom, Berkeleya adeliensis, was more abundant  concentration was significantly lower in treatments with low light (4.44 ± 4.10 mg chla L−1) compared to both the control (24.86 ± 4.27 mg chla L−1) and high light treatments . In comparison, in treatments transitioned from high to low light (HL → LL) chla decreased by 38% after 72 h, although this was not significant  between chla and snow depth in treatments before the change in irradiance transition. There was no significant correlation between snow depth and chla following the transition.After the 10 day acclimation period, chlorophyll nificant . Furtherow depth ; the resF2, 18 = 4.856, p < 0.001, pairwise t-test Bonferroni adjusted p < 0.0001; a did not change. In contrast, Fv/Fm improved in the high light to low light treatment (HL → LL); Fv/Fm increased from a mean of 0.235 ± 0.139 to 0.337 ± 0.03, however, this was not statistically significant (p > 0.05). Fv/Fm in the control treatment declined slightly (p > 0.05).The rapid transition from LL → HL, resulted in a statistically significant reduction in maximum quantum yield of PSII (Fv/Fm) from a mean of 0.444 ± 0.02 to 0.095 ± 0.027 after 72 h  = 7.273, p < 0.05) and HL → LL treatments (p < 0.002). There was no significant difference in the marker for RNA oxidative damage in any other treatments over time  in LL → HL treatment at 72 h when compared to the mid-range light . Furthermore, the community composition differed with irradiance level; B. adeliensis appeared to dominate when exposed to high light, whereas N. stellata was more prominent in both low and mid-range irradiances. In treatments acclimated to low light, the subsequent exposure to high irradiance resulted in a ~400× increase in the production of H2O2 and a 10× increase in NO concentration after 24 h. This is the first study to observe a real-time response of H2O2 and NO as a function of irradiance in an Antarctic sea ice microbial community.Rapid oscillations in light level can trigger large variations in the degree of under-algal biomass in polar habitats . The timl stress . Rapid cl stress . In contl stress  and incrl stress . Importal stress . The res−2 s−1  was correlated with an increase in the production of H2O2 and NO, suggesting that significant photo-oxidative stress was evident which may have contributed to the decline of the dominant species. The uncharged nature of H2O2 ensures easy diffusion across cell membranes, which under saturating conditions can result in an extracellular overflow . Furthermore, a similar pulse in NO was observed following exposure from low to high light (24 h). This correlated with the pulse in H2O2 and may also be explained in the breakdown of photosynthetic electron transport. The production of NO in the sea-ice diatom Fragilariopsis cylindrus occurs only in the dark or when the photosynthetic chain is inhibited  in both the Antarctic  and less under low light plots (thick snow). Conversely, the maximum quantum yield of PSII (Fv/Fm) was higher when exposed to low light and considerably less in plots with high irradiance. Moreover, alterations in the distribution of the algal mat at the ice-water interface (I-PAM) were consistent with irradiance-induced shifts in species composition. This suggests that the community (dominated by B. adelienis) exposed to HL was either photophysiologically stressed or that photosynthetic capacity was reduced as a mechanism to limit photodamage  as a measure of biomass is viewed with caution because ice-associated taxa can rapidly modulate their pigment composition in response to irradiance  and deposition  events during spring. As a result, seasonal variation in spectral intensity and wavelength will intensify. In principle, we show that deeper snow will reduce light stress and enhance microbial growth, and this could mitigate the seasonal photoinhibition that would otherwise occur because of thinner ice. However, our data also illustrate the negative short-term effects of exposure to relatively high light associated with the oscillation frequency of snow loading. Because our experimental design reflects the constraints of deep-field Antarctic research, extrapolation to large regional and temporal scales would be overly simplistic, however, we clearly demonstrate that advances in the application of microsensors warrants future research. A predictive understanding of how shifts in ecosystem drivers in the Antarctic will impact sea-ice primary production will require an experimental framework that couples in-situ stress in sea-ice microbial communities. This aligns with a fundamentally important goal of assessing the current state of Antarctic ecosystems and generating a baseline against which future change in ecosystem structure and function can be measured.In summary, our results demonstrate that microsensors offer an enhanced level of resolution for tracking real-time The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.FK collected and analyzed the samples and wrote the manuscript. AMa and KR wrote the grant, designed the research, collected the samples, and assisted with the manuscript. KR and KC collected the samples and assisted with the manuscript. EC collected the under-ice irradiance data. AMc assisted with the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Acoustic radiation force impulse (ARFI) elastography, the aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), aspartate aminotransferase-to-platelet ratio index (APRI), and the fibrosis-4 (FIB-4) index are widely used to assess liver fibrosis. However, efficacies of these methods in the evaluation of hepatic functional reserve remain unclear. In this study, we investigated the relationship between ARFI elastography combined with either AAR, APRI, or FIB-4 index and Child-Pugh (CP) class for the evaluation of hepatic functional reserve in patients with chronic hepatitis B (CHB)-related cirrhosis.The shear wave velocities of 104 patients with clinically confirmed CHB-related cirrhosis were determined using the ARFI; and clinical serum markers  were used to calculate the AAR, APRI, and FIB-4 index. Cirrhosis patients were scored according to their CP class. The ARFI, AAR, APRI, and FIB-4 index were compared with the CP class. The efficacy of each indicator in diagnosis was analyzed using the receiver operating characteristic (ROC) curve and the ARFI combined with either the AAR, APRI, or FIB-4 index, which is used to predict decompensated cirrhosis.p>0.05). The ARFI values and the AAR, APRI, and FIB-4 index of patients with CP classes A, B, and C were significantly different (p<0.05). With an increasing CP class, the ARFI, AAR, APRI, and FIB-4 values increased. The correlation between the ARFI and the CP class was stronger than that between the AAR, APRI, and FIB-4 index and the CP class. The area under the ROC curve for the diagnosis of decompensated cirrhosis using the ARFI was 0.841, which was higher than that for the AAR, APRI, and FIB-4 index. According to the area under the curve results, no significant differences were found when the ARFI was combined with either the AAR, APRI, or FIB-4 index and when the ARFI alone was used.No significant differences were observed in gender and age among CP classes A, B, and C patients (The ARFI value has a strong correlation with the CP class. Therefore, ARFI elastography complements CP class in the assessment of the hepatic functional reserve in patients with CHB-related cirrhosis. Hepatitis B virus (HBV) infection is the most common cause of liver disease in China. About 97 million people are HBV carriers, among which more than 20 million suffer from active chronic HBV infection ,2. PersiThe most commonly used method for the assessment of the hepatic functional reserve in the clinic is the Child-Pugh (CP) class. It consists of five clinical features including ascites, HE, total bilirubin, albumin, and prothrombin time or international normalized ratio (INR) , and is Recently, a number of alternative methods including the use of serum markers and acoustic radiation force impulse (ARFI) elastography based on ultrasound -10 were However, the efficacy of the ARFI combined with either the APRI, AAR, or FIB-4 index in the evaluation of hepatic functional reserve during CHB-related cirrhosis remains unclear. Previous studies reported that liver stiffness is correlated with the CP class ,20. Thus2; and patients who did not cooperate during the procedures were excluded from the study. The ARFI and serum markers  were evaluated in all 107 patients before treatment.The study was approved by the local ethics committee, and signed informed consent was provided before participation. A total of 107 patients with CHB-related cirrhosis were enrolled in this study from August 2017 to July 2018. Cirrhosis was diagnosed based on the results of histological examination of liver tissue or based on clinical, laboratory, and radiological examination results. Patients with cirrhosis caused by hepatitis C, alcohol, drugs, or other factors; those with suspected or confirmed primary liver cancer or malignant tumors at other sites; those who had undergone spleen resection and other surgery to relieve portal hypertension symptoms; those with a body mass index ≥30 kg/mARFI imaging was performed using the Siemens Acuson S2000TM ultrasound system with a 4C1 convex array probe. All patients underwent ARFI examination performed by a senior physician blinded to the clinical information of patients throughout the study. During the examination, patients were placed in the slight left lateral position and the right upper limb was raised. The sampling frame was placed in the right lobe of the liver, 1-3 cm below the liver capsule, avoiding the visible structure of the large blood vessels and bile ducts. The UPDATE button was clicked to obtain the shear wave velocity (m/s) in the breath-holding state, and was repeated 10 times to calculate the median value. When a value was significantly different from the others due to the patient’s respiratory motion or adjacent small blood vessels in the sample frame, the number of measurements was increased by more than 15 times. Successful reliable measurements were characterized by: median value of liver stiffness measurements with a success rate ≥60% and an interquartile range/median ratio (IQR/M) <30% , and theInformation on HE, ascites, bilirubin, albumin, and prothrombin time were collected, and the CP class was evaluated using the CP scoring system by a senior doctor who was engaged in research on liver disease and was blinded to the ARFI examination results throughout the study. According to their CP scores, patients were classified as follows: CP class A, 5-6; B, 7-9; and C, 10-15. The APRI, AAR, and FIB-4 were calculated as follows:9/L),APRI=[(AST/ULN) 100]/PLT .9/L)×ALT(U/L)]1/2AAR=AST(U/L)/ALT(U/L), FIB-4=age (years)×AST(U/L)/[PLT. For variables with normal distribution, differences in the ARFI, AAR, APRI, and FIB-4 index among the CP class groups were analyzed using one-way analysis of variance; otherwise the Kruskal-Wallis test was used. The correlation of the ARFI, AAR, APRI, and FIB-4 index with the CP class was analyzed using Spearman’s correlation. MedCalc software was used to draw the receiver operating characteristic (ROC) curve to evaluate the performance of the ARFI, AAR, APRI, and FIB-4 index in the diagnosis of decompensated cirrhosis and to determine the optimal cut-off value (maximum of the sum of sensitivity and specificity). The combined prediction model  was constructed using logistic regression analysis. The ROC curve was drawn using the predicted probability and the accuracy of decompensated cirrhosis diagnosis using each combined model was assessed using the area under the curve (AUC). The AUC in each combined model was compared using the DeLong test. p>0.05; A total of 107 patients were enrolled; however, three were excluded because their IQR/M ARFI values were >30%. Thus, the analysis involved 104 patients, which comprised of 80.9% (n=84) men and 19.2% (n=20) women. The ages of patients ranged from 25 to 80 years, with an average age of 52.73±11.93 years. There were 24, 45, and 35 cases of CP classes A, B, and C, respectively. There were no significant differences in gender and age p>0.05; .s=0.457, p=0.000) and the CP class was stronger than that between the AAR , APRI , and FIB-4 index , and negative predictive value (NPV) of the ARFI, AAR, APRI, and FIB-4 index for the diagnosis of patients with decompensated cirrhosis (at least CP class B) are shown in p=0.543), ARFI+APRI , and ARFI+FIB-4  and ARFI alone .The results showed that the ARFI, AAR, APRI, and FIB-4 index all increased when the CP class increased. As the patient’s condition deteriorated, the degree of liver fibrosis and cirrhosis also increased, which is expected to provide a reference value for predicting the progress and prognosis of patients with cirrhosis. In addition, the correlation between the ARFI value and the CP class was stronger than that between the AAR, APRI, and FIB-4 index and the CP class, indicating that the predictive value of ARFI was significantly better than that of the other markers. These results were partly consistent with those of previous studies that showed that the higher the CP class, the more severe the liver function damage and the worse the liver functional reserve. Hashimoto et al.  found thThe comparison of ARFI values for the corresponding CP class groups showed that the ARFI values of CP class A patients were significantly lower than those of CP classes B and C patients, similar to the results of Bota et al. . The AARCompared to the general population, the mortality rate of patients with compensated cirrhosis is five-fold higher, whereas the mortality rate of patients with decompensation is 10-fold higher . StudiesThere were several limitations in the current study. First, the number of enrolled patients was small. Second, the scores of patients in CP classes B and C were close, which may have affected the results. In future studies, the sample size should be increased and multi-center experiments should be performed.In conclusion, we identified relationships between the ARFI, AAR, APRI, and FIB-4 index and the CP grade. The accuracy of ARFI in predicting decompensated cirrhosis was higher than that of the AAR, APRI, and FIB-4 index. Compared to the ARFI, the combined assessment using the ARFI+AAR, ARFI+APRI, and ARFI+FIB-4 did not further improve the accuracy of diagnosing decompensated cirrhosis. This study identified a convenient non-invasive diagnostic method for predicting decompensated cirrhosis in CHB patients in clinical practice.Wei L and Chen L conceived and designed the study. Wei L and Bao Z conducted the study. Lin X provided clinical information. Wei L and Xu X analyzed the data. Wei L wrote the manuscript. Ye Z was responsible for the revision of the manuscript. All authors read and approved the final version of the manuscript.