E2-25K/Hip2 is an unusual ubiquitin-conjugating enzyme that interacts with the frameshift mutant of ubiquitin B (UBB ؉1 ) and has been identified as a crucial factor regulating amyloid-␤ neurotoxicity. To study the structural basis of the neurotoxicity mediated by the E2-25K-UBB ؉1 interaction, we determined the three-dimensional structures of UBB ؉1 , E2-25K and the ), and polyglutamine-expanded huntingtin (2). UBB ϩ1 was first identified as a frameshift mutant of the ubiquitin (Ub) B protein in the brains of neurodegenerative disease patients (3) and is composed of a Ub moiety (75 residues) with a 19-residue C-terminal extension. Neither A␤ nor UBB ϩ1 is found in young patients not suffering from dementia, but they are observed in older Alzheimer disease patients (4). The genes from which UBB ϩ1 mRNAs are transcribed contain several GAGAG motifs, and dinucleotide deletions (⌬GA) from within the GAGAG motif result in an abnormal C-terminal sequence. Normally these aberrant proteins are removed by the Ub-proteasome system (UPS), which executes the proteolytic degradation of aberrant proteins via a Ub-tagging mechanism (3, 5). E2-25K/ubiquitin, and E2-25K/UBBWithin the UPS, Ub tagging of target molecules entails enzymatic reactions catalyzed by the E1 (Ub-activating), E2 (Ubconjugating), and E3 (Ub-ligating) enzymes. Once E3 tags a target molecule with mono-or polyUb, the tagged molecule is recognized by the 26 S proteasome and degraded (6). In the healthy brain both ␤-amyloid precursor protein and UBB ϩ1 molecules are targets for the UPS and are degraded by the 26 S proteasome (7,8). In the brains of Alzheimer patients, however, both UBB ϩ1 and Ub are present within aggregation plaques also containing ␤-amyloid precursor protein, which is indicative of UPS dysfunction (9, 10). When at normal basal levels, UBB ϩ1 can be removed by the UPS. But when its expression is up-regulated, UBB ϩ1 inhibits the 26 S proteasome in a dose-dependent manner, resulting in the accumulation of aberrant proteins (11). The aberrant C terminus of UBB ϩ1 prevents its activation and, therefore, subsequent ligation to substrates due to , the frame shift mutant of ubiquitin B; UPS, ubiquitin-proteasome system; HSQC, heteronuclear single quantum correlation; TROSY, transverse relaxation optimized spectroscopy; DsRed, Discosoma sp. red fluorescent protein.

Purpose A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web‐based transfusion risk‐assessment system for clinical use. Methods This study retrospectively reviewed 1686 patients who underwent TKA at our institution. Data for 43 preoperative variables, including medication history, laboratory values, and demographic characteristics, were collected. Variable selection was conducted using the recursive feature elimination algorithm. The transfusion group was defined as patients with haemoglobin (Hb) < 7 g/dL after TKA. A predictive model was developed using the gradient boosting machine, and the performance of the model was assessed by the area under the receiver operating characteristic curve (AUC). Data sets from an independent institution were tested with the model for external validation. Results Of the 1686 patients who underwent TKA, 108 (6.4%) were categorized into the transfusion group. Six preoperative variables were selected, including preoperative Hb, platelet count, type of surgery, tranexamic acid, age, and body weight. The predictive model demonstrated good predictive performance using the six variables [AUC 0.842; 95% confidence interval (CI) 0.820–0.856]. Performance was also good according to the external validation using 400 data from an independent institution (AUC 0.880; 95% CI 0.844–0.910). This web‐based blood transfusion risk‐assessment system can be accessed at http://safetka.net. Conclusions A web‐based predictive model for transfusion after TKA using a machine learning algorithm was developed using six preoperative variables. The model is simple, has been validated, showed good performance, and can be used before TKA to predict the risk of transfusion and guide appropriate precautions for high‐risk patients. Level of evidence Diagnostic level II.

PurposeAcute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web‐based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD. MethodThe study included 5757 patients treated in three tertiary teaching hospitals. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the third hospital. Eighteen preoperative variables were collected and feature selection was performed. A gradient boosting machine (GBM) was used to predict AKI. A tenfold‐stratified area under the curve (AUC) served as the metric for internal validation. Calibration was performed via isotonic regression and evaluated using a calibration plot. End‐stage renal disease (ESRD) was followed up for an average of 41.7 months. ResultsAKI develops in up to 10% of patients undergoing TKA, increasing the risk of progression to ESRD. The ESRD odds ratio of AKI patients (compared to non‐AKI patients) was 9.8 (95% confidence interval 4.3–22.4). Six key predictors of postoperative AKI were selected: higher preoperative levels of creatinine in serum, the use of general anesthesia, male sex, a higher ASA class (> 3), use of a renin–angiotensin–aldosterone system inhibitor, and no use of tranexamic acid (all p < 0.001). The predictive performance of our model was good (area under the curve 0.78 [95% CI 0.74–0.81] in the developmental cohort and improved in the external validation cohort (0.89). Our model can be accessed at https://safetka.net. ConclusionsA web‐based predictive model for AKI after TKA was developed using a machine‐learning algorithm featuring six preoperative variables. The model is simple and has been validated to improve both short‐ and long‐term prognoses of TKA patients. Postoperative AKI may lead to ESRD, which surgeons should strive to avoid. Level of evidenceDiagnostic level II.

Objectives: This study aimed to develop and validate a new method for measuring injury severity, the excess mortality ratio-adjusted Injury Severity Score (EMR-ISS), using the International Classification of Diseases 10th Edition (ICD-10).Methods: An injury severity grade similar to the Abbreviated Injury Scale (AIS) was converted from the ICD-10 codes on the basis of quintiles of the EMR for each ICD-10 code. Like the New Injury Severity Score (NISS), the EMR-ISS was calculated from three maximum severity grades using data from the Korean National Injury Database. The EMR-ISS was then validated using the Hosmer-Lemeshow goodness-of-fit chi-square (HL chi-square, with lower values preferable), the area under the receiver operating characteristic curve (AUC-ROC), and the Pearson correlation coefficient to compare it with the International Classification of Diseases 9th Edition-based Injury Severity Score (ICISS). Nationwide hospital discharge abstract data (DAD) from stratified-sample general hospitals (n = 150) in 2004 were used for an external validation.Results: The total number of study subjects was 29,282,531, with five subgroups of particular interest identified for further study: traumatic brain injury (TBI, n = 3,768,670), traumatic chest injury (TCI, n = 1,169,828), poisoning (n = 251,565), burns (n = 869,020), and DAD (n = 26,374). The HL chi-square was lower for EMR-ISS than for ICISS in all groups: 42,410.8 versus 55,721.9 in total injury, 7,139.6 versus 20,653.9 in TBI,6,603.3 versus 4,531.8 Conclusions: The EMR-ISS showed better calibration and discrimination power for prediction of death than the ICISS in most injury groups. The EMR-ISS appears to be a feasible tool for passive injury surveillance of large data sets, such as insurance data sets or community injury registries containing diagnosis codes. Additional further studies for external validation on prospectively collected data sets should be considered.

The interaction between the orphan nuclear receptor FTZ-F1 (Fushi tarazu factor 1) and the segmentation gene protein FTZ is critical for specifying alternate parasegments in the Drosophila embryo. Here, we have determined the structure of the FTZ-F1 ligand-binding domain (LBD)⅐FTZ peptide complex using x-ray crystallography. Strikingly, the ligand-binding pocket of the FTZ-F1 LBD is completely occupied by helix 6 (H6) of the receptor, whereas the cofactor FTZ binds the co-activator cleft site of the FTZ-F1 LBD. Our findings suggest that H6 is essential for transcriptional activity of FTZ-F1; this is further supported by data from mutagenesis and activity assays. These data suggest that FTZ-F1 might belong to a novel class of ligand-independent nuclear receptors. Our findings are intriguing given that the highly homologous human steroidogenic factor-1 and liver receptor homolog-1 LBDs exhibit sizable ligand-binding pockets occupied by putative ligand molecules.FTZ-F1 (Fushi tarazu factor 1)is a member of the orphan nuclear receptor (NR) 3 family, NR5A3, which interacts with the homeodomain protein FTZ to define alternate parasegmental regions in the Drosophila embryo (1-4). FTZ-F1 was originally isolated as a putative transcriptional activator of the ftz (fushi tarazu) gene (5). In blastoderm stage embryos, FTZ-F1 is uniformly distributed, whereas FTZ is expressed in seven stripes representing even-numbered parasegments (6). Both proteins are required for the formation of these even-numbered parasegments (7). FTZ-F1 has two conserved structural domains: a DNA-binding domain consisting of two zinc fingers and a putative ligandbinding domain (LBD) at the C terminus. Sequence alignments with other NRs and secondary structure predictions suggest that the FTZ-F1 LBD, like that of other NR proteins, is composed of 12 helices. Recent reports have also shown that transcriptional regulation by ligand-dependent NRs is achieved by interactions with cognate lipophilic ligands, which convert apo-NRs into the holo-conformation (8 -14) and lead to transcriptional activation (15, 16) or repression (17,18). These studies suggest that ligand binding to NR LBDs induces a conformational change in the LBD such that the AF-2 helix, which is the last ␣-helix at the C terminus, reorients its position to facilitate the binding of cofactors. However, FTZ-F1 has been considered an orphan NR because no cognate ligand has yet been identified. Therefore, the molecular mechanism governing FTZ-F1 activation and regulation has remained largely unknown.Among the vertebrate NRs, those most similar to FTZ-F1 are steroidogenic factor-1 (SF-1/NR5A1) and liver receptor homologue-1 (LRH-1/NR5A2), which share ϳ40% sequence identity with FTZ-F1 in the LBD. Recently, the structure of the mouse LRH-1 LBD has been solved, revealing that the AF-2 helix forms an active conformation despite the presence of a large and empty ligand-binding pocket (LBP) (19). Unlike mouse LRH-1, human LRH-1 requires the putative ligand phosphatidylinositol for optimum activity (...

The syndecan proteoglycans are an ancient class of receptor, bearing heparan sulfate chains that interact with numerous potential ligands including growth factors, morphogens, and extracellular matrix molecules. The single syndecan of invertebrates appears not to have cell adhesion roles, but these have been described for mammalian paralogues, especially syndecan-4. This member is best understood in terms of interactions, signaling, and structure of its cytoplasmic domain. The zebrafish homologue of syndecan-4 has been genetically linked to cell adhesion and migration in zebrafish embryos, but no molecular and cellular studies have been reported. Here it is demonstrated that key functional attributes of syndecan-4 are common to both zebrafish and mammalian homologues. These include glycosaminoglycan substitution, a NXIP motif in the extracellular domain that promotes integrin-mediated cell adhesion, and a transmembrane GXXXG motif that promotes dimer formation. In addition, despite some amino acid substitutions in the cytoplasmic domain, its ability to form twisted clamp dimers is preserved, as revealed by nuclear magnetic resonance spectroscopy. This technique also showed that phosphatidylinositol 4,5-bisphosphate can interact with the zebrafish syndecan-4 cytoplasmic domain, and that the molecule in its entirety supports focal adhesion formation, and complements the murine null cells to restore a normal actin cytoskeleton identically to the rat homologue. Therefore, the cell adhesion properties of syndecan-4 are consistent across the vertebrate spectrum and reflect an early acquisition of specialization after syndecan gene duplication events at the invertebrate/early chordate boundary.Syndecans are heparan sulfate bearing type-1 transmembrane proteins intimately associated with cell adhesion and linkage to the cytoskeleton. In mammals there are four syndecan family members, syndecan-1, -2, -3, and -4. These are characterized by a short highly conserved cytoplasmic domain, a transmembrane domain, and a less conserved ectodomain that is substituted with heparan sulfate chains (for reviews, see Refs. 1-3). Syndecan-4 is expressed in nearly all cell types and tissues and is a focal adhesion component (4 -5). In fibroblasts seeded on individual fibronectin domains there is a requirement for the engagement of syndecan-4 through its heparan sulfate chains for focal adhesion formation. Cells seeded on the RGD containing integrin-binding domain of fibronectin only form focal adhesions after the addition of the Hep II fibronectin heparin-binding domain (6, 7). In the syndecan-4 knock-out mouse vascular, wound healing, and migration defects have been reported and the ability of syndecan-4 null fibroblasts to form focal adhesions in response to fibronectin fragments is compromised (8).The syndecan-4 cytoplasmic domain shares two highly conserved regions, the C1 and C2, with the other syndecan family members and they flank a variable region (V region) unique to syndecan-4. The membrane distal C2 region interacts with PDZ p...

Telomeres are protein–DNA elements that are located at the ends of linear eukaryotic chromosomes. In concert with various telomere-binding proteins, they play an essential role in genome stability. We determined the structure of the DNA-binding domain of NgTRF1, a double-stranded telomere-binding protein of tobacco, using multidimensional NMR spectroscopy and X-ray crystallography. The DNA-binding domain of NgTRF1 contained the Myb-like domain and C-terminal Myb-extension that is characteristic of plant double-stranded telomere-binding proteins. It encompassed amino acids 561–681 (NgTRF1561–681), and was composed of 4 α-helices. We also determined the structure of NgTRF1561–681 bound to plant telomeric DNA. We identified several amino acid residues that interacted directly with DNA, and confirmed their role in the binding of NgTRF1 to telomere using site-directed mutagenesis. Based on a structural comparison of the DNA-binding domains of NgTRF1 and human TRF1 (hTRF1), NgTRF1 has both common and unique DNA-binding properties. Interaction of Myb-like domain with telomeric sequences is almost identical in NgTRF1561–681 with the DNA-binding domain of hTRF1. The interaction of Arg-638 with the telomeric DNA, which is unique in NgTRF1561–681, may provide the structural explanation for the specificity of NgTRF1 to the plant telomere sequences, (TTTAGGG)n.

No abstract