Identification of a novel ubiquitin binding site of STAM1 VHS domain by NMR spectroscopy

AbstractInteraction between the signal-transducing adapter molecule 1 (STAM1) Vps27/Hrs/Stam (VHS) domain and ubiquitin was investigated by nuclear magnetic resonance (NMR) spectroscopy. NMR evidence showed that the structure of STAM1 VHS domain resembles that of other VHS domains, especially the homologous domain of STAM2. We found that the VHS domain binds to ubiquitin via its hydrophobic patch consisting of N-terminus of helix 2 and C-terminus of helix 4 in which Trp26 on helix 2 plays a pivotal role in the binding. The binding between VHS and ubiquitin seems to be very similar to that between ubiquitin associated domain (UBA) and ubiquitin, however, the direction of α-helices involved in the ubiquitin binding is opposite. Here, we propose a novel ubiquitin binding site and the manner of ubiquitin recognition of the STAM1 VHS domain.Structured summaryMINT-6804185:STAM1 (uniprotkb:Q92783) binds (MI:0407) to ubiquitin (uniprotkb:P62988) by nuclear magnetic resonance (MI:0077

Divergent lineage of a novel hantavirus in the banana pipistrelle (Neoromicia nanus) in Côte d'Ivoire

Recently identified hantaviruses harbored by shrews and moles (order Soricomorpha) suggest that other mammals having shared ancestry may serve as reservoirs. To investigate this possibility, archival tissues from 213 insectivorous bats (order Chiroptera) were analyzed for hantavirus RNA by RT-PCR. Following numerous failed attempts, hantavirus RNA was detected in ethanol-fixed liver tissue from two banana pipistrelles (Neoromicia nanus), captured near Mouyassué village in Côte d'Ivoire, West Africa, in June 2011. Phylogenetic analysis of partial L-segment sequences using maximum-likelihood and Bayesian methods revealed that the newfound hantavirus, designated Mouyassué virus (MOUV), was highly divergent and basal to all other rodent- and soricomorph-borne hantaviruses, except for Nova virus in the European common mole (Talpa europaea). Full genome sequencing of MOUV and further surveys of other bat species for hantaviruses, now underway, will provide critical insights into the evolution and diversification of hantaviruses

Long-term Results of Primary Total Knee Arthroplasty with and without Patellar Resurfacing

Among patients that underwent total knee arthroplasty from June, 1990 to January, 1999, 61 cases (44 patients) that could be followed for more than 10 years were included in this study. The patients were divided into a patellar retention group and a patellar resurfacing group, and were compared with regard to their clinical and radiological outcomes. In patients undergoing primary TKA, a selective patellar resurfacing protocol was used. The indications for patellar retention were a small patella, nearly normal articular cartilage, minimal preoperative patellofemoral pain, poor patellar bone quality, and young patient age. When patellar retention was performed, osteophytes of the patella were removed and marginal electrocauterization was carried out. There were 25 cases (20 patients) in the patellar retention group and 36 cases (29 patients) in the patellar resurfacing group. The mean follow-up period was 140.7 months in the patellar retention group and 149.0 months in the patellar resurfacing group. The selective patellar resurfacing with total knee arthroplasty had a favorable outcome;there were a significant difference noted between the 2 groups in the functional scores, which showed better outcomes in the patellar resurfacing group than in the patellar retention group

Negative Conversion of Polymerase Chain Reaction and Clinical Outcomes according to the SARS-CoV-2 Variant in Critically Ill Patients with COVID-19

Background Coronavirus disease 2019 (COVID-19) is an ongoing global public health threat and different variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified. This study aimed to analyse the factors associated with negative conversion of polymerase chain reaction (PCR) and prognosis in critically ill patients according to the SARS-CoV-2 variant. Methods This study retrospectively analysed 259 critically ill patients with COVID-19 who were admitted to the intensive care unit of a tertiary medical center between January 2020 and May 2022. The Charlson comorbidity index (CCI) was used to evaluate comorbidity, and a negative PCR test result within 2 weeks was used to define negative PCR conversion. The cases were divided into the following three variant groups, according to the documented variant of SARS-CoV-2 at the time of diagnosis: non-Delta (January 20, 2020–July 6, 2021), Delta (July 7, 2021– January 1, 2022), and Omicron (January 30, 2022–April 24, 2022). Results The mean age of the 259 patients was 67.1 years and 93 (35.9%) patients were female. Fifty (19.3%) patients were smokers, and 50 (19.3%) patients were vaccinated. The CCI (hazard ratio [HR], 1.555; p<0.001), vaccination (HR, 0.492; p=0.033), and Delta variant (HR, 2.469; p=0.002) were significant factors for in-hospital mortality. The Delta variant (odds ratio, 0.288; p=0.003) was associated with fewer negative PCR conversion; however, vaccination (p=0.163) and remdesivir (p=0.124) treatments did not. Conclusion The Delta variant of SARS-CoV-2 is associated with lower survival and negative PCR conversion. Contrary to expectations, vaccination and remdesivir may not affect negative PCR conversion in critically ill patients with COVID-19

Microporation is a valuable transfection method for efficient gene delivery into human umbilical cord blood-derived mesenchymal stem cells

<p>Abstract</p> <p>Background</p> <p>Mesenchymal stem cells (MSCs) are an attractive source of adult stem cells for therapeutic application in clinical study. Genetic modification of MSCs with beneficial genes makes them more effective for therapeutic use. However, it is difficult to transduce genes into MSCs by common transfection methods, especially nonviral methods. In this study, we applied microporation technology as a novel electroporation technique to introduce enhanced green fluorescent protein (EGFP) and brain-derived neurotropfic factor (BDNF) plasmid DNA into human umbilical cord blood-derived MSCs (hUCB-MSCs) with significant efficiency, and investigated the stem cell potentiality of engineered MSCs through their phenotypes, proliferative capacity, ability to differentiate into multiple lineages, and migration ability towards malignant glioma cells.</p> <p>Results</p> <p>Using microporation with EGFP as a reporter gene, hUCB-MSCs were transfected with higher efficiency (83%) and only minimal cell damage than when conventional liposome-based reagent (<20%) or established electroporation methods were used (30-40%). More importantly, microporation did not affect the immunophenotype of hUCB-MSCs, their proliferation activity, ability to differentiate into mesodermal and ectodermal lineages, or migration ability towards cancer cells. In addition, the BDNF gene could be successfully transfected into hUCB-MSCs, and BDNF expression remained fairly constant for the first 2 weeks <it>in vitro </it>and <it>in vivo</it>. Moreover, microporation of BDNF gene into hUCB-MSCs promoted their <it>in vitro </it>differentiation into neural cells.</p> <p>Conclusion</p> <p>Taken together, the present data demonstrates the value of microporation as an efficient means of transfection of MSCs without changing their multiple properties. Gene delivery by microporation may enhance the feasibility of transgenic stem cell therapy.</p

Type 2 diabetes genetic association database manually curated for the study design and odds ratio

<p>Abstract</p> <p>Background</p> <p>The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.</p> <p>Description</p> <p>The list of publications used in this study was collected from the HuGE Navigator, which is an online database of published genome epidemiology literature. Because type 2 diabetes genetic association database (T2DGADB) aims to provide specialized information on the genetic risk factors involved in the development of type 2 diabetes, 701 of the 1,771 publications in the type 2 Diabetes case-control study for the development of the disease were extracted.</p> <p>Conclusions</p> <p>In the database, the association results were grouped as either positive or negative. The gene and SNP names were replaced with gene symbols and rsSNP numbers, the association p-values were determined manually, and the results are displayed by graphs and tables. In addition, the study design in publications, such as the population type and size are described. This database can be used for research purposes, such as an association and functional study of type 2 diabetes related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.</p

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings