The minimal kinome of Giardia lamblia illuminates early kinase evolution and unique parasite biology

Background: The major human intestinal pathogen Giardia lamblia is a very early branching eukaryote with a minimal genome of broad evolutionary and biological interest. Results: To explore early kinase evolution and regulation of Giardia biology, we cataloged the kinomes of three sequenced strains. Comparison with published kinomes and those of the excavates Trichomonas vaginalis and Leishmania major shows that Giardia's 80 core kinases constitute the smallest known core kinome of any eukaryote that can be grown in pure culture, reflecting both its early origin and secondary gene loss. Kinase losses in DNA repair, mitochondrial function, transcription, splicing, and stress response reflect this reduced genome, while the presence of other kinases helps define the kinome of the last common eukaryotic ancestor. Immunofluorescence analysis shows abundant phospho-staining in trophozoites, with phosphotyrosine abundant in the nuclei and phosphothreonine and phosphoserine in distinct cytoskeletal organelles. The Nek kinase family has been massively expanded, accounting for 198 of the 278 protein kinases in Giardia. Most Neks are catalytically inactive, have very divergent sequences and undergo extensive duplication and loss between strains. Many Neks are highly induced during development. We localized four catalytically active Neks to distinct parts of the cytoskeleton and one inactive Nek to the cytoplasm. Conclusions: The reduced kinome of Giardia sheds new light on early kinase evolution, and its highly divergent sequences add to the definition of individual kinase families as well as offering specific drug targets. Giardia's massive Nek expansion may reflect its distinctive lifestyle, biphasic life cycle and complex cytoskeleton

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Periprosthetic osteolysis after total hip replacement: molecular pathology and clinical management

Periprosthetic osteolysis is a serious complication of total hip replacement (THR) in the medium to long term. Although often asymptomatic, osteolysis can lead to prosthesis loosening and periprosthetic fracture. These complications cause significant morbidity and require complex revision surgery. Here, we review advances in our understanding of the cell and tissue response to particles produced by wear of the articular and non-articular surfaces of prostheses. We discuss the molecular and cellular regulators of osteoclast formation and bone resorptive activity, a better understanding of which may lead to pharmacological treatments for periprosthetic osteolysis. We describe the development of imaging techniques for the detection and measurement of osteolysis around THR prostheses, which enable improved clinical management of patients, provide a means of evaluating outcomes of non-surgical treatments for periprosthetic osteolysis, and assist in pre-operative planning for revision surgery. Finally, there have been advances in the materials used for bearing surfaces to minimise wear, and we review the literature regarding the performance of these new materials to date.Donald W. Howie, Susan D. Neale, David R. Haynes, Oksana T. Holubowycz, Margaret A. McGee, Lucian B. Solomon, Stuart A. Callary, Gerald J. Atkins, David M. Findla

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

BACKGROUND: Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. METHODS: In this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals. FINDINGS: The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%). INTERPRETATION: Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. FUNDING: Charles Wolfson Charitable Trust and Guts UK