Genetic and functional evaluation of the role of CXCR1 and CXCR2 in susceptibility to visceral leishmaniasis in north-east India.

BACKGROUND: IL8RA and IL8RB, encoded by CXCR1 and CXCR2, are receptors for interleukin (IL)-8 and other CXC chemokines involved in chemotaxis and activation of polymorphonuclear neutrophils (PMN). Variants at CXCR1 and CXCR2 have been associated with susceptibility to cutaneous and mucocutaneous leishmaniasis in Brazil. Here we investigate the role of CXCR1/CXCR2 in visceral leishmaniasis (VL) in India. METHODS: Three single nucleotide polymorphisms (SNPs) (rs4674259, rs2234671, rs3138060) that tag linkage disequilibrium blocks across CXCR1/CXCR2 were genotyped in primary family-based (313 cases; 176 nuclear families; 836 individuals) and replication (941 cases; 992 controls) samples. Family- and population-based analyses were performed to look for association between CXCR1/CXCR2 variants and VL. Quantitative RT/PCR was used to compare CXCR1/CXCR2 expression in mRNA from paired splenic aspirates taken before and after treatment from 19 VL patients. RESULTS: Family-based analysis using FBAT showed association between VL and SNPs CXCR1_rs2234671 (Z-score = 2.935, P = 0.003) and CXCR1_rs3138060 (Z-score = 2.22, P = 0.026), but not with CXCR2_rs4674259. Logistic regression analysis of the case-control data under an additive model of inheritance showed association between VL and SNPs CXCR2_rs4674259 (OR = 1.15, 95%CI = 1.01-1.31, P = 0.027) and CXCR1_rs3138060 (OR = 1.25, 95%CI = 1.02-1.53, P = 0.028), but not with CXCR1_rs2234671. The 3-locus haplotype T_G_C across these SNPs was shown to be the risk haplotype in both family- (TRANSMIT; P = 0.014) and population- (OR = 1.16, P = 0.028) samples (combined P = 0.002). CXCR2, but not CXCR1, expression was down regulated in pre-treatment compared to post-treatment splenic aspirates (P = 0.021). CONCLUSIONS: This well-powered primary and replication genetic study, together with functional analysis of gene expression, implicate CXCR2 in determining outcome of VL in India.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

LEARN: A multi-centre, cross-sectional evaluation of Urology teaching in UK medical schools

OBJECTIVE: To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. MATERIALS AND METHODS: LEARN was a national multicentre cross-sectional study. Year 2 to Year 5 medical students and FY1 doctors were invited to complete a survey between 3rd October and 20th December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS: 7,063/8,346 (84.6%) responses from all 39 UK medical schools were included; 1,127/7,063 (16.0%) were from Foundation Year (FY) 1 doctors, who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and clinical placements in the later years of medical school. 20.1% of FY1 doctors reported no undergraduate clinical attachment in urology. CONCLUSION: LEARN is the largest ever evaluation of undergraduate urology teaching. In the UK, teaching seemed satisfactory as evaluated by the BAUS undergraduate syllabus. However, many students report having no clinical attachments in Urology and some newly qualified doctors report never having inserted a catheter, which is a GMC mandated requirement. We recommend a greater emphasis on undergraduate clinical exposure to urology and stricter adherence to GMC mandated procedures

An SVM-based method for assessment of transcription factor-DNA complex models

Abstract Background Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models. Results We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models. Conclusions A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models

Protein-Protein Interface Detection Using the Energy Centrality Relationship (ECR) Characteristic of Proteins

<div><p>Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.</p></div

Summary of protein and protein interface counts in FLIPdb.

<p>* Proteins chains are common to multiple sub-categories though the interfaces are distinct.</p><p>‡ Interfaces are constructed from existing FLIPs through coordinate transformations arising from the symmetry of the source X-ray crystal structure (XFunCs).</p><p>FLIPdb contains 160 interfaces in 94 structures involving 219 individual protein chains. These interfaces have been assigned to FLIP or FunC functional categories and 9 functional sub-categories based on a review of the literature (see Supplement <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097115#pone.0097115.s002" target="_blank">Table S1</a>). Due to the reuse of some chains, the totals represented in the first two columns do not sum across sub-categories.</p

Accuracy of clustering in Training and Test-18 sets.

<p>†) TP: FLIP found in Cluster 1TN: FUNC found in Cluster 2</p><p>FP: FUNC found in Cluster 1FN: FLIP found in Cluster 2</p><p>The accuracy and Matthews correlation coefficient (MCC, a measure of the quality of a binary classification) of the results of the clusterings shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097115#pone-0097115-g004" target="_blank">Figure 4</a> are indicated. The overall accuracy is 76% and 78% for both training Test-18 sets, respectively. TPs are quite readily identified in both training and Test-18 sets (80% and 69% <i>sensitivity</i>, respectively). The majority of TPs are enzymes and immunoglobin heavy chain-light chain interactions. TNs are less well identified (70% and 56% <i>negative predictive values</i>, respectively). MCCs of 0.50 and 0.62 indicate that our simple two-category approach is generally appropriate.</p