Experimental ionization of atomic hydrogen with few-cycle pulses

We present the first experimental data on strong-field ionization of atomic hydrogen by few-cycle laser pulses. We obtain quantitative agreement at the 10% level between the data and an {\it ab initio} simulation over a wide range of laser intensities and electron energies

Integrating Candida albicans metabolism with biofilm heterogeneity by transcriptome mapping

This work was supported by the Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377/Z/11/Z. We are grateful to microbiology colleagues throughout Scotland for clinical isolates collection.Peer reviewedPublisher PD

Polycation-π Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC–AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF β -1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia

Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers-reaching the frontiers of individual risk prediction

AIMS: This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. METHODS AND RESULTS: Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27–55]. During a median follow-up of 4.3 years (IQR 1.7–7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability {C-statistic 0.83 [95% confidence interval (CI) 0.78–0.88]}. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61–0.75). CONCLUSION: This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach

Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies

The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today. While we tend to define protein evolution in terms of sequence level mutations, insertions and deletions, it is hard to translate these processes to a more complete picture incorporating a polypeptide's structure and function. By considering how protein structures change over time we can gain an entirely new appreciation of their long-term evolutionary dynamics. In this work we seek to identify how populations of proteins at different stages of evolution explore their possible structure space. We use an annotation of superfamily age to this space and explore the relationship between these ages and a diverse set of properties pertaining to a superfamily's sequence, structure and function. We note several marked differences between the populations of newly evolved and ancient structures, such as in their length distributions, secondary structure content and tertiary packing arrangements. In particular, many of these differences suggest a less elaborate structure for newly evolved superfamilies when compared with their ancient counterparts. We show that the structural preferences we report are not a residual effect of a more fundamental relationship with function. Furthermore, we demonstrate the robustness of our results, using significant variation in the algorithm used to estimate the ages. We present these age estimates as a useful tool to analyse protein populations. In particularly, we apply this in a comparison of domains containing greek key or jelly roll motifs

The immunological landscape of peripheral blood in glioblastoma patients and immunological consequences of age and dexamethasone treatment

BackgroundGlioblastomas manipulate the immune system both locally and systemically, yet, glioblastoma-associated changes in peripheral blood immune composition are poorly studied. Age and dexamethasone administration in glioblastoma patients have been hypothesized to limit the effectiveness of immunotherapy, but their effects remain unclear. We compared peripheral blood immune composition in patients with different types of brain tumor to determine the influence of age, dexamethasone treatment, and tumor volume.MethodsHigh-dimensional mass cytometry was used to characterise peripheral blood mononuclear cells of 169 patients with glioblastoma, lower grade astrocytoma, metastases and meningioma. We used blood from medically-refractory epilepsy patients and healthy controls as control groups. Immune phenotyping was performed using FlowSOM and t-SNE analysis in R followed by supervised annotation of the resulting clusters. We conducted multiple linear regression analysis between intracranial pathology and cell type abundance, corrected for clinical variables. We tested correlations between cell type abundance and survival with Cox-regression analyses.ResultsGlioblastoma patients had significantly fewer naive CD4+ T cells, but higher percentages of mature NK cells than controls. Decreases of naive CD8+ T cells and alternative monocytes and an increase of memory B cells in glioblastoma patients were influenced by age and dexamethasone treatment, and only memory B cells by tumor volume. Progression free survival was associated with percentages of CD4+ regulatory T cells and double negative T cells.ConclusionHigh-dimensional mass cytometry of peripheral blood in patients with different types of intracranial tumor provides insight into the relation between intracranial pathology and peripheral immune status. Wide immunosuppression associated with age and pre-operative dexamethasone treatment provide further evidence for their deleterious effects on treatment with immunotherapy

Malignant mixed Mullerian tumors of the uterus: histopathological evaluation of cell cycle and apoptotic regulatory proteins

<p>Abstract</p> <p>Aim</p> <p>The aim of our study was to evaluate survival outcomes in malignant mixed Mullerian tumors (MMMT) of the uterus with respect to the role of cell cycle and apoptotic regulatory proteins in the carcinomatous and sarcomatous components.</p> <p>Methods</p> <p>23 cases of uterine MMMT identified from the Saskatchewan Cancer Agency (1970-1999) were evaluated. Immunohistochemical expression of Bad, Mcl-1, bcl-x, bak, mdm2, bax, p16, p21, p53, p27, EMA, Bcl-2, Ki67 and PCNA was correlated with clinico-pathological data including survival outcomes.</p> <p>Results</p> <p>Histopathological examination confirmed malignant epithelial component with homologous (12 cases) and heterologous (11 cases) sarcomatous elements. P53 was strongly expressed (70-95%) in 15 cases and negative in 5 cases. The average survival in the p53+ve cases was 3.56 years as opposed to 8.94 years in p53-ve cases. Overexpression of p16 and Mcl-1 were observed in patients with longer survival outcomes (> 2 years). P16 and p21 were overexpressed in the carcinomatous and sarcomatous elements respectively. Cyclin-D1 was focally expressed only in the carcinomatous elements.</p> <p>Conclusions</p> <p>Our study supports that a) cell cycle and apoptotic regulatory protein dysregulation is an important pathway for tumorigenesis and b) p53 is an important immunoprognostic marker in MMMT of the uterus.</p