Correlation between CD4/CD8 ratio and neurocognitive performance during early HIV infection

INTRODUCTION: CD4/CD8 ratio is a marker of immune activation in HIV infection and has been associated with neurocognitive performance during chronic infection, but little is known about the early phases. The aim of this study was to examine the relationship between blood CD4/CD8 ratio and central nervous system endpoints in primary HIV infection (PHI) before and after antiretroviral treatment (ART). METHODS: This was a retrospective analysis of the Primary Infection Stage CNS Events Study (PISCES) cohort. We longitudinally assessed blood and cerebrospinal fluid (CSF) markers of inflammation, immune activation and neuronal injury, and neuropsychological testing performance (NPZ4, an average of three motor and one processing speed tests, and a summarized total score, NPZ11, including also executive function, learning and memory) in ART-naïve participants enrolled during PHI. Spearman correlation and linear mixed models assessed the relationships between the trajectory of CD4/CD8 ratio over time and neurocognitive performance, blood and CSF markers of immune activation and neuronal injury. RESULTS: In all, 109 PHI participants were enrolled. The mean CD4/CD8 ratio decreased with longer time from infection to starting treatment (p < 0.001). Every unit increase in NPZ4 score was independently associated with a 0.15 increase in CD4/CD8 ratio (95% CI: 0.002-0.29; p = 0.047), whereas no correlation was found between CD4/CD8 ratio and NPZ11. Among the cognitive domains, only a change in processing speed was correlated with CD4/CD8 ratio over time (p = 0.03). The trajectory of the CD4/CD8 ratio was negatively correlated with change in CSF neurofilament light chain (p = 0.04). CONCLUSIONS: The trajectory of CD4/CD8 ratio was independently associated with motor/psychomotor speed performance, suggesting that immune activation is involved in brain injury during the early stages of the infection

IL-2 production correlates with effector cell differentiation in HIV-specific CD8+ T cells

BACKGROUND: Diminished IL-2 production and lack of effector differentiation have been reported for HIV-specific T cells. In this study, we examined the prevalence of these phenomena using 8-color cytokine flow cytometry, and tested the hypothesis that these two findings were causally related. We analyzed cytokine profiles and memory/effector phenotypes of HIV-specific and CMV-specific T cells using short-term in vitro stimulation with HIV or CMV peptide pools. Nineteen HIV-positive subjects with progressive disease and twenty healthy, HIV-negative subjects were examined. RESULTS: Among HIV-infected subjects, there were significantly fewer CD8+ IL-2+ T cells responding to HIV compared to CMV, with no significant difference in CD4+ IL-2+ T cells. The majority of CMV-specific T cells in both HIV-negative and HIV-positive subjects appeared to be terminally differentiated effector cells (CD8+ CD27- CD28- CD45RA+ or CD8+ CD27- CD28- CD45RA-). In HIV-positive subjects, the most common phenotype of HIV-specific T cells was intermediate in differentiation (CD8+ CD27+ CD28- CD45RA-). These differences were statistically significant, both as absolute cell frequencies and as percentages. There was a significant correlation between the absolute number of HIV-specific CD8+ IL-2+ T cells and HIV-specific CD8+ CD27- CD28- CD45RA+ terminal effector cells. CONCLUSION: IL-2 production from antigen-specific CD8+ T cells correlates with effector cell differentiation of those cells

Magnetic crystalline-symmetry-protected axion electrodynamics and field-tunable unpinned Dirac cones in EuIn2As2

Knowledge of magnetic symmetry is vital for exploiting nontrivial surface states of magnetic topological materials. EuIn$_{2}$As$_{2}$ is an excellent example, as it is predicted to have collinear antiferromagnetic order where the magnetic moment direction determines either a topological-crystalline-insulator phase supporting axion electrodynamics or a higher-order-topological-insulator phase with chiral hinge states. Here, we use neutron diffraction, symmetry analysis, and density functional theory results to demonstrate that EuIn$_{2}$As$_{2}$ actually exhibits low-symmetry helical antiferromagnetic order which makes it a stoichiometric magnetic topological-crystalline axion insulator protected by the combination of a 180$^{\circ}$ rotation and time-reversal symmetries: $C_{2}\times\mathcal{T}=2^{\prime}$. Surfaces protected by $2^{\prime}$ are expected to have an exotic gapless Dirac cone which is unpinned to specific crystal momenta. All other surfaces have gapped Dirac cones and exhibit half-integer quantum anomalous Hall conductivity. We predict that the direction of a modest applied magnetic field of $H\approx1$ to $2$ T can tune between gapless and gapped surface states.Comment: 49 pages, 26 figure

Human Tyrosine Hydroxylase Natural Allelic Variation: Influence on Autonomic Function and Hypertension

The catecholamine biosynthetic pathway consists of several enzymatic steps in series, beginning with the amino acids phenylalanine and tyrosine, and eventuating in the catecholamines norepinephrine (noradrenaline) and epinephrine (adrenaline). Since the enzyme tyrosine hydroxylase (TH; tyrosine 3-mono-oxygenase; EC 1.14.16.2; chromosome 11p15.5) is generally considered to be rate-limiting in this pathway, probed as to whether common genetic variation at the TH gene occurred, and whether such variants contributed to inter-individual alterations in autonomic function, either biochemical or physiological. We began with sequencing a tetranucleotide (TCAT) repeat in the first intron, and found that the two most common versions, (TCAT)6 and (TCAT)10i, predicted heritable autonomic traits in twin pairs. We then conducted systematic polymorphism discovery across the ~8 kbp locus, and discovered numerous variants, principally non-coding. The proximal promoter block contained four common variants, and its haplotypes and SNPs (especially C-824T, rs10770141) predicted catecholamine secretion, environmental stress-induced BP increments, and hypertension. Finally, we found that two of the common promoter variants, C-824T (rs10770141) and A-581G (rs10770140), were functional in that they differentially affected transcriptional activity of the isolated promoter, disrupted recognition motifs for specific transcription factor binding, altered the promoter responses to the co-transfected (exogenous) factors, and bound the endogenous factors in the chromatin fraction of the nucleus. We concluded that common variation in the proximal TH promoter is functional, giving rise to changes in autonomic function and consequently cardiovascular risk

Computational Prediction of Heme-Binding Residues by Exploiting Residue Interaction Network

Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based features significantly improved the prediction performance. We also compared the residue interaction networks of heme proteins before and after heme binding and found that the topological features can well characterize the heme-binding sites of apo structures as well as those of holo structures, which led to reliable performance improvement as we applied HemeNet to predicting the binding residues of proteins in the heme-free state. HemeNet web server is freely accessible at http://mleg.cse.sc.edu/hemeNet/

False positive reduction in protein-protein interaction predictions using gene ontology annotations

<p>Abstract</p> <p>Background</p> <p>Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.</p> <p>Results</p> <p>Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The '<it>strength</it>', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the <it>strength </it>varies between two and ten-fold of randomly removing protein pairs from the datasets.</p> <p>Conclusion</p> <p>Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.</p

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

Positive mental health in schizophrenia and healthy comparison groups: relationships with overall health and biomarkers

ObjectivePositive psychological factors (PPFs) have been reported to have a significant impact on health in the general population. However, little is known about the relationship of these factors with mental and physical health in schizophrenia.MethodOne hundred and thirty-five outpatients with schizophrenia and 127 healthy comparison subjects (HCs), aged 26-65&nbsp;years, were evaluated with scales of resilience, optimism, happiness, and perceived stress. Measures of mental and physical health were also obtained. Regression analyses examined associations of a PPF composite with health variables.ResultsRelative to the HCs, the schizophrenia group had lower levels of PPFs. However, there was considerable heterogeneity, with over one-third of schizophrenia participants having values within the 'normative' range. The PPF composite was positively related to mental and physical health variables and with biomarkers of inflammation and insulin resistance. The relationship between PPFs and mental health was particularly strong for individuals with schizophrenia.ConclusionA sizable minority of adults with chronic schizophrenia have levels of resilience, optimism, happiness, and perceived stress similar to HCs. Psychosocial interventions to enhance PPFs should be tested in patients with serious mental illnesses, with the goal of improving their mental health (beyond controlling symptoms of psychosis) and their physical health

Modeling allosteric signal propagation using protein structure networks

Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains