Blocking TLR7- and TLR9-mediated IFN-α Production by Plasmacytoid Dendritic Cells Does Not Diminish Immune Activation in Early SIV Infection

Persistent production of type I interferon (IFN) by activated plasmacytoid dendritic cells (pDC) is a leading model to explain chronic immune activation in human immunodeficiency virus (HIV) infection but direct evidence for this is lacking. We used a dual antagonist of Toll-like receptor (TLR) 7 and TLR9 to selectively inhibit responses of pDC but not other mononuclear phagocytes to viral RNA prior to and for 8 weeks following pathogenic simian immunodeficiency virus (SIV) infection of rhesus macaques. We show that pDC are major but not exclusive producers of IFN-α that rapidly become unresponsive to virus stimulation following SIV infection, whereas myeloid DC gain the capacity to produce IFN-α, albeit at low levels. pDC mediate a marked but transient IFN-α response in lymph nodes during the acute phase that is blocked by administration of TLR7 and TLR9 antagonist without impacting pDC recruitment. TLR7 and TLR9 blockade did not impact virus load or the acute IFN-α response in plasma and had minimal effect on expression of IFN-stimulated genes in both blood and lymph node. TLR7 and TLR9 blockade did not prevent activation of memory CD4+ and CD8+ T cells in blood or lymph node but led to significant increases in proliferation of both subsets in blood following SIV infection. Our findings reveal that virus-mediated activation of pDC through TLR7 and TLR9 contributes to substantial but transient IFN-α production following pathogenic SIV infection. However, the data indicate that pDC activation and IFN-α production are unlikely to be major factors in driving immune activation in early infection. Based on these findings therapeutic strategies aimed at blocking pDC function and IFN-α production may not reduce HIV-associated immunopathology. © 2013 Kader et al

Biallelic SQSTM1 mutations in early-onset, variably progressive neurodegeneration.

OBJECTIVE: To characterize clinically and molecularly an early-onset, variably progressive neurodegenerative disorder characterized by a cerebellar syndrome with severe ataxia, gaze palsy, dyskinesia, dystonia, and cognitive decline affecting 11 individuals from 3 consanguineous families. METHODS: We used whole-exome sequencing (WES) (families 1 and 2) and a combined approach based on homozygosity mapping and WES (family 3). We performed in vitro studies to explore the effect of the nontruncating SQSTM1 mutation on protein function and the effect of impaired SQSTM1 function on autophagy. We analyzed the consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in vivo using zebrafish as a model. RESULTS: We identified 3 homozygous inactivating variants, including a splice site substitution (c.301+2T>A) causing aberrant transcript processing and accelerated degradation of a resulting protein lacking exon 2, as well as 2 truncating changes (c.875_876insT and c.934_936delinsTGA). We show that loss of SQSTM1 causes impaired production of ubiquitin-positive protein aggregates in response to misfolded protein stress and decelerated autophagic flux. The consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in zebrafish documented a variable but reproducible phenotype characterized by cerebellum anomalies ranging from depletion of axonal connections to complete atrophy. We provide a detailed clinical characterization of the disorder; the natural history is reported for 2 siblings who have been followed up for >20 years. CONCLUSIONS: This study offers an accurate clinical characterization of this recently recognized neurodegenerative disorder caused by biallelic inactivating mutations in SQSTM1 and links this phenotype to defective selective autophagy

Drug-Class Specific Impact of Antivirals on the Reproductive Capacity of HIV

Predictive markers linking drug efficacy to clinical outcome are a key component in the drug discovery and development process. In HIV infection, two different measures, viral load decay and phenotypic assays, are used to assess drug efficacy in vivo and in vitro. For the newly introduced class of integrase inhibitors, a huge discrepancy between these two measures of efficacy was observed. Hence, a thorough understanding of the relation between these two measures of drug efficacy is imperative for guiding future drug discovery and development activities in HIV. In this article, we developed a novel viral dynamics model, which allows for a mechanistic integration of the mode of action of all approved drugs and drugs in late clinical trials. Subsequently, we established a link between in vivo and in vitro measures of drug efficacy, and extract important determinants of drug efficacy in vivo. The analysis is based on a new quantity—the reproductive capacity—that represents in mathematical terms the in vivo analog of the read-out of a phenotypic assay. Our results suggest a drug-class specific impact of antivirals on the total amount of viral replication. Moreover, we showed that the (drug-)target half life, dominated by immune-system related clearance processes, is a key characteristic that affects both the emergence of resistance as well as the in vitro–in vivo correlation of efficacy measures in HIV treatment. We found that protease- and maturation inhibitors, due to their target half-life, decrease the total amount of viral replication and the emergence of resistance most efficiently

Exact model reduction of combinatorial reaction networks

Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models

ALC: automated reduction of rule-based models

<p>Abstract</p> <p>Background</p> <p>Combinatorial complexity is a challenging problem for the modeling of cellular signal transduction since the association of a few proteins can give rise to an enormous amount of feasible protein complexes. The layer-based approach is an approximative, but accurate method for the mathematical modeling of signaling systems with inherent combinatorial complexity. The number of variables in the simulation equations is highly reduced and the resulting dynamic models show a pronounced modularity. Layer-based modeling allows for the modeling of systems not accessible previously.</p> <p>Results</p> <p>ALC (Automated Layer Construction) is a computer program that highly simplifies the building of reduced modular models, according to the layer-based approach. The model is defined using a simple but powerful rule-based syntax that supports the concepts of modularity and macrostates. ALC performs consistency checks on the model definition and provides the model output in different formats (C MEX, MATLAB, <it>Mathematica </it>and SBML) as ready-to-run simulation files. ALC also provides additional documentation files that simplify the publication or presentation of the models. The tool can be used offline or via a form on the ALC website.</p> <p>Conclusion</p> <p>ALC allows for a simple rule-based generation of layer-based reduced models. The model files are given in different formats as ready-to-run simulation files.</p

The Effect of Raltegravir Intensification on Low-level Residual Viremia in HIV-Infected Patients on Antiretroviral Therapy: A Randomized Controlled Trial

In a double-blind trial, Rajesh Gandhi and colleagues detect no significant reduction in viral load after people with low-level HIV viremia added an integrase inhibitor to their treatment regimen

Relationships of PBMC microRNA expression, plasma viral load, and CD4+ T-cell count in HIV-1-infected elite suppressors and viremic patients

<p>Abstract</p> <p>Background</p> <p>HIV-1-infected elite controllers or suppressors (ES) maintain undetectable viral loads (< 50 copies/mL) without antiretroviral therapy. The mechanisms of suppression are incompletely understood. Modulation of HIV-1 replication by miRNAs has been reported, but the role of small RNAs in ES is unknown. Using samples from a well-characterized ES cohort, untreated viremic patients, and uninfected controls, we explored the PBMC miRNA profile and probed the relationships of miRNA expression, CD4+ T-cell counts, and viral load.</p> <p>Results</p> <p>miRNA profiles, obtained using multiple acquisition, data processing, and analysis methods, distinguished ES and uninfected controls from viremic HIV-1-infected patients. For several miRNAs, however, ES and viremic patients shared similar expression patterns. Differentially expressed miRNAs included those with reported roles in HIV-1 latency (miR-29 family members, miRs -125b and -150). Others, such as miR-31 and miR-31*, had no previously reported connection with HIV-1 infection but were found here to differ significantly with uncontrolled HIV-1 replication. Correlations of miRNA expression with CD4+ T-cell count and viral load were found, and we observed that ES with low CD4+ T-cell counts had miRNA profiles more closely related to viremic patients than controls. However, expression patterns indicate that miRNA variability cannot be explained solely by CD4+ T-cell variation.</p> <p>Conclusions</p> <p>The intimate involvement of miRNAs in disease processes is underscored by connections of miRNA expression with the HIV disease clinical parameters of CD4 count and plasma viral load. However, miRNA profile changes are not explained completely by these variables. Significant declines of miRs-125b and -150, among others, in both ES and viremic patients indicate the persistence of host miRNA responses or ongoing effects of infection despite viral suppression by ES. We found no negative correlations with viral load in viremic patients, not even those that have been reported to silence HIV-1 in vitro, suggesting that the effects of these miRNAs are exerted in a focused, cell-type-specific manner. Finally, the observation that some ES with low CD4 counts were consistently related to viremic patients suggests that miRNAs may serve as biomarkers for risk of disease progression even in the presence of viral suppression.</p

Confidence from uncertainty - A multi-target drug screening method from robust control theory

<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p