Protein misfolding and dysregulated protein homeostasis in autoinflammatory diseases and beyond.

Cells have a number of mechanisms to maintain protein homeostasis, including proteasome-mediated degradation of ubiquitinated proteins and autophagy, a regulated process of ‘self-eating’ where the contents of entire organelles can be recycled for other uses. The unfolded protein response prevents protein overload in the secretory pathway. In the past decade, it has become clear that these fundamental cellular processes also help contain inflammation though degrading pro-inflammatory protein complexes such as the NLRP3 inflammasome. Signaling pathways such as the UPR can also be co-opted by toll-like receptor and mitochondrial reactive oxygen species signaling to induce inflammatory responses. Mutations that alter key inflammatory proteins, such as NLRP3 or TNFR1, can overcome normal protein homeostasis mechanisms, resulting in autoinflammatory diseases. Conversely, Mendelian defects in the proteasome cause protein accumulation, which can trigger interferon-dependent autoinflammatory disease. In non-Mendelian inflammatory diseases, polymorphisms in genes affecting the UPR or autophagy pathways can contribute to disease, and in diseases not formerly considered inflammatory such as neurodegenerative conditions and type 2 diabetes, there is increasing evidence that cell intrinsic or environmental alterations in protein homeostasis may contribute to pathogenesis

Using death to one's advantage: HIV modulation of apoptosis

Infection by human immunodeficiency virus (HIV) is associated with an early immune dysfunction and progressive destruction of CD4+ T lymphocytes. This progressive disappearance of T cells leads to a lack of immune control of HIV replication and to the development of immune deficiency resulting in the increased occurrence of opportunistic infections associated with acquired immune deficiency syndrome (AIDS). The HIV-induced, premature destruction of lymphocytes is associated with the continuous production of HIV viral proteins that modulate apoptotic pathways. The viral proteins, such as Tat, Env, and Nef, are associated with chronic immune activation and the continuous induction of apoptotic factors. Viral protein expression predisposes lymphocytes, particularly CD4+ T cells, CD8+ T cells, and antigen-presenting cells, to evolve into effectors of apoptosis and as a result, to lead to the destruction of healthy, non-infected T cells. Tat and Nef, along with Vpu, can also protect HIV-infected cells from apoptosis by increasing anti-apoptotic proteins and down- regulating cell surface receptors recognized by immune system cells. This review will discuss the validity of the apoptosis hypothesis in HIV disease and the potential mechanism(s) that HIV proteins perform in the progressive T cell depletion observed in AIDS pathogenesis. Originally published Leukemia, Vol. 15, No. 3, Mar 200

Estimating time since infection in early homogeneous HIV-1 samples using a poisson model

<p>Abstract</p> <p>Background</p> <p>The occurrence of a genetic bottleneck in HIV sexual or mother-to-infant transmission has been well documented. This results in a majority of new infections being homogeneous, <it>i.e</it>., initiated by a single genetic strain. Early after infection, prior to the onset of the host immune response, the viral population grows exponentially. In this simple setting, an approach for estimating evolutionary and demographic parameters based on comparison of diversity measures is a feasible alternative to the existing Bayesian methods (<it>e.g</it>., BEAST), which are instead based on the simulation of genealogies.</p> <p>Results</p> <p>We have devised a web tool that analyzes genetic diversity in acutely infected HIV-1 patients by comparing it to a model of neutral growth. More specifically, we consider a homogeneous infection (<it>i.e</it>., initiated by a unique genetic strain) prior to the onset of host-induced selection, where we can assume a random accumulation of mutations. Previously, we have shown that such a model successfully describes about 80% of sexual HIV-1 transmissions provided the samples are drawn early enough in the infection. Violation of the model is an indicator of either heterogeneous infections or the initiation of selection.</p> <p>Conclusions</p> <p>When the underlying assumptions of our model (homogeneous infection prior to selection and fast exponential growth) are met, we are under a very particular scenario for which we can use a forward approach (instead of backwards in time as provided by coalescent methods). This allows for more computationally efficient methods to derive the time since the most recent common ancestor. Furthermore, the tool performs statistical tests on the Hamming distance frequency distribution, and outputs summary statistics (mean of the best fitting Poisson distribution, goodness of fit p-value, etc). The tool runs within minutes and can readily accommodate the tens of thousands of sequences generated through new ultradeep pyrosequencing technologies. The tool is available on the LANL website.</p

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

Molecular Evolution of HIV-1 CRF01_AE Env in Thai Patients

BACKGROUND: The envelope glycoproteins (Env), gp120 and gp41, are the most variable proteins of human immunodeficiency virus type 1 (HIV-1), and are the major targets of humoral immune responses against HIV-1. A circulating recombinant form of HIV-1, CRF01_AE, is prevalent throughout Southeast Asia; however, only limited information regarding the immunological characteristics of CRF01_AE Env is currently available. In this study, we attempted to examine the evolutionary pattern of CRF01_AE Env under the selection pressure of host immune responses. METHODOLOGY/PRINCIPAL FINDINGS: Peripheral blood samples were collected periodically over 3 years from 15 HIV-1-infected individuals residing in northern Thailand, and amplified env genes from the samples were subjected to computational analysis. The V5 region of gp120 showed highest variability in several samples over 3 years, whereas the V1/V2 and/or V4 regions of gp120 also showed high variability in many samples. In addition, the N-terminal part of the C3 region of gp120 showed highest amino acid diversity among the conserved regions of gp120. Chronological changes in the numbers of amino acid residues in gp120 variable regions and potential N-linked glycosylation (PNLG) sites are involved in increasing the variability of Env gp120. Furthermore, the C3 region contained several amino acid residues potentially under positive selection, and APOBEC3 family protein-mediated G to A mutations were frequently detected in such residues. CONCLUSIONS/SIGNIFICANCE: Several factors, including amino acid substitutions particularly in gp120 C3 and V5 regions as well as changes in the number of PNLG sites and in the length of gp120 variable regions, were revealed to be involved in the molecular evolution of CRF01_AE Env. In addition, a similar tendency was observed between CRF01_AE and subtype C Env with regard to the amino acid variation of gp120 V3 and C3 regions. These results may provide important information for understanding the immunological characteristics of CRF01_AE Env

Quantitative Deep Sequencing Reveals Dynamic HIV-1 Escape and Large Population Shifts during CCR5 Antagonist Therapy In Vivo

High-throughput sequencing platforms provide an approach for detecting rare HIV-1 variants and documenting more fully quasispecies diversity. We applied this technology to the V3 loop-coding region of env in samples collected from 4 chronically HIV-infected subjects in whom CCR5 antagonist (vicriviroc [VVC]) therapy failed. Between 25,000–140,000 amplified sequences were obtained per sample. Profound baseline V3 loop sequence heterogeneity existed; predicted CXCR4-using populations were identified in a largely CCR5-using population. The V3 loop forms associated with subsequent virologic failure, either through CXCR4 use or the emergence of high-level VVC resistance, were present as minor variants at 0.8–2.8% of baseline samples. Extreme, rapid shifts in population frequencies toward these forms occurred, and deep sequencing provided a detailed view of the rapid evolutionary impact of VVC selection. Greater V3 diversity was observed post-selection. This previously unreported degree of V3 loop sequence diversity has implications for viral pathogenesis, vaccine design, and the optimal use of HIV-1 CCR5 antagonists

Biochemical Characterization of a Structure-Specific Resolving Enzyme from Sulfolobus islandicus Rod-Shaped Virus 2

Sulfolobus islandicus rod shaped virus 2 (SIRV2) infects the archaeon Sulfolobus islandicus at extreme temperature (70°C–80°C) and acidity (pH 3). SIRV2 encodes a Holliday junction resolving enzyme (SIRV2 Hjr) that has been proposed as a key enzyme in SIRV2 genome replication. The molecular mechanism for SIRV2 Hjr four-way junction cleavage bias, minimal requirements for four-way junction cleavage, and substrate specificity were determined. SIRV2 Hjr cleaves four-way DNA junctions with a preference for cleavage of exchange strand pairs, in contrast to host-derived resolving enzymes, suggesting fundamental differences in substrate recognition and cleavage among closely related Sulfolobus resolving enzymes. Unlike other viral resolving enzymes, such as T4 endonuclease VII or T7 endonuclease I, that cleave branched DNA replication intermediates, SIRV2 Hjr cleavage is specific to four-way DNA junctions and inactive on other branched DNA molecules. In addition, a specific interaction was detected between SIRV2 Hjr and the SIRV2 virion body coat protein (SIRV2gp26). Based on this observation, a model is proposed linking SIRV2 Hjr genome resolution to viral particle assembly