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BACKGROUND Molecular epidemiological evaluation of HIV-1 transmission networks can elucidate behavioral components of transmission that can be targets for intervention. METHODS We combined phylogenetic and statistical approaches using pol sequences from patients diagnosed between 2004 and 2011 at a large HIV center in Rhode Island, following 75% of the state's HIV population. Phylogenetic trees were constructed using maximum likelihood, and putative transmission clusters were evaluated using latent class analyses to determine association of cluster size with underlying demographic/behavioral characteristics. A logistic growth model was used to assess intracluster dynamics over time and predict "active" clusters that were more likely to harbor undiagnosed infections. RESULTS Of the 1166 HIV-1 subtype B sequences, 31% were distributed among 114 statistically supported, monophyletic clusters (range: 2-15 sequences/cluster). Sequences from men who have sex with men (MSM) formed 52% of clusters. Latent class analyses demonstrated that sequences from recently diagnosed (2008-2011) MSM with primary HIV infection (PHI) and other sexually transmitted infections (STIs) were more likely to form larger clusters (odds ratio: 1.62-11.25, P < 0.01). MSM in clusters were more likely to have anonymous partners and meet partners at sex clubs and pornographic stores. Four large clusters with 38 sequences (100% male, 89% MSM) had a high probability of harboring undiagnosed infections and included younger MSM with PHI and STIs. CONCLUSIONS In this first large-scale molecular epidemiological investigation of HIV-1 transmission in New England, sexual networks among recently diagnosed MSM with PHI and concomitant STIs contributed to the ongoing transmission. Characterization of transmission dynamics revealed actively growing clusters, which may be targets for intervention.

One of the obstacles for cancer immunotherapy is the inefficiency of CD8+ T-cell recruitment to tumors. STAT3 has been shown to suppress CD8+ T-cell antitumor functions in various cancer models, in part by restricting accumulation of CD8+ T cells. However, the underlying molecular mechanism by which STAT3 in CD8+ T cells inhibits their accumulation in tumors remains to be defined. Here, we show that STAT3 signaling in CD8+ T cells inhibits chemokine CXCL10 production by tumor-associated myeloid cells by reducing IFNγ expression by T cells. We further demonstrate that ablating STAT3 in T cells allows expression of CXCR3, the receptor of CXCL10, on CD8+ T cells, resulting in efficient accumulation of CD8+ T cells at tumor sites. Blocking IFNγ or CXCR3 impairs the accumulation of STAT3-deficient CD8+ T cells in tumor and their antitumor effects. Together, our study reveals a negative regulation by STAT3 signaling in T cells on cross-talk between myeloid cells and T cells through IFNγ/CXCR3/CXCL10, which is important for CD8+ T cells homing to tumors. Our results thus provide new insights applicable to cancer immunotherapy and adoptive T-cell strategies. Cancer Immunol Res; 3(8); 864–70. ©2015 AACR.

The characterization of intrinsically disordered proteins is challenging because accurate models of these systems require a description of both their thermally accessible conformers and the associated relative stabilities or weights. These structures and weights are typically chosen such that calculated ensemble averages agree with some set of prespecified experimental measurements; however, the large number of degrees of freedom in these systems typically leads to multiple conformational ensembles that are degenerate with respect to any given set of experimental observables. In this work we demonstrate that estimates of the relative stabilities of conformers within an ensemble are often incorrect when one does not account for the underlying uncertainty in the estimates themselves. Therefore, we present a method for modeling the conformational properties of disordered proteins that estimates the uncertainty in the weights of each conformer. The Bayesian weighting (BW) formalism incorporates information from both experimental data and theoretical predictions to calculate a probability density over all possible ways of weighting the conformers in the ensemble. This probability density is then used to estimate the values of the weights. A unique and powerful feature of the approach is that it provides a built-in error measure that allows one to assess the accuracy of the ensemble. We validate the approach using reference ensembles constructed from the five-residue peptide met-enkephalin and then apply the BW method to construct an ensemble of the K18 isoform of the tau protein. Using this ensemble, we indentify a specific pattern of long-range contacts in K18 that correlates with the known aggregation properties of the sequence.

Probably the most unusual class of proteins in nature is the intrinsically unstructured proteins (IUPs), because they are not structured yet play essential roles in protein-protein signaling. Many IUPs can bind different proteins, and in many cases, adopt different bound conformations. The p21 protein is a small IUP (164 residues) that is ubiquitous in cellular signaling, for example, cell cycle control, apoptosis, transcription, differentiation, and so forth; it binds to approximately 25 targets. How does this small, unstructured protein recognize each of these targets with high affinity? Here, we characterize residual structural elements of the C-terminal segment of p21 encompassing residues 145-164 using a combination of NMR measurements and molecular dynamics simulations. The N-terminal half of the peptide has a significant helical propensity which is recognized by calmodulin while the C-terminal half of the peptide prefers extended conformations that facilitate binding to the proliferating cell nuclear antigen (PCNA). Our results suggest that the final bound conformations of p21 (145-164) pre-exist in the free peptide even without its binding partners. While the conformational flexibility of the p21 peptide is essential for adapting to diverse binding environments, the intrinsic structural preferences of the free peptide enable promiscuous yet high affinity binding to a diverse array of molecular targets.

Aims: HIV-1 sequence diversity can affect host immune responses and phenotypic characteristics such as antiretroviral drug resistance. Current HIV-1 sequence diversity classification uses phylogeny-based methods to identify subtypes and recombinants, which may overlook distinct subpopulations within subtypes. While local epidemic studies have characterized sequence-level clustering within subtypes using phylogeny, identification of new genotype–phenotype associations are based on mutational correlations at individual sequence positions. We perform a systematic, global analysis of position-specific pol gene sequence variation across geographic regions within HIV-1 subtypes to characterize subpopulation differences that may be missed by standard subtyping methods and sequence-level phylogenetic clustering analyses. Materials & methods: Analysis was performed on a large, globally diverse, cross-sectional pol sequence dataset. Sequences were partitioned into subtypes and geographic subpopulations within subtypes. For each subtype, we identified positions that varied according to geography using VESPA (viral epidemiology signature pattern analysis) to identify sequence signature differences and a likelihood ratio test adjusted for multiple comparisons to characterize differences in amino acid (AA) frequencies, including minority mutations. Synonymous nonsynonymous analysis program (SNAP) was used to explore the role of evolutionary selection witihin subtype C. Results: In 7693 protease (PR) and reverse transcriptase (RT) sequences from untreated patients in multiple geographic regions, 11 PR and 11 RT positions exhibited sequence signature differences within subtypes. Thirty six PR and 80 RT positions exhibited within-subtype geography-dependent differences in AA distributions, including minority mutations, at both conserved and variable loci. Among subtype C samples from India and South Africa, nine PR and nine RT positions had significantly different AA distributions, including one PR and five RT positions that differed in consensus AA between regions. A selection analysis of subtype C using SNAP demonstrated that estimated rates of nonsynonymous and synonymous mutations are consistent with the possibility of positive selection across geographic subpopulations within subtypes. Conclusion: We characterized systematic genotypic pol differences across geographic regions within subtypes that are not captured by the subtyping nomenclature. Awareness of such differences may improve the interpretation of future studies determining the phenotypic consequences of genetic backgrounds.

Next generation sequencing technologies have recently been applied to characterize mutational spectra of the heterogeneous population of viral genotypes (known as a quasispecies) within HIV-infected patients. Such information is clinically relevant because minority genetic subpopulations of HIV within patients enable viral escape from selection pressures such as the immune response and antiretroviral therapy. However, methods for quasispecies sequence reconstruction from next generation sequencing reads are not yet widely used and remains an emerging area of research. Furthermore, the majority of research methodology in HIV has focused on 454 sequencing, while many next-generation sequencing platforms used in practice are limited to shorter read lengths relative to 454 sequencing. Little work has been done in determining how best to address the read length limitations of other platforms. The approach described here incorporates graph representations of both read differences and read overlap to conservatively determine the regions of the sequence with sufficient variability to separate quasispecies sequences. Within these tractable regions of quasispecies inference, we use constraint programming to solve for an optimal quasispecies subsequence determination via vertex coloring of the conflict graph, a representation which also lends itself to data with non-contiguous reads such as paired-end sequencing. We demonstrate the utility of the method by applying it to simulations based on actual intra-patient clonal HIV-1 sequencing data.

Capturing and quantifying dynamic changes in three-dimensional cellular geometries on fast time scales is a challenge because of mechanical limitations of imaging systems as well as of the inherent tradeoffs between temporal resolution and image quality. We have combined a custom high-speed two-photon microscopy approach with a novel image segmentation method, the weighted directional adaptive-threshold (WDAT), to quantify the dimensions of intercellular spaces of cells under compressive stress on timescales previously inaccessible. The adaptation of a high-speed two-photon microscope addressed the need to capture events occurring on short timescales, while the WDAT method was developed to address artifacts of standard intensity-based analysis methods when applied to this system. Our novel approach is demonstrated by the enhanced temporal analysis of the three-dimensional cellular and extracellular deformations that accompany compressive loading of airway epithelial cells.