HIV transmission. Selection bias at the heterosexual HIV-1 transmission bottleneck.

Heterosexual transmission of HIV-1 typically results in one genetic variant establishing systemic infection. We compared, for 137 linked transmission pairs, the amino acid sequences encoded by non-envelope genes of viruses in both partners and demonstrate a selection bias for transmission of residues that are predicted to confer increased in vivo fitness on viruses in the newly infected, immunologically naïve recipient. Although tempered by transmission risk factors, such as donor viral load, genital inflammation, and recipient gender, this selection bias provides an overall transmission advantage for viral quasispecies that are dominated by viruses with high in vivo fitness. Thus, preventative or therapeutic approaches that even marginally reduce viral fitness may lower the overall transmission rates and offer long-term benefits even upon successful transmission.

Frequent and variable cytotoxic-T-lymphocyte escape-associated fitness costs in the human immunodeficiency virus type 1 subtype B Gag proteins.

Cytotoxic-T-lymphocyte (CTL) escape mutations undermine the durability of effective human immunodeficiency virus type 1 (HIV-1)-specific CD8(+) T cell responses. The rate of CTL escape from a given response is largely governed by the net of all escape-associated viral fitness costs and benefits. The observation that CTL escape mutations can carry an associated fitness cost in terms of reduced virus replication capacity (RC) suggests a fitness cost-benefit trade-off that could delay CTL escape and thereby prolong CD8 response effectiveness. However, our understanding of this potential fitness trade-off is limited by the small number of CTL escape mutations for which a fitness cost has been quantified. Here, we quantified the fitness cost of the 29 most common HIV-1B Gag CTL escape mutations using an in vitro RC assay. The majority (20/29) of mutations reduced RC by more than the benchmark M184V antiretroviral drug resistance mutation, with impacts ranging from 8% to 69%. Notably, the reduction in RC was significantly greater for CTL escape mutations associated with protective HLA class I alleles than for those associated with nonprotective alleles. To speed the future evaluation of CTL escape costs, we also developed an in silico approach for inferring the relative impact of a mutation on RC based on its computed impact on protein thermodynamic stability. These data illustrate that the magnitude of CTL escape-associated fitness costs, and thus the barrier to CTL escape, varies widely even in the conserved Gag proteins and suggest that differential escape costs may contribute to the relative efficacy of CD8 responses.

Learning cellular sorting pathways using protein interactions and sequence motifs.

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.

Discriminative motif finding for predicting protein subcellular localization.

Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.

Alignment and classification of time series gene expression in clinical studies.

MOTIVATION: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class). RESULTS: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers. AVAILABILITY: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/.

Interpreting anonymous DNA samples from mass disasters--probabilistic forensic inference using genetic markers.

MOTIVATION: The problem of identifying victims in a mass disaster using DNA fingerprints involves a scale of computation that requires efficient and accurate algorithms. In a typical scenario there are hundreds of samples taken from remains that must be matched to the pedigrees of the alleged victim's surviving relatives. Moreover the samples are often degraded due to heat and exposure. To develop a competent method for this type of forensic inference problem, the complicated quality issues of DNA typing need to be handled appropriately, the matches between every sample and every family must be considered, and the confidence of matches need to be provided. RESULTS: We present a unified probabilistic framework that efficiently clusters samples, conservatively eliminates implausible sample-pedigree pairings, and handles both degraded samples (missing values) and experimental errors in producing and/or reading a genotype. We present a method that confidently exclude forensically unambiguous sample-family matches from the large hypothesis space of candidate matches, based on posterior probabilistic inference. Due to the high confidentiality of disaster DNA data, simulation experiments are commonly performed and used here for validation. Our framework is shown to be robust to these errors at levels typical in real applications. Furthermore, the flexibility in the probabilistic models makes it possible to extend this framework to include other biological factors such as interdependent markers, mitochondrial sequences, and blood type. AVAILABILITY: The software and data sets are available from the authors upon request.