Efficient inference, potential, and limitations of site-specific substitution models.

Natural selection imposes a complex filter on which variants persist in a population resulting in evolutionary patterns that vary greatly along the genome. Some sites evolve close to neutrally, while others are highly conserved, allow only specific states, or only change in concert with other sites. On one hand, such constraints on sequence evolution can be to infer biological function, one the other hand they need to be accounted for in phylogenetic reconstruction. Phylogenetic models often account for this complexity by partitioning sites into a small number of discrete classes with different rates and/or state preferences. Appropriate model complexity is typically determined by model selection procedures. Here, we present an efficient algorithm to estimate more complex models that allow for different preferences at every site and explore the accuracy at which such models can be estimated from simulated data. Our iterative approximate maximum likelihood scheme uses information in the data efficiently and accurately estimates site-specific preferences from large data sets with moderately diverged sequences and known topology. However, the joint estimation of site-specific rates, and site-specific preferences, and phylogenetic branch length can suffer from identifiability problems, while ignoring variation in preferences across sites results in branch length underestimates. Site-specific preferences estimated from large HIV pol alignments show qualitative concordance with intra-host estimates of fitness costs. Analysis of these substitution models suggests near saturation of divergence after a few hundred years. Such saturation can explain the inability to infer deep divergence times of HIV and SIVs using molecular clock approaches and time-dependent rate estimates.

TreeTime: Maximum-likelihood phylodynamic analysis.

Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. Here, we present TreeTime, a Python-based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The runtime of TreeTime scales linearly with dataset size.

Estimating time of HIV-1 infection from next-generation sequence diversity.

Estimating the time since infection (TI) in newly diagnosed HIV-1 patients is challenging, but important to understand the epidemiology of the infection. Here we explore the utility of virus diversity estimated by next-generation sequencing (NGS) as novel biomarker by using a recent genome-wide longitudinal dataset obtained from 11 untreated HIV-1-infected patients with known dates of infection. The results were validated on a second dataset from 31 patients. Virus diversity increased linearly with time, particularly at 3rd codon positions, with little inter-patient variation. The precision of the TI estimate improved with increasing sequencing depth, showing that diversity in NGS data yields superior estimates to the number of ambiguous sites in Sanger sequences, which is one of the alternative biomarkers. The full advantage of deep NGS was utilized with continuous diversity measures such as average pairwise distance or site entropy, rather than the fraction of polymorphic sites. The precision depended on the genomic region and codon position and was highest when 3rd codon positions in the entire pol gene were used. For these data, TI estimates had a mean absolute error of around 1 year. The error increased only slightly from around 0.6 years at a TI of 6 months to around 1.1 years at 6 years. Our results show that virus diversity determined by NGS can be used to estimate time since HIV-1 infection many years after the infection, in contrast to most alternative biomarkers. We provide the regression coefficients as well as web tool for TI estimation.

In vivo mutation rates and the landscape of fitness costs of HIV-1.

Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of evolution. Using whole genome deep sequencing data from longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and fitness costs in HIV-1 from the dynamics of genetic variation. At approximately neutral sites, mutations accumulate with a rate of 1.2 × 10-5 per site per day, in agreement with the rate measured in cell cultures. We estimated the rate from G to A to be the largest, followed by the other transitions C to T, T to C, and A to G, while transversions are less frequent. At other sites, mutations tend to reduce virus replication. We estimated the fitness cost of mutations at every site in the HIV-1 genome using a model of mutation selection balance. About half of all non-synonymous mutations have large fitness costs (>10 percent), while most synonymous mutations have costs <1 percent. The cost of synonymous mutations is especially low in most of pol where we could not detect measurable costs for the majority of synonymous mutations. In contrast, we find high costs for synonymous mutations in important RNA structures and regulatory regions. The intra-patient fitness cost estimates are consistent across multiple patients, indicating that the deleterious part of the fitness landscape is universal and explains a large fraction of global HIV-1 group M diversity.

Optimizing culture conditions for free-living stages of the nematode parasite Strongyloides ratti.

The rat parasitic nematode Strongyloides ratti (S. ratti) has recently emerged as a model system for various aspects of parasite biology and evolution. In addition to parasitic parthenogenetic females, this species can also form facultative free-living generations of sexually reproducing adults. These free-living worms are bacteriovorous and grow very well when cultured in the feces of their host. However, in fecal cultures the worms are rather difficult to find for observation and experimental manipulation. Therefore, it has also been attempted to raise S. ratti on Nematode Growth Media (NGM) plates with Escherichia coli OP50 as food, exactly as described for the model nematode Caenorhabditis elegans. Whilst worms did grow on these plates, their longevity and reproductive output compared to fecal cultures were dramatically reduced. In order to improve the culture success we tested other plates occasionally used for C. elegans and, starting from the best performing one, systematically varied the plate composition, the temperature and the food in order to further optimize the conditions. Here we present a plate culturing protocol for free-living stages of S. ratti with strongly improved reproductive success and longevity.

Single molecule detection of nanomechanical motion.

We investigate theoretically how single molecule spectroscopy techniques can be used to perform fast and high resolution displacement detection and manipulation of nanomechanical oscillators, such as singly clamped carbon nanotubes. We analyze the possibility of real time displacement detection by the luminescence signal and of displacement fluctuations by the degree of second order coherence. Estimates of the electromechanical coupling constant indicate that intriguing regimes of strong backaction between the two-level system of a molecule and the oscillator can be realized.

How to measure the transmission phase through a quantum dot in a two-terminal interferometer.

Measurement of the transmission phase through a quantum dot (QD) embedded in an arm of a two-terminal Aharonov-Bohm (AB) interferometer is inhibited by phase symmetry, i.e., the property that the linear response conductance of a two-terminal device is an even function of the magnetic field. It is demonstrated that in a setup consisting of an interferometer with a QD in each of its arms, with one of the QDs capacitively coupled to a nearby quantum point contact (QPC), phase symmetry is broken when a finite voltage bias is applied to the QPC. The transmission phase via the uncoupled QD can then be deduced from the amplitude of the odd component of the AB oscillations.