neherlab is migrating

we are changing location, both physically and on the web.

the new lab page is:

our new home is the:

Biozentrum in Basel, Switzerland


Establishment and dynamics of the latent HIV-1 reservoir

Our new study (work with the group of Jan Albert) on HIV-1 evolution and turnover during suppressive anti-retroviral therapy has just come out in eLife. In this paper, we combined our previous data on HIV-1 evolution in plasma prior to therapy (Zanini et al, 2015) with HIV-1 DNA sequences from peripheral blood cells (PBMC) after many years of therapy. This combination of pre-therapy and on-therapy data from the same individuals allowed us to investigate the origin of integrated HIV-1 DNA and determine whether viral DNA in cells change during therapy:

  • We find no evidence of replication/evolution during suppressive therapy
  • Even after 18y of therapy HIV-1 DNA looks very similar to the HIV-1 RNA from samples right before treatment
  • The HIV reservoir is turning over fast in absence of therapy. This turnover is dramatically slowed by therapy, suggesting that HIV-1 infection is a major contributor to T-cell death.

Our results are at odds with a recent study by Lorenzo-Redondo et al 2016. Using sequence data from HIV RNA at treatment initiation and HIV DNA 3 and 6 month into therapy, Lorenzo-Redondo et al estimated a very high rate of sequence evolution. The evolution of the root-to-tip distance predicted on the basis of their rate estimate is included in the graph below as shaded area – clearly incompatible with our results. In fact, the rate estimated by Lorenzo-Redondo et al is faster than the pre-therapy rate in the individuals we investigated. combined_root_to_tip_clustered_good_hap_count

Lorenzo-Redondo et al studied sequences from blood and lymph tissue, while we had only access to blood samples. This, however, is unlikely to explain this discrepancy: Lorenzo-Redondo et al estimate similar rates in PBMCs and lymp tissue. Furthermore, several studies, including Lorenzo-Redondo et al, estimate that HIV sequences from lymph and PBMCs mix on a time scale of a few month such that PBMCs should be an accurate reporter. The rapid evolution inferred by Lorenzo-Redondo might be explained in part by the following factors:

  • The samples come from a six month interval, which is much shorter than the coalescence time scale of HIV. With sequences from such small time intervals, rooting of the phylogenetic tree to maximize the correlation between root-to-tip distance and sampling date can generate an exaggerated temporal signal.
  • With increasing time since start of therapy, the HIV-1 DNA positive pool of cells will become dominated by long-lived cells which sample deeper into the history of the HIV infection prior to therapy. This could generate a signal of spurious signal “backward” evolution.

The graph below illustrates the latter. If HIV positive cells are a mix of short-lived (blue) and long-lived (red) cells, a sample taken at treatment start will be dominated by short-lived cells and virus that was replicating very recently. A few month into treatment, short-lived cells will be mostly HIV negative while HIV positive cells tend to be long-lived cells that sample deeper into the history of the infection. This shift can generate a spurious signal of evolution.back_sampling

While we cannot rule out that HIV does replicate in compartments that are missed by our sequencing of HIV from PBMCs, ongoing replication is not the dominant mechanism by which HIV DNA is maintained in circulating cells.


Tracking MERS-CoV evolution

About 3 years ago, a novel respiratory disease was diagnosed in Saudi Arabia and called Middle East Respiratory Syndrome. The responsible agent was identified as a betacoronavirus, a single stranded RNA virus, and is called MERS-CoV. Up until a few weeks ago, the disease was restricted to the middle east with most of the 1200 reported cases in Saudi Arabia. In early May, a business traveller brought the virus to South Korea, where it rapidly spread within a hospital — 120+ confirmed cases so far. From there, one infected person travelled to China. The chart below shows case numbers over time.


Sequences of recently isolated viruses from China and Korea have been deposited in genbank this week. To track the spread and evolution of the virus, we adapted nextflu to MERS-CoV. This is now live at

Embedded image permalink allows to color the tree by country, host (human/camel) and sampling date.

The tree is characterized by multiple clusters of virus sequences that are very similar and are isolated almost at the same time (see tree colored by sampling data below. The clusters contain almost mers_dateonly viruses isolated from humans, whereas the viruses isolated from camels are more isolated and scattered around in the tree, pointing towards multiple camel-human transmissions followed by localized transmissions among humans.

Among RNA viruses MERS-CoV has an exceptionally large genome of more than 30kb in length. Together with the high mutation rate of RNA viruses, this would in principle result in well resolved trees with a mutation every couple of days. However, MERS-CoV also seems to recombine, which makes the interpretation of sequence differences between viruses difficult. Viruses can be similar in one region of the genome, and quite different in another. This is illustrated in the matrices of pairwise distances below. Trees constructed from sequences that have undergone recombination are still useful as a summary of the average differences between sequences, but deep branches in particular don’t necessarily represent the true history of the virus. The tight clusters associated with recent outbreaks don’t suffer from this problem as much. For the virus, recombination is probably a necessity since maintaining a long genome with high mutation rate is next to impossible without recombination.

Color encodes distance between viral sequences (blue = similar, red = distant). The part of the genome used to compare sequences is indicated above the panels. The order of sequences is the same as in the tree with the oldest sequences at the bottom left.
Color encodes distance between viral sequences (blue = similar, red = distant). The part of the genome used to compare sequences is indicated above the panels. The order of sequences is the same as in the tree with the oldest sequences at the bottom left.

Look here for a more detailed analysis of recombination in MERS-CoV by Gytis Dudas and Andrew Rambaut.