Deep Learning from Phylogenies for Diversification Analyses.

Birth-death models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models such formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time constant homogeneous birth-death model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for deployment of future models in the field.

Correction: Cuban history of CRF19 recombinant subtype of HIV-1.

[This corrects the article DOI: 10.1371/journal.ppat.1009786.].

Experimental and mathematical insights on the interactions between poliovirus and a defective interfering genome.

During replication, RNA viruses accumulate genome alterations, such as mutations and deletions. The interactions between individual variants can determine the fitness of the virus population and, thus, the outcome of infection. To investigate the effects of defective interfering genomes (DI) on wild-type (WT) poliovirus replication, we developed an ordinary differential equation model, which enables exploring the parameter space of the WT and DI competition. We also experimentally examined virus and DI replication kinetics during co-infection, and used these data to infer model parameters. Our model identifies, and our experimental measurements confirm, that the efficiencies of DI genome replication and encapsidation are two most critical parameters determining the outcome of WT replication. However, an equilibrium can be established which enables WT to replicate, albeit to reduced levels.

Cuban history of CRF19 recombinant subtype of HIV-1.

CRF19 is a recombinant form of HIV-1 subtypes D, A1 and G, which was first sampled in Cuba in 1999, but was already present there in 1980s. CRF19 was reported almost uniquely in Cuba, where it accounts for ∼25% of new HIV-positive patients and causes rapid progression to AIDS (∼3 years). We analyzed a large data set comprising ∼350 pol and env sequences sampled in Cuba over the last 15 years and ∼350 from Los Alamos database. This data set contained both CRF19 (∼315), and A1, D and G sequences. We performed and combined analyses for the three A1, G and D regions, using fast maximum likelihood approaches, including: (1) phylogeny reconstruction, (2) spatio-temporal analysis of the virus spread, and ancestral character reconstruction for (3) transmission mode and (4) drug resistance mutations (DRMs). We verified these results with a Bayesian approach. This allowed us to acquire new insights on the CRF19 origin and transmission patterns. We showed that CRF19 recombined between 1966 and 1977, most likely in Cuban community stationed in Congo region. We further investigated CRF19 spread on the Cuban province level, and discovered that the epidemic started in 1970s, most probably in Villa Clara, that it was at first carried by heterosexual transmissions, and then quickly spread in the 1980s within the "men having sex with men" (MSM) community, with multiple transmissions back to heterosexuals. The analysis of the transmission patterns of common DRMs found very few resistance transmission clusters. Our results show a very early introduction of CRF19 in Cuba, which could explain its local epidemiological success. Ignited by a major founder event, the epidemic then followed a similar pattern as other subtypes and CRFs in Cuba. The reason for the short time to AIDS remains to be understood and requires specific surveillance, in Cuba and elsewhere.

COVID-Align: accurate online alignment of hCoV-19 genomes using a profile HMM.

MOTIVATION: The first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1000 and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. RESULTS: hCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1000 genomes requires ∼50 minutes on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). AVAILABILITY AND IMPLEMENTATION: https://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Origin, evolution and global spread of SARS-CoV-2.

SARS-CoV-2 is the virus responsible for the global COVID19 pandemic. We review what is known about the origin of this virus, detected in China at the end of December 2019. The genome of this virus mainly evolves under the effect of point mutations. These are generally neutral and have no impact on virulence and severity, but some appear to influence infectivity, notably the D614G mutation of the Spike protein. To date (30/09/2020) no recombination of the virus has been documented in the human host, and very few insertions and deletions. The worldwide spread of the virus was the subject of controversies that we summarize, before proposing a new approach free from the limitations of previous methods. The results show a complex scenario with, for example, numerous introductions to the USA and returns of the virus from the USA to certain countries including France.

Le SARS-CoV-2 est le virus responsable de la pandémie mondiale de COVID19. On dresse ici un bilan de ce qui est connu sur l'origine de ce virus, détecté en Chine fin décembre 2019. Le génome de ce virus évolue sous l'effet de mutations ponctuelles. Celles-ci sont généralement neutres et sans impact sur la virulence et la sévérité, mais certaines semblent influer sur l'infectiosité, notamment la mutation D614G de la protéine Spike. A l'inverse, on n'a à ce jour (30/09/2020) documenté aucune recombinaison du virus chez l'hôte humain, et très peu d'insertions et de délétions. La propagation mondiale du virus a fait l'objet de polémiques sur lesquelles nous revenons, avant de proposer une nouvelle approche débarrassée des limites des méthodes précédentes. Les résultats montrent une propagation complexe avec, par exemple, de très nombreuses introductions aux USA et des retours du virus depuis les USA vers certains pays dont la France.

High-throughput Microscopic Analysis of Salmonella Invasion of Host Cells.

Salmonella is a Gram-negative bacterium causing a gastro-enteric disease called salmonellosis. During the first phase of infection, Salmonella uses its flagella to swim near the surface of the epithelial cells and to target specific site of infection. In order to study the selection criteria that determine which host cells are targeted by the pathogen, and to analyze the relation between infecting Salmonella (i.e., cooperation or competition), we have established a high-throughput microscopic assay of HeLa cells sequentially infected with fluorescent bacteria. Using an automated pipeline of image analysis, we quantitatively characterized a multitude of parameters of infected and non-infected cells. Based on this, we established a predictive model that allowed us to identify those parameters involved in host cell vulnerability towards infection. We revealed that host cell vulnerability has two origins: a pathogen-induced cellular vulnerability emerging from Salmonella uptake and persisting at later stages of the infection process; and a host cell-inherent vulnerability linked with cell inherent attributes, such as local cell crowding, and cholesterol content. Our method forecasts the probability of Salmonella infection within monolayers of epithelial cells based on morphological or molecular host cell parameters. Here, we provide a detailed description of the workflow including the computer-based analysis pipeline. Our method has the potential to be applied to study other combinations of host-pathogen interactions.

Identification of Parameters of Host Cell Vulnerability during Salmonella Infection by Quantitative Image Analysis and Modeling.

Salmonella targets and enters epithelial cells at permissive entry sites: some cells are more likely to be infected than others. However, the parameters that lead to host cell heterogeneity are not known. Here, we quantitatively characterized host cell vulnerability to Salmonella infection based on imaged parameters. We performed successive infections of the same host cell population followed by automated high-throughput microscopy and observed that infected cells have a higher probability of being reinfected. Establishing a predictive model, we identified two combined origins of host cell vulnerability: pathogen-induced cellular vulnerability emerging from Salmonella uptake and persisting at later stages of the infection and host cell-inherent vulnerability. We linked the host cell-inherent vulnerability with its morphological attributes, such as local cell crowding, and with host cell cholesterol content. This showed that the probability of Salmonella infection success can be forecast from morphological or molecular host cell parameters.