Paul-Michael Agapow

Evolutionary dynamics of local pandemic H1N1/2009 influenza virus lineages revealed by whole-genome analysis

ABSTRACT Virus gene sequencing and phylogenetics can be used to study the epidemiological dynamics of rapidly evolving viruses. With complete genome data, it becomes possible to identify and trace individual transmission chains of viruses such as influenza virus during the course of an epidemic. Here we sequenced 153 pandemic influenza H1N1/09 virus genomes from United Kingdom isolates from the first (127 isolates) and second (26 isolates) waves of the 2009 pandemic and used their sequences, dates of isolation, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingdom. We demonstrate that the epidemic in the United Kingdom was composed of many cocirculating lineages, among which at least 13 were exclusively or predominantly United Kingdom clusters. The estimated divergence times of two of the clusters predate the detection of pandemic H1N1/09 virus in the United Kingdom, suggesting that the pandemic H1N1/09 virus was already circulating in the United Kingdom before the first clinical case. Crucially, three clusters contain isolates from the second wave of infections in the United Kingdom, two of which represent chains of transmission that appear to have persisted within the United Kingdom between the first and second waves. This demonstrates that whole-genome analysis can track in fine detail the behavior of individual influenza virus lineages during the course of a single epidemic or pandemic.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural networks prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

PHYLOGENETICALLY NESTED COMPARISONS FOR TESTING CORRELATES OF SPECIES RICHNESS: A SIMULATION STUDY OF CONTINUOUS VARIABLES

Abstract.— Explaining the uneven distribution of species among lineages is one of the oldest questions in evolution. Proposed correlations between biological traits and species diversity are routinely tested by making comparisons between phylogenetic sister clades. Several recent studies have used nested sister‐clade comparisons to test hypotheses linking continuously varying traits, such as body size, with diversity. Evaluating the findings of these studies is complicated because they differ in the index of species richness difference used, the way in which trait differences were treated, and the statistical tests employed. In this paper, we use simulations to compare the performance of four species richness indices, two choices about the branch lengths used to estimate trait values for internal nodes and two statistical tests under a range of models of clade growth and character evolution. All four indices returned appropriate Type I error rates when the assumptions of the method were met and when branch lengths were set proportional to time. Only two of the indices were robust to the different evolutionary models and to different choices of branch lengths and statistical tests. These robust indices had comparable power under one nonnull scenario. Regression through the origin was consistently more powerful than the t‐test, and the choice of branch lengths exerts a strong effect on both the validity and power. In the light of our simulations, we re‐evaluate the findings of those who have previously used nested comparisons in the context of species richness. We provide a set of simple guidelines to maximize the performance of phylogenetically nested comparisons in tests of putative correlates of species richness.

Phylogeny Imbalance: Taxonomic Level Matters

Two lines of evidence indicate that the degree of symmetry in phylogenetic topologies differs at different hierarchical levels. First, in a set of 61 phylogenies with superspecific taxa as their terminals, trees were on average more unbalanced (asymmetric) when the species richness of terminals was considered than when it was not. Second, nodes with a given number of higher taxa descended from them were on average more unbalanced than were nodes with the same number of species as descendants. There are several possible reasons--some biological, some artifactual--for the differences. Whatever the reason, these results caution against treating species-level and higher level phylogenies as equivalent when considering tree shape. The imbalance measure adopted here permits the use of trees that contain polytomies, facilitating a larger sample than has been achieved previously.

Evolutionary pathways of the pandemic influenza A (H1N1) 2009 in the UK.

The emergence of the influenza (H1N1) 2009 virus provided a unique opportunity to study the evolution of a pandemic virus following its introduction into the human population. Virological and clinical surveillance in the UK were comprehensive during the first and second waves of the pandemic in 2009, with extensive laboratory confirmation of infection allowing a detailed sampling of representative circulating viruses. We sequenced the complete coding region of the haemagglutinin (HA) segment of 685 H1N1 pandemic viruses selected without bias during two waves of pandemic in the UK (April-December 2009). Phylogenetic analysis showed that although temporal accumulation of amino acid changes was observed in the HA sequences, the overall diversity was less than that typically seen for seasonal influenza A H1N1 or H3N2. There was co-circulation of multiple variants as characterised by signature amino acid changes in the HA. A specific substitution (S203T) became predominant both in UK and global isolates. No antigenic drift occurred during 2009 as viruses with greater than four-fold reduction in their haemagglutination inhibition (HI) titre (“low reactors”) were detected in a low proportion (3%) and occurred sporadically. Although some limited antigenic divergence in viruses with four-fold reduction in HI titre might be related to the presence of 203T, additional studies are needed to test this hypothesis.

ALES: cell lineage analysis and mapping of developmental events

MOTIVATION Animals build their bodies by altering the fates of cells. The way in which they do so is reflected in the topology of cell lineages and the fates of terminal cells. Cell lineages should, therefore, contain information about the molecular events that determined them. Here we introduce new tools for visualizing, manipulating, and extracting the information contained in cell lineages. Our tools enable us to analyze very large cell lineages, where previously analyses have only been carried out on cell lineages no larger than a few dozen cells. RESULTS Ales (A Lineage Evaluation System) allows the display, evaluation and comparison of cell lineages with the aim of identifying molecular and cellular events underlying development. Ales introduces a series of algorithms that locate putative developmental events. The distribution of these predicted events can then be compared to gene expression patterns or other cellular characteristics. In addition, artificial lineages can be generated, or existing lineages modified, according to a range of models, in order to test hypotheses about lineage evolution. AVAILABILITY The program can run on any operating system with a compliant Java 2 environment. Ales is free for academic use and can be downloaded from http://mbi.dkfz-heidelberg.de/mbi/research/cellsim/ales.

Allowance for effects of electrostatic repulsion on protein dimerization

A simple procedure for assessing the extent of electrostatic effects on protein dimerization is described and illustrated by application to published results on the ionic strength dependence of the dimerization constant for alpha-chymotrypsin at pH 4 (Aune, K.C., Goldsmith, L.C. and Timasheff, S.N. (1971) Biochemistry 10, 1617-1622). From the analysis it is concluded that the inverse dependence of alpha-chymotrypsin dimerization upon ionic strength is predominantly a general electrostatic effect, rather than a consequence of repulsion between two specific charged residues on the adjacent monomers comprising dimer.

Mycobacterium tuberculosis exploits a molecular off switch of the immune system for intracellular survival

Mycobacterium tuberculosis (M. tuberculosis) survives and multiplies inside human macrophages by subversion of immune mechanisms. Although these immune evasion strategies are well characterised functionally, the underlying molecular mechanisms are poorly understood. Here we show that during infection of human whole blood with M. tuberculosis, host gene transcriptional suppression, rather than activation, is the predominant response. Spatial, temporal and functional characterisation of repressed genes revealed their involvement in pathogen sensing and phagocytosis, degradation within the phagolysosome and antigen processing and presentation. To identify mechanisms underlying suppression of multiple immune genes we undertook epigenetic analyses. We identified significantly differentially expressed microRNAs with known targets in suppressed genes. In addition, after searching regions upstream of the start of transcription of suppressed genes for common sequence motifs, we discovered novel enriched composite sequence patterns, which corresponded to Alu repeat elements, transposable elements known to have wide ranging influences on gene expression. Our findings suggest that to survive within infected cells, mycobacteria exploit a complex immune “molecular off switch” controlled by both microRNAs and Alu regulatory elements.

The tree balance signature of mass extinction is erased by continued evolution in clades of constrained size with trait-dependent speciation

[This corrects the article DOI: 10.1371/journal.pone.0179553.].

Preferential ligand binding to multi-state acceptor systems: comparisons of the calcium-binding and dimerization characteristics of prothrombin and fragment 1.

Consideration is given to the interactions of a ligand with self-associating acceptor systems for which preferential binding is an ambiguous term in that ligand-mediated self-association does not necessarily imply a greater binding constant for polymeric acceptor--even in instances where binding sites are preserved in the self-association process. This dilemma is shown to arise in situations involving the binding of ligand to monomeric and polymeric forms of an acceptor that also coexist in equilibrium with inactive isomeric states. For example, the ten-fold increase in the measured dimerization constant for prothrombin Fragment 1 in the presence of a saturating concentration of Ca2+ ion may well reflect the existence of a 12% greater binding constant for the interaction of metal ion with dimeric acceptor. However, that result, as well as the detailed form of the sigmoidal binding curve, are also reasonably described by another extreme model in which the monomeric and dimeric forms of the acceptor possess equal affinities for Ca2+ ion. Likewise, the fact that the same experimental dimerization constant applies to prothrombin and its Ca(2+)-saturated complex does not preclude the possibility that the active form of dimeric zymogen exhibits a 12% greater affinity for metal ion. Numerical simulations have established that characterization of the dimerization behaviour as a function of free ligand concentration should allow greater discrimination between such models of the interplay between calcium binding and self-association of prothrombin and Fragment 1. Finally, by illustrating the likelihood that the disparity in self-association behaviour of prothrombin and Fragment 1 merely reflects minor differences in the relative magnitudes of isomerization constants and/or binding constants for monomeric and dimeric states of the two acceptors, the present investigation serves to allay concern about the validity of employing the proteolytic fragment as a model of the intact zymogen.