Christopher John Illingworth

High-Definition Reconstruction of Clonal Composition in Cancer

Summary The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.

Addicted? Reduced host resistance in populations with defensive symbionts.

Heritable symbionts that protect their hosts from pathogens have been described in a wide range of insect species. By reducing the incidence or severity of infection, these symbionts have the potential to reduce the strength of selection on genes in the insect genome that increase resistance. Therefore, the presence of such symbionts may slow down the evolution of resistance. Here we investigated this idea by exposing Drosophila melanogaster populations to infection with the pathogenic Drosophila C virus (DCV) in the presence or absence of Wolbachia, a heritable symbiont of arthropods that confers protection against viruses. After nine generations of selection, we found that resistance to DCV had increased in all populations. However, in the presence of Wolbachia the resistant allele of pastrel—a gene that has a major effect on resistance to DCV—was at a lower frequency than in the symbiont-free populations. This finding suggests that defensive symbionts have the potential to hamper the evolution of insect resistance genes, potentially leading to a state of evolutionary addiction where the genetically susceptible insect host mostly relies on its symbiont to fight pathogens.

In vitro selection of miltefosine resistance in promastigotes of Leishmania donovani from Nepal: genomic and metabolomic characterization.

In this study, we followed the genomic, lipidomic and metabolomic changes associated with the selection of miltefosine (MIL) resistance in two clinically derived Leishmania donovani strains with different inherent resistance to antimonial drugs (antimony sensitive strain Sb‐S; and antimony resistant Sb‐R). MIL‐R was easily induced in both strains using the promastigote‐stage, but a significant increase in MIL‐R in the intracellular amastigote compared to the corresponding wild‐type did not occur until promastigotes had adapted to 12.2 μM MIL. A variety of common and strain‐specific genetic changes were discovered in MIL‐adapted parasites, including deletions at the LdMT transporter gene, single‐base mutations and changes in somy. The most obvious lipid changes in MIL‐R promastigotes occurred to phosphatidylcholines and lysophosphatidylcholines and results indicate that the Kennedy pathway is involved in MIL resistance. The inherent Sb resistance of the parasite had an impact on the changes that occurred in MIL‐R parasites, with more genetic changes occurring in Sb‐R compared with Sb‐S parasites. Initial interpretation of the changes identified in this study does not support synergies with Sb‐R in the mechanisms of MIL resistance, though this requires an enhanced understanding of the parasites biochemical pathways and how they are genetically regulated to be verified fully.

Identifying selection in the within-host evolution of influenza using viral sequence data.

The within-host evolution of influenza is a vital component of its epidemiology. A question of particular interest is the role that selection plays in shaping the viral population over the course of a single infection. We here describe a method to measure selection acting upon the influenza virus within an individual host, based upon time-resolved genome sequence data from an infection. Analysing sequence data from a transmission study conducted in pigs, describing part of the haemagglutinin gene (HA1) of an influenza virus, we find signatures of non-neutrality in six of a total of sixteen infections. We find evidence for both positive and negative selection acting upon specific alleles, while in three cases, the data suggest the presence of time-dependent selection. In one infection we observe what is potentially a specific immune response against the virus; a non-synonymous mutation in an epitope region of the virus is found to be under initially positive, then strongly negative selection. Crucially, given the lack of homologous recombination in influenza, our method accounts for linkage disequilibrium between nucleotides at different positions in the haemagglutinin gene, allowing for the analysis of populations in which multiple mutations are present at any given time. Our approach offers a new insight into the dynamics of influenza infection, providing a detailed characterisation of the forces that underlie viral evolution.

Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites

Single-cell RNA-sequencing is revolutionising our understanding of seemingly homogeneous cell populations but has not yet been widely applied to single-celled organisms. Transcriptional variation in unicellular malaria parasites from the Plasmodium genus is associated with critical phenotypes including red blood cell invasion and immune evasion, yet transcriptional variation at an individual parasite level has not been examined in depth. Here, we describe the adaptation of a single-cell RNA-sequencing (scRNA-seq) protocol to deconvolute transcriptional variation for more than 500 individual parasites of both rodent and human malaria comprising asexual and sexual life-cycle stages. We uncover previously hidden discrete transcriptional signatures during the pathogenic part of the life cycle, suggesting that expression over development is not as continuous as commonly thought. In transmission stages, we find novel, sex-specific roles for differential expression of contingency gene families that are usually associated with immune evasion and pathogenesis.

Fitness inference from short-read data: within-host evolution of a reassortant H5N1 influenza virus

We present a method to infer the role of selection acting during the within-host evolution of the influenza virus from short-read genome sequence data. Linkage disequilibrium between loci is accounted for by treating short-read sequences as noisy multilocus emissions from an underlying model of haplotype evolution. A hierarchical model-selection procedure is used to infer the underlying fitness landscape of the virus insofar as that landscape is explored by the viral population. In a first application of our method, we analyze data from an evolutionary experiment describing the growth of a reassortant H5N1 virus in ferrets. Across two sets of replica experiments we infer multiple alleles to be under selection, including variants associated with receptor binding specificity, glycosylation, and with the increased transmissibility of the virus. We identify epistasis as an important component of the within-host fitness landscape, and show that adaptation can proceed through multiple genetic pathways.

SAMFIRE: multi-locus variant calling for time-resolved sequence data

UNLABELLED An increasingly common method for studying evolution is the collection of time-resolved short-read sequence data. Such datasets allow for the direct observation of rapid evolutionary processes, as might occur in natural microbial populations and in evolutionary experiments. In many circumstances, evolutionary pressure acting upon single variants can cause genomic changes at multiple nearby loci. SAMFIRE is an open-access software package for processing and analyzing sequence reads from time-resolved data, calling important single- and multi-locus variants over time, identifying alleles potentially affected by selection, calculating linkage disequilibrium statistics, performing haplotype reconstruction and exploiting time-resolved information to estimate the extent of uncertainty in reported genomic data. AVAILABILITY AND IMPLEMENTATION C ++ code may be found at https://github.com/cjri/samfire/ CONTACT chris.illingworth@gen.cam.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Single-cell transcriptomics of malaria parasites

Single-cell RNA-sequencing is revolutionising our understanding of seemingly homogeneous cell populations, but has not yet been applied to single cell organisms. Here, we established a method to successfully investigate transcriptional variation across individual malaria parasites. We discover an unexpected, discontinuous program of transcription during asexual growth previously masked by bulk analyses, and uncover novel variation among sexual stage parasites in their expression of gene families important in host-parasite interactions.

On the effective depth of viral sequence data.

Abstract Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

Evaluating genetic drift in time-series evolutionary analysis

Highlights • We assess the inferrability of a Wright–Fisher drift model from time-resolved genome sequence data.• We identify thresholds at which a Wright–Fisher model can be distinguished from Gaussian diffusion.• Considering a recent experimental dataset, a Wright–Fisher model is favoured.• We infer chromosome dependent effective population sizes for this dataset.