Biology subjects

Hadfield, J.

Publications and source records attributed to Hadfield, J..

12 recordsLinked to original sources

bioRxiv2026

Nextstrain automates real-time phylodynamic analysis of open data for endemic and emerging pathogens

MotivationGenome sequencing provides an exceptional window into the evolutionary and epidemiological dynamics of endemic and emerging pathogens, and thus allows for better, more targeted, public health interventions. Online genomic surveillance platforms can provide near real-time insight into these dynamics. ResultsNextstrain provides continually updated real-time genomic surveillance for 21 viruses and the bacterial pathogen Mycobacterium tuberculosis, with most analyses relying solely on open sequence data. Each pathogen includes steps to fetch and curate open data, classify sequences using established nomenclature systems, perform phylogenetic analyses, and share the results publicly. These analyses are automated, with most running daily to provide continually updated snapshots of pathogen evolution. Availability and ImplementationAll source code is available at https://github.com/nextstrain. Phylogenetic results can be visualized and downloaded at https://nextstrain.org/pathogens, and open sequence data and curated metadata are available at https://nextstrain.org/pathogens/files.

bioinformatics↗

bioRxiv2020

A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS-CoV-2 lineages

Since the start of the COVID-19 pandemic, an unprecedented number of genomic sequences of the causative virus (SARS-CoV-2) have been generated and shared with the scientific community. The unparalleled volume of available genetic data presents a unique opportunity to gain real-time insights into the virus transmission during the pandemic, but also a daunting computational hurdle if analysed with gold-standard phylogeographic approaches. We here describe and apply an analytical pipeline that is a compromise between fast and rigorous analytical steps. As a proof of concept, we focus on the Belgium epidemic, with one of the highest spatial density of available SARS-CoV-2 genomes. At the global scale, our analyses confirm the importance of external introduction events in establishing multiple transmission chains in the country. At the country scale, our spatially-explicit phylogeographic analyses highlight that the national lockdown had a relatively low impact on both the lineage dispersal velocity and the long-distance dispersal events within Belgium. Our pipeline has the potential to be quickly applied to other countries or regions, with key benefits in complementing epidemiological analyses in assessing the impact of intervention measures or their progressive easement.

evolutionary biology↗

bioRxiv2020

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summaryAs shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.

evolutionary biology↗

bioRxiv2020

High-resolution influenza mapping of a city reveals socioeconomic determinants of transmission within and between urban quarters

With two-thirds of the global population projected to be living in urban areas by 2050, understanding the transmission patterns of viral pathogens within cities is crucial for effective prevention strategies. Here, in unprecedented spatial resolution, we analysed the socioeconomic determinants of influenza transmission in a European city. We combined geographical and epidemiological data with whole genome sequencing of influenza viruses at the scale of urban quarters and statistical blocks, the smallest geographic subdivisions within a city. We observed annually re-occurring geographic clusters of influenza incidences, mainly associated with net income, and independent of population density and living space. Vaccination against influenza was also mainly associated with household income and was linked to the likelihood of influenza-like illness within an urban quarter. Transmissions patterns within and between quarters were complex. High-resolution city-level epidemiological studies combined with social science surveys such as this will be essential for understanding seasonal and pandemic transmission chains and delivering tailored public health information and vaccination programs at the municipal level.

microbiology↗

bioRxiv2020

An emergent clade of SARS-CoV-2 linked to returned travellers from Iran

The SARS-CoV-2 epidemic has rapidly spread outside China with major outbreaks occurring in Italy, South Korea and Iran. Phylogenetic analyses of whole genome sequencing data identified a distinct SARS-CoV-2 clade linked to travellers returning from Iran to Australia and New Zealand. This study highlights potential viral diversity driving the epidemic in Iran, and underscores the power of rapid genome sequencing and public data sharing to improve the detection and management of emerging infectious diseases.

microbiology↗

bioRxiv2019

Independence of HIF1a and androgen signaling pathways in prostate cancer

Androgen signaling drives prostate cancer progression and is a therapeutic target. Hypoxia/HIF1a signaling is associated with resistance to hormone therapy and a poor prognosis in patients treated with surgery or radiotherapy. It is not known whether the pathways operate in cooperation or independently. Using LNCaP cells with and without stable transfection of a HIF1a expression vector, we show that combined AR and HIF1a signaling promotes tumor growth in vitro and in vivo, and the capacity of HIF1a to promote tumor growth in the absence of endogenous androgen in vivo. Gene expression analysis identified 7 genes that were upregulated by both androgen and HIF1a. ChIP-Seq analysis showed that the AR and HIF/hypoxia signaling pathways function independently regulating the transcription of different genes with few shared targets. In clinical datasets elevated expression of 5 of the 7 genes was associated with a poor prognosis. Our findings suggest that simultaneous therapeutic inhibition of AR and HIF1a signaling pathways should be explored as a potential therapeutic strategy.

cancer biology↗

bioRxiv2019

Integrated clonal analysis reveals circulating tumor DNA in urine and plasma of glioma patients

Glioma-derived cell-free tumor DNA is challenging to detect using standard liquid biopsy techniques as its levels in body fluids are very low, similar to those in patients with early stage carcinomas. By sequencing cell-free DNA across thousands of clonal and private mutations identified individually in each patients tumor we detected tumor-derived DNA in plasma (10/12, 83%) and urine samples (8/11, 72%) from the majority (7/8, 87.5%) of glioma patients tested.\n\nOne Sentence SummaryCirculating tumor DNA can be detected in the majority of plasma and urine samples from primary brain tumor patients using sequencing guided by mutations detected in multi-region tumor biopsies.

genomics↗

bioRxiv2019

Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the viruss North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70% of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, HI assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics.

genomics↗

bioRxiv2017

Nextstrain: real-time tracking of pathogen evolution

SummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike.\n\nAvailability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

evolutionary biology↗

bioRxiv2017

Population-based analysis of ocular Chlamydia trachomatis in trachoma-endemic West African communities identifies genomic markers of disease severity

Chlamydia trachomatis (Ct) is the most common infectious cause of blindness and bacterial sexually transmitted infection worldwide. Using Ct whole genome sequences obtained directly from conjunctival swabs, we studied Ct genomic diversity and associations between Ct genetic polymorphisms with ocular localization and disease severity in a treatment-naive trachoma-endemic population in Guinea Bissau, West Africa. All sequences fall within the T2 ocular clade phylogenetically. This is consistent with the presence of the characteristic deletion in trpA resulting in a truncated non-functional protein and the ocular tyrosine repeat regions present in tarP associated with ocular tissue localization. We have identified twenty-one Ct non-synonymous single nucleotide polymorphisms (SNPs) associated with ocular localization, including SNPs within pmpD (OR=4.07, p*=0.001) and tarP (OR=0.34, p*=0.009). Eight SNPs associated with disease severity were found in yjfH (rlmB) (OR=0.13, p*=0.037), CTA0273 (OR=0.12, p*=0.027), trmD (OR=0.12, p*=0.032), CTA0744 (OR=0.12, p*=0.041), glgA (OR=0.10, p*=0.026), alaS (OR=0.10, p*=0.032), pmpE (OR=0.08, p*=0.001) and the intergenic region CTA0744-CTA0745 (OR=0.13, p*=0.043). This study demonstrates the extent of genomic diversity within a naturally circulating population of ocular Ct, and the first to describe novel genomic associations with disease severity. These findings direct investigation of host-pathogen interactions that may be important in ocular Ct pathogenesis and disease transmission.

genomics↗

bioRxiv2017

RNA interference pathways display high rates of adaptive protein evolution across multiple invertebrates

Conflict between organisms can lead to reciprocal adaptation that manifests itself as an increased evolutionary rate in the genes mediating the conflict. This adaptive signature has been observed in RNA interference (RNAi) pathway genes involved in the suppression of viruses and transposable elements in Drosophila melanogaster, suggesting that a subset of Drosophila RNAi genes may be locked into an arms race with these parasites. However, it is not known whether rapid evolution of RNAi genes is a general phenomenon across invertebrates, or which RNAi genes generally evolve adaptively. Here we use population genomic data from eight invertebrate species to infer rates of adaptive sequence evolution, and to test for past and ongoing selective sweeps in RNAi genes. We assess rates of adaptive protein evolution across species by using a formal meta-analytic framework to combine data across species, and by implementing a multispecies generalised linear mixed model of mutation counts. In all species, we find that RNAi genes display a greater rate of adaptive protein substitution than other genes, and that this is primarily mediated by positive selection acting on the subset of genes that are most likely to defend against viruses and transposable elements. In contrast, evidence for recent selective sweeps is broadly spread across functional classes of RNAi genes and differs substantially among species. Finally, we identify genes that exhibit elevated adaptive evolution across the analysed insect species combined, perhaps due to concurrent parasite-mediated arms races.

evolutionary biology↗

bioRxiv2017

Phandango: an interactive viewer for bacterial population genomics.

SummaryFully exploiting the wealth of data in current bacterial population genomics datasets requires synthesising and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner.\n\nAvailabilityPhandango is a web application freely available for use at https://jameshadfield.github.io/phandango and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango\n\nContactjh22@sanger.ac.uk, sh16@sanger.ac.uk

bioinformatics↗