Ecology of emerging diseases: virulence and transmissibility of human RNA viruses

Emerging infectious diseases continue to represent serious threats to global human health. Novel zoonotic pathogens are continually being recognised, and some ultimately cause significant disease burdens and extensive epidemics. Research and public health initiatives often face emerging pathogens with limited knowledge and resources. Inferences from empirical modelling have begun to uncover the factors determining cross-species transmission and emergence in humans, and subsequently guide risk assessments. However, the dynamics of virulence and transmissibility during the process of emergence are not well understood. Here, I focus on RNA viruses, a priority pathogen type because of their potential for rapid evolution. I use comparative trait-based analyses to investigate how aspects of both host and virus ecology contribute to the risk of virulence and transmissibility within human RNA viruses. To explore these questions, data were collected via systematic literature search protocols. In the first half of this thesis, I focus on viral determinants of virulence and transmissibility. I ask whether virulence can be predicted by viral traits of tissue tropism, transmission route, transmissibility and taxonomic classification. Using a machine learning approach, the most prominent predictors of severe virulence were breadth of tissue tropism, and nonvector-borne transmission routes. When applied to newly reported viruses as test set, the final model predicted disease severity with 87% accuracy. Next, I assess support for hypothesised routes of adaptation during emergence using phylogenetic state-switching models. Propensity for adaptation in small ‘stepwise’ movements versus large ‘off-the-shelf’ jumps differed between virus taxa, though no single route dominated, suggesting multiple independent trajectories of adaptation to human hosts. In addition, phylogenetic regressions showed vector and respiratory-transmitted viruses to be more likely to progress through early stages of emergence. In the second half of this thesis, I focus on how dynamics of virulence and transmissibility differ with respect to nonhuman host diversity, identity, and ecology. Using a regression framework, I observe that viruses with a broader mammalian host range exhibited higher risk of severe virulence, but lower risk of transmissibility, which may reflect potential trade-offs of host specificity. Furthermore, viruses with artiodactyl hosts exhibited lower risk of severe virulence and viruses with bat or nonhuman primate hosts exhibited higher risk of transmissibility. Next, I test hypotheses that mammal species with faster-paced life history may be predisposed to host viruses with greater virulence and transmissibility. Mammal body mass was used as an established proxy for pace of life history. In regression analyses, mammals with faster-paced life history hosted more viruses with severe virulence, though evidence for a relationship with transmissibility was limited. The broad-scale associations presented in this thesis suggest the evolution of virulence and human-to-human transmissibility during zoonotic emergence is a multifactorial, highly dynamic process influenced by both virus and host ecology. Despite this, general characteristics of high-risk emerging viruses are evident. For example, severe virulence was associated with broad niche diversity of both tissue tropisms at the within-host scale, and host species at the macroecological scale. However, risk factors for virulence and human-to-human transmissibility often did not coincide, which may imply an overarching trade-off between these traits. These analyses can contribute to preparedness and direction within public health strategies by identifying likely candidates for high-impact emergence events among previously known and newly discovered human viruses. The inherent connectivity between RNA viruses, their nonhuman hosts and the resulting implications for human health emphasise the holistic nature of emerging diseases and supports the One Health perspective for infectious disease research

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 225 and 187 viruses belonging to the family Coronaviridae , respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ∼73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases

Assessing the epidemic potential of RNA and DNA viruses

Many new and emerging RNA and DNA viruses are zoonotic or have zoonotic origins in an animal reservoir that is usually mammalian and sometimes avian. Not all zoonotic viruses are transmissible (directly or by an arthropod vector) between human hosts. Virus genome sequence data provide the best evidence of transmission. Of human transmissible virus, 37 species have so far been restricted to self-limiting outbreaks. These viruses are priorities for surveillance because relatively minor changes in their epidemiologies can potentially lead to major changes in the threat they pose to public health. On the basis of comparisons across all recognized human viruses, we consider the characteristics of these priority viruses and assess the likelihood that they will further emerge in human populations. We also assess the likelihood that a virus that can infect humans but is not capable of transmission (directly or by a vector) between human hosts can acquire that capability

Tissue tropism and transmission ecology predict virulence of human RNA viruses

Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-to-human transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R0 ≤ 1) human-to-human transmissibility. We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections

Global discovery of human-infective RNA viruses:A modelling analysis

RNA viruses are a leading cause of human infectious diseases and the prediction of where new RNA viruses are likely to be discovered is a significant public health concern. Here, we geocoded the first peer-reviewed reports of 223 human RNA viruses. Using a boosted regression tree model, we matched these virus data with 33 explanatory factors related to natural virus distribution and research effort to predict the probability of virus discovery across the globe in 2010-2019. Stratified analyses by virus transmissibility and transmission mode were also performed. The historical discovery of human RNA viruses has been concentrated in eastern North America, Europe, central Africa, eastern Australia, and north-eastern South America. The virus discovery can be predicted by a combination of socio-economic, land use, climate, and biodiversity variables. Remarkably, vector-borne viruses and strictly zoonotic viruses are more associated with climate and biodiversity whereas non-vector-borne viruses and human transmissible viruses are more associated with GDP and urbanization. The areas with the highest predicted probability for 2010-2019 include three new regions including East and Southeast Asia, India, and Central America, which likely reflect both increasing surveillance and diversity of their virome. Our findings can inform priority regions for investment in surveillance systems for new human RNA viruses

The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape.

The world continues to face a life-threatening viral pandemic. The virus underlying the Coronavirus Disease 2019 (COVID-19), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has caused over 98 million confirmed cases and 2.2 million deaths since January 2020. Although the most recent respiratory viral pandemic swept the globe only a decade ago, the way science operates and responds to current events has experienced a cultural shift in the interim. The scientific community has responded rapidly to the COVID-19 pandemic, releasing over 125,000 COVID-19-related scientific articles within 10 months of the first confirmed case, of which more than 30,000 were hosted by preprint servers. We focused our analysis on bioRxiv and medRxiv, 2 growing preprint servers for biomedical research, investigating the attributes of COVID-19 preprints, their access and usage rates, as well as characteristics of their propagation on online platforms. Our data provide evidence for increased scientific and public engagement with preprints related to COVID-19 (COVID-19 preprints are accessed more, cited more, and shared more on various online platforms than non-COVID-19 preprints), as well as changes in the use of preprints by journalists and policymakers. We also find evidence for changes in preprinting and publishing behaviour: COVID-19 preprints are shorter and reviewed faster. Our results highlight the unprecedented role of preprints and preprint servers in the dissemination of COVID-19 science and the impact of the pandemic on the scientific communication landscape

The effect of dapagliflozin on glycaemic control and other cardiovascular disease risk factors in type 2 diabetes mellitus:a real-world observational study

Aims/hypothesis: Dapagliflozin, a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is indicated for improving glycaemic control in type 2 diabetes mellitus. Whether its effects on HbA1c and other variables, including safety outcomes, in clinical trials are obtained in real-world practice needs to be established. Methods: We used data from the comprehensive national diabetes register, the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database, available from 2004 to mid-2016. Data within this database were linked to mortality data from the General Registrar, available from the Information Services Division (ISD) of the National Health Service in Scotland. We calculated crude within-person differences between pre- and post-drug-initiation values of HbA1c, BMI, body weight, systolic blood pressure (SBP) and eGFR. We used mixed-effects regression models to adjust for within-person time trajectories in these measures. For completeness, we evaluated safety outcomes, cardiovascular disease events, lower-limb amputation and diabetic ketoacidosis, focusing on cumulative exposure effects, using Cox proportional hazard models, though power to detect such effects was limited. Results: Among 8566 people exposed to dapagliflozin over a median of 210 days the crude within-person change in HbA1c was −10.41 mmol/mol (−0.95%) after 3 months’ exposure. The crude change after 12 months was −12.99 mmol/mol (−1.19%) but considering the expected rise over time in HbA1c gave a dapagliflozin-exposure-effect estimate of −15.14 mmol/mol (95% CI −15.87, −14.41) (−1.39% [95% CI −1.45, −1.32]) at 12 months that was maintained thereafter. A drop in SBP of −4.32 mmHg (95% CI −4.84, −3.79) on exposure within the first 3 months was also maintained thereafter. Reductions in BMI and body weight stabilised by 6 months at −0.82 kg/m2 (95% CI −0.87, −0.77) and −2.20 kg (95% CI −2.34, −2.06) and were maintained thereafter. eGFR declined initially by −1.81 ml min−1 [1.73 m]−2 (95% CI −2.10, −1.52) at 3 months but varied thereafter. There were no significant effects of cumulative drug exposure on safety outcomes. Conclusions/interpretation: Dapagliflozin exposure was associated with reductions in HbA1c, SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA1c and SBP over the period of study

Data proliferation, reconciliation, and synthesis in viral ecology

The fields of viral ecology and evolution have rapidly expanded in the last two decades, driven by technological improvements, and motivated by efforts to discover potentially zoonotic wildlife viruses under the rubric of pandemic prevention. One consequence has been a massive proliferation of host-virus association data, which comprise the backbone of research in viral macroecology and zoonotic risk prediction. These data remain fragmented across numerous data portals and projects, each with their own scope, structure, and reporting standards. Here, we propose that synthesis of host-virus association data is a central challenge to improve our understanding of the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open reconciled mammal-virus database from four key published datasets, applying a standardized taxonomy and metadata. We show that reconciling these datasets provides a substantially richer view of the mammal virome than that offered by any one individual database. We argue for a shift in best practice towards the incremental development and use of synthetic datasets in viral ecology research, both to improve comparability and replicability across studies, and to facilitate future efforts to use machine learning to predict the structure and dynamics of the global virome

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in ∼25% of patients (‘chr21amp’). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A, a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo, and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target

The International Virus Bioinformatics Meeting 2023

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24&ndash;26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting