Biology subjects

Taylor, N. P.

Publications and source records attributed to Taylor, N. P..

4 recordsLinked to original sources

bioRxiv2022

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete - i.e. resistant strains are unaffected by the chemical and resistance requires only a single genetic change - using the lowest possible dose ensuring sufficient control is well-known as the optimal resistance management strategy. However, partial resistance (where resistant strains are still partially suppressed by the fungicide) and quantitative resistance (where a range of resistant strains are present) remain ill-understood. Here we use a model of quantitative fungicide resistance (parameterised for the economically-important fungal pathogen Zymoseptoria tritici) which handles qualitative partial resistance as a special case. We show that - for both qualitative partial resistance and quantitative resistance - although low doses are optimal for resistance management, for some model parameterisations the benefit does not outweigh the improvement in control from increasing doses. Via a machine learning approach (a gradient-boosted trees model combined with Shapley values to facilitate interpretability) we interpret the effect of parameters controlling pathogen mutation and characterising the fungicide, in addition to the timescale of interest.

evolutionary biology↗

bioRxiv2022

Single-cell analysis of non-alcoholic fatty livers identifies a role for the constitutive androstane receptor

Non-alcoholic fatty liver disease is a heterogeneous disease with unclear underlying molecular mechanisms. While several genetic risk factors have been identified, the cellular and molecular heterogeneity associated with the development of hepatic steatosis are still not fully understood. Here, we perform single-cell RNA sequencing of hepatocytes and hepatic nonparenchymal cells to map the lipid signatures in mice with non-alcoholic fatty liver disease (NAFLD). We uncover previously unidentified clusters of hepatocytes characterized by either high or low srebp1 expression with unique molecular signatures of lipid synthesis. We find that NAFLD livers have elevated expression of the constitutive androstane receptor (CAR), a gene previously associated with lipid synthesis. Furthermore, nuclear expression of CAR positively correlates with steatohepatitis in humans. These findings demonstrate significant cellular differences in lipid signatures and identify a gene with a high likelihood of being linked to hepatic steatosis in humans.

cell biology↗

bioRxiv2021

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vectors own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding - as well as potential effects of infection on vector population density - on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics. AUTHOR SUMMARYPlant virus diseases - which cause devastating epidemics in plant populations worldwide - are most often transmitted by insect vectors. Recent experimental evidence indicates how vectors do not choose between plants at random, but instead can be affected by whether plants are infected (or not). Virus infection can cause plants to "smell" different, because they produce different combinations of volatile chemicals, or "taste" different, due to chemical changes in infected tissues. Vector reproduction rates can also be affected when colonising infected versus uninfected plants. Potential effects on epidemic spread through a population of plants are not yet entirely understood. There are also interactions with the mode of virus transmission. Some viruses can be transmitted after only a brief probe by a vector, whereas others are only picked up after an extended feed on an infected plant. Furthermore there are differences in how long vectors remain able to transmit the virus. This ranges from a matter of minutes, right up to the entire lifetime of the insect, depending on the plant-virus-vector combination under consideration. Here we use mathematical modelling to synthesise all this complexity into a coherent theoretical framework. We illustrate our model via an online interface https://plantdiseasevectorpreference.herokuapp.com/.

ecology↗

bioRxiv2020

Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system

Delimiting species boundaries is a major goal in evolutionary biology. An increasing body of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and demographic fluctuations. New methods based on model selection, such as approximate Bayesian computation, approximate likelihoods, and machine learning are promising tools arising in this field. Here, we introduce a framework for species delimitation using the multispecies coalescent model coupled with a deep learning algorithm based on convolutional neural networks (CNNs). We compared this strategy with a similar ABC approach. We applied both methods to test species boundary hypotheses based on current and previous taxonomic delimitations as well as genetic data (sequences from 41 loci) in Pilosocereus aurisetus, a cactus species complex with a sky-island distribution and taxonomic uncertainty. To validate our method, we also applied the same strategy on data from widely accepted species from the genus Drosophila. The results show that our CNN approach has high capacity to distinguish among the simulated species delimitation scenarios, with higher accuracy than ABC. For the cactus dataset, a splitter hypothesis without gene flow showed the highest probability in both CNN and ABC approaches, a result agreeing with previous taxonomic classifications and in line with the sky-island distribution and low dispersal features of P. aurisetus. Our results highlight the cryptic diversity within the P. aurisetus complex and show that CNNs are a promising approach for distinguishing complex evolutionary histories, even outperforming the accuracy of other model-based approaches such as ABC.

evolutionary biology↗