Biology subjects

Davis, C.

Publications and source records attributed to Davis, C..

44 records · Page 3Linked to original sources

bioRxiv2019

Spatiotemporal limits of optogenetic manipulations in cortical circuits

Neuronal inactivation is commonly used to assess the involvement of groups of neurons in specific brain functions. Optogenetic tools allow manipulations of genetically and spatially defined neuronal populations with excellent temporal resolution. However, the targeted neurons are coupled with other neural populations over multiple length scales. As a result, the effects of localized optogenetic manipulations are not limited to the targeted neurons, but produces spatially extended excitation and inhibition with rich dynamics. Here we benchmarked several optogenetic silencers in transgenic mice and with viral gene transduction, with the goal to inactivate excitatory neurons in small regions of neocortex. We analyzed the effects of the perturbations in vivo using electrophysiology. Channelrhodopsin activation of GABAergic neurons produced more effective photoinhibition of pyramidal neurons than direct photoinhibition using light-gated ion pumps. We made transgenic mice expressing the light-dependent chloride channel GtACR under the control of Cre-recombinase. Activation of GtACR produced the most potent photoinhibition. For all methods, localized photostimuli produced photoinhibition that extended substantially beyond the spread of light in tissue, although different methods had slightly different resolution limits (radius of inactivation, 0.5 mm to 1 mm). The spatial profile of photoinhibition was likely shaped by strong coupling between cortical neurons. Over some range of photostimulation, circuits produced the \"paradoxical effect\", where excitation of inhibitory neurons reduced activity in these neurons, together with pyramidal neurons, a signature of inhibition-stabilized neural networks. The offset of optogenetic inactivation was followed by rebound excitation in a light dose-dependent manner, which can be mitigated by slowly varying photostimuli, but at the expense of time resolution. Our data offer guidance for the design of in vivo optogenetics experiments and suggest how these experiments can reveal operating principles of neural circuits.

neuroscience↗

bioRxiv2019

The use of mixture-density networks in the emulation of complex epidemiological individual-based models

Complex, highly computational, individual-based models are abundant in epidemiology. For epidemics such as macro-parasitic diseases, detailed modelling of human behaviour and pathogen life-cycle are required in order to produce accurate results. This can often lead to models that are computationally-expensive to analyse and perform model fitting, and often require many simulation runs in order to build up sufficient statistics. Emulation can provide a more computationally-efficient output of the individual-based model, by approximating it using a statistical model. Previous work has used Gaussian processes in order to achieve this, but these can not deal with multi-modal, heavy-tailed, or discrete distributions. Here, we introduce the concept of a mixture density network (MDN) in its application in the emulation of epidemiological models. MDNs incorporate both a mixture model and a neural network to provide a flexible tool for emulating a variety of models and outputs. We develop an MDN emulation methodology and demonstrate its use on a number of simple models incorporating both normal, gamma and beta distribution outputs. We then explore its use on the stochastic SIR model to predict the final size distribution and infection dynamics. MDNs have the potential to faithfully reproduce multiple outputs of an individual-based model and allow for rapid analysis from a range of users. As such, an open-access library of the method has been released alongside this manuscript. Author summaryInfectious disease modellers have a growing need to expose their models to a variety of stakeholders in interactive, engaging ways that allow them to explore different scenarios. This approach can come with a considerable computational cost that motivates providing a simpler representation of the complex model. We propose the use of mixture density networks as a solution to this problem. These are highly flexible, deep neural network-based models that can emulate a variety of data, including counts and over-dispersion. We explore their use firstly through emulating a negative-binomial distribution, which arises in many places in ecology and parasite epidemiology. We then explore the approach using a stochastic SIR model. We also provide an accompanying Python library with code for all examples given in the manuscript. We believe that the use of emulation will provide a method to package an infectious disease model such that it can be disseminated to the widest audience possible.

epidemiology↗

bioRxiv2018

Dynamics of microRNA expression during mouse prenatal development

MicroRNAs (miRNAs) play a critical role as post-transcriptional regulators of gene expression. The ENCODE project profiled the expression of miRNAs in a comprehensive set of tissues during a time-course of mouse embryonic development and captured the expression dynamics of 785 miRNAs. We found distinct tissue and developmental stage specific miRNA expression clusters, with an overall pattern of increasing tissue specific expression as development proceeds. Comparative analysis of conserved miRNAs in mouse and human revealed stronger clustering of expression patterns by tissue types rather than by species. An analysis of messenger RNA gene expression clusters compared with miRNA expression clusters identifies the potential role of specific miRNA expression clusters in suppressing the expression of mRNAs specific to other developmental programs in the tissue where these microRNAs are expressed during embryonic development. Our results provide the most comprehensive timecourse of miRNA expression as an integrated part of the ENCODE reference dataset for mouse embryonic development.

genomics↗

bioRxiv2018

Functional clustering of dendritic activity during decision-making

The active properties of dendrites support local nonlinear operations, but previous imaging and electrophysiological measurements have produced conflicting views regarding the prevalence of local nonlinearities in vivo. We imaged calcium signals in pyramidal cell dendrites in the motor cortex of mice performing a tactile decision task. A custom microscope allowed us to image the soma and up to 300 m of contiguous dendrite at 15 Hz, while resolving individual spines. New analysis methods were used to estimate the frequency and spatial scales of activity in dendritic branches and spines. The majority of dendritic calcium transients were coincident with global events. However, task-associated calcium signals in dendrites and spines were compartmentalized by dendritic branching and clustered within branches over approximately 10 m. Diverse behavior-related signals were intermingled and distributed throughout the dendritic arbor, potentially supporting a large computational repertoire and learning capacity in individual neurons.

neuroscience↗

bioRxiv2018

Engineered Microbes to Sense and Respond to Enterotoxigenic Escherichia coli

Every year, Enterotoxigenic Escherichia coli (ETEC), the most common form of travelers diarrhea, affects thousands of military personnel deployed overseas. The goal of this research was to engineer non-pathogenic E. coli to sense ETEC, respond to its presence, and package the non-pathogenic E. coli in a cellulose matrix to enable environmental detection of ETEC. Two plasmids were created: sense-respond; and packaging. The sense-respond plasmid detected autoinducer 2 (AI-2), a quorum sensing molecule created by most ETEC strains, by expressing LsrR which switches on the Lsr promoter. Activation of the Lsr promoter expresses superfolder green fluorescent protein (sfGFP), indicating the presence of ETEC. The packaging plasmid expresses a fusion protein consisting of curli fibers and cellulose binding domains. These modified surface proteins permit the bacteria to bind to cellulose, encapsulating the sense-response module. This genetically engineered machine could be deployed in both the internal and external environment to detect ETEC.

synthetic biology↗

bioRxiv2017

Reconstructing deep-time paleoclimate legacies unveil the demise and turnover of the ancient (boreo)tropical flora

AimSince the Late Cretaceous, the Earth has gone through periods of climate change similar in scale and pace to the warming trend observed today in the Anthropocene. The impact of these ancient climatic events on the evolutionary trajectories of organisms provides clues on the organismal response to climate change, including extinction, migration or persistence. Here, we examine the evolutionary response to climate cooling/warming events of the clusioid families Calophyllaceae, Podostemaceae and Hypericaceae (CPH), and the genus Hypericum as test cases.\n\nLocationHolarctic.\n\nTime periodLate Cretaceous-Cenozoic\n\nMajor taxa studiedangiosperms\n\nMethodsWe use paleoclimate simulations, species distribution models and phylogenetic comparative approaches calibrated with fossils.\n\nResultsAncestral CPH lineages could have been distributed in the Holarctic 100 Ma, occupying tropical subhumid assemblages, a finding supported by the fossil record. Expansion to closed-canopy tropical rain forests occurred after 60 Ma, in the Cenozoic, in agreement with earlier ideas of a post-Cretaceous origin of current tropical rain forest. Cooling during this period triggered diversification declines on CPH tropical lineages, and was associated with a climatic shift towards temperate affinities in Hypericum. Hypericum subsequently migrated to tropical mountains where it encountered different temperate conditions than in the Holarctic.\n\nMain conclusionsWe hypothesize that most clusioid CPH lineages failed to adapt to temperate regimes during periods of Cenozoic climate change, and thus went extinct in the Holarctic. In contrast, boreotropical descendants including Hypericum that underwent niche evolution demonstrated selective advantages as climates became colder. Our results points toward macroevolutionary trajectories involving the altering fates of closely related clades that adapt to periods of global climate change versus those that do not. Moreover, they suggest the hypothesis that potentially many clades, particularly inhabitants of boreotropical floras, were likely extirpated from the Holarctic and persist today (if at all) in more southern tropical locations.

evolutionary biology↗

bioRxiv2017

Mineral analysis of complete dog and cat foods in the UK and compliance with European guidelines

The mineral content of complete pet food is regulated to ensure health of the companion animal population. A comprehensive analysis of adherence to these regulatory guidelines has not been conducted. We measured mineral composition of a range of complete wet (n=97) and dry (n=80) canine and feline pet food sold in the UK to assess compliance with EU guidelines. While a majority of foods complied with [≥]8 of 11 guidelines (99% and 83% for dry and wet food, respectively), many failed to provide nutritional minimum (e.g. Cu, 20 % of wet food) or exceeded nutritional maximum (e.g. Se, 76% of wet food). Only 6% (6/97) of wet and 39% (34/80) of dry food were fully compliant. Some foods (20-30% of all analysed) had mineral imbalances such as not having the recommended balance of Ca:P (between 1:1 to 2:1). Foods with high fish content had high levels of undesirable metal elements such as arsenic. The study highlights broad non-compliance of a range of popular pet foods sold in the UK with EU guidelines (95% and 61% of wet and dry foods, respectively). If fed exclusively and over an extended period, a number of these pet foods could impact the general health of companion animals.

zoology↗

bioRxiv2017

High-throughput annotation of full-length long noncoding RNAs with Capture Long-Read Sequencing (CLS)

Accurate annotations of genes and their transcripts is a foundation of genomics, but no annotation technique presently combines throughput and accuracy. As a result, reference gene collections remain incomplete: many gene models are fragmentary, while thousands more remain uncatalogued-particularly for long noncoding RNAs (lncRNAs). To accelerate lncRNA annotation, the GENCODE consortium has developed RNA Capture Long Seq (CLS), combining targeted RNA capture with third-generation long-read sequencing. We present an experimental re-annotation of the GENCODE intergenic lncRNA population in matched human and mouse tissues, resulting in novel transcript models for 3574 / 561 gene loci, respectively. CLS approximately doubles the annotated complexity of targeted loci, outperforming existing short-read techniques. Full-length transcript models produced by CLS enable us to definitively characterize the genomic features of lncRNAs, including promoter- and gene-structure, and protein-coding potential. Thus CLS removes a longstanding bottleneck of transcriptome annotation, generating manual-quality full-length transcript models at high-throughput scales.\n\nAbbreviations

genomics↗