Biology subjects

Yates, J.

Publications and source records attributed to Yates, J..

31 records · Page 2Linked to original sources

bioRxiv2022

Activity in primate visual cortex is minimally driven by spontaneous movements

Organisms process sensory information in the context of their own moving bodies, an idea referred to as embodiment. This idea is important for developmental neuroscience, and increasingly plays a role in robotics and systems neuroscience. The mechanisms that support such embodiment are unknown, but a manifestation could be the observation in mice of brain-wide neuromodulation, including in the primary visual cortex, driven by task-irrelevant spontaneous body movements. Here we tested this hypothesis in macaque monkeys, a primate model for human vision, by simultaneously recording visual cortex activity and facial and body movements. Activity in the visual cortex (V1, V2, V3/V3A) was associated with the animals own movements, but this modulation was largely explained by the impact of the movements on the retinal image. These results suggest that embodiment in primate vision may be realized by input provided by the eyes themselves.

neuroscience↗

bioRxiv2022

CanSig: Discovering de novo shared transcriptional programs in single cancer cells

Single-cell RNA sequencing (scRNA-seq) facilitates the discovery of gene signatures that define cell states across patients, which could be used in patient stratification and drug discovery. However, the lack of standardization in computational methodologies to analyse these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a gene signature correlation metric to score according to rediscovery, cross-dataset reproducibility, and clinical relevance. We applied CanSig to ten methods and to ten scRNA-seq datasets from four human cancer types--glioblastoma, breast cancer, lung adenocarcinoma, and cutaneous squamous cell carcinoma-- representing 116 patients and 105,000 malignant cells. Our results identify BBKNN as a leading method. We showed that the signatures identified with these methods correlate with clinically relevant outcomes, including patient survival and lymph node metastasis. Thus, CanSig establishes a standardized framework for reproducible cancer transcriptomics analysis.

bioinformatics↗

bioRxiv2022

scROSHI - robust supervised hierarchical identification of single cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene sets and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.

bioinformatics↗

bioRxiv2022

Comparative host-pathogen dynamics of Snake Fungal Disease in sympatric species of water snakes (Nerodia)

The ascomycete fungus Ophidiomyces ophiodiicola (Oo) is the causative agent of ophidiomycosis (Snake Fungal Disease), which has been detected globally. However, surveillance efforts in the central U.S., specifically Texas, have been minimal. The threatened and rare Brazos water snake (Nerodia harteri harteri) is one of the most range restricted snakes in the U.S. and is sympatric with two wide-ranging congeners, N. erythrogaster transversa and N. rhombifer, in north central Texas; thus, providing an opportunity to test comparative host-pathogen dynamics in this system. To accomplish this, we surveyed a portion of the Brazos river drainage (~400 river km) over 29 months and tested 150 Nerodia spp. for the presence of Oo via quantitative PCR and recorded any potential signs of Oo infection. We found Oo was distributed across the entire range of N. h. harteri, Oo prevalence was 46 % overall, and there was a significant association between Oo occurrence and signs of infection in our sample. Models indicated adults had a higher probability of Oo infection than juveniles and subadults, and adult N. h. harteri had a higher probability of infection than adult N. rhombifer but not higher than adult N. e. transversa. High Oo prevalence estimates (94.4%) in adult N. h. harteri has implications for their conservation and management owing to their patchy distribution, comparatively low genetic diversity, and threats from anthropogenic habitat modification.

ecology↗

bioRxiv2021

Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neurons decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

neuroscience↗

bioRxiv2021

A spatially resolved mechanistic growth law for cancer drug development.

Mathematical models used in pre-clinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumour volume, however, they typically have little connection with the underlying physiological processes. This lack of a mechanistic underpinning restricts their flexibility and inhibits their direct translation across studies including from animal to human. Here we present a mathematical model describing tumour growth for the evaluation of single agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumours. Importantly, we demonstrate the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for pre-clinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft (CDX) and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current pre-clinical modelling studies to also incorporating spatially resolved and multi-modal data without significant added complexity and creates the opportunity to improve translation and tumour response predictions.

pharmacology and toxicology↗

bioRxiv2020

Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes

High-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B cell receptor (BCR) and T cell receptor (TCR) repertoires from single-cell sequencing experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression, and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by increasing accessibility to the broader immunology community by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.

immunology↗

bioRxiv2020

Endpoint and Epitope-specific Antibody Responses as Correlates of Vaccine-mediated Protection of Mice against Ricin Toxin

The successful licensure of vaccines for biodefense is contingent upon the availability of well-established correlates of protection (CoP) in at least two animal species that can then be applied to humans, without the need to assess efficacy in the clinic. In this report we describe a multivariate model that combines pre-challenge serum antibody endpoint titers (EPT) and values derived from an epitope profiling immune-competition capture (EPICC) assay as a predictor in mice of vaccine-mediated immunity against ricin toxin (RT), a Category B biothreat. EPICC is a modified competition ELISA in which serum samples from vaccinated mice were assessed for their ability to inhibit the capture of soluble, biotinylated (b)-RT by a panel of immobilized monoclonal antibodies (mAbs) directed against four immunodominant toxin-neutralizing regions on the enzymatic A chain (RTA) of RT. In a test cohort of mice (n=40) vaccinated with suboptimal doses of the RTA subunit vaccine, RiVax(R), we identified two mAbs, PB10 and SyH7, which had EPICC inhibition values in pre-challenge serum samples that correlated with survival following a challenge with 10 x LD50 of RT administered by intraperitoneal (IP) injection. Analysis of a larger cohort of mice (n=645) revealed that a multivariate model combining endpoint titers and an epitope-profiling immune-competition capture (EPICC) assay values for PB10 and SyH7 as predictive variables had significantly higher statistical power than any one of the independent variables alone. Establishing the correlates of vaccine-mediated protection in mice represents an important steppingstone in the development of RiVax(R) as a medical countermeasure under the United States Food and Drug Administrations "Two Animal Rule."

immunology↗

bioRxiv2019

Targeting Cellular DNA Damage Responses: Predicting in vivo treatment responses using an in vitro-calibrated agent-based mathematical model.

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia telangiectasia mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately eight days post tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

cancer biology↗

bioRxiv2019

Unbiased identification of trans regulators of ADAR and A-to-I RNA editing

Adenosine-to-Inosine RNA editing is catalyzed by ADAR enzymes that deaminate adenosine to inosine. While many RNA editing sites are known, few trans regulators have been identified. We perform BioID followed by mass-spectrometry to identify trans regulators of ADAR1 and ADAR2 in HeLa and M17 neuroblastoma cells. We identify known and novel ADAR-interacting proteins. Using ENCODE data we validate and characterize a subset of the novel interactors as global or site-specific RNA editing regulators. Our set of novel trans regulators includes all four members of the DZF-domain-containing family of proteins: ILF3, ILF2, STRBP, and ZFR. We show that these proteins interact with each ADAR and modulate RNA editing levels. We find ILF3 is a global negative regulator of editing. This work demonstrates the broad roles RNA binding proteins play in regulating editing levels and establishes DZF-domain containing proteins as a group of highly influential RNA editing regulators.

genetics↗

bioRxiv2018

A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observers temporal bias changed as the result of changing the key stimulus statistics identified by our model. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

animal behavior and cognition↗

bioRxiv2017

Spliceosome profiling visualizes the operations of a dynamic RNP in vivo at nucleotide resolution

Tools to understand how the spliceosome functions in vivo have lagged behind advances in its structural biology. We describe methods to globally profile spliceosome-bound precursor, intermediates and products at nucleotide resolution. We apply these tools to three divergent yeast species that span 600 million years of evolution. The sensitivity of the approach enables detection of novel cases of non-canonical catalysis including interrupted, recursive and nested splicing. Employing statistical modeling to understand the quantitative relationships between RNA features and the data, we uncover independent roles for intron size, position and number in substrate progression through the two catalytic stages. These include species-specific inputs suggestive of spliceosome-transcriptome coevolution. Further investigations reveal ATP-dependent discard of numerous endogenous substrates at both the precursor and lariat-intermediate stages and connect discard to intron retention, a form of splicing regulation. Spliceosome profiling is a quantitative, generalizable global technology to investigate an RNP central to eukaryotic gene expression.\n\nHighlightsO_LIMeasurement of spliceosome-bound precursor and intermediate in three species\nC_LIO_LINon-canonical splicing events revealed\nC_LIO_LIStatistical modeling uncovers substrate features that predict catalytic efficiency\nC_LIO_LIDiscard of suboptimal substrates occurs in vivo and predicts intron-retained mRNAs\nC_LI

molecular biology↗

bioRxiv2017

Exosomes regulate Neurogenesis and Circuit Assembly in a Model of Rett Syndrome

Exosomes are thought to be secreted by all cells in the body and to be involved in intercellular communication. Here, we tested whether neural exosomes regulate the development of neural circuits and whether exosome-mediated signaling may be aberrant in the neurodevelopmental disorder Rett Syndrome (RTT). Quantitative proteomic analysis comparing exosomes from human induced pluripotent stem cells (hiPSC) - derived RTT patient or control neural cultures indicates that control exosomes contain signaling components capable of influencing neuronal development and function, which are lacking in RTT exosomes. Moreover, treatment with control exosomes rescues neuron number, apoptosis, synaptic puncta and synchronized firing phenotypes of MeCP2 knockdown in human primary neurons, indicating that exosomes have the capacity to influence neural development and may be a promising avenue to treat neurodevelopmental disorders like Rett Syndrome.

neuroscience↗