Biology subjects

Snook, E.

Publications and source records attributed to Snook, E..

6 recordsLinked to original sources

bioRxiv2024

IgSeqR: a protocol for the identification, assembly, and characterization of full-length tumor Immunoglobulin transcripts from unselected RNA sequencing data

Immunoglobulin (IG) gene analysis provides fundamental insight into B-cell receptor structure and function. In B-cell tumors, it can inform the cell of origin and clinical outcomes. Its clinical value has been established in the two types of chronic lymphocytic leukemia with unmutated or mutated IGHV genes and is emerging in other B-cell tumors. The traditional PCR-based techniques, which are labor-intensive, rely on the attainment of either a dominant sequence or a small number of subclonal sequences and do not allow automated matching with the clonal phenotypic features. Extraction of the expressed tumor IG transcripts using high-throughput RNA sequencing (RNA-seq) can be faster and allow the collection of multiple sequences matched with the transcriptome profile. Analytical tools are regularly sought to increase the accuracy, depth, and speed of acquisition of the full IGV-(IGD)-IGJ-IGC sequences and combine the IG characteristics with other RNA-seq data. We provide here a user-friendly protocol for the rapid extraction, identification, and accurate determination of the full (leader to constant region) tumor IG templated and non-templated transcript sequence from RNA-seq. The derived amino acid sequences can be interrogated for their physico-chemical characteristics and, in certain lymphomas, predict tumor glycan types occupying acquired N-glycosylation sites. These features will then be available for association studies with the tumor transcriptome. The resulting information can also help refine diagnosis, prognosis, and potential therapeutic targeting in the most common lymphomas.

bioinformatics↗

bioRxiv2022

Examining Litter Specific Variability in Mice and its Impact on Neurodevelopmental Studies

Our current understanding of litter variability in neurodevelopmental studies using mouse may limit translation of neuroscientific findings. Higher variance of measures across litters than within, often termed intra-litter likeness, may be attributable to pre- and postnatal environment. This study aimed to assess the litter-effect within behavioral assessments (2 timepoints), and anatomy using T1-weighted magnetic resonance images (4 timepoints) across 72 brain region volumes (36 C57bl/6J inbred mice; 7 litters: 19F/17M). Between-litter comparisons of brain and behavioral measures and their associations were evaluated using univariate and multivariate techniques. A power analysis using simulation methods was then performed modeling neurodevelopment and evaluating trade-offs between number-of-litters, mice-per-litter, and sample size. Our results show litter-specific developmental effects, from the adolescent period to adulthood for brain structure volumes and behaviors, and their associations in adulthood. Our power simulation analysis results suggest increasing the number-of-litters in experimental design to achieve the smallest total sample size for detecting different rates of change in specific brain regions. Our results also demonstrate how litter-specific effects may influence development and that increasing the litters to the total sample size ratio should be strongly considered when designing neurodevelopmental studies.

neuroscience↗

bioRxiv2021

Subtle alterations in neonatal neurodevelopment following early or late exposure to prenatal maternal immune activation

Prenatal exposure to maternal immune activation (MIA) is a risk factor for a variety of neurodevelopmental and psychiatric disorders. The timing of MIA-exposure has been shown to affect adolescent and adult offspring neurodevelopment, however, less is known about these effects in the neonatal period. To better understand the impact of MIA-exposure on neonatal brain development, we first assess neonate communicative abilities with the ultrasonic vocalization task, followed by high-resolution ex vivo magnetic resonance imaging (MRI) on the neonatal (postnatal day 8) brain. Early exposed offspring displayed decreased communicative ability, while brain anatomy appeared largely unaffected, apart from some subtle alterations. By integrating MRI and behavioural assays to investigate the effects of MIA-expsoure on neonatal neurodevelopment we show that offspring neuroanatomy and behaviour are only subtly affected by both early and late exposure. This suggests that the deficits often observed in later stages of life may be dormant, not yet developed in the neonatal period, or not as easily detectable using a cross-sectional approach.

neuroscience↗

bioRxiv2021

Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment

Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental and psychiatric disorders. MIA-induced deficits in adolescent and adult offspring have been well characterized, however, less is known about the effects of MIA-exposure on embryo development. To address this gap, we performed high-resolution ex vivo magnetic resonance imaging (MRI) to investigate the effects of early (gestational day [GD]9) and late (GD17) MIA-exposure on embryo (GD18) brain structure. We identify striking neuroanatomical changes in the embryo brain, particularly in the late exposed offspring. We further examined hippocampal neuroanatomy using electron microscopy and identified differential effects due to MIA-timing. An increase in apoptotic cell density was observed in the GD9 exposed offspring, while an increase in the density of dark neurons and glia, putative markers for increased neuroinflammation and oxidative stress, was observed in GD17 exposed offspring, particularly in females. Overall, our findings integrate imaging techniques across different scales to identify differential impact of MIA-timing on the earliest stages of neurodevelopment.

neuroscience↗

bioRxiv2020

Early or late gestational exposure to maternal immune activation alters neurodevelopmental trajectories in mice: an integrated neuroimaging, behavioural, and transcriptional study

Prenatal maternal immune activation (MIA) is a risk factor for neurodevelopmental disorders. How gestational timing of MIA-exposure differentially impacts downstream development remains unclear. Here, we characterize neurodevelopmental trajectories of mice exposed to MIA induced by poly I:C either early (gestational day [GD]9) or late (GD17) in gestation using longitudinal structural magnetic resonance imaging from weaning to adulthood. Early MIA-exposure associated with accelerated brain volume increases in adolescence/early-adulthood that normalized in later adulthood, in regions including the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety, stereotypic, and sensorimotor gating behaviours observed in adolescence normalized in adulthood. In contrast, MIA-exposure in late gestation had less impact on anatomical and behavioural profiles. Using a multivariate technique to relate imaging and behavioural variables for the time of greatest alteration, i.e. adolescence/early adulthood, we demonstrate that variation in anxiety, social, and sensorimotor gating associates significantly with volume of regions including the dorsal and ventral hippocampus, and anterior cingulate cortex. Using RNA sequencing to explore the molecular underpinnings of region-specific alterations in early MIA-exposed mice in adolescence, we observed the most transcriptional changes in the dorsal hippocampus, with regulated genes enriched for fibroblast growth factor regulation, autistic behaviours, inflammatory pathways, and microRNA regulation. This indicates that MIA in early gestation perturbs brain development mechanisms implicated in neurodevelopmental disorders. Our findings demonstrate the inherent strength of an integrated hypothesis- and data-driven approach in linking brain-behavioural alterations to the transcriptome to understand how MIA confers risk for major mental illness.

neuroscience↗

bioRxiv2019

Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

BackgroundDepression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability.\n\nMethodsUsing STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature selection. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population.\n\nResultsOur deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% improvement in population remission rate (p= 0.01, C.I. 2.48% {+/-} .5%).\n\nConclusionOur model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from a number of treatment options. These models may have significant real-world clinical implications.

neuroscience↗