Biology subjects

Johnson, G.

Publications and source records attributed to Johnson, G..

32 records · Page 2Linked to original sources

bioRxiv2021

Drosophila nicotinic acetylcholine receptor subunits and their native interactions with insecticidal peptide toxins

Drosophila nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that represent a target for insecticides. Peptide neurotoxins are known to block nAChRs by binding to their target subunits, however, a better understanding of receptor subunit composition is needed for effective design of insecticides. To facilitate the analysis of nAChRs we used a CRISPR/Cas9 strategy to generate null alleles for all ten nAChR subunit genes in a common genetic background. We studied interactions of nAChR subunits with peptide neurotoxins by larval injections and styrene maleic acid lipid particles (SMALPs) pull-down assays. For the null alleles we determined the effects of -Bungarotoxin (-Btx) and {omega}-Hexatoxin-Hv1a (Hv1a) administration, identifying potential receptor subunits implicated in the binding of these toxins. We employed pull-down assays to confirm -Btx interactions with the D5, D6, D7 subunits. Finally, we report the localization of fluorescent tagged endogenous D6 during nervous system development. Taken together this study elucidates native Drosophila nAChR subunit interactions with insecticidal peptide toxins and provides a resource for the in vivo analysis of insect nAChRs.

neuroscience↗

bioRxiv2021

A deep generative model of 3D single-cell organization

1 AbstractWe introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional {beta}-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs. 2 Author summaryIts impossible to acquire all the information we want about every cell were interested in in a single experiment. Even just limiting ourselves to imaging, we can only image a small set of subcellular structures in each cell. If we are interested in integrating those images into a holistic picture of cellular organization directly from data, there are a number of approaches one might take. Here, we leverage the fact that of the three channels we image in each cell, two stay the same across the data set; these two channels assess the cells shape and nuclear morphology. Given these two reference channels, we learn a model of cell and nuclear morphology, and then use this as a reference frame in which to learn a representation of the localization of each subcellular structure as measured by the third channel. We use {beta}-variational autoencoders to learn representations of both the reference channels and representations of each subcellular structure (conditioned on the reference channels of the cell in which it was imaged). Since these models are both probabilistic and generative, we can use them to understand the variation in the data from which they were trained, to generate instantiations of new cell morphologies, and to generate imputations of structures in real cell images to create an integrated model of subcellular organization.

cell biology↗

bioRxiv2021

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 - 5.0 {micro}g/mL) and/or carbendazim (0.8 - 1.6 {micro}g/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for mononucleates, binucleates, mononucleates with MN and binucleates with MN, respectively. Successful classifications of trinucleates (90%) and tetranucleates (88%) in addition to other or unscorable phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful ([≤] 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

pharmacology and toxicology↗

bioRxiv2020

Functional analysis of caspase cleavable proteoforms from the Drosophila GSK-3 gene shaggy.

The Drosophila shaggy (sgg) gene encodes the major fly orthologue of Glycogen Synthase Kinase -3 (GSK-3), a key highly conserved kinase at the heart of many signalling pathways. The sgg locus is complex, encoding multiple protein isoforms that are expressed in distinct temporal and tissue-specific patterns across development. Its isoforms predominantly differ at the carboxy and amino termini due to the use of different transcriptional start sites and alternative splicing events that include internal and terminal exons. One interesting class of proteins isoforms is represented by the Sgg-PD class (Sgg46), three proteoforms that contain a large 582 amino acid N-terminal domain which contains recognition sites for caspase-mediated cleavage. Regulated cleavage at these sites by non-apoptotic caspases has previously been implicated in the regulation of Sgg activity in adult bristle development. Here, we take a genome engineering approach to introduce specific tags into this unique Sgg-PD exon and utilise these for localisation and protein interaction studies. We also generated new loss of function alleles and specific mutations in the caspase cleavage motifs. We find that loss of functions Sgg-PD class alleles are viable and fertile, but exhibit adult locomotor and bristle defects. Expression analysis of lines carrying tags on both sides of the caspase cleavage sites indicates that the cleavage is developmentally regulated during embryogenesis. Surprisingly, we found that in some cells, particularly embryonic hemocytes, the N-terminal domain released by caspase cleavage is retained while the polypeptide containing the conserved kinase domain is apparently lost. Transcriptomic analysis of embryos homozygous for the new caspase-insensitive allele indicates a role for Sgg-PD in the regulation of cytoskeletal and cell junction functions, which is supported by proteomics analysis using specific in locus tags to identify common and unique protein interaction partners with N- and C-terminal domains. Taken together, our work identifies new activities for the Sgg protein and uncovers unexpected roles for caspase cleavage in Sgg biology.

developmental biology↗

bioRxiv2020

Breast cancer endocrine therapy exhausts adipocyte progenitors promoting weight gain and glucose intolerance

Breast cancer survivors treated with anti-estrogen therapies report weight gain and have an elevated risk of type 2 diabetes. Here, we show that current tamoxifen use associated with larger breast adipocyte diameter only in women with a BMI >30 kg/m2. To understand the mechanisms behind these clinical findings, we investigated the impact of estrogen deprivation and tamoxifen in a relevant pre-clinical model of obesity. Specifically, mature female mice were housed at thermoneutrality and fed either a low-fat/low-sucrose (LFLS) or a high-fat/high-sucrose (HFHS) diet. Consistent with the high expression of Esr1 observed in single-cell RNA sequencing of mesenchymal stem cells from adipose tissue, endocrine therapies induced adipose accumulation and preadipocyte expansion, but resulted in adipocyte progenitor depletion only in the context of HFHS. Consequently, 7-week endocrine therapy supported adipocyte hypertrophy and was associated with hepatic steatosis, hyperinsulinemia, insulin resistance, and glucose intolerance, particularly in HFHS fed females. Metformin or pioglitazone, glucose lowering drugs used to treat diabetes, prevented the effects of tamoxifen but not estrogen deprivation on adipocyte size and insulin resistance in HFHS-fed mice. This translational study suggests that endocrine therapies act via ER to directly disrupt adipocyte progenitors and support adipocyte hypertrophy, leading to ectopic lipid deposition that may promote hyperinsulinemia, insulin resistance and type 2 diabetes. Interventions that target insulin action should be considered for some women receiving life-saving endocrine therapies for breast cancer.

physiology↗

bioRxiv2020

Alzheimer's disease-relevant tau modifications selectively impact neurodegeneration and mitophagy in a novel C. elegans single-copy transgenic model

BackgroundA defining pathological hallmark of the progressive neurodegenerative disorder Alzheimers disease (AD) is the accumulation of misfolded tau with abnormal post-translational modifications (PTMs). These include phosphorylation at Threonine 231 (T231) and acetylation at Lysine 274 (K274) and at Lysine 281 (K281). Although tau is recognized to play a central role in pathogenesis of AD, the precise mechanisms by which these abnormal PTMs contribute to the neural toxicity of tau is unclear. MethodsHuman 0N4R tau (wild type) was expressed in touch receptor neurons of the genetic model organism C. elegans through single-copy gene insertion. Defined mutations were then introduced into the single-copy tau transgene through CRISPR-Cas9 genome editing. These mutations included T231E and T231A, to mimic phosphorylation and phospho-ablation of a commonly observed pathological epitope, respectively, and K274/281Q, to mimic disease-associated lysine acetylation. Stereotypical touch response assays were used to assess behavioral defects in the transgenic strains as a function of age, and genetically-encoded fluorescent biosensors were used to measure the morphological dynamics and turnover of touch neuron mitochondria. ResultsUnlike existing tau overexpression models, C. elegans single-copy expression of tau did not elicit overt pathological phenotypes at baseline. However, strains expressing disease associated PTM-mimetics (T231E and K274/281Q) exhibited reduced touch sensation and morphological abnormalities that increased with age. In addition, the PTM-mimetic mutants lacked the ability to engage mitophagy in response to mitochondrial stress. ConclusionsLimiting the expression of tau results in a genetic model where pathological modifications and age result in evolving phenotypes, which may more closely resemble the normal progression of AD. The finding that disease-associated PTMs suppress compensatory responses to mitochondrial stress provides a new perspective into the pathogenic mechanisms underlying AD.

neuroscience↗

bioRxiv2020

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

Pre-mRNA splicing is a key control point in human gene expression. Disturbances in splicing due to mutation or aberrant splicing regulatory networks lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of active and selective splicing modulator compounds have been recently identified, thus proving that pre-mRNA splicing is a viable target for therapy. We describe herein the identification of BPN-15477, a novel splicing modulator compound, that restores correct splicing of exon 20 in the Elongator complex protein 1 (ELP1) gene carrying the major IVS20+6T>C mutation responsible for familial dysautonomia. We then developed a machine learning approach to evaluate the therapeutic potential of BPN-15477 to correct splicing in other human genetic diseases. Using transcriptome sequencing from compound-treated fibroblast cells, we identified treatment responsive sequence signatures, the majority of which center at the 5 splice site of exons whose inclusion or exclusion is modulated by SMC treatment. We then leveraged this model to identify 155 human disease genes that harbor ClinVar mutations predicted to alter pre-mRNA splicing as potential targets for BPN-15477 treatment. Using in vitro splicing assays, we validated representative predictions by demonstrating successful correction of splicing defects caused by mutations in genes responsible for cystic fibrosis (CFTR), cholesterol ester storage disease (LIPA), Lynch syndrome (MLH1) and familial frontotemporal dementia (MAPT). Our study shows that deep learning techniques can identify a complex set of sequence signatures and predict response to pharmacological modulation, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate splicing.

bioinformatics↗

bioRxiv2019

Characterisation of protein isoforms encoded by the Drosophila Glycogen Synthase Kinase 3 gene shaggy

The Drosophila shaggy gene (sgg, GSK-3) encodes multiple protein isoforms with serine/threonine kinase activity and is a key player in diverse developmental signalling pathways. Currently it is unclear whether different Sgg proteoforms are similarly involved in signalling or if different proteoforms have distinct functions. We used CRISPR/Cas9 genome engineering to tag eight different Sgg proteoform classes and determined their localization during embryonic development. We performed proteomic analysis of the two major proteoform classes and generated mutant lines for both of these for transcriptomic and phenotypic analysis. We uncovered distinct tissue-specific localization patterns for all of the tagged proteoforms we examined, most of which have not previously been characterised directly at the protein level, including one proteoform initiating with a non-standard codon. Collectively this suggests complex developmentally regulated splicing of the sgg primary transcript. Further, affinity purification followed by mass spectrometric analyses indicate a different repertoire of interacting proteins for the two major proteoform classes we examined, one with ubiquitous expression (Sgg-PB) and one with nervous system specific expression (Sgg-PA). Specific mutation of these proteoforms shows that Sgg-PB performs the well characterised maternal and zygotic segmentations functions of the sgg locus, while Sgg-PA mutants show adult lifespan and locomotor defects consistent with its nervous system localisation. Our findings provide new insights into the role of GSK-3 proteoforms and intriguing links with the GSK-3 and GSK-3{beta} encoded by independent vertebrate genes. Our analysis suggests that different protein isoforms generated by alternative splicing perform distinct functions.

genetics↗

bioRxiv2019

Prefrontal high-gamma in ECoG tags periodicity of musical rhythms in perception and imagination

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope-tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners, to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACC) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelation of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms structure as opposed to mere auditory phenomena. The ACC approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms.

neuroscience↗

bioRxiv2019

Human bone marrow mesenchymal stem/stromal cell behaviour is coordinated via mechanically activated osteocyte-derived extracellular vesicles

Osteocytes are mechanosensitive cells that are believed to play a fundamental role in coordinating bone mechanoadaptation via the secretion of paracrine factors. However, the exact mechanisms by which osteocytes relay mechanical signals to effector cells is poorly understood. In this study, we demonstrated that osteocytes subjected to a physiologic fluid shear secrete a distinct collection of factors that significantly enhance human MSC recruitment and osteogenesis. Utilising proteomics we generated an extensive map of proteins within the mechanically activated osteocyte secretome, identifying numerous paracrine factors that are modified by mechanical stimulation. Moreover, we identified the presence of extracellular vesicles (EVs) and further demonstrated that these mechanically activated osteocyte derived EVs (MAEVs) coordinate human MSCs recruitment and osteogenesis. This indicates that mechanical conditioning of parent cells can modify EVs and demonstrates the pro-osteogenic potential of MAEVs as a cell-free therapy to enhance bone regeneration and repair in diseases such as osteoporosis.

physiology↗

bioRxiv2018

Dissecting the sharp response of a canonical developmental enhancer reveals multiple sources of cooperativity

Developmental enhancers integrate graded concentrations of input transcription factors (TFs) to create sharp gene expression boundaries. Here we examine the hunchback P2 (HbP2) enhancer which drives a sharp expression pattern in the Drosophila blastoderm embryo in response to the transcriptional activator Bicoid (Bcd). We systematically interrogate cis and transfactors that influence the shape and position of expression driven by HbP2, and find that the prevailing model, based on cooperative binding of Bcd to HbP2 is not adequate. We demonstrate that other proteins, such as pioneer factors, mediator and histone modifiers influence the shape and position of the HbP2 expression pattern. By comparing our results to theory, we assess how higher-order cooperativity and energy expenditure impact boundary location and sharpness. Our results emphasize that the bacterial view of transcription regulation, where pairwise interactions between regulatory proteins dominate, must be re-examined in animals, where multiple molecular mechanisms collaborate to shape the gene regulatory function.

genetics↗

bioRxiv2018

Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.

cell biology↗

bioRxiv2018

Principles of Computer Numerical Controlled Machining Applied to Cranial Microsurgery

Over the last decade, a plethora of tools have been developed for neuroscientists to interface with the brain. Implementing these tools requires precise removal of sections of the skull to access the brain. These delicate cranial microsurgical procedures need to be performed on sub-millimeter thick bone without damaging the underlying tissue and therefore, require significant training. Automating some of these procedures would not only enable more precise microsurgical operations, but also democratize use of advanced neurotechnologies. Here, we describe the Craniobot, a cranial microsurgery platform that combines automated skull surface profiling with a computer numerical controlled (CNC) milling machine to perform a variety of cranial microsurgical procedures in mice. The Craniobot utilizes a low force contact sensor to profile the skull surface and uses this information to perform micrometer-scale precise milling operations within minutes. We have used the Craniobot to drill pilot holes to anchor cranial implants, perform skull thinning, and open small to large craniotomies. The Craniobot is built using off-the-shelf components for under $1000 and is controlled using open-source CNC programming software.

bioengineering↗

bioRxiv2017

Three-dimensional actin-based protrusions of migrating neutrophils are intrinsically lamellar and facilitate direction changes

Leukocytes and other amoeboid cells change shape as they move, forming highly dynamic, actin-filled pseudopods. Although we understand much about the architecture and dynamics of thin lamellipodia made by slow-moving cells on flat surfaces, conventional light microscopy lacks the spatial and temporal resolution required to track complex pseudopods of cells moving in three dimensions. We therefore employed lattice light sheet microscopy to perform three-dimensional, time-lapse imaging of neutrophil-like HL-60 cells crawling through collagen matrices. To analyze three-dimensional pseudopods we: (i) developed fluorescent probe combinations that distinguish cortical actin from dynamic, pseudopod-forming actin networks, and (ii) adapted molecular visualization tools from structural biology to render and analyze complex cell surfaces. Surprisingly, three-dimensional pseudopods turn out to be composed of thin (<0.75 m), flat sheets that sometimes interleave to form rosettes. Their laminar nature is not templated by an external surface, but likely reflects a linear arrangement of regulatory molecules. Although we find that pseudopods are dispensable for three-dimensional locomotion, their elimination dramatically decreases the frequency of cell turning, and pseudopod dynamics increase when cells change direction, highlighting the important role pseudopods play in pathfinding.

cell biology↗