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Crawford, J.

Publications and source records attributed to Crawford, J..

At least 37 records · Page 2Linked to original sources

Identification of efflux substrates using a riboswitch-based reporter in Pseudomonas aeruginosa

Pseudomonas aeruginosa is intrinsically resistant to many classes of antibiotics, reflecting the restrictive nature of its outer membrane and the action of its numerous efflux systems. However, the dynamics of compound uptake, retention and efflux in this bacterium remain incompletely understood. Here, we exploited the sensor capabilities of a Z-nucleotide sensing riboswitch to create an experimental system able to identify physicochemical and structural properties of compounds that permeate the bacterial cell, avoid efflux, and perturb the folate cycle or de novo purine synthesis. In a first step, a collection of structurally diverse compounds enriched in antifolate drugs was screened for ZTP riboswitch reporter activity in efflux-deficient P. aeruginosa, allowing us to identify compounds that entered the cell and disrupted the folate pathway. These initial hits were then rescreened using isogenic efflux-proficient bacteria, allowing us to separate efflux substrates from efflux avoiders. We confirmed this categorization by measuring intracellular levels of select compounds in the efflux-deficient and - proficient strain using high resolution LC-MS. This simple yet powerful method, optimized for high throughput screening, enables the discovery of numerous permeable compounds that avoid efflux and paves the way for further refinement of the physicochemical and structural rules governing efflux in this multi-drug resistant Gram-negative pathogen. ImportanceTreatment of Pseudomonas aeruginosa infections has become increasingly challenging. The development of novel antibiotics against this multi-drug resistant bacterium is a priority, but many drug candidates never achieve effective concentrations in the bacterial cell due due to its highly restrictive outer membrane and the action of multiple efflux pumps. Here, we develop a robust and simple reporter system in P. aeruginosa to screen chemical libraries and identify compounds that either enter the cell and remain inside, or enter the cell and are exported by efflux systems. This approach enables developing rules of compound uptake and retention in P. aeruginosa that will lead to more rational design of novel antibiotics.

microbiology↗

Spatial transcriptomic analysis of Sonic Hedgehog Medulloblastoma identifies that the loss of heterogeneity and promotion of differentiation underlies the response to CDK4/6 inhibition

BackgroundMedulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. MethodsHere, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. ResultsWe distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. ConclusionsOur data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

genomics↗

Benchmarking robust spatial transcriptomics approaches to capture the molecular landscape and pathological architecture of archived cancer tissues

AbtractsApplying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient samples were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.

pathology↗

Gestational stress decreases postpartum mitochondrial respiration in the prefrontal cortex of female rats

Postpartum depression (PPD) is a major psychiatric complication of childbirth, affecting up to 20% of mothers, yet remains understudied. Mitochondria, dynamic organelles crucial for cell homeostasis and energy production, share links with many of the proposed mechanisms underlying PPD pathology. Brain mitochondrial function is affected by stress, a major risk factor for development of PPD, and is linked to anxiety-like and social behaviors. Considering the importance of mitochondria in regulating brain function and behavior, we hypothesized that mitochondrial dysfunction is associated with behavioral alterations in a chronic stress-induced rat model of PPD. Using a validated and translationally relevant chronic mild unpredictable stress paradigm during late gestation, we induced PPD-relevant behaviors in adult postpartum Wistar rats. In the mid-postpartum, we measured mitochondrial function in the prefrontal cortex (PFC) and nucleus accumbens (NAc) using high-resolution respirometry. We then measured protein expression of mitochondrial complex proteins and 4-hydroxynonenal (a marker of oxidative stress), and Th1/Th2 cytokine levels in PFC and plasma. We report novel findings that gestational stress decreased mitochondrial function in the PFC, but not the NAc of postpartum dams. However, in groups controlling for the effects of either stress or parity alone, no differences in mitochondrial respiration measured in either brain regions were observed compared to nulliparous controls. This decrease in PFC mitochondrial function in stressed dams was accompanied by negative behavioral consequences in the postpartum, complex-I specific deficits in protein expression, and increased Tumor Necrosis Factor alpha cytokine levels in plasma and PFC. Overall, we report an association between PFC mitochondrial respiration, PPD-relevant behaviors, and inflammation following gestational stress, highlighting a potential role for mitochondrial function in postpartum health.

neuroscience↗

The Effects of Nonlinear Signal on Expression-Based Prediction Performance

Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will better match complex biological systems. The second, imagining that complex systems will still be well predicted by simple dividing lines prefers linear models that are easier to interpret. We compare multi-layer neural networks and logistic regression across multiple prediction tasks on GTEx and Recount3 datasets and find evidence in favor of both possibilities. We verified the presence of non-linear signal for transcriptomic prediction tasks by removing the predictive linear signal with Limma, and showed the removal ablated the performance of linear methods but not non-linear ones. However, we also found that the presence of non-linear signal was not necessarily sufficient for neural networks to outperform logistic regression. Our results demonstrate that while multi-layer neural networks may be useful for making predictions from gene expression data, including a linear baseline model is critical because while biological systems are highdimensional, effective dividing lines for predictive models may not be.

bioinformatics↗

A general framework for the combined morphometric, transcriptomic, and physiological analysis of cells using metric geometry

High-resolution imaging-based single-cell profiling has transformed the study of cells in their spatial context. However, the lack of quantitative methods that can summarize the great diversity of complex cell shapes found in tissues and infer associations with other single-cell data modalities limits current analyses. Here, we report a general computational framework for the multi-modal analysis and integration of single-cell morphological data. We build upon metric geometry to construct cell morphology latent spaces, where distances in these spaces indicate the amount of physical deformation needed to change the morphology of one cell into that of another. Using these spaces, we integrate morphological data across technologies and leverage associated single-cell RNA-seq data to infer relations between morphological and transcriptomic cellular processes. We apply this framework to imaging and multi-modal data of neurons and glia to uncover genes related to neuronal plasticity. Our approach represents a strategy for incorporating cell morphological data into single-cell omics analyses.

bioinformatics↗

wenda_gpu: fast domain adaptation for genomic data

MotivationDomain adaptation allows for development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets. ResultsWe developed wenda_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net. Availabilitywenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/. Contactcasey.s.greene@cuanschutz.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Prediction of cancer mutation states using multiple data modalities reveals the utility and consistency of gene expression and DNA methylation

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal may be unclear. In this study, we consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem. Since functional signatures of cancer mutation have been identified across many data types, this problem presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. The TCGA Pan-Cancer Atlas contains genetic alteration data including somatic mutations and copy number variants (CNVs), as well as several -omics data types. From TCGA, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. Surprisingly, we found that for many of the genes we considered, one or more other data types are approximately equally effective predictors. Performance was more variable between mutations than it was between data types for the same mutation, and there was often little difference between the top data types. We also found that combining data types into a single multi-omics model provided little or no improvement in predictive ability over the best individual data type. Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

bioinformatics↗

Generative neural networks separate common and specific transcriptional responses

Genome-wide transcriptome profiling identifies genes that are prone to differential expression across contexts ("common DEGs"), as well as genes with changes specific to the experimental manipulation. Distinguishing common DEGs from those that are specifically changed in a context of interest allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, commonly differentially expressed genes or pathways can only be identified through the laborious manual curation of highly controlled experiments, an inordinately time-consuming and impractical endeavor. Here we pioneer an approach for identifying common patterns using generative neural networks. This approach produces a background set of transcriptomic experiments from which a null distribution of gene and pathway changes can be generated. By comparing the set of differentially expressed genes found in a target experiment against the generated background set, common results can be easily separated from specific ones. This "Specific cOntext Pattern Highlighting In Expression data" (SOPHIE) approach is broadly applicable to new platforms or any species with a large collection of gene expression data. We apply SOPHIE to diverse datasets including those from human, human cancer, and the bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further, molecular validation indicates that SOPHIE detects highly specific, but low magnitude, biologically relevant, transcriptional changes. SOPHIEs measure of specificity can complement log fold change values generated from traditional differential expression analyses. For example, by filtering the set of differentially expressed genes, one can identify those genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions.

bioinformatics↗

Face processing of social cognition in patients with first episode psychosis: Its deficits and association with the right subcallosal anterior cingulate cortex

The clinical importance of social cognition is well acknowledged in patients with psychosis, in particular those with first episode psychosis (FEP). Nevertheless, its brain substrates and circuitries remain elusive, lacking precise analysis between multimodal brain characteristics and behavioral sub-dimensions within social cognition. In the present study, we examined face processing of social cognition in 79 FEP patients and 80 healthy controls (HCs). We looked for a possible correlation between face processing and multimodal MRI characteristics such as resting-state functional connectivity (rsFC) and brain volume. We observed worse recognition accuracy, longer recognition response time, and longer memory response time in FEP patients when compared with HCs. Of these, memory response time was selectively correlated with specific rsFCs, which included the right subcallosal sub-region of BA24 in the ACC (scACC), only in FEP patients. The volume of this region was also correlated with memory response time in FEP patients. The scACC is functionally and structurally important in FEP-associated abnormalities of face processing measures in social cognition.

neuroscience↗

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

molecular biology↗

Latrophilins are essential for endothelial junctional fluid shear stress mechanotransduction

Endothelial cell (EC) responses to fluid shear stress (FSS) are crucial for vascular development, adult physiology and disease. PECAM1 is an important transducer but earlier events remain poorly understood. We therefore investigated heterotrimeric G proteins in FSS sensing. Knockdown (KD) in ECs of single G proteins had little effect but combined depletion of Gi and Gq/11 blocked all known PECAM1-dependent responses. Re-expression of Gi2 and Gq but not Gi1 and Gi3 rescued these effects. Sequence alignment and mutational studies identified that K307 in Gi2 and Gq/11 (Q306 in Gi1/3), determines participation in flow signaling. We developed pull-down assays for measuring G activation and found that this residue, localized to the GPCR interface, determines activation by FSS. We developed a protocol for affinity purification of GPCRs on activated Gs, which identified latrophilins (ADGRLs) as specific upstream interactors for Gi2 and Gq/11. Depletion of latrophilin-2 blocked EC activation of Gi2 and Gq, downstream events in vitro, and flow-dependent vascular morphogenesis in zebrafish embryos. Surprisingly, latrophilin-2 depletion also blocked flow activation of two additional pathways activated at cell-cell junctions, Smad1/5 and Notch1, independently of G proteins. Latrophilins are thus central mediators of junctional shear stress mechanotransduction via G protein-dependent and -independent mechanisms.

molecular biology↗

Graph biased feature selection of genes is better than random for many genes

Recent work suggests that gene expression dependencies can be predicted almost as well by using random networks as by using experimentally derived interaction networks. We hypothesize that this effect is highly variable across genes, as useful and robust experimental evidence exists for some genes but not others. To explore this variation, we take the k-core decomposition of the STRING network, and compare it to a degree-matched random model. We show that when low-degree nodes are removed, expression dependencies in the remaining genes can be predicted better by the resulting network than by the random model.

bioinformatics↗

Heterozygous variants in the mechanosensitive ion channel TMEM63A result in transient hypomyelination during infancy

Mechanically activated (MA) ion channels convert physical forces into electrical signals. Despite the importance of this function, the involvement of mechanosensitive ion channels in human disease is poorly understood. Here we report heterozygous missense mutations in the gene encoding the MA ion channel TMEM63A that result in an infantile disorder resembling a hypomyelinating leukodystrophy. Four unrelated individuals presented with congenital nystagmus, motor delay, and deficient myelination on serial scans in infancy, prompting the diagnosis of Pelizaeus-Merzbacher (like) disease. Genomic sequencing revealed all four individuals carry heterozygous missense variants in the pore-forming domain of TMEM63A. These variants were confirmed to have arisen de novo in three of the four individuals. While the physiological role of TMEM63A is incompletely understood, it is highly expressed in oligodendrocytes and it has recently been shown to be a mechanically activated (MA) ion channel. Using patch clamp electrophysiology, we demonstrated that each of the modelled variants results in strongly attenuated stretch-activated currents when expressed in naive cells. Unexpectedly, the clinical evolution of all four individuals has been surprisingly favorable, with substantial improvements in neurological signs and developmental progression. In the three individuals with follow-up scans after four years of age, the myelin deficit had almost completely resolved. Our results suggest a previously unappreciated role for mechanosensitive ion channels in myelin development.

genomics↗

DreamDMI: a tool for Disease Module Identification in molecular networks

SummaryWe define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful bio-markers. To this end, we launched the \"Disease Module Identification (DMI) DREAM Challenge\", a community effort to build and evaluate unsupervised molecular network modularisation algorithms (Choobdar et al., 2018). Here we present MONET, a toolbox providing easy and unified access to the three top methods from the DMI DREAM Challenge for the bioinformatics community.\n\nAvailability and ImplementationMONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git\n\nContactmattia.tomasoni@unil.ch (MT); sven.bergmann@unil.ch (SB)

bioinformatics↗

Neuronal histone methyltransferase EZH2 regulates neuronal morphogenesis, synaptic plasticity, and cognitive behavior of mice

Recent studies showed that in the nervous system histone methyltransferase EZH2-mediated trimethylation of histone H3 lysine 27 (H3K27me3) acts to regulate neural stem cell proliferation and fate specificity through silencing different gene sets. Here we explored the function of EZH2 in early post-mitotic excitatory neurons by generating a neuronal specific Ezh2 conditional knockout mouse line. The results showed that lack of neuronal EZH2 led to delayed neuronal migration, more complex dendritic arborization, and significantly increased dendritic spine density. RNA-sequencing (RNA-seq) experiments comparing control and Ezh2 knockout neurons revealed that neuronal EZH2 regulated genes related to neuronal morphogenesis. In particular, Pak3 was identified as a target gene suppressed by EZH2 and H3K27me3, and expression of dominant negative PAK3 reversed Ezh2 knockout-induced higher dendritic spine density. Lastly, lack of neuronal EZH2 resulted in impaired memory behaviors in adult mice. Our results demonstrated that neuronal EZH2 played important roles in controlling multiple steps of neuronal morphogenesis during development, which had long-lasting effects on cognitive function in adult mice.

neuroscience↗

Innate Immune Priming by cGAS as a Preparatory Countermeasure Against RNA Virus Infection

The detection of nucleic acids by pattern recognition receptors is an ancient and conserved component of the innate immune system. Notably, RNA virus genomes are sensed by mammalian cytosolic RIG-I-like receptors, thereby activating interferon-stimulated gene (ISG) expression to restrict viral replication. However, recent evidence indicates that the cGAS-STING DNA sensing pathway also protects against RNA viruses. So far, the mechanisms responsible for DNA sensing of RNA viruses, which replicate without known DNA intermediates, remain unclear. By using cGAS gene knockout and reconstitution in human and mouse cell cultures, we discovered that DNA sensing and cGAMP synthase activities are required for cGAS-mediated restriction of vesicular stomatitis virus and Sindbis virus. The level of cGAMP produced in response to RNA virus infection was below the threshold of detection, suggesting that only transient and/or low levels of cGAMP are produced during RNA virus infections. To clarify the DNA ligands that activate cGAS activity, we confirmed that cGAS binds mitochondrial DNA in the cytosol of both uninfected and infected cells; however, the amount of cGAS-associated mitochondrial DNA did not change in response to virus infection. Rather, a variety of pre-existing cytosolic DNAs, including mitochondrial DNA and endogenous cDNAs, may serve as stimuli for basal cGAS activation. Importantly, cGAS knockout and reconstitution experiments demonstrated that cGAS drives low-level ISG expression at steady state. We propose that cGAS-STING restricts RNA viruses by promoting a preparatory immune activation state within cells, likely primed by endogenous cellular DNA ligands.\n\nImportanceMany medically important RNA viruses are restricted by the cGAS-STING DNA-sensing pathway of innate immune activation. Since these viruses replicate without DNA intermediates, it is unclear what DNA ligand(s) are responsible for triggering this pathway. We show here that cGASs DNA binding and signaling activities are required for RNA virus restriction, similar to the mechanisms by which it restricts DNA viruses. Furthermore, we confirmed that cGAS continuously binds host DNA, which was unaffected by RNA virus infection. Finally, cGAS expression correlated with the low-level expression of interferon-stimulated genes in uninfected cells, both in vitro and in vivo. We propose that cGAS-mediated sensing of endogenous DNA ligands contributes to RNA virus restriction by establishing a baseline of innate immune activation.

immunology↗

Genome-Wide Associations with Resistance to Bipolaris Leaf Spot (Bipolaris oryzae (Breda de Haan) Shoemaker) in a Northern Switchgrass Population (Panicum virgatum)

Switchgrass (Panicum virgatum L.), a northern native perennial grass, suffers from yield reduction from Bipolaris leaf spot caused by Bipolaris oryzae (Breda de Haan) Shoe-maker. This study aimed for determining the resistant populations via multiple phenotyping approaches and identifying potential resistance genes to the disease from genome-wide association studies in the switchgrass northern association panel. The disease resistance was evaluated from both natural (field evaluations in NY and PA) and artificial inoculations (detached leaf and leaf disk assays). There are ten out of the 66 populations showed the most resistant based on a combination of detached leaf, leaf disk, and mean from two locations. The GWAS from five subgroups from the association panel to different disease evaluation combinations yielded 27 significant SNPs on 12 chromosomes: 1K, 2K, 2N, 3K, 3N, 4N, 5K, 5N, 6N, 7K, 7N, and 9N accumulatively explaining phenotypic variance of BLUPs of detached leaf percent lesion via image analysis 26.52% at most and BLUPs of leaf disk percent lesion via image analysis 3.28% at least. Within linkage disequilibrium of 20 kb, these SNP markers linked with the potential resistance genes including genes encoding for NBS-LRR, PPR, cell wall related proteins, homeostatic proteins, anti-apoptotic proteins, and ABC transporters.

bioinformatics↗