Biology subjects

Turner, J.

Publications and source records attributed to Turner, J..

52 records · Page 3Linked to original sources

bioRxiv2021

Host-directed therapies against early-lineage SARS-CoV-2 retain efficacy against B.1.1.7 variant

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in millions of deaths worldwide and massive societal and economic burden. Recently, a new variant of SARS-CoV-2, known as B.1.1.7, was first detected in the United Kingdom and is spreading in several other countries, heightening public health concern and raising questions as to the resulting effectiveness of vaccines and therapeutic interventions. We and others previously identified host-directed therapies with antiviral efficacy against SARS-CoV-2 infection. Less prone to the development of therapy resistance, host-directed drugs represent promising therapeutic options to combat emerging viral variants as host genes possess a lower propensity to mutate compared to viral genes. Here, in the first study of the full-length B.1.1.7 variant virus, we find two host-directed drugs, plitidepsin (aplidin; inhibits translation elongation factor eEF1A) and ralimetinib (inhibits p38 MAP kinase cascade), as well as remdesivir, to possess similar antiviral activity against both the early-lineage SARS-CoV-2 and the B.1.1.7 variant, evaluated in both human gastrointestinal and lung epithelial cell lines. We find that plitidepsin is over an order of magnitude more potent than remdesivir against both viruses. These results highlight the importance of continued development of host-directed therapeutics to combat current and future coronavirus variant outbreaks.

microbiology↗

bioRxiv2021

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases

Pro-inflammatory fibroblasts are critical to pathogenesis in rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease, and Sjogrens syndrome, and represent a novel therapeutic target for chronic inflammatory disease. However, the heterogeneity of fibroblast phenotypes, exacerbated by the lack of a common cross-tissue taxonomy, has limited the understanding of which pathways are shared by multiple diseases. To investigate, we profiled patient-derived fibroblasts from inflamed and non-inflamed synovium, intestine, lung, and salivary glands with single-cell RNA-sequencing. We integrated all fibroblasts into a multi-tissue atlas to characterize shared and tissue-specific phenotypes. Two shared clusters, CXCL10+CCL19+ immune-interacting and SPARC+COL3A1+ vascular-interacting fibroblasts were expanded in all inflamed tissues and additionally mapped to dermal analogues in a public atopic dermatitis atlas. We further confirmed these human pro-inflammatory fibroblasts in animal models of lung, joint, and intestinal inflammation. This work represents the first cross-tissue, single-cell fibroblast atlas revealing shared pathogenic activation states across four chronic inflammatory diseases.

genomics↗

bioRxiv2021

Acute inflammation, mediated by lung neutrophils, confers enhanced protection against Mycobacterium tuberculosis infection in mice

Inflammation plays a crucial role in the control of Mycobacterium tuberculosis (M.tb) infection. In this study, we demonstrate that an inflammatory pulmonary environment at the time of infection mediated by liposaccharide (LPS) treatment in mice confers enhanced protection against M.tb for up to 6 months post infection. This transient protective inflammatory environment was associated with a neutrophil and monocyte/macrophage influx as well as increased inflammatory cytokines. In vitro infection of neutrophils from LPS treated mice demonstrated that LPS neutrophils exhibited increased recognition of M.tb, and had a greater innate capacity for killing M.tb. Finally, partial depletion of neutrophils in LPS treated mice showed an increase in M.tb burden, suggesting neutrophils conferred the enhanced protection observed in LPS treated mice. These results indicate a positive role of an inflammatory environment during initial M.tb infection, and suggests that acute inflammation at the time of M.tb infection can positively alter disease outcome.

immunology↗

bioRxiv2021

Multi-Spatial Scale Dynamic Interactions between Functional Sources Reveal Sex-Specific Changes in Schizophrenia

We introduce an extension of independent component analysis (ICA), called multiscale ICA (msICA), and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. msICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and -common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (1) within the subcortical domain, (2) between the somatomotor and cerebellum domains, and (3) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

neuroscience↗

bioRxiv2020

Impact of Model Order Choice on the Results of Parallel Independent Component Analysis

Parallel independent component analysis (pICA) is a data-driven method that identifies the maximally independent components of multiple imaging modalities while simultaneously investigating the strength of their correlations. Researchers using pICA are given the option to use the suggested model order calculated by the minimum descriptive length (MDL) algorithm, or they can choose their own model order. To date, there are no suggested guidelines for this choice. To test the sensitivity of pICA to the selection of model order, we applied it to a well-researched brain disorder, schizophrenia, looking at the correlations between patterns of grey matter volume (GM) volume and white matter integrity, measured using fractional anisotropy (FA). We varied model orders from low to high, and tested the sensitivity to disorder effects (cases vs controls), similarity of spatial maps identified across model orders, consolidation or distribution effects related to model order selection, and the performance of the minimum descriptive length (MDL) algorithm. The pICA results (multimodal analysis) were also compared to the ICA (unimodal analysis) for each imaging modality. Across model orders, there was consistent sensitivity to disorder effects, and clustered patterns of spatial maps for both the GM and FA reflecting those differences. The MDL-estimated model order captured the majority, but not all, of the spatial patterns present in the GM and FA. There was not the expected consolidation of spatial maps at lower model orders, nor the distribution of spatial maps at higher model orders. The spatial patterns identified in the ICA closely resemble those found in the pICA, although lacking the benefit of the optimization algorithm, were not as highly correlated. This offers some insight and guidance for researchers interested in using pICA with regard to selecting model order for their particular analysis of multiple imaging modalities.

neuroscience↗

bioRxiv2020

A novel method for tri-clustering dynamic functional network connectivity (dFNC) identifies significant schizophrenia effects across multiple states in distinct subgroups of individuals

BackgroundBrain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). Though, such an approach does not factor in the homogeneity of underlying data and may end up with a less meaningful subgrouping of the dataset. MethodsDynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more apples-to-apples comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. ResultsResulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared to HC, SZ in most tri-clusters show hypoconnectivity (low positive) among subcortical, default mode, cognitive control but hyper-connectivity (high positive) between sensory networks. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks compared to SZ. Results also provide statistically significant difference in reoccurrence time between SZ and HC subjects for two distinct dFNC states. ConclusionsOutcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in the study of a heterogeneous disorder like schizophrenia and in unconstrained experimental conditions such as resting fMRI.

neuroscience↗

bioRxiv2020

Lethality of SARS-CoV-2 infection in K18 human angiotensin converting enzyme 2 transgenic mice

ABSTRACTVaccine and antiviral development against SARS-CoV-2 infection or COVID-19 disease currently lacks a validated small animal model. Here, we show that transgenic mice expressing human angiotensin converting enzyme 2 (hACE2) by the human cytokeratin 18 promoter (K18 hACE2) represent a susceptible rodent model. K18 hACE2-transgenic mice succumbed to SARS-CoV-2 infection by day 6, with virus detected in lung airway epithelium and brain. K18 ACE2-transgenic mice produced a modest TH1/2/17 cytokine storm in the lung and spleen that peaked by day 2, and an extended chemokine storm that was detected in both lungs and brain. This chemokine storm was also detected in the brain at day 4. K18 hACE2-transgenic mice are, therefore, highly susceptible to SARS-CoV-2 infection and represent a suitable animal model for the study of viral pathogenesis, and for identification and characterization of vaccines (prophylactic) and antivirals (therapeutics) for SARS-CoV-2 infection and associated severe COVID-19 disease.

microbiology↗

bioRxiv2019

Structural Brain Alterations and Their Association with Cognitive Function and Symptoms in Attention-Deficit/Hyperactivity Disorder Families

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 - 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a case/control difference with sub-clinical cases being intermediate between cases and controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to cases, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

neuroscience↗

bioRxiv2019

Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans

Tuberculosis (TB) is the leading cause of death due to a single infectious disease. Knowing when a person was infected with Mycobacterium tuberculosis (M.tb) is critical as recent infection is the strongest clinical risk factor for progression to TB disease in immunocompetent individuals. However, time since M.tb infection is challenging to determine in routine clinical practice. To define a biomarker for recent TB exposure, we determined whether gene expression patterns in blood RNA correlated with time since M.tb infection or exposure. First, we found RNA signatures that accurately discriminated early and late time periods after experimental infection in mice and cynomolgus macaques. Next, we found a 6-gene blood RNA signature that identified recently exposed individuals in two independent human cohorts, including adult household contacts of TB cases and adolescents who recently acquired M.tb infection. Our work supports the need for future longitudinal studies of recent TB contacts to determine whether biomarkers of recent infection can provide prognostic information of TB disease risk in individuals and help map recent transmission in communities.

immunology↗

bioRxiv2019

Age-related Structural and Functional Variations in 5967 Individuals across the Adult Lifespan

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging data, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC) and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: 1)GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. 2) FNC tended to linearly decrease within resting-state networks (RSNs), especially in visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. 3) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

neuroscience↗

bioRxiv2019

Micro-scale fluid behavior during cryo-EM sample blotting

Blotting has been the standard technique for preparing aqueous samples for single-particle electron cryo-microscopy (cryo-EM) for over three decades. This technique removes excess solution from a TEM grid by pressing absorbent filter paper against the specimen prior to vitrification. However, this standard technique produces vitreous ice with inconsistent thickness from specimen to specimen and from region to region within the same specimen, the reasons for which are not understood. Here, high-speed interference-contrast microscopy is used to demonstrate that the irregular pattern of fibers in the filter paper imposes tortuous, highly variable boundaries during removal of excess liquid from a flat, hydrophilic surface. As a result, aqueous films of nonuniform thickness are formed while the filter paper is pressed against the substrate. This pattern of nonuniform liquid thickness changes again after the filter paper is pulled away, but the thickness still does not become completely uniform. We suggest that similar topological features of the liquid film are produced during the standard technique used to blot EM grids and that these manifest in nonuniform ice after vitrification. These observations suggest that alternative thinning techniques, which do not rely on direct contact between the filter paper and the grid, may result in more repeatable and uniform sample thicknesses.\n\nSTATEMENT OF SIGNIFICANCEMultiple imaging techniques are used to observe dynamic, micro-scale events as excess water is removed from a substrate by blotting with filter paper. As a result, new insight is gained about why the thickness values of remaining sample material are so variable across a single EM grid, as well as from one grid to the next. In addition, quantitative estimates are made of the shear forces to which macromolecular complexes can be exposed during blotting. The fact that sample thicknesses and flow rates are seen to be inherently under poor control during blotting suggests that other methods of removing excess water may be better suited for consistently achieving large sample areas that are suitable for use in electron cryo-microscopy.

biophysics↗

bioRxiv2019

Connectivity domain analysis: characterization of connectivity frequency profile using filter banks

Studying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach. First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.

neuroscience↗

bioRxiv2018

Resting-State Brain Fluctuation and Functional Connectivity Dissociate Moral Injury from Posttraumatic Stress Disorder

Moral injury is closely associated with posttraumatic stress disorder (PTSD) and is characterized by disturbances in social and moral cognition. Little is known about the neural underpinnings of moral injury, and whether the neural correlates are different between moral injury and PTSD.\n\nA sample of 26 US military veterans (2 females; 28~55 years old) were investigated to determine how moral injury experiences and PTSD symptoms are differentially related to spontaneous fluctuations indexed by low frequency fluctuation (ALFF) as well as functional connectivity during resting-state functional magnetic resonance imaging (fMRI) scanning.\n\nALFF in the left inferior parietal lobule (L IPL) was positively associated with moral injury sub-scores of transgressions, negatively associated with sub-scores of betrayals, and not related with PTSD symptoms. Moreover, functional connectivity between the L IPL and bilateral precuneus was positively related with PTSD symptoms and negatively related with moral injury total scores.\n\nOur results provide the first evidence that moral injury and PTSD have dissociable neural underpinnings, and behaviorally distinct sub-components of moral injury are different in neural responses. The findings increase our knowledge of the neural distinctions between moral injury and PTSD and may contribute to developing nosology and interventions for military veterans afflicted with moral injury.

neuroscience↗

bioRxiv2018

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns are changing in fundamental and recurring ways through time. Most previous work has assumed fixed spatial nodes/networks, ignoring the possibility that spatial nodes or networks may vary in time, particularly at the level of the voxel. Here, we introduce an approach allowing for a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variation in spatiotemporal coupling at the voxel level within each network. We identify a novel set of spatially dynamic features which can be obtained and evaluated under different conditions. Results reveal transient spatially fluid interactions between intra- and inter-network relationships in which brain networks transiently merge and then separate again, emphasizing the dynamic interplay between segregation and integration. We also show that brain networks exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies which assumed static networks. Moreover, we show for the first time that anticorrelative connections to the default mode network, are transient as opposed to constant across the entire scan. Preliminary assessments of the approach using a multi-site dataset collected from 160 healthy subjects and 149 patients with schizophrenia (SZ) revealed the ability of the approach to obtain new information and nuanced alterations of brain networks that remain undetected during static analysis. For example, patients with SZ display transient decreases in voxel-wise network coupling including within visual and auditory networks that are not detectable in a spatially static analysis. Our approach also enabled calculation of a novel parameter, the intra-domain coupling variability which was higher within patients with SZ. The significant association between spatiotemporal uniformity and attention/vigilance cognitive domain highlights the cognitive relevance of the spatial chronnectome. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks that are transient at the voxel level and identification of mechanisms for within and between-subject spatial variability to study functional brain homeostasis.

neuroscience↗

bioRxiv2018

Pathologically distinct fibroblast subsets drive inflammation and tissue damage in arthritis

The identification of lymphocyte subsets with non-overlapping effector functions has been pivotal to the development of targeted therapies in immune mediated inflammatory diseases (IMIDs). However it remains unclear whether fibroblast subclasses with non-overlapping functions also exist and are responsible for the wide variety of tissue driven processes observed in IMIDs such as inflammation and damage. Here we identify and describe the biology of distinct subsets of fibroblasts responsible for mediating either inflammation or tissue damage in arthritis. We show that deletion of FAP+ synovial cells suppressed both inflammation and bone erosions in murine models of resolving and persistent arthritis. Single cell transcriptional analysis identified two distinct fibroblast subsets: FAP+ THY1+ immune effector fibroblasts located in the synovial sub-lining, and FAP+ THY1- destructive fibroblasts restricted to the synovial lining. When adoptively transferred into the joint, FAP + THY1- fibroblasts selectively mediate bone and cartilage damage with little effect on inflammation whereas transfer of FAP + THY1+ fibroblasts resulted in a more severe and persistent inflammatory arthritis, with minimal effect on bone and cartilage. Our findings describing anatomically discrete, functionally distinct fibroblast subsets with non-overlapping functions have important implications for cell based therapies aimed at modulating inflammation and tissue damage.

immunology↗

bioRxiv2017

The sequence of a male-specific genome region containing the sex determination switch in Aedes aegypti

Aedes aegypti is the principal vector of several important arboviruses. Among the methods of vector control to limit transmission of disease are genetic strategies that involve the release of sterile or genetically modified non-biting males1,, which has generated interest in manipulating mosquito sex ratios2,3. Sex determination in Ae. aegypti is controlled by a non-recombining Y chromosome-like region called the M locus4, yet characterisation of this locus has been thwarted by the repetitive nature of the genome5. In 2015, an M locus gene named Nix was identified that displays the qualities of a sex determination switch5. With the use of a whole-genome BAC library, we amplified and sequenced a [~]200kb region containing this male-determining gene. In this study, w ...

genetics↗