Biology subjects

Hwang, S.

Publications and source records attributed to Hwang, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Robust and durable prophylactic protection conferred by RNA interference in preclinical models of SARS-CoV-2

RNA interference is a natural antiviral mechanism that could be harnessed to combat SARS-CoV-2 infection by targeting and destroying the viral genome. We screened lipophilic small-interfering RNA (siRNA) conjugates targeting highly conserved regions of the SARS-CoV-2 genome and identified leads targeting outside of the spike-encoding region capable of achieving [≥]3-log viral reduction. Serial passaging studies demonstrated that a two-siRNA combination prevented development of resistance compared to a single-siRNA approach. A two-siRNA combination delivered intranasally protected Syrian hamsters from weight loss and lung pathology by viral infection upon prophylactic administration but not following onset of infection. Together, the data support potential utility of RNAi as a prophylactic approach to limit SARS-CoV-2 infection that may help combat emergent variants, complement existing interventions, or protect populations where vaccines are less effective. Most importantly, this strategy has implications for developing medicines that may be valuable in protecting against future coronavirus pandemics.

pharmacology and toxicology↗

bioRxiv2022

TrmB family transcription factor as a thiol-based regulator of oxidative stress response

Oxidative stress causes cellular damage including DNA mutations, protein dysfunction and loss of membrane integrity. Here we discovered TrmB (transcription regulator of mal operon) family proteins (Pfam PF01978) composed of a single winged-helix DNA binding domain (InterPro IPR002831) can function as thiol-based transcriptional regulators of oxidative stress responses. Using the archaeon Haloferax volcanii as a model system, we demonstrate that the TrmB-like OxsR is important for recovery of cells from hypochlorite stress. OxsR is shown to bind specific regions of genomic DNA, particularly during hypochlorite stress. OxsR-bound intergenic regions were found proximal to oxidative stress operons including genes associated with thiol relay and low molecular weight thiol biosynthesis. Further analysis of a subset of these sites, revealed OxsR to function during hypochlorite stress as a transcriptional activator and repressor. OxsR was shown to require a conserved cysteine (C24) for function and to use a CG-rich motif upstream of conserved BRE/TATA box promoter elements for transcriptional activation. Protein modeling suggested the C24 is located at a homodimer interface formed by antiparallel helices, and that oxidation of this cysteine would result in the formation of an intersubunit disulfide bond. This covalent linkage may promote stabilization of an OxsR homodimer with the enhanced DNA binding properties observed in the presence of hypochlorite stress. The phylogenetic distribution TrmB family proteins, like OxsR, that have a single winged-helix DNA binding domain and conserved cysteine residue suggests this type of redox signaling mechanism is widespread in Archaea. ImportanceTrmB-like proteins, while not yet correlated with redox stress, are found in bacteria and widespread in archaea. Here we expand annotation of a large group of TrmB-like single winged-helix DNA binding domain proteins from diverse archaea to function as thiol-based transcriptional regulators of oxidative stress response. Using Haloferax volcanii as a model, we reveal the TrmB-like OxsR functions during hypochlorite stress as a transcriptional activator and repressor of an extensive gene co-expression network associated with thiol relay and other related activities. A conserved cysteine residue of OxxR serves as the thiol-based sensor for this function and likely forms an intersubunit disulfide bond during hypochlorite stress that stabilizes a homodimeric configuration with enhanced DNA binding properties. A CG-rich DNA motif in the promoter region of a subset of sites identified to be OxsR-bound is required for regulation; however, not all sites have this motif suggesting added complexity to the regulatory network.

microbiology↗

bioRxiv2022

Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

The study of pattern formation has greatly benefited from our ability to reverse-engineer gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, and focus on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN and mechanistic insight can be obtained from the GRNs alone. However, this is not the case in most animal patterning systems, where patterning and morphogenesis are co-occurring and tightly linked. To address the mechanisms driving pattern formation in such systems we need to adapt our GRN inference methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues undergoing morphogenetic changes and cell rearrangements. By integrating quantitative data from live and fixed embryos, we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework generates candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

systems biology↗

bioRxiv2021

CyanoHABs: inevitable evolutionary ecological consequence for low nutrient-requiring cyanobacteria in water eutrophication

Cyanobacterial harmful algal blooms (CyanoHABs) arise as cyanobacteria dominate phytoplankton communities when nutrient levels increase from oligotrophic state. From a wholistic perspective, this longstanding altered phytoplankton structure results from two conditions: one sufficient condition that cyanobacteria can grow maximally with elevated nutrients; one necessary condition that co-living algae cannot grow fast or dominate at the same levels. The sufficient condition, the special mechanism of CyanoHABs at the population level, has been established as the synergistic interaction between superior cyanobacterial ecophysiology and elevated nutrients. But it is unknown how these functions arise or whether they are under directed evolution to water eutrophication. The necessary condition, the general mechanism of CyanoHABs at the community level, is little understood: why co-living algae cannot form blooms as cyanobacteria? Literature and bioinformatics analyses show that the superior ecophysiology undergoes no directed positive evolution to worldwide eutrophication in general or any local eutrophic waters in particular; instead, these functions are under strong purifying selection and likely acquired through early adaptive radiation in nutrient-deficient conditions, as functions enabling extant cyanobacteria to occupy other niches. The general mechanism turns out to be quite straightforward: cyanobacteria are simple life forms and thus have lower per capita nutrient demand for growth, compared to co-existing eukaryotic algae in cell size and structure, genome size, size of genome-scale metabolic networks, cell content, nutrient requirement. Lower nutrient demand is proved by existing field nutrient supplementation. Both the special and general mechanisms of CyanoHABs are tentative frameworks awaiting further theoretic improvement and empirical assessment.

evolutionary biology↗

bioRxiv2021

Structural and mechanistic insight into CRISPR-Cas9 inhibition by anti-CRISPR protein AcrIIC4

Phages, plasmids, and other mobile genetic elements express inhibitors of CRISPR-Cas immune systems, known as anti-CRISPR proteins, to protect themselves from targeted destruction. These anti-CRISPRs have been shown to function through very diverse mechanisms. In this work we investigate the activity of an anti-CRISPR isolated from a prophage in Haemophilus parainfluenzae that blocks CRISPR-Cas9 DNA cleavage activity. We determine the three-dimensional crystal struture of AcrIIC4 and show that it binds to the Cas9 Recognition Domain. This binding does not prevent the Cas9-anti-CRISPR complex from interacting with target DNA but does inhibit DNA cleavage. AcrIIC4 likely acts by blocking the conformational changes that allow the HNH and RuvC endonuclease domains to contact the DNA sites to be nicked.

biochemistry↗

bioRxiv2021

Autophagy gene-dependent intracellular immunity triggered by interferon-γ

Genes required for the lysosomal degradation pathway of autophagy play key roles in topologically distinct cellular processes with significant physiologic importance. One of the first-described of these ATG gene-dependent processes is the requirement for a subset of ATG genes in interferon-{gamma} (IFN{gamma})-induced inhibition of norovirus and Toxoplasma gondii replication. Herein we identified new genes that are required for or that negatively regulate this immune mechanism. Enzymes involved in the conjugation of UFM1 to target proteins including UFC1 and UBA5, negatively regulated IFN{gamma}-induced inhibition of norovirus replication via effects of Ern1. IFN{gamma}-induced inhibition of norovirus replication required Wipi2b and Atg9a, but not Becn1 (encoding Beclin1), Atg14, or Sqstm1. The phosphatidylinositol-3-phosphate and ATG16L1 binding domains of WIPI2B were required for IFN{gamma}-induced inhibition of norovirus replication. Both WIPI2 and SQSTM1 were required for IFN{gamma}-induced inhibition of Toxoplasma gondii replication in HeLa cells. These studies further delineate the mechanisms of a programmable form of cytokine-induced intracellular immunity that relies on an expanding cassette of essential ATG genes to restrict the growth of phylogenetically diverse pathogens. ImportanceInterferon-{gamma} is a critical mediator of cell-intrinsic immunity to intracellular pathogens. Understanding the complex cellular mechanisms supporting robust interferon-{gamma}-induced host defenses could aid in developing new therapeutics to treat infections. Here, we examined the impact of autophagy in the interferon-{gamma} induced host response. We demonstrate that CRISPR-Cas9 screens specifically targeting the autophagy pathway uncover a role for WIPI2 in IFN{gamma}-induced inhibition of Norovirus replication in mouse cells and IFN{gamma} mediated restriction of the Toxoplasma gondii parasitophorous vacuole in human cells. Furthermore, we found perturbation of UFMylation pathway components led to more robust IFN{gamma}-induced inhibition of Norovirus due to ER stress in vitro. Enhancing or inhibiting these dynamic cellular components could serve as a strategy to weaken intracellular pathogens and maintain an effective immune response.

microbiology↗

bioRxiv2021

The dual function monoclonal antibodies VIR-7831 and VIR-7832 demonstrate potent in vitro and in vivo activity against SARS-CoV-2

Sotrovimab (VIR-7831) and VIR-7832 are dual action monoclonal antibodies (mAbs) targeting the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sotrovimab and VIR-7832 were derived from a parent antibody (S309) isolated from memory B cells of a 2003 severe acute respiratory syndrome coronavirus (SARS-CoV) survivor. Both mAbs contain an "LS" mutation in the Fc region to prolong serum half-life. In addition, VIR-7832 encodes an Fc GAALIE mutation that has been shown previously to evoke CD8+ T-cells in the context of an in vivo viral respiratory infection. Sotrovimab and VIR-7832 neutralize wild-type and variant pseudotyped viruses and authentic virus in vitro. In addition, they retain activity against monoclonal antibody resistance mutations conferring reduced susceptibility to previously authorized mAbs. The sotrovimab/VIR-7832 epitope continues to be highly conserved among circulating sequences consistent with the high barrier to resistance observed in vitro. Furthermore, both mAbs can recruit effector mechanisms in vitro that may contribute to clinical efficacy via elimination of infected host cells. In vitro studies with these mAbs demonstrated no enhancement of infection. In a Syrian Golden hamster proof-of concept wildtype SARS-CoV-2 infection model, animals treated with sotrovimab had less weight loss, and significantly decreased total viral load and infectious virus levels in the lung compared to a control mAb. Taken together, these data indicate that sotrovimab and VIR-7832 are key agents in the fight against COVID-19.

immunology↗

bioRxiv2021

Morphogenetic coupling leads to pattern emergence in the pre somitic mesoderm

As tissues elongate, cell rearrangement alters positional information in manner that must be accounted for to generate gene expression pattern. How this is achieved during paraxial mesoderm elongation is unknown. By reverse-engineering gene regulatory networks that predict single cell expression trajectories across the tissue, we find a network capable of recapitulating the full range of dynamic differentiation profiles observed both in vivo and in vitro. Simulating gene expression profiles on in toto cell tracking data sets reveal that temporal exposure to Wnt and FGF is generated by cell movement. The absence of reversal in gene expression towards a more premature gene expression state predicts the generation of aberrant tbx6 expression in the posterior progenitor zone that we then confirm by quantitative single cell imaging. Taken together, these results demonstrate cell rearrangement tunes the dynamics of mesoderm progenitor differentiation to generate pattern emergence as a function of temporal Wnt and FGF exposure.

developmental biology↗

bioRxiv2021

Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution

Both mutations with large benefits and mutations occurring at high rates may cause parallel evolution, but their contribution is expected to depend on population size. We show that small and large bacterial populations adapt to a novel antibiotic using similar numbers, but different types of mutations. Small populations repeatedly substitute similar high-rate structural variants, including the deletion of a nonfunctional {beta}-lactamase, and evolve modest resistance levels. Hundred-fold larger populations more frequently use the same low-rate, large-benefit point mutations, including those activating the {beta}-lactamase, and reach 50-fold higher resistance levels. Our results demonstrate a key role of clonal interference in mediating the contribution of high-rate and large-benefit mutations in populations of different size, facilitated by a tradeoff between rates and fitness effects of different mutation classes.

evolutionary biology↗

bioRxiv2020

Alternative drug-response metric based on cellular response phenotypes

Background and PurposeAssessment of drug response is typically performed by constructing a dose-response curve of viability and summarizing it to a representative value. However, this is limited by its dependency on the assay duration and lack of reflections regarding actual cellular response phenotypes. To resolve these limitations, we considered contribution of each response phenotype upon a drug treatment to the overall growth behavior. Experimental ApproachThe differential equation of phenotype population dynamics was solved analytically without numerical computation. By using the properly figured population dynamics, we explored how the conventional assessment method itself affects the assessment result of drug response, in the context of drug screening. Alternative phenotype metric was compared with the conventional metrics through evaluation of the publicly available drug response data. Key ResultsThe conventional assessment showed several limitations in the comparative analysis of drug response: a significant time-dependency, and ambiguities in assessment results based on a dose-response curve. Instead, the alternative phenotype metrics provide time-independent phenotype rates of change, that contain all the information of the drug response at a given dose, and better classification including the mechanism underlying growth inhibition. Conclusion and ImplicationsThe conventional dose-response curve is useful for a visual presentation of overall drug responses upon a certain molecular feature qualitatively. In contrast, the phenotype metric is better for assessing therapeutic effectiveness, and would improve preclinical pharmacogenomic analysis through its relevance to a response phenotype. Bullet point summaryO_ST_ABSWhat is already knownC_ST_ABSO_LIDrug response is investigated by constructing a dose-response curve in wide range. C_LIO_LIConventional assessment metrics of drug response lack reflections regarding actual cellular response phenotypes. C_LI What this study addsO_LILimitations of conventional assessments are due to time-dependency of dose-response curve and customary summarization. C_LIO_LIPhenotype metric evaluate a single dose-response that is time-independent and phenotype-relevant. C_LI Clinical significanceO_LIPhenotype metric would improve pharmacogenomic analysis with better classification and phenotype-relevance of drug response. C_LIO_LIImprovement in preclinical pharmacology would bring better translation and useful information in clinical studies. C_LI

pharmacology and toxicology↗

bioRxiv2020

Designed PKC-targeting bryostatin analogs modulate innate immunity and neuroinflammation

Neuroinflammation characterizes multiple neurologic diseases, including primary inflammatory conditions such as multiple sclerosis (MS) and classical neurodegenerative diseases. Aberrant activation of the innate immune system contributes to disease progression in these conditions, but drugs that modulate innate immunity, particularly within the central nervous system (CNS), are lacking. The CNS-pene-trant natural product bryostatin-1 (bryo-1) attenuates neuroinflammation by targeting innate myeloid cells. Supplies of natural bryo-1 are limited but a recent scalable synthesis has enabled access to it and its analogs (termed bryologs), the latter providing a path to more efficacious, better tolerated, and more accessible agents. Here, we show that multiple synthetically accessible bryologs replicate the anti-inflammatory effects of bryo-1 on innate immune cells in vitro, and a lead bryolog attenuates neuroinflammation in vivo - actions mechanistically dependent on PKC binding. Our findings identify bryologs as promising drug candidates for targeting innate immunity in neuroinflammation and create a platform for evaluation of synthetic PKC modulators in neuroinflammatory diseases such as MS. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗

bioRxiv2020

Consequences of aneuploidy in human fibroblasts with trisomy 21

An extra copy of chromosome 21 causes Down syndrome, the most common genetic disease in humans. The mechanisms by which the aneuploid status of the cell, independent of the identity of the triplicated genes, contributes to the pathologies associated with this syndrome are not well defined. To characterize aneuploidy driven phenotypes in trisomy 21 cells, we performed global transcriptome, proteome, and phenotypic analysis of primary human fibroblasts from individuals with Patau (trisomy 13), Edwards (trisomy 18), or Down syndromes. On average, mRNA and protein levels show a 1.5 fold increase in all trisomies with a subset of proteins enriched for subunits of macromolecular complexes showing signs of post-transcriptional regulation. Furthermore, we show several aneuploidy-associated phenotypes are present in trisomy 21 cells, including lower viability and an increased dependency on the serine-driven lipid biosynthesis pathway to proliferate. Our studies present a novel paradigm to study how aneuploidy contributes to Down syndrome.

cell biology↗

bioRxiv2020

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches. Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.

developmental biology↗

bioRxiv2020

Epoxy fatty acid dysregulation and neuroinflammation in Alzheimer's disease is resolved by a soluble epoxide hydrolase inhibitor

Neuroinflammation has been increasingly recognized to play critical roles in Alzheimers disease (AD). The epoxy fatty acids (EpFAs) are derivatives of the arachidonic acid metabolism with anti-inflammatory activities. However, their efficacy is limited due to the rapid hydrolysis by the soluble epoxide hydrolase (sEH). We found that sEH is predominantly expressed in astrocytes where its levels are significantly elevated in postmortem human AD brains and in {beta}-amyloid mouse models, and the latter is correlated with drastic reductions of brain EpFA levels. Using a highly potent and specific small molecule sEH inhibitor, 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea (TPPU), we report here that TPPU treatment potently protected against LPS-induced inflammation in vitro and in vivo. Long-term administration of TPPU to the 5xFAD mouse model via drinking water reversed microglia and astrocyte reactivity and immune pathway dysregulation, and this is associated with reduced {beta}-amyloid pathology and improved synaptic integrity and cognitive function. Importantly, TPPU treatment reinstated and positively correlated EpFA levels in the 5xFAD mouse brain, demonstrating its brain penetration and target engagement. These findings support TPPU as a novel therapeutic target for the treatment of AD and related disorders. One Sentence SummaryWe show that soluble epoxide hydrolase is upregulated in AD patients and mouse models, and that inhibition of this lipid metabolic pathway using an orally bioavailable small molecule inhibitor is effective in restoring brain epoxy fatty acids, ameliorating AD neuropathology and improving synaptic and cognitive function.

neuroscience↗

bioRxiv2020

SDE2 Integrates into the TIMELESS-TIPIN Complex to Protect Stalled Replication Forks

Protecting replication fork integrity during DNA replication is essential for maintaining genome stability. Here, we report that SDE2, a PCNA-associated protein, plays a key role in maintaining active replication and counteracting replication stress by regulating the replication fork protection complex (FPC). SDE2 directly interacts with the FPC component TIMELESS (TIM) and enhances TIM stability and its localization to replication forks, thereby aiding the coordination of replisome progression. Like TIM deficiency, knockdown of SDE2 leads to impaired fork progression and stalled fork recovery, along with a failure to activate CHK1 phosphorylation. Moreover, loss of SDE2 or TIM results in an excessive MRE11-dependent degradation of reversed forks. Together, our study uncovers an essential role for SDE2 in maintaining genomic integrity by stabilizing the FPC and describes a new role for TIM in protecting stalled replication forks. We propose that TIM-mediated fork protection may represent a way to cooperate with BRCA-dependent fork stabilization.

cell biology↗

bioRxiv2020

Simple Behavioral Analysis (SimBA): an open source toolkit for computer classification of complex social behaviors in experimental animals

Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study of complex social behavior in freely moving rodents is relatively infrequently incorporated into preclinical models. This likely contributes to limited translational impact. A major bottleneck for the adoption of socially complex, ethology-rich, preclinical procedures are the technical limitations for consistently annotating detailed behavioral repertoires of rodent social behavior. Manual annotation is subjective, prone to observer drift, and extremely time-intensive. Commercial approaches are expensive and inferior to manual annotation. Open-source alternatives often require significant investments in specialized hardware and significant computational and programming knowledge. By combining recent computational advances in convolutional neural networks and pose-estimation with further machine learning analysis, complex rodent social behavior is primed for inclusion under the umbrella of computational neuroethology. Here we present an open-source package with graphical interface and workflow (Simple Behavioral Analysis, SimBA) that uses pose-estimation to create supervised machine learning predictive classifiers of rodent social behavior, with millisecond resolution and accuracies that can out-perform human observers. SimBA does not require specialized video acquisition hardware nor extensive computational background. Standard descriptive statistical analysis, along with graphical region of interest annotation, are provided in addition to predictive classifier generation. To increase ease-of-use for behavioural neuroscientists, we designed SimBA with accessible menus for pre-processing videos, annotating behavioural training datasets, selecting advanced machine learning options, robust classifier validation functions and flexible visualizations tools. This allows for predictive classifier transparency, explainability and tunability prior to, and during, experimental use. We demonstrate that this approach is flexible and robust in both mice and rats by classifying social behaviors that are commonly central to the study of brain function and social motivation. Finally, we provide a library of poseestimation weights and behavioral predictive classifiers for resident-intruder behaviors in mice and rats. All code and data, together with detailed tutorials and documentation, are available on the SimBA GitHub repository. Graphical abstractSimBA graphical interface (GUI) for creating supervised machine learning classifiers of rodent social behavior. (a) Pre-process videos. SimBA supports common video pre-processing functions (e.g., cropping, clipping, sampling, format conversion, etc.) that can be performed either on single videos, or as a batch. (b) Managing poseestimation data and creating classification projects. Pose-estimation tracking projects in DeepLabCut and DeepPoseKit can be either imported or created and managed within the SimBA graphical user interface, and the tracking results are imported into SimBA classification projects. SimBA also supports userdrawn region-of-interests (ROIs) for descriptive statistics of animal movements, or as features in machine learning classification projects. (c) Create classifiers, perform classifications, and analyze classification data. SimBA has graphical tools for correcting poseestimation tracking inaccuracies when multiple subjects are within a single frame, annotating behavioral events from videos, and optimizing machine learning hyperparameters and discrimination thresholds. A number of validation checkpoints and logs are included for increased classifier explainability and tunability prior to, and during, experimental use. Both detailed and summary data are provided at the end of classifier analysis. SimBA accepts behavioral annotations generated elsewhere (such as through JWatcher) that can be imported into SimBA classification projects. (d) Visualize classification results. SimBA has several options for visualizing machine learning classifications, animal movements and ROI data, and analyzing the durations and frequencies of classified behaviors. See the SimBA GitHub repository for a comprehensive documentation and user tutorials. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

animal behavior and cognition↗

bioRxiv2020

Dynamin-related Irgm proteins modulate LPS-induced caspase-4 activation and septic shock

Inflammation associated with gram-negative bacterial infections is often instigated by the bacterial cell wall component lipopolysaccharide (LPS). LPS-induced inflammation and resulting life-threatening sepsis are mediated by the two distinct LPS receptors TLR4 and caspase-4. Whereas the regulation of TLR4 activation by extracellular and phago-endosomal LPS has been studied in great detail, auxiliary host factors that specifically modulate recognition of cytosolic LPS by caspase-4 are largely unknown. This study identifies dynamin-related membrane remodeling proteins belonging to the family of Immunity related GTPases M clade (IRGM) as negative regulators of caspase-4 activation in macrophages. Phagocytes lacking expression of mouse isoform Irgm2 aberrantly activate caspase-4-dependent inflammatory responses when exposed to extracellular LPS, bacterial outer membrane vesicles or gram-negative bacteria. Consequently, Irgm2-deficient mice display increased susceptibility to caspase-4-mediated septic shock in vivo. This Irgm2 phenotype is partly reversed by the simultaneous genetic deletion of the two additional Irgm paralogs Irgm1 and Irgm3, indicating that dysregulated Irgm isoform expression disrupts intracellular LPS processing pathways that limit LPS availability for caspase-4 activation.

immunology↗

bioRxiv2019

Convolutional neural network model to predict causal risk factors that share complex regulatory features

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of > 2,000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of causal noncoding variants in post-GWAS analyses.

genetics↗