Biology subjects

Lee, S.

Publications and source records attributed to Lee, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Three-dimensional refractive index distributions of individual angiosperm pollen grains

Three-dimensional (3D) refractive index (RI) imaging and quantitative analyses of angiosperm pollen grains are presented. Using optical diffraction tomography, the 3D RI structures of individual angiosperm pollen grains were measured without using labeling or other preparation techniques. Various physical quantities, including volume, surface area, exine volume, and sphericity, were determined from the measured RI tomograms of pollen grains. Exine skeletons, the distinct internal structures of angiosperm pollen grains, were identified and systematically analyzed.

plant biology↗

bioRxiv2018

Data-Constrained Biophysical Modeling Of Human VIM To Assess Contributions Of Intrinsic Oscillatory Activity To Essential Tremor

Essential tremor (ET) is the most common movement disorder, in which the primary symptom is a prominent, involuntary 4-10 Hz rhythmic movement. The presence of tremor frequency oscillations (TFOs) in the ventral intermediate nucleus of the thalamus (VIM) is well-established, but it is often assumed that it is driven by cerebellar tremor frequency activity, while the role of intrinsic oscillatory activity in VIM is not clear. An improved understanding of the mechanisms of tremor and non-tremor frequency activity in VIM is critical to the development of improved pharmacological and neuromodulatory therapies. Starting from a canonical model of thalamus, we developed a biophysically-principled computational model of tremor field activity in the VIM, coupled with the thalamic reticular nucleus (TRN). We simulated TFOs in the model generated either by extrinsic tremor-periodic drive or intrinsic VIM-TRN interaction to understand whether these networks exhibited distinct biophysical properties, which may impact the efficacy of pharmacological or stimulation treatment for TFOs. Extrinsic and intrinsic TFOs in the model depended on T-type Ca2+ channels in different ways. Each also depended on GABA modulation in a site- and type-specific manner. These results suggested that efficacy of pharmacological manipulations may depend upon the mechanisms generating TFOs in VIM. Simulated non-tremor-related motor activity from cerebellum decreased extrinsic but increased intrinsic TFOs. Our results suggest that both mechanisms may be important to understand the emergence and cessation of TFOs in VIM and lead to experimentally testable predictions on how to modulate tremor frequency activity to improve treatment strategies for ET.\n\nSignificance StatementEssential Tremor (ET) is a movement disorder in which the primary symptom is a prominent, involuntary, and rhythmic shaking, often of the hands. Electrical activity in many areas of the brain exhibit rhythmicity related to the patients tremor. One such area resides in a structure called the thalamus, but it is not fully known what gives rise to tremor-related activity. We created a computational model of this activity, which suggested how to differentiate tremor mechanisms and how these differences may contribute to other impairments in ET. Knowledge of the biophysical mechanisms contributing to tremor can ultimately lead to improvements in treatments to alleviate symptoms of ET.

neuroscience↗

bioRxiv2018

Identification of pathogens in culture-negative infective endocarditis with metagenomic analysis

Pathogens identification is critical for the proper diagnosis and precise treatment of infective endocarditis. Although blood and valve cultures are the gold standard for IE pathogens detection, many cases are culture-negative, especially in patients who had received long-term antibiotic treatment, and precise diagnosis has therefore become a major challenge in the clinic. Metagenomic sequencing can provide both information on the pathogenic strain and the antibiotic susceptibility profile of patient samples without culturing, offering a powerful method to deal with culture-negative cases. In this work, we assessed the feasibility of a metagenomic approach to detect the causative pathogens in resected valves from IE patients.\n\nUsing our in-house developed bioinformatics pipeline, we analyzed the sequencing results generated from both next-generation sequencing and Oxford Nanopore Technologies MinION nanopore sequencing for the direct identification of pathogens from the resected valves of seven clinically culture-negative IE patients according to the modified Duke criteria. Moreover, we were able to simultaneously characterize respective antimicrobial resistance features. This provides clinicians with valuable information to diagnose and treat IE patients after valve replacement surgery.

microbiology↗

bioRxiv2018

Coupled Mixed Model for joint genetic analysis of complex disorders from independently collected data sets: application to Alzheimer’s disease and substance use disorder

In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratication, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimers disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases.\n\nThe software is available at https://github.com/HaohanWang/CMM

genetics↗

bioRxiv2018

Highly Multiplexed Genome Engineering Using CRISPR/Cas9 gRNA Arrays

The CRISPR/Cas9 system is an RNA guided nuclease system that evolved as a mechanism of adaptive immunity in bacteria. This system has been adopted for numerous genome engineering applications in research and recently, therapeutics. The CRISPR/Cas9 system has been largely implemented by delivery of Cas9 as protein, RNA, or plasmid along with a chimeric crRNA-tracrRNA guide RNA (gRNA) under the expression of a pol III promoter, such as U6. Using this approach, multiplex genome engineering has been achieved by delivering several U6-gRNA plasmids targeting multiple loci. However, this approach is limiting due to the efficiently of delivering multiple plasmids to a single cell at one time. To augment the capability and accessibility of multiplexed genome engineering, we developed an efficient golden gate based method to assemble gRNAs linked by optimal Csy4 ribonuclease sequences to deliver up to 10 gRNAs as a single gRNA array transcript. Here we report the optimal expression of our guide RNA array under a strong pol II promoter. This system can be implemented alongside the myriad of CRISPR applications, allowing users to model complex biological processes requiring numerous gRNAs.

bioengineering↗

bioRxiv2018

Subset-Based Analysis using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes

ObjectivesClassical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. We aim to enhance the discovery of novel associated loci in the presence of heterogeneity of genetic effects in sub-groups defined by an environmental factor.\n\nMethodsWe present a p-value Assisted Subset Testing for Associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure. We conduct simulation studies and provide two data examples.\n\nResultsSimulation studies show that our proposal is more powerful than methods based on marginal associations in the presence of G-E interactions and maintains comparable power even in their absence. Both data examples demonstrate that our method can increase power to detect overall genetic associations and identify novel studies/phenotypes that contribute to the association.\n\nConclusionsOur proposed method can be a useful screening tool to identify candidate single nucleotide polymorphisms (SNPs) that are potentially associated with the trait(s) of interest for further validation. It also allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.

genetics↗

bioRxiv2018

plyranges: A grammar of genomic data transformation

The Bioconductor project provides many interoperable data abstractions for analyzing high-throughput genomics experiments; however implementing a typical genomic workflow with Bioconductor requires learning these abstractions and understanding them at an integrative level. This places a large cognitive burden on the user, especially for non-programmers. To reduce this burden we have created a grammar of genomic data transformation that operates on a single, central Bioconductor data structure, GRanges, which naturally represents genomic intervals and their associated measurements. The grammar defines verbs for performing actions on and between genomic interval data through a simplified, coherent interface to existing Bioconductor infrastructure, resulting in fluent analysis workflows. We have implemented this grammar as an R/Bioconductor package called plyranges.

bioinformatics↗

bioRxiv2018

Simple but Robust Improvement in Multivoxel Pattern Classification

Multivoxel pattern analysis (MVPA) typically begins with the estimation of single trial activation levels, and several studies have examined how different procedures for estimating single trial activity affect the ultimate classification accuracy of MVPA. Here we show that the currently preferred estimation procedures impart spurious shifts in run-level means that cause the estimated activities to be misaligned across runs. These shifts are caused by positive correlations between the means of different category activity estimates within the same scanner run. In other words, if the mean of the estimates for one type of trials is high (low) in a given scanner run, then the mean of the other type of trials is also high (low) for that same scanner run, and the mean across all trials therefore shifts from run to run. Simulations show that these correlations are unavoidable whenever there is a need to deconvolve overlapping trial activities in the presence of noise. We show that subtracting the mean across all trials of a run from all the estimates within that run (i.e., run-level mean centering of estimates), by cancelling out these mean shifts, leads to robust and significant improvements in MVPA classification accuracy. These improvements are seen in both simulated and real data across a wide variety of situations and can provide significant direct benefits with no computational cost. However, we also point out that there could be cases when mean activations are expected to shift across runs and that run-level mean centering could be detrimental in some of these cases (e.g., different proportion of trial types between different runs).

neuroscience↗

bioRxiv2018

A 3D Topographical Model of Parenchymal Infiltration and Perivascular Invasion in Glioblastoma

Glioblastoma (GBM) is the most common and invasive primary brain cancer. GBM tumors are characterized by diffuse infiltration, with tumor cells invading slowly through the hyaluronic acid (HA)-rich parenchyma toward vascular beds and then migrating rapidly along microvasculature. Progress in understanding local infiltration, vascular homing, and perivascular invasion is limited by an absence of culture models that recapitulate these hallmark processes. Here we introduce a platform for GBM invasion consisting of a tumor-like cell reservoir and a parallel open channel \"vessel\" embedded in 3D HA-RGD matrix. We show that this simple paradigm is sufficient to capture multi-step invasion and transitions in cell morphology and speed reminiscent of those seen in GBM. Specifically, seeded tumor cells grow into multicellular masses that expand and invade the surrounding HA-RGD matrices while extending long (10-100 {micro}m), thin protrusions resembling those observed for GBM in vivo. Upon encountering the channel, cells orient along the channel wall, adopt a 2D-like morphology, and migrate rapidly along the channel. Structured illumination microscopy reveals distinct cytoskeletal architectures for cells invading through the HA matrix versus those migrating along the vascular channel. Substitution of collagen I in place of HA-RGD supports the same sequence of events but with faster local invasion and a more mesenchymal morphology. These results indicate that topographical effects are generalizable across matrix formulations, but that mechanisms underlying invasion are matrix-dependent. We anticipate that our reductionist paradigm should speed the development of mechanistic hypotheses that could be tested in more complex tumor models.

bioengineering↗

bioRxiv2018

Coupling initial hydrogel microenvironments with mesenchymal stem cell remodeling to define a dynamic hematopoietic stem cell niche

Hematopoietic stem cells (HSCs) reside in the bone marrow within discrete niches defined by a complex milieu of external signals including biophysical cues, bound and diffusible biomolecules, and heterotypic cell-cell interactions. Recent studies have shown the importance of autocrine-mediated feedback of cell-secreted signals and the interplay between matrix architecture and biochemical diffusion on hematopoietic stem cell activity. Autocrine and paracrine signaling from HSCs and niche-associated mesenchymal stromal cells (MSCs) have both been suggested to support HSC maintenance in vivo and in vitro. Here we report the development of a library of methacrylamide-functionalized gelatin (GelMA) hydrogels to explore the balance between autocrine feedback and paracrine signals from co-encapsulated murine bone marrow MSCs on murine HSCs. The use of a degradable GelMA hydrogel enables the possibility for significant MSC-mediated remodeling, yielding dynamic shifts in the matrix environment surrounding HSCs. We identify a combination of an initially low-diffusivity hydrogel and a 1:1 HSPC:MSC seeding ratio as conducive to enhanced HSC population maintenance and quiescence. Further, gene expression and serial mechanical testing data suggests that MSC-mediated matrix remodeling is significant for the long-term HSC culture, reducing HSC autocrine feedback and potentially enhancing MSC-mediated signaling over 7-day culture in vitro. This work demonstrates the design of an HSC culture system that couples initial hydrogel properties, MSC co-culture, and concepts of dynamic reciprocity mediated by MSC remodeling to achieve enhanced HSC maintenance. One Sentence SummaryCoupling effects of hydrogel biotransport, heterotypic cell culture, and matrix remodeling enhances hematopoietic stem cell culture and quiescence.

bioengineering↗

bioRxiv2018

Pseudomonas syringae evades phagocytosis in animal cells through type III effector-mediated inhibition of the LIM kinase-cofilin system

Certain animal and plant pathogenic bacteria have developed virulence factors (including effector proteins) that enable them to overcome host immunity. A plant pathogen, Pseudomonas syringae pv. tomato (Pto), secretes a large repertoire of effectors into plant cells via a type III secretory apparatus, thereby suppressing plant immunity. Here, we show that exposure to Pto caused sepsis in mice. Surprisingly, the effector HopQ1 disrupted phagocytosis by inhibiting actin rearrangement via a direct interaction with the LIM domain of the animal target protein LIM kinase, a key regulator of actin polymerization. The results provide new insights into cross-kingdom pathogenicity of bacteria. The current studies demonstrate that certain plant pathogenic bacteria such as Pto can be fatal in animals due to cross-kingdom host immune suppression.

microbiology↗

bioRxiv2018

Internal gain modulations, but not changes in stimulus contrast, preserve the neural code

Neurons in primary visual cortex (V1) are strongly modulated both by stimulus contrast and by fluctuations of internal inputs. An important question is whether population codes are preserved under these conditions. Changes in stimulus contrast are thought to leave population codes invariant, whereas the effect of internal gain modulations remains unknown. To address these questions we studied how the direction-of-motion of oriented gratings is encoded in layer 2/3 of mouse V1. Surprisingly, we found that, because contrast gain responses across cells are heterogeneous, a change in contrast alters the information distribution profile across cells leading to the failure of contrast invariance. Remarkably, internal input fluctuations that cause commensurate firing rate modulations at the single-cell level, do respect population code invariance. These observations have important implications for visual information encoding, and argue that the brain strives to maintain the stability of the neural code in the face of fluctuating internal inputs.

neuroscience↗

bioRxiv2018

Accurate Prediction of Alzheimer’s Disease Using Multi-Modal MRI and High-Throughput Brain Phenotyping

Accurate, reliable prediction of risk for Alzheimers disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping--morphometry and connectomics--and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimers Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid {beta}, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multi-site dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD.

neuroscience↗

bioRxiv2018

Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation

To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

genetics↗

bioRxiv2017

Somatic Mutations Predict Acute Myeloid Leukemia Years Before Diagnosis

BackgroundSomatic mutations observed in clonal hematopoiesis are associated with increased age and risk of hematological malignancies. However, the limited number of acute myeloid leukemia (AML) patients in studies of clonal hematopoiesis thus far has precluded determination of the spectrum of mutations leading to AML and their impact on risk and time to diagnosis.\n\nMethodsThe relationship between baseline mutations and subsequent AML was determined in a case-control study design. 212 women eventually diagnosed with AML (median time: 9.8 years) were identified from the Womens Health Initiative cohort alongside 212 matched AML-free controls. Deep sequencing of 67 genes was performed on DNA isolated from peripheral blood to detect subclonal mutations.\n\nResultsThe presence of any mutation was associated with increased odds of eventual AML (odds ratio [OR] 4.0; 95% confidence interval [CI], 2.5-6.3). These odds were further elevated with mutations in IDH1/2 (OR 8.4; 95% CI, 1.4-51.9), TP53 (OR 54.2; 95% CI, 2.9-1017.7), or spliceosome genes (OR 5.6; 95% CI, 1.5-20.6). Eventual AML diagnosis occurred in all or most participants with mutations in TP53 (N=23/23) or IDH1/2 (N=15/16). Mutations indicated sooner AML diagnosis (median 8.0 vs. 11.9 years; P < 0.001) with TP53 mutations demonstrating increased odds of AML within 5 years (OR 3.6; 95% CI, 1.4-9.1).\n\nConclusionsMutations are present in peripheral blood of AML patients a decade prior to AML diagnosis with mutations in TP53 producing especially rapid presentation. Strategies for monitoring of high-risk populations are needed and clinical trials of potential early interventions can be considered.

cancer biology↗

bioRxiv2017

Multi-SKAT: General framework to test multiple phenotype associations of rare variants

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants and the violation of these assumptions can reduce power to detect association. Here we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models effect sizes of variants on the phenotypes through a kernel matrix and performs a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METSIM study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.\n\nGrant NumbersThis research was conducted with support from the following grants from National Institute of Health: O_LIR01 HG008773 (D.D. and S.L.)\nC_LIO_LIR01 LM012535 (D.D. and S.L.)\nC_LIO_LIR01 HG000376 (L.S. and M.B.)\nC_LIO_LIU01 DK062370 (L.S. and M.B.)\nC_LI

genetics↗

bioRxiv2017

Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography

Microalgae are promising candidates for biofuel production due to their high lipid content. To facilitate utilization of the microalgae for biofuel, rapid quantification of the lipid contents in microalgae is necessary. However, conventional methods based on the chemical extraction of lipids require a time-consuming destructive extraction process. Here, we demonstrate label-free, non-invasive, rapid quantification of the lipid contents in individual micro-algal cells measuring the three-dimensional refractive index tomograms. We measure three-dimensional refractive index distributions within Nannochloropsis oculata cells and find that lipid droplets are identifiable in tomograms by their high refractive index. In addition, we alter N. oculata under nitrogen deficiency by measuring the volume, lipid weight, and dry cell weight of individual cells. Characterization of individual cells allows correlative analysis between the lipid content and size of individual cells.

bioengineering↗