Biology subjects

Seal, S.

Publications and source records attributed to Seal, S..

50 records · Page 3Linked to original sources

bioRxiv2022

From neural border to migratory stage: A comprehensive single cell roadmap of the timing and regulatory logic driving cranial and vagal neural crest emergence

Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification towards definitive neural crest. Here, we combine ultra-dense single cell transcriptomes with machine-learning and large-scale experimental validation to provide a comprehensive GRN underlying neural crest fate diversification from induction to early migration stages in the frog Xenopus tropicalis. During gastrulation, a transient neural border zone state precedes the choice between neural crest and placodes which includes multiple and converging gene programs. Transcription factor connectome and bifurcation analyses demonstrate the early emergence of neural crest fates at the neural plate stage, alongside an unbiased multipotent lineage persisting until after epithelial-mesenchymal transition. We decipher the circuits driving cranial and vagal neural crest formation and provide a broadly applicable strategy for investigating SC transcriptomes in vertebrate GRNs in development, evolution, and disease.

developmental biology↗

bioRxiv2022

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

MotivationStudying the interaction or co-expression of the proteins or markers in the tumor microenvironment (TME) of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for each marker, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness. ResultsWe present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies. AvailabilityThe associated R package can be found here, https://github.com/sealx017/MIAMI. Contactsouvik.seal@cuanschutz.edu Supplementary informationThe Supplementary Material is attached.

bioinformatics↗

bioRxiv2022

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a fast alternative of CFGL known as rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in several simulation scenarios. We use RCFGL to jointly estimate the gene co-expression networks of different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL. Author summaryInferring gene co-expression networks can be useful for understanding pathway activity and gene regulation. While jointly estimating co-expression networks of multiple conditions, taking into account condition specificity such as information about an edge being present only in a specific condition, substantially increases the power. In this paper, a computationally rapid condition adaptive method for jointly estimating gene co-expression networks of multiple conditions is proposed. The novelty of the method is demonstrated through a broad range of simulation studies and a real data analysis with multiple brain regions from a genetically diverse cohort of rats.

bioinformatics↗

bioRxiv2022

Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/475326v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f29341org.highwire.dtl.DTLVardef@1a1970borg.highwire.dtl.DTLVardef@1c3581dorg.highwire.dtl.DTLVardef@1ce00d4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2021

AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

In this study, we introduce and implement an interpretable graph-based deep learning prediction model, which utilizes protein binding sites along with self-attention to learn which protein binding sites interact with a given ligand. Our proposed model enables interpretability by identifying the protein binding sites that contribute the most towards the Drug-Target Interaction. Results on three benchmark datasets show improved performance compared to previous graph-based models. More significantly, unlike previous studies our model performance remains close to the optimal performance when tested with new proteins (ie., high generalizablity). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally-observed (measured) DTIs illustrates the potential of our method as an effective pre-screening tool in drug re-purposing applications.

bioinformatics↗

bioRxiv2021

DenVar: Density-based Variation analysis of multiplex imaging data

Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions, often known as functional markers, in the TME becomes extremely crucial for subject-specific assessment of risks, such as risk of recurrence and risk of death. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular functional marker, and the other to classify the subjects based on the proportion of the positive cells. The selection of the thresholds has a large impact on the results and an arbitrary selection can lead to an incomprehensible conclusion. In light of this problem, we present a threshold-free distance between the subjects based on the probability densities of the functional markers. The distance can be used to classify the subjects into meaningful groups or can be used in a linear mixed model setup for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling an easier but interpretable analysis of these types of data. With the proposed method, we analyze a lung cancer dataset from an mIHC platform, finding the difference in the density of functional marker HLA-DR to be significantly associated with the overall survival. The approach is also applied on an MIBI triple-negative breast cancer dataset to analyze effects of multiple functional markers. Finally, we demonstrate the reliability of our method through extensive simulation studies.

bioinformatics↗

bioRxiv2021

Rapid Estimation of SNP Heritability using Predictive Process approximation in Large scale Cohort Studies

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.

bioinformatics↗

bioRxiv2020

Estimating SNP heritability in presence of population substructure in large biobank-scale data

SNP heritability of a trait is measured by the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits. The basic concept behind this approach is to model genetic contribution as a random effect, where the variance of this genetic contribution attributes to the heritability of the trait. This linear mixed model approach requires estimation of relatedness among individuals in the sample, which is usually captured by estimating a genetic relationship matrix (GRM). Heritability is estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches, and this estimation relies heavily on the GRM computed from the genetic data on individuals. Presence of population substructure in the data could significantly impact the GRM estimation and may introduce bias in heritability estimation. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. Here we propose an alternative way of estimating heritability in multi-ethnic studies. Our proposed approach is a MOM estimator derived from the Haseman-Elston regression and gives an asymptotically unbiased estimate of heritability in presence of population stratification. It introduces adjustments for the population stratification in a second-order estimating equation and allows for the total phenotypic variance vary by ethnicity. We study the performance of different MOM and REML approaches in presence of population stratification through extensive simulation studies. We estimate the heritability of height, weight and other anthropometric traits in the UK Biobank cohort to investigate the impact of subtle population substructure on SNP heritability estimation.

genetics↗

bioRxiv2020

CARCASS SCAVENGING RELAXES CHEMICAL-DRIVEN FEMALE INTERFERENCE COMPETITION

Female-female nonsexual interference competition is rapidly emerging as a major fitness determinant of biased sex-ratio groups with high female density. How do females overcome such competition? We used adult flour beetle Tribolium castaneum to answer this question, where females from female-biased groups suppressed each others fecundity by secreting toxic quinones from their stink glands, revealing a chemical-driven interference competition. The added natal resource did not alleviate these fitness costs. Females also did not disperse more at high female-density. Hence, the competition was neither limited by the total resource availability nor the inability to avoid chemical interference. Instead, protein sequestered via scavenging of nutrient-rich carcasses relaxed the female competition, by increasing their fecundity and reducing the quinone content. Even infected carcasses were scavenged to extract fitness benefits, despite the infection-risk. Finally, individual stink gland components triggered carcass-scavenging to increase fecundity, indicating a potentially novel chemical feedback loop to reduce the competition.

ecology↗

bioRxiv2020

Mutational spectra of SARS-CoV-2 orf1ab polyprotein and Signature mutations in the United States of America

Pandemic COVID-19 outbreak has been caused due to SARS-COV2 pathogen, resulting millions of infection and death worldwide, USA being on top at the present moment. The long, complex orf1ab polyproteins of SARS-COV2 play an important role in viral RNA synthesis. To assess the impact of mutations in this important domain, we analyzed 1134 complete protein sequences of orf1ab polyprotein from NCBI Virus database from affected patients across various states of USA from December 2019 to 25th April, 2020. Multiple sequence alignment using Clustal Omega followed by statistical significance was calculated. Four significant mutations T265I (nsp 2), P4715L (nsp 12) and P5828L and Y5865C (both at nsp 13) were identified in important non-structural proteins, which function either as replicase or helicase. A comparative analysis shows 265T>I, 5828P>L and 5865Y>C are unique to USA and not reported from Europe or Asia; while one, 4715P>L is predominant in both Europe and USA. Mutational changes in amino acids are predicted to alter structure and function of corresponding proteins, thereby it is imperative to consider the mutational spectra while designing new antiviral therapeutics targeting viral orf1ab.

bioinformatics↗

bioRxiv2019

A staphylococcal cyclophilin carries a single domain and unfolds via the formation of an intermediate that preserves cyclosporin A binding activity

Cyclophilin (Cyp), a peptidyl-prolyl cis-trans isomerase (PPIase), acts as a virulence factor in many bacteria including Staphylococcus aureus. The enzymatic activity of Cyp is inhibited by cyclosporin A (CsA), an immunosuppressive drug. To precisely determine the unfolding mechanism and the domain structure of Cyp, we have investigated a chimeric S. aureus Cyp (rCyp) using various probes. Our limited proteolysis and the consequent analysis of the proteolytic fragments indicate that rCyp is composed of one domain with a short flexible tail at the C-terminal end. We also show that the urea-induced unfolding of both rCyp and rCyp-CsA is completely reversible and proceeds via the synthesis of at least one stable intermediate. The secondary structure, tertiary structure, and the hydrophobic surface area of no intermediate are fully identical to those of other intermediate or the related native protein. Further analyses reveal no loss of CsA binding activity in rCyp intermediate. The thermodynamic stability of rCyp was also significantly increased in the presence of CsA, recommending that this protein could be employed to screen new CsA derivatives in future.

biochemistry↗

bioRxiv2018

nPIST: A Novel Actin Binding Protein of trans-Golgi Network

npist is the neuronal isoform of PIST, a trans-golgi associated protein involved in major modulation of vesicular trafficking. nPIST interacts with glutamate delta2 receptor (GluR{delta}2) in Purkinje cells. Our study shows nPIST as a novel actin binding protein. Our structure based sequence analysis shows nPIST contains one WH2-like domain. Further our experimental analysis illustrates that fragment of nPIST consisting of WH2-like domain binds to actin. Moreover it was found that nPIST contains several regions involved in interaction with actin. The binding of nPIST to actin through multiple actin binding regions facilitated actin filament stabilization in vitro. In vivo, nPIST localized actin in perinuclear region as a blotch when ectopically expressed.

cell biology↗

bioRxiv2017

Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only [~]1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as Linked-Reads. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

genomics↗

bioRxiv2017

An interlaboratory study of complex variant detection

Next-generation sequencing (NGS) is widely used and cost-effective. Depending on the specific methods, NGS can have limitations detecting certain technically challenging variant types even though they are both prevalent in patients and medically important. These types are underrepresented in validation studies, hindering the uniform assessment of test methodologies by laboratory directors and clinicians. Specimens containing such variants can be difficult to obtain; thus, we evaluated a novel solution to this problem in which a diverse set of technically challenging variants was synthesized and introduced into a known genomic background. This specimen was sequenced by 7 laboratories using 10 different NGS workflows. The specimen was compatible with all 10 workflows and presented biochemical and bioinformatic challenges similar to those of patient specimens. Only 10 of 22 challenging variants were correctly identified by all 10 workflows, and only 3 workflows detected all 22. Many, but not all, of the sensitivity limitations were bioinformatic in nature. We conclude that Synthetic controls can provide an efficient and informative mechanism to augment studies with technically challenging variants that are difficult to obtain otherwise. Data from such specimens can facilitate inter-laboratory methodologic comparisons and can help establish standards that improve communication between clinicians and laboratories.

genetics↗