1
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Tejeda-Garibay S, Zhao L, Hum NR, Pimentel M, Diep AL, Amiri B, Sindi SS, Weilhammer DR, Loots GG, Hoyer KK. Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro. bioRxiv 2023:2023.10.23.563655. [PMID: 37961490 PMCID: PMC10634762 DOI: 10.1101/2023.10.23.563655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides. Unfortunately, patients are often misdiagnosed with bacterial pneumonia leading to inappropriate antibiotic treatment. Soil bacteria B. subtilis-like species exhibits antagonistic properties against Coccidioides in vitro; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro. We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2xGYE (GYE) and CNA w/ 5% sheep's blood agar (5%SB-CNA) inhibited the growth of Coccidioides, but that grown on chocolate agar does not. Partial depletion of the microbiota through antibiotic disk diffusion revealed that microbiota depletion leads to diminished inhibition and comparable growth of Coccidioides growth to controls. To characterize the bacteria grown and narrow down potential candidates contributing to the inhibition of Coccidioides, 16s rRNA sequencing of tracheal and intestinal agar cultures and murine lung extracts was performed. The identity of host bacteria that may be responsible for this inhibition was revealed. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo.
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Affiliation(s)
- Susana Tejeda-Garibay
- Quantitative and Systems Biology, Graduate Program, University of California Merced, CA
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
| | - Lihong Zhao
- Department of Applied Mathematics, University of California, Merced, CA
- Health Sciences Research Institute, University of California Merced, Merced, CA
| | - Nicholas R Hum
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
| | - Maria Pimentel
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California Merced, CA
| | - Anh L Diep
- Quantitative and Systems Biology, Graduate Program, University of California Merced, CA
| | - Beheshta Amiri
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, CA
- Health Sciences Research Institute, University of California Merced, Merced, CA
| | - Dina R Weilhammer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
| | - Gabriela G Loots
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
- University of California Davis Health, Department of Orthopaedic Surgery, Lawrence J. Ellison Musculo-skeletal Research Center, 2700 Stockton Blvd, Sacramento, CA 95817, CA
| | - Katrina K Hoyer
- Quantitative and Systems Biology, Graduate Program, University of California Merced, CA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California Merced, CA
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratories, Livermore CA
- Health Sciences Research Institute, University of California Merced, Merced, CA
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2
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Li R, Rozum JC, Quail MM, Qasim MN, Sindi SS, Nobile CJ, Albert R, Hernday AD. Inferring gene regulatory networks using transcriptional profiles as dynamical attractors. PLoS Comput Biol 2023; 19:e1010991. [PMID: 37607190 PMCID: PMC10473541 DOI: 10.1371/journal.pcbi.1010991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 09/01/2023] [Accepted: 07/19/2023] [Indexed: 08/24/2023] Open
Abstract
Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.
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Affiliation(s)
- Ruihao Li
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Jordan C. Rozum
- Department of Systems Science and Industrial Engineering, Binghamton University (State University of New York), Binghamton, New York, United States of America
| | - Morgan M. Quail
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Mohammad N. Qasim
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, California, United States of America
| | - Clarissa J. Nobile
- Department of Molecular Cell Biology, University of California, Merced, Merced, California, United States of America
- Health Sciences Research Institute, University of California, Merced, Merced, California, United States of America
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, University Park, Pennsylvania, United States of America
- Department of Biology, Pennsylvania State University, University Park, University Park, Pennsylvania, United States of America
| | - Aaron D. Hernday
- Department of Molecular Cell Biology, University of California, Merced, Merced, California, United States of America
- Health Sciences Research Institute, University of California, Merced, Merced, California, United States of America
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3
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Davalos OA, Heydari AA, Fertig EJ, Sindi SS, Hoyer KK. Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention. bioRxiv 2023:2023.05.29.542760. [PMID: 37398136 PMCID: PMC10312486 DOI: 10.1101/2023.05.29.542760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.
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Affiliation(s)
- Oscar A. Davalos
- Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA
| | - A. Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA
- Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Elana J. Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California, Merced, CA, USA
- Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Katrina K. Hoyer
- Health Sciences Research Institute, University of California, Merced, CA, USA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
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4
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Heydari AA, Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. Biophys Rev (Melville) 2023; 4:011306. [PMID: 38505815 PMCID: PMC10903438 DOI: 10.1063/5.0091135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/19/2022] [Indexed: 03/21/2024]
Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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Paropkari AD, Bapat PS, Sindi SS, Nobile CJ. A Computational Workflow for Analysis of 3' Tag-Seq Data. Curr Protoc 2023; 3:e664. [PMID: 36779816 PMCID: PMC9930165 DOI: 10.1002/cpz1.664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.
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Affiliation(s)
- Akshay D. Paropkari
- Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
| | - Priyanka S. Bapat
- Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
| | - Suzanne S. Sindi
- Department of Applied Mathematics, School of Natural Sciences, University of California, Merced, CA, USA
| | - Clarissa J. Nobile
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
- Health Science Research Institute, University of California, Merced, CA, USA
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6
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Zhao L, Santiago F, Rutter EM, Khatri S, Sindi SS. Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus. Bull Math Biol 2023; 85:13. [PMID: 36637563 PMCID: PMC9837465 DOI: 10.1007/s11538-022-01107-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 11/13/2022] [Indexed: 01/14/2023]
Abstract
In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to "normal" in-person operations, but it is not clear if-or for how long-campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infectious surrounding community will maintain infections on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infectious individuals.
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Affiliation(s)
- Lihong Zhao
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA
| | - Fabian Santiago
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA
| | - Erica M. Rutter
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA ,Health Sciences Research Institute, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA
| | - Shilpa Khatri
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA ,Health Sciences Research Institute, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA ,Health Sciences Research Institute, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343 USA
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7
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Qasim MN, Valle Arevalo A, Paropkari AD, Ennis CL, Sindi SS, Nobile CJ, Hernday AD. Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in <em>Candida albicans</em>: A Comprehensive CUT&RUN Method and Data Analysis Workflow. J Vis Exp 2022. [DOI: 10.3791/63655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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8
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Heydari AA, Davalos OA, Zhao L, Hoyer KK, Sindi SS. ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders. Bioinformatics 2022; 38:2194-2201. [PMID: 35179571 PMCID: PMC9004654 DOI: 10.1093/bioinformatics/btac095] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 01/19/2022] [Accepted: 02/15/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. RESULTS We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. AVAILABILITY AND IMPLEMENTATION The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA 95343, USA,Health Sciences Research Institute, University of California, Merced, CA 95343, USA
| | - Oscar A Davalos
- Health Sciences Research Institute, University of California, Merced, CA 95343, USA,Quantitative and Systems Biology Graduate Program, University of California, Merced, CA 95343, USA
| | - Lihong Zhao
- Department of Applied Mathematics, University of California, Merced, CA 95343, USA
| | - Katrina K Hoyer
- Health Sciences Research Institute, University of California, Merced, CA 95343, USA,Department of Molecular and Cell Biology, University of California, Merced, CA 95343, USA
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9
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Griesemer M, Sindi SS. Rules of Engagement: A Guide to Developing Agent-Based Models. Methods Mol Biol 2022; 2349:367-380. [PMID: 34719003 DOI: 10.1007/978-1-0716-1585-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).
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Affiliation(s)
- Marc Griesemer
- Controls and Data Systems Division, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA.
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10
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Way GP, Greene CS, Carninci P, Carvalho BS, de Hoon M, Finley SD, Gosline SJC, Lȇ Cao KA, Lee JSH, Marchionni L, Robine N, Sindi SS, Theis FJ, Yang JYH, Carpenter AE, Fertig EJ. A field guide to cultivating computational biology. PLoS Biol 2021; 19:e3001419. [PMID: 34618807 PMCID: PMC8525744 DOI: 10.1371/journal.pbio.3001419] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 10/19/2021] [Indexed: 11/18/2022] Open
Abstract
Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.
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Affiliation(s)
- Gregory P. Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Casey S. Greene
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
- Human Technopole, Milan, Italy
| | - Benilton S. Carvalho
- Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas, Brazil
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
| | - Stacey D. Finley
- Department of Biomedical Engineering, Quantitative and Computational Biology, and Chemical Engineering & Materials Science, University of Southern California, Los Angeles, California, United States of America
| | - Sara J. C. Gosline
- Pacific Northwest National Laboratory, Seattle, Washington, United States of America
| | - Kim-Anh Lȇ Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jerry S. H. Lee
- Ellison Institute and Departments of Medicine/Oncology, Chemical Engineering, and Material Sciences, University of Southern California, Los Angeles, California, United States of America
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill-Cornell Medicine, New York, New York, United States of America
| | - Nicolas Robine
- Computational Biology Lab, New York Genome Center, New York, New York, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Center Munich and Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Jean Y. H. Yang
- Charles Perkins Centre and School of Mathematics and Statistics, The University of Sydney, Australia
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elana J. Fertig
- Convergence Institute, Departments of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America
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11
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Seher TD, Nguyen N, Ramos D, Bapat P, Nobile CJ, Sindi SS, Hernday AD. AddTag, a two-step approach with supporting software package that facilitates CRISPR/Cas-mediated precision genome editing. G3 (Bethesda) 2021; 11:jkab216. [PMID: 34544122 PMCID: PMC8496238 DOI: 10.1093/g3journal/jkab216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/13/2021] [Indexed: 12/22/2022]
Abstract
CRISPR/Cas-induced genome editing is a powerful tool for genetic engineering, however, targeting constraints limit which loci are editable with this method. Since the length of a DNA sequence impacts the likelihood it overlaps a unique target site, precision editing of small genomic features with CRISPR/Cas remains an obstacle. We introduce a two-step genome editing strategy that virtually eliminates CRISPR/Cas targeting constraints and facilitates precision genome editing of elements as short as a single base-pair at virtually any locus in any organism that supports CRISPR/Cas-induced genome editing. Our two-step approach first replaces the locus of interest with an "AddTag" sequence, which is subsequently replaced with any engineered sequence, and thus circumvents the need for direct overlap with a unique CRISPR/Cas target site. In this study, we demonstrate the feasibility of our approach by editing transcription factor binding sites within Candida albicans that could not be targeted directly using the traditional gene-editing approach. We also demonstrate the utility of the AddTag approach for combinatorial genome editing and gene complementation analysis, and we present a software package that automates the design of AddTag editing.
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Affiliation(s)
- Thaddeus D Seher
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343, USA
| | - Namkha Nguyen
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343, USA
| | - Diana Ramos
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
| | - Priyanka Bapat
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343, USA
| | - Clarissa J Nobile
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
- Health Sciences Research Institute, University of California, Merced, Merced, CA 95343, USA
| | - Suzanne S Sindi
- Health Sciences Research Institute, University of California, Merced, Merced, CA 95343, USA
- Department of Applied Mathematics, School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
| | - Aaron D Hernday
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
- Health Sciences Research Institute, University of California, Merced, Merced, CA 95343, USA
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12
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Abstract
Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.
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Affiliation(s)
- Karin Leiderman
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, California
| | - Dougald M Monroe
- Department of Medicine, UNC Blood Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Aaron L Fogelson
- Departments of Mathematics and Biomedical Engineering, University of Utah, Salt Lake City, Utah
| | - Keith B Neeves
- Department of Bioengineering, Department of Pediatrics, Section of Hematology, Oncology, and Bone Marrow Transplant, Hemophilia and Thrombosis Center, University of Colorado, Denver, Colorado
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13
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Abstract
Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa (activated factor X).
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Affiliation(s)
- Kathryn G Link
- Department of Mathematics, University of California Davis (K.G.L.)
| | - Michael T Stobb
- Department of Mathematics and Computer Science, Coe College, Cedar Rapids, IA (M.T.S.)
| | - Dougald M Monroe
- Department of Medicine, UNC Blood Research Center, University of North Carolina at Chapel Hill (D.M.M.)
| | - Aaron L Fogelson
- Departments of Mathematics and Biomedical Engineering, University of Utah, Salt Lake City (A.L.F.)
| | - Keith B Neeves
- Departments of Bioengineering and Pediatrics, Section of Hematology, Oncology, and Bone Marrow Transplant, Hemophilia and Thrombosis Center, University of Colorado, Denver (K.B.N.)
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced (S.S.S.)
| | - Karin Leiderman
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden (K.L.)
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14
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Lemarre P, Pujo-Menjouet L, Sindi SS. A unifying model for the propagation of prion proteins in yeast brings insight into the [PSI+] prion. PLoS Comput Biol 2020; 16:e1007647. [PMID: 32453794 PMCID: PMC7274466 DOI: 10.1371/journal.pcbi.1007647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 06/05/2020] [Accepted: 03/06/2020] [Indexed: 11/18/2022] Open
Abstract
The use of yeast systems to study the propagation of prions and amyloids has emerged as a crucial aspect of the global endeavor to understand those mechanisms. Yeast prion systems are intrinsically multi-scale: the molecular chemical processes are indeed coupled to the cellular processes of cell growth and division to influence phenotypical traits, observable at the scale of colonies. We introduce a novel modeling framework to tackle this difficulty using impulsive differential equations. We apply this approach to the [PSI+] yeast prion, which is associated with the misconformation and aggregation of Sup35. We build a model that reproduces and unifies previously conflicting experimental observations on [PSI+] and thus sheds light onto characteristics of the intracellular molecular processes driving aggregate replication. In particular our model uncovers a kinetic barrier for aggregate replication at low densities, meaning the change between prion or prion-free phenotype is a bi-stable transition. This result is based on the study of prion curing experiments, as well as the phenomenon of colony sectoring, a phenotype which is often ignored in experimental assays and has never been modeled. Furthermore, our results provide further insight into the effect of guanidine hydrochloride (GdnHCl) on Sup35 aggregates. To qualitatively reproduce the GdnHCl curing experiment, aggregate replication must not be completely inhibited, which suggests the existence of a mechanism different than Hsp104-mediated fragmentation. Those results are promising for further development of the [PSI+] model, but also for extending the use of this novel framework to other yeast prion or amyloid systems.
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Affiliation(s)
- Paul Lemarre
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, F-69622 Villeurbanne cedex, France
- INRIA Rhônes-Alpes, INRIA, Villeurbanne, France
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
| | - Laurent Pujo-Menjouet
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, F-69622 Villeurbanne cedex, France
- INRIA Rhônes-Alpes, INRIA, Villeurbanne, France
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
- * E-mail:
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15
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Villali J, Dark J, Brechtel TM, Pei F, Sindi SS, Serio TR. Nucleation seed size determines amyloid clearance and establishes a barrier to prion appearance in yeast. Nat Struct Mol Biol 2020; 27:540-549. [PMID: 32367069 PMCID: PMC7293557 DOI: 10.1038/s41594-020-0416-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 03/18/2020] [Indexed: 12/14/2022]
Abstract
Amyloid appearance is a rare event that is promoted in the presence of
other aggregated proteins. These aggregates were thought to act by templating
the formation of an assembly-competent nucleation seed, but we find an
unanticipated role for them in enhancing the persistence of amyloid after it
arises. Specifically, Saccharoymyces cerevisiae Rnq1 amyloid
reduces chaperone-mediated disassembly of Sup35 amyloid, promoting its
persistence in yeast. Mathematical modeling and corresponding in
vivo experiments link amyloid persistence to the conformationally
defined size of the Sup35 nucleation seed and suggest that amyloid is actively
cleared by disassembly below this threshold to suppress appearance of the
[PSI+] prion in vivo.
Remarkably, this framework resolves multiple known inconsistencies in the
appearance and curing of yeast prions. Thus, our observations establish the size
of the nucleation seed as a previously unappreciated characteristic of prion
variants that is key to understanding transitions between prion states.
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Affiliation(s)
- Janice Villali
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA.,Relay Therapeutics, Cambridge, MA, USA
| | - Jason Dark
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA
| | - Teal M Brechtel
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Fen Pei
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.,BioLegend, San Diego, CA, USA
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA.
| | - Tricia R Serio
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA, USA.
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16
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Wilson SN, Sindi SS, Brooks HZ, Hohn ME, Price CR, Radunskaya AE, Williams ND, Fefferman NH. How Emergent Social Patterns in Allogrooming Combat Parasitic Infections. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2020.00054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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17
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Link KG, Stobb MT, Sorrells MG, Bortot M, Ruegg K, Manco-Johnson MJ, Di Paola JA, Sindi SS, Fogelson AL, Leiderman K, Neeves KB. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A. J Thromb Haemost 2020; 18:306-317. [PMID: 31562694 PMCID: PMC6994344 DOI: 10.1111/jth.14653] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/24/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND The variability in bleeding patterns among individuals with hemophilia A, who have similar factor VIII (FVIII) levels, is significant and the origins are unknown. OBJECTIVE To use a previously validated mathematical model of flow-mediated coagulation as a screening tool to identify parameters that are most likely to enhance thrombin generation in the context of FVIII deficiency. METHODS We performed a global sensitivity analysis (GSA) on our mathematical model to identify potential modifiers of thrombin generation. Candidates from the GSA were confirmed by calibrated automated thrombography (CAT) and flow assays on collagen-tissue factor (TF) surfaces at a shear rate of 100 per second. RESULTS Simulations identified low-normal factor V (FV) (50%) as the strongest modifier, with additional thrombin enhancement when combined with high-normal prothrombin (150%). Low-normal FV levels or partial FV inhibition (60% activity) augmented thrombin generation in FVIII-inhibited or FVIII-deficient plasma in CAT. Partial FV inhibition (60%) boosted fibrin deposition in flow assays performed with whole blood from individuals with mild and moderate FVIII deficiencies. These effects were amplified by high-normal prothrombin levels in both experimental models. CONCLUSIONS These results show that low-normal FV levels can enhance thrombin generation in hemophilia A. Further explorations with the mathematical model suggest a potential mechanism: lowering FV reduces competition between FV and FVIII for factor Xa (FXa) on activated platelet surfaces (APS), which enhances FVIII activation and rescues thrombin generation in FVIII-deficient blood.
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Affiliation(s)
- Kathryn G. Link
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA
| | - Michael T. Stobb
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Matthew G. Sorrells
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA
| | - Maria Bortot
- Department of Bioengineering, University of Colorado, Denver | Anschutz Medical Campus, Aurora, CO, USA
| | - Katherine Ruegg
- Hemophilia and Thrombosis Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Marilyn J. Manco-Johnson
- Hemophilia and Thrombosis Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Jorge A. Di Paola
- Hemophilia and Thrombosis Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Suzanne S. Sindi
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Aaron L. Fogelson
- Department of Applied Mathematics, University of California, Merced, Merced, CA, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Karin Leiderman
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, USA
| | - Keith B. Neeves
- Department of Bioengineering, University of Colorado, Denver | Anschutz Medical Campus, Aurora, CO, USA
- Hemophilia and Thrombosis Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
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18
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Stobb MT, Monroe DM, Leiderman K, Sindi SS. Assessing the impact of product inhibition in a chromogenic assay. Anal Biochem 2019; 580:62-71. [PMID: 31091429 DOI: 10.1016/j.ab.2019.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/29/2019] [Accepted: 05/01/2019] [Indexed: 12/30/2022]
Abstract
Chromogenic substrates (CS) are synthetic substrates used to monitor the activity of a target enzyme. It has been reported that some CSs display competitive product inhibition with their target enzyme. Thus, in assays where enzyme activity is continuously monitored over long periods of time, the product inhibition may significantly interfere with the reactions being monitored. Despite this knowledge, it is rare for CSs to be directly incorporated into mathematical models that simulate these assays. This devalues the predictive power of the models. In this study, we examined the interactions between a single enzyme, coagulation factor Xa, and its chromogenic substrate. We developed, and experimentally validated, a mathematical model of a chromogenic assay for factor Xa that explicitly included product inhibition from the CS. We employed Bayesian inference, in the form of Markov-Chain Monte Carlo, to estimate the strength of the product inhibition and other sources of uncertainty such as pipetting error and kinetic rate constants. Our model, together with carefully calibrated biochemistry experiments, allowed for full characterization of the strength and impact of product inhibition in the assay. The effect of CS product inhibition in more complex reaction mixtures was further explored using mathematical models.
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Affiliation(s)
- Michael T Stobb
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Road, Merced, CA, 95340, USA
| | - Dougald M Monroe
- Hematology/Oncology, 8202B Mary Ellen Jones Building, Campus Box 7035, Chapel Hill, NC, 27599-7035, USA
| | - Karin Leiderman
- Department of Applied Mathematics and Statistics, Colorado School of Mines, 1500 Illinois St, Golden, CO, 80401, USA.
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, 5200 North Lake Road, Merced, CA, 95340, USA
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19
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Lemarre P, Pujo-Menjouet L, Sindi SS. Generalizing a mathematical model of prion aggregation allows strain coexistence and co-stability by including a novel misfolded species. J Math Biol 2018; 78:465-495. [PMID: 30116882 PMCID: PMC6399074 DOI: 10.1007/s00285-018-1280-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/20/2018] [Indexed: 11/29/2022]
Abstract
Prions are proteins capable of adopting misfolded conformations and transmitting these conformations to other normally folded proteins. Prions are most commonly known for causing fatal neurodegenerative diseases in mammals but are also associated with several harmless phenotypes in yeast. A distinct feature of prion propagation is the existence of different phenotypical variants, called strains. It is widely accepted that these strains correspond to different conformational states of the protein, but the mechanisms driving their interactions remain poorly understood. This study uses mathematical modeling to provide insight into this problem. We show that the classical model of prion dynamics allows at most one conformational strain to stably propagate. In order to conform to biological observations of strain coexistence and co-stability, we develop an extension of the classical model by introducing a novel prion species consistent with biological studies. Qualitative analysis of this model reveals a new variety of behavior. Indeed, it allows for stable coexistence of different strains in a wide parameter range, and it also introduces intricate initial condition dependency. These new behaviors are consistent with experimental observations of prions in both mammals and yeast. As such, our model provides a valuable tool for investigating the underlying mechanisms of prion propagation and the link between prion strains and strain specific phenotypes. The consideration of a novel prion species brings a change in perspective on prion biology and we use our model to generate hypotheses about prion infectivity.
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Affiliation(s)
- Paul Lemarre
- School of Natural Sciences, University of California, Merced, 5200 North Lake Road, Merced, CA, 95343, USA
| | - Laurent Pujo-Menjouet
- Institut Camille Jordan, Université de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, 43 blvd. du 11 novembre 1918, 69622, Villeurbanne cedex, France.,Team Dracula, INRIA, 69603, Villeurbanne cedex, France
| | - Suzanne S Sindi
- Applied Mathematics School of Natural Sciences, University of California, Merced, 5200 North Lake Road, Merced, CA, 95343, USA.
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20
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Link KG, Stobb MT, Di Paola J, Neeves KB, Fogelson AL, Sindi SS, Leiderman K. A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow. PLoS One 2018; 13:e0200917. [PMID: 30048479 PMCID: PMC6062055 DOI: 10.1371/journal.pone.0200917] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/04/2018] [Indexed: 11/23/2022] Open
Abstract
The hemostatic response involves blood coagulation and platelet aggregation to stop blood loss from an injured blood vessel. The complexity of these processes make it difficult to intuit the overall hemostatic response without quantitative methods. Mathematical models aim to address this challenge but are often accompanied by numerous parameters choices and thus need to be analyzed for sensitivity to such choices. Here we use local and global sensitivity analyses to study a model of coagulation and platelet deposition under flow. To relate with clinical assays, we measured the sensitivity of three specific thrombin metrics: lag time, maximum relative rate of generation, and final concentration after 20 minutes. In addition, we varied parameters of three different classes: plasma protein levels, kinetic rate constants, and platelet characteristics. In terms of an overall ranking of the model’s sensitivities, we found that the local and global methods provided similar information. Our local analysis, in agreement with previous findings, shows that varying parameters within 50-150% of baseline values, in a one-at-a-time (OAT) fashion, always leads to significant thrombin generation in 20 minutes. Our global analysis gave a different and novel result highlighting groups of parameters, still varying within the normal 50-150%, that produced little or no thrombin in 20 minutes. Variations in either plasma levels or platelet characteristics, using either OAT or simultaneous variations, always led to strong thrombin production and overall, relatively low output variance. Simultaneous variation in kinetics rate constants or in a subset of all three parameter classes led to the highest overall output variance, incorporating instances with little to no thrombin production. The global analysis revealed multiple parameter interactions in the lag time and final concentration leading to relatively high variance; high variance was also observed in the thrombin generation rate, but parameters attributed to that variance acted independently and additively.
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Affiliation(s)
- Kathryn G. Link
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States of America
| | - Michael T. Stobb
- Department of Applied Mathematics, University of California Merced, Merced, CA, United States of America
| | - Jorge Di Paola
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Keith B. Neeves
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States of America
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, United States of America
| | - Aaron L. Fogelson
- Department of Mathematics, University of Utah, Salt Lake City, UT, United States of America
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, CA, United States of America
| | - Karin Leiderman
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, United States of America
- * E-mail:
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21
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Valentine KM, Davini D, Lawrence TJ, Mullins GN, Manansala M, Al-Kuhlani M, Pinney JM, Davis JK, Beaudin AE, Sindi SS, Gravano DM, Hoyer KK. CD8 Follicular T Cells Promote B Cell Antibody Class Switch in Autoimmune Disease. J Immunol 2018; 201:31-40. [PMID: 29743314 DOI: 10.4049/jimmunol.1701079] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 04/22/2018] [Indexed: 02/04/2023]
Abstract
CD8 T cells can play both a protective and pathogenic role in inflammation and autoimmune development. Recent studies have highlighted the ability of CD8 T cells to function as T follicular helper (Tfh) cells in the germinal center in the context of infection. However, whether this phenomenon occurs in autoimmunity and contributes to autoimmune pathogenesis is largely unexplored. In this study, we show that CD8 T cells acquire a CD4 Tfh profile in the absence of functional regulatory T cells in both the IL-2-deficient and scurfy mouse models. Depletion of CD8 T cells mitigates autoimmune pathogenesis in IL-2-deficient mice. CD8 T cells express the B cell follicle-localizing chemokine receptor CXCR5, a principal Tfh transcription factor Bcl6, and the Tfh effector cytokine IL-21. CD8 T cells localize to the B cell follicle, express B cell costimulatory proteins, and promote B cell differentiation and Ab isotype class switching. These data reveal a novel contribution of autoreactive CD8 T cells to autoimmune disease, in part, through CD4 follicular-like differentiation and functionality.
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Affiliation(s)
- Kristen M Valentine
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343
| | - Dan Davini
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343
| | - Travis J Lawrence
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343
| | - Genevieve N Mullins
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, CA 95343
| | - Miguel Manansala
- Stem Cell Instrumentation Foundry, University of California, Merced, Merced, CA 95343; and
| | - Mufadhal Al-Kuhlani
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343
| | - James M Pinney
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343
| | - Jason K Davis
- Department of Applied Mathematics, University of California, Merced, Merced, CA 95343
| | - Anna E Beaudin
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343
| | - Suzanne S Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, CA 95343
| | - David M Gravano
- Stem Cell Instrumentation Foundry, University of California, Merced, Merced, CA 95343; and
| | - Katrina K Hoyer
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, Merced, CA 95343;
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22
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Brechtel TM, Villali J, Davis J, Pei F, Sindi SS, Serio TR. [
PIN
+
]ning down [
PSI
+
] inducibility. FASEB J 2018. [DOI: 10.1096/fasebj.2018.32.1_supplement.lb42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | | | - Fen Pei
- BMCBUniversity of ArizonaTucsonAZ
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23
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Barron M, Dhillon J, Asghar SA, Kuse Q, De La Cruz N, Vu E, Sindi SS, Ortiz RM. Modeling Effects of a High‐Fat (Almonds) vs. High‐Carbohydrate (Graham Crackers) Snack on Metabolic Outcomes in College Freshmen. FASEB J 2018. [DOI: 10.1096/fasebj.2018.32.1_supplement.767.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | | | | | | | - Emily Vu
- University of CaliforniaMercedMercedCA
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24
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Pei F, DiSalvo S, Sindi SS, Serio TR. A dominant-negative mutant inhibits multiple prion variants through a common mechanism. PLoS Genet 2017; 13:e1007085. [PMID: 29084237 PMCID: PMC5679637 DOI: 10.1371/journal.pgen.1007085] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/09/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Prions adopt alternative, self-replicating protein conformations and thereby determine novel phenotypes that are often irreversible. Nevertheless, dominant-negative prion mutants can revert phenotypes associated with some conformations. These observations suggest that, while intervention is possible, distinct inhibitors must be developed to overcome the conformational plasticity of prions. To understand the basis of this specificity, we determined the impact of the G58D mutant of the Sup35 prion on three of its conformational variants, which form amyloids in S. cerevisiae. G58D had been previously proposed to have unique effects on these variants, but our studies suggest a common mechanism. All variants, including those reported to be resistant, are inhibited by G58D but at distinct doses. G58D lowers the kinetic stability of the associated amyloid, enhancing its fragmentation by molecular chaperones, promoting Sup35 resolubilization, and leading to amyloid clearance particularly in daughter cells. Reducing the availability or activity of the chaperone Hsp104, even transiently, reverses curing. Thus, the specificity of inhibition is determined by the sensitivity of variants to the mutant dosage rather than mode of action, challenging the view that a unique inhibitor must be developed to combat each variant.
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Affiliation(s)
- Fen Pei
- The University of Arizona, Department of Molecular and Cellular Biology, Tucson, Arizona, United States of America
| | - Susanne DiSalvo
- Brown University, Department of Molecular and Cell Biology, Providence, Rhode Island, United States of America
| | - Suzanne S. Sindi
- University of California, Merced, Applied Mathematics, School of Natural Sciences, Merced, California, United States of America
- * E-mail: (SS); (TRS)
| | - Tricia R. Serio
- The University of Arizona, Department of Molecular and Cellular Biology, Tucson, Arizona, United States of America
- * E-mail: (SS); (TRS)
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25
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Abstract
The yeast Saccharomyces cerevisiae has emerged as an ideal model system to study the dynamics of prion proteins which are responsible for a number of fatal neurodegenerative diseases in humans. Within an infected cell, prion proteins aggregate in complexes which may increase in size or be fragmented and are transmitted upon cell division. Recent work in yeast suggests that only aggregates below a critical size are transmitted efficiently. We formulate a continuous-time branching process model of a yeast colony under conditions of prion curing. We generalize previous approaches by providing an explicit formula approximating prion loss as influenced by both aggregate growth and size-dependent transmission.
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26
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Abstract
The formation of a stable protein aggregate is regarded as the rate limiting step in the establishment of prion diseases. In these systems, once aggregates reach a critical size the growth process accelerates and thus the waiting time until the appearance of the first critically sized aggregate is a key determinant of disease onset. In addition to prion diseases, aggregation and nucleation is a central step of many physical, chemical, and biological process. Previous studies have examined the first-arrival time at a critical nucleus size during homogeneous self-assembly under the assumption that at time t=0 the system was in the all-monomer state. However, in order to compare to in vivo biological experiments where protein constituents inherited by a newly born cell likely contain intermediate aggregates, other possibilities must be considered. We consider one such possibility by conditioning the unique ergodic size distribution on subcritical aggregate sizes; this least-informed distribution is then used as an initial condition. We make the claim that this initial condition carries fewer assumptions than an all-monomer one and verify that it can yield significantly different averaged waiting times relative to the all-monomer condition under various models of assembly.
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Affiliation(s)
- Jason K Davis
- University of California, Merced, 5200 N Lake Road, Merced, California 95343, USA
| | - Suzanne S Sindi
- University of California, Merced, 5200 N Lake Road, Merced, California 95343, USA
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27
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Abstract
The nucleated polymerization model is a mathematical framework that has been applied to aggregation and fragmentation processes in both the discrete and continuous setting. In particular, this model has been the canonical framework for analyzing the dynamics of protein aggregates arising in prion and amyloid diseases such as as Alzheimer's and Parkinson's disease. We present an explicit steady-state solution to the aggregate size distribution governed by the discrete nucleated polymerization equations. Steady-state solutions have been previously obtained under the assumption of continuous aggregate sizes; however, the discrete solution allows for direct computation and parameter inference, as well as facilitates estimates on the accuracy of the continuous approximation.
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Affiliation(s)
- Jason K. Davis
- University of California, Merced, School of Natural Sciences, 5200 N Lake Rd, Merced, CA 95343
| | - Suzanne S. Sindi
- University of California, Merced, School of Natural Sciences, 5200 N Lake Rd, Merced, CA 95343
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28
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Abstract
The infectious agent of many neurodegenerative disorders is thought to be aggregates of prion protein, which are transmitted between cells. Recent work in yeast supports this hypothesis, but suggests that only aggregates below a critical size are transmitted efficiently. The total number of transmissible aggregates in a typical cell is a key determinant of strain infectivity. In a discrete-time branching process model of a yeast colony with prions, prion aggregates increase in size according to a Poisson process and only aggregates below a threshold size are transmitted during cell division. The total number of cells with aggregates in a growing population of yeast is expressed.
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Sindi SS, Onal S, Peng LC, Wu HT, Raphael BJ. An integrative probabilistic model for identification of structural variation in sequencing data. Genome Biol 2012; 13:R22. [PMID: 22452995 PMCID: PMC3439973 DOI: 10.1186/gb-2012-13-3-r22] [Citation(s) in RCA: 110] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Accepted: 03/27/2012] [Indexed: 12/12/2022] Open
Abstract
Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model that can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50 to 90% improvement in specificity on deletions and a 50% improvement on inversions. GASVPro is available at http://compbio.cs.brown.edu/software.
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Affiliation(s)
- Suzanne S Sindi
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
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Cáceres A, Sindi SS, Raphael BJ, Cáceres M, González JR. Identification of polymorphic inversions from genotypes. BMC Bioinformatics 2012; 13:28. [PMID: 22321652 PMCID: PMC3296650 DOI: 10.1186/1471-2105-13-28] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Accepted: 02/09/2012] [Indexed: 01/19/2023] Open
Abstract
Background Polymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies. Results We present a novel method to both identify polymorphic inversions from genome-wide genotype data and classify individuals as containing a normal or inverted allele. Our method, a generalization of a published method for haplotype data [1], utilizes linkage between groups of SNPs to partition a set of individuals into normal and inverted subpopulations. We employ a sliding window scan to identify regions likely to have an inversion, and accumulation of evidence from neighboring SNPs is used to accurately determine the inversion status of each subject. Further, our approach detects inversions directly from genotype data, thus increasing its usability to current genome-wide association studies (GWAS). Conclusions We demonstrate the accuracy of our method to detect inversions and classify individuals on principled-simulated genotypes, produced by the evolution of an inversion event within a coalescent model [2]. We applied our method to real genotype data from HapMap Phase III to characterize the inversion status of two known inversions within the regions 17q21 and 8p23 across 1184 individuals. Finally, we scan the full genomes of the European Origin (CEU) and Yoruba (YRI) HapMap samples. We find population-based evidence for 9 out of 15 well-established autosomic inversions, and for 52 regions previously predicted by independent experimental methods in ten (9+1) individuals [3,4]. We provide efficient implementations of both genotype and haplotype methods as a unified R package inveRsion.
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Affiliation(s)
- Alejandro Cáceres
- Center for Research in Environmental Epidemiology, and Institut Municipal d'Investigació Mèdica, Barcelona 08003, Spain.
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31
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Abstract
According to the prion hypothesis, atypical phenotypes arise when a prion protein adopts an alternative conformation and persist when that form assembles into self-replicating aggregates. Amyloid formation in vitro provides a model for this protein-misfolding pathway, but the mechanism by which this process interacts with the cellular environment to produce transmissible phenotypes is poorly understood. Using the yeast prion Sup35/[PSI(+)], we found that protein conformation determined the size distribution of aggregates through its interactions with a molecular chaperone. Shifts in this range created variations in aggregate abundance among cells because of a size threshold for transmission, and this heterogeneity, along with aggregate growth and fragmentation, induced age-dependent fluctuations in phenotype. Thus, prion conformations may specify phenotypes as population averages in a dynamic system.
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Affiliation(s)
- Aaron Derdowski
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, 185 Meeting Street, Post Office Box G-L2, Providence, RI 02912, USA
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32
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Abstract
Structural rearrangements, including copy-number alterations and inversions, are increasingly recognized as an important contributor to human genetic variation. Copy number variants are readily measured via array-based techniques like comparative genomic hybridization, but copy-neutral variants such as inversion polymorphisms remain difficult to identify without whole genome sequencing. We introduce a method to identify inversion polymorphisms and estimate their frequency in a population using readily available single nucleotide polymorphism (SNP) data. Our method uses a probabilistic model to describe a population as a mixture of forward and inverted chromosomes and identifies putative inversions by characteristic differences in haplotype frequencies around inversion breakpoints. On simulated data, our method accurately predicts inversions with frequencies as low as 25% in the population and reliably estimates inversion frequencies over a wide range. On the human HapMap Phase 2 data, we predict between 88 and 142 inversion polymorphisms with frequency ranging from 20 to 81 percent. Many of these correspond to known inversions or have other evidence supporting them, and the predicted inversion frequencies largely agree with the limited information presently available.
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Affiliation(s)
- Suzanne S Sindi
- Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA
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Sindi SS, Serio TR. Prion dynamics and the quest for the genetic determinant in protein-only inheritance. Curr Opin Microbiol 2009; 12:623-30. [PMID: 19864176 DOI: 10.1016/j.mib.2009.09.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2009] [Revised: 09/12/2009] [Accepted: 09/14/2009] [Indexed: 01/24/2023]
Abstract
According to the prion hypothesis, proteins may act in atypical roles as genetic elements of infectivity and inheritance by undergoing self-replicating changes in physical state. While the preponderance of evidence strongly supports this concept particularly in fungi, the detailed mechanisms by which distinct protein forms specify unique phenotypes are emerging concepts. A particularly active area of investigation is the molecular nature of the heritable species, which has been probed through genetic, biochemical, and cell biological experimentation as well as by mathematical modeling. Here, we suggest that these studies are converging to implicate small aggregates composed of prion-state conformers as the transmissible genetic determinants of protein-based phenotypes.
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Affiliation(s)
- Suzanne S Sindi
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, 185 Meeting St., Box G-L2, Providence, RI 02912, USA
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Sindi SS, Hunt BR, Yorke JA. Duplication count distributions in DNA sequences. Phys Rev E Stat Nonlin Soft Matter Phys 2008; 78:061912. [PMID: 19256873 PMCID: PMC3121164 DOI: 10.1103/physreve.78.061912] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2007] [Revised: 04/22/2008] [Indexed: 05/13/2023]
Abstract
We study quantitative features of complex repetitive DNA in several genomes by studying sequences that are sufficiently long that they are unlikely to have repeated by chance. For each genome we study, we determine the number of identical copies, the "duplication count," of each sequence of length 40, that is of each "40-mer." We say a 40-mer is "repeated" if its duplication count is at least 2. We focus mainly on "complex" 40-mers, those without short internal repetitions. We find that we can classify most of the complex repeated 40-mers into two categories: one category has its copies clustered closely together on one chromosome, the other has its copies distributed widely across multiple chromosomes. For each genome and each of the categories above, we compute N(c), the number of 40-mers that have duplication count c, for each integer c. In each case, we observe a power-law-like decay in N(c) as c increases from 3 to 50 or higher. In particular, we find that N(c) decays much more slowly than would be predicted by evolutionary models where each 40-mer is equally likely to be duplicated. We also analyze an evolutionary model that does reflect the slow decay of N(c).
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Affiliation(s)
- Suzanne S Sindi
- Institute for Physical Sciences and Technology, University of Maryland, College Park, Maryland 20742, USA.
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