Browsing by Subject "Computational Biology"

Now showing 1 - 20 of 58
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    A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.
    (Genome Res, 2014-07) Naccache, Samia N; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jerome; Greninger, Alexander L; Luk, Ka-Cheung; Enge, Barryett; Wadford, Debra A; Messenger, Sharon L; Genrich, Gillian L; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric; Schneider, Bradley S; Fair, Joseph N; Martínez, Miguel A; Isa, Pavel; Crump, John A; DeRisi, Joseph L; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y
    Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI ("sequence-based ultrarapid pathogen identification"), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.
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    A framework for integrating the songbird brain.
    (J Comp Physiol A Neuroethol Sens Neural Behav Physiol, 2002-12) Jarvis, ED; Smith, VA; Wada, K; Rivas, MV; McElroy, M; Smulders, TV; Carninci, P; Hayashizaki, Y; Dietrich, F; Wu, X; McConnell, P; Yu, J; Wang, PP; Hartemink, AJ; Lin, S
    Biological systems by default involve complex components with complex relationships. To decipher how biological systems work, we assume that one needs to integrate information over multiple levels of complexity. The songbird vocal communication system is ideal for such integration due to many years of ethological investigation and a discreet dedicated brain network. Here we announce the beginnings of a songbird brain integrative project that involves high-throughput, molecular, anatomical, electrophysiological and behavioral levels of analysis. We first formed a rationale for inclusion of specific biological levels of analysis, then developed high-throughput molecular technologies on songbird brains, developed technologies for combined analysis of electrophysiological activity and gene regulation in awake behaving animals, and developed bioinformatic tools that predict causal interactions within and between biological levels of organization. This integrative brain project is fitting for the interdisciplinary approaches taken in the current songbird issue of the Journal of Comparative Physiology A and is expected to be conducive to deciphering how brains generate and perceive complex behaviors.
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    A functional analysis of the spacer of V(D)J recombination signal sequences.
    (PLoS Biol, 2003-10) Lee, Alfred Ian; Fugmann, Sebastian D; Cowell, Lindsay G; Ptaszek, Leon M; Kelsoe, Garnett; Schatz, David G
    During lymphocyte development, V(D)J recombination assembles antigen receptor genes from component V, D, and J gene segments. These gene segments are flanked by a recombination signal sequence (RSS), which serves as the binding site for the recombination machinery. The murine Jbeta2.6 gene segment is a recombinationally inactive pseudogene, but examination of its RSS reveals no obvious reason for its failure to recombine. Mutagenesis of the Jbeta2.6 RSS demonstrates that the sequences of the heptamer, nonamer, and spacer are all important. Strikingly, changes solely in the spacer sequence can result in dramatic differences in the level of recombination. The subsequent analysis of a library of more than 4,000 spacer variants revealed that spacer residues of particular functional importance are correlated with their degree of conservation. Biochemical assays indicate distinct cooperation between the spacer and heptamer/nonamer along each step of the reaction pathway. The results suggest that the spacer serves not only to ensure the appropriate distance between the heptamer and nonamer but also regulates RSS activity by providing additional RAG:RSS interaction surfaces. We conclude that while RSSs are defined by a "digital" requirement for absolutely conserved nucleotides, the quality of RSS function is determined in an "analog" manner by numerous complex interactions between the RAG proteins and the less-well conserved nucleotides in the heptamer, the nonamer, and, importantly, the spacer. Those modulatory effects are accurately predicted by a new computational algorithm for "RSS information content." The interplay between such binary and multiplicative modes of interactions provides a general model for analyzing protein-DNA interactions in various biological systems.
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    A genetic memory initiates the epigenetic loop necessary to preserve centromere position.
    (The EMBO journal, 2020-10) Hoffmann, Sebastian; Izquierdo, Helena M; Gamba, Riccardo; Chardon, Florian; Dumont, Marie; Keizer, Veer; Hervé, Solène; McNulty, Shannon M; Sullivan, Beth A; Manel, Nicolas; Fachinetti, Daniele
    Centromeres are built on repetitive DNA sequences (CenDNA) and a specific chromatin enriched with the histone H3 variant CENP-A, the epigenetic mark that identifies centromere position. Here, we interrogate the importance of CenDNA in centromere specification by developing a system to rapidly remove and reactivate CENP-A (CENP-AOFF/ON ). Using this system, we define the temporal cascade of events necessary to maintain centromere position. We unveil that CENP-B bound to CenDNA provides memory for maintenance on human centromeres by promoting de novo CENP-A deposition. Indeed, lack of CENP-B favors neocentromere formation under selective pressure. Occasionally, CENP-B triggers centromere re-activation initiated by CENP-C, but not CENP-A, recruitment at both ectopic and native centromeres. This is then sufficient to initiate the CENP-A-based epigenetic loop. Finally, we identify a population of CENP-A-negative, CENP-B/C-positive resting CD4+ T cells capable to re-express and reassembles CENP-A upon cell cycle entry, demonstrating the physiological importance of the genetic memory.
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    A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
    (PLoS Comput Biol, 2015-08) Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo
    Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns.
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    An active learning approach for rapid characterization of endothelial cells in human tumors.
    (PLoS One, 2014) Padmanabhan, Raghav K; Somasundar, Vinay H; Griffith, Sandra D; Zhu, Jianliang; Samoyedny, Drew; Tan, Kay See; Hu, Jiahao; Liao, Xuejun; Carin, Lawrence; Yoon, Sam S; Flaherty, Keith T; Dipaola, Robert S; Heitjan, Daniel F; Lal, Priti; Feldman, Michael D; Roysam, Badrinath; Lee, William MF
    Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.
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    An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease.
    (Cell host & microbe, 2018-08) Wang, Liuyang; Pittman, Kelly J; Barker, Jeffrey R; Salinas, Raul E; Stanaway, Ian B; Williams, Graham D; Carroll, Robert J; Balmat, Tom; Ingham, Andy; Gopalakrishnan, Anusha M; Gibbs, Kyle D; Antonia, Alejandro L; eMERGE Network; Heitman, Joseph; Lee, Soo Chan; Jarvik, Gail P; Denny, Joshua C; Horner, Stacy M; DeLong, Mark R; Valdivia, Raphael H; Crosslin, David R; Ko, Dennis C
    Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.
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    An ERCC4 regulatory variant predicts grade-3 or -4 toxicities in patients with advanced non-small cell lung cancer treated by platinum-based therapy.
    (International journal of cancer, 2018-03) Zhang, Ruoxin; Jia, Ming; Xu, Yuan; Qian, Danwen; Wang, Mengyun; Zhu, Meiling; Sun, Menghong; Chang, Jianhua; Wei, Qingyi
    Platinum-based chemotherapy (PBC) in combination with the 3rd generation drugs is the first-line treatment for patients with advanced non-small cell lung cancer (NSCLC); however, the efficacy is severely hampered by grade 3-4 toxicities. Nucleotide excision repair (NER) pathway is the main mechanism of removing platinum-induced DNA adducts that contribute to the toxicity and outcome of PBC. We analyzed data from 710 Chinese NSCLC patients treated with PBC and assessed the associations of 25 potentially functional single nucleotide polymorphisms (SNPs) in nine NER core genes with overall, gastrointestinal and hematologic toxicities. Through a two-phase study, we found that ERCC4 rs1799798 was significantly associated with overall and gastrointestinal toxicities [all patients: GA/AA vs. GG, odds ratio (OR)adj =1.61 and 2.35, 95% confidence interval (CI)=1.11-2.33 and 1.25-4.41, and Padj =0.012 and 0.008, respectively]. Our prediction model for the overall toxicity incorporating rs1799798 demonstrated a significant increase in the area under the curve (AUC) value, compared to that for clinical factors only (all patients: AUC = 0.61 vs. 0.59, 95% CI = 0.57-0.65 vs. 0.55-0.63, P = 0.010). Furthermore, the ERCC4 rs1799798 A allele was associated with lower ERCC4 mRNA expression levels according to the expression quantitative trait loci (eQTL) analysis. Our study provided some new clue in future development of biomarkers for assessing toxicity and outcomes of platinum drugs in lung cancer treatment.
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    apex: phylogenetics with multiple genes.
    (Mol Ecol Resour, 2017-01) Jombart, Thibaut; Archer, Frederick; Schliep, Klaus; Kamvar, Zhian; Harris, Rebecca; Paradis, Emmanuel; Goudet, Jérome; Lapp, Hilmar
    Genetic sequences of multiple genes are becoming increasingly common for a wide range of organisms including viruses, bacteria and eukaryotes. While such data may sometimes be treated as a single locus, in practice, a number of biological and statistical phenomena can lead to phylogenetic incongruence. In such cases, different loci should, at least as a preliminary step, be examined and analysed separately. The r software has become a popular platform for phylogenetics, with several packages implementing distance-based, parsimony and likelihood-based phylogenetic reconstruction, and an even greater number of packages implementing phylogenetic comparative methods. Unfortunately, basic data structures and tools for analysing multiple genes have so far been lacking, thereby limiting potential for investigating phylogenetic incongruence. In this study, we introduce the new r package apex to fill this gap. apex implements new object classes, which extend existing standards for storing DNA and amino acid sequences, and provides a number of convenient tools for handling, visualizing and analysing these data. In this study, we introduce the main features of the package and illustrate its functionalities through the analysis of a simple data set.
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    Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements.
    (PLoS One, 2011) Lekprasert, Parawee; Mayhew, Michael; Ohler, Uwe
    BACKGROUND: Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5'seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement. METHODOLOGY/PRINCIPAL FINDINGS: We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3-5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline. CONCLUSIONS/SIGNIFICANCE: In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.
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    Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
    (PLoS Comput Biol, 2011-07) Pruteanu-Malinici, Iulian; Mace, Daniel L; Ohler, Uwe
    Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.
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    Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.
    (PLoS Comput Biol, 2011-07) Xing, Chuanhua; Dunson, David B
    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.
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    Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
    (PLoS computational biology, 2024-05) Lopez, Velma K; Cramer, Estee Y; Pagano, Robert; Drake, John M; O'Dea, Eamon B; Adee, Madeline; Ayer, Turgay; Chhatwal, Jagpreet; Dalgic, Ozden O; Ladd, Mary A; Linas, Benjamin P; Mueller, Peter P; Xiao, Jade; Bracher, Johannes; Castro Rivadeneira, Alvaro J; Gerding, Aaron; Gneiting, Tilmann; Huang, Yuxin; Jayawardena, Dasuni; Kanji, Abdul H; Le, Khoa; Mühlemann, Anja; Niemi, Jarad; Ray, Evan L; Stark, Ariane; Wang, Yijin; Wattanachit, Nutcha; Zorn, Martha W; Pei, Sen; Shaman, Jeffrey; Yamana, Teresa K; Tarasewicz, Samuel R; Wilson, Daniel J; Baccam, Sid; Gurung, Heidi; Stage, Steve; Suchoski, Brad; Gao, Lei; Gu, Zhiling; Kim, Myungjin; Li, Xinyi; Wang, Guannan; Wang, Lily; Wang, Yueying; Yu, Shan; Gardner, Lauren; Jindal, Sonia; Marshall, Maximilian; Nixon, Kristen; Dent, Juan; Hill, Alison L; Kaminsky, Joshua; Lee, Elizabeth C; Lemaitre, Joseph C; Lessler, Justin; Smith, Claire P; Truelove, Shaun; Kinsey, Matt; Mullany, Luke C; Rainwater-Lovett, Kaitlin; Shin, Lauren; Tallaksen, Katharine; Wilson, Shelby; Karlen, Dean; Castro, Lauren; Fairchild, Geoffrey; Michaud, Isaac; Osthus, Dave; Bian, Jiang; Cao, Wei; Gao, Zhifeng; Lavista Ferres, Juan; Li, Chaozhuo; Liu, Tie-Yan; Xie, Xing; Zhang, Shun; Zheng, Shun; Chinazzi, Matteo; Davis, Jessica T; Mu, Kunpeng; Pastore Y Piontti, Ana; Vespignani, Alessandro; Xiong, Xinyue; Walraven, Robert; Chen, Jinghui; Gu, Quanquan; Wang, Lingxiao; Xu, Pan; Zhang, Weitong; Zou, Difan; Gibson, Graham Casey; Sheldon, Daniel; Srivastava, Ajitesh; Adiga, Aniruddha; Hurt, Benjamin; Kaur, Gursharn; Lewis, Bryan; Marathe, Madhav; Peddireddy, Akhil Sai; Porebski, Przemyslaw; Venkatramanan, Srinivasan; Wang, Lijing; Prasad, Pragati V; Walker, Jo W; Webber, Alexander E; Slayton, Rachel B; Biggerstaff, Matthew; Reich, Nicholas G; Johansson, Michael A
    During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
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    Chemotactic movement of a polarity site enables yeast cells to find their mates.
    (Proceedings of the National Academy of Sciences of the United States of America, 2021-06) Ghose, Debraj; Jacobs, Katherine; Ramirez, Samuel; Elston, Timothy; Lew, Daniel
    How small eukaryotic cells can interpret dynamic, noisy, and spatially complex chemical gradients to orient growth or movement is poorly understood. We address this question using Saccharomyces cerevisiae, where cells orient polarity up pheromone gradients during mating. Initial orientation is often incorrect, but polarity sites then move around the cortex in a search for partners. We find that this movement is biased by local pheromone gradients across the polarity site: that is, movement of the polarity site is chemotactic. A bottom-up computational model recapitulates this biased movement. The model reveals how even though pheromone-bound receptors do not mimic the shape of external pheromone gradients, nonlinear and stochastic effects combine to generate effective gradient tracking. This mechanism for gradient tracking may be applicable to any cell that searches for a target in a complex chemical landscape.
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    Co-regulation of nuclear respiratory factor-1 by NFkappaB and CREB links LPS-induced inflammation to mitochondrial biogenesis.
    (J Cell Sci, 2010-08-01) Suliman, Hagir B; Sweeney, Timothy E; Withers, Crystal M; Piantadosi, Claude A
    The nuclear respiratory factor-1 (NRF1) gene is activated by lipopolysaccharide (LPS), which might reflect TLR4-mediated mitigation of cellular inflammatory damage via initiation of mitochondrial biogenesis. To test this hypothesis, we examined NRF1 promoter regulation by NFκB, and identified interspecies-conserved κB-responsive promoter and intronic elements in the NRF1 locus. In mice, activation of Nrf1 and its downstream target, Tfam, by Escherichia coli was contingent on NFκB, and in LPS-treated hepatocytes, NFκB served as an NRF1 enhancer element in conjunction with NFκB promoter binding. Unexpectedly, optimal NRF1 promoter activity after LPS also required binding by the energy-state-dependent transcription factor CREB. EMSA and ChIP assays confirmed p65 and CREB binding to the NRF1 promoter and p65 binding to intron 1. Functionality for both transcription factors was validated by gene-knockdown studies. LPS regulation of NRF1 led to mtDNA-encoded gene expression and expansion of mtDNA copy number. In cells expressing plasmid constructs containing the NRF-1 promoter and GFP, LPS-dependent reporter activity was abolished by cis-acting κB-element mutations, and nuclear accumulation of NFκB and CREB demonstrated dependence on mitochondrial H(2)O(2). These findings indicate that TLR4-dependent NFκB and CREB activation co-regulate the NRF1 promoter with NFκB intronic enhancement and redox-regulated nuclear translocation, leading to downstream target-gene expression, and identify NRF-1 as an early-phase component of the host antibacterial defenses.
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    Computational analysis of antibody dynamics identifies recent HIV-1 infection.
    (JCI insight, 2017-12-21) Seaton, Kelly E; Vandergrift, Nathan A; Deal, Aaron W; Rountree, Wes; Bainbridge, John; Grebe, Eduard; Anderson, David A; Sawant, Sheetal; Shen, Xiaoying; Yates, Nicole L; Denny, Thomas N; Liao, Hua-Xin; Haynes, Barton F; Robb, Merlin L; Parkin, Neil; Santos, Breno R; Garrett, Nigel; Price, Matthew A; Naniche, Denise; Duerr, Ann C; CEPHIA group; Keating, Sheila; Hampton, Dylan; Facente, Shelley; Marson, Kara; Welte, Alex; Pilcher, Christopher D; Cohen, Myron S; Tomaras, Georgia D
    Accurate HIV-1 incidence estimation is critical to the success of HIV-1 prevention strategies. Current assays are limited by high false recent rates (FRRs) in certain populations and a short mean duration of recent infection (MDRI). Dynamic early HIV-1 antibody response kinetics were harnessed to identify biomarkers for improved incidence assays. We conducted retrospective analyses on circulating antibodies from known recent and longstanding infections and evaluated binding and avidity measurements of Env and non-Env antigens and multiple antibody forms (i.e., IgG, IgA, IgG3, IgG4, dIgA, and IgM) in a diverse panel of 164 HIV-1-infected participants (clades A, B, C). Discriminant function analysis identified an optimal set of measurements that were subsequently evaluated in a 324-specimen blinded biomarker validation panel. These biomarkers included clade C gp140 IgG3, transmitted/founder clade C gp140 IgG4 avidity, clade B gp140 IgG4 avidity, and gp41 immunodominant region IgG avidity. MDRI was estimated at 215 day or alternatively, 267 days. FRRs in untreated and treated subjects were 5.0% and 3.6%, respectively. Thus, computational analysis of dynamic HIV-1 antibody isotype and antigen interactions during infection enabled design of a promising HIV-1 recency assay for improved cross-sectional incidence estimation.
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    Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.
    (Nature genetics, 2017-12) Meyers, Robin M; Bryan, Jordan G; McFarland, James M; Weir, Barbara A; Sizemore, Ann E; Xu, Han; Dharia, Neekesh V; Montgomery, Phillip G; Cowley, Glenn S; Pantel, Sasha; Goodale, Amy; Lee, Yenarae; Ali, Levi D; Jiang, Guozhi; Lubonja, Rakela; Harrington, William F; Strickland, Matthew; Wu, Ting; Hawes, Derek C; Zhivich, Victor A; Wyatt, Meghan R; Kalani, Zohra; Chang, Jaime J; Okamoto, Michael; Stegmaier, Kimberly; Golub, Todd R; Boehm, Jesse S; Vazquez, Francisca; Root, David E; Hahn, William C; Tsherniak, Aviad
    The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.
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    Computational Methods for RNA Structure Validation and Improvement.
    (Methods Enzymol, 2015) Jain, Swati; Richardson, David C; Richardson, Jane S
    With increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.
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    Domain-oriented edge-based alignment of protein interaction networks.
    (Bioinformatics, 2009-06-15) Guo, Xin; Hartemink, Alexander J
    MOTIVATION: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein-protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment. RESULTS: Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast-fly and yeast-worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics. AVAILABILITY: Supplementary material and source code can be found at http://www.cs.duke.edu/ approximately amink/.
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    ItemOpen Access
    Drosophila muller f elements maintain a distinct set of genomic properties over 40 million years of evolution.
    (G3 (Bethesda, Md.), 2015-03-04) Leung, Wilson; Shaffer, Christopher D; Reed, Laura K; Smith, Sheryl T; Barshop, William; Dirkes, William; Dothager, Matthew; Lee, Paul; Wong, Jeannette; Xiong, David; Yuan, Han; Bedard, James EJ; Machone, Joshua F; Patterson, Seantay D; Price, Amber L; Turner, Bryce A; Robic, Srebrenka; Luippold, Erin K; McCartha, Shannon R; Walji, Tezin A; Walker, Chelsea A; Saville, Kenneth; Abrams, Marita K; Armstrong, Andrew R; Armstrong, William; Bailey, Robert J; Barberi, Chelsea R; Beck, Lauren R; Blaker, Amanda L; Blunden, Christopher E; Brand, Jordan P; Brock, Ethan J; Brooks, Dana W; Brown, Marie; Butzler, Sarah C; Clark, Eric M; Clark, Nicole B; Collins, Ashley A; Cotteleer, Rebecca J; Cullimore, Peterson R; Dawson, Seth G; Docking, Carter T; Dorsett, Sasha L; Dougherty, Grace A; Downey, Kaitlyn A; Drake, Andrew P; Earl, Erica K; Floyd, Trevor G; Forsyth, Joshua D; Foust, Jonathan D; Franchi, Spencer L; Geary, James F; Hanson, Cynthia K; Harding, Taylor S; Harris, Cameron B; Heckman, Jonathan M; Holderness, Heather L; Howey, Nicole A; Jacobs, Dontae A; Jewell, Elizabeth S; Kaisler, Maria; Karaska, Elizabeth A; Kehoe, James L; Koaches, Hannah C; Koehler, Jessica; Koenig, Dana; Kujawski, Alexander J; Kus, Jordan E; Lammers, Jennifer A; Leads, Rachel R; Leatherman, Emily C; Lippert, Rachel N; Messenger, Gregory S; Morrow, Adam T; Newcomb, Victoria; Plasman, Haley J; Potocny, Stephanie J; Powers, Michelle K; Reem, Rachel M; Rennhack, Jonathan P; Reynolds, Katherine R; Reynolds, Lyndsey A; Rhee, Dong K; Rivard, Allyson B; Ronk, Adam J; Rooney, Meghan B; Rubin, Lainey S; Salbert, Luke R; Saluja, Rasleen K; Schauder, Taylor; Schneiter, Allison R; Schulz, Robert W; Smith, Karl E; Spencer, Sarah; Swanson, Bryant R; Tache, Melissa A; Tewilliager, Ashley A; Tilot, Amanda K; VanEck, Eve; Villerot, Matthew M; Vylonis, Megan B; Watson, David T; Wurzler, Juliana A; Wysocki, Lauren M; Yalamanchili, Monica; Zaborowicz, Matthew A; Emerson, Julia A; Ortiz, Carlos; Deuschle, Frederic J; DiLorenzo, Lauren A; Goeller, Katie L; Macchi, Christopher R; Muller, Sarah E; Pasierb, Brittany D; Sable, Joseph E; Tucci, Jessica M; Tynon, Marykathryn; Dunbar, David A; Beken, Levent H; Conturso, Alaina C; Danner, Benjamin L; DeMichele, Gabriella A; Gonzales, Justin A; Hammond, Maureen S; Kelley, Colleen V; Kelly, Elisabeth A; Kulich, Danielle; Mageeney, Catherine M; McCabe, Nikie L; Newman, Alyssa M; Spaeder, Lindsay A; Tumminello, Richard A; Revie, Dennis; Benson, Jonathon M; Cristostomo, Michael C; DaSilva, Paolo A; Harker, Katherine S; Jarrell, Jenifer N; Jimenez, Luis A; Katz, Brandon M; Kennedy, William R; Kolibas, Kimberly S; LeBlanc, Mark T; Nguyen, Trung T; Nicolas, Daniel S; Patao, Melissa D; Patao, Shane M; Rupley, Bryan J; Sessions, Bridget J; Weaver, Jennifer A; Goodman, Anya L; Alvendia, Erica L; Baldassari, Shana M; Brown, Ashley S; Chase, Ian O; Chen, Maida; Chiang, Scott; Cromwell, Avery B; Custer, Ashley F; DiTommaso, Tia M; El-Adaimi, Jad; Goscinski, Nora C; Grove, Ryan A; Gutierrez, Nestor; Harnoto, Raechel S; Hedeen, Heather; Hong, Emily L; Hopkins, Barbara L; Huerta, Vilma F; Khoshabian, Colin; LaForge, Kristin M; Lee, Cassidy T; Lewis, Benjamin M; Lydon, Anniken M; Maniaci, Brian J; Mitchell, Ryan D; Morlock, Elaine V; Morris, William M; Naik, Priyanka; Olson, Nicole C; Osterloh, Jeannette M; Perez, Marcos A; Presley, Jonathan D; Randazzo, Matt J; Regan, Melanie K; Rossi, Franca G; Smith, Melanie A; Soliterman, Eugenia A; Sparks, Ciani J; Tran, Danny L; Wan, Tiffany; Welker, Anne A; Wong, Jeremy N; Sreenivasan, Aparna; Youngblom, Jim; Adams, Andrew; Alldredge, Justin; Bryant, Ashley; Carranza, David; Cifelli, Alyssa; Coulson, Kevin; Debow, Calise; Delacruz, Noelle; Emerson, Charlene; Farrar, Cassandra; Foret, Don; Garibay, Edgar; Gooch, John; Heslop, Michelle; Kaur, Sukhjit; Khan, Ambreen; Kim, Van; Lamb, Travis; Lindbeck, Peter; Lucas, Gabi; Macias, Elizabeth; Martiniuc, Daniela; Mayorga, Lissett; Medina, Joseph; Membreno, Nelson; Messiah, Shady; Neufeld, Lacey; Nguyen, San Francisco; Nichols, Zachary; Odisho, George; Peterson, Daymon; Rodela, Laura; Rodriguez, Priscilla; Rodriguez, Vanessa; Ruiz, Jorge; Sherrill, Will; Silva, Valeria; Sparks, Jeri; Statton, Geeta; Townsend, Ashley; Valdez, Isabel; Waters, Mary; Westphal, Kyle; Winkler, Stacey; Zumkehr, Joannee; DeJong, Randall J; Hoogewerf, Arlene J; Ackerman, Cheri M; Armistead, Isaac O; Baatenburg, Lara; Borr, Matthew J; Brouwer, Lindsay K; Burkhart, Brandon J; Bushhouse, Kelsey T; Cesko, Lejla; Choi, Tiffany YY; Cohen, Heather; Damsteegt, Amanda M; Darusz, Jess M; Dauphin, Cory M; Davis, Yelena P; Diekema, Emily J; Drewry, Melissa; Eisen, Michelle EM; Faber, Hayley M; Faber, Katherine J; Feenstra, Elizabeth; Felzer-Kim, Isabella T; Hammond, Brandy L; Hendriksma, Jesse; Herrold, Milton R; Hilbrands, Julia A; Howell, Emily J; Jelgerhuis, Sarah A; Jelsema, Timothy R; Johnson, Benjamin K; Jones, Kelly K; Kim, Anna; Kooienga, Ross D; Menyes, Erika E; Nollet, Eric A; Plescher, Brittany E; 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    The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25-50%) than euchromatic reference regions (3-11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11-27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4-3.6 vs. 8.4-8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu.