Physical Sciences and Mathematics Commons^™

Complex Dynamics Of Coral Gene Expression Responses To Low Ph Across Species, Veronica Z. Radice, Ana Martinez, Adina Paytan, Donald C. Potts, Daniel J. Barshis

Biological Sciences Faculty Publications

Coral capacity to tolerate low pH affects coral community composition and, ultimately, reef ecosystem function. Low pH submarine discharges (‘Ojo’; Yucatán, México) represent a natural laboratory to study plasticity and acclimatization to low pH in relation to ocean acidification. A previous >2‐year coral transplant experiment to ambient and low pH common garden sites revealed differential survivorship across species and sites, providing a framework to compare mechanistic responses to differential pH exposures. Here, we examined gene expression responses of transplants of three species of reef‐building corals (Porites astreoides, Porites porites and Siderastrea siderea) and their algal endosymbiont communities …

Physiological And Transcriptomic Responses Of Two Artemisia Californica Populations To Drought: Implications For Restoring Drought-Resilient Native Communities, Hagop S. Atamian Dr., Jennifer L. Funk

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

As climate change brings drier and more variable rainfall patterns to many arid and semi-arid regions, land managers must re-assemble appropriate plant communities for these conditions. Transcriptome sequencing can elucidate the molecular mechanisms underlying plant responses to changing environmental conditions, potentially enhancing our ability to screen suitable genotypes and species for restoration. We examined physiological and morphological traits and transcriptome sequences of coastal and inland populations of California sagebrush (Artemisia californica), a critical shrub used to restore coastal sage scrub vegetation communities, grown under low and high rainfall environments. The populations are located approximately 36 km apart but …

Elevated Atmospheric Co2 Concentration Triggers Redistribution Of Nitrogen To Promote Tillering In Rice, Juan Zhou, Yingbo Gao, Junpeng Wang, Chang Liu, Zi Wang, Minjia Lv, Xiaoxiang Zhang, Yong Zhou, Guichun Dong, Yulong Wang, Jianye Huang, Dafeng Hui, Zefeng Yang, Youli Yao

Biology Faculty Research

Elevated atmospheric CO₂ concentration (eCO₂) often reduces nitrogen (N) content in rice plants and stimulates tillering. However, there is a general consensus that reduced N would constrain rice tillering. To resolve this contradiction, we investigated N distribution and transcriptomic changes in different rice plant organs after subjecting them to eCO₂ and different N application rates. Our results showed that eCO₂ significantly promoted rice tillers (by 0.6, 1.1, 1.7, and 2.1 tillers/plant at 0, 75, 150, and 225 kg N ha⁻¹ N application rates, respectively) and more tillers were produced under higher N application rates, …

Fast And Pervasive Transcriptomic Resilience And Acclimation Of Extremely Heat-Tolerant Coral Holobionts From The Northern Red Sea, Romain Savary, Daniel J. Barshis, Christian R. Voolstra, Anny Cárdenas, Nicolas R. Evensen, Guilhem Banc-Prandi, Maoz Fine, Anders Meiborn

Biological Sciences Faculty Publications

Corals from the northern Red Sea and Gulf of Aqaba exhibit extreme thermal tolerance. To examine the underlying gene expression dynamics, we exposed Stylophora pistillata from the Gulf of Aqaba to short-term (hours) and long-term (weeks) heat stress with peak seawater temperatures ranging from their maximum monthly mean of 27 °C (baseline) to 29.5 °C, 32 °C, and 34.5 °C. Corals were sampled at the end of the heat stress as well as after a recovery period at baseline temperature. Changes in coral host and symbiotic algal gene expression were determined via RNA-sequencing (RNA-Seq). Shifts in coral microbiome composition were …

Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang

Biostatistics Faculty Publications

With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) …

Expression Of Wnt-Signaling Pathway Genes And Their Associations With Mirnas In Colorectal Cancer, Martha L. Slattery, Lila E. Mullany, Lori C. Sakoda, Wade S. Samowitz, Roger K. Wolff, John R. Stevens, Jennifer S. Herrick

Mathematics and Statistics Faculty Publications

The Wnt-signaling pathway functions in regulating cell growth and thus is involved in the carcinogenic process of several cancers, including colorectal cancer. We tested the hypothesis that multiple genes in this signaling pathway are dysregulated and that miRNAs are associated with these dysregulated genes. We used data from 217 colorectal cancer (CRC) cases to evaluate differences in Wnt-signaling pathway gene expression between paired CRC and normal mucosa and identify miRNAs that are associated with these genes. Gene expression data from RNA-Seq and miRNA expression data from Agilent Human miRNA Microarray V19.0 were analyzed. We focused on genes most strongly associated …

Fruit Weight Is Controlled By Cell Size Regulator Encoding A Novel Protein That Is Expressed In Maturing Tomato Fruits, Qi Mu, Zejun Huang, Manohar Chakrabarti, Eudald Illa-Berenguer, Xiaoxi Liu, Yanping Wang, Alexis Ramos, Esther Van Der Knaap

Plant and Soil Sciences Faculty Publications

Increases in fruit weight of cultivated vegetables and fruits accompanied the domestication of these crops. Here we report on the positional cloning of a quantitative trait locus (QTL) controlling fruit weight in tomato. The derived allele of Cell Size Regulator (CSR-D) increases fruit weight predominantly through enlargement of the pericarp areas. The expanded pericarp tissues result from increased mesocarp cell size and not from increased number of cell layers. The effect of CSR on fruit weight and cell size is found across different genetic backgrounds implying a consistent impact of the locus on the trait. In fruits, CSR …

Comparative Transcriptomic Analysis Of Two Brassica Napus Near-Isogenic Lines Reveals A Network Of Genes That Influences Seed Oil Accumulation, Jingxue Wang, Sanjay Kumar Singh, Chunfang Du, Chen Li, Jianchun Fan, Sitakanta Pattanaik, Ling Yuan

Plant and Soil Sciences Faculty Publications

Rapeseed (Brassica napus) is an important oil seed crop, providing more than 13% of the world’s supply of edible oils. An in-depth knowledge of the gene network involved in biosynthesis and accumulation of seed oil is critical for the improvement of B. napus. Using available genomic and transcriptomic resources, we identified 1,750 acyl-lipid metabolism (ALM) genes that are distributed over 19 chromosomes in the B. napus genome. B. rapa and B. oleracea, two diploid progenitors of B. napus, contributed almost equally to the ALM genes. Genome collinearity analysis demonstrated that the majority of the …

A Gene-Based Association Method For Mapping Traits Using Reference Transcriptome Data, Eric R. Gamazon, Heather Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, Gtex Consortium, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im

Bioinformatics Faculty Publications

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates ‘imputed’ gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys …

Gene And Protein Sequence Optimization For High-Level Production Of Fully Active And Aglycosylated Lysostaphin In Pichia Pastoris, Hongliang Zhao, Kristina Blazanovic, Yoonjoo Choi, Chris Bailey-Kellogg, Karl E. Griswold

Dartmouth Scholarship

Lysostaphin represents a promising therapeutic agent for the treatment of staphylococcal infections, in particular those of methicillin-resistant Staphylococcus aureus (MRSA). However, conventional expression systems for the enzyme suffer from various limitations, and there remains a need for an efficient and cost-effective production process to facilitate clinical translation and the development of nonmedical applications. While Pichia pastoris is widely used for high-level production of recombinant proteins, there are two major barriers to the production of lysostaphin in this industrially relevant host: lack of expression from the wild-type lysostaphin gene and aberrant glycosylation of the wild-type protein sequence. The first barrier can …

A New Essential Protein Discovery Method Based On The Integration Of Protein-Protein Interaction And Gene Expression Data, Min Li, Hanhui Zhang, Jian-Xin Wang, Yi Pan

Computer Science Faculty Publications

The article offers information on a study conducted on the essential protein discovery method, PeC, which is based on the integration of protein-protein interaction and gene expression data. It states that PeC was developed on the basis of the definitions of edge clustering coefficient (ECC) and Pearson's correlation coefficient (PCC). It mentions that a list of essential proteins of Saccharomyces cerevisiae were collected.

Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have …

Selective Capture Of Transcribed Sequences: A Promising Approach For Investigating Bacterium-Insect Interactions, Ruisheng An, Parwinder Grewal

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Bacterial interactions with eukaryotic hosts are complex processes which vary from pathogenic to mutualistic. Identification of bacterial genes differentially expressed in the host, promises to unravel molecular mechanisms driving and maintaining such interactions. Several techniques have been developed in the past 20 years to investigate bacterial gene expression within their hosts. The most commonly used techniques include in-vivo expression technology, signature-tagged mutagenesis, differential fluorescence induction, and cDNA microarrays. However, the limitations of these techniques in analyzing bacterial in-vivo gene expression indicate the need to develop alternative tools. With many advantages over the other methods for analyzing bacterial in-vivo gene expression, …

Integration Of Breast Cancer Gene Signatures Based On Graph Centrality, Jianxin Wang, Gang Chen, Min Li, Yi Pan

Computer Science Faculty Publications

Background: Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome.

Results: In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of …

Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki

Masters Theses & Specialist Projects

Stages in Drosophila development denote the time after fertilization at which certain specific events occur in the developmental cycle. Stage information of a host embryo, as well as spatial information of a gene expression region is indispensable input for the discovery of the pattern of gene-gene interaction. Manual labeling of stages is becoming a bottleneck under the circumstance of high throughput embryo images. Automatic recognition based on the appearances of embryos is becoming a more desirable scheme. This problem, however, is very challenging due to severe variations of illumination and gene expressions. In this research thesis, we propose an appearance …

The Cell Cycle–Regulated Genes Of Schizosaccharomyces Pombe, Anna Oliva, Adan Rosebrock, Francisco Ferrezuelo, Haiying Chen, Saumyadipta Pyne, Steve Skiena, Bruce Futcher, Janet Leatherwood

Department of Molecular Genetics and Microbiology Faculty Publications

Many genes are regulated as an innate part of the eukaryotic cell cycle, and a complex transcriptional network helps enable the cyclic behavior of dividing cells. This transcriptional network has been studied in Saccharomyces cerevisiae (budding yeast) and elsewhere. To provide more perspective on these regulatory mechanisms, we have used microarrays to measure gene expression through the cell cycle of Schizosaccharomyces pombe (fission yeast). The 750 genes with the most significant oscillations were identified and analyzed. There were two broad waves of cell cycle transcription, one in early/mid G2 phase, and the other near the G2/M transition. The early/mid G2 …

Cluster Analysis Of Genomic Data With Applications In R, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in R. We discuss statistical issues and methods in choosing the number of clusters, the choice of clustering algorithm, and the choice of dissimilarity matrix. In particular, we illustrate how the bootstrap can be employed as a statistical method in cluster analysis to establish the reproducibility of the clusters and the overall variability of the followed procedure. We also show how to visualize a clustering result by plotting ordered dissimilarity matrices in R. We present a new R package, hopach, which implements the hybrid clustering method, …

Nonparametric Methods For Analyzing Replication Origins In Genomewide Data, Debashis Ghosh

The University of Michigan Department of Biostatistics Working Paper Series

Due to the advent of high-throughput genomic technology, it has become possible to globally monitor cellular activities on a genomewide basis. With these new methods, scientists can begin to address important biological questions. One such question involves the identification of replication origins, which are regions in chromosomes where DNA replication is initiated. In addition, one hypothesis regarding replication origins is that their locations are non-random throughout the genome. In this article, we develop methods for identification of and cluster inference regarding replication origins involving genomewide expression data. We compare several nonparametric regression methods for the identification of replication origin locations. …

Semiparametric Methods For Identification Of Tumor Progression Genes From Microarray Data, Debashis Ghosh, Arul Chinnaiyan

The University of Michigan Department of Biostatistics Working Paper Series

The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression. In this article, we develop statistical procedures for the identification of such genes, which we term tumor progression genes. Two methods are considered in this paper. The first is use of a proportional odds procedure, combined with false discovery rate estimation techniques to adjust for the multiple testing problem. The second method is based on order-restricted estimation …

The False Discovery Rate: A Variable Selection Perspective, Debashis Ghosh, Wei Chen, Trivellore E. Raghuanthan

The University of Michigan Department of Biostatistics Working Paper Series

In many scientific and medical settings, large-scale experiments are generating large quantities of data that lead to inferential problems involving multiple hypotheses. This has led to recent tremendous interest in statistical methods regarding the false discovery rate (FDR). Several authors have studied the properties involving FDR in a univariate mixture model setting. In this article, we turn the problem on its side; in this manuscript, we show that FDR is a by-product of Bayesian analysis of variable selection problem for a hierarchical linear regression model. This equivalence gives many Bayesian insights as to why FDR is a natural quantity to …

Classification Using Generalized Partial Least Squares, Beiying Ding, Robert Gentleman

Bioconductor Project Working Papers

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in …

Mixture Models For Assessing Differential Expression In Complex Tissues Using Microarray Data, Debashis Ghosh

The University of Michigan Department of Biostatistics Working Paper Series

The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous. In this article, we outline a general framework for determining …

A New Partitioning Around Medoids Algorithm, Mark J. Van Der Laan, Katherine S. Pollard, Jennifer Bryan

U.C. Berkeley Division of Biostatistics Working Paper Series

Kaufman & Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a distance matrix into a specified number of clusters. A particularly nice property is that PAM allows clustering with respect to any specified distance metric. In addition, the medoids are robust representations of the cluster centers, which is particularly important in the common context that many elements do not belong well to any cluster. Based on our experience in clustering gene expression data, we have noticed that PAM does have problems recognizing relatively small clusters in situations where good partitions around medoids clearly exist. In this …

Identification Of Regulatory Elements Using A Feature Selection Method, Sunduz Keles, Mark J. Van Der Laan, Michael B. Eisen

U.C. Berkeley Division of Biostatistics Working Paper Series

Many methods have been described to identify regulatory motifs in the transcription control regions of genes that exhibit similar patterns of gene expression across a variety of experimental conditions. Here we focus on a single experimental condition, and utilize gene expression data to identify sequence motifs associated with genes that are activated under this experimental condition. We use a linear model with two way interactions to model gene expression as a function of sequence features (words) present in presumptive transcription control regions. The most relevant features are selected by a feature selection method called stepwise selection with monte carlo cross …

Statistical Inference For Simultaneous Clustering Of Gene Expression Data, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as …

Molecular And Physiological Responses Of Diatoms To Variable Levels Of Irradiance And Nitrogen Availability: Growth Of Skeletonema Costatum In Simulated Upwelling Conditions, G. Jason Smith, Richard C. Zimmerman, Randall S. Alberte

OES Faculty Publications

Molecular mechanisms that drive metabolic acclimation to environmental shifts have been poorly characterized in phytoplankton. In this laboratory study. the response of light- and N-limited Skeletonema costatum cells to an increase in light and NO₃ availability was examined. C assimilation was depressed relative to N assimilation early in enrichment, and the photosynthetic quotient (O₂: CO₂) increased, consistent with the shunting of reducing equivalents from CO₂ fixation to NO₃- reduction. The concomitant increase in dark respiration was consistent with the increased energetic demand associated with macromolecular synthesis. The accelerations of N-specific rates of …