The timing of transcription of RpoS-dependent genes varies across multiple stresses in Escherichia coli K-12.

IMPORTANCE: Bacteria adapt to changing environments by altering the transcription of their genes. Specific proteins can regulate these changes. This study explored how a single protein called RpoS controls how many genes change expression during adaptation to three stresses. We found that: (i) RpoS is responsible for activating different genes in different stresses; (ii) that during a stress, the timing of gene activation depends on the what stress it is; and (iii) that how much RpoS a gene needs in order to be activated can predict when that gene will be activated during the stress of stationary phase.

Chemical Inhibition of Bromodomain Proteins in Insect-Stage African Trypanosomes Perturbs Silencing of the Variant Surface Glycoprotein Repertoire and Results in Widespread Changes in the Transcriptome.

The eukaryotic protozoan parasite Trypanosoma brucei is transmitted by the tsetse fly to both humans and animals, where it causes a fatal disease called African trypanosomiasis. While the parasite lacks canonical DNA sequence-specific transcription factors, it does possess histones, histone modifications, and proteins that write, erase, and read histone marks. Chemical inhibition of chromatin-interacting bromodomain proteins has previously been shown to perturb bloodstream specific trypanosome processes, including silencing of the variant surface glycoprotein (VSG) genes and immune evasion. Transcriptomic changes that occur in bromodomain-inhibited bloodstream parasites mirror many of the changes that occur as parasites developmentally progress from the bloodstream to the insect stage. We performed transcriptome sequencing (RNA-seq) time courses to determine the effects of chemical bromodomain inhibition in insect-stage parasites using the compound I-BET151. We found that treatment with I-BET151 causes large changes in the transcriptome of insect-stage parasites and also perturbs silencing of VSG genes. The transcriptomes of bromodomain-inhibited parasites share some features with early metacyclic-stage parasites in the fly salivary gland, implicating bromodomain proteins as important for regulating transcript levels for developmentally relevant genes. However, the downregulation of surface procyclin protein that typically accompanies developmental progression is absent in bromodomain-inhibited insect-stage parasites. We conclude that chemical modulation of bromodomain proteins causes widespread transcriptomic changes in multiple trypanosome life cycle stages. Understanding the gene-regulatory processes that facilitate transcriptome remodeling in this highly diverged eukaryote may shed light on how these mechanisms evolved. IMPORTANCE The disease African trypanosomiasis imposes a severe human and economic burden for communities in sub-Saharan Africa. The parasite that causes the disease is transmitted to the bloodstream of a human or ungulate via the tsetse fly. Because the environments of the fly and the bloodstream differ, the parasite modulates the expression of its genes to accommodate two different lifestyles in these disparate niches. Perturbation of bromodomain proteins that interact with histone proteins around which DNA is wrapped (chromatin) causes profound changes in gene expression in bloodstream-stage parasites. This paper reports that gene expression is also affected by chemical bromodomain inhibition in insect-stage parasites but that the genes affected differ depending on life cycle stage. Because trypanosomes diverged early from model eukaryotes, an understanding of how trypanosomes regulate gene expression may lend insight into how gene-regulatory mechanisms evolved. This could also be leveraged to generate new therapeutic strategies.

Genomic Occupancy of the Bromodomain Protein Bdf3 Is Dynamic during Differentiation of African Trypanosomes from Bloodstream to Procyclic Forms.

Trypanosoma brucei, the causative agent of human and animal African trypanosomiasis, cycles between a mammalian host and a tsetse fly vector. The parasite undergoes huge changes in morphology and metabolism during adaptation to each host environment. These changes are reflected in the different transcriptomes of parasites living in each host. However, it remains unclear whether chromatin-interacting proteins help mediate these changes. Bromodomain proteins localize to transcription start sites in bloodstream parasites, but whether the localization of bromodomain proteins changes as parasites differentiate from bloodstream to insect stages remains unknown. To address this question, we performed cleavage under target and release using nuclease (CUT&RUN) against bromodomain protein 3 (Bdf3) in parasites differentiating from bloodstream to insect forms. We found that Bdf3 occupancy at most loci increased at 3 h following onset of differentiation and decreased thereafter. A number of sites with increased bromodomain protein occupancy lie proximal to genes with altered transcript levels during differentiation, such as procyclins, procyclin-associated genes, and invariant surface glycoproteins. Most Bdf3-occupied sites are observed throughout differentiation. However, one site appears de novo during differentiation and lies proximal to the procyclin gene locus housing genes essential for remodeling surface proteins following transition to the insect stage. These studies indicate that occupancy of chromatin-interacting proteins is dynamic during life cycle stage transitions and provide the groundwork for future studies on the effects of changes in bromodomain protein occupancy. Additionally, the adaptation of CUT&RUN for Trypanosoma brucei provides other researchers with an alternative to chromatin immunoprecipitation (ChIP). IMPORTANCE The parasite Trypanosoma brucei is the causative agent of human and animal African trypanosomiasis (sleeping sickness). Trypanosomiasis, which affects humans and cattle, is fatal if untreated. Existing drugs have significant side effects. Thus, these parasites impose a significant human and economic burden in sub-Saharan Africa, where trypanosomiasis is endemic. T. brucei cycles between the mammalian host and a tsetse fly vector, and parasites undergo huge changes in morphology and metabolism to adapt to different hosts. Here, we show that DNA-interacting bromodomain protein 3 (Bdf3) shows changes in occupancy at its binding sites as parasites transition from the bloodstream to the insect stage. Additionally, a new binding site appears near the locus responsible for remodeling of parasite surface proteins during transition to the insect stage. Understanding the mechanisms behind host adaptation is important for understanding the life cycle of the parasite.

Increasing the speed of frequency-domain, homodyne thermoreflectance imaging.

Charge coupled device (CCD)-based thermoreflectance imaging using a "4-bucket" lock-in imaging algorithm is a well-established, powerful method for obtaining high spatial and thermal resolution two-dimensional thermal maps of optoelectronic, electronic, and micro-electro-mechanical systems devices. However, the technique is relatively slow, limiting broad commercial adoption. In this work, we examine the underlying limit on the image acquisition speed using the conventional "4-bucket" algorithm and show that the straightforward extension to an n-bucket technique by faster sampling does not address the underlying statistical bias in the data analysis and hence does not reduce the image acquisition time. Instead, we develop a modified "enhanced n-bucket" algorithm that halves the image acquisition time for every doubling of the number of buckets. We derive detailed statistical models of the algorithms and confirm both the models and the resulting speed enhancement experimentally, resulting in a practical means of significantly enhancing the speed and utility of CCD-based frequency domain, homodyne thermoreflectance imaging.

Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes.

This paper begins to build a theoretical framework that would enable the pharmaceutical industry to use network complexity measures as a way to identify drug targets. The variability of a betweenness measure for a network node is examined through different methods of network perturbation. Our results indicate a robustness of betweenness centrality in the identification of target genes.

Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma.

Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology.

Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions.

RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.

Genome-Wide Transcriptional Response to Varying RpoS Levels in Escherichia coli K-12.

The alternative sigma factor RpoS is a central regulator of many stress responses in Escherichia coli The level of functional RpoS differs depending on the stress. The effect of these differing concentrations of RpoS on global transcriptional responses remains unclear. We investigated the effect of RpoS concentration on the transcriptome during stationary phase in rich media. We found that 23% of genes in the E. coli genome are regulated by RpoS, and we identified many RpoS-transcribed genes and promoters. We observed three distinct classes of response to RpoS by genes in the regulon: genes whose expression changes linearly with increasing RpoS level, genes whose expression changes dramatically with the production of only a little RpoS ("sensitive" genes), and genes whose expression changes very little with the production of a little RpoS ("insensitive"). We show that sequences outside the core promoter region determine whether an RpoS-regulated gene is sensitive or insensitive. Moreover, we show that sensitive and insensitive genes are enriched for specific functional classes and that the sensitivity of a gene to RpoS corresponds to the timing of induction as cells enter stationary phase. Thus, promoter sensitivity to RpoS is a mechanism to coordinate specific cellular processes with growth phase and may also contribute to the diversity of stress responses directed by RpoS.IMPORTANCE The sigma factor RpoS is a global regulator that controls the response to many stresses in Escherichia coli Different stresses result in different levels of RpoS production, but the consequences of this variation are unknown. We describe how changing the level of RpoS does not influence all RpoS-regulated genes equally. The cause of this variation is likely the action of transcription factors that bind the promoters of the genes. We show that the sensitivity of a gene to RpoS levels explains the timing of expression as cells enter stationary phase and that genes with different RpoS sensitivities are enriched for specific functional groups. Thus, promoter sensitivity to RpoS is a mechanism that coordinates specific cellular processes in response to stresses.

Resistant multiple sparse canonical correlation.

Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca.

A note on oligonucleotide expression values not being normally distributed.

Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.

Pathways change in expression during replicative aging in Saccharomyces cerevisiae.

Yeast replicative aging is a process resembling replicative aging in mammalian cells. During aging, wild-type haploid yeast cells enlarge, become sterile, and undergo nucleolar enlargement and fragmentation; we sought gene expression changes during the time of these phenotypic changes. Gene expression studied via microarrays and quantitative real-time reverse-transcription polymerase chain reaction (qPCR) has shown reproducible, statistically significant changes in messenger RNA (mRNA) of genes at 12 and 18-20 generations. Our findings support previously described changes towards aerobic metabolism, decreased ribosome gene expression, and a partial environmental stress response. Our findings include a pseudostationary phase, downregulation of methylation-related metabolism, increased nucleotide excision repair-related mRNA, and a strong upregulation of many of the regulatory subunits of protein phosphatase I (Glc7). These findings are correlated with aging changes in higher organisms as well as with the known involvement of protein phosphorylation states during yeast aging.

A robust measure of correlation between two genes on a microarray.

BACKGROUND: The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.) RESULTS: We propose a resistant similarity metric based on Tukey's biweight estimate of multivariate scale and location. The resistant metric is simply the correlation obtained from a resistant covariance matrix of scale. We give results which demonstrate that our correlation metric is much more resistant than the Pearson correlation while being more efficient than other nonparametric measures of correlation (e.g., Spearman correlation.) Additionally, our method gives a systematic gene flagging procedure which is useful when dealing with large amounts of noisy data. CONCLUSION: When dealing with microarray data, which are known to be quite noisy, robust methods should be used. Specifically, robust distances, including the biweight correlation, should be used in clustering and gene network analysis.

Evaluation of multiple models to distinguish closely related forms of disease using DNA microarray data: an application to multiple myeloma.

MOTIVATION: Standard laboratory classification of the plasma cell dyscrasia monoclonal gammopathy of undetermined significance (MGUS) and the overt plasma cell neoplasm multiple myeloma (MM) is quite accurate, yet, for the most part, biologically uninformative. Most, if not all, cancers are caused by inherited or acquired genetic mutations that manifest themselves in altered gene expression patterns in the clonally related cancer cells. Microarray technology allows for qualitative and quantitative measurements of the expression levels of thousands of genes simultaneously, and it has now been used both to classify cancers that are morphologically indistinguishable and to predict response to therapy. It is anticipated that this information can also be used to develop molecular diagnostic models and to provide insight into mechanisms of disease progression, e.g., transition from healthy to benign hyperplasia or conversion of a benign hyperplasia to overt malignancy. However, standard data analysis techniques are not trivial to employ on these large data sets. Methodology designed to handle large data sets (or modified to do so) is needed to access the vital information contained in the genetic samples, which in turn can be used to develop more robust and accurate methods of clinical diagnostics and prognostics. RESULTS: Here we report on the application of a panel of statistical and data mining methodologies to classify groups of samples based on expression of 12,000 genes derived from a high density oligonucleotide microarray analysis of highly purified plasma cells from newly diagnosed MM, MGUS, and normal healthy donors. The three groups of samples are each tested against each other. The methods are found to be similar in their ability to predict group membership; all do quite well at predicting MM vs. normal and MGUS vs. normal. However, no method appears to be able to distinguish explicitly the genetic mechanisms between MM and MGUS. We believe this might be due to the lack of genetic differences between these two conditions, and may not be due to the failure of the models. We report the prediction errors for each of the models and each of the methods. Additionally, we report ROC curves for the results on group prediction. AVAILABILITY: Logistic regression: standard software, available, for example in SAS. Decision trees and boosted trees: C5.0 from www.rulequest.com. SVM: SVM-light is publicly available from svmlight.joachims.org. Naïve Bayes and ensemble of voters are publicly available from www.biostat.wisc.edu/~mwaddell/eov.html. Nearest Shrunken Centroids is publicly available from http://www-stat.stanford.edu/~tibs/PAM.

Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells.

Bone marrow plasma cells (PCs) from 74 patients with newly diagnosed multiple myeloma (MM), 5 with monoclonal gammopathy of undetermined significance (MGUS), and 31 healthy volunteers (normal PCs) were purified by CD138(+) selection. Gene expression of purified PCs and 7 MM cell lines were profiled using high-density oligonucleotide microarrays interrogating about 6800 genes. On hierarchical clustering analysis, normal and MM PCs were differentiated and 4 distinct subgroups of MM (MM1, MM2, MM3, and MM4) were identified. The expression pattern of MM1 was similar to normal PCs and MGUS, whereas MM4 was similar to MM cell lines. Clinical parameters linked to poor prognosis, abnormal karyotype (P =.002) and high serum beta(2)-microglobulin levels (P =.0005), were most prevalent in MM4. Also, genes involved in DNA metabolism and cell cycle control were overexpressed in a comparison of MM1 and MM4. In addition, using chi(2) and Wilcoxon rank sum tests, 120 novel candidate disease genes were identified that discriminate normal and malignant PCs (P <.0001); many are involved in adhesion, apoptosis, cell cycle, drug resistance, growth arrest, oncogenesis, signaling, and transcription. A total of 156 genes, including FGFR3 and CCND1, exhibited highly elevated ("spiked") expression in at least 4 of the 74 MM cases (range, 4-25 spikes). Elevated expression of these 2 genes was caused by the translocation t(4;14)(p16;q32) or t(11;14)(q13;q32). Thus, novel candidate MM disease genes have been identified using gene expression profiling and this profiling has led to the development of a gene-based classification system for MM.