Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range

Algorithms to Preprocess Microarray Image Data

Microarray is a powerful technology that enables the monitoring of expression levels for thousands of genes simultaneously, providing scientists with a full overview about DNA and RNA investigation. The process is made of three main phases: interaction with biological samples, data extraction, and data analysis. In particular, the data extraction phase strongly relies on image processing algorithms, since the expression levels are revealed by the interaction of light with fluorescent markers. More in detail, in order to extract quantitative information from probes image, three steps are required: (1) gridding, (2) segmentation, and (3) intensity quantification. Errors in one of these steps can deeply affect the process outcome. In this chapter each of the above mentioned steps will be analyzed and discussed. Software platforms dedicated to this purpose will be reported as well.

Comparison of NF-κB from the protists Capsaspora owczarzaki and Acanthoeca spectabilis reveals extensive evolutionary diversification of this transcription factor

We provide a functional characterization of transcription factor NF-κB in protists and provide information about the evolution and diversification of this biologically important protein. We characterized NF-κB in two protists using phylogenetic, cellular, and biochemical techniques. NF-κB of the holozoan Capsaspora owczarzaki (Co) has an N-terminal DNA-binding domain and a C-terminal Ankyrin repeat (ANK) domain, and its DNA-binding specificity is more similar to metazoan NF-κB proteins than to Rel proteins. Removal of the ANK domain allows Co-NF-κB to enter the nucleus, bind DNA, and activate transcription. However, C-terminal processing of Co-NF-κB is not induced by IκB kinases in human cells. Overexpressed Co-NF-κB localizes to the cytoplasm in Co cells. Co-NF-κB mRNA and DNA-binding levels differ across three Capsaspora life stages. RNA-sequencing and GO analyses identify possible gene targets of Co-NF-κB. Three NF-κB-like proteins from the choanoflagellate Acanthoeca spectabilis (As) contain conserved Rel Homology domain sequences, but lack C-terminal ANK repeats. All three As-NF-κB proteins constitutively enter the nucleus of cells, but differ in their DNA-binding abilities, transcriptional activation activities, and dimerization properties. These results provide a basis for understanding the evolutionary origins of this key transcription factor and could have implications for the origins of regulated immunity in higher taxa.

Transcription factor NF-ĸB is a key regulator of immunity in mammals, but its function in protists like Capsaspora and choanoflagellates is not known. Here, Leah Williams et al. characterize and compare the structure, activity, and regulation of NF-ĸB from Capsaspora and one choanoflagellate, providing further insight into the origins of NF-ĸB.

A modified CUT&RUN protocol and analysis pipeline to identify transcription factor binding sites in human cell lines

CUT&RUN is a recently developed in situ chromatin profiling technique that enables high-resolution chromatin mapping and probing. Herein, we describe our adapted CUT&RUN protocol for transcription factors (TFs). Our protocol outlines all necessary steps for TF profiling including the procedure to obtain proteinA-Mnase, while also outlining the bioinformatic pipeline steps required to process, analyze, and identify novel binding sites and sequences. Due to the small number of cells required, this method will allow the elucidation of cell context-dependent functions of many TFs.

For details on the use and execution of this protocol, please refer to Kong et al. (2021).

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CUT&RUN was recently developed for in situ chromatin mapping and probing

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Herein, we describe our modified CUT&RUN protocol to profile TF binding sites and motifs

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Modifications relate to nuclear TF targeting, rather than whole-cell histone targeting

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Bespoke bioinformatics pipeline simplifies analysis enabling binding site identification

Zinc Finger Protein SALL4 Functions through an AT-Rich Motif to Regulate Gene Expression

The zinc finger transcription factor SALL4 is highly expressed in embryonic stem cells, downregulated in most adult tissues, but reactivated in many aggressive cancers. This unique expression pattern makes SALL4 an attractive therapeutic target. However, whether SALL4 binds DNA directly to regulate gene expression is unclear, and many of its targets in cancer cells remain elusive. Here, through an unbiased screen of protein binding microarray (PBM) and cleavage under targets and release using nuclease (CUT&RUN) experiments, we identify and validate the DNA binding domain of SALL4 and its consensus binding sequence. Combined with RNA sequencing (RNA-seq) analyses after SALL4 knockdown, we discover hundreds of new SALL4 target genes that it directly regulates in aggressive liver cancer cells, including genes encoding a family of histone 3 lysine 9-specific demethylases (KDMs). Taken together, these results elucidate the mechanism of SALL4 DNA binding and reveal pathways and molecules to target in SALL4-dependent tumors.

In this paper, Kong et al. elucidate the DNA binding mechanisms of the transcription factor SALL4 and an epigenetic pathway that it regulates. Due to its important role in driving aggressive cancers, better understanding of SALL4 function will lead to strategies to target this protein in cancer.

Context and number of noncanonical repeat variable diresidues impede the design of TALE proteins with improved DNA targeting

Transcription activator-like effector (TALE) proteins have been used extensively for targeted binding of fusion proteins to loci of interest in (epi)genome engineering. Such approaches typically utilize four canonical TALE repeat variable diresidue (RVD) types, corresponding to the identities of two key amino acids, to target each nucleotide. Alternate RVDs with improved specificity are desired. Here, we focused on seven noncanonical RVDs that have been suggested to have improved specificity for their target nucleotides. We used custom protein binding microarrays to characterize the DNA-binding activity of 65 TALEs containing these alternate or corresponding canonical RVDs at multiple positions to ~5,000 unique DNA sequences per protein. We found that none of the noncanonical thymine-targeting RVDs displayed stronger preference for thymine than did the canonical RVD. Of the noncanonical RVDs with putatively improved specificity for guanine, only EN and NH showed greater discrimination of guanine over adenine. This improved specificity, however, comes at a cost: more substitutions of a noncanonical RVD for a canonical RVD generally decreased the protein's DNA-binding activity. Our results highlight the need to investigate RVD-nucleotide specificities in multiple protein contexts and suggest that a balance between canonical and noncanonical RVDs is needed to build TALEs with improved specificity.

Selective deployment of transcription factor paralogs with submaximal strength facilitates gene regulation in the immune system

In multicellular organisms, duplicated genes can diverge through tissue-specific gene expression patterns, as exemplified by highly regulated expression of Runx transcription factor paralogs with apparent functional redundancy. Here we asked what cell type-specific biologies might be supported by the selective expression of Runx paralogs during Langerhans cell and inducible regulatory T cell differentiation. We uncovered functional non-equivalence between Runx paralogs. Selective expression of native paralogs allowed integration of transcription factor activity with extrinsic signals, while non-native paralogs enforced differentiation even in the absence of exogenous inducers. DNA-binding affinity was controlled by divergent amino acids within the otherwise highly conserved RUNT domain, and evolutionary reconstruction suggested convergence of RUNT domain residues towards sub-maximal strength. Hence, the selective expression of gene duplicates in specialized cell types can synergize with the acquisition of functional differences to enable appropriate gene expression, lineage choice and differentiation in the mammalian immune system.

A multiplex RNA quantification method to determine the absolute amounts of mRNA without reverse transcription

We have developed a highly sensitive microarray-based method that determines the absolute amounts of mRNA in a total RNA sample in a multiplex manner without reverse transcription. This direct mRNA measurement promotes high-throughput testing and reduces bias in transcriptome analyses. Furthermore, quantification of the absolute amount of mRNA allows transcriptome analysis without common controls or additional, complicated normalization. The method, called Photo-DEAN, was validated using chemically synthesized RNAs of known quantities and mouse liver total RNA samples. We found that the absolute amounts of mRNA were successfully measured without the cDNA synthesis step, with a sensitivity of 15 zmol achieved in 7 h.

Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them

Translation rate per mRNA molecule correlates positively with mRNA abundance. As a result, protein levels do not scale linearly with mRNA levels, but instead scale with the abundance of mRNA raised to the power of an ‘amplification exponent’. Here we show that to quantitate translational control, the translation rate must be decomposed into two components. One, TR_mD, depends on the mRNA level and defines the amplification exponent. The other, TR_mIND, is independent of mRNA amount and impacts the correlation coefficient between protein and mRNA levels. We show that in Saccharomyces cerevisiae TR_mD represents ∼20% of the variance in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1.26]. TR_mIND constitutes the remaining ∼80% of the variance in translation and explains ∼5% of the variance in protein expression. We also find that TR_mD and TR_mIND are preferentially determined by different mRNA sequence features: TR_mIND by the length of the open reading frame and TR_mD both by a ∼60 nucleotide element that spans the initiating AUG and by codon and amino acid frequency. Our work provides more appropriate estimates of translational control and implies that TR_mIND is under different evolutionary selective pressures than TR_mD.

Transcription factor NF-κB is modulated by symbiotic status in a sea anemone model of cnidarian bleaching

Transcription factor NF-κB plays a central role in immunity from fruit flies to humans, and NF-κB activity is altered in many human diseases. To investigate a role for NF-κB in immunity and disease on a broader evolutionary scale we have characterized NF-κB in a sea anemone (Exaiptasia pallida; called Aiptasia herein) model for cnidarian symbiosis and dysbiosis (i.e., “bleaching”). We show that the DNA-binding site specificity of Aiptasia NF-κB is similar to NF-κB proteins from a broad expanse of organisms. Analyses of NF-κB and IκB kinase proteins from Aiptasia suggest that non-canonical NF-κB processing is an evolutionarily ancient pathway, which can be reconstituted in human cells. In Aiptasia, NF-κB protein levels, DNA-binding activity, and tissue expression increase when loss of the algal symbiont Symbiodinium is induced by heat or chemical treatment. Kinetic analysis of NF-κB levels following loss of symbiosis show that NF-κB levels increase only after Symbiodinium is cleared. Moreover, introduction of Symbiodinium into naïve Aiptasia larvae results in a decrease in NF-κB expression. Our results suggest that Symbiodinium suppresses NF-κB in order to enable establishment of symbiosis in Aiptasia. These results are the first to demonstrate a link between changes in the conserved immune regulatory protein NF-κB and cnidarian symbiotic status.

Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds

Transcription factors (TFs) control cellular processes by binding specific DNA motifs to modulate gene expression. Motif enrichment analysis of regulatory regions can identify direct and indirect TF binding sites. Here, we created a glossary of 108 non-redundant TF-8mer "modules" of shared specificity for 671 metazoan TFs from publicly available and new universal protein binding microarray data. Analysis of 239 ENCODE TF chromatin immunoprecipitation sequencing datasets and associated RNA sequencing profiles suggest the 8mer modules are more precise than position weight matrices in identifying indirect binding motifs and their associated tethering TFs. We also developed GENRE (genomically equivalent negative regions), a tunable tool for construction of matched genomic background sequences for analysis of regulatory regions. GENRE outperformed four state-of-the-art approaches to background sequence construction. We used our TF-8mer glossary and GENRE in the analysis of the indirect binding motifs for the co-occurrence of tethering factors, suggesting novel TF-TF interactions. We anticipate that these tools will aid in elucidating tissue-specific gene-regulatory programs.

[A possibility to use the DNA-based probes as internal standards for Agilent Technologies microarray transcriptomic analysis]

Accuracy of the microarray technology results is raised by using the multi-stage normalization of results. One of the principal requirements of such normalization is usage of internal standards. The routine Agilent microarray-based gene expression analysis protocol utilizes a Spike-In Kit during preparation of the samples representing a mixture of RNA fragments in different ratios. RNA probes which were synthesized in vitro conditions could be also used to establish how the magnitude of the fluorescent signal reflects the presence of RNA in the sample. A significant disadvantage of this type of standards is a difficulty of their production and the low RNA stability. In accordance with the Agilent protocol, the presence of the T7 promoter is necessary for the synthesis of labeled cRNA during sample preparation procedure. We hypothesized that we can successfully synthesize any RNA sequence having such type of promoter in its start position. Moreover, DNA sequence would serve as a matrix in this case. Using a set of different genes attached downstream of the T7-promoter in the plasmid DNA we have demonstrated in this study that such system can serve as a reliable template for the fluorescent labeled RNA sequence synthesis. In comparison with the routinely used internal RNA based controls, this template is stable, easy to manufacture and can be easily obtained in large quantities.

Comparison of Normal and Pre-Eclamptic Placental Gene Expression: A Systematic Review with Meta-Analysis

Pre-eclampsia (PE) is a serious multi-factorial disorder of human pregnancy. It is associated with changes in the expression of placental genes. Recent transcription profiling of placental genes with microarray analyses have offered better opportunities to define the molecular pathology of this disorder. However, the extent to which placental gene expression changes in PE is not fully understood. We conducted a systematic review of published PE and normal pregnancy (NP) control placental RNA microarrays to describe the similarities and differences between NP and PE placental gene expression, and examined how these differences could contribute to the molecular pathology of the disease. A total of 167 microarray samples were available for meta-analysis. We found the expression pattern of one group of genes was the same in PE and NP. The review also identified a set of genes (PE unique genes) including a subset, that were significantly (p < 0.05) down-regulated in pre-eclamptic placentae only. Using class prediction analysis, we further identified the expression of 88 genes that were highly associated with PE (p < 0.05), 10 of which (LEP, HTRA4, SPAG4, LHB, TREM1, FSTL3, CGB, INHA, PROCR, and LTF) were significant at p < 0.001. Our review also suggested that about 30% of genes currently being investigated as possibly of importance in PE placenta were not consistently and significantly affected in the PE placentae. We recommend further work to confirm the roles of the PE unique and associated genes, currently not being investigated in the molecular pathology of the disease.

Expansion of GA Dinucleotide Repeats Increases the Density of CLAMP Binding Sites on the X-Chromosome to Promote Drosophila Dosage Compensation

Dosage compensation is an essential process that equalizes transcript levels of X-linked genes between sexes by forming a domain of coordinated gene expression. Throughout the evolution of Diptera, many different X-chromosomes acquired the ability to be dosage compensated. Once each newly evolved X-chromosome is targeted for dosage compensation in XY males, its active genes are upregulated two-fold to equalize gene expression with XX females. In Drosophila melanogaster, the CLAMP zinc finger protein links the dosage compensation complex to the X-chromosome. However, the mechanism for X-chromosome identification has remained unknown. Here, we combine biochemical, genomic and evolutionary approaches to reveal that expansion of GA-dinucleotide repeats likely accumulated on the X-chromosome over evolutionary time to increase the density of CLAMP binding sites, thereby driving the evolution of dosage compensation. Overall, we present new insight into how subtle changes in genomic architecture, such as expansions of a simple sequence repeat, promote the evolution of coordinated gene expression.

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

Background

Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes.

Results

We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l² norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by ¹³C metabolic flux analysis (¹³C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).

Conclusion

Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Survey of variation in human transcription factors reveals prevalent DNA binding changes

Sequencing of exomes and genomes has revealed abundant genetic variation affecting the coding sequences of human transcription factors (TFs), but the consequences of such variation remain largely unexplored. We developed a computational, structure-based approach to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activity across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases. We found 77 variants in 28 genes that affect DNA binding affinity or specificity and identified thousands of rare alleles likely to alter the DNA binding activity of human sequence-specific TFs. Our results suggest that most individuals have unique repertoires of TF DNA binding activities, which may contribute to phenotypic variation.

Systematic Determination of Transcription Factor DNA-Binding Specificities in Yeast

Understanding how genes are regulated, decoding their "regulome", is one of the main challenges of the post-genomic era. Here, we describe the in vitro method we used to associate cis-regulatory sites with cognate trans-regulators by characterizing the DNA-binding specificity of the vast majority of yeast transcription factors using Protein Binding Microarrays. This approach can be implemented to any given organism.

Infectious hematopoietic necrosis virus (IHNV) persistence in Sockeye Salmon: influence on brain transcriptome and subsequent response to the viral mimic poly(I:C)

Background

Sockeye Salmon are an iconic species widely distributed throughout the North Pacific. A devastating pathogen of Sockeye Salmon is infectious hematopoietic necrosis virus (IHNV, genus Novirhabdovirus, family Rhabdoviridae). It has been postulated that IHNV is maintained in salmon populations by persisting over the life of its host and/or by residing in natural reservoirs other than its susceptible hosts. Herein we demonstrate the presence of IHNV in the brain of Sockeye Salmon that survived an experimentally-induced outbreak, suggesting the presence of viral persistence in this susceptible species. To understand the viral persistent state in Sockeye Salmon we profiled the transcriptome to evaluate the host response in asymptomatic carriers and to determine what effects (if any) IHNV exposure may have on subsequent virus challenges.

Results

A laboratory disease model to simulate a natural IHNV outbreak in Sockeye Salmon resulted in over a third of the population incurring acute IHN disease and mortality during the first four months after initial exposure. Nine months post IHNV exposure, despite the absence of disease and mortality, a small percentage (<4 %) of the surviving population contained IHNV in brain. Transcriptome analysis in brain of asymptomatic virus carriers and survivors without virus exhibited distinct transcriptional profiles in comparison to naïve fish. Characteristic for carriers was the up-regulation of genes involved in antibody production and antigen presentation. In both carriers and survivors a down-regulation of genes related to cholesterol biosynthesis, resembling an antiviral mechanism observed in higher vertebrates was revealed along with differences in nervous system development. Moreover, following challenge with poly(I:C), survivors and carriers displayed an elevated antiviral immune response in comparison to naïve fish.

Conclusions

IHN virus persistence was identified in Sockeye Salmon where it elicited a unique brain transcriptome profile suggesting an ongoing adaptive immune response. IHNV carriers remained uncompromised in mounting efficient innate antiviral responses when exposed to a viral mimic. The capacity of IHNV to reside in asymptomatic hosts supports a virus carrier hypothesis and if proven infectious, could have significant epidemiological consequences towards maintaining and spreading IHNV among susceptible host populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1759-y) contains supplementary material, which is available to authorized users.

Molecular Evidence for Functional Divergence and Decay of a Transcription Factor Derived from Whole-Genome Duplication in Arabidopsis thaliana

Functional divergence between duplicate transcription factors (TFs) has been linked to critical events in the evolution of land plants and can result from changes in patterns of expression, binding site divergence, and/or interactions with other proteins. Although plant TFs tend to be retained post polyploidization, many are lost within tens to hundreds of million years. Thus, it can be hypothesized that some TFs in plant genomes are in the process of becoming pseudogenes. Here, we use a pair of salt tolerance-conferring transcription factors, DWARF AND DELAYED FLOWERING1 (DDF1) and DDF2, that duplicated through paleopolyploidy 50 to 65 million years ago, as examples to illustrate potential mechanisms leading to duplicate retention and loss. We found that the expression patterns of Arabidopsis thaliana (At)DDF1 and AtDDF2 have diverged in a highly asymmetric manner, and AtDDF2 has lost most inferred ancestral stress responses. Consistent with promoter disablement, the AtDDF2 promoter has fewer predicted cis-elements and a methylated repetitive element. Through comparisons of AtDDF1, AtDDF2, and their Arabidopsis lyrata orthologs, we identified significant differences in binding affinities and binding site preference. In particular, an AtDDF2-specific substitution within the DNA-binding domain significantly reduces binding affinity. Cross-species analyses indicate that both AtDDF1 and AtDDF2 are under selective constraint, but among A. thaliana accessions, AtDDF2 has a higher level of nonsynonymous nucleotide diversity compared with AtDDF1. This may be the result of selection in different environments or may point toward the possibility of ongoing functional decay despite retention for millions of years after gene duplication.

Context influences on TALE-DNA binding revealed by quantitative profiling

Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE-DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000-20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE-DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Ionotropic glutamate receptors mediate inducible defense in the water flea Daphnia pulex

Phenotypic plasticity is the ability held in many organisms to produce different phenotypes with a given genome in response to environmental stimuli, such as temperature, nutrition and various biological interactions. It seems likely that environmental signals induce a variety of mechanistic responses that influence ontogenetic processes. Inducible defenses, in which prey animals alter their morphology, behavior and/or other traits to help protect against direct or latent predation threats, are among the most striking examples of phenotypic plasticity. The freshwater microcrustacean Daphnia pulex forms tooth-like defensive structures, "neckteeth," in response to chemical cues or signals, referred to as "kairomones," in this case released from phantom midge larvae, a predator of D. pulex. To identify factors involved in the reception and/or transmission of a kairomone, we used microarray analysis to identify genes up-regulated following a short period of exposure to the midge kairomone. In addition to identifying differentially expressed genes of unknown function, we also found significant up-regulation of genes encoding ionotropic glutamate receptors, which are known to be involved in neurotransmission in many animal species. Specific antagonists of these receptors strongly inhibit the formation of neckteeth in D. pulex, although agonists did not induce neckteeth by themselves, indicating that ionotropic glutamate receptors are necessary but not sufficient for early steps of neckteeth formation in D. pulex. Moreover, using co-exposure of D. pulex to antagonists and juvenile hormone (JH), which physiologically mediates neckteeth formation, we found evidence suggesting that the inhibitory effect of antagonists is not due to direct inhibition of JH synthesis/secretion. Our findings not only provide a candidate molecule required for the inducible defense response in D. pulex, but also will contribute to the understanding of complex mechanisms underlying the recognition of environmental changes, which form the basis of phenotypic plasticity.

Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology

We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and human, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast data sets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels-a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable missing data on estimates of the correlation between high dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.

Characterizing the DNA binding site specificity of NF-κB with protein-binding microarrays (PBMs)

NF-κB transcription factors control a wide array of important cellular and organismal processes in eukaryotes. All NF-κB transcription factors bind to DNA target sites as dimers. In vertebrates, there are five NF-κB subunits, p50, p52, RelA (p65), c-Rel, and RelB, that can form almost all combinations of homodimers and heterodimers, which recognize distinct, but overlapping, target sequences. In this chapter, we describe the use of protein-binding microarrays (PBMs), a high-throughput method to measure the binding of proteins to different DNA sequences. PBM datasets allow for sensitive comparisons of NF-κB dimer DNA-binding differences and can aid in the computational and experimental prediction of NF-κB target genes.

Enrichment of spermatogonial stem cells using side population in teleost

Spermatogenesis originates from a small population of spermatogonial stem cells; this population can maintain continuous sperm production throughout the life of fish via self-renewal and differentiation. Despite their biological importance, spermatogonial stem cells are not thoroughly characterized because they are difficult to distinguish from their progeny cells that become committed to differentiation. We previously established a novel technique for germ cell transplantation to identify spermatogonial stem cells based on their colonizing activity and their ability to initiate donor-derived gametogenesis in the rainbow trout (Oncorhynchus mykiss). Although spermatogonial stem cells can be retrospectively identified after transplantation, there is currently no technique to prospectively enrich for or purify spermatogonial stem cells. Here, we describe a method for spermatogonial stem cell enrichment using a side population. With optimized Hoechst 33342 staining conditions, we successfully identified side-population cells among type A spermatogonia. Side-population cells were transcriptomically and morphologically distinct from non-side-population cells. To functionally determine whether the transplantable spermatogonial stem cells were enriched in the side-population fraction, we compared the colonization activity of side-population cells with that of non-side-population cells. Colonization efficiency was significantly higher with side-population cells than with non-side-population cells or with total type A spermatogonia. In addition, side-population cells could produce billions of sperm in recipients. These results indicated that transplantable spermatogonial stem cells were enriched in the side-population fraction. This method will provide biological information that may advance our understanding of spermatogonial stem cells in teleosts. Additionally, this technique will increase the efficiency of germ cell transplantation used in surrogate broodstock technology.

Tissue-specific expression and regulatory networks of pig microRNAome

Background

Despite the economic and medical importance of the pig, knowledge about its genome organization, gene expression regulation, and molecular mechanisms involved in physiological processes is far from that achieved for mouse and rat, the two most used model organisms in biomedical research. MicroRNAs (miRNAs) are a wide class of molecules that exert a recognized role in gene expression modulation, but only 280 miRNAs in pig have been characterized to date.

Results

We applied a novel computational approach to predict species-specific and conserved miRNAs in the pig genome, which were then subjected to experimental validation. We experimentally identified candidate miRNAs sequences grouped in high-confidence (424) and medium-confidence (353) miRNAs according to RNA-seq results. A group of miRNAs was also validated by PCR experiments. We established the subtle variability in expression of isomiRs and miRNA-miRNA star couples supporting a biological function for these molecules. Finally, miRNA and mRNA expression profiles produced from the same sample of 20 different tissue of the animal were combined, using a correlation threshold to filter miRNA-target predictions, to identify tissue-specific regulatory networks.

Conclusions

Our data represent a significant progress in the current understanding of miRNAome in pig. The identification of miRNAs, their target mRNAs, and the construction of regulatory circuits will provide new insights into the complex biological networks in several tissues of this important animal model.

BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE

Both clinical and analytical metrics produced by microarray-based assay technology have recognized problems in reproducibility, reliability and analytical sensitivity. These issues are often attributed to poor understanding and control of nucleic acid behaviors and properties at solid-liquid interfaces. Nucleic acid hybridization, central to DNA and RNA microarray formats, depends on the properties and behaviors of single strand (ss) nucleic acids (e.g., probe oligomeric DNA) bound to surfaces. ssDNA's persistence length, radius of gyration, electrostatics, conformations on different surfaces and under various assay conditions, its chain flexibility and curvature, charging effects in ionic solutions, and fluorescent labeling all influence its physical chemistry and hybridization under assay conditions. Nucleic acid (e.g., both RNA and DNA) target interactions with immobilized ssDNA strands are highly impacted by these biophysical states. Furthermore, the kinetics, thermodynamics, and enthalpic and entropic contributions to DNA hybridization reflect global probe/target structures and interaction dynamics. Here we review several biophysical issues relevant to oligomeric nucleic acid molecular behaviors at surfaces and their influences on duplex formation that influence microarray assay performance. Correlation of biophysical aspects of single and double-stranded nucleic acids with their complexes in bulk solution is common. Such analysis at surfaces is not commonly reported, despite its importance to microarray assays. We seek to provide further insight into nucleic acid-surface challenges facing microarray diagnostic formats that have hindered their clinical adoption and compromise their research quality and value as genomics tools.

Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq

BACKGROUND

High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

RESULTS

To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

CONCLUSIONS

Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

A history of microarrays in biomedicine

The fundamental strategy of the current postgenomic era or the era of functional genomics is to expand the scale of biologic research from studying single genes or proteins to studying all genes or proteins simultaneously using a systematic approach. As recently developed methods for obtaining genome-wide mRNA expression data, oligonucleotide and DNA microarrays are particularly powerful in the context of knowing the entire genome sequence and can provide a global view of changes in gene expression patterns in response to physiologic alterations or manipulation of transcriptional regulators. In biomedical research, such an approach will ultimately determine biologic behavior of both normal and diseased tissues, which may provide insights into disease mechanisms and identify novel markers and candidates for diagnostic, prognostic and therapeutic intervention. However, microarray technology is still in a continuous state of evolution and development, and it may take time to implement microarrays as a routine medical device. Many limitations exist and many challenges remain to be achieved to help inclusion of microarrays in clinical medicine. In this review, a brief history of microarrays in biomedical research is provided, including experimental overview, limitations, challenges and future developments.

High-throughput analysis of protein-DNA binding affinity

Sequence-specific protein-DNA interactions mediate most regulatory processes underlying gene expression, such as transcriptional regulation by transcription factors (TFs) or chromatin organization. Current knowledge about DNA-binding specificities of TFs is based mostly on low- to medium-throughput methodologies that are time-consuming and often fail to identify DNA motifs recognized by a TF with lower affinity but retaining biological relevance. The use of protein-binding microarrays (PBMs) offers a high-throughput alternative for the identification of protein-DNA specificities. PBM consists in an array of pseudorandomized DNA sequences that are optimized to include all the possible 10- or 11-mer DNA sequences, allowing the determination of binding specificities of most eukaryotic TFs. PBMs that can be synthesized by several manufacturing companies as single-stranded DNA are converted into double-stranded in a simple primer extension reaction. The protein of interest fused to an epitope tag is then incubated onto the PBM, and specific DNA-protein complexes are revealed in a series of immunological reactions coupled to a fluorophore. After scanning and quantifying PBMs, specific DNA motifs recognized by the protein are identified with ready-to-use scripts, generating comprehensive but accessible information about the DNA-binding specificity of the protein. This chapter describes detailed procedures for preparation of double-stranded PBMs, incubation with recombinant protein, and detection of protein-DNA complexes. Finally, we outline some cues for evaluating the biological role of DNA motifs obtained in vitro.

Assessing the detection capacity of microarrays as bio/nanosensing platforms

Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis. Expanding the utility of microarrays as a detection tool for various biological and biomedical applications requires information about performance such as the limits of detection and quantification, which are considered as an essential information to decide the detection sensitivity of sensing devices. Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system. Two different types of limits of detection and quantification are proposed by the prediction or tolerance interval for two common cyanine fluorescence dyes, Cy3 and Cy5. Besides oligonucleotide, the proposed method can be generalized to other microarray formats with various biomolecules such as complementary DNA, protein, peptide, carbohydrate, tissue, or other small biomolecules. Also, it can be easily applied to other fluorescence dyes for further dye chemistry improvement.

Transcriptome analysis of acyl-homoserine lactone-based quorum sensing regulation in Yersinia pestis [corrected]

The etiologic agent of bubonic plague, Yersinia pestis, senses self-produced, secreted chemical signals in a process named quorum sensing. Though the closely related enteric pathogen Y. pseudotuberculosis uses quorum sensing system to regulate motility, the role of quorum sensing in Y. pestis has been unclear. In this study we performed transcriptional profiling experiments to identify Y. pestis quorum sensing regulated functions. Our analysis revealed that acyl-homoserine lactone-based quorum sensing controls the expression of several metabolic functions. Maltose fermentation and the glyoxylate bypass are induced by acyl-homoserine lactone signaling. This effect was observed at 30°C, indicating a potential role for quorum sensing regulation of metabolism at temperatures below the normal mammalian temperature. It is proposed that utilization of alternative carbon sources may enhance growth and/or survival during prolonged periods in natural habitats with limited nutrient sources, contributing to maintenance of plague in nature.

Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein-binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case studies, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the three-dimensional structure of DNA binding sites. Thus, the local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.

Towards Optimal Microarray Universal Reference Sample Designs

In this paper, the authors present an assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal universal reference rna (urr) samples to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Toward this target, the authors present an in-silico (binary) optimization process the solutions of which present optimal urr sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.

Measurement of MicroRNA: a regulator of gene expression

MicroRNAs (miRs) are epigenetic regulators of messenger RNAs' (mRNA) expression of polypeptides. As such, miRs represent an intriguing mechanism by which gene-environment interactions are hypothesized to occur on the level of epigenetic control over gene expression. In addition to promising findings from in vitro studies indicating that miRs have the potential to function as therapeutic agents in modifying the course of pathophysiologic conditions, recent human studies revealed changes in miR expression patterns in response to behavioral interventions. The authors provide an overview of how miRs are preserved and isolated from other genetic material and describe commonly used methods for measuring miR in the research setting, including Northern blot, polymerase chain reaction, and microarray. The authors also introduce bioinformatic approaches to analysis of high-throughput miR expression and techniques used to create predictive models of miR-mRNA binding to describe possible physiologic pathways affected by specific miRs.

Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights

Background

Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis-regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking.

Results

We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae TFs. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8-bp sequences. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHT1 regulator (VHR) TF families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR TFs, newly characterized by protein binding microarrays in this study, recognize bZIP-like DNA motifs, while the bZIP TF Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) TFs. We identified several TFs with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo TF binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific TFs.

Conclusions

The comprehensive data in this curated collection allow for more accurate analyses of regulatory TF-DNA interactions, in-depth structural studies of TF-DNA specificity determinants, and future experimental investigations of the TFs' predicted target genes and regulatory roles.

Segmentation and intensity estimation for microarray images with saturated pixels

Background

Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶ - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.

Results

We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.

Conclusions

The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

Role of SPI-1 secreted effectors in acute bovine response to Salmonella enterica Serovar Typhimurium: a systems biology analysis approach

Salmonella enterica Serovar Typhimurium (S. Typhimurium) causes enterocolitis with diarrhea and polymorphonuclear cell (PMN) influx into the intestinal mucosa in humans and calves. The Salmonella Type III Secretion System (T3SS) encoded at Pathogenicity Island I translocates Salmonella effector proteins SipA, SopA, SopB, SopD, and SopE2 into epithelial cells and is required for induction of diarrhea. These effector proteins act together to induce intestinal fluid secretion and transcription of C-X-C chemokines, recruiting PMNs to the infection site. While individual molecular interactions of the effectors with cultured host cells have been characterized, their combined role in intestinal fluid secretion and inflammation is less understood. We hypothesized that comparison of the bovine intestinal mucosal response to wild type Salmonella and a SipA, SopABDE2 effector mutant relative to uninfected bovine ileum would reveal heretofore unidentified diarrhea-associated host cellular pathways. To determine the coordinated effects of these virulence factors, a bovine ligated ileal loop model was used to measure responses to wild type S. Typhimurium (WT) and a ΔsipA, sopABDE2 mutant (MUT) across 12 hours of infection using a bovine microarray. Data were analyzed using standard microarray analysis and a dynamic bayesian network modeling approach (DBN). Both analytical methods confirmed increased expression of immune response genes to Salmonella infection and novel gene expression. Gene expression changes mapped to 219 molecular interaction pathways and 1620 gene ontology groups. Bayesian network modeling identified effects of infection on several interrelated signaling pathways including MAPK, Phosphatidylinositol, mTOR, Calcium, Toll-like Receptor, CCR3, Wnt, TGF-β, and Regulation of Actin Cytoskeleton and Apoptosis that were used to model of host-pathogen interactions. Comparison of WT and MUT demonstrated significantly different patterns of host response at early time points of infection (15 minutes, 30 minutes and one hour) within phosphatidylinositol, CCR3, Wnt, and TGF-β signaling pathways and the regulation of actin cytoskeleton pathway.

Quantification of codon selection for comparative bacterial genomics

Background

Statistics measuring codon selection seek to compare genes by their sensitivity to selection for translational efficiency, but existing statistics lack a model for testing the significance of differences between genes. Here, we introduce a new statistic for measuring codon selection, the Adaptive Codon Enrichment (ACE).

Results

This statistic represents codon usage bias in terms of a probabilistic distribution, quantifying the extent that preferred codons are over-represented in the gene of interest relative to the mean and variance that would result from stochastic sampling of codons. Expected codon frequencies are derived from the observed codon usage frequencies of a broad set of genes, such that they are likely to reflect nonselective, genome wide influences on codon usage (e.g. mutational biases). The relative adaptiveness of synonymous codons is deduced from the frequency of codon usage in a pre-selected set of genes relative to the expected frequency. The ACE can predict both transcript abundance during rapid growth and the rate of synonymous substitutions, with accuracy comparable to or greater than existing metrics. We further examine how the composition of reference gene sets affects the accuracy of the statistic, and suggest methods for selecting appropriate reference sets for any genome, including bacteriophages. Finally, we demonstrate that the ACE may naturally be extended to quantify the genome-wide influence of codon selection in a manner that is sensitive to a large fraction of codons in the genome. This reveals substantial variation among genomes, correlated with the tRNA gene number, even among groups of bacteria where previously proposed whole-genome measures show little variation.

Conclusions

The statistical framework of the ACE allows rigorous comparison of the level of codon selection acting on genes, both within a genome and between genomes.

Linking phytoplankton community composition to seasonal changes in f-ratio

Seasonal changes in nitrogen assimilation have been studied in the western English Channel by sampling at approximately weekly intervals for 12 months. Nitrate concentrations showed strong seasonal variations. Available nitrogen in the winter was dominated by nitrate but this was close to limit of detection from May to September, after the spring phytoplankton bloom. The (15)N uptake experiments showed that nitrate was the nitrogen source for the spring phytoplankton bloom but regenerated nitrogen supported phytoplankton productivity throughout the summer. The average annual f-ratio was 0.35, which demonstrated the importance of ammonia regeneration in this dynamic temperate region. Nitrogen uptake rate measurements were related to the phytoplankton responsible by assessing the relative abundance of nitrate reductase (NR) genes and the expression of NR among eukaryotic phytoplankton. Strong signals were detected from NR sequences that are not associated with known phylotypes or cultures. NR sequences from the diatom Phaeodactylum tricornutum were highly represented in gene abundance and expression, and were significantly correlated with f-ratio. The results demonstrate that analysis of functional genes provides additional information, and may be able to give better indications of which phytoplankton species are responsible for the observed seasonal changes in f-ratio than microscopic phytoplankton identification.

Simultaneous detection of multiple biomarkers with over three orders of concentration difference using phase change nanoparticles

A big challenge for multiplexed detection of cancer biomarkers is that biomarker concentrations in body fluid differs several orders of magnitude. Existing techniques are not suitable to detect low- and high-concentration biomarkers (protein and DNA) at the same time, and liquid chromatography or electrophoresis is used to separate or purify target biomarkers before analysis. This paper describes a new broad-range biomarker assay using solid to liquid phase change nanoparticles, where a panel of metallic nanoparticles (i.e., metals and eutectic alloys) are modified with a panel of ligands to establish a one-to-one correspondence and attached onto ligand-modified substrates by forming sandwiched complexes. The melting peak and fusion enthalpy of phase change nanoparticles during thermal analysis reflect the type and concentration of biomarkers, respectively. The thermal readout condition can be adjusted in such a way that multiple biomarkers with concentration difference over 3 orders of magnitude have been simultaneously detected under the same condition.

Robust protein profiling with complex antibody microarrays in a dual-colour mode

Antibody microarrays are a multiplexing technique for the analyses of hundreds of different analytes in parallel from small sample volumes of few microlitres only. With sensitivities in the picomolar to femtomolar range, they are gaining importance in proteomic analyses. These sensitivities can be obtained for complex protein samples without any pre-fractionation or signal amplification. Also, no expensive or elaborate protein depletion steps are needed. As with custom DNA-microarrays, the implementation of a dual-colour assay adds to assay robustness and reproducibility and was therefore a focus of our technical implementation. In order to perform antibody microarray experiments for large sets of samples and analytes in a robust manner, it was essential to optimise the experimental layout, the protein extraction, labelling and incubation as well as data processing steps. Here, we present our current protocol, which is used for the simultaneous analysis of the abundance of more than 800 proteins in plasma, urine, and tissue samples.

Notch and MAML-1 complexation do not detectably alter the DNA binding specificity of the transcription factor CSL

BACKGROUND

Canonical Notch signaling is initiated when ligand binding induces proteolytic release of the intracellular part of Notch (ICN) from the cell membrane. ICN then travels into the nucleus where it drives the assembly of a transcriptional activation complex containing the DNA-binding transcription factor CSL, ICN, and a specialized co-activator of the Mastermind family. A consensus DNA binding site motif for the CSL protein was previously defined using selection-based methods, but whether subsequent association of Notch and Mastermind-like proteins affects the DNA binding preferences of CSL has not previously been examined.

PRINCIPAL FINDINGS

Here, we utilized protein-binding microarrays (PBMs) to compare the binding site preferences of isolated CSL with the preferred binding sites of CSL when bound to the CSL-binding domains of all four different human Notch receptors. Measurements were taken both in the absence and in the presence of Mastermind-like-1 (MAML1). Our data show no detectable difference in the DNA binding site preferences of CSL before and after loading of Notch and MAML1 proteins.

CONCLUSIONS/SIGNIFICANCE

These findings support the conclusion that accrual of Notch and MAML1 promote transcriptional activation without dramatically altering the preferred sites of DNA binding, and illustrate the potential of PBMs to analyze the binding site preferences of multiprotein-DNA complexes.

Development of an environmental functional gene microarray for soil microbial communities

Functional attributes of microbial communities are difficult to study, and most current techniques rely on DNA- and rRNA-based profiling of taxa and genes, including microarrays containing sequences of known microorganisms. To quantify gene expression in environmental samples in a culture-independent manner, we constructed an environmental functional gene microarray (E-FGA) consisting of 13,056 mRNA-enriched anonymous microbial clones from diverse microbial communities to profile microbial gene transcripts. A new normalization method using internal spot standards was devised to overcome spotting and hybridization bias, enabling direct comparisons of microarrays. To evaluate potential applications of this metatranscriptomic approach for studying microbes in environmental samples, we tested the E-FGA by profiling the microbial activity of agricultural soils with a low or high flux of N₂O. A total of 109 genes displayed expression that differed significantly between soils with low and high N₂O emissions. We conclude that mRNA-based approaches such as the one presented here may complement existing techniques for assessing functional attributes of microbial communities.

Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.