Linking DNA-binding proteins to their recognition sequences by using protein microarrays

GSM1 Requires Hap4 for Expression and Plays a Role in Gluconeogenesis and Utilization of Nonfermentable Carbon Sources

Multiple transcription factors in the budding yeast Saccharomyces cerevisiae are required for the switch from fermentative growth to respiratory growth. The Hap2/3/4/5 complex is a transcriptional activator that binds to CCAAT sequence elements in the promoters of many genes involved in the tricarboxylic acid cycle and oxidative phosphorylation and activates gene expression. Adr1 and Cat8 are required to activate the expression of genes involved in the glyoxylate cycle, gluconeogenesis, and utilization of nonfermentable carbon sources. Here, we characterize the regulation and function of the zinc cluster transcription factor Gsm1 using Western blotting and lacZ reporter-gene analysis. GSM1 is subject to glucose repression, and it requires a CCAAT sequence element for Hap2/3/4/5-dependent expression under glucose-derepression conditions. Genome-wide CHIP analyses revealed many potential targets. We analyzed 29 of them and found that FBP1, LPX1, PCK1, SFC1, and YAT1 require both Gsm1 and Hap4 for optimal expression. FBP1, PCK1, SFC1, and YAT1 play important roles in gluconeogenesis and utilization of two-carbon compounds, and they are known to be regulated by Cat8. GSM1 overexpression in cat8Δ mutant cells increases the expression of these target genes and suppresses growth defects in cat8Δ mutants on lactate medium. We propose that Gsm1 and Cat8 have shared functions in gluconeogenesis and utilization of nonfermentable carbon sources and that Cat8 is the primary regulator.

A deep learning-based method for the prediction of DNA interacting residues in a protein

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Proteins via Weighted Convolutional Features

Protein-DNA interactions exist ubiquitously and play important roles in the life cycles of living cells. The accurate identification of DNA-binding proteins (DBPs) is one of the key steps to understand the mechanisms of protein-DNA interactions. Although many DBP identification methods have been proposed, the current performance is still unsatisfactory. In this study, a new method, called TargetDBP+, is developed to further enhance the performance of identifying DBPs. In TargetDBP+, five convolutional features are first extracted from five feature sources, i.e., amino acid one-hot matrix (AAOHM), position-specific scoring matrix (PSSM), predicted secondary structure probability matrix (PSSPM), predicted solvent accessibility probability matrix (PSAPM), and predicted probabilities of DNA-binding sites (PPDBSs); second, the five features are weightedly and serially combined using the weights of all of the elements learned by the differential evolution algorithm; and finally, the DBP identification model of TargetDBP+ is trained using the support vector machine (SVM) algorithm. To evaluate the developed TargetDBP+ and compare it with other existing methods, a new gold-standard benchmark data set, called UniSwiss, is constructed, which consists of 4881 DBPs and 4881 non-DBPs extracted from the UniprotKB/Swiss-Prot database. Experimental results demonstrate that TargetDBP+ can obtain an accuracy of 85.83% and precision of 88.45% covering 82.41% of all DBP data on the independent validation subset of UniSwiss, with the MCC value (0.718) being significantly higher than those of other state-of-the-art control methods. The web server of TargetDBP+ is accessible at http://csbio.njust.edu.cn/bioinf/targetdbpplus/; the UniSwiss data set and stand-alone program of TargetDBP+ are accessible at https://github.com/jun-csbio/TargetDBPplus.

I-Block: a simple Escherichia coli-based assay for studying sequence-specific DNA binding of proteins

We have developed a simple method called I-Block assay, which can detect sequence-specific binding of proteins to DNA in Escherichia coli. The method works by detecting competition between the protein of interest and RNA polymerase for binding to overlapping target sites in a plasmid-borne lacI promoter variant. The assay utilizes two plasmids and an E. coli host strain, from which the gene of the Lac repressor (lacI) has been deleted. One of the plasmids carries the lacI gene with a unique NheI restriction site created in the lacI promoter. The potential recognition sequences of the tested protein are inserted into the NheI site. Introduction of the plasmids into the E. coliΔlacI host represses the constitutive β-galactosidase synthesis of the host bacterium. If the studied protein expressed from a compatible plasmid binds to its target site in the lacI promoter, it will interfere with lacI transcription and lead to increased β-galactosidase activity. The method was tested with two zinc finger proteins, with the lambda phage cI857 repressor, and with CRISPR-dCas9 targeted to the lacI promoter. The I-Block assay was shown to work with standard liquid cultures, with cultures grown in microplate and with colonies on X-gal indicator plates.

iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

BACKGROUND

Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done.

RESULTS

Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%.

CONCLUSIONS

According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

A semi-synthetic regulon enables rapid growth of yeast on xylose

Nutrient assimilation is the first step that allows biological systems to proliferate and produce value-added products. Yet, implementation of heterologous catabolic pathways has so far relied on constitutive gene expression without consideration for global regulatory systems that may enhance nutrient assimilation and cell growth. In contrast, natural systems prefer nutrient-responsive gene regulation (called regulons) that control multiple cellular functions necessary for cell survival and growth. Here, in Saccharomyces cerevisiae, by partially- and fully uncoupling galactose (GAL)-responsive regulation and metabolism, we demonstrate the significant growth benefits conferred by the GAL regulon. Next, by adapting the various aspects of the GAL regulon for a non-native nutrient, xylose, we build a semi-synthetic regulon that exhibits higher growth rate, better nutrient consumption, and improved growth fitness compared to the traditional and ubiquitous constitutive expression strategy. This work provides an elegant paradigm to integrate non-native nutrient catabolism with native, global cellular responses to support fast growth.

Efficient assimilation of nutrients is essential for the production of value-added products in microbial fermentation. Here the authors design a semi-synthetic xylose regulon to improve growth characteristics of Saccharomyces cerevisiae on this non-native sugar.

Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs

Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features. Based on the weightedly combined feature, multiple initial predictors with SVM as classifiers are trained by applying a random under-sampling technique to the original dataset, the purpose of which is to cope with the severe imbalance phenomenon that exists between the number of DNA-binding and non-binding residues. The final ensembled predictor is obtained by boosting the multiple initially trained predictors. Experimental simulation results demonstrate that the proposed TargetDNA achieves a high prediction performance and outperforms many existing sequence-based protein-DNA binding residue predictors. The TargetDNA web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/TargetDNA/ for academic use.

DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins

Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.

The Potentials and Pitfalls of Microarrays in Neglected Tropical Diseases: A Focus on Human Filarial Infections

Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines.

Compartmentation of metabolites in regulating epigenome of cancer

Covalent modification of DNA and histones are important epigenetic events and the genome wide reshaping of epigenetic markers is common in cancer. The epigenetic markers are produced by enzymatic reactions and some of these reactions require the presence of metabolites as cofactors (termed Epigenetic Enzyme Required Metabolites, EERMs). Recent studies found that the abundance of these EERMs correlates with epigenetic enzyme activities. Also, the subcellular compartmentation, especially the nuclear localization of these EERMs may play a role in regulating the activities of epigenetic enzymes. Moreover, gene specific recruitment of enzymes which produce the EERMs in the proximity of the epigenetic modification events accompanying the gene expression regulation, were proposed. Therefore, it is of importance to summarize these findings of the EERMs in regulating the epigenetic modifications at both DNA and histone levels, and to understand how EERMs contribute to cancer development by addressing their global versus local distribution.

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

Transcription factor binding sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA motif) models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement if choosing di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites

With the prevalence of chromatin immunoprecipitation (ChIP) with sequencing (ChIP-Seq) technology, massive ChIP-Seq data has been accumulated. The ChIP-Seq technology measures the genome-wide occupancy of DNA-binding proteins in vivo. It is well-known that different DNA-binding protein occupancies may result in a gene being regulated in different conditions (e.g. different cell types). To fully understand a gene's function, it is essential to develop probabilistic models on multiple ChIP-Seq profiles for deciphering the gene transcription causalities. In this work, we propose and describe two probabilistic models. Assuming the conditional independence of different DNA-binding proteins' occupancies, the first method (SignalRanker) is developed as an intuitive method for ChIP-Seq genome-wide signal profile inference. Unfortunately, such an assumption may not always hold in some gene regulation cases. Thus, we propose and describe another method (FullSignalRanker) which does not make the conditional independence assumption. The proposed methods are compared with other existing methods on ENCODE ChIP-Seq datasets, demonstrating its regression and classification ability. The results suggest that FullSignalRanker is the best-performing method for recovering the signal ranks on the promoter and enhancer regions. In addition, FullSignalRanker is also the best-performing method for peak sequence classification. We envision that SignalRanker and FullSignalRanker will become important in the era of next generation sequencing. FullSignalRanker program is available on the following website: http://www.cs.toronto.edu/~wkc/FullSignalRanker/.

SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles

MOTIVATION

Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-Seq) measures the genome-wide occupancy of transcription factors in vivo. Different combinations of DNA-binding protein occupancies may result in a gene being expressed in different tissues or at different developmental stages. To fully understand the functions of genes, it is essential to develop probabilistic models on multiple ChIP-Seq profiles to decipher the combinatorial regulatory mechanisms by multiple transcription factors.

RESULTS

In this work, we describe a probabilistic model (SignalSpider) to decipher the combinatorial binding events of multiple transcription factors. Comparing with similar existing methods, we found SignalSpider performs better in clustering promoter and enhancer regions. Notably, SignalSpider can learn higher-order combinatorial patterns from multiple ChIP-Seq profiles. We have applied SignalSpider on the normalized ChIP-Seq profiles from the ENCODE consortium and learned model instances. We observed different higher-order enrichment and depletion patterns across sets of proteins. Those clustering patterns are supported by Gene Ontology (GO) enrichment, evolutionary conservation and chromatin interaction enrichment, offering biological insights for further focused studies. We also proposed a specific enrichment map visualization method to reveal the genome-wide transcription factor combinatorial patterns from the models built, which extend our existing fine-scale knowledge on gene regulation to a genome-wide level.

AVAILABILITY AND IMPLEMENTATION

The matrix-algebra-optimized executables and source codes are available at the authors' websites: http://www.cs.toronto.edu/∼wkc/SignalSpider.

Identification of bacterial factors involved in type 1 fimbria expression using an Escherichia coli K12 proteome chip

Type 1 fimbriae are filamentous structures on Escherichia coli. These structures are important adherence factors. Because binding to the host cells is the first step of infection, type 1 fimbria is an important virulence factor of pathogenic E. coli. Expression of type 1 fimbria is regulated by a phase variation in which each individual bacterium can alternate between fimbriated (phase-ON) and nonfimbriated (phase-OFF) states. The phase variation is regulated by the flipping of the 314-bp fimS fragment, which contains the promoter driving the expression of the genes required for the synthesis of type 1 fimbria. Thus, the bacterial proteins able to interact with fimS are likely to be involved in regulating the expression of type 1 fimbria. To identify novel type 1 fimbria-regulating factors, we used an E. coli K12 proteome chip to screen for the bacterial factors able to interact with a 602-bp DNA fragment containing fimS and its adjacent regions. The Spr protein was identified by the proteome chip-based screening and further confirmed to be able to interact with fimS by electrophoretic mobility shift assay. Deletion of spr in the neonatal meningitis E. coli strain RS218 significantly increased the ratio of the bacterial colonies that contained the type 1 fimbria phase-ON cells on agar plates. In addition, Spr interfered with the interactions of fimS with the site-specific recombinases, FimB and FimE, which are responsible for mediating the flipping of fimS. These results suggest that Spr is involved in the regulation of type 1 fimbria expression through direct interaction with the invertible element fimS. These findings facilitate our understanding of the regulation of type 1 fimbria.

Using functional proteome microarrays to study protein lysine acetylation

Emergence of proteome microarray provides a versatile platform to globally explore biological functions of broad significance. In the past decade, researchers have successfully fabricated functional proteome microarrays by printing individually purified proteins at a high-throughput, proteome-wide scale on one single slide. These arrays have been used to profile protein posttranslational modifications, including phosphorylation, ubiquitylation, acetylation, and nitrosylation. In this chapter, we summarize our work of using the yeast proteome microarrays to connect protein lysine acetylation substrates to their upstream modifying enzyme, the nucleosome acetyltransferase of H4 (NuA4), which is the only essential acetyltransferase in yeast. We further prove that the reversible acetylation on critical cell metabolism-related enzymes controls life span in yeast. Our studies represent a paradigm shift for the functional dissection of a crucial acetylation enzyme affecting aging and longevity pathways.

DNA motif elucidation using belief propagation

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors’ websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

Overview of protein microarrays

Protein microarray technology is an emerging field that provides a versatile platform for the characterization of hundreds of thousands of proteins in a highly parallel and high-throughput manner. Protein microarrays are composed of two major classes: analytical and functional. In addition, tissue or cell lysates can also be fractionated and spotted on a slide to form a reverse-phase protein microarray. Applications of protein microarrays, especially functional protein microarrays, have flourished over the past decade as the fabrication technology has matured. In this unit, advances in protein microarray technologies are reviewed, and then a series of examples are presented to illustrate the applications of analytical and functional protein microarrays in both basic and clinical research. Relevant areas of research include the detection of various binding properties of proteins, the study of protein post-translational modifications, the analysis of host-microbe interactions, profiling antibody specificity, and the identification of biomarkers in autoimmune diseases.

Applications of functional protein microarrays in basic and clinical research

The protein microarray technology provides a versatile platform for characterization of hundreds of thousands of proteins in a highly parallel and high-throughput manner. It is viewed as a new tool that overcomes the limitation of DNA microarrays. On the basis of its application, protein microarrays fall into two major classes: analytical and functional protein microarrays. In addition, tissue or cell lysates can also be directly spotted on a slide to form the so-called "reverse-phase" protein microarray. In the last decade, applications of functional protein microarrays in particular have flourished in studying protein function and construction of networks and pathways. In this chapter, we will review the recent advancements in the protein microarray technology, followed by presenting a series of examples to illustrate the power and versatility of protein microarrays in both basic and clinical research. As a powerful technology platform, it would not be surprising if protein microarrays will become one of the leading technologies in proteomic and diagnostic fields in the next decade.

Repression of mitochondrial translation, respiration and a metabolic cycle-regulated gene, SLF1, by the yeast Pumilio-family protein Puf3p

Synthesis and assembly of the mitochondrial oxidative phosphorylation (OXPHOS) system requires genes located both in the nuclear and mitochondrial genomes, but how gene expression is coordinated between these two compartments is not fully understood. One level of control is through regulated expression mitochondrial ribosomal proteins and other factors required for mitochondrial translation and OXPHOS assembly, which are all products of nuclear genes that are subsequently imported into mitochondria. Interestingly, this cadre of genes in budding yeast has in common a 3′-UTR element that is bound by the Pumilio family protein, Puf3p, and is coordinately regulated under many conditions, including during the yeast metabolic cycle. Multiple functions have been assigned to Puf3p, including promoting mRNA degradation, localizing nucleus-encoded mitochondrial transcripts to the outer mitochondrial membrane, and facilitating mitochondria-cytoskeletal interactions and motility. Here we show that Puf3p has a general repressive effect on mitochondrial OXPHOS abundance, translation, and respiration that does not involve changes in overall mitochondrial biogenesis and largely independent of TORC1-mitochondrial signaling. We also identified the cytoplasmic translation factor Slf1p as yeast metabolic cycle-regulated gene that is repressed by Puf3p at the post-transcriptional level and promotes respiration and extension of yeast chronological life span when over-expressed. Altogether, these results should facilitate future studies on which of the many functions of Puf3p is most relevant for regulating mitochondrial gene expression and the role of nuclear-mitochondrial communication in aging and longevity.

Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress

The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5'-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3'. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (K(D) of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (K(D) of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (K(D) of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5'-(G/C)(A/C)GG(G/C)G-3'. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed.

Functional protein microarray technology

Functional protein microarrays are emerging as a promising new tool for large-scale and high-throughput studies. In this article, we review their applications in basic proteomics research, where various types of assays have been developed to probe binding activities to other biomolecules, such as proteins, DNA, RNA, small molecules, and glycans. We also report recent progress of using functional protein microarrays in profiling protein post-translational modifications, including phosphorylation, ubiquitylation, acetylation, and nitrosylation. Finally, we discuss potential of functional protein microarrays in biomarker identification and clinical diagnostics. We strongly believe that functional protein microarrays will soon become an indispensible and invaluable tool in proteomics research and systems biology.

Systematic characterization of protein-DNA interactions

Sequence-specific protein-DNA interactions (PDIs) are critical for regulating many cellular processes, including transcription, DNA replication, repair, and rearrangement. We review recent experimental advances in high-throughput technologies designed to characterize PDIs and discuss recent studies that use these tools, including ChIP-chip/seq, SELEX-based approaches, yeast one-hybrid, bacterial one-hybrid, protein binding microarray, and protein microarray. The results of these studies have challenged some long-standing concepts of PDI and provide valuable insights into the complex transcriptional regulatory networks.

Characterization of protein-DNA interactions using protein microarrays

INTRODUCTION

Protein-DNA interactions (PDIs) are critical for many cellular processes. We present here a protocol for the identification of PDIs in vitro using protein microarray technology. The procedure involves double-stranding synthesized DNA oligonucleotides with a fluorescent-labeled primer, binding the labeled double-stranded DNA directly to the protein microarray, and analyzing binding of the resulting PDIs. This approach provides simultaneous identification of PDIs for thousands of proteins, and multiple carefully designed DNA probes can be tested in parallel, which enables a rapid mapping of PDIs on a proteome-wide scale.

Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling

Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We identified a large number of unanticipated PDIs for known TFs, as well as for previously uncharacterized TFs. We also found that over three hundred unconventional DNA-binding proteins (uDBPs)--which include RNA-binding proteins, mitochondrial proteins, and protein kinases--showed sequence-specific PDIs. One such uDBP, ERK2, acts as a transcriptional repressor for interferon gamma-induced genes, suggesting important biological roles for such proteins.

Quadruple 9-mer-based protein binding microarray with DsRed fusion protein

BACKGROUND

The interaction between a transcription factor and DNA motif (cis-acting element) is an important regulatory step in gene regulation. Comprehensive genome-wide methods have been developed to characterize protein-DNA interactions. Recently, the universal protein binding microarray (PBM) was introduced to determine if a DNA motif interacts with proteins in a genome-wide manner.

RESULTS

We facilitated the PBM technology using a DsRed fluorescent protein and a concatenated sequence of oligonucleotides. The PBM was designed in such a way that target probes were synthesized as quadruples of all possible 9-mer combinations, permitting unequivocal interpretation of the cis-acting elements. The complimentary DNA strands of the features were synthesized with a primer and DNA polymerase on microarray slides. Proteins were labeled via N-terminal fusion with DsRed fluorescent protein, which circumvents the need for a multi-step incubation. The PBM presented herein confirmed the well-known DNA binding sequences of Cbf1 and CBF1/DREB1B, and it was also applied to elucidate the unidentified cis-acting element of the OsNAC6 rice transcription factor.

CONCLUSION

Our method demonstrated PBM can be conveniently performed by adopting: (1) quadruple 9-mers may increase protein-DNA binding interactions in the microarray, and (2) a one-step incubation shortens the wash and hybridization steps. This technology will facilitate greater understanding of genome-wide interactions between proteins and DNA.

Protein microarrays

Protein microarrays containing nearly the entire yeast proteome have been constructed. They are typically prepared by overexpression and high-throughput purification and printing onto microscope slides. The arrays can be used to screen nearly the entire proteome in an unbiased fashion and have enormous utility for a variety of applications. These include protein-protein interactions, identification of novel lipid- and nucleic acid-binding proteins, and finding targets of small molecules, protein kinases, and other modification enzymes. Protein microarrays are thus powerful tools for individual studies as well as systematic characterization of proteins and their biochemical activities and regulation.

Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors

Protein-binding microarray (PBM) technology provides a rapid, high-throughput means of characterizing the in vitro DNA-binding specificities of transcription factors (TFs). Using high-density, custom-designed microarrays containing all 10-mer sequence variants, one can obtain comprehensive binding-site measurements for any TF, regardless of its structural class or species of origin. Here, we present a protocol for the examination and analysis of TF-binding specificities at high resolution using such 'all 10-mer' universal PBMs. This procedure involves double-stranding a commercially synthesized DNA oligonucleotide array, binding a TF directly to the double-stranded DNA microarray and labeling the protein-bound microarray with a fluorophore-conjugated antibody. We describe how to computationally extract the relative binding preferences of the examined TF for all possible contiguous and gapped 8-mers over the full range of affinities, from highest affinity sites to nonspecific sites. Multiple proteins can be tested in parallel in separate chambers on a single microarray, enabling the processing of a dozen or more TFs in a single day.

Protein microarrays: high-throughput tools for proteomics

Protein microarrays are versatile tools for parallel, miniaturized screening of binding events involving large numbers of immobilized proteins in a time- and cost-effective manner. They are increasingly applied for high-throughput protein analyses in many research areas, such as protein interactions, expression profiling and target discovery. While conventionally made by the spotting of purified proteins, recent advances in technology have made it possible to produce protein microarrays through in situ cell-free synthesis directly from corresponding DNA arrays. This article reviews recent developments in the generation of protein microarrays and their applications in proteomics and diagnostics.

Efg1-mediated recruitment of NuA4 to promoters is required for hypha-specific Swi/Snf binding and activation in Candida albicans

Efg1 is essential for hyphal development and virulence in the human pathogenic fungus Candida albicans. How Efg1 regulates gene expression is unknown. Here, we show that Efg1 interacts with components of the nucleosome acetyltransferase of H4 (NuA4) histone acetyltransferase (HAT) complex in both yeast and hyphal cells. Deleting YNG2, a subunit of the NuA4 HAT module, results in a significant decrease in the acetylation level of nucleosomal H4 and a profound defect in hyphal development, as well as a defect in the expression of hypha-specific genes. Using chromatin immunoprecipitation, Efg1 and the NuA4 complex are found at the UAS regions of hypha-specific genes in both yeast and hyphal cells, and Efg1 is required for the recruitment of NuA4. Nucleosomal H4 acetylation at the promoters peaks during initial hyphal induction in an Efg1-dependent manner. We also find that Efg1 bound to the promoters of hypha-specific genes is critical for recruitment of the Swi/Snf chromatin remodeling complex during hyphal induction. Our data show that the recruitment of the NuA4 complex by Efg1 to the promoters of hypha-specific genes is required for nucleosomal H4 acetylation at the promoters during hyphal induction and for subsequent binding of Swi/Snf and transcriptional activation.

Development of a novel oligonucleotide array-based transcription factor assay platform for genome-wide active transcription factor profiling in Saccharomyces cerevisiae

Transcription factors (TFs) play a central role in regulating gene expression and in providing interconnecting regulatory networks between related pathway elements. Although single TF assays provide some insights into pathway regulation, a method that allows the parallel investigation of all active TFs is highly desired to elucidate the complex inter-regulated cellular mechanisms. We have developed a novel oligonucleotide array-based transcription factor assay platform for genome-wide active TF profiling of Saccharomyces cerevisiae, which can simultaneously analyze the activities of 93 different TFs. The platform has been validated using 28 purified TFs produced in Escherichia coli, cell extracts from yeast strains overexpressing particular TFs, and by detailed control experiments. We then used the platform to examine the activity changes of all yeast TFs during diauxic shift, and results showed, in good agreement with previous studies, that the Sip4 was induced specifically. Other individual TFs required for growth in synthetic complete medium were also identified. Genome-wide analysis of TF activity is extremely useful in investigating complex gene regulatory networks and for the development of systematic understanding of the complexity of genomic functions. These results obtained in this report demonstrate the validity, and for the first time the utility, of this technology for genome-wide investigation of TF activities.

Improved genome-wide localization by ChIP-chip using double-round T7 RNA polymerase-based amplification

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a powerful technique to detect in vivo protein–DNA interactions. Due to low yields, ChIP assays of transcription factors generally require amplification of immunoprecipitated genomic DNA. Here, we present an adapted linear amplification method that involves two rounds of T7 RNA polymerase amplification (double-T7). Using this we could successfully amplify as little as 0.4 ng of ChIP DNA to sufficient amounts for microarray analysis. In addition, we compared the double-T7 method to the ligation-mediated polymerase chain reaction (LM-PCR) method in a ChIP-chip of the yeast transcription factor Gsm1p. The double-T7 protocol showed lower noise levels and stronger binding signals compared to LM-PCR. Both LM-PCR and double-T7 identified strongly bound genomic regions, but the double-T7 method increased sensitivity and specificity to allow detection of weaker binding sites.

The development of protein microarrays and their applications in DNA-protein and protein-protein interaction analyses of Arabidopsis transcription factors

We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale.

Microbial regulatory and metabolic networks

Reconstruction of transcriptional regulatory and metabolic networks is the foundation of large-scale microbial systems and synthetic biology. An enormous amount of information including the annotated genomic sequences and the genomic locations of DNA-binding regulatory proteins can be used to define metabolic and regulatory networks in cells. In particular, advances in experimental methods to map regulatory networks in microbial cells have allowed reliable data-driven reconstruction of these networks. Recent work on metabolic engineering and experimental evolution of microbes highlights the key role of global regulatory networks in controlling specific metabolic processes and the need to consider the integrated function of multiple types of networks for both scientific and engineering purposes.

Function and regulation in MAPK signaling pathways: lessons learned from the yeast Saccharomyces cerevisiae

Signaling pathways that activate different mitogen-activated protein kinases (MAPKs) elicit many of the responses that are evoked in cells by changes in certain environmental conditions and upon exposure to a variety of hormonal and other stimuli. These pathways were first elucidated in the unicellular eukaryote Saccharomyces cerevisiae (budding yeast). Studies of MAPK pathways in this organism continue to be especially informative in revealing the molecular mechanisms by which MAPK cascades operate, propagate signals, modulate cellular processes, and are controlled by regulatory factors both internal to and external to the pathways. Here we highlight recent advances and new insights about MAPK-based signaling that have been made through studies in yeast, which provide lessons directly applicable to, and that enhance our understanding of, MAPK-mediated signaling in mammalian cells.

Avidin Decorated Core–Shell Nanoparticles for Biorecognition Studies by Elastic Light Scattering

In this paper, a straightforward method based on elastic light scattering is shown to provide a sensitive and reliable tool for the quantitative determination of protein-ligand interactions that occur at the surface of suitably designed core-shell nanoparticles. The assay makes use of monodisperse nanocolloids that have minimal optical contrast with the aqueous environment. By properly coating the particles with avidin and oligo(ethylene glycol)-based amphiphiles, we developed a hybrid system that combines the availability of standard ligands with the necessary bioinvisibility towards the accidental adsorption of nonspecific macromolecules. This probe was employed to detect interactions between different kinds of biotinylated proteins, and it revealed high specificity and affinities in the low nanomolar range. In particular, we obtained an efficient avidin anchorage of biotinylated protein A on the surface of the nanoparticles, which we exploited as a functional probe for the rapid, quantitative, picomolar detection of human IgG antibodies. Overall, these light-scattering-based nanosensors appear as a simple and highly informative tool for proteomics studies.

General transfer matrix formalism to calculate DNA-protein-drug binding in gene regulation: application to OR operator of phage lambda

The transfer matrix methodology is proposed as a systematic tool for the statistical-mechanical description of DNA-protein-drug binding involved in gene regulation. We show that a genetic system of several cis-regulatory modules is calculable using this method, considering explicitly the site-overlapping, competitive, cooperative binding of regulatory proteins, their multilayer assembly and DNA looping. In the methodological section, the matrix models are solved for the basic types of short- and long-range interactions between DNA-bound proteins, drugs and nucleosomes. We apply the matrix method to gene regulation at the O(R) operator of phage lambda. The transfer matrix formalism allowed the description of the lambda-switch at a single-nucleotide resolution, taking into account the effects of a range of inter-protein distances. Our calculations confirm previously established roles of the contact CI-Cro-RNAP interactions. Concerning long-range interactions, we show that while the DNA loop between the O(R) and O(L) operators is important at the lysogenic CI concentrations, the interference between the adjacent promoters P(R) and P(RM) becomes more important at small CI concentrations. A large change in the expression pattern may arise in this regime due to anticooperative interactions between DNA-bound RNA polymerases. The applicability of the matrix method to more complex systems is discussed.

High-throughput methods of regulatory element discovery

With the number of organisms whose genomes have been sequenced, a vast amount of information concerning the genetic structure of an organism's genome has been collected. However, effective experiment means to study how this information is accessed have only recently been developed. In this review, three basic methods for identifying regions of protein-DNA interaction will be introduced. The first two, chromatin immunoprecipitation (ChIP)-chip and ChIP-PET (for paired-end ditag), rely on the enrichment provided by chromosomal immunoprecipitation to interrogate the genomic sequence for the interaction sites of a protein of interest. In contrast, protein microarrays allow the identification of DNA binding protein that interacts with a DNA sequence of interest. These complementary methods of exploring protein-DNA interactions will increase our fundamental knowledge of how the information contained within the genome sequence is accessed and processed.