Exponential Combination of a and e/g Intracellular Peptide Libraries Identifies a Selective ATF3 Inhibitor.

Activating transcription factor 3 (ATF3) is an activation transcription factor/cyclic adenosine monophosphate (cAMP) responsive element-binding (CREB) protein family member. It is recognized as an important regulator of cancer progression by repressing expression of key inflammatory factors such as interferon-γ and chemokine (C-C motif) ligand 4 (CCL4). Here, we describe a novel library screening approach that probes individual leucine zipper components before combining them to search exponentially larger sequence spaces not normally accessible to intracellular screening. To do so, we employ two individual semirational library design approaches and screen using a protein-fragment complementation assay (PCA). First, a 248,832-member library explored 12 amino acid positions at all five a positions to identify those that provided improved binding, with all e/g positions fixed as Q, placing selection pressure onto the library options provided. Next, a 59,049-member library probed all ten e/g positions with 3 options. Similarly, during e/g library screening, a positions were locked into a generically bindable sequence pattern (AIAIA), weakly favoring leucine zipper formation, while placing selection pressure onto e/g options provided. The combined a/e/g library represents ∼14.7 billion members, with the resulting peptide, ATF3W_aeg, binding ATF3 with high affinity (Tm = 60 °C; Kd = 151 nM) while strongly disfavoring homodimerization. Moreover, ATF3W_aeg is notably improved over component PCA hits, with target specificity found to be driven predominantly by electrostatic interactions. The combined a/e/g exponential library screening approach provides a robust, accelerated platform for exploring larger peptide libraries, toward derivation of potent yet selective antagonists that avoid homoassociation to provide new insight into rational peptide design.

Anticancer Activity of ST101, A Novel Antagonist of CCAAT/Enhancer Binding Protein ß.

CCAAT/enhancer binding protein ß (C/EBPß) is a basic leucine zipper (bZIP) family transcription factor, which is upregulated or overactivated in many cancers, resulting in a gene expression profile that drives oncogenesis. C/EBPß dimerization regulates binding to DNA at the canonical TTGCGCAA motif and subsequent transcriptional activity, suggesting that disruption of dimerization represents a powerful approach to inhibit this previously "undruggable" oncogenic target. Here we describe the mechanism of action and antitumor activity of ST101, a novel and selective peptide antagonist of C/EBPß that is currently in clinical evaluation in patients with advanced solid tumors. ST101 binds the leucine zipper domain of C/EBPß, preventing its dimerization and enhancing ubiquitin-proteasome dependent C/EBPß degradation. ST101 exposure attenuates transcription of C/EBPß target genes, including a significant decrease in expression of survival, transcription factors, and cell-cycle-related proteins. The result of ST101 exposure is potent, tumor-specific in vitro cytotoxic activity in cancer cell lines including glioblastoma, breast, melanoma, prostate, and lung cancer, whereas normal human immune and epithelial cells are not impacted. Further, in mouse xenograft models ST101 exposure results in potent tumor growth inhibition or regression, both as a single agent and in combination studies. These data provide the First Disclosure of ST101, and support continued clinical development of ST101 as a novel strategy for targeting C/EBPß-dependent cancers.

Combined computational and intracellular peptide library screening: towards a potent and selective Fra1 inhibitor.

To date, most research into the inhibition of oncogenic transcriptional regulator, Activator Protein 1 (AP-1), has focused on heterodimers of cJun and cFos. However, the Fra1 homologue remains an important cancer target. Here we describe library design coupled with computational and intracellular screening as an effective methodology to derive an antagonist that is selective for Fra1 relative to Jun counterparts. To do so the isCAN computational tool was used to rapidly screen >75 million peptide library members, narrowing the library size by >99.8% to one accessible to intracellular PCA selection. The resulting 131 072-member library was predicted to contain high quality binders with both a high likelihood of target engagement, while simultaneously avoiding homodimerization and off-target interaction with Jun homologues. PCA screening was next performed to enrich those members that meet these criteria. In particular, optimization was achieved via inclusion of options designed to generate the potential for compromised intermolecular contacts in both desired and non-desired species. This is an often-overlooked prerequisite in the conflicting design requirement of libraries that must be selective for their target in the context of a range of alternative potential interactions. Here we demonstrate that specificity is achieved via a combination of both hydrophobic and electrostatic contacts as exhibited by the selected peptide (Fra1W). In vitro analysis of the desired Fra1-Fra1W interaction further validates high Fra1 affinity (917 nM) yet selective binding relative to Fra1W homodimers or affinity for cJun. The isCAN → PCA based multidisciplinary approach provides a robust screening pipeline in generating target-specific hits, as well as new insight into rational peptide design in the search for novel bZIP family inhibitors.

A High-Throughput Strategy for Dissecting Mammalian Genetic Interactions.

Comprehensive delineation of complex cellular networks requires high-throughput interrogation of genetic interactions. To address this challenge, we describe the development of a multiplex combinatorial strategy to assess pairwise genetic interactions using CRISPR-Cas9 genome editing and next-generation sequencing. We characterize the performance of combinatorial genome editing and analysis using different promoter and gRNA designs and identified regions of the chimeric RNA that are compatible with next-generation sequencing preparation and quantification. This approach is an important step towards elucidating genetic networks relevant to human diseases and the development of more efficient Cas9-based therapeutics.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

An inter-species protein-protein interaction network across vast evolutionary distance.

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life.

While conceptual principles governing plant immunity are becoming clear, its systems-level organization and the evolutionary dynamic of the host-pathogen interface are still obscure. We generated a systematic protein-protein interaction network of virulence effectors from the ascomycete pathogen Golovinomyces orontii and Arabidopsis thaliana host proteins. We combined this data set with corresponding data for the eubacterial pathogen Pseudomonas syringae and the oomycete pathogen Hyaloperonospora arabidopsidis. The resulting network identifies host proteins onto which intraspecies and interspecies pathogen effectors converge. Phenotyping of 124 Arabidopsis effector-interactor mutants revealed a correlation between intraspecies and interspecies convergence and several altered immune response phenotypes. Several effectors and the most heavily targeted host protein colocalized in subnuclear foci. Products of adaptively selected Arabidopsis genes are enriched for interactions with effector targets. Our data suggest the existence of a molecular host-pathogen interface that is conserved across Arabidopsis accessions, while evolutionary adaptation occurs in the immediate network neighborhood of effector targets.

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism.

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.

Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii.

BACKGROUND: Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale. RESULTS: We assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST. In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate. CONCLUSIONS: We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.

Independently evolved virulence effectors converge onto hubs in a plant immune system network.

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

A public genome-scale lentiviral expression library of human ORFs.

Functional characterization of the human genome requires tools for systematically modulating gene expression in both loss-of-function and gain-of-function experiments. We describe the production of a sequence-confirmed, clonal collection of over 16,100 human open-reading frames (ORFs) encoded in a versatile Gateway vector system. Using this ORFeome resource, we created a genome-scale expression collection in a lentiviral vector, thereby enabling both targeted experiments and high-throughput screens in diverse cell types.

Large-scale RACE approach for proactive experimental definition of C. elegans ORFeome.

Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.

Metabolic network analysis integrated with transcript verification for sequenced genomes.

With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.

A kinetic, spectroscopic, and redox study of human tryptophan 2,3-dioxygenase.

The family of heme dioxygenases, as exemplified by indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase, catalyzes the oxidative cleavage of L-tryptophan to N-formylkynurenine. Here, we describe a bacterial expression system for human tryptophan 2,3-dioxygenase (rhTDO) together with spectroscopic, kinetic, and redox analyses. We find unexpected differences between human tryptophan 2,3-dioxygenase and human indoleamine 2,3-dioxygenase [Chauhan et al. (2008) Biochemistry 47, 4761-4769 ]. Thus, in contrast to indoleamine 2,3-dioxygenase, the catalytic ferrous-oxy complex of rhTDO is not observed, nor does the enzyme discriminate against substrate binding to the ferric derivative. In addition, we show that the rhTDO is also catalytically active in the ferric form. These new findings illustrate that significant mechanistic differences exist across the heme dioxygenase family, and the data are discussed within this broader framework.

Kinetics properties of guaiacol peroxidase activity in Crocus sativus L. corm during rooting.

BACKGROUND: Guaiacol peroxidases (GP) are haem-containing enzymes participating in many physiological processes in plants. The expression pattern of these enzymes is organ-specific and developmentally regulated. METHODS: The presence of GP activity in extract samples, prepared from Crocus sativus L. corms that were either dormant or rooting for 3, 6 and 10 days, was investigated. RESULTS: Kinetic studies revealed a significant similarity among GP activities detectable in the corm at different stages of development: in all extract samples, the activity was maximal at pH 7.5 and after preincubation at 30-40 degrees C. When guaiacol was used as the varying substrate, Michaelis-Menten kinetics behavior was observed in all extract samples and resulted in similar KM values; catalytic efficiencies were also very similar. The corm GP activity was inhibited by cyanide, azide and ascorbate. The GP activities from different extract samples had the same sensitivities to azide, cyanide and ascorbate and the type of inhibition by azide and cyanide was competitive and uncompetitive, respectively, while ascorbate inhibited the GP activity non-competitively. Corm extract samples from different stages of rooting similarly responded to temperature treatment and a biphasic Arrhenius plot resulted for each extract sample studied. When dormant, 3-, 6- and 10-days-rooting corm extracts were submitted to non-denaturing polyacrylamide gel electrophoresis, the GP-specific activity staining revealed one band on the gel, with the same migrating distances. CONCLUSION: This finding in combination with kinetic studies demonstrated that at least one form of GP, with an apparent molecular weight of 68 kDa, was expressed during development of Crocus sativus L. corm.