Synergistic experimental and computational approach identifies novel strategies for polyhydroxybutyrate overproduction

Polyhydroxybutyrate (PHB) is a sustainable bioplastic produced by bacteria that is a potential replacement for conventional plastics. This study delivers an integrated experimental and computational modeling approach to decipher metabolic factors controlling PHB production and offers engineering design strategies to boost production. In the metabolically robust Rhodopseudomonas palustris CGA009, PHB production significantly increased when grown on the carbon- and electron-rich lignin breakdown product p-coumarate (C₉H₈O₃) compared to virtually no PHB titer from acetate (C₂H₃NaO₂). The maximum yield did not improve further when grown on coniferyl alcohol (C₁₀H₁₂O₃), but comparison of the PHB profiles showed that coniferyl alcohol's higher carbon content resulted in a higher rate of PHB production. Combined experimental results revealed that cytoplasmic space may be a limiting factor for maximum PHB titer. In order to obtain a systems-level understanding of factors driving PHB yield, a model-driven investigation was performed. The model yielded several engineering design strategies including utilizing reduced, high molecular weight substrates that bypass the thiolase reaction (phaA). Based on these strategies, utilization of butyrate was predicted and subsequently validated to produce PHB. Model analysis also explained why nitrogen starvation was not essential for PHB production and revealed that renewable and abundant lignin aromatics are ideal candidates for PHB production. Most importantly, the generality of the derived design rules allows them to be applied to any PHB-producing microbe with similar metabolic features.     

A microarray-based approach identifies ADP ribosylation factor-like protein 2 as a target of microRNA-16

microRNAs (miRNAs) are generally thought to negatively regulate the expression of their target genes by mRNA degradation or by translation repression. Here we show an efficient way to identify miRNA target genes by screening alterations in global mRNA levels following changes in miRNA levels. In this study, we used mRNA microarrays to measure global mRNA expression in three cell lines with increased or decreased levels of miR-16 and performed bioinformatics analysis based on multiple target prediction algorithms. For further investigation among the predicted miR-16 target genes, we selected genes that show an expression pattern opposite to that of miR-16. One of the candidate target genes that may interact with miR-16, ADP-ribosylation factor-like protein 2 (ARL2), was further investigated. First, ARL2 was deduced to be an ideal miR-16 target by computational predictions. Second, ARL2 mRNA and protein levels were significantly abolished by treatment with miR-16 precursors, whereas a miR-16 inhibitor increased ARL2 mRNA and protein levels. Third, a luciferase reporter assay confirmed that miR-16 directly recognizes the 3'-untranslated region (3'-UTR) of ARL2. Finally, we showed that miR-16 could regulate proliferation and induce a significant G0/G1 cell cycle arrest, which was due at least in part, to the down-regulation of ARL2. In summary, the present study suggests that integrating global mRNA profiling and bioinformatics tools may provide the basis for further investigation of the potential targets of a given miRNA. These results also illustrate a novel function of miR-16 targeting ARL2 in modulating proliferation and cell cycle progression.     

A new computational approach to analyze human protein complexes and predict novel protein interactions

We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated α-tubulin and early endosome antigen 1 as its novel interactors.     

A computational approach to finding novel targets for existing drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.     

A novel microarray approach reveals new tissue-specific signatures of known and predicted mammalian microRNAs

Microarrays to examine the global expression levels of microRNAs (miRNAs) in a systematic in-parallel manner have become important tools to help unravel the functions of miRNAs and to understand their roles in RNA-based regulation and their implications in human diseases. We have established a novel miRNA-specific microarray platform that enables the simultaneous expression analysis of both known and predicted miRNAs obtained from human or mouse origin. Chemically modified 2′-O-(2-methoxyethyl)-(MOE) oligoribonucleotide probes were arrayed onto Evanescent Resonance (ER) microchips by robotic spotting. Supplementing the complementary probes against miRNAs with carefully designed mismatch controls allowed for accurate sequence-specific determination of miRNA expression profiles obtained from a panel of mouse tissues. This revealed new expression signatures of known miRNAs as well as of novel miRNAs previously predicted using bioinformatic methods. Systematic confirmation of the array data with northern blotting and, in particular, real-time PCR suggests that the described microarray platform is a powerful tool to analyze miRNA expression patterns with rapid throughput and high fidelity.     

A novel potassium channel encoded by Ectocarpus siliculosus virus

Kcv, the first identified viral potassium channel encoded by the green algae Paramecium bursaria chlorella virus (PBCV-1), conducted K(+) selective currents when expressed in heterologous systems. This K(+) channel was proposed to be important for PBCV-1 infection and replication. In the present study, we identified and functionally characterized a novel K(+) channel Kesv, encoded by Ectocarpus siliculosus virus that infects filamentous marine brown algae. Kesv encodes a protein of 124 amino acids and is 21.8% identical and 37.1% homologous to Kcv. Membrane topology programs predicted that Kesv consists of three transmembrane domains. When expressed in Xenopus oocytes, Kesv induced largely instantaneous, K(+) selective currents that were sensitive to block by Ba(2+) and amantadine. Thus, Kesv along with Kcv, constitutes an emerging family of viral potassium channels, which may play important roles in the life cycle of viruses.     

A population-based statistical approach identifies parameters characteristic of human microRNA-mRNA interactions

Background  

MicroRNAs are ~17–24 nt. noncoding RNAs found in all eukaryotes that degrade messenger RNAs via RNA interference (if they bind in a perfect or near-perfect complementarity to the target mRNA), or arrest translation (if the binding is imperfect). Several microRNA targets have been identified in lower organisms, but only one mammalian microRNA target has yet been validated experimentally.     

Identification and analysis of a novel heparin-binding glycoprotein encoded by human herpesvirus 7

Human herpesvirus 6 (HHV-6) and HHV-7 are closely related betaherpesviruses that encode a number of genes with no known counterparts in other herpesviruses. The product of one such gene is the HHV-6 glycoprotein gp82-105, which is a major virion component and a target for neutralizing antibodies. A 1.7-kb cDNA clone from HHV-7 was identified which contains a large open reading frame capable of encoding a predicted primary translational product of 468 amino acids (54 kDa) with 13 cysteine residues and 9 potential N-linked glycosylation sites. This putative protein, which we have termed gp65, was homologous to HHV-6 gp105 (30% identity) and contained a single potential membrane-spanning domain located near its amino terminus. Comparison of the cDNA sequence with that of the viral genome revealed that the gene encoding gp65 contains eight exons, spanning almost 6 kb of the viral genome at the right (3') end of the HHV-7 genome. Northern (RNA) blot analysis with poly(A)(+) RNA from HHV-7-infected cells revealed that the cDNA insert hybridized to a single major RNA species of 1.7 kb. Antiserum raised against a purified, recombinant form of gp65 recognized a protein of roughly 65 kDa in sucrose density gradient-purified HHV-7 preparations; treatment with PNGase F reduced this glycoprotein to a putative precursor of approximately 50 kDa. Gp65-specific antiserum also neutralized the infectivity of HHV-7, while matched preimmune serum did not do so. Finally, analysis of the biochemical properties of recombinant gp65 revealed a specific interaction with heparin and heparan sulfate proteoglycans and not with closely related molecules such as N-acetylheparin and de-N-sulfated heparin. At least two domains of the protein were found to contribute to heparin binding. Taken together, these findings suggest that HHV-7 gp65 may contribute to viral attachment to cell surface proteoglycans.     

A novel bioinformatics approach identifies candidate genes for the synthesis and feruloylation of arabinoxylan

Arabinoxylans (AXs) are major components of graminaceous plant cell walls, including those in the grain and straw of economically important cereals. Despite some recent advances in identifying the genes encoding biosynthetic enzymes for a number of other plant cell wall polysaccharides, the genes encoding enzymes of the final stages of AX synthesis have not been identified. We have therefore adopted a novel bioinformatics approach based on estimation of differential expression of orthologous genes between taxonomic divisions of species. Over 3 million public domain cereal and dicot expressed sequence tags were mapped onto the complete sets of rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genes, respectively. It was assumed that genes in cereals involved in AX biosynthesis would be expressed at high levels and that their orthologs in dicotyledonous plants would be expressed at much lower levels. Considering all rice genes encoding putative glycosyl transferases (GTs) predicted to be integral membrane proteins, genes in the GT43, GT47, and GT61 families emerged as much the strongest candidates. When the search was widened to all other rice or Arabidopsis genes predicted to encode integral membrane proteins, cereal genes in Pfam family PF02458 emerged as candidates for the feruloylation of AX. Our analysis, known activities, and recent findings elsewhere are most consistent with genes in the GT43 families encoding beta-1,4-xylan synthases, genes in the GT47 family encoding xylan alpha-1,2- or alpha-1,3-arabinosyl transferases, and genes in the GT61 family encoding feruloyl-AX beta-1,2-xylosyl transferases.     

Computational and experimental analysis identifies many novel human genes

Because of advances in automation, human genomic sequences are being deposited in public databases at a dramatic rate. However, the process of detecting genes in these sequences is still something of an art. Here we describe the implementation and testing of a relatively straightforward computational approach, the Virtual Transcribed Sequence project, which analyzes their gene content using the gene prediction program GENSCAN (GENSCAN 1.0 1,2) in combination with similarity-based methods. This approach identifies many novel human genes not found even in EST databases.     

Expression of a novel human sialidase encoded by the NEU2 gene

Sialidases (E.C.3.2.1.18) belong to a group of glycohydrolytic enzymes, widely distributed in nature, which remove sialic acid residues from glycoproteins and glycolipids. All of the sialidase so far characterized at the molecular level share an Asp block, repeated three to five times in the primary structure, and an F/YRIP sequence motif which is part of the active site. Using a sequence homology-based approach, we previously identified a human gene, named NEU2, mapping to chromosome 2q37. NEU2 encoded protein is a polypeptide of 380 amino acids with two Asp block consensuses and the YRIP sequence in the amino terminal part of the primary structure. Here we demonstrate that NEU2 encodes a functional sialidase. NEU2 was expressed in COS7 cells, giving rise to a dramatic increase in the sialidase activity measured in cell extracts with the artificial substrate 4-MU-NANA. Using a rabbit polyclonal antiserum, on Western blots a protein band with a molecular weight of about 42 kDa was detectable, and its cytosolic localization was demonstrated with cell fractionation experiments. These results were confirmed using immunohistochemical techniques. NEU2 expression in E.coli cells allowed purification of the recombinant protein. As already observed in the enzyme expressed in COS7 cells, NEU2 pH optimum corresponds to 5.6 and the polypeptide showed a K(m)for 4-MU-NANA of 0.07 mM. In addition, based on the detectable similarities between the NEU2 amino acid sequence and bacterial sialidases, a prediction of the three-dimensional structure of the enzyme was carried out using a protein homology modeling approach.     

A novel proteomic approach identifies new interaction partners for proliferating cell nuclear antigen

During DNA replication and repair, many proteins bind to and dissociate in a highly specific and ordered manner from proliferating cell nuclear antigen (PCNA). We describe a combined approach of in silico searches at the genome level and combinatorial peptide synthesis to investigate the binding properties of hundreds of short PCNA-interacting peptides (PIP-peptides) to archaeal and eukaryal PCNAs. Biological relevance of our combined approach was demonstrated by identification an inactive complex of Pyrococcus abyssi ribonuclease HII with PCNA. Furthermore we show that PIP-peptides interact with PCNA largely in a sequence independent manner. Our experimental approach also identified many so far unidentified PCNA interacting peptides in a number of human proteins.     

Designing and optimization of novel human LMTK3 inhibitors against breast cancer – a computational approach

Estrogen receptor-α (ERα) is expressed more in patients with breast cancer and its level correlated with endocrine resistance. LMTK3 is reported as breast cancer target with regulation of estrogen receptor-α (ERα) through phosphorylation. In this computational study, structure-based inhibitor screening was performed on human LMTK3 using ZINC database. ATP-binding cavity with critical residues involved in the LMTK3 phosphorylation was used as target site for the screening. From the large ligand library, the best compounds were screen with three-phase virtual screening methods in Dockblaster, AutoDock Vina and AutoDock, respectively. The evaluation of ligands was carried out by binding energy and weak interactions, such as hydrogen bond interactions and hydrophobic contacts, in the target site that favors LMTK3 inhibition. Top compounds were found to be more effective in druglikeness activity by ADME prediction. The stability and binding affinity of ligand complexes were optimized by trajectory analysis such as RMSD, Rg, SASA and interhydrogen bonds from molecular dynamics simulations. The behavior of protein motion after ligand binding was illustrated by eigenvectors from principal component analysis (PCA). In addition, binding free energy of the LMTK3–ligand complexes were calculated by MM/PBSA methods and results supported the strong binding in dynamic system. Thus, the computational studies illustrated the structural insights on LMTK3 inhibition mechanism by ligands ZINC04670539, ZINC05607079 and ZINC04344028, also proposed as potent lead candidates. Our findings step towards developing novel LMTK3 inhibitors and identified lead candidates can be future breast cancer drugs with further experimental studies.     

A proteomic approach to identify phosphoproteins encoded by cDNA libraries

We report a method for large-scale rapid analysis of phosphoproteins in tissues or cells by combining immobilized metal affinity chromatography (IMAC) with phage display cDNA library screening. We expressed a testis cDNA library as fusion proteins on phage and, using IMAC, enriched for sequences encoding phosphoproteins. Selected clones were polymerase chain reaction amplified and sequenced. The majority of the clones sequenced (80%) encoded known proteins previously identified as phosphoproteins. Immunoblotting with phosphotyrosine antibodies confirmed that some of the selected sequences encoded tyrosine phosphorylated proteins when expressed on phage. An advantage of this method is the rapid identification of phosphoproteins encoded by a cDNA library, which can identify proteins that are potentially phosphorylated in vivo. When this method is combined with limited enzymatic digestion and tandem mass spectrometric techniques, the specific phosphorylation site in a protein can be identified. This technique can be used in proteomics studies to effectively detect phosphorylated proteins and avoid time-consuming and expensive peptide sequencing.     

A combined proteomics and computational approach provides a better understanding of HCV-induced liver disease

Evaluation of: Diamond DL, Krasnoselsky AL, Burnum KE et al. Proteome and computational analyses reveal new insights into the mechanisms of hepatitis C virus-mediated liver disease posttransplantation. Hepatology 56(1), 28–38 (2012).

HCV is a major cause of chronic liver disease worldwide and is a formidable therapeutic challenge. Recently, Diamond et al. analyzed the proteomic profiles of liver samples from HCV-positive liver transplant recipients, supplemented with an independent metabolite analysis. They used a computational approach, which highlighted the enriched functional themes and topological attributes associated with the protein association network based on their clinical data and suggested a crucial role of oxidative stress in fibrosis progression in HCV infection. Their findings provide new insights into the mechanisms that regulate the progression of HCV-associated liver fibrosis, which may be useful for identification of suitable biomarkers to evaluate the onset and severity of hepatic fibrosis and the development of new therapeutic and anti-HCV strategies.