Jun-tao Guo

Direct activation of human and mouse Oct4 genes using engineered TALE and Cas9 transcription factors

The newly developed transcription activator-like effector protein (TALE) and clustered regularly interspaced short palindromic repeats/Cas9 transcription factors (TF) offered a powerful and precise approach for modulating gene expression. In this article, we systematically investigated the potential of these new tools in activating the stringently silenced pluripotency gene Oct4 (Pou5f1) in mouse and human somatic cells. First, with a number of TALEs and sgRNAs targeting various regions in the mouse and human Oct4 promoters, we found that the most efficient TALE-VP64s bound around −120 to −80 bp, while highly effective sgRNAs targeted from −147 to −89-bp upstream of the transcription start sites to induce high activity of luciferase reporters. In addition, we observed significant transcriptional synergy when multiple TFs were applied simultaneously. Although individual TFs exhibited marginal activity to up-regulate endogenous gene expression, optimized combinations of TALE-VP64s could enhance endogenous Oct4 transcription up to 30-fold in mouse NIH3T3 cells and 20-fold in human HEK293T cells. More importantly, the enhancement of OCT4 transcription ultimately generated OCT4 proteins. Furthermore, examination of different epigenetic modifiers showed that histone acetyltransferase p300 could enhance both TALE-VP64 and sgRNA/dCas9-VP64 induced transcription of endogenous OCT4. Taken together, our study suggested that engineered TALE-TF and dCas9-TF are useful tools for modulating gene expression in mammalian cells.

Molecular modeling of the core of Aβ amyloid fibrils

Amyloid fibrils, a key pathological feature of Alzheimers disease (AD) and other amyloidosis implicated in neurodegeneration, have a characteristic cross‐β structure. Here we present a structural model for the core of amyloid fibrils formed by the Aβ peptide using computational approaches and experimental data. Aβ(15–36) was threaded against the parallel β‐helical proteins. Our multi‐layer model was constructed using the top scoring template 1lxa, a left‐handed parallel β‐helical protein. This six‐rung helical model has in‐register repeats of the Aβ(15–36) sequence. Each rung has three β‐strands separated by two turns. The model was tested using molecular dynamics simulations in explicit water, and is in good agreement with a number of experimental observations. In addition, a model based on right‐handed helical proteins is also described. The core structural model described here might serve as the building block of the Aβ(1–40) amyloid fibril as well as some other amyloid fibrils. Proteins 2004.

Porphyrin and heme metabolism and the porphyrias.

Porphyrins and metalloporphyrins are the key pigments of life on earth as we know it, because they include chlorophyll (a magnesium-containing metalloporphyrin) and heme (iron protoporphyrin). In eukaryotes, porphyrins and heme are synthesized by a multistep pathway that involves eight enzymes. The first and rate-controlling step is the formation of delta-aminolevulinic acid (ALA) from glycine plus succinyl CoA, catalyzed by ALA synthase. Intermediate steps occur in the cytoplasm, with formation of the monopyrrole porphobilinogen and the tetrapyrroles hydroxymethylbilane and a series of porphyrinogens, which are serially decarboxylated. Heme is utilized chiefly for the formation of hemoglobin in erythrocytes, myoglobin in muscle cells, cytochromes P-450 and mitochondrial cytochromes, and other hemoproteins in hepatocytes. The rate-controlling step of heme breakdown is catalyzed by heme oxygenase (HMOX), of which there are two isoforms, called HMOX1 and HMOX2. HMOX breaks down heme to form biliverdin, carbon monoxide, and iron. The porphyrias are a group of disorders, mainly inherited, in which there are defects in normal porphyrin and heme synthesis. The cardinal clinical features are cutaneous (due to the skin-damaging effects of excess deposited porphyrins) or neurovisceral attacks of pain, sometimes with weakness, delirium, seizures, and the like (probably due mainly to neurotoxic effects of ALA). The treatment of choice for the acute hepatic porphyrias is intravenous heme therapy, which repletes a critical regulatory heme pool in hepatocytes and leads to downregulation of hepatic ALA synthase, which is a biochemical hallmark of all forms of acute porphyria in relapse.

Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potential

Computational evaluation of protein–DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein–DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein–DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF–DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences.

Expression of glutathione-S-transferase isozyme in the SY5Y neuroblastoma cell line increases resistance to oxidative stress

Glutathione-S-transferases (GSTs) are a superfamily of enzymes that function to catalyze the nucleophilic attack of glutathione on electrophilic groups of a second substrate. GSTs are present in many organs and have been implicated in the detoxification of endogenous alpha, beta unsaturated aldehydes, including 4-hydroxynonenal (HNE). Exogenous GST protects hippocampal neurons against HNE in culture. To test the hypothesis that overexpression of GST in cells would increase resistance to exogenous or endogenous HNE induced by oxidative stress, stable transfectants of SY5Y neuroblastoma cells with GST were established. Stable GST transfectants demonstrated enzyme activities 13.7 times (Clone 1) and 30 times (Clone 2) higher than cells transfected with vector alone. GST transfectants (both Clones 1 and 2) demonstrated significantly (p <.05) increased resistance to ferrous sulfate/hydrogen peroxide (20.9% for Clone 1; 46.5% for Clone 2), amyloid beta-peptide (12.2% for Clone 1; 27.5.% for Clone 2), and peroxynitrite (24.3% for Clone 1; 43.9% for Clone 2), but not to exogenous application of HNE in culture medium. GST transfectants treated with 1,1,4-tris (acetyloxy)nonane, a nontoxic derivative of HNE that is degraded to HNE intracellularly, demonstrated a statistically significant (p <.05) increase in viability in a dose-dependent manner compared with SY5Y cells transfected with vector alone. These results suggest that overexpression of GST increases resistance to endogenous HNE induced by oxidative stress or released in the degradation of 1,1,4-tris (acetyloxy)nonane, but not to exogenous application of HNE.

Structural analysis of heme proteins: implications for design and prediction

BackgroundHeme is an essential molecule and plays vital roles in many biological processes. The structural determination of a large number of heme proteins has made it possible to study the detailed chemical and structural properties of heme binding environment. Knowledge of these characteristics can provide valuable guidelines in the design of novel heme proteins and help us predict unknown heme binding proteins.ResultsIn this paper, we constructed a non-redundant dataset of 125 heme-binding protein chains and found that these heme proteins encompass at least 31 different structural folds with all-α class as the dominating scaffold. Heme binding pockets are enriched in aromatic and non-polar amino acids with fewer charged residues. The differences between apo and holo forms of heme proteins in terms of the structure and the binding pockets have been investigated. In most cases the proteins undergo small conformational changes upon heme binding. We also examined the CP (cysteine-proline) heme regulatory motifs and demonstrated that the conserved dipeptide has structural implications in protein-heme interactions.ConclusionsOur analysis revealed that heme binding pockets show special features and that most of the heme proteins undergo small conformational changes after heme binding, suggesting the apo structures can be used for structure-based heme protein prediction and as scaffolds for future heme protein design.

Analysis of chameleon sequences and their implications in biological processes.

Chameleon sequences have been implicated in amyloid related diseases. Here we report an analysis of two types of chameleon sequences, chameleon‐HS (Helix vs. Strand) and chameleon‐HE (Helix vs. Sheet), based on known structures in Protein Data Bank. Our survey shows that the longest chameleon‐HS is eight residues while the longest chameleon‐HE is seven residues. We have done a detailed analysis on the local and global environment that might contribute to the unique conformation of a chameleon sequence. We found that the existence of chameleon sequences does not present a problem for secondary structure prediction programs, including the first generation prediction programs, such as Chou–Fasman algorithm, and the third generation prediction programs that utilize evolution information. We have also investigated the possible implication of chameleon sequences in structural conservation and functional diversity of alternatively spliced protein isoforms. Proteins 2007.

Structure‐based prediction of transcription factor binding sites using a protein‐DNA docking approach

Accurate identification of transcription factor binding sites is critical to our understanding of transcriptional regulatory networks. To overcome the issue of high false‐positive predictions that trouble the sequence‐based prediction techniques, we have developed a structure‐based prediction method that takes into consideration of interactions between the amino acids of a transcription factor and the nucleotides of its DNA binding sequence at structural level, along with an efficient protein‐DNA docking algorithm. The docked structures between a protein and a DNA are evaluated using a knowledge‐based energy function, in conjunction with van der Waals energy. Our docking algorithm supports quasi‐flexible docking, overcoming a number of limiting issues faced by similar docking algorithms. Our rigid‐body docking algorithm is tested on a dataset of 141 nonredundant transcription factor‐DNA complex structures. The test results show that 63.1% of the 141 complex structures are reconstructed with accuracies better than 1.0 Å RMSDs (root mean square deviation) and 79.4% of the complexes are predicted with accuracies better than 3.0 Å RMSDs when using the native DNA structures. Our quasi‐flexible docking algorithm, assuming that the DNA structures are not known, is tested on a separate set of 45 transcription factor‐DNA complexes, of which 57.8% of the docked complex conformations achieve better than 1.0 Å RMSDs while 71.1% of the complexes have RMSDs less than 3.0 Å. We have also applied our method to predict the binding motifs of the ferric uptake regulator in E. coli and showed that most of the experimentally identified sites can be predicted accurately. Proteins 2008.

Apolipoprotein Serum Amyloid A in Alzheimer's Disease

Alzheimers disease is characterized by the tissue deposition of beta-amyloid peptide (Abeta) in the brain. Recent studies have shown apoproteins (apo) in amyloid plaques and associated with high-density lipoprotein (HDL) particles in the cerebrospinal fluid (CSF). Western blot analysis revealed that serum amyloid A (apoSAA) protein was present in control and AD patients at low levels compared to apoE and apoA-I, however, AD brains showed a significant increase over control values. Analysis of CSF-HDL from control and AD individuals showed that apoA-I, apoE and apoSAA were on the particle. Immuno-cytochemical analysis showed that SAA was detected in senile plaques in AD tissue, but was predominantly localized to neuritic plaques. ApoE staining of AD brain confirmed that most plaques contained the apoprotein, similar to Abeta immunoreactivity, whereas apoA-I expressed little staining of senile plaques. No significant differences were detected in the level of apoSAA when compared to APOE genotype in AD samples, suggesting that interactions with apoE were non-specific. These data imply that the specific interactions of SAA with Abeta in the neuritic plaques may play a role in AD.

PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction

Knowledge of the detailed structure of a protein is crucial to our understanding of the biological functions of that protein. The gap between the number of solved protein structures and the number of protein sequences continues to widen rapidly in the post-genomics era due to long and expensive processes for solving structures experimentally. Computational prediction of structures from amino acid sequence has come to play a key role in narrowing the gap and has been successful in providing useful information for the biological research community. We have developed a prediction pipeline, PROSPECT-PSPP, an integration of multiple computational tools, for fully automated protein structure prediction. The pipeline consists of tools for (i) preprocessing of protein sequences, which includes signal peptide prediction, protein type prediction (membrane or soluble) and protein domain partition, (ii) secondary structure prediction, (iii) fold recognition and (iv) atomic structural model generation. The centerpiece of the pipeline is our threading-based program PROSPECT. The pipeline is implemented using SOAP (Simple Object Access Protocol), which makes it easier to share our tools and resources. The pipeline has an easy-to-use user interface and is implemented on a 64-node dual processor Linux cluster. It can be used for genome-scale protein structure prediction. The pipeline is accessible at http://csbl.bmb.uga.edu/protein_pipeline.