W. Jim Zheng

β-Arrestin-Selective G Protein-Coupled Receptor Agonists Engender Unique Biological Efficacy in Vivo

Biased G protein-coupled receptor agonists are orthosteric ligands that possess pathway-selective efficacy, activating or inhibiting only a subset of the signaling repertoire of their cognate receptors. In vitro, D-Trp(12),Tyr(34)-bPTH(7-34) [bPTH(7-34)], a biased agonist for the type 1 PTH receptor, antagonizes receptor-G protein coupling but activates arrestin-dependent signaling. In vivo, both bPTH(7-34) and the conventional agonist hPTH(1-34) stimulate anabolic bone formation. To understand how two PTH receptor ligands with markedly different in vitro efficacy could elicit similar in vivo responses, we analyzed transcriptional profiles from calvarial bone of mice treated for 8 wk with vehicle, bPTH(7-34) or hPTH(1-34). Treatment of wild-type mice with bPTH(7-34) primarily affected pathways that promote expansion of the osteoblast pool, notably cell cycle regulation, cell survival, and migration. These responses were absent in β-arrestin2-null mice, identifying them as downstream targets of β-arrestin2-mediated signaling. In contrast, hPTH(1-34) primarily affected pathways classically associated with enhanced bone formation, including collagen synthesis and matrix mineralization. hPTH(1-34) actions were less dependent on β-arrestin2, as might be expected of a ligand capable of G protein activation. In vitro, bPTH(7-34) slowed the rate of preosteoblast proliferation, enhanced osteoblast survival when exposed to an apoptotic stimulus, and stimulated cell migration in wild-type, but not β-arrestin2-null, calvarial osteoblasts. These results suggest that bPTH(7-34) and hPTH(1-34) affect bone mass in vivo through predominantly separate genomic mechanisms created by largely distinct receptor-signaling networks and demonstrate that functional selectivity can be exploited to change the quality of G protein-coupled receptor efficacy.

Molecular Profiling of Multiple Human Cancers Defines an Inflammatory Cancer-Associated Molecular Pattern and Uncovers KPNA2 as a Uniform Poor Prognostic Cancer Marker

Background Immune evasion is one of the recognized hallmarks of cancer. Inflammatory responses to cancer can also contribute directly to oncogenesis. Since the immune system is hardwired to protect the host, there is a possibility that cancers, regardless of their histological origins, endow themselves with a common and shared inflammatory cancer-associated molecular pattern (iCAMP) to promote oncoinflammation. However, the definition of iCAMP has not been conceptually and experimentally investigated. Methods and Findings Genome-wide cDNA expression data was analyzed for 221 normal and 324 cancer specimens from 7 cancer types: breast, prostate, lung, colon, gastric, oral and pancreatic. A total of 96 inflammatory genes with consistent dysregulation were identified, including 44 up-regulated and 52 down-regulated genes. Protein expression was confirmed by immunohistochemistry for some of these genes. The iCAMP contains proteins whose roles in cancer have been implicated and others which are yet to be appreciated. The clinical significance of many iCAMP genes was confirmed in multiple independent cohorts of colon and ovarian cancer patients. In both cases, better prognosis correlated strongly with high CXCL13 and low level of GREM1, LOX, TNFAIP6, CD36, and EDNRA. An “Inflammatory Gene Integrated Score” was further developed from the combination of 18 iCAMP genes in ovarian cancer, which predicted overall survival. Noticeably, as a selective nuclear import protein whose immuno-regulatory function just begins to emerge, karyopherin alpha 2 (KPNA2) is uniformly up-regulated across cancer types. For the first time, the cancer-specific up-regulation of KPNA2 and its clinical significance were verified by tissue microarray analysis in colon and head-neck cancers. Conclusion This work defines an inflammatory signature shared by seven epithelial cancer types and KPNA2 as a consistently up-regulated protein in cancer. Identification of iCAMP may not only serve as a novel biomarker for prognostication and individualized treatment of cancer, but also have significant biological implications.

Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome

BackgroundNew technologies are enabling the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is increasingly structural in nature, and is often difficult to coordinate with other data sets. There is a legitimate need for integrating and visualizing these disparate data sets to reveal structural relationships not apparent when looking at these data in isolation.ResultsWe have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In order to integrate and visualize large volume of data, novel statistical and mathematical approaches have been developed to reduce the size of the data. To our knowledge, this is the first such tool developed that can visualize human genome in three-dimension. We describe here the major features of Genome3D and discuss our multi-scale data framework using a representative basic physical model. We then demonstrate many of the issues and benefits of multi-resolution data integration.ConclusionsGenome3D is a software visualization tool that explores a wide range of structural genomic and epigenetic data. Data from various sources of differing scales can be integrated within a hierarchical framework that is easily adapted to new developments concerning the structure of the physical genome. In addition, our tool has a simple annotation mechanism to incorporate non-structural information. Genome3D is unique is its ability to manipulate large amounts of multi-resolution data from diverse sources to uncover complex and new structural relationships within the genome.

A role for Fli-1 in B cell proliferation: implications for SLE pathogenesis

Transgenic overexpression of Fli-1 in normal mice leads to SLE-like disease and increased expression was reported in SLE-affected human and murine lymphocytes. Reducing Fli-1 expression in MRL/lpr mice decreased antibody production, proteinuria, renal pathology, and mortality. Compared to those with wild-type expression of Fli-1, we report here that proliferative responses of Fli-1-deficient naïve B cells to several mitogens were reduced in lupus-prone and control mice. Expression of mitogen receptors, including BCR, TLR4, and TLR9, was not significantly impacted in Fli-1-deficient naïve B cells. IL12a transcripts were upregulated and NFAT transcripts were downregulated in Fli-1-deficient MRL/lpr B cells. These results demonstrate that Fli-1 deficiency affects B cell proliferative responses to mitogens, independent of BCR and TLR expression. IL12a and NFAT, known to influence proliferation, were identified as potential mediators of this effect. This may be a mechanism by which overexpression of Fli-1 contributes to B cell hyperactivity and subsequent SLE pathogenesis.

Evaluation of genome-wide association study results through development of ontology fingerprints

MOTIVATION Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important. RESULTS We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint--the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation.

Ranking gene-drug relationships in biomedical literature using Latent Dirichlet Allocation.

Drug responses vary greatly among individuals due to human genetic variations, which is known as pharmacogenomics (PGx). Much of the PGx knowledge has been embedded in biomedical literature and there is a growing interest to develop text mining approaches to extract such knowledge. In this paper, we present a study to rank candidate gene-drug relations using Latent Dirichlet Allocation (LDA) model. Our approach consists of three steps: 1) recognize gene and drug entities in MEDLINE abstracts; 2) extract candidate gene-drug pairs based on different levels of co-occurrence, including abstract level, sentence level, and phrase level; and 3) rank candidate gene-drug pairs using multiple different methods including term frequency, Chi-square test, Mutual Information (MI), a reported Kullback-Leibler (KL) distance based on topics derived from LDA (LDA-KL), and a newly defined probabilistic KL distance based on LDA (LDA-PKL). We systematically evaluated these methods by using a gold standard data set of gene-drug relations derived from PharmGKB. Our results showed that the proposed LDA-PKL method achieved better Mean Average Precision (MAP) than any other methods, suggesting its promising uses for ranking and detecting PGx relations.

Improving transmission efficiency of large sequence alignment/map (SAM) files.

Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly.

Cellular Morphogenesis Under Stress Is Influenced by the Sphingolipid Pathway Gene ISC1 and DNA Integrity Checkpoint Genes in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, replication stress induced by hydroxyurea (HU) and methyl methanesulfonate (MMS) activates DNA integrity checkpoints; in checkpoint-defective yeast strains, HU treatment also induces morphological aberrations. We find that the sphingolipid pathway gene ISC1, the product of which catalyzes the generation of bioactive ceramides from complex sphingolipids, plays a novel role in determining cellular morphology following HU/MMS treatment. HU-treated isc1Δ cells display morphological aberrations, cell-wall defects, and defects in actin depolymerization. Swe1, a morphogenesis checkpoint regulator, and the cell cycle regulator Cdk1 play key roles in these morphological defects of isc1Δ cells. A genetic approach reveals that ISC1 interacts with other checkpoint proteins to control cell morphology. That is, yeast carrying deletions of both ISC1 and a replication checkpoint mediator gene including MRC1, TOF1, or CSM3 display basal morphological defects, which increase following HU treatment. Interestingly, strains with deletions of both ISC1 and the DNA damage checkpoint mediator gene RAD9 display reduced morphological aberrations irrespective of HU treatment, suggesting a role for RAD9 in determining the morphology of isc1Δ cells. Mechanistically, the checkpoint regulator Rad53 partially influences isc1Δ cell morphology in a dosage-dependent manner.

Object-oriented biological system integration: a SARS coronavirus example

Abstract Motivation: The importance of studying biology at the system level has been well recognized, yet there is no well-defined process or consistent methodology to integrate and represent biological information at this level. To overcome this hurdle, a blending of disciplines such as computer science and biology is necessary. Results: By applying an adapted, sequential software engineering process, a complex biological system (severe acquired respiratory syndrome-coronavirus viral infection) has been reverse-engineered and represented as an object-oriented software system. The scalability of this object-oriented software engineering approach indicates that we can apply this technology for the integration of large complex biological systems. Availability: A navigable web-based version of the system is freely available at http://people.musc.edu/~zhengw/SARS/Software-Process.htm Contact: zhengw@musc.edu Supplementary information: Supplemental data: Table 1 and Figures 1–16.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.