Duration of SARS-CoV-2 RNA shedding and factors associated with prolonged viral shedding in patients with COVID-19.

To investigate the factors associated with the duration of severe acute respiratory syndrome coronavirus 2 RNA shedding in patients with coronavirus disease 2019 (COVID-19). A retrospective cohort of COVID-19 patients admitted to a designated hospital in Beijing was analyzed to study the factors affecting the duration of viral shedding. The median duration of viral shedding was 11 days (IQR, 8-14.3 days) as measured from illness onset. Univariate regression analysis showed that disease severity, corticosteroid therapy, fever (temperature>38.5°C), and time from onset to hospitalization were associated with prolonged duration of viral shedding (P < .05). Multivariate regression analysis showed that fever (temperature>38.5°C) (OR, 5.1, 95%CI: 1.5-18.1), corticosteroid therapy (OR, 6.3, 95%CI: 1.5-27.8), and time from onset to hospitalization (OR, 1.8, 95%CI: 1.19-2.7) were associated with increased odds of prolonged duration of viral shedding. Corticosteroid treatment, fever (temperature>38.5°C), and longer time from onset to hospitalization were associated with prolonged viral shedding in COVID-19 patients.

The efficacy and safety of methylprednisolone in hepatitis B virus-related acute-on-chronic liver failure: a prospective multi-center clinical trial.

BACKGROUND: Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a severe condition with high mortality due to lack of efficient therapy. Until now, the use of methylprednisolone (MP) in HBV-ACLF is still controversial. We aimed to evaluate the efficacy and safety of MP in HBV-ACLF. METHODS: Totally 171 HBV-ACLF patients from three medical centers were randomly allocated into MP group (83 patients treated with MP intravenously guttae for 7 days plus standard treatment: 1.5 mg/kg/day [day 1-3], 1 mg/kg/day [day 4-5], and 0.5 mg/kg/day [day 6-7]) and control group (88 patients treated with standard treatment). The primary endpoints were 6-month mortality and prognostic factors for 6-month survival. The survival time, cause of death, adverse events, liver function, and HBV DNA replication were analyzed. RESULTS: The 6-month mortality was significantly lower in MP group than control group [32.4% vs. 42.5%, P = 0.0037]. MP treatment was an independent prognostic factor for 6-month survival [HR (95% CI) 0.547(0.308-0.973); P = 0.040]. Factors associated with reduced 6-month mortality in MP group included HBV DNA and lymphocyte/monocyte ratio (LMR) (P < 0.05). Based on ROC curve, LMR+MELD had a better predictive value for prognosis of HBV-ACLF under MP treatment. No significant difference in HBV DNA replication was observed between groups (P > 0.05). CONCLUSIONS: MP therapy is an effective and safe clinical strategy in HBV-ACLF, increasing the 6-month survival rate. Clinical trials registered at http://www.chictr.org.cn as ChiCTR-TRC-13003113 registered on 16 March 2013.

Prevalence and clinical impact of sarcopenia in liver transplant recipients: A meta-analysis.

BACKGROUND: The prevalence of sarcopenia in patients undergoing liver transplantation (LT) remains to be determined partly because of different diagnostic criteria. Sarcopenia has recently been recognized as a new prognostic factor for predicting outcomes in LT candidates. AIM: To estimate the prevalence of sarcopenia and evaluate its clinical effect on LT candidates. METHODS: This systematic search was conducted in PubMed, Web of Science, Embase, and Cochrane Library for original English-language articles that investigated the prevalence and influence of sarcopenia in patients undergoing LT from database inception to November 30, 2022. Cohort studies of the definition of sarcopenia that estimate sarcopenia prevalence and evaluate its effect on clinical outcomes and the risk of mortality were included. RESULTS: Twenty-five studies involving 7760 patients undergoing LT were included. The pooled prevalence of sarcopenia in patients undergoing LT was 40.7% [95% confidence intervals (95%CI): 32.1-49.6]. The 1-, 3-, and 5-year cumulative probabilities of post-LT survival in patients with preoperative sarcopenia were all lower than those without sarcopenia (P < 0.05). Sarcopenia was associated with an increased risk of post-LT mortality in patients undergoing LT (adjusted hazard ratio: 1.58; 95%CI: 1.21-2.07). Patients with preoperative sarcopenia had a longer intensive care unit stay, a high risk ratio of sepsis, and serious post-LT complications than those without sarcopenia. CONCLUSION: Sarcopenia is prevalent in a substantial proportion of patients undergoing LT and is strongly and independently associated with higher a risk of mortality risk.

miRNA-548c: a specific signature in circulating PBMCs from dilated cardiomyopathy patients.

High fidelity genome-wide expression analysis has strengthened the idea that microRNA (miRNA) signatures in peripheral blood mononuclear cells (PBMCs) can be potentially used to predict the pathology when anatomical samples are inaccessible like the heart. PBMCs from 48 non-failing controls and 44 patients with relatively stable chronic heart failure (ejection fraction of ≤ 40%) associated with dilated cardiomyopathy (DCM) were used for miRNA analysis. Genome-wide miRNA-microarray on PBMCs from chronic heart failure patients identified miRNA signature uniquely characterized by the downregulation of miRNA-548 family members. We have also independently validated downregulation of miRNA-548 family members (miRNA-548c & 548i) using real time-PCR in a large cohort of independent patient samples. Independent in silico Ingenuity Pathway Analysis (IPA) of miRNA-548 targets shows unique enrichment of signaling molecules and pathways associated with cardiovascular disease and hypertrophy. Consistent with specificity of miRNA changes with pathology, PBMCs from breast cancer patients showed no alterations in miRNA-548c expression compared to healthy controls. These studies suggest that miRNA-548 family signature in PBMCs can therefore be used to detect early heart failure. Our studies show that cognate networking of predicted miRNA-548 targets in heart failure can be used as a powerful ancillary tool to predict the ongoing pathology.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Fold change and p-value cutoffs significantly alter microarray interpretations.

BACKGROUND: As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis. RESULTS: The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient. CONCLUSIONS: Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.

Amino acid function and docking site prediction through combining disease variants, structure alignments, sequence alignments, and molecular dynamics: a study of the HMG domain.

BACKGROUND: The DNA binding domain of HMG proteins is known to be important in many diseases, with the Sox sub-family of HMG proteins of particular significance. Numerous natural variants in HMG proteins are associated with disease phenotypes. Integrating these natural variants, molecular dynamic simulations of DNA interaction and sequence and structure alignments give detailed molecular knowledge of potential amino acid function such as DNA or protein interaction. RESULTS: A total of 33 amino acids in HMG proteins are known to have natural variants in diseases. Eight of these amino acids are normally conserved in human HMG proteins and 27 are conserved in the human Sox sub-family. Among the six non-Sox conserved amino acids, amino acids 16 and 45 are likely targets for interaction with other proteins. Docking studies between the androgen receptor and Sry/Sox9 reveals a stable amino acid specific interaction involving several Sox conserved residues. CONCLUSION: The HMG box has structural conservation between the first two of the three helixes in the domain as well as some DNA contact points. Individual sub-groups of the HMG family have specificity in the location of the third helix, DNA specific contact points (such as amino acids 4 and 29), and conserved amino acids interacting with other proteins such as androgen receptor. Studies such as this help to distinguish individual members of a much larger family of proteins and can be applied to any protein family of interest.

Unique microRNA profile in end-stage heart failure indicates alterations in specific cardiovascular signaling networks.

It is well established that gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, but the reasons for such differences are not clear. MicroRNAs (miRNAs) are short noncoding RNAs that provide a novel mechanism for gene regulation. The goal of this study was to comprehensively test for alterations in miRNA expression using human heart failure samples with an aim to build signaling pathway networks using predicted targets for the miRNAs and to identify nodal molecules that control these networks. Genome-wide profiling of miRNAs was performed using custom-designed miRNA microarray followed by validation on an independent set of samples. Eight miRNAs are significantly altered in heart failure of which we have identified two novel miRNAs that are yet to be implicated in cardiac pathophysiology. To gain an unbiased global perspective on regulation by altered miRNAs, predicted targets of eight miRNAs were analyzed using the Ingenuity Pathways Analysis network algorithm to build signaling networks and identify nodal molecules. The majority of nodal molecules identified in our analysis are targets of altered miRNAs and are known regulators of cardiovascular signaling. A heart failure gene expression data base was used to analyze changes in expression patterns for these target nodal molecules. Indeed, expression of nodal molecules was altered in heart failure and inversely correlated to miRNA changes validating our analysis. Importantly, using network analysis we have identified a limited number of key functional targets that may regulate expression of the myriad proteins in heart failure and could be potential therapeutic targets.

Serum level of adiponectin correlated with gender and genotype in patients with chronic hepatitis C.

Adiponectin is well recognized as plasma physiologically active polypeptide hormone exclusively derived from human and animal mature adipocytes, with vigorous property in antidiabetic, antiobesity, antiatherogenic, and anti-inflammatory processes. In this study, we investigated the correlation between serum adiponectin level and clinical and pathological parameters in patients with chronic hepatitis C (CHC). The study included 127 patients with CHC and 42 healthy volunteers as controls whose laboratory parameters and serum adiponectin and tumor necrosis factor-alpha (TNF-alpha) were assessed using enzyme-linked immunosorbent assay (ELISA). We demonstrated that a lower serum adiponectin level was associated with male gender, higher gamma-glutamyltransferase (gamma-GGT), higher albumin, higher TNF-alpha, and steatosis grade. The higher level of serum adiponectin in patients with genotype 2a was demonstrated when compared with that in the patients with genotype 1b. Furthermore, of great interest, results suggested that the significant differences regarding viral genotype seemed to occur only in male patients with CHC but not in female patients. In conclusion, serum adiponectin was associated with gender, genotype, liver steatosis, and TNF-alpha in a Chinese population with CHC.

Cancer Genetic Network Inference Using Gaussian Graphical Models.

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

A Novel Role for CAMKK1 in the Regulation of the Mesenchymal Stem Cell Secretome.

Transplantation of adult stem cells into myocardial tissue after acute myocardial infarction (AMI), has been shown to improve tissue recovery and prevent progression to ischemic cardiomyopathy. Studies suggest that the effects of mesenchymal stem cells (MSC) are due to paracrine factors released by MSC, as the benefits of MSC can be achieved through delivery of conditioned media (CM) alone. We previously demonstrated that downregulation of Dab2 enhances MSC cardiac protein expression and improves cardiac function after AMI following MSC engraftment. In order to define the molecular mechanisms that regulate MSC secretome, we analyzed gene arrays in MSC following downregulation of Dab2 via TGFß1 pretreatment or transfection with Dab2:siRNA or miR-145. We identified 23 genes whose expressions were significantly changed in all three conditions. Among these genes, we have initially focused our validation and functional work on calcium/calmodulin-dependent protein kinase kinase-1 (CAMKK1). We quantified the effects of CAMKK1 overexpression in MSC following injection of CM after AMI. Injections of CM from MSC with CAMKK1 over-expression correlated with an increase in vascular density (CAMKK1 CM: 2,794.95 ± 44.2 versus Control: 1,290.69 ± 2.8 vessels/mm2 ) and decreased scar formation (CAMKK1 CM 50% ± 3.2% versus Control: 28% ± 1.4%), as well as improved cardiac function. Direct overexpression of CAMKK1 in infarcted tissue using a CAMKK1-encoding plasmid significantly improved ejection fraction (CAMKK1: 83.2% ± 5.4% versus saline: 51.7% ± 5.8%. Baseline: 91.3% ± 4.3%) and decreased infarct size after AMI. Our data identify a novel role for CAMKK1 as regulator of the MSC secretome and demonstrate that direct overexpression of CAMKK1 in infarcted cardiac tissue, results in therapeutic beneficial effects. Stem Cells Translational Medicine 2017;6:1759-1766.

A unique microRNA profile in end-stage heart failure indicates alterations in specific cardiovascular signaling networks.

It is well established that the gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, however, less is known about the reasons behind such global differences. MicroRNAs (miRNAs) are short non-coding RNAs that can target multiple molecules to regulate wide array of proteins in diverse pathways. The goal of the study was to profile alterations in miRNA expression using end-stage human heart failure samples with an aim to build signaling network pathways using predicted targets for the altered miRNA and to determine nodal molecules regulating individual networks. Profiling of miRNAs using custom designed microarray and validation with an independent set of samples identified eight miRNAs that are altered in human heart failure including one novel miRNA yet to be implicated in cardiac pathology. To gain an unbiased perspective on global regulation by top eight altered miRNAs, functional relationship of predicted targets for these eight miRNAs were examined by network analysis. Ingenuity Pathways Analysis network algorithm was used to build global signaling networks based on the targets of altered miRNAs which allowed us to identify participating networks and nodal molecules that could contribute to cardiac pathophysiology. Majority of the nodal molecules identified in our analysis are targets of altered miRNAs and known regulators of cardiovascular signaling. Cardio-genomics heart failure gene expression public data base was used to analyze trends in expression pattern for target nodal molecules and indeed changes in expression of nodal molecules inversely correlated to miRNA alterations. We have used NF kappa B network as an example to show that targeting other molecules in the network could alter the nodal NF kappa B despite not being a miRNA target suggesting an integrated network response. Thus, using network analysis we show that altering key functional target proteins may regulate expression of the myriad signaling pathways underlying the cardiac pathology.

The relationship between protein sequences and their gene ontology functions.

BACKGROUND: One main research challenge in the post-genomic era is to understand the relationship between protein sequences and their biological functions. In recent years, several automated annotation systems have been developed for the functional assignment of uncharacterized proteins. The underlying assumption of these systems is that similar sequences imply similar biological functions. However, it has been noted that matching sequences do not always infer similar functions. RESULTS: In this paper, we present the correlation between protein sequences and protein functions for the yeast proteome in the context of gene ontology. A novel measure is introduced to define the overall similarity between two protein sequences. The effects of the level as well as the size of a gene ontology group on the degree of similarity were studied. The similarity distributions at different levels of gene ontology trees are presented. To evaluate the theoretical prediction power of similar sequences, we computed the posterior probability of correct predictions. CONCLUSION: The results indicate that protein pairs of similar biological functions tend to have higher sequence similarity, although the similarity distribution in each functional group is heterogeneous and varies from group to group. We conclude that sequence similarity can serve as a key measure in protein function prediction. However, the resulting annotations must be verified through other means. A method that combines a broader range of measures is more likely to provide more accurate prediction. Our study indicates that the posterior probability of a correct prediction could serve as one of the key measures.

[Characteristics of acid-base balance in patients with chronic severe hepatitis: analysis of 126 cases].

OBJECTIVE: To investigate the characteristics of acid-base balance in patients with chronic severe hepatitis. METHODS: Samples of venous blood and arterial blood were collected from 126 patients with chronic severe hepatitis, 106 males and 20 females, aged 44 +/- 13 (25 - 74), to undergo measurement of the potassium, sodium, and chloride ions, and urea nitrogen and creatine, blood electrolytes and blood gas analysis respectively. RESULTS: Acid-base disturbance (ABD) was found in 115 of the 126 patients (91.3%). 40 of the 115 patients with ABD (31.7%) had respiratory alkalosis combined with metabolic alkalosis, 32 of them (25.4%) had respiratory alkalosis combined with metabolic acidosis; 28 of them (22.2%) had pure respiratory alkalosis, and 8 (6.3%) had pure metabolic acidosis. Five patients had triple acid-base disturbance, 4 of which had respiratory alkalosis (combined with metabolic alkalosis and metabolic acidosis, 3.2%), and 1 of which had respiratory acidosis (combined with metabolic alkalosis and metabolic acidosis, 0.8%). There was no significantly difference in the prevalence rates of pure acid-base disturbance and multiple acid-base disturbances between the patients with cirrhosis-base chronic severe hepatitis and those with chronic hepatitis-based chronic severe hepatitis. Hypoxia symptoms were seen in 34 patients (27%). 103 patients died. All the 15 patients with their blood pH < 7.35 died. CONCLUSION: ABD in the patients with chronic severe hepatitis is mainly alkalosis with respiratory alkalosis as the basic ABD type. The appearance of ABD is not associated with the underlying illness. Patients of chronic severe hepatitis often suffer from hypoxia. Low blood pH is an important factor causing death.

Correction: Interaction of G-Protein ßγ Complex with Chromatin Modulates GPCR-Dependent Gene Regulation.

[This corrects the article DOI: 10.1371/journal.pone.0052689.].

Effects of resveratrol on gene expression in renal cell carcinoma.

Studies have shown that Resveratrol (RE) can inhibit cancer initiation, promotion, and progression. However the gene expression profile in renal cell carcinoma (RCC) in response to RE treatment has never been reported. To understand the potential anticancer effect of RE on RCC at molecular level, we profiled and analyzed the expression of 2059 cancer-related genes in a RCC cell line RCC54 treated with RE. Biological functions of 633 genes were annotated based on biological process ontology and clustered into functional categories. Twenty-nine highly differentially expressed genes in RE treated RCC54 were identified and the potential implications of some gene expression alterations in RCC carcinogenesis were identified. RE was also shown to inhibit cell growth and induce cell death of RCC cells. The expression alterations of selected genes were validated using reverse transcription polymerase chain reaction. In addition, the gene expression profiles under different RE treatments were analyzed and visualized using singular value decomposition. The findings from this study support the hypothesis that RE induces differential expression of genes that are directly or indirectly related to the inhibition of RCC cell growth and induction of RCC cell death. In addition, it is apparent that the gene expression alterations due to RE treatment depend strongly on RE concentration. This study provides a general understanding of the overall genetic response of RCC54 to RE treatment and yields insights into the understanding of the cancer preventive mechanism of RE in RCC.

Microarray gene expression profiling and analysis in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays. METHODS: Total RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC. RESULTS: Selected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR). Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved. CONCLUSIONS: This is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most notably, genes involved in cell adhesion were dominantly up-regulated whereas genes involved in transport were dominantly down-regulated. This study reveals significant gene expression alterations in key biological pathways and provides potential insights into understanding the molecular mechanism of renal cell carcinogenesis.

Interaction of G-protein ßγ complex with chromatin modulates GPCR-dependent gene regulation.

Heterotrimeric G-protein signal transduction initiated by G-protein-coupled receptors (GPCRs) in the plasma membrane is thought to propagate through protein-protein interactions of subunits, Gα and Gßγ in the cytosol. In this study, we show novel nuclear functions of Gßγ through demonstrating interaction of Gß(2) with integral components of chromatin and effects of Gß(2) depletion on global gene expression. Agonist activation of several GPCRs including the angiotensin II type 1 receptor specifically augmented Gß(2) levels in the nucleus and Gß(2) interacted with specific nucleosome core histones and transcriptional modulators. Depletion of Gß(2) repressed the basal and angiotensin II-dependent transcriptional activities of myocyte enhancer factor 2. Gß(2) interacted with a sequence motif that was present in several transcription factors, whose genome-wide binding accounted for the Gß(2)-dependent regulation of approximately 2% genes. These findings suggest a wide-ranging mechanism by which direct interaction of Gßγ with specific chromatin bound transcription factors regulates functional gene networks in response to GPCR activation in cells.

Aberrant signaling pathways in squamous cell lung carcinoma.

Lung cancer is the second most commonly occurring non-cutaneous cancer in the United States with the highest mortality rate among both men and women. In this study, we utilized three lung cancer microarray datasets generated by previous researchers to identify differentially expressed genes, altered signaling pathways, and assess the involvement of Hedgehog (Hh) pathway. The three datasets contain the expression levels of tens of thousands genes in normal lung tissues and squamous cell lung carcinoma. The datasets were combined and analyzed. The dysregulated genes and altered signaling pathways were identified using statistical methods. We then performed Fisher's exact test on the significance of the association of Hh pathway downstream genes and squamous cell lung carcinoma.395 genes were found commonly differentially expressed in squamous cell lung carcinoma. The genes encoding fibrous structural protein keratins and cell cycle dependent genes encoding cyclin-dependent kinases were significantly up-regulated while the ones encoding LIM domains were down. Over 100 signaling pathways were implicated in squamous cell lung carcinoma, including cell cycle regulation pathway, p53 tumor-suppressor pathway, IL-8 signaling, Wnt-ß-catenin pathway, mTOR signaling and EGF signaling. In addition, 37 out of 223 downstream molecules of Hh pathway were altered. The P-value from the Fisher's exact test indicates that Hh signaling is implicated in squamous cell lung carcinoma.Numerous genes were altered and multiple pathways were dysfunctional in squamous cell lung carcinoma. Many of the altered genes have been implicated in different types of carcinoma while some are organ-specific. Hh signaling is implicated in squamous cell lung cancer, opening the door for exploring new cancer therapeutic treatment using GLI antagonist GANT 61.