The initiators caspase-9 (CASP9) and caspase-10 (CASP10) are two key controllers of apoptosis and play important roles in carcinogenesis. This study aims to explore the association between CASPs gene polymorphisms and colorectal cancer (CRC) susceptibility in a population-based study. A two-stage designed population-based case-control study was carried out, including a testing set with 300 cases and 296 controls and a validation set with 206 cases and 845 controls. A total of eight tag selected single nucleotide polymorphisms (SNPs) in CASP9 and CASP10 were chosen based on HapMap and the National Center of Biotechnology Information (NCBI) datasets and genotyped by restriction fragment length polymorphism (RFLP) assay. Multivariate logistic regression models were applied to evaluate the association of SNPs with CRC risk. In the first stage, from eight tag SNPs, three polymorphisms rs4646077 (odds ratio (OR)AA+AG: 0.654, 95% confidence interval (CI): 0.406–1.055; P=0.082), rs4233532 (ORCC: 1.667, 95% CI: 0.967–2.876; ORCT: 1.435, 95% CI: 0.998–2.063; P=0.077), and rs2881930 (ORCC: 0.263, 95% CI: 0.095–0.728, P=0.036) showed possible association with CRC risk. However, none of the three SNPs, rs4646077 (ORAA+AG: 1.233, 95% CI: 0.903–1.683), rs4233532 (ORCC: 0.892, 95% CI: 0.640–1.243; ORCT: 1.134, 95% CI: 0.897–1.433), and rs2881930 (ORCC: 1.096, 95% CI: 0.620–1.938; ORCT: 1.009, 95% CI: 0.801–1.271), remained significant with CRC risk in the validation set, even after stratification for different tumor locations (colon or rectum). In addition, never tea drinking was associated with a significantly increased risk of CRC in testing set together with validation set (OR: 1.755, 95% CI: 1.319–2.334). Our results found that polymorphisms of CASP9 and CASP10 genes may not contribute to CRC risk in Chinese population and thereby the large-scale case-control studies might be in consideration. In addition, tea drinking was a protective factor for CRC.

Dysregulation of the complement system has been linked to pathogenesis of hypertension. However, whether genetic changes of complement factor H (CFH) and its related genes are associated with hypertension is unknown. We genotyped three SNPs in the CFH gene cluster that are closely linked to age-related macular degeneration, namely rs1061170 (Y402H), rs2274700 (A473A) and rs7542235 (CFHR1–3Δ), and tested for their associations with blood pressure and hypertension risk in a population-based cohort including 3,210 unrelated Chinese Hans (50–70 years of age) from Beijing and Shanghai. We found that rs2274700 (A473A) and rs7542235 (CFHR1–3Δ) were both significantly associated with diastolic blood pressure (DBP) (β = 0.632–1.431, P≤0.038) and systolic blood pressure (SBP) (β = 1.567–4.445, P≤0.008), and rs2274700 (A473A) was associated with hypertension risk (OR [95%CI]: 1.175 [1.005–1.373], P = 0.048). Notably, the associations of rs2274700 (A473A) with DBP (P = 2.1×10−3), SBP (P = 8×10−5) and hypertension risk (P = 7.9×10−3) were significant only in the individuals with low CRP levels (<2.0 mg/l), but not in those with CRP levels ≥2.0 mg/l (P≥0.0807) (P for interaction ≤0.0467). However, no significant association between rs1061170 (Y402H) and blood pressure or hypertension risk was observed (P≥0.259). In conclusion, our results suggest that genetic variations in CFH and its related genes may contribute to hypertension risk in Chinese Hans.

Background

Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged.

Results

By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.

Conclusions

The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Vertebrate lens development depends on a complex network of signaling molecules to coordinate cell proliferation, migration and differentiation. In this study, we have studied the role of heparan sulfate in lens specific signaling by generating a conditional ablation of heparan sulfate modification genes, Ndst1 and Ndst2. In this mutant, N-sulfation of heparan sulfate was disrupted after the lens induction stage, resulting in reduced lens cell proliferation, increased cell death and defective lens fiber differentiation in later lens development. The loss of Ndst function also prevented the assembly of Fgf/Fgfr complexes on the lens cell surface and disrupted ERK signaling within the lens. We further demonstrated that Ndst mutation completely inhibited the FGF1 and Fgf3 overexpression phenotypes, but Kras reactivation was sufficient to reverse the Ndst deficient lens differentiation defect. The epistatic relationship between Ndst and FGF-Ras signaling demonstrates that FGF signaling is the predominant signaling pathway controlled by Ndst in lens development.

Background

Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.

Results

In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.

Conclusions

The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.

OBJECTIVE

Most animals experience fasting–feeding cycles throughout their lives. It is well known that the liver plays a central role in regulating glycogen metabolism. However, how hepatic glycogenesis is coordinated with the fasting–feeding cycle to control postprandial glucose homeostasis remains largely unknown. This study determines the molecular mechanism underlying the coupling of hepatic glycogenesis with the fasting–feeding cycle.

RESEARCH DESIGN AND METHODS

Through a series of molecular, cellular, and animal studies, we investigated how PPP1R3G, a glycogen-targeting regulatory subunit of protein phosphatase 1 (PP1), is implicated in regulating hepatic glycogenesis and glucose homeostasis in a manner tightly orchestrated with the fasting–feeding cycle.

RESULTS

PPP1R3G in the liver is upregulated during fasting and downregulated after feeding. PPP1R3G associates with glycogen pellet, interacts with the catalytic subunit of PP1, and regulates glycogen synthase (GS) activity. Fasting glucose level is reduced when PPP1R3G is overexpressed in the liver. Hepatic knockdown of PPP1R3G reduces postprandial elevation of GS activity, decreases postprandial accumulation of liver glycogen, and decelerates postprandial clearance of blood glucose. Other glycogen-targeting regulatory subunits of PP1, such as PPP1R3B, PPP1R3C, and PPP1R3D, are downregulated by fasting and increased by feeding in the liver.

CONCLUSIONS

We propose that the opposite expression pattern of PPP1R3G versus other PP1 regulatory subunits comprise an intricate regulatory machinery to control hepatic glycogenesis during the fasting–feeding cycle. Because of its unique expression pattern, PPP1R3G plays a major role to control postprandial glucose homeostasis during the fasting–feeding transition via its regulation on liver glycogenesis.

FAK opposes Rho activity and vimentin intermediate filament formation to promote podosome rosette assembly.

Podosomes are dynamic actin-enriched membrane structures that play an important role in invasive cell motility and extracellular matrix degradation. They are often found to assemble into large rosettelike structures in highly invasive cells. However, the mechanism of this assembly remains obscure. In this study, we identified focal adhesion kinase (FAK) as a key molecule necessary for assembly. Moreover, phosphorylation of p130Cas and suppression of Rho signaling by FAK were found to be important for FAK to induce the assembly of podosome rosettes. Finally, we found that suppression of vimentin intermediate filaments by FAK facilitates the assembly of podosome rosettes. Collectively, our results strongly suggest a link between FAK, podosome rosettes, and tumor invasion and unveil a negative role for Rho signaling and vimentin filaments in podosome rosette assembly.

The carboxylesterase Est55 has been cloned and expressed in Bacillus subtilis strains. Est55, which lacks a classical, cleavable N-terminal signal sequence, was found to be secreted during the stationary phase of growth such that there is more Est55 in the medium than inside the cells. Several cytoplasmic proteins were also secreted in large amounts during late stationary phase, indicating that secretion in B. subtilis is not unique to Est55. These proteins, which all have defined cytoplasmic functions, include GroEL, DnaK, enolase, pyruvate dehydrogenase subunits PdhB and PdhD, and SodA. The release of Est55 and those proteins into the growth medium is not due to gross cell lysis, a conclusion that is supported by several lines of evidence: constant cell density and secretion in the presence of chloramphenicol, constant viability count, the absence of EF-Tu and SecA in the culture medium, and the lack of effect of autolysin-deficient mutants. The shedding of these proteins by membrane vesicles into the medium is minimal. More importantly, we have identified a hydrophobic α-helical domain within enolase that contributes to its secretion. Thus, upon the genetic deletion or replacement of a potential membrane-embedding domain, the secretion of plasmid gene-encoded mutant enolase is totally blocked, while the wild-type chromosomal enolase is secreted normally in the same cultures during the stationary phase, indicating differential specificity. We conclude that the secretion of Est55 and several cytoplasmic proteins without signal peptides in B. subtilis is a general phenomenon and is not a consequence of cell lysis or membrane shedding; instead, their secretion is through a process(es) in which protein domain structure plays a contributing factor.

Klebsiella pneumoniae is the common cause of a global emerging infectious disease, community-acquired pyogenic liver abscess (PLA). Capsular polysaccharide (CPS) and lipopolysaccharide (LPS) are critical for this microorganism's ability to spread through the blood and to cause sepsis. While CPS type K1 is an important virulence factor in K. pneumoniae causing PLA, the role of LPS in PLA is not clear. Here, we characterize the role of LPS O antigen in the pathogenesis of K. pneumoniae causing PLA. NTUH-K2044 is a LPS O1 clinical strain; the presence of the O antigen was shown via the presence of 1,3-galactan in the LPS, and of sequences that align with the wb gene cluster, known to produce O-antigen. Serologic analysis of K. pneumoniae clinical isolates demonstrated that the O1 serotype was more prevalent in PLA strains than that in non-tissue-invasive strains (38/42 vs. 9/32, P<0.0001). O1 serotype isolates had a higher frequency of serum resistance, and mutation of the O1 antigen changed serum resistance in K. pneumoniae. A PLA-causing strain of CPS capsular type K2 and LPS serotype O1 (i.e., O1:K2 PLA strain) deleted for the O1 synthesizing genes was profoundly attenuated in virulence, as demonstrated in separate mouse models of septicemia and liver abscess. Immunization of mice with the K2044 magA-mutant (K1− O1) against LPS O1 provided protection against infection with an O1:K2 PLA strain, but not against infection with an O1:K1 PLA strain. Our findings indicate that the O1 antigen of PLA-associated K. pneumoniae contributes to virulence by conveying resistance to serum killing, promoting bacterial dissemination to and colonization of internal organs after the onset of bacteremia, and could be a useful vaccine candidate against infection by an O1:K2 PLA strain.

Background

Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.

Results

In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized α-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.

Conclusions

We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

Background

Environmental factors-induced dysfunction of esophageal squamous epithelium, including genomic DNA impairment and apoptosis, play an important role in the pathogenesis of esophageal squamous cell cancer. DNA damage-induced 45α (GADD45α) has been found promoting DNA repair and removing methylation marker, Therefore, in this study we will investigate whether GADD45α expression is induced and its mechanism in esophageal squamous cell cancer.

Methods

Two human esophageal squamous cell lines (ESCC), ECA109 and KYSE510 were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS). Lipofectamine 2000 was used to transfect cells. mRNA level of GADD45α was measured by reverse transcription-quantitive PCR (RT-qPCR), protein level of GADD45α was detected by western blot and Immunohistochemistry. Global DNA methylation of tissue sample was measured using the Methylamp Global DNA Methylation Quantification Ultra kit (Epigentek Group) and promoter methylation was measured by bisulfite sequencing.

Results

GADD45a mRNA and protein levels were increased significantly in tumor tissue than that in adjacent normal tissue. Hypomethylation of global genomic DNA and GADD45α promoter were found in ESCC. The cell sensitivity to Cisplatin DDP was decreased significantly in Eca109 and Kyse510 cells, in which GADD45α expression was down-regulated by RNA interference (RNAi). In addition, silence of GADD45a expression in ESCC cells inhibited proliferation and promoted apoptosis.

Conclusion

Overexpression of GADD45α gene is due to DNA hypomethylation in ESCC. GADD45α may be a protective factor in DDP chemotherapy for esophageal squamous cell carcinoma.

Chronic alcohol exposure affects the central nervous system, influences behavior, and induces neuroadaptive changes in vertebrate species including our own. The molecular mechanisms responsible for chronic alcohol effects have not been fully elucidated due to the complexity of alcohol’s actions. Here we use zebrafish, a novel tool in alcohol research, to reveal a large number of genes that respond to chronic alcohol treatment. We demonstrate differential gene expression in response to chronic alcohol treatment using full genome DNA microarrays and find a total of 1914 genes to show a minimum of 2-fold and significant expression level change (1127 were up- and 787 were down-regulated). Approximately two-thirds of these genes had no known previous functional annotation. The results of the microarray analyses correlated well with those obtained on a selected subset of genes analyzed by quantitative real-time RT-PCR. Analyses of the differentially expressed genes with known annotations were enriched for a variety of molecular functions. Only a fraction of these known genes has been reported in the literature to be alcohol related. We conclude that the zebrafish is an excellent tool for the analysis of genes associated with alcohol’s actions in vertebrates, one which may facilitate the discovery and better understanding of the mechanisms of alcohol abuse.

Semaphorin4D (SEMA4D) has been regarded as an important protein in tumor angiogenesis, though originally identified in neurodevelopment. SEMA4D is extensively expressed in several malignant solid tumors. Nevertheless, the function and expression of SEMA4D in epithelial ovarian cancer (EOC) is as yet not well understood. The aim of this study was to investigate SEMA4D expression in EOC and evaluate its clinical–pathological and prognostic significance. Immunohistochemistry was used to analyze SEMA4D expression and tumor angiogenesis-related proteins (HIF-1α and VEGF) in tissues from 40 patients with normal ovarian epithelia and 124 EOC patients. SEMA4D was found to be expressed in 61.3% of the 124 EOC tissues, which was significantly higher than in the normal ovarian epithelia (p < 0.001). SEMA4D expression correlated with HIF-1α and VEGF closely (ρ = 0.349 and 0.263, p < 0.001). Positive SEMA4D staining was significantly higher in tissues from patients with low histological grade, FIGO stage III-IV, lymph node metastasis and residual disease ≥1 cm (p < 0.05). In the Cox proportional hazard mode, SEMA4D expression and histologic grade were independent indicators of overall survival (OS) and progress-free survival (PFS) for EOC patients. These findings suggest that the cooperation of SEMA4D, HIF-1α, and VEGF may indicate poor prognosis for patients with EOC, thereby demonstrating that SEMA4D and its role in angiogenesis in EOC warrants further study.

Background

One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.

Results

We design O(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align m sequences with max length n, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from O(m × n4) to O(m) using O(m × n3) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes O(m × n4) processing units and completes in O(m) time.

Conclusions

To our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with O(m) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with O(1) run-time using O(n3) processing units. This is a big improvement over the current best constant-time algorithm that uses O(n4) processing units.

Background

Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome.

Results

In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well-known breast cancer genes, such as TP53 and BRCA1, are ranked extremely high in our results. Compared with previous results by functional enrichment analysis, graph centralities, especially the eigenvector centrality and subgraph centrality, based gene signatures are more tightly related to breast cancer. We validate these signatures on genome-wide microarray dataset and found strong association between the expression of these signature genes and pathologic parameters.

Conclusions

In summary, graph centralities provide a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is not only can be used on breast cancer, but also can be used on other gene expression related diseases and drug studies.

Background

Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.

Results

We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well.

Conclusion

We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

We investigated the effects of chronic mineralocorticoid receptor blockade with eplerenone on the development and progression of hypertension and end organ damage in Dahl salt-sensitive rats. Eplerenone significantly attenuated the progressive rise in systolic blood pressure (SBP) (204 ± 3 vs. 179±3 mmHg, p < 0.05), reduced proteinuria (605.5 ± 29.6 vs. 479.7 ± 26.1 mg/24h, p < 0.05), improved injury scores of glomeruli, tubules, renal interstitium, and vasculature in Dahl salt-sensitive rats fed a high-salt diet. These results demonstrate that mineralocorticoid receptor antagonism provides target organ protection and attenuates the development of elevated blood pressure (BP) in a model of salt-sensitive hypertension.

Neurovascular coupling in response to stimulation of the rat barrel cortex was investigated using concurrent multichannel electrophysiology and laser Doppler flowmetry. The data was used to build a linear dynamic model relating neural activity to blood flow. Local field potential time series were subject to current source density analysis, and the time series of a layer IV sink of the barrel cortex was used as the input to the model. The model output was the time series of the changes in regional cerebral blood flow (CBF). We show that this model can provide excellent fit of the CBF responses for stimulus durations of up to 16s. The structure of the model consisted of two coupled components representing vascular dilation and constriction. The complex temporal characteristics of the CBF time series was reproduced by the relatively simple balance of these two components. We show that the impulse response obtained under the 16s duration stimulation condition generalised to provide a good prediction to the data from the shorter duration stimulation conditions. Furthermore by optimising three out of the total of nine model parameters, the variability in the data can be well accounted for over a wide range of stimulus conditions. By establishing linearity, classic system analysis methods can be used to generate and explore a range of equivalent model structures (e.g. feed-forward or feedback) to guide the experimental investigation of the control of vascular dilation and constriction following stimulation.

Phagocytosis resistance is an important virulence factor in Klebsiella pneumoniae. Dictyostelium has been used to study the interaction between phagocytes and bacteria because of its similarity to mammalian macrophages. In this study, we used a Dictyostelium model to investigate genes for resistance to phagocytosis in NTUH-K2044, a strain of K. pneumoniae causing pyogenic liver abscess that is highly resistant to phagocytosis. A total of 2,500 transposon mutants were screened by plaque assay, and 29 of them permitted phagocytosis by Dictyostelium. In the 29 mutants, six loci were identified; three were capsular synthesis genes. Of the other three, one was related to carnitine metabolism, one encoded a subunit of protease (clpX), and one encoded a lipopolysaccharide O-antigen transporter (wzm). Deletion and complementation of these genes showed that only ΔclpX and Δwzm mutants became susceptible to Dictyostelium phagocytosis, and their complementation restored the phagocytosis resistance phenotype. These two mutants were also susceptible to phagocytosis by human neutrophils and revealed attenuated virulence in a mouse model, implying that they play important roles in the pathogenesis of K. pneumoniae. Furthermore, we demonstrated that clpP, which exists in an operon with clpX, was also involved in resistance to phagocytosis. The transcriptional profile of ΔclpX was examined by microarray analysis and revealed a 3-fold lower level of expression of capsular synthesis genes. Therefore, we have identified genes involved in resistance to phagocytosis in K. pneumoniae using Dictyostelium, and this model is useful to explore genes associated with resistance to phagocytosis in heavily encapsulated bacteria.

Interleukin 22 (IL-22) is a T-cell secreted cytokine that modulates inflammatory response in nonhematopoietic tissues such as epithelium and liver. The function of IL-22 in adipose tissue is currently unknown. We generated a transgenic mouse model with overexpression of IL-22 specifically in adipose tissue. The IL-22 transgenic mice had no apparent changes in obesity and insulin resistance after feeding with high fat diet (HFD). Unexpectedly, all the IL-22 transgenic mice fed with HFD for four months developed spontaneous tumors in epididymal adipose tissue. Histological analysis indicated that the tumors were well-differentiated liposarcomas with infiltration of inflammatory cells. IL-22 overexpression promotes production of inflammatory cytokines such as IL-1β and IL-10 and stimulates ERK phosphorylation in adipose tissue. Furthermore, IL-22 treatment in differentiated 3T3-L1 adipocytes could induce IL-1β and IL-10 expression, together with stimulation of ERK phosphorylation. Taken together, our study not only established a novel mouse model with spontaneous liposarcoma, but also revealed that IL-22 overexpression may collaborate with diet-induced obesity to impact on tumor development in mouse.

Background

Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.

Results

In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks.

Conclusions

The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at http://bioinfo.csu.edu.cn/txw/TC-PINs.

Summary

While cellular responses to low oxygen (O2) or hypoxia have been studied extensively, the precise identity of mammalian cellular O2 sensors remains controversial. Using murine embryonic cells lacking cytochrome c, and therefore mitochondrial activity, we show that mitochondrial reactive oxygen species (mtROS) are essential for proper O2 sensing and subsequent HIF-1α and HIF-2α stabilization at 1.5% O2. In the absence of this signal, HIF-α subunits continue to be degraded. Furthermore, exogenous treatment with H2O2 or severe O2 deprivation is sufficient to stabilize HIF-α even in the absence of cytochrome c and functional mitochondria. These results provide genetic evidence indicating that mtROS act upstream of prolyl hydroxylases in regulating HIF-1α and HIF-2α in this O2 sensing pathway.

Emerging evidence suggests that cancer stem cells account for the initiation and progression of cancer. While many types of cancer stem cells with specific markers have been isolated and identified, a variety of differences among them began to be appreciated. Cancer stem cells are hierarchical populations that consist of precancerous stem cells, primary cancer stem cells, migrating cancer stem cells and chemoradioresistant cancer stem cells, playing different roles in cancer initiation and progression. Here we propose a new concept "horizontal hierarchy of cancer stem cells" to distinguish them from vertical hierarchy cancer stem cells, cancer transient-amplifying cells and cancer differentiated cells, and summarize our current understanding of these subsets of cancer stem cells with the aim to open up novel therapeutic strategies for cancer based on this understanding.

The coefficients of individual agreement (CIA’s), which are based on the ratio of the intra-and inter-observer disagreement, provide a general approach for evaluating agreement between two fixed methods of measurements or human observers. In this paper, programs in both SAS and R are presented for estimation of the CIA’s between two observers with quantitative or binary measurements. A detailed illustration of the computations, macro variable definitions, input and output for the SAS and R programs are also included in the text. The programs provide estimations of CIA’s, their standard errors as well as confidence intervals, for the cases with or without a reference method. Data from a carotid stenosis screening study is used as an example of quantitative measurements. Data from a study involving the evaluation of mammograms by ten radiologists is used to illustrate a binary data example.

Study Objective

To evaluate the pharmacokinetic compatibility of a ritonavir-boosted indinavir-fosamprenavir combination among patients with human immunodeficiency virus (HIV).

Design

Single-center, nonrandomized, prospective, multiple-dose, two-phase pharmacokinetic study.

Setting

University research center.

Patients

Eight adult patients with HIV infection who had been receiving and tolerating indinavir 800 mg–ritonavir 100 mg twice/day for at least 2 weeks.

Intervention

After 12-hour pharmacokinetic sampling was performed on all patients (period A), fosamprenavir (a prodrug of amprenavir) 700 mg twice/day was coadministered for 5 days, with a repeat 12-hour pharmacokinetic sampling performed on the fifth day (period B).

Measurements and Main Results

Pharmacokinetic parameters were determined for indinavir, ritonavir, and amprenavir: area under the concentration-time curve from time 0 to 12 hours after dosing (AUC0–12), maximum plasma concentration (Cmax), and 12-hour plasma concentration (C12). For each parameter, the geometric mean, as well as the geometric mean ratio (GMR) comparing period B with period A, were calculated. Indinavir Cmax was lowered by 20% (GMR 0.80, 95% confidence interval [CI] 0.67–0.96), AUC0–12 was lowered by 6% (GMR 0.94, 95% CI 0.74–1.21), and C12 was increased by 28% (GMR 1.28, 95% CI 0.78–2.10). Ritonavir AUC0–12 was 20% lower (GMR 0.80, 95% CI 0.54–1.19), Cmax was 15% lower (GMR 0.85, 95% CI 0.55–1.32), and C12 was 7% lower (GMR 0.93, 95% CI 0.49–1.76). With the exception of indinavir Cmax, the changes in indinavir and ritonavir pharmacokinetic parameters observed after fosamprenavir coadministration were not statistically significant. The geometric means of amprenavir AUC0–12, Cmax, and C12 were 41,517 ng•hour/ml (95% CI 30,317–56,854 ng•hr/ml), 5572 ng/ml (95% CI 4330–7170 ng/ml), and 2421 ng/ml (95% CI 1578–3712 ng/ml), respectively.

Conclusion

The combination of indinavir 800 mg–ritonavir 100 mg–fosamprenavir 700 mg twice/day appears to be devoid of a clinically significant drug-drug interaction and should be evaluated as an alternative regimen in salvage HIV treatment. This combination may be suitable as part of a background regimen to optimize the therapeutic benefit of newer classes of antiretroviral agents such as the integrase and coreceptor inhibitors in the treatment of multidrug-resistant viruses.