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The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The "shock-and-kill" strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV- infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat-transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the "block and lock" strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks. [ABSTRACT FROM AUTHOR]

The discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state space by O(n!), exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11- node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks. [ABSTRACT FROM AUTHOR]

There is a concern of whether the structure and diversity of a microbial community can be effectively revealed by short-length pyrosequencing reads. In this study, we performed a microbial community analysis on a sample from a high-efficiency denitrifying quinoline-degrading bioreactor and compared the results generated by pyrosequencing with those generated by clone library technology. By both technologies, 16S rRNA gene analysis indicated that the bacteria in the sample were closely related to, for example, Proteobacteria, Actinobacteria, and Bacteroidetes. The sequences belonging to Rhodococcus were the most predominant, and Pseudomonas, Sphingomonas, Acidovorax, and Zoogloea were also abundant. Both methods revealed a similar overall bacterial community structure. However, the 622 pyrosequencing reads of the hypervariable V3 region of the 16S rRNA gene revealed much higher bacterial diversity than the 130 sequences from the full-length 16S rRNA gene clone library. The 92 operational taxonomic unit (OTUs) detected using pyrosequencing belonged to 45 families, whereas the 37 OTUs found in the clone library belonged to 25 families. Most sequences obtained from the clone library had equivalents in the pyrosequencing reads. However, 64 OTUs detected by pyrosequencing were not represented in the clone library. Our results demonstrate that pyrosequencing of the V3 region of the 16S rRNA gene is not only a powerful tool for discovering low-abundance bacterial populations but is also reliable for dissecting the bacterial community structure in a wastewater environment. [ABSTRACT FROM AUTHOR]

Both genetic variations and diet-disrupted gut microbiota can predispose animals to metabolic syndromes (MS). This study assessed the relative contributions of host genetics and diet in shaping the gut microbiota and modulating MS-relevant phenotypes in mice. Together with its wild-type (Wt) counterpart, the Apoa-I knockout mouse, which has impaired glucose tolerance (IGT) and increased body fat, was fed a high-fat diet (HFD) or normal chow (NC) diet for 25 weeks. DNA fingerprinting and bar-coded pyrosequencing of 16S rRNA genes were used to profile gut microbiota structures and to identify the key population changes relevant to MS development by Partial Least Square Discriminate Analysis. Diet changes explained 57% of the total structural variation in gut microbiota, whereas genetic mutation accounted for no more than 12%. All three groups with IGT had significantly different gut microbiota relative to healthy Wt/NC-fed animals. In all, 65 species-level phylotypes were identified as key members with differential responses to changes in diet, genotype and MS phenotype. Most notably, gut barrier-protecting Bifidobacterium spp. were nearly absent in all animals on HFD, regardless of genotype. Sulphate-reducing, endotoxin-producing bacteria of the family, Desulfovibrionaceae, were enhanced in all animals with IGT, most significantly in the Wt/HFD group, which had the highest calorie intake and the most serious MS phenotypes. Thus, diet has a dominating role in shaping gut microbiota and changes of some key populations may transform the gut microbiota of Wt animals into a pathogen-like entity relevant to development of MS, despite a complete host genome. [ABSTRACT FROM AUTHOR]

Background: Stochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 µM to 10nM (about 100 to 10 copies in a cell). The chemical master equation provides a fundamental framework for studying these networks, and the time-varying landscape probability distribution over the full microstates, i.e., the combination of copy numbers of molecular species, provide a full characterization of the network dynamics. A complete characterization of the space of the microstates is a prerequisite for obtaining the full landscape probability distribution of a network. However, there are neither closed-form solutions nor algorithms fully describing all microstates for a given molecular network. Results: We have developed an algorithm that can exhaustively enumerate the microstates of a molecular network of small copy numbers under the condition that the net gain in newly synthesized molecules is smaller than a predefined limit. We also describe a simple method for computing the exact mean or steady state landscape probability distribution over microstates. We show how the full landscape probability for the gene networks of the self-regulating gene and the toggle-switch in the steady state can be fully characterized. We also give an example using the MAPK cascade network. Data and server will be available at URL: http://scsb.sjtu.edu.cn/statespace. Conclusion: Our algorithm works for networks of small copy numbers buffered with a finite copy number of net molecules that can be synthesized, regardless of the reaction stoichiometry, and is optimal in both storage and time complexity. The algorithm can also be used to calculate the rates of all transitions between microstates from given reactions and reaction rates. The buffer size is limited by the available memory or disk storage. Our algorithm is applicable to a class of biological networks when the copy numbers of molecules are small and the network is closed, or the network is open but the net gain in newly synthesized molecules does not exceed a predefined buffer capacity. For these networks, our method allows full stochastic characterization of the mean landscape probability distribution, and the steady state when it exists. [ABSTRACT FROM AUTHOR]

GbERF belongs to the ERF (ethylene responsive factor) family of transcription factors and regulates the GCC-box containing pathogen-related (PR) genes in the ethylene signal transduction pathway. To study the function of GbERF in the process of biotic stress, transgenic tobacco plants expressing GbERF were generated. Overexpression of GbERF did not change transgenic plant's phenotype and endogenous ethylene level. However, the expression profile of some ethylene-inducible GCC-box and non-GCC-box containing genes was altered, such as PR1b, PR2, PR3, PR4, Osmotin, CHN50, ACC oxidase and ACC synthase genes. These data indicate that the cotton GbERF could act as a transcriptional activator or repressor to regulate the differential expression of ethylene-inducible genes via GCC and non-GCC cis-elements. Moreover, the constitutive expression of GbERF in transgenic tobacco enhanced the plant's resistance to Pseudomonas syringae pv tabaci infection. In conclusion, GbERF mediates the expression of a wide array of PR and ethylene-responsive genes and plays an important role in the plant's response to biotic stress. [ABSTRACT FROM AUTHOR]

Background: A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system. After reducing the decision system, decision rules are generated, which can be used to classify new objects. Results: In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729-738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches. Conclusion: The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics. [ABSTRACT FROM AUTHOR]

Background: A new algorithm for assessing similarity between primer and template has been developed based on the hypothesis that annealing of primer to template is an information transfer process. Results: Primer sequence is converted to a vector of the full potential hydrogen numbers (3 for G or C, 2 for A or T), while template sequence is converted to a vector of the actual hydrogen bond numbers formed after primer annealing. The former is considered as source information and the latter destination information. An information coefficient is calculated as a measure for fidelity of this information transfer process and thus a measure of similarity between primer and potential annealing site on template. Conclusion: Successful prediction of PCR products from whole genomic sequences with a computer program based on the algorithm demonstrated the potential of this new algorithm in areas like in silico PCR and gene finding. [ABSTRACT FROM AUTHOR]

A new stress-responsive gene BoRS1 (GenBank accession No.) was isolated from Brassica oleracea var. acephala by rapid amplification of cDNA ends (RACE). The full-length cDNA of BoRS1 was 2076 bp and contained a 1851 bp open reading frame (ORF) encoding 617 amino acids. Sequence analysis indicated that the deduced BoRS1 shared some identities with , RD29A, RD29B and from Arabidopsis thaliana. Southern blot analysis of genomic DNA indicated that other related genes existed and there were two copies of BoRS1 in the genome of B. oleracea. Northern blot analysis revealed that BoRS1 was up-regulated by cold, mannitol, NaCl and abscisic acid (ABA). Expressional fluctuation of time course with ABA implied a two-step induction process. Tissue-specific expression analysis indicated that BoRS1 was expressed in all the tested plant tissues including leaves, stems and roots. Our studies imply that BoRS1 is a new gene that is responsive to environmental stresses such as low temperature, salinity and osmotic stress. [ABSTRACT FROM AUTHOR]

A full-length cDNA clone with high homology with Brassica napus peptide methionine sulfoxide reductase (PMSR) gene ( BnPMSR ), which could reverse oxidation of methionine residues into MetSO in protein, was cloned by suppression subtraction hybridization and cDNA library screening from Gossypium barbadense . This gene (named as cMsrA ; Accession number: AY224208) had a total length of 1059 bp with an open reading frame of 765 bp, and encoded a predicted polypeptide of 255 amino acids with a molecular weight of 28.4 kDa. The cMsrA protein shared 68.6, 66.3 and 65.8% identity with those PMSR proteins isolated from B. napus , A. thaliana and L. sativa respectively. Expression patterns revealed that cMsrA was enriched later and weaker in resistant variety 7124 ( Gossypium barbadense ) than sensitive variety Ejing-1 ( G. hirsutum ) under salt treatment and pathogens attack. These results indicated that cMsrA played an important role in protecting the cells against oxidative damage during the pathogens and salt stress. [ABSTRACT FROM AUTHOR]

A correction to the article "Interactions Between Gut Microbiota, Host Genetics & Diet Relevant to Development of Metabolic Syndromes in Mice" that was published in the October 29, 2009 issue is presented.