Youfang Cao

Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice.

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.

Prediction of protein structural class with Rough Sets

BackgroundA 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.ResultsIn 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.ConclusionThe 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.

Probability landscape of heritable and robust epigenetic state of lysogeny in phage lambda.

Computational studies of biological networks can help to identify components and wirings responsible for observed phenotypes. However, studying stochastic networks controlling many biological processes is challenging. Similar to Schrödinger’s equation in quantum mechanics, the chemical master equation (CME) provides a basic framework for understanding stochastic networks. However, except for simple problems, the CME cannot be solved analytically. Here we use a method called discrete chemical master equation (dCME) to compute directly the full steady-state probability landscape of the lysogeny maintenance network in phage lambda from its CME. Results show that wild-type phage lambda can maintain a constant level of repressor over a wide range of repressor degradation rate and is stable against UV irradiation, ensuring heritability of the lysogenic state. Furthermore, it can switch efficiently to the lytic state once repressor degradation increases past a high threshold by a small amount. We find that beyond bistability and nonlinear dimerization, cooperativity between repressors bound to OR1 and OR2 is required for stable and heritable epigenetic state of lysogeny that can switch efficiently. Mutants of phage lambda lack stability and do not possess a high threshold. Instead, they are leaky and respond to gradual changes in degradation rate. Our computation faithfully reproduces the hair triggers for UV-induced lysis observed in mutants and the limitation in robustness against mutations. The landscape approach computed from dCME is general and can be applied to study broad issues in systems biology.

Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability

BackgroundStochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 μ M to 10n M (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.ResultsWe 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.ConclusionOur 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.

A diverse bacterial community in an anoxic quinoline-degrading bioreactor determined by using pyrosequencing and clone library analysis

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.

Overexpression ofGbERF confers alteration of ethylene-responsive gene expression and enhanced resistance toPseudomonas syringae in transgenic tobacco

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 expressingGbERF were generated. Overexpression ofGbERF 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 asPR1b, 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-GCCcis-elements. Moreover, the constitutive expression ofGbERF in transgenic tobacco enhanced the plant’s resistance toPseudomonas syringae pvtabaci infection. In conclusion,GbERF mediates the expression of a wide array ofPR and ethylene-responsive genes and plays an important role in the plant’s response to biotic stress.

Information theory-based algorithm for in silico prediction of PCR products with whole genomic sequences as templates

BackgroundA 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.ResultsPrimer 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.ConclusionSuccessful 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.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Modeling spatial population dynamics of stem cell lineage in tissue growth

Understanding the dynamics of cell population allows insight into the control mechanism of the growth and development of mammalian tissues. It is well known that the proliferation and differentiation among stem cells (SCs), intermediate progenitor cells (IPCs), and fully differentiated cells (FDCs) are under different activation and inhibition controls. Secreted factors in negative feedback loops have already been identified as major elements in regulating the numbers of different cell types and in maintaining the equilibrium of cell populations. We have developed a novel spatial dynamic model of cells. We can characterize not only overall cell population dynamics, but also details of temporal-spatial relationship of individual cells within a tissue. In our model, the shape, growth, and division of each cell are modeled using a realistic geometric model. Furthermore, the inhibited growth rate, proliferation and differentiation probabilities of individual cells are modeled through feedback loops controlled by secreted factors of neighboring cells within a proper diffusion radius. With specific proliferation and differentiation probabilities, the actual division type that each cell will take is chosen by a Monte Carlo sampling process. With simulations we found that with proper strengths of inhibitions to growth and stem cell divisions, the whole tissue is capable of achieving a homeostatic size control. We discuss our findings on control mechanisms of the stability of the tissue development. Our model can be applied to study broad issues on tissue development and pattern formation in stem cell and cancer research.

Stochastic probability landscape model for switching efficiency, robustness, and differential threshold for induction of genetic circuit in phage λ

The genetic switch of phage lambda provides a paradigm for studying developmental biology and cell fate. Although there have been numerous experimental and theoretical studies, the mechanisms for switching efficiency, stability and robustness, maintenance of lysogenic state, and the induction lytic state are not fully understood. In this paper, a new method is adopted to account for the full stochasticity typical in small copy events and the exact steady state probability landscape of the genetic circuit of the switch network is computed. The stability and sensitivity of phage λ switch and its robustness against perturbations derived from our model for wild type and mutants are consistent with experimental data. Our study has revealed likely mechanism of the phage switch network that was previously unknown.