An Efficient Proteomic Approach to Analyze Agriculture Crop Biomass

While a plant cell wall is formed by a complex of various components, including polysaccharides and structural proteins, its composition and representation may vary during cell growth. Currently, plant research targets the proteins participating in wall loosening. Multiple classes of enzymes, including various hemicellulases and cellulases, are required for plant material degradation to achieve the maximum decomposition. Identifying the set of proteins involved in the breakdown of cell-wall polymers is important to understand plant material conversion into suitable products. The objective of this study was to describe a method which can be used to carry out proteomics analysis of complex plant samples and identify enzymes degrading biomass. For this purpose we used proteomic techniques including gel electrophoresis, high pressure liquid chromatography combinated with mass spectrometry followed by data evaluation using databases searching. Results show that more than 50 % of these activities correspond to enzymes with proteolytic function. This study was focused primarily on enzymes able to breakdown the lignocellulosic and hemicellulosic parts that are very important for the material conversion into required products of degradation.     

A Proteomic Approach to Analyze the Aspirin-mediated Lysine Acetylome

Aspirin, or acetylsalicylic acid is widely used to control pain, inflammation and fever. Important to this function is its ability to irreversibly acetylate cyclooxygenases at active site serines. Aspirin has the potential to acetylate other amino acid side-chains, leading to the possibility that aspirin-mediated lysine acetylation could explain some of its as-yet unexplained drug actions or side-effects. Using isotopically labeled aspirin-d₃, in combination with acetylated lysine purification and LC-MS/MS, we identified over 12000 sites of lysine acetylation from cultured human cells. Although aspirin amplifies endogenous acetylation signals at the majority of detectable endogenous sites, cells tolerate aspirin mediated acetylation very well unless cellular deacetylases are inhibited. Although most endogenous acetylations are amplified by orders of magnitude, lysine acetylation site occupancies remain very low even after high doses of aspirin. This work shows that while aspirin has enormous potential to alter protein function, in the majority of cases aspirin-mediated acetylations do not accumulate to levels likely to elicit biological effects. These findings are consistent with an emerging model for cellular acetylation whereby stoichiometry correlates with biological relevance, and deacetylases act to minimize the biological consequences of nonspecific chemical acetylations.     

Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets

The advances in proteomics technologies offer an unprecedented opportunity and valuable resources to understand how living organisms execute necessary functions at systems levels. However, little work has been done up to date to utilize the highly accurate spatio-temporal dynamic proteome data generated by phosphoprotemics for mathematical modeling of complex cell signaling pathways. This work proposed a novel computational framework to develop mathematical models based on proteomic datasets. Using the MAP kinase pathway as the test system, we developed a mathematical model including the cytosolic and nuclear subsystems; and applied the genetic algorithm to infer unknown model parameters. Robustness property of the mathematical model was used as a criterion to select the appropriate rate constants from the estimated candidates. Quantitative information regarding the absolute protein concentrations was used to refine the mathematical model. We have demonstrated that the incorporation of more experimental data could significantly enhance both the simulation accuracy and robustness property of the proposed model. In addition, we used the MAP kinase pathway inhibited by phosphatases with different concentrations to predict the signal output influenced by different cellular conditions. Our predictions are in good agreement with the experimental observations when the MAP kinase pathway was inhibited by phosphatase PP2A and MKP3. The successful application of the proposed modeling framework to the MAP kinase pathway suggests that our method is very promising for developing accurate mathematical models and yielding insights into the regulatory mechanisms of complex cell signaling pathways.     

光敏色素互作因子PIFs是整合多种信号调控植物生长发育的核心元件

光敏色素互作因子（PIFs）蛋白家族隶属于bHLH转录因子家族,参与多种信号转导途径,调控植物的生长发育进程,如抑制种子萌发、促进幼苗的暗形态建成和调控开花时间等。作为一个胞内信号调控的重要组分,PIFs广泛参与到由多种植物内源激素如赤霉素、乙烯、生长素、油菜素内酯、脱落酸和外部环境因素如高温、光等所介导的信号网络中。本文AKPIFs的结构、参与途径及调控植物发育进程3个方面,简要介绍近年来国内外对PIFs在信号网络调控方面的最新研究进展。     

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.     

利用双标图分析作物区试数据

品种区试中品种的基因型和环境存在着交互作用（G×E）,使区试数据的分析比较棘手．主效可加和互作可乘模型（简称AMMI模型）是分析区试数据的一新类模型,与常规的方差分析模型和线性回归模型相比,这一方法应用范围广而且更有效．而双标图（biplot）不仅是解释AMMI分析结果的直观有效的工具,而且可以帮助我们选择分析数据的合适模型．本文以95年南方水稻区试单季晚粳组数据为例,对AMMI模型及双标图作一扼要介绍,给出双标图的一些新的性质和用途,和一种可用于产量预测的双标图.     

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher''s experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.     

FASMA: A Service to Format and Analyze Sequences in Multiple Alignments

Multiple sequence alignments are successfully applied in many studies for under- standing the structural and functional relations among single nucleic acids and pro- tein sequences as well as whole families. Because of the rapid growth of sequence databases, multiple sequence alignments can often be very large and difficult to visualize and analyze. We offer a new service aimed to visualize and analyze the multiple alignments obtained with different external algorithms, with new features useful for the comparison of the aligned sequences as well as for the creation of a final image of the alignment. The service is named FASMA and is available at http: //bioinformatica.isa.cnr.it /FASMA /.     

FASMA： A Service to Format and Analyze Sequences in Multiple Alignments

Multiple sequence alignments are successfully applied in many studies for under- standing the structural and functional relations among single nucleic acids and pro- tein sequences as well as whole families. Because of the rapid growth of sequence databases, multiple sequence alignments can often be very large and difficult to visualize and analyze. We offer a new service aimed to visualize and analyze the multiple alignments obtained with different external algorithms, with new features useful for the comparison of the aligned sequences as well as for the creation of a final image of the alignment. The service is named FASMA and is available at http: //bioinformatica.isa.cnr.it /FASMA /.     

Proteomic Approaches to Decipher Mechanisms Underlying Pathogenesis in Multiple Sclerosis Patients

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS). The cause of MS is unknown, with no effective therapies available to halt the progressive neurological disability. Development of new and improvement of existing therapeutic strategies therefore require a better understanding of MS pathogenesis, especially during the progressive phase of the disease. This can be achieved through development of biomarkers that can help to identify disease pathophysiology and monitor disease progression. Proteomics is a powerful and promising tool to accelerate biomarker detection and contribute to novel therapeutics. In this review, an overview of how proteomic technology using CNS tissues and biofluids from MS patients has provided important clues to the pathogenesis of MS is provided. Current publications, pitfalls, as well as directions of future research involving proteomic approaches to understand the pathogenesis of MS are discussed.     

Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models

Summary .  Regression models are often used to test for cause-effect relationships from data collected in randomized trials or experiments. This practice has deservedly come under heavy scrutiny, because commonly used models such as linear and logistic regression will often not capture the actual relationships between variables, and incorrectly specified models potentially lead to incorrect conclusions. In this article, we focus on hypothesis tests of whether the treatment given in a randomized trial has any effect on the mean of the primary outcome, within strata of baseline variables such as age, sex, and health status. Our primary concern is ensuring that such hypothesis tests have correct type I error for large samples. Our main result is that for a surprisingly large class of commonly used regression models, standard regression-based hypothesis tests (but using robust variance estimators) are guaranteed to have correct type I error for large samples, even when the models are incorrectly specified. To the best of our knowledge, this robustness of such model-based hypothesis tests to incorrectly specified models was previously unknown for Poisson regression models and for other commonly used models we consider. Our results have practical implications for understanding the reliability of commonly used, model-based tests for analyzing randomized trials.     

On the Mechanistic Derivation of Various Discrete-Time Population Models

We present a derivation of various discrete-time population models within a single unifying mechanistic context. By systematically varying the within-year patterns of reproduction and aggression between individuals we can derive various discrete-time population models. These models include classical examples such as the Ricker model, the Beverton-Holt model, the Skellam model, the Hassell model, and others. Some of these models until now lacked a good mechanistic interpretation or have been derived in a different context. By using this mechanistic approach, the model parameters can be interpreted in terms of individual behavior.     

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing(sc RNA-seq)analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in sc RNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model(sc LM), a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, sc LM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. sc LM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that sc LM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of sc LM, we apply it to our in-house and public experimental sc RNA-seq datasets. sc LM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the sc LM method is available at https://github.com/QSong-github/sc LM.     

On the Use of Ripley's K-Function and Its Derivatives to Analyze Domain Size

Ripley's K-, H-, and L-functions are used increasingly to identify clustering of proteins in membrane microdomains. In this approach, aggregation (or clustering) is identified if the average number of proteins within a distance r of another protein is statistically greater than that expected for a random distribution. However, it is not entirely clear how the function may be used to quantitatively determine the size of domains in which clustering occurs. Here, we evaluate the extent to which the domain radius can be determined by different interpretations of Ripley's K-statistic in a theoretical, idealized context. We also evaluate the measures for noisy experimental data and use Monte Carlo simulations to separate the effects of different types of experimental noise. We find that the radius of maximal aggregation approximates the domain radius, while identifying the domain boundary with the minimum of the derivative of H(r) is highly accurate in idealized conditions. The accuracy of both measures is impacted by the noise present in experimental data; for example, here, the presence of a large fraction of particles distributed as monomers and interdomain interactions. These findings help to delineate the limitations and potential of Ripley's K in real-life scenarios.     

The Use of Mouse Models to Study Epigenetics

Much of what we know about the role of epigenetics in the determination of phenotype has come from studies of inbred mice. Some unusual expression patterns arising from endogenous and transgenic murine alleles, such as the Agouti coat color alleles, have allowed the study of variegation, variable expressivity, transgenerational epigenetic inheritance, parent-of-origin effects, and position effects. These phenomena have taught us much about gene silencing and the probabilistic nature of epigenetic processes. Based on some of these alleles, large-scale mutagenesis screens have broadened our knowledge of epigenetic control by identifying and characterizing novel genes involved in these processes.