Metabolic specialization and the assembly of microbial communities

Metabolic specialization is a general biological principle that shapes the assembly of microbial communities. Individual cell types rarely metabolize a wide range of substrates within their environment. Instead, different cell types often specialize at metabolizing only subsets of the available substrates. What is the advantage of metabolizing subsets of the available substrates rather than all of them? In this perspective piece, we argue that biochemical conflicts between different metabolic processes can promote metabolic specialization and that a better understanding of these conflicts is therefore important for revealing the general principles and rules that govern the assembly of microbial communities. We first discuss three types of biochemical conflicts that could promote metabolic specialization. Next, we demonstrate how knowledge about the consequences of biochemical conflicts can be used to predict whether different metabolic processes are likely to be performed by the same cell type or by different cell types. We then discuss the major challenges in identifying and assessing biochemical conflicts between different metabolic processes and propose several approaches for their measurement. Finally, we argue that a deeper understanding of the biochemical causes of metabolic specialization could serve as a foundation for the field of synthetic ecology, where the objective would be to rationally engineer the assembly of a microbial community to perform a desired biotransformation.     

Composite functional module inference: detecting cooperation between transcriptional regulation and protein interaction by mantel test

Background  

Functional modules are basic units of cell function, and exploring them is important for understanding the organization, regulation and execution of cell processes. Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies. Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes.     

Problems with dimensionless measurement models of synchrony in biological systems

Synchrony is surprisingly complex even in the simplest cases. One strategy for simplifying complex phenomena is to define a dimensionless measurement model with the aim of (1) finding order, (2) comparing complex phenomena, and (3) making decisions about statistical significance. However, a model is only as good as its assumptions. In this paper, several types of dimensionless measurement models of synchrony among biological states are evaluated using the preceding criteria. These dimensionless measurement models are found to be inadequate even in the simplest cases of N individuals cycling through k non-overlapping states. Moreover, independent of their adequacy as measures of synchrony, there is the additional problem of the applicability of biological-state measurement models to rhythmic biological processes. Biological states are often just quantized observations of the phases of rhythmic biological processes. With the help of a concrete example, it is shown that quantizing the phases of a process into discrete states can lead to serious errors. These conclusions do not imply that the study of synchrony in biological systems is intractable. There are statistical approaches for detecting synchrony in groups and researchers are making progress towards understanding the general mechanisms of rhythmic phenomena in biological systems. Am. J. Primatol. 41:65–85, 1997. © 1997 Wiley-Liss, Inc.     

Seasonal shifts in the assembly dynamics of benthic macroinvertebrate and diatom communities in a subtropical river

Identifying seasonal shifts in community assembly for multiple biological groups is important to help enhance our understanding of their ecological dynamics. However, such knowledge on lotic assemblages is still limited. In this study, we used biological traits and functional diversity indices in association with null model analyses to detect seasonal shifts in the community assembly mechanisms of lotic macroinvertebrates and diatoms in an unregulated subtropical river in China. We found that functional composition and functional diversity (FRic, FEve, FDis, MNN, and SDNN) showed seasonal variation for macroinvertebrate and diatom assemblages. Null models suggested that environmental filtering, competitive exclusion, and neutral process were all important community assembly mechanisms for both biological groups. However, environmental filtering had a stronger effect on spring macroinvertebrate assemblages than autumn assemblages, but the effect on diatom assemblages was the same in both seasons. Moreover, macroinvertebrate and diatom assemblages were shaped by different environmental factors. Macroinvertebrates were filtered mainly by substrate types, velocity, and COD_Mn, while diatoms were mainly shaped by altitude, substrate types, and water quality. Therefore, our study showed (a) that different biological assemblages in a river system presented similarities and differences in community assembly mechanisms, (b) that multiple processes play important roles in maintaining benthic community structure, and (c) that these patterns and underlying mechanisms are seasonally variable. Thus, we highlight the importance of exploring the community assembly mechanisms of multiple biological groups, especially in different seasons, as this is crucial to improve the understanding of river community changes and their responses to environmental degradation.     

Genome-wide prediction of genetic interactions in a metazoan

Genetic interactions provide information about genes and processes with overlapping functions in biological systems. For Saccharomyces cerevisiae, computational integration of multiple types of functional genomic data is used to generate genome-wide predictions of genetic interactions. However, this methodology cannot be applied to the vastly more complex genome of metazoans, and only recently has the first metazoan genome-wide prediction of genetic interactions been reported. The prediction for Caenorhabditis elegans was generated by computationally integrating functional genomic data from S. cerevisiae, C. elegans and Drosophila melanogaster. This achievement is an important step toward system-level understanding of biological systems and human diseases.     

Crystal Structure of a Periplasmic Substrate-Binding Protein in Complex with Calcium Lactate

Lactate is utilized in many biological processes, and its transport across biological membranes is mediated with various types of transporters. Here, we report the crystal structures of a lactate-binding protein of a TRAP (tripartite ATP-independent periplasmic) secondary transporter from Thermus thermophilus HB8. The folding of the protein is typical for a type II periplasmic solute-binding protein and forms a dimer in a back-to-back manner. One molecule of l-lactate is clearly identified in a cleft of the protein as a complex with a calcium ion. Detailed crystallographic and biochemical analyses revealed that the calcium ion can be removed from the protein and replaced with other divalent cations. This characterization of the structure of a protein binding with calcium lactate makes a significant contribution to our understanding of the mechanisms by which calcium and lactate are accommodated in cells.     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.     

Overcoming the Effect of the Socio-cultural Context: Impact of Teaching Evolution in Tunisia

In Tunisia, even though it is an Arab-Muslim country, the teaching of evolution is not forbidden. Nevertheless, the Muslim perspective makes learning about the biological basis of evolution difficult because of the harmony that exists between religion and science. Tunisian students have a mixed misconception: They explain the diversity of life as both a result of God’s works and a result of evolutionary processes at the same time. This paper presents the external evaluation that assesses the impact of an approach to teaching evolution designed to help students distinguish between theological and biological (scientific) explanations. The comparative analysis between the outcomes of the pre- and post-teaching interviews shows some success in helping students to distinguish between the two types of arguments and to develop better understanding of evolution as scientific knowledge.     

The molecular action of tumor necrosis factor-alpha.

Tumor necrosis factor-alpha (TNF-alpha) is a polypeptide hormone newly synthesized by different cell types upon stimulation with endotoxin, inflammatory mediators (C5a anaphylatoxin), or cytokines such as interleukin-1 and, in an autocrine manner, TNF itself. The net biological effect of TNF-alpha may vary depending on relative concentration, duration of cell exposure and presence of other mediators which may act in synergism with this cytokine. TNF-alpha may be relevant either in pathological events occurring in cachexia and endotoxic shock and inflammation or in beneficial processes such as host defense, immunity and tissue homeostasis. The biological effects of TNF-alpha are triggered by the binding to specific cell surface receptors. The formation of TNF-alpha-receptor complex activates a variety of biochemical pathways that include the transduction of the signal at least in part controlled by guanine-nucleotide-binding regulatory proteins (G proteins), its amplification through activation of adenyl cyclase, phospholipases and protein kinases with the generation of second messenger pathways. The transduction of selected genes in different cell types determines the characteristics of the cell response to TNF-alpha. The full understanding of the molecular mechanisms of TNF-alpha will provide the basis for a pharmacological approach intended to inhibit or potentiate selected biological actions of this cytokine.     

微生物产氢研究的进展

氢能由于其清洁、高效、可再生的特点而成为一种最有吸引力的化石燃料的替代能源。与传统的热化学和电化学制氢技术相比,生物制氢具有低能耗、少污染等特点。本文主要对各种微生物的生物产氢方法作一综合概括,着重介绍光合紫色非硫细菌(PNS)产氢研究的最新进展。     

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes

Increasingly, various ‘omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new ‘omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.     

Mitochondrial quality control: an integrated network of pathways

Mitochondria are organelles of eukaryotic cells with various functions. Best known is their role in energy transduction leading to the formation of ATP. As byproducts of this process, reactive oxygen species (ROS) are formed that can damage different types of molecules leading to mitochondrial dysfunction. Different quality control (QC) mechanisms keep mitochondria functional. Although several components involved in mitochondrial QC have been characterized in some detail, others remain to be integrated into what is currently emerging as a hierarchical network of interacting pathways. The elucidation of this network holds the key to the understanding of complex biological processes such as aging and the development of age-related diseases.     

植物组蛋白变体生物学功能

组蛋白变体是重要的表观遗传调控因子,能够在染色质特定位置替换常规组蛋白,维持染色质结构进而保证转录激活或抑制的顺利进行.目前,组蛋白变体的调控功能已成为植物学研究领域的一个热点.近年来,随着植物组蛋白变体生物学功能研究的不断深入,发现组蛋白变体能够在植物生长发育和环境应答调控等多个生物学过程中发挥重要作用.该文简要介绍...     

Statistical analysis and parsimonious modelling of dendrograms of in vitro neurones

The processes whereby developing neurones acquire morphological features that are common to entire populations (thereby allowing the definition of neuronal types) are still poorly understood. A mathematical model of neuronal arborizations may be useful to extract basic parameters or organization rules, hence helping to achieve a better understanding of the underlying growth processes. We present a parsimonious statistical model, intended to describe the topological organization of neuritic arborizations with a minimal number of parameters. It is based on a probability of splitting which depends only on the centrifugal order of segments. We compare the predictions made by the model of several topological properties of neurones with the corresponding actual values measured on a sample of honeybee (olfactory) antennal lobe neurones grown in primary culture, described in a previous study. The comparison is performed for three populations of segments corresponding to three neuronal morphological types previously identified and described in this sample. We show that simple assumptions together with the knowledge of a very small number of parameters allow the topological reconstruction of representative (bi-dimensional) biological neurones. We discuss the biological significance (in terms of possible factors involved in the determinism of neuronal types) of both common properties and cell-type specific features, observed on the neurones and predicted by the model. These authors contributed equally to this work.