Bacterial secretome: the assembly manual and operating instructions (Review)

Bacterial protein secretion is a complex multi-stage reaction that is central to membrane and cell wall biosynthesis and essential for cell viability. An impressive array of experimental tools have been used to dissect this reaction into discreet sub-reactions. Synthesis of these data reveals a fascinating cascade of inter- and intra-molecular interactions that select, sort and target secretory polypeptides to the membrane and then spend metabolic energy to bias their vectorial movement across the membrane plane through a lipid-inaccessible proteinaceous environment. Transmembrane crossing is catalyzed by protein translocase, an astonishingly dynamic molecular machine. The unusual molecular features of the Sec pathway components allows a handful of proteins to catalyze the export of hundreds of secretory polypeptide substrates with astonishing fidelity. Knowledge of the molecular details of the secretion pathway allows us to rationally exploit these features in heterologous protein production biotechnologies and in the development of novel antibiotics.     

Bacterial secretome: the assembly manual and operating instructions (Review)

Bacterial protein secretion is a complex multi-stage reaction that is central to membrane and cell wall biosynthesis and essential for cell viability. An impressive array of experimental tools have been used to dissect this reaction into discreet sub-reactions. Synthesis of these data reveals a fascinating cascade of inter- and intra-molecular interactions that select, sort and target secretory polypeptides to the membrane and then spend metabolic energy to bias their vectorial movement across the membrane plane through a lipid-inaccessible proteinaceous environment. Transmembrane crossing is catalyzed by protein translocase, an astonishingly dynamic molecular machine. The unusual molecular features of the Sec pathway components allows a handful of proteins to catalyze the export of hundreds of secretory polypeptide substrates with astonishing fidelity. Knowledge of the molecular details of the secretion pathway allows us to rationally exploit these features in heterologous protein production biotechnologies and in the development of novel antibiotics.     

From seed to seed: the role of microbial inheritance in the assembly of the plant microbiome

Despite evidence that the microbiome extends host genetic and phenotypic traits, information on how the microbiome is transmitted and maintained across generations remains fragmented. For seed-bearing plants, seeds harbor a distinct microbiome and play a unique role by linking one generation to the next. Studies on microbial inheritance, a process we suggest including both vertical transmission and the subsequent migration of seed microorganisms to the new plant, thus become essential for our understanding of host evolutionary potential and host–microbiome coevolution. We propose dividing the inheritance process into three stages: (i) plant to seed, (ii) seed dormancy, and (iii) seed to seedling. We discuss the factors affecting the assembly of the microbiome during the three stages, highlight future research directions, and emphasize the implications of microbial inheritance for fundamental science and society.     

The root microbiome: Community assembly and its contributions to plant fitness

The root microbiome refers to the community of microbes living in association with a plant's roots, and includes mutualists, pathogens, and commensals. Here we focus on recent advances in the study of root commensal community which is the major research object of microbiome-related researches. With the rapid development of new technologies, plant–commensal interactions can be explored with unprecedented breadth and depth. Both the soil environment and the host plant drive commensal community assembly. The bulk soil is the seed bank of potential commensals, and plants use root exudates and immune responses to build healthy microbial communities from the available microbes. The plant microbiome extends the functional system of plants by participating in a variety of processes, including nutrient absorption, growth promotion, and resistance to biotic and abiotic stresses. Plants and their microbiomes have evolved adaptation strategies over time. However, there is still a huge gap in our understanding of the regulatory mechanisms of plant–commensal interactions. In this review, we summarize recent research on the assembly of root microbial communities and the effects of these communities on plant growth and development, and look at the prospects for promoting sustainable agricultural development through the study of the root microbiome.     

Assisted assembly: how to improve a de novo genome assembly by using related species

We describe a new assembly algorithm, where a genome assembly with low sequence coverage, either throughout the genome or locally, due to cloning bias, is considerably improved through an assisting process via a related genome. We show that the information provided by aligning the whole-genome shotgun reads of the target against a reference genome can be used to substantially improve the quality of the resulting assembly.     

From infection to cancer: how DNA tumour viruses alter host cell central carbon and lipid metabolism

Infections cause 13% of all cancers globally, and DNA tumour viruses account for almost 60% of these cancers. All viruses are obligate intracellular parasites and hijack host cell functions to replicate and complete their life cycles to produce progeny virions. While many aspects of viral manipulation of host cells have been studied, how DNA tumour viruses manipulate host cell metabolism and whether metabolic alterations in the virus life cycle contribute to carcinogenesis are not well understood. In this review, we compare the differences in central carbon and fatty acid metabolism in host cells following infection, oncogenic transformation, and virus-driven cancer of DNA tumour viruses including: Epstein–Barr virus, hepatitis B virus, human papillomavirus, Kaposi''s sarcoma-associated herpesvirus and Merkel cell polyomavirus.     

Molecular motors: how to make models that can be used to convey the concept of molecular ratchets and thermal capture

A wide variety of cellular processes use molecular motors, including processive motors that move along some form of track (e.g., myosin with actin, kinesin or dynein with tubulin) and polymerases that move along a template (e.g., DNA and RNA polymerases, ribosomes). In trying to understand how these molecular motors actually move, many apply their understanding of how man-made motors work: the latter use some form of energy to exert a force or torque on its load. However, quite a different mechanism has been proposed to possibly account for the movement of molecular motors. Rather than hydrolyzing ATP to push or pull their load, they might use their own thermal vibrational energy as well as that of their load and their environment to move the load, capturing those movements that occur along a desired vector or axis and resisting others; ATP hydrolysis is required to make backward movements impossible. This intriguing thermal capture or Brownian ratchet model is relatively more difficult to convey to students. In this report, we describe several teaching aids that are very easily constructed using widely available household materials to convey the concept of a molecular ratchet.     

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.     

From pore to kinetochore and back: regulating envelope assembly

Reassembly of the nuclear envelope following mitosis is a fundamental process that remains only partially understood. Two recent reports by and in the September 5 issue of Current Biology identify a novel protein, MEL-28, that shuttles between the nuclear pore complex and kinetochore and is essential for envelope assembly in C. elegans early embryos.     

From diversity indices to community assembly processes: a test with simulated data

Ecological theory suggests that spatial distribution of biodiversity is strongly driven by community assembly processes. Thus the study of diversity patterns combined with null model testing has become increasingly common to infer assembly processes from observed distributions of diversity indices. However, results in both empirical and simulation studies are inconsistent. The aim of our study is to determine with simulated data which facets of biodiversity, if any, may unravel the processes driving its spatial patterns, and to provide practical considerations about the combination of diversity indices that would produce significant and congruent signals when using null models. The study is based on simulated species’ assemblages that emerge under various landscape structures in a spatially explicit individual‐based model with contrasting, predefined assembly processes. We focus on four assembly processes (species‐sorting, mass effect, neutral dynamics and competition colonization trade‐off) and investigate the emerging species’ distributions with varied diversity indices (alpha, beta and gamma) measured at different spatial scales and for different diversity facets (taxonomic, functional and phylogenetic). We find that 1) the four assembly processes result in distinct spatial distributions of species under any landscape structure, 2) a broad range of diversity indices allows distinguishing between communities driven by different assembly processes, 3) null models provide congruent results only for a small fraction of diversity indices and 4) only a combination of these diversity indices allows identifying the correct assembly processes. Our study supports the inference of assembly processes from patterns of diversity only when different types of indices are combined. It highlights the need to combine phylogenetic, functional and taxonomic diversity indices at multiple spatial scales to effectively infer underlying assembly processes from diversity patterns by illustrating how combination of different indices might help disentangling the complex question of coexistence.     

Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.