Bacterial microevolution and the Pangenome

The comparison of multiple genome sequences sampled from a bacterial population reveals considerable diversity in both the core and the accessory parts of the pangenome. This diversity can be analysed in terms of microevolutionary events that took place since the genomes shared a common ancestor, especially deletion, duplication, and recombination. We review the basic modelling ingredients used implicitly or explicitly when performing such a pangenome analysis. In particular, we describe a basic neutral phylogenetic framework of bacterial pangenome microevolution, which is not incompatible with evaluating the role of natural selection. We survey the different ways in which pangenome data is summarised in order to be included in microevolutionary models, as well as the main methodological approaches that have been proposed to reconstruct pangenome microevolutionary history

Structure of a bacterial type III secretion system in contact with a host membrane in situ

Many bacterial pathogens of animals and plants use a conserved type III secretion system (T3SS) to inject virulence effector proteins directly into eukaryotic cells to subvert host functions. Contact with host membranes is critical for T3SS activation, yet little is known about T3SS architecture in this state or the conformational changes that drive effector translocation. Here we use cryo-electron tomography and sub-tomogram averaging to derive the intact structure of the primordial Chlamydia trachomatis T3SS in the presence and absence of host membrane contact. Comparison of the averaged structures demonstrates a marked compaction of the basal body (4 nm) occurs when the needle tip contacts the host cell membrane. This compaction is coupled to a stabilization of the cytosolic sorting platform– ATPase. Our findings reveal the first structure of a bacterial T3SS from a major human pathogen engaged with a eukaryotic host, and reveal striking ‘pump-action’ conformational changes that underpin effector injection

3,3′-Diindolylmethane Induces G1 Arrest and Apoptosis in Human Acute T-Cell Lymphoblastic Leukemia Cells

Certain bioactive food components, including indole-3-carbinol (I3C) and 3,3′-diindolylmethane (DIM) from cruciferous vegetables, have been shown to target cellular pathways regulating carcinogenesis. Previously, our laboratory showed that dietary I3C is an effective transplacental chemopreventive agent in a dibenzo[def,p]chrysene (DBC)-dependent model of murine T-cell lymphoblastic lymphoma. The primary objective of the present study was to extend our chemoprevention studies in mice to an analogous human neoplasm in cell culture. Therefore, we tested the hypothesis that I3C or DIM may be chemotherapeutic in human T-cell acute lymphoblastic leukemia (T-ALL) cells. Treatment of the T-ALL cell lines CCRF-CEM, CCRF-HSB2, SUP-T1 and Jurkat with DIM in vitro significantly reduced cell proliferation and viability at concentrations 8- to 25-fold lower than the parent compound I3C. DIM (7.5 µM) arrested CEM and HSB2 cells at the G1 phase of the cell cycle and 15 µM DIM significantly increased the percentage of apoptotic cells in all T-ALL lines. In CEM cells, DIM reduced protein expression of cyclin dependent kinases 4 and 6 (CDK4, CDK6) and D-type cyclin 3 (CCND3); DIM also significantly altered expression of eight transcripts related to human apoptosis (BCL2L10, CD40LG, HRK, TNF, TNFRSF1A, TNFRSF25, TNFSF8, TRAF4). Similar anticancer effects of DIM were observed in vivo. Dietary exposure to 100 ppm DIM significantly decreased the rate of growth of human CEM xenografts in immunodeficient SCID mice, reduced final tumor size by 44% and increased the apoptotic index compared to control-fed mice. Taken together, our results demonstrate a potential for therapeutic application of DIM in T-ALL

Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica

Salmonella enterica subspecies enterica is traditionally subdivided into serovars by serological and nutritional characteristics. We used Multilocus Sequence Typing (MLST) to assign 4,257 isolates from 554 serovars to 1092 sequence types (STs). The majority of the isolates and many STs were grouped into 138 genetically closely related clusters called eBurstGroups (eBGs). Many eBGs correspond to a serovar, for example most Typhimurium are in eBG1 and most Enteritidis are in eBG4, but many eBGs contained more than one serovar. Furthermore, most serovars were polyphyletic and are distributed across multiple unrelated eBGs. Thus, serovar designations confounded genetically unrelated isolates and failed to recognize natural evolutionary groupings. An inability of serotyping to correctly group isolates was most apparent for Paratyphi B and its variant Java. Most Paratyphi B were included within a sub-cluster of STs belonging to eBG5, which also encompasses a separate sub-cluster of Java STs. However, diphasic Java variants were also found in two other eBGs and monophasic Java variants were in four other eBGs or STs, one of which is in subspecies salamae and a second of which includes isolates assigned to Enteritidis, Dublin and monophasic Paratyphi B. Similarly, Choleraesuis was found in eBG6 and is closely related to Paratyphi C, which is in eBG20. However, Choleraesuis var. Decatur consists of isolates from seven other, unrelated eBGs or STs. The serological assignment of these Decatur isolates to Choleraesuis likely reflects lateral gene transfer of flagellar genes between unrelated bacteria plus purifying selection. By confounding multiple evolutionary groups, serotyping can be misleading about the disease potential of S. enterica. Unlike serotyping, MLST recognizes evolutionary groupings and we recommend that Salmonella classification by serotyping should be replaced by MLST or its equivalents

Metabolic profiling identifies trehalose as an abundant and diurnally fluctuating metabolite in the microalga Ostreococcus tauri

© 2017, The Author(s).Introduction: The picoeukaryotic alga Ostreococcus tauri (Chlorophyta) belongs to the widespread group of marine prasinophytes. Despite its ecological importance, little is known about the metabolism of this alga. Objectives: In this work, changes in the metabolome were quantified when O. tauri was grown under alternating cycles of 12 h light and 12 h darkness. Methods: Algal metabolism was analyzed by gas chromatography-mass spectrometry. Using fluorescence-activated cell sorting, the bacteria associated with O. tauri were depleted to below 0.1% of total cells at the time of metabolic profiling. Results: Of 111 metabolites quantified over light–dark cycles, 20 (18%) showed clear diurnal variations. The strongest fluctuations were found for trehalose. With an intracellular concentration of 1.6 mM in the dark, this disaccharide was six times more abundant at night than during the day. This fluctuation pattern of trehalose may be a consequence of starch degradation or of the synchronized cell cycle. On the other hand, maltose (and also sucrose) was below the detection limit (~10 μM). Accumulation of glycine in the light is in agreement with the presence of a classical glycolate pathway of photorespiration. We also provide evidence for the presence of fatty acid methyl and ethyl esters in O. tauri. Conclusions: This study shows how the metabolism of O. tauri adapts to day and night and gives new insights into the configuration of the carbon metabolism. In addition, several less common metabolites were identified

Identifying Conjugative Plasmids and Integrative Conjugative Elements with CONJscan

Part of the Methods in Molecular Biology book series (MIMB, volume 2075)International audienceWe present a computational method to identify conjugative systems in plasmids and chromosomes using the CONJscan module of MacSyFinder. The method relies on the identification of the protein components of the system using hidden Markov model profiles and then checking that the composition and genetic organization of the system is consistent with that expected from a conjugative system. The method can be assessed online using the Galaxy workflow or locally using a standalone software. The latter version allows to modify the models of the module (i.e., to change the expected components, their number, and their organization).CONJscan identifies conjugative systems, but when the mobile genetic element is integrative (ICE), one often also wants to delimit it from the chromosome. We present a method, with a script, to use the results of CONJscan and comparative genomics to delimit ICE in chromosomes. The method provides a visual representation of the ICE location. Together, these methods facilitate the identification of conjugative elements in bacterial genomes