Biocommunication, pp. 257-283 (2017) No Access10: Paenibacillus vortex — A Bacterial Guide to the Wisdom of the CrowdAlin Finkelshtein, Alexandra Sirota-Madi, Dalit Roth, Colin J. Ingham, and Eshel Ben JacobAlin FinkelshteinSackler School of Medicine, Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo, Israel, Alexandra Sirota-MadiSackler School of Medicine, Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo, IsraelDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel, Dalit RothSackler School of Medicine, Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo, Israel, Colin J. InghamMicroDish BV, Utrecht, NL, Netherlands, and Eshel Ben JacobSackler School of Medicine, Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo, IsraelCenter for Theoretical Biological Physics, Rice University, Houston, TX, USAhttps://doi.org/10.1142/9781786340450_0010Cited by:3 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Complex organizations of bacteria emerge through the communication-based interplay between individuals. Each bacterium is, by itself, a biotic autonomous system with its own internal cellular informatics capabilities (storage and processing of information triggering an appropriate response). The cell plasticity allows the bacteria to select its response to biochemical messages it receives, including self-alteration (exposure to antibiotics causes enrichment of one subpopulation in Paenibacillus vortex) and the broadcasting of messages to influence on other bacteria. New features can collectively emerge during self-organization from the intracellular level to the whole colony. P. vortex, is a highly social, motile, pattern forming bacteria. Its information processing abilities are supported by exceptionally high number of communication related genes (two-component system (TCS), transcription factors (TFs), transport and defense related genes) suggesting a high "Bacterial Social Intelligence Quotient (IQ) Score". P. vortex contains two subpopulations with different locations and functions, that contribute to creation of complex pattern colonies. The coexistence of two subpopulations within P. vortex colonies allows flexibility and adjustment to heterogeneous environments including other microorganisms such as fungi. An unusual intraspecies temporary mutualism facilitates the spreading of both species. While P. vortex transporting fungal conidia with his swarming masses, the fungi bridges air gaps with its mycelia and allows bacteria to traverse these and colonize new niches. FiguresReferencesRelatedDetailsCited By 3BAFFLE: A 3D Printable Device for Macroscopic Quantification of Fluorescent Bacteria in Space and TimeCarles Tardío Pi, Daniela Reyes-González, Andrea Fernández-Duque, Ayari Fuentes-Hernández and Fernando Santos-Escobar et al.27 Oct 2022 | Journal of Open Hardware, Vol. 6, No. 1Vortex Swarm Optimization: New Metaheuristic AlgorithmAhmed Sabry A. Elrahman and Hesham A. Hefny24 March 2020Bacterial colonies as complex adaptive systemsDanilo Cunha, Rafael Xavier and Leandro Nunes de Castro20 June 2018 | Natural Computing, Vol. 17, No. 4 BiocommunicationMetrics History PDF download

ABSTRACT Swarming bacteria are challenged by the need to invade hostile environments. Swarms of the flagellated bacterium Paenibacillus vortex can collectively transport other microorganisms. Here we show that P. vortex can invade toxic environments by carrying antibiotic-degrading bacteria; this transport is mediated by a specialized, phenotypic subpopulation utilizing a process not dependent on cargo motility. Swarms of beta-lactam antibiotic (BLA)-sensitive P. vortex used beta-lactamase-producing, resistant, cargo bacteria to detoxify BLAs in their path. In the presence of BLAs, both transporter and cargo bacteria gained from this temporary cooperation; there was a positive correlation between BLA resistance and dispersal. P. vortex transported only the most beneficial antibiotic-resistant cargo (including environmental and clinical isolates) in a sustained way. P. vortex displayed a bet-hedging strategy that promoted the colonization of nontoxic niches by P. vortex alone; when detoxifying cargo bacteria were not needed, they were lost. This work has relevance for the dispersal of antibiotic-resistant microorganisms and for strategies for asymmetric cooperation with agricultural and medical implications. IMPORTANCE Antibiotic resistance is a major health threat. We show a novel mechanism for the local spread of antibiotic resistance. This involves interactions between different bacteria: one species provides an enzyme that detoxifies the antibiotic (a sessile cargo bacterium carrying a resistance gene), while the other (Paenibacillus vortex) moves itself and transports the cargo. P. vortex used a bet-hedging strategy, colonizing new environments alone when the cargo added no benefit, but cooperating when the cargo was needed. This work is of interest in an evolutionary context and sheds light on fundamental questions, such as how environmental antibiotic resistance may lead to clinical resistance and also microbial social organization, as well as the costs, benefits, and risks of dispersal in the environment.

Bacteria often use sophisticated cooperative behaviours, such as the development of complex colonies, elaborate biofilms and advanced dispersal strategies, to cope with the harsh and variable conditions of natural habitats, including the presence of antibiotics. Paenibacillus vortex uses swarming motility and cell-to-cell communication to form complex, structured colonies. The modular organization of P.vortex colony has been found to facilitate its dispersal on agar surfaces. The current study reveals that the complex structure of the colony is generated by the coexistence and transition between two morphotypes - builders' and explorers' - with distinct functions in colony formation. Here, we focused on the explorers, which are highly motile and spearhead colonial expansion. Explorers are characterized by high expression levels of flagellar genes, such as flagellin (hag), motA, fliI, flgK and sigD, hyperflagellation, decrease in ATP (adenosine-5-triphosphate) levels, and increased resistance to antibiotics. Their tolerance to many antibiotics gives them the advantage of translocation through antibiotics-containing areas. This work gives new insights on the importance of cell differentiation and task distribution in colony morphogenesis and adaptation to antibiotics.

ABSTRACT Paenibacillus dendritiformis is a Gram-positive, soil-dwelling, spore-forming social microorganism. An intriguing collective faculty of this strain is manifested by its ability to switch between different morphotypes, such as the branching (T) and the chiral (C) morphotypes. Here we report the 6.3-Mb draft genome sequence of the P. dendritiformis C454 chiral morphotype.

Microarray technology has played an important role in promoting the understanding of gene network regulations. Different supervised and unsupervised analysis methods have been devised to extract meaningful information from gene-expression data. In this chapter, we introduce the Genome Holography method (GH) for the analysis of gene-expression data and discuss some of its possible applications, such as clique finding technique and Functional Holography Minimal Spanning Tree (FHMST). We employ this new technique to analyze a database of gene expression of Bacillus subtilis exposed to sublethal levels of 37 different antibiotics. Using this method, we present a new way to visualize and investigate the relationships between genes in different gene regulatory networks, and how these relationships change over time due to an environmental stress.

Background The pattern-forming bacterium Paenibacillus vortex is notable for its advanced social behavior, which is reflected in development of colonies with highly intricate architectures. Prior to this study, only two other Paenibacillus species ( Paenibacillus sp. JDR-2 and Paenibacillus larvae ) have been sequenced. However, no genomic data is available on the Paenibacillus species with pattern-forming and complex social motility. Here we report the de novo genome sequence of this Gram-positive, soil-dwelling, sporulating bacterium. Results The complete P. vortex genome was sequenced by a hybrid approach using 454 Life Sciences and Illumina, achieving a total of 289× coverage, with 99.8% sequence identity between the two methods. The sequencing results were validated using a custom designed Agilent microarray expression chip which represented the coding and the non-coding regions. Analysis of the P. vortex genome revealed 6,437 open reading frames (ORFs) and 73 non-coding RNA genes. Comparative genomic analysis with 500 complete bacterial genomes revealed exceptionally high number of two-component system (TCS) genes, transcription factors (TFs), transport and defense related genes. Additionally, we have identified genes involved in the production of antimicrobial compounds and extracellular degrading enzymes. Conclusions These findings suggest that P. vortex has advanced faculties to perceive and react to a wide range of signaling molecules and environmental conditions, which could be associated with its ability to reconfigure and replicate complex colony architectures. Additionally, P. vortex is likely to serve as a rich source of genes important for agricultural, medical and industrial applications and it has the potential to advance the study of social microbiology within Gram-positive bacteria.

Supplemental Figure S7. Increase in the frequencies of parallel deletion events in two adaptive recovery populations (66D, and 66E) and one control population (C3) containing another overlapping region on Chromosome X. The average copy-number per haploid genome was calculated from qPCR results and is indicated on the vertical axis. The number of recovery generations is indicated on the horizontal axis. The results show a strong decline in average copy-number of these three independent deletions that were initially detected by oaCGH. The deletions have reached fixation when the average copynumber has reached 0.

Background DNA chips allow simultaneous measurements of genome-wide response of thousands of genes, i.e. system level monitoring of the gene-network activity. Advanced analysis methods have been developed to extract meaningful information from the vast amount of raw gene-expression data obtained from the microarray measurements. These methods usually aimed to distinguish between groups of subjects (e.g., cancer patients vs. healthy subjects) or identifying marker genes that help to distinguish between those groups. We assumed that motifs related to the internal structure of operons and gene-networks regulation are also embedded in microarray and can be deciphered by using proper analysis. Methodology/Principal Findings The analysis presented here is based on investigating the gene-gene correlations. We analyze a database of gene expression of Bacillus subtilis exposed to sub-lethal levels of 37 different antibiotics. Using unsupervised analysis (dendrogram) of the matrix of normalized gene-gene correlations, we identified the operons as they form distinct clusters of genes in the sorted correlation matrix. Applying dimension-reduction algorithm (Principal Component Analysis, PCA) to the matrices of normalized correlations reveals functional motifs. The genes are placed in a reduced 3-dimensional space of the three leading PCA eigen-vectors according to their corresponding eigen-values. We found that the organization of the genes in the reduced PCA space recovers motifs of the operon internal structure, such as the order of the genes along the genome, gene separation by non-coding segments, and translational start and end regions. In addition to the intra-operon structure, it is also possible to predict inter-operon relationships, operons sharing functional regulation factors, and more. In particular, we demonstrate the above in the context of the competence and sporulation pathways. Conclusions/Significance We demonstrated that by analyzing gene-gene correlation from gene-expression data it is possible to identify operons and to predict unknown internal structure of operons and gene-networks regulation.