Systematic Genome Analysis of a Novel Arachidonic Acid-Producing Strain Uncovered Unique Metabolic Traits in the Production of acetyl-CoA-derived Products in Mortierellale Fungi

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Tayvich Vorapreeda, Chinae Thammarongtham, Thanaporn Palasak, Tanawut Srisuk, Piroon Jenjaroenpun, Thidathip Wongsurawat, Intawat Nookaew, Kobkul Laoteng

[published online ahead of print, 2020 Mar 10]. Gene. 2020;144559. doi:10.1016/j.gene.2020.144559

Abstract

The fungi in order Mortierellales are attractive producers for long-chain polyunsaturated fatty acids (PUFAs). Here, the genome sequencing and assembly of a novel strain of Mortierella sp. BCC40632 were done, yielding 65 contigs spanning of 49,964,116 total bases with predicted 12,149 protein-coding genes. We focused on the acetyl-CoA in relevant to its derived metabolic pathways for biosynthesis of macromolecules with biological functions, including PUFAs, eicosanoids and carotenoids. By comparative genome analysis between Mortierellales and Mucorales, the signature genetic characteristics of the arachidonic acid-producing strains, including Δ5-desaturase and GLELO-like elongase, were also identified in the strain BCC40632. Remarkably, this fungal strain contained only n-6 pathway of PUFA biosynthesis due to the absence of Δ15-desaturase or ω3-desaturase gene in contrast to other Mortierella species. Four putative enzyme sequences in the eicosanoid biosynthetic pathways were identified in the strain BCC40632 and others Mortierellale fungi, but were not detected in the Mucorales. Another unique metabolic trait of the Mortierellales was the inability in carotenoid synthesis as a result of the lack of phytoene synthase and phytoene desaturase genes. The findings provide a perspective in strain optimization for production of tailored-made products with industrial applications.

Keywords Arachidonic acid; Carotenoids, Eicosanoids; Genome analysis; Microbial lipids; Mortierella sp.; Oleaginous fungi

Read the publication here: https://www.sciencedirect.com/science/article/abs/pii/S0378111920302286

MEMOTE for standardized genome-scale metabolic testing

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Christian Lieven, Moritz E Beber, Brett G Olivier, Frank T Bergmann, Meric Ataman, Parizad Babaei, Jennifer A Bartell, Lars M Blank, Siddharth Chauhan, Kevin Correia, Christian Diener, Andreas Dräger, Birgitta E Ebert, Janaka N Edirisinghe, José P Faria, Adam M Feist, Georgios Fengos, Ronan MT Fleming, Beatriz García-Jiménez, Vassily Hatzimanikatis, Wout van Helvoirt, Christopher S Henry, Henning Hermjakob, Markus J Herrgård, Ali Kaafarani, Hyun Uk Kim, Zachary King, Steffen Klamt, Edda Klipp, Jasper J Koehorst, Matthias König, Meiyappan Lakshmanan, Dong-Yup Lee, Sang Yup Lee, Sunjae Lee, Nathan E Lewis, Filipe Liu, Hongwu Ma, Daniel Machado, Radhakrishnan Mahadevan, Paulo Maia, Adil Mardinoglu, Gregory L Medlock, Jonathan M Monk, Jens Nielsen, Lars Keld Nielsen, Juan Nogales, Intawat Nookaew, Bernhard O Palsson, Jason A Papin, Kiran R Patil, Mark Poolman, Nathan D Price, Osbaldo Resendis-Antonio, Anne Richelle, Isabel Rocha, Benjamín J Sánchez, Peter J Schaap, Rahuman S Malik Sheriff, Saeed Shoaie, Nikolaus Sonnenschein, Bas Teusink, Paulo Vilaça, Jon Olav Vik, Judith AH Wodke, Joana C Xavier, Qianqian Yuan, Maksim Zakhartsev, Cheng Zhang

Nature Biotechnology. 2020 Mar 2:1-5.

Abstract

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.

Read the publication here: https://www.nature.com/articles/s41587-020-0446-y

Report of the 2019 NIST-FDA workshop on standards for next generation sequencing detection of viral adventitious agents in biologics and biomanufacturing

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Megan H Cleveland, Bharathi Anekella, Michael Brewer, Pei-Ju Chin, Heather Couch, Eric Delwart, Jim Huggett, Scott Jackson, Javier Martin, Serge Monpoeho, Tom Morrison, Siemon HS Ng, David Ussery, Arifa S Khan

Biologicals, 2020

Abstract

Adventitious virus testing assures product safety by demonstrating the absence of viruses that could be unintentionally introduced during the manufacturing process. The capabilities of next-generation sequencing (NGS) for broad virus detection in biologics have been demonstrated by the detection of known and novel viruses that were previously missed using the recommended routine assays for adventitious agent testing. A meeting was co-organized by the National Institute of Standards and Technology and the U.S. Food and Drug Administration on September 18–19, 2019 in Gaithersburg, Maryland, USA, to facilitate standardization of NGS technologies for applications of adventitious virus testing in biologics. The goal was to assess the currently used standards for virus detection by NGS and their public availability, and to identify additional needs for different types of reference materials and standards (natural and synthetic). The meeting focused on the NGS processes from sample preparation through sequencing but did not thoroughly cover bioinformatics, since this was considered to be the topic of a separate meeting.

Keywords Adventitious virusesBiologicsVaccinesNext generation sequencingHigh-throughput sequencingBioinformaticsAlternative methodsVirus detectionStandardsReference materialsAssay standardizationAssay validation

Read the publication here: https://www.sciencedirect.com/science/article/pii/S1045105620300245

A Glioblastoma Genomics Primer for Clinicians

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JD Patterson, T Wongsurawat, and A Rodriguez

Medical Research Archives, [S.l.], v. 8, n. 2, feb. 2020. ISSN 2375-1924. doi: https://doi.org/10.18103/mra.v8i2.2034.

Abstract

New discoveries in Glioblastoma (GBM) biology have been made using genomics data. Genomic markers are routinely integrated into clinical neurosurgical practice. In this manuscript, we review fundamentals of genomics such as the differences between first, second, and third generation sequencing technology. We also review the impact of single cell genomics in understanding the complex heterogenous GBM microenvironment. Finally, we will discuss advances in epigenetics that have lent insights into treatment resistance. The integration of genomics into neuro-oncology clinical practice is routine and will continue to expand with the expansion of precision of medicine. We provide a primer for clinicians.

Read the publication here: https://journals.ke-i.org/mra/article/view/2034

Full transcriptomic approach of Pseudomonas aeruginosa to the fructooligosaccharide

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José Manuel Rubio-Gómez, Carlos Molina Santiago, Zulema Udaondo, Mireia Tena Garitaonaindia, Tino Krell, Juan-Luis Ramos, Abdelali Daddaoua

Frontiers in Microbiology 11, 202

Abstract

Pseudomonas aeruginosa is an ubiquitous gram-negative opportunistic human pathogen which is not considered part of the human commensal gut microbiota. However, depletion of the intestinal microbiota (Dysbiosis) following antibiotic treatment facilitates the colonization of the intestinal tract by Multidrug-Resistant P. aeruginosa. One possible strategy is based on the use of functional foods with prebiotic activity. The bifidogenic effect of the prebiotic inulin and its hydrolyzed form (fructooligosaccharide: FOS) is well established since they promote the growth of specific beneficial (probiotic) gut bacteria such as bifidobacteria. Previous studies of the opportunistic nosocomial pathogen Pseudomonas aeruginosa PAO1 have shown that inulin and to a greater extent FOS reduce growth and biofilm formation, which was found to be due to a decrease in motility and exotoxin secretion. However, the transcriptional basis for these phenotypic alterations remains unclear. To address this question we conducted RNA-sequence analysis. Changes in the transcript level induced by inulin and FOS were similar, but a set of transcript levels were increased in response to inulin and reduced in the presence of FOS. In the presence of inulin or FOS, 260 and 217 transcript levels, respectively, were altered compared to the control to which no polysaccharide was added. Importantly, changes in transcript levels of 57 and 83 genes were found to be specific for either inulin or FOS, respectively, indicating that both compounds trigger different changes. Gene pathway analyses of differentially expressed genes (DEG) revealed a specific FOS-mediated reduction in transcript levels of genes that participate in several canonical pathways involved in metabolism and growth, motility, biofilm formation, β-lactamase resistance, and in the modulation of type III and VI secretion systems; results that have been partially verified by real time quantitative PCR measurements. Moreover, we have identified a genomic island formed by a cluster of 15 genes, encoding uncharacterized proteins, which were repressed in the presence of FOS. The analysis of isogenic mutants has shown that genes of this genomic island encode proteins involved in growth, biofilm formation and motility. These results indicate that FOS selectively modulates bacterial pathogenicity by interfering with different signaling pathways.

Read the publication here: https://www.frontiersin.org/articles/10.3389/fmicb.2020.00202/full

A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome.

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John L. Darcy, Alex D. Washburne, Michael S. Robeson, Tiffany Prest, Steven K. Schmidt & Catherine A. Lozupone

ISME J. 2020 Feb 19. doi: 10.1038/s41396-020-0613-7. [Epub ahead of print]

Abstract

Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

Read the publication here: https://www.nature.com/articles/s41396-020-0613-7

What can we learn from over 100,000 Escherichia coli genomes?

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Kaleb Z Abram, Zulema Udaondo, Carissa Bleker, Visanu Wanchai, Trudy M Wassenaar, Michael S Robeson, David W Ussery

bioRxiv 708131; doi: https://doi.org/10.1101/708131

Abstract

The explosion of microbial genome sequences in public databases allows for large-scale population genomic studies of bacterial species, such as Escherichia coli. In this study, we examine and classify more than one hundred thousand E. coli and Shigella genomes. After removing outliers, a semi-automated Mash-based analysis of 10,667 assembled genomes reveals 14 distinct phylogroups. A representative genome or medoid identified for each phylogroup serves as a proxy to classify more than 95,000 unassembled genomes. This analysis shows that most sequenced E. coli genomes belong to 4 phylogroups (A, C, B1 and E2(O157)). Authenticity of the 14 phylogroups described is supported by pangenomic and phylogenetic analyses, which show differences in gene preservation between phylogroups. A phylogenetic tree constructed with 2,613 single copy core genes along with a matrix of phylogenetic profiles is used to confirm that the 14 phylogroups change at different rates of gene gain/loss/duplication. The methodology used in this work is able to identify previously uncharacterized phylogroups in E. coli species. Some of these new phylogroups harbor clonal strains that have undergone a process of genomic adaptation to the acquisition of new genomic elements related to virulence or antibiotic resistance. This is, to our knowledge, the largest E. coli genome dataset analyzed to date and provides valuable insights into the population structure of the species.

Read the publication here: https://www.biorxiv.org/content/10.1101/708131v2

2019_nCoV: Rapid classification of betacoronaviruses and identification of traditional Chinese medicine as potential origin of zoonotic coronaviruses

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Trudy M. Wassenaar and Ying Zou

Lett Appl Microbiol. 2020 Feb 14. doi: 10.1111/lam.13285. [Epub ahead of print]

Abstract

The current outbreak of a novel SARS‐like coronavirus, 2019_nCoV, illustrated difficulties in identifying a novel coronavirus and its natural host, as the coding sequences of various Betacoronavirus species can be highly diverse. By means of whole‐genome sequence comparisons, we demonstrate that the non‐coding flanks of the viral genome can be used to correctly separate the recognized four betacoronavirus subspecies. The conservation would be sufficient to define target sequences that could, in theory, classify novel virus species into their subspecies. Only 253 upstream non‐coding sequences of Sarbecovirus are sufficient to identify genetic similarities between species of this subgenus. Further, it was investigated which bat species have commercial value in China, and would thus likely be handled for trading purposes. A number of coronavirus genomes have been published that were obtained from such bat species. These bats are used in Traditional Chinese Medicine, and their handling poses a potential risk to cause zoonotic coronavirus epidemics.

Keywords Sarbecovirus ; Coronavirus; Traditional Chinese Medicine; bats; epidemic; whole-genome comparison; zoonosis

Read the publication here: https://sfamjournals.onlinelibrary.wiley.com/doi/abs/10.1111/lam.13285

Comment on “Enumeration of Escherichia coli in Probiotic Products. Microorganisms 2019, 7, 437”

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Wassenaar TM, von Bünau R, and Zimmermann

Microorganisms 2020, 8(2), 245; https://doi.org/10.3390/microorganisms8020245

Abstract

Recently, Zimmer and Dorea published a communication on the enumeration of Escherichiaácoli in probiotic products containing this species [1]. They investigated the content of two commercial products, Mutaflor (produced by Pharma-Zentrale GmbH, Herdecke, Germany) and Symbioflor 2 (produced by SymbioPharm GmbH, Herborn, Germany). Mutaflor is available as viable, lyophilized E. ácoli bacteria inside an acid-resistant capsule, while Symbioflor 2 contains viable E. ácoliábacteria in a liquid suspension. The authors tested the viability of the bacteria for three batches of each product. They report that the products contained E. ácoli in numbers several orders of magnitude less than claimed on the product information [1].The authors assessed the number of viable bacteria by applying the methodology optimized for enumeration of coliform bacteria in surface waters. Serial dilutions were made in Ringers solution (the content of the Mutaflor capsules was resuspended for this) and these were enumerated in triplicate using a Colilert Quanti-train/2000 system, from which the most probable number (MPN) was obtained. This method is validated and recommended in multiple countries, including Germany [2], to estimate the number of coliform bacteria in samples expected to contain low numbers, for instance, surface water samples. For such samples the MPN methodology has been developed and optimized [3].

Read the publication here: https://www.mdpi.com/2076-2607/8/2/245

Comparative genomics of Hepatitis A virus, Hepatitis C virus and Hepatitis E virus provides insights into the evolutionary history of Hepatovirus species

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Trudy M. Wassenaar, Se-Ran Jun, Michael Robeson and David W. Ussery

MicrobiologyOpen. 2019 Nov 19:e973. doi: 10.1002/mbo3.973.

Abstract

The intraspecies genomic diversity of the single-strand RNA (+) virus species hepatitis A virus (Hepatovirus), hepatitis C virus (Hepacivirus), and hepatitis E virus (Orthohepevirus) was compared. These viral species all can cause liver inflammation (hepatitis), but share no gene similarity. The codon usage of human hepatitis A virus (HAV) is suboptimal for replication in its host, a characteristic it shares with taxonomically related rodent, simian, and bat hepatitis A virus species. We found this codon usage to be strikingly similar to that of Triatoma virus that infects blood-sucking kissing bugs. The codon usage of that virus is well adapted to its insect host. The codon usage of HAV is also similar to other invertebrate viruses of various taxonomic families. An evolutionary ancestor of HAV and related virus species is hypothesized to be an insect virus that underwent a host jump to infect mammals. The similarity between HAV and invertebrate viruses goes beyond codon usage, as they also share amino acid composition characteristics, while not sharing direct sequence homology. In contrast, hepatitis C virus and hepatitis E virus are highly similar in codon usage preference, nucleotide composition, and amino acid composition, and share these characteristics with Human pegivirus A, West Nile virus, and Zika virus. We present evidence that these observations are only partly explained by differences in nucleotide composition of the complete viral codon regions. We consider the combination of nucleotide composition, amino acid composition, and codon usage preference suitable to provide information on possible evolutionary similarities between distant virus species that cannot be investigated by phylogeny.

Keywords Hepactovirus A; codon bias; comparative genomics; evolution; hepatitis A virus

Read the publication here: https://onlinelibrary.wiley.com/doi/full/10.1002/mbo3.973