Genome Sequence of Thermotolerant Bacillus methanolicus: Features and Regulation Related to Methylotrophy and Production of l-Lysine and l-Glutamate from Methanol

ABSTRACTBacillus methanolicuscan utilize methanol as its sole carbon and energy source, and the scientific interest in this thermotolerant bacterium has focused largely on exploring its potential as a biocatalyst for the conversion of methanol intol-lysine andl-glutamate. We present here the genome sequences of the importantB. methanolicusmodel strain MGA3 (ATCC 53907) and the alternative wild-type strain PB1 (NCIMB13113). The physiological diversity of these two strains was demonstrated by a comparative fed-batch methanol cultivation displaying highly different methanol consumption and respiration profiles, as well as major differences in theirl-glutamate production levels (406 mmol liter−1and 11 mmol liter−1, respectively). Both genomes are small (ca 3.4 Mbp) compared to those of other related bacilli, and MGA3 has two plasmids (pBM19 and pBM69), while PB1 has only one (pBM20). In particular, we focus here on genes representing biochemical pathways for methanol oxidation and concomitant formaldehyde assimilation and dissimilation, the important phosphoenol pyruvate/pyruvate anaplerotic node, the tricarboxylic acid cycle including the glyoxylate pathway, and the biosynthetic pathways forl-lysine andl-glutamate. Several unique findings were made, including the discovery of three different methanol dehydrogenase genes in each of the twoB. methanolicusstrains, and the genomic analyses were accompanied by gene expression studies. Our results provide new insight into a number of peculiar physiological and metabolic traits ofB. methanolicusand open up possibilities for system-level metabolic engineering of this bacterium for the production of amino acids and other useful compounds from methanol.

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Gene co-expression network analysis reveals common system-level properties of genes involved in tuberculosis across independent gene expression studies

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-016-0131-8 ◽

2016 ◽

Vol 5 (1) ◽

Cited By ~ 2

Author(s):

Mohit Jha ◽

Anvita Gupta Malhotra ◽

Sudha Singh ◽

Khushhali Menaria Pandey

Keyword(s):

Gene Expression ◽

Network Analysis ◽

System Level ◽

Common System ◽

Independent Gene ◽

Expression Studies ◽

Gene Expression Studies

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Co-Expression Networks for Causal Gene Identification Based on RNA-seq Data of Corynebacterium Pseudotuberculosis

10.20944/preprints202006.0144.v1 ◽

2020 ◽

Author(s):

Edian F. Franco ◽

Pratip Rana ◽

Ana Lidia Queiroz Cavalcante ◽

Artur Luiz da Silva ◽

Adriana R Carneiro Folador ◽

...

Keyword(s):

Network Inference ◽

Data Sets ◽

Rna Seq ◽

Cellular Functions ◽

Expression Studies ◽

Genome Wide ◽

Causal Genes ◽

Gene Expression Studies ◽

Membrane Components ◽

Insight Into

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

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Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

Microbial Genomics ◽

10.1099/mgen.0.000500 ◽

2021 ◽

Author(s):

Kara K. Tsang ◽

Finlay Maguire ◽

Haley L. Zubyk ◽

Sommer Chou ◽

Arman Edalatmand ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Type Species ◽

Multidrug Resistant ◽

Content Type ◽

Logistic Regression Models ◽

Link Type ◽

Expression Studies ◽

Gene Expression Studies ◽

Targeted Gene Expression ◽

Phenotypic Methods

Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.

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Biosynthesis and Secretion of Indole-3-Acetic Acid and Its Morphological Effects on Tricholoma vaccinum-Spruce Ectomycorrhiza

Applied and Environmental Microbiology ◽

10.1128/aem.01991-15 ◽

2015 ◽

Vol 81 (20) ◽

pp. 7003-7011 ◽

Cited By ~ 40

Author(s):

Katrin Krause ◽

Catarina Henke ◽

Theodore Asiimwe ◽

Andrea Ulbricht ◽

Sandra Klemmer ◽

...

Keyword(s):

Acetic Acid ◽

Mycorrhizal Fungi ◽

Schizophyllum Commune ◽

Content Type ◽

Saprobic Fungi ◽

Hartig Net ◽

Expression Studies ◽

Fungal Morphogenesis ◽

Gene Expression Studies ◽

Indole 3 Acetic Acid

ABSTRACTFungus-derived indole-3-acetic acid (IAA), which is involved in development of ectomycorrhiza, affects both partners, i.e., the tree and the fungus. The biosynthesis pathway, excretion from fungal hyphae, the induction of branching in fungal cultures, and enhanced Hartig net formation in mycorrhiza were shown. Gene expression studies, incorporation of labeled compounds into IAA, heterologous expression of a transporter, and bioinformatics were applied to study the effect of IAA on fungal morphogenesis and on ectomycorrhiza.Tricholoma vaccinumproduces IAA from tryptophan via indole-3-pyruvate, with the last step of this biosynthetic pathway being catalyzed by an aldehyde dehydrogenase. The geneald1was found to be highly expressed in ectomycorrhiza and induced by indole-3-acetaldehyde. The export of IAA from fungal cells is supported by the multidrug and toxic extrusion (MATE) transporter Mte1 found inT. vaccinum. The addition of IAA and its precursors induced elongated cells and hyphal ramification of mycorrhizal fungi; in contrast, in saprobic fungi such asSchizophyllum commune, IAA did not induce morphogenetic changes. Mycorrhiza responded by increasing its Hartig net formation. The IAA of fungal origin acts as a diffusible signal, influencing root colonization and increasing Hartig net formation in ectomycorrhiza.

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Method Enabling Gene Expression Studies of Pathogens in a Complex Food Matrix

Applied and Environmental Microbiology ◽

10.1128/aem.05471-11 ◽

2011 ◽

Vol 77 (23) ◽

pp. 8456-8458 ◽

Cited By ~ 12

Author(s):

Jette Kjeldgaard ◽

Sidsel Henriksen ◽

Marianne Thorup Cohn ◽

Søren Aabo ◽

Hanne Ingmer

Keyword(s):

Gene Expression ◽

Food Matrix ◽

Simple Method ◽

Bacterial Gene ◽

Content Type ◽

Reverse Transcription Pcr ◽

Bacterial Gene Expression ◽

Expression Studies ◽

Gene Expression Studies ◽

Minced Meat

ABSTRACTWe describe a simple method for stabilizing and extracting high-quality prokaryotic RNA from meat. Heat and salt stress ofEscherichia coliandSalmonellaspp. in minced meat reproducibly induceddnaKandotsBexpression, respectively, as observed by quantitative reverse transcription-PCR (>5-fold relative changes). Thus, the method is applicable in studies of bacterial gene expression in a meat matrix.

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Analysis of the Contribution of cyp51 Genes to Azole Resistance in Aspergillus Section Nigri with the CRISPR-Cas9 Technique

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01996-20 ◽

2021 ◽

Vol 65 (5) ◽

Author(s):

Alba Pérez-Cantero ◽

Adela Martin-Vicente ◽

Josep Guarro ◽

Jarrod R. Fortwendel ◽

Javier Capilla

Keyword(s):

Single Gene ◽

Transcriptional Response ◽

Azole Resistance ◽

Gene Deletions ◽

Content Type ◽

Ergosterol Content ◽

Expression Studies ◽

Gene Expression Studies ◽

Induced Changes ◽

The Impact

ABSTRACT Cyp51 contribution to azole resistance has been broadly studied and characterized in Aspergillus fumigatus, whereas it remains poorly investigated in other clinically relevant species of the genus, such as those of section Nigri. In this work, we aimed to analyze the impact of cyp51 genes (cyp51A and cyp51B) on the voriconazole (VRC) response and resistance of Aspergillus niger and Aspergillus tubingensis. We generated CRISPR-Cas9 cyp51A and cyp51B knockout mutants from strains with different genetic backgrounds and diverse patterns of azole susceptibility. Single-gene deletions of cyp51 genes resulted in 2- to 16-fold decreases of the VRC MIC values, which were below the VRC epidemiological cutoff value (ECV) established by the Clinical and Laboratory Standards Institute (CLSI), irrespective of their parental strains’ susceptibilities. Gene expression studies in the tested species confirmed that cyp51A participates more actively than cyp51B in the transcriptional response of azole stress. However, ergosterol quantification revealed that both enzymes comparably impact the total ergosterol content within the cell, as basal- and VRC-induced changes to ergosterol content were similar in all cases. These data contribute to our understanding of Aspergillus azole resistance, especially in non-A. fumigatus species.

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Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

Genes ◽

10.3390/genes11070794 ◽

2020 ◽

Vol 11 (7) ◽

pp. 794

Author(s):

Edian F. Franco ◽

Pratip Rana ◽

Ana Lidia Queiroz Cavalcante ◽

Artur Luiz da Silva ◽

Anne Cybelle Pinto Gomide ◽

...

Keyword(s):

Network Inference ◽

Corynebacterium Pseudotuberculosis ◽

Rna Seq ◽

Cellular Functions ◽

Expression Studies ◽

Genome Wide ◽

Causal Genes ◽

Gene Expression Studies ◽

Membrane Components ◽

Insight Into

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

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Global Analysis and Comparison of the Transcriptomes and Proteomes of Group A Streptococcus Biofilms

mSystems ◽

10.1128/msystems.00149-16 ◽

2016 ◽

Vol 1 (6) ◽

Cited By ~ 10

Author(s):

Jeffrey A. Freiberg ◽

Yoann Le Breton ◽

Bao Q. Tran ◽

Alison J. Scott ◽

Janette M. Harro ◽

...

Keyword(s):

Shotgun Proteomics ◽

Rna Seq ◽

Content Type ◽

Expression Studies ◽

Proteome Level ◽

Group A ◽

Gene Expression Studies ◽

Microbial Samples ◽

Transcriptome Level ◽

Planktonic State

ABSTRACT Prokaryotes are thought to regulate their proteomes largely at the level of transcription. However, the results from this first set of global transcriptomic and proteomic analyses of paired microbial samples presented here show that this assumption is false for the majority of genes and their products in S. pyogenes. In addition, the tenuousness of the link between transcription and translation becomes even more pronounced when microbes exist in a biofilm or a stationary planktonic state. Since the transcriptome level does not usually equal the proteome level, the validity attributed to gene expression studies as well as proteomic studies in microbial analyses must be brought into question. Therefore, the results attained by either approach, whether RNA-seq or shotgun proteomics, must be taken in context and evaluated with particular care since they are by no means interchangeable. To gain a better understanding of the genes and proteins involved in group A Streptococcus (GAS; Streptococcus pyogenes) biofilm growth, we analyzed the transcriptome, cellular proteome, and cell wall proteome from biofilms at different stages and compared them to those of plankton-stage GAS. Using high-throughput RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics, we found distinct expression profiles in the transcriptome and proteome. A total of 46 genes and 41 proteins showed expression across the majority of biofilm time points that was consistently higher or consistently lower than that seen across the majority of planktonic time points. However, there was little overlap between the genes and proteins on these two lists. In line with other studies comparing transcriptomic and proteomic data, the overall correlation between the two data sets was modest. Furthermore, correlation was poorest for biofilm samples. This suggests a high degree of regulation of protein expression by nontranscriptional mechanisms. This report illustrates the benefits and weaknesses of two different approaches to global expression profiling, and it also demonstrates the advantage of using proteomics in conjunction with transcriptomics to gain a more complete picture of global expression within biofilms. In addition, this report provides the fullest characterization of expression patterns in GAS biofilms currently available. IMPORTANCE Prokaryotes are thought to regulate their proteomes largely at the level of transcription. However, the results from this first set of global transcriptomic and proteomic analyses of paired microbial samples presented here show that this assumption is false for the majority of genes and their products in S. pyogenes. In addition, the tenuousness of the link between transcription and translation becomes even more pronounced when microbes exist in a biofilm or a stationary planktonic state. Since the transcriptome level does not usually equal the proteome level, the validity attributed to gene expression studies as well as proteomic studies in microbial analyses must be brought into question. Therefore, the results attained by either approach, whether RNA-seq or shotgun proteomics, must be taken in context and evaluated with particular care since they are by no means interchangeable.

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