. Weissella paramesenteroides
. has potential as an industrial biocatalyst due to its ability to produce lactic acid. A novel strain of
. W. paramesenteroides
. was isolated from ensiled sorghum. The genome was sequenced using a hybrid assembly of Oxford Nanopore and Illumina data to produce a 2-Mbp genome and 22-kbp plasmid sequence.
.

The formation of persister cells is one mechanism by which bacteria can survive exposure to environmental stresses. We show that Campylobacter jejuni 11168H forms persister cells at a frequency of 10−3 after exposure to 100 × MIC of penicillin G for 24 h. Staining the cell population with a redox sensitive fluorescent dye revealed that penicillin G treatment resulted in the appearance of a population of cells with increased fluorescence. We present evidence, to show this could be a consequence of increased redox protein activity in, or associated with, the electron transport chain. These data suggest that a population of penicillin G treated C. jejuni cells could undergo a remodeling of the electron transport chain in order to moderate membrane hyperpolarization and intracellular alkalization; thus reducing the antibiotic efficacy and potentially assisting in persister cell formation.

Designing improved vaccines against mutable viruses such as dengue and influenza would be helped by a better understanding of how the B-cell memory compartment responds to variant antigens. Towards this we have recently shown, after secondary immunization of mice with a widely variant dengue virus envelope protein with only 63% amino acid identity, that IgM+ memory B cells with few mutations supported an efficient secondary germinal centre (GC) and serum response, superior to a primary response to the same protein. Here, further investigation of memory responses to variant proteins, using more closely related influenza virus haemagglutinins (HA) that were 82% identical, produced a variant-induced boost response in the GC dominated by highly mutated B cells that failed, not efficiently improving serum avidity even in the presence of extra adjuvant, and that was worse than a primary response. This supports a hypothesis that over a certain level of antigenic differences, cross-reactive memory B-cell populations have reduced competency for affinity maturation. Combined with our previous observations, these findings also provide new parameters of success and failure in antibody memory responses.

This procedure can be used to calibrate OD600 to colony forming unit (CFU) counts, which are directly relatable to the cell concentration of the culture, i.e. viable cell counts per mL. This protocol assumes that 1 bacterial cell will give rise to 1 colony. For the CFU protocol, you will need to count colonies for your two Positive Control (BBa_I20270) cultures and your two Negative Control (BBa_R0040) cultures.

Plate readers report fluorescence values in arbitrary units that vary widely from instrument to instrument. Therefore absolute fluorescence values cannot be directly compared from one instrument to another. In order to compare fluorescence output of test devices between teams, it is necessary for each team to create a standard fluorescence curve. Although distribution of a known concentration of GFP protein would be an ideal way to standardize the amount of GFP fluorescence in our E. coli cells, the stability of the protein and the high cost of its purification are problematic. We therefore use the small molecule fluorescein, which has similar excitation and emission properties to GFP, but is cost-effective and easy to prepare. (The version of GFP used in the devices, GFP mut3b, has an excitation maximum at 501 nm and an emission maximum at 511 nm; fluorescein has an excitation maximum at 494 nm and an emission maximum at 525nm). You will prepare a dilution series of fluorescein in four replicates and measure the fluorescence in a 96 well plate in your plate reader. By measuring these in your plate reader, you will generate a standard curve of fluorescence for fluorescein concentration. You will be able to use this to convert your cell based readings to an equivalent fluorescein concentration. Before beginning this protocol, ensure that you are familiar with the GFP settings and measurement modes of your instrument. You will need to know what filters your instrument has for measuring GFP, including information about the bandpass width (530 nm / 30 nm bandpass, 25-30 nm width is recommended), excitation (485 nm is recommended) and emission (520-530 nm is recommended) of this filter. Note: the iGEM Particle Standard Curve with Microspheres calibration method is a pre-requisite for carrying out this protocol. You will need data from that calibration to analyse the results of this protocol.

You will prepare a dilution series of monodisperse silica microspheres and measure the Abs600 in your plate reader. The size and optical characteristics of these microspheres are similar to cells, and there is a known amount of particles per volume. This measurement will allow you to construct a standard curve of particle concentration which can be used to convert 600 nm absorbance measurements into an estimated equivalent number of cells.

This is a protocol from the 2018 iGEM InterLab study which tests various 'standard' parts from the Registry.

Conversion from forward scatter to Eμm is not a linear function, so data cannot be converted simply by multiplying with a scaling factor. We thus recommend use of software tools for processing data with size calibration. This protocol can be combined with bead-based fluorescence calibration.

This protocol can be applied to any strain of cell that can be safely run through a flow cytometer. For clarity, we have written it assuming E. coli DH5-alpha; to apply the protocol to another cell type, substitute the other cell type for any place where the protocol says [E. coli DH5-alpha]. This protocol has been written for measurement of GFP, YFP, or other yellow/green fluorescent proteins into MEFL units. To apply it to fluorescent proteins of other colors:. Replace BBa_J364001 with a construct for strong expression of the other protein. For blue proteins (e.g. mTagBFP), measure with 405nm excitation and 450nm/50nm emission filter. Units will be MEC30. For red/orange proteins (e.g. mCherry), measure with 561nm excitation and 610nm/20nm or 620nm/15nm emission filter. Units will be MEPTR. For far-red / near-infrared proteins (e.g. IRFP), measure with 635nm excitation and 780nm/60nm or 750nm long-pass (LP) emission filter. Units will be MEAPCY7. To apply the protocol to multiple colors, add a positive process control for each color and use one of the tools on the iGEM Measurement Resources page to compensate measurements for spectral overlap. This protocol can be combined with bead-based cell size calibration.

AbstractThe sediment microbiome is a demographically diverse and functionally active biosphere. Ensuring that data acquired from sediment is truly representative of the microbiome is critical to achieving robust analyses. Sample storage and the processing and timing of nucleic acid purification after environmental sample extraction may fundamentally affect the detectable microbial community and thereby significantly alter resultant data. Direct sequencing of environmental samples is increasingly commonplace due to the advent of the portable Oxford Nanopore MinION sequencing device. Here we demonstrate that storing sediment subsamples at − 20 °C or storing the cores at 4 °C for 10 weeks prior to analysis, has a significant effect on the sediment microbiome analysed using sedimentary DNA (sedDNA), especially for Alpha-, Beta- and Deltaproteobacteria species. Furthermore, these significant differences are observed regardless of sediment type. We show that the taxa which are predominantly affected by storage are Proteobacteria, and therefore recommend on-site purifications are performed to ensure an accurate representation of these taxa are observed in the microbiome. Comparisons of sedimentary RNA (sedRNA) analyses, revealed substantial differences between samples purified and sequenced immediately on-site, samples that were frozen before transportation, and cores that were stored at 4 °C prior to analysis. Our data therefore suggest that a more accurate representation of the sediment microbiome demography and functionality may be achieved by environmental sequencing as rapidly as possible to minimise confounding effects of storage.

. Weissella paramesenteroides
. has potential as an industrial biocatalyst due to its ability to produce lactic acid. A novel strain of
. W. paramesenteroides
. was isolated from ensiled sorghum. The genome was sequenced using a hybrid assembly of Oxford Nanopore and Illumina data to produce a 2-Mbp genome and 22-kbp plasmid sequence.
.

The formation of persister cells is one mechanism by which bacteria can survive exposure to environmental stresses. We show that Campylobacter jejuni 11168H forms persister cells at a frequency of 10−3 after exposure to 100 × MIC of penicillin G for 24 h. Staining the cell population with a redox sensitive fluorescent dye revealed that penicillin G treatment resulted in the appearance of a population of cells with increased fluorescence. We present evidence, to show this could be a consequence of increased redox protein activity in, or associated with, the electron transport chain. These data suggest that a population of penicillin G treated C. jejuni cells could undergo a remodeling of the electron transport chain in order to moderate membrane hyperpolarization and intracellular alkalization; thus reducing the antibiotic efficacy and potentially assisting in persister cell formation.

Designing improved vaccines against mutable viruses such as dengue and influenza would be helped by a better understanding of how the B-cell memory compartment responds to variant antigens. Towards this we have recently shown, after secondary immunization of mice with a widely variant dengue virus envelope protein with only 63% amino acid identity, that IgM+ memory B cells with few mutations supported an efficient secondary germinal centre (GC) and serum response, superior to a primary response to the same protein. Here, further investigation of memory responses to variant proteins, using more closely related influenza virus haemagglutinins (HA) that were 82% identical, produced a variant-induced boost response in the GC dominated by highly mutated B cells that failed, not efficiently improving serum avidity even in the presence of extra adjuvant, and that was worse than a primary response. This supports a hypothesis that over a certain level of antigenic differences, cross-reactive memory B-cell populations have reduced competency for affinity maturation. Combined with our previous observations, these findings also provide new parameters of success and failure in antibody memory responses.

Well-characterized promoter collections for synthetic biology applications are not always available in industrially relevant hosts. We developed a broadly applicable method for promoter identification in atypical microbial hosts that requires no a priori understanding of cis-regulatory element structure. This novel approach combines bioinformatic filtering with rapid empirical characterization to expand the promoter toolkit and uses machine learning to improve the understanding of the relationship between DNA sequence and function. Here, we apply the method in Geobacillus thermoglucosidasius, a thermophilic organism with high potential as a synthetic biology chassis for industrial applications. Bioinformatic screening of G. kaustophilus, G. stearothermophilus, G. thermodenitrificans, and G. thermoglucosidasius resulted in the identification of 636 100 bp putative promoters, encompassing the genome-wide design space and lacking known transcription factor binding sites. Eighty of these sequences were characterized in vivo, and activities covered a 2-log range of predictable expression levels. Seven sequences were shown to function consistently regardless of the downstream coding sequence. Partition modeling identified sequence positions upstream of the canonical -35 and -10 consensus motifs that were predicted to strongly influence regulatory activity in Geobacillus, and artificial neural network and partial least squares regression models were derived to assess if there were a simple, forward, quantitative method for in silico prediction of promoter function. However, the models were insufficiently general to predict pre hoc promoter activity in vivo, most probably as a result of the relatively small size of the training data set compared to the size of the modeled design space.