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TmRNA

TmRNA, also known as transfer-messenger RNA, is a unique bacterial RNA molecule that plays a crucial role in bacterial protein synthesis and quality control.
It functions as both a tRNA and an mRNA, helping to rescue stalled ribosomes and tag incomplete or aberrant proteins for degradation.
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Most cited protocols related to «TmRNA»

PHAST accepts both raw DNA sequence and GenBank annotated genomes. If given a raw genomic sequence (FASTA format), PHAST identifies all ORFs using GLIMMER 3.02 (14 (link)). This ORF identification step takes about 45 s for an average bacterial genome of 5.0 Mb. The translated ORFs are then rapidly annotated via BLAST using PHAST's non-redundant bacterial protein library (∼2–3 min/genome). Because tRNA and tmRNA sites provide valuable information for identifying the attachment sites, they are calculated using the programs tRNAscan-SE (15 (link)) and ARAGORN (16 (link)). If an input (GenBank formatted) file is provided with complete protein and tRNA information, these steps are skipped. Phage or phage-like proteins are then identified by performing a BLAST search against PHAST's local phage/prophage sequence database along with specific keywords searches to facilitate further refinement and identification. Matched phage or phage-like sequences with BLAST e-values less than 10−4 are saved as hits and their positions tracked for subsequent evaluation for local phage density by DBSCAN (17 ).
Publication 2011
Bacterial Proteins Bacteriophages DNA Library Genome Genome, Bacterial Open Reading Frames Prophages Proteins tmRNA Transfer RNA
We have developed a tool, called ICEfinder, available online and as a standalone version for the rapid detection of ICEs and IMEs in bacterial genome sequences. ICEfinder employs a method we called ‘Pattern-based hit co-localization’ (see the Supplementary Methods) that detects the signature sequences of the recombination modules and conjugation modules based on their profile HMMs (19 (link)) (Supplementary Table S2, S3 and Figure S4). It also searches for the oriT region using the approach proposed by oriTfinder (18 (link)). It then co-localizes, filters and groups the corresponding genes. At last, those elements carrying an integrase gene, a relaxase gene and T4SS gene clusters (12 (link),20 (link)) are considered as T4SS-type ICEs, while those without T4SS but with integrase, replication and the AICE translocation-related proteins are thought to be putative AICEs. Those without T4SS but with integrase and relaxase are tagged as putative IMEs. ICEfinder also tries to detect some particular IMEs with integrase and an oriT but no relaxase. ICEfinder employs ARAGORN (21 (link)) with the default parameters to identify the 3′ termini of the tRNA/tmRNA genes as the putative ICE insertion sites. It also uses Vmatch (http://vmatch.de/) with the default options to detect the directed repeats as the tRNA-distal boundaries. The acquired antibiotic resistance genes and virulence factors are also identified by NCBI BLASTp (22 (link)) with the cut-off of Ha-value of 0.64 (12 (link)).
The ICEfinder online tool allows users to submit a GenBank file containing a nucleotide sequence and its annotation as a query. A FASTA format file of a raw nucleotide sequence is also accepted, which is annotated using our gene annotation tool CDSeasy (12 (link)) and is then used as the input for the following ICE detection. ICEfinder uses the CGView circular genome visualization tool (23 (link)) to display the distribution of the predicted T4SS-type ICEs, IMEs and AICEs in the query bacterial genome. In addition, the ICEfinder has a comparison module (Supplementary Figure S5) that allows performing the alignment between the identified ICE loci against the ICEberg-archived ICEs using MultiGeneBlast (24 (link)).
Publication 2018
Antibiotic Resistance, Microbial Base Sequence DNA Replication Gene Annotation Gene Clusters Genes Genome Genome, Bacterial Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Ice Icebergs Integrase Proteins Recombination, Genetic tmRNA Transfer RNA Translocation, Chromosomal Virulence Factors
Our algorithm detects islands primarily by their target site, while other orthogonal methods look for phage-like genome content or for anomalous nucleotide composition. We evaluated the whole genomes in our study with PHAST (29 (link)) to identify prophage-like regions and with Alien_hunter (30 (link)) to find regions with biased composition. Figure 3A shows base-pair coverage of the genomes by Islander (2.34% of the total genome length), PHAST (1.70%) and Alien_hunter (19.3%), and their considerable overlaps. A key overlap is the 17.6% of the DNA of Islander islands that is covered by PHAST calls; the remaining PHAST regions may indicate prophages in sites other than tDNAs, or having lost an integrase gene or the tDNA fragment. Alien_hunter regions, despite their high genomic coverage, are enriched in Islander islands.
Figure 3B illustrates the island length distribution among Islander islands. There are distinct peaks centered at 13.6 kbp and 39.9 kbp, the latter mostly due to islands with PHAST overlap. These peaks are both found in the size profile of RefSeq prokaryotic viruses, which we show broken down by virus phylogeny in Figure 3C, with interesting trends. (Note that neither size profile necessarily represents the frequencies of islands or viruses in the natural world; each is biased by the selections researchers have made for genome sequencing.) The RefSeq profile matches best to the portion of the island profile with PHAST overlap (red segments in Figure 3B). The 40 kbp peak is enriched in Podoviridae and Siphoviridae. That the 40 kbp peak is negligible among non-PHAST islands (blue segments in Figure 3B) indicates that PHAST finds most of the Islander islands that are prophages, at least at this size range. Extrapolating, if nearly all self-mobilizing prophages among Islander islands are among the 1119 found by PHAST, that leaves ∼71% of Islander islands whose mobility must be explained by other means, perhaps either as PHAST-undetectable satellite phages or by conjugation. PHAST-overlapping islands are also enriched in the 13.6 kbp peak, but less so than in 40 kbp peak, and there may be a relative shift between PHAST and non-PHAST components within this peak. This peak is not explained by the single stranded DNA viruses populating that size range of Figure 3C, since none of them encode integrases.
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Publication 2014
Aliens Bacteriophages Base Pairing DNA, Single-Stranded Genes Genome Integrase Lanugo Nucleotides Podoviridae Prokaryotic Cells Prophages Range of Motion, Articular Siphoviridae T-DNA Virus
One of the original intentions of Phage_Finder was to have a program that can distinguish between largely intact, possibly functional prophages versus small regions or clusters of prophage remnants and other mobile elements. It takes advantage of several features of functional prophages to filter out unwanted fragmented regions. Since functional temperate phages integrate as linear molecules in a size range of 18–150 kb, good candidate prophage regions should have clusters of phage-like genes in this size range. Functional phages also have a large fraction of hypothetical or conserved hypothetical proteins. These stretches of phage-like and unknown genes are consecutive, not broken up by operons of house-keeping genes, although an occasional metabolic enzyme can be encoded on a phage. Tailed phages will have a conserved late gene operon that is responsible for packaging and head morphogenesis (21 (link)–23 (link)). This conserved region includes a small and large terminase subunit to recognize pac or cos sites and to cleave phage genome concatemers for packaging of the phage genome into capsids (24 (link)); a portal protein to form a hole for passage of the phage genome during packaging and release (25 ); a prohead protease to generate mature capsids (23 (link),26 ); and the major capsid protein that forms the bacteriophage capsid or head (23 (link)). A functional prophage region will also lack ribosomal RNA sequences.
The boundaries of functional prophages that integrated into specific locations can be determined by locating a site-specific recombinase at one of the ends of the phage region. Phages can integrate into tRNA/tmRNA genes, other conserved genes or intragenic regions. Since many phages and genetic elements tend to have an affinity for tRNA genes as the target for integration, any tRNA/tmRNA gene present within the putative phage region is checked first as a target for integration. The integrase can integrate into the anticodon-loop, the T-loop, or the 3′ end of the tRNA/tmRNA gene (27 (link)). The phage genome will contain sequence near the integrase gene that is homologous to the 3′ part of its target to avoid inactivating the gene after insertion (28 (link)). Following integration, the target gene will be a fusion with the 5′ end being of bacterial origin and the 3′ end of phage origin and the original bacterial-derived 3′ end on the other side of the inserted phage genome. By searching the other end of the putative phage region with the sequence of the tRNA/tmRNA gene, including extra sequence in case of miscalculation of the boundaries, one can identify what looks like a target-site duplication, the sequence of the replaced 3′ end of the tRNA/tmRNA gene. This homologous sequence, flanking the genome of the integrated phage genome, is referred to as the core attachment site (attcore) and the two half sites are attL (the phage-derived sequence) and attR (the original bacterial sequence). Sequences that are 3′ of the tRNA/tmRNA gene can also be part of the attcore (27 (link)).
Publication 2006
Anticodon Bacteria Bacteriophages Capsid Capsid Proteins Caudovirales Enzymes Gene Components Gene Insertion Genes Genes, Bacterial Genes, Housekeeping Genome Head Homologous Sequences Integrase Morphogenesis Operon Peptide Hydrolases Procapsid Prophages Proteins Protein Subunits Ribosomal RNA Site-specific recombinase terminase tmRNA Transfer RNA TTR protein, human
To obtain the set of isolate genomes to be analyzed, we downloaded all archaeal and bacterial genomes (220 561 genomes) of the GenBank database at the date of the 17th of April 2019. We removed genome assemblies that do not respect quality control criteria defined by GenBank. They correspond to entries with an assembly status flag different from “status = latest” in the “assembly_status.txt” files. In addition, genomes were discarded if they had more than 1000 contigs or a L90 > 100. These filters allowed us to exclude poor quality assemblies, some of which may correspond to contaminated genomes and others to incomplete ones. For each species (identified by its NCBI species taxid), a pairwise genomic distance matrix was computed using Mash (version 2.0) [37 (link)]. To avoid redundancy, if several genomes are at a Mash distance < 0.0001, only one was kept (the one having the lowest number of contigs). A single linkage clustering using SiLiX (version 1.2.11) [53 (link)] was then performed on the adjacency graph of the Mash distance matrix considering only distances below or equal to 0.06. This Mash distance corresponds to a 94% Average Nucleotide Identity (ANI) cutoff which is a usual value to define species [54 (link)]. Genomes that were not in the largest connected component were discarded to remove potential taxonomic assignation errors. Only species having at least 15 remaining genomes were then considered for the analysis. The list of all the GenBank assembly accessions used after filtering is available in S3 File. This dataset consists of 439 species encompassing 136 287 genomes (see S1 File). MAGs from the Pasolli et al. study [41 (link)] were downloaded from https://opendata.lifebit.ai/table/SGB. In this dataset, the genomes are already grouped in Species Genome Bins. These SGBs do not exactly match the GenBank taxonomy. Thus, SGBs assigned with the same species name (column “estimated taxonomy” in the supplementary table S4 of [41 (link)]) were merged to allow comparison with GenBank. SGBs that do not have a taxonomy assigned at the species level were not considered. A total of 583 species encompassing 698 SGBs and 71 766 MAGs were analyzed but only MAGs from 78 species were finally compared to GenBank genomes. To avoid introducing a bias in our analysis due to heterogeneous gene calling, GenBank annotations were not considered as they were obtained using a variety of annotation workflows. Genomes from GenBank and Pasolli et al were consistently annotated using the procedure implemented in PPanGGOLiN. Prodigal (version 2.6.2) [55 (link)] is used to detect the coding genes (CDS). tRNA and tmRNA genes are predicted using Aragorn (version 1.2.38) [56 (link)] whereas the rRNA are detected using Infernal (version 1.1.2) [57 (link)] with HMM models from Rfam [58 (link)]. In the case of overlaps between a RNA and a CDS, the overlapping CDS are discarded. Homologous gene families were determined using MMseqs2 (version 8-fac81) [59 (link)] with the following parameters: coverage = 80% with cov-mode = 0, minimal amino acid sequence identity = 80% and cluster-mode = 0 corresponding to the Greedy Set Cover clustering mode. PPanGGOLiN partitioning was executed on each species using the NEM approach with a parameter β = 2.5. The nodes having a degree above 10 (which is the default parameter) were not considered to smooth the partitioning via the MRF. The number of partitions (K) was determined automatically for each NCBI species using a δICL = 0.05 and iterating between 3 and 20 for the possible values of K. K was fixed at 3 for the MAG analysis. The partitioning was done using chunks of 500 genomes when there were more than 500 genomes in a species. To compare PPanGGOLiN results between MAGs and GenBank genomes for each species, the representative sequences of each MAG gene family (extracted using the mmseqs2 subcommand: “result2repseq”) were aligned (using mmseqs2 “search”) on those of GenBank genomes. If the best hit of the query had a sequence identity > 80% and a coverage > 80% of the target, the 2 corresponding gene families of each dataset were associated.
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Publication 2020
Amino Acid Sequence Archaea Genes Genetic Heterogeneity Genome Genome, Bacterial Nucleotides Ribosomal RNA Simpson-Golabi-Behmel Syndrome, Type 1 tmRNA Transfer RNA

Most recents protocols related to «TmRNA»

CTF parameter determination, reference-free particle picking, and stack creation were carried out in cisTEM (v1.0-beta; Grant et al., 2018 (link)). Particle alignment and refinement were carried out in FREALIGNX (Lyumkis et al., 2013 (link)). Data processing was initially performed independently for each dataset (Supplementary Figure S1A). To speed up the processing, 2×− and 4×−image stacks were prepared using resample.exe, which is part of the FREALIGN distribution (Lyumkis et al., 2013 (link)). The initial model for particle alignment of 70S maps was the 11.5 Å resolution EMDB-1003 (Gabashvili et al., 2000 (link)), sampled to match 4×−binned image stack using resample.exe. Three rounds of mode 3 search alignment to 25 Å were run using the 4×−binned stack. Next, 25–30 rounds of mode 1 refinement were run with the 4×−binned, 2×−binned, and eventually unbinned stacks until the resolutions stopped improving, to the final resolutions of 2.8 Å and 2.7 Å of the overall maps. 3D maximum-likelihood classification into 20 classes was performed in FREALIGN v9.11 to separate 70S conformations, 50S subunits, and junk (poorly aligned or damaged) particles. An unexpected class emerged in each stack, featuring density near the 30S head, which connects the mRNA tunnel with the A-site finger and P site. The 70S classes with different tRNA occupancies and ribosome conformations (including the tmRNA class) were extracted into a stack per data set, using merge_classes.exe from FREALIGN distribution. Two 70S stacks were merged using IMOD 4.7 (Kremer et al., 1996 (link)).
The merged 70S stack was refined as described above, yielding a final average 70S reconstruction at 2.8 Å resolution. The refined parameters were used to run a 3D maximum-likelihood classification into 32 classes without a mask, with an ASF-covering mask, or with the A-site-covering mask. All masks were “spherical,” also known as “2D” masks on micrographs (Grigorieff, 2016 (link)), as opposed to specifically shaped “3D” masks (Supplementary Figure S1A). The tmRNA class was found in the no-mask and ASF-mask classifications. Particles with tmRNA density resulting from the ASF-mask classification were extracted into a substack. The tmRNA substack was classified at 4× with a P- and A-site covering mask or the E-site mask to further purify the tmRNA-containing density (P-A mask) or the E-tRNA-containing density (E mask). The unbinned stack was used to yield the resulting cryo-EM reconstructions with tmRNA (with A-tRNA and partial E-tRNA) and with tmRNA (with full-occupancy A-tRNA and E-tRNA) at resolutions of 3.7 Å and 3.9 Å, respectively (Supplementary Figure S1A).
Fourier Shell Correlation (FSC) curves were calculated by FREALIGNX for even and odd particle half-sets (Supplementary Figure S1B). The maps used for structure refinements was B-factor sharpened using the B factor of −100 Å2 up to 3.4 Å (tmRNA with A-tRNA) and −50 Å2 to 3.8 Å (tmRNA with A-tRNA and E-tRNA), using bfactor.exe (included with the FREALIGN distribution; Lyumkis et al., 2013 (link)).
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Publication 2024
Structure of the non-rotated 70S•tRNA3 complex (V-B; PDB 6WDE; Loveland et al., 2020 (link)) and structures of 70S•tmRNA complexes (PDB: 6Q98 and 7ACJ; Rae et al., 2019 (link); D’Urso et al., 2023 (link)) were used as starting models for 70S ribosome and tmRNA fitting, respectively. The model of tRNAAla (GGC anticodon) from PDB:6OF6 (Nguyen et al., 2020 (link)) was modified to fit the cryo-EM map and match the nucleotide sequence of tRNAAla (UGC). The 50S, 30S and tmRNA domains were fitted using UCSF Chimera 1.6 (Goddard et al., 2018 (link); Pettersen et al., 2021 (link)) and locally modeled in Pymol1.2r1 (DeLano, 2002 ). The fitted structures were refined conservatively, using secondary-structure restraints and low simulated-annealing temperatures (100 K, 300 K or 500 K), against cryo-EM maps using phenix.real_space_refine v1.19.2 (Adams et al., 2010 (link)). Refinement parameters, such as the relative weighting of stereochemical restraints and experimental energy term, were optimized to produce the optimal structure stereochemistry and real-space correlation coefficients (Supplementary Table S1). B-factors of the models were refined at the final stages using phenix.real_space_refine. Structure stereochemistry validation was performed using phenix.molprobity.
Structure superpositions and distance calculations were performed in PyMOL. To calculate the angles of the 30S rotation and head tilt, 23S rRNAs of corresponding structures were aligned using PyMOL, and the angle between 16S domains were measured in Chimera. Figures were prepared in PyMOL and Chimera.
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Publication 2024

Example 2

In the following examples, the method to perform antimicrobial sensitivity testing of bacteria in whole blood sample after partial lysis of blood cells and enrichment of the blood culture was described. Klebsiella pneumoniae species was used as an example. The blood cells in the K. pneumoniae spiked whole blood were partially lysed by a lysing reagent, and most of the supernatant was removed after centrifugation. The remaining minimal volume of the liquid was enriched with TSB growth medium and pre-incubated at 37° C. for to allow the bacteria in the mixture multiply. At the end the pre-incubation period, the antimicrobial sensitivity test was performed by aliquoting the culture into multiple tubes, one without antibiotic and the remaining with different antibiotics.

K. pneumoniae cells were spiked into 2 mL of EDTA-treated whole blood at 100 CFU/mL concentrations. Blood cell lysing reagent containing 40 mg/mL Saponin, 10 mg/mL SPS and 1% PPG in water of 200 uL was added to the blood. The content was thoroughly mixed by inverting followed by vortexing at low speed for 10 sec, centrifugation at 12,000 rpm for 2 min and removal of 1.8 mL of the supernatant, leaving 200 μL of the liquid and the sedimented remaining blood cells and the microbial cells. One wash cycle was performed by adding 1.8 mL of TSB medium to the remaining liquid supernatant of 200 uL, mixing by inverting, centrifugation at 12,000 rpm for 2 min and removal of 1.8 mL of the supernatant, 200 μL of the liquid and the sedimented remaining blood cells and the microbial cells. The remaining minimal volume of the liquid was supplemented with 3.8 mL TSB growth medium, mixed thoroughly by inverting and pre-incubated at 37° C. for 2 hours to allow the bacteria in the mixture multiply. At the end the pre-incubation period, 1 mL each of the culture was distributed into multiple tubes. One tube was designated as non-treated growth control tube without any antibiotic, and 8 ug/mL final concentrations of norfloxacin and tetracycline were added to the remaining respective tubes. The culture tubes were incubated at 37° C. for 2 hours and at the end of the incubation time the respective TSB-enriched blood culture of 1 mL was subjected to blood sample pretreatment procedure which includes blood cell lysis step followed by 4 wash cycles was performed as described above. K. pneumoniae cells in the resulting pretreated sample was lysed by electrical lysis and the cell lysate of 1 uL containing nominal 1 cell was subjected to RT-PCR.

The rRNA primers used in this example are rRNA Primer #1 (enterob2) forward (5′-GTGCCCTTGAGGCGTGGCTTC-3′) (SEQ. ID. 5), rRNA Primer #1 (enterob2) reverse (5′-GCGGGACTTAACCGAACATTCAC-3′) (SEQ. ID. 6), rRNA Primer #2 (enterob4) forward (5′-ACAAGCGGTGGAGCATGTGG-3′) (SEQ. ID. 7), rRNA Primer #2 (enterob4) reverse (5′-GCGGGACTTAACCCAACATTTCAC-3′) (SEQ. ID. 8), rRNA Primer #3 (ebGN3) forward (5′-ACTTTCAGCGGGGAGGAAGG-3′) (SEQ. ID. 9) and rRNA Primer #3 (ebGN3) reverse (5′-GCGGGACTTAACCCAACATTTCAC-3′) (SEQ. ID. 10). The 16S rRNA fragments of 203 base pairs (nucleotides 504 to 707 using K. pneumoniae str. Kp52.145 as a reference) were amplified by primer pair #1, 166 base pairs by primer pair #2 and 666 base pairs by primer pair #3.

The tmRNA primers used in this example are tmRNA Primer #A forward (5′-GCAAACGACGAAAACTACGCTTTAGC-3′) (SEQ. ID 11), tmRNA Primer #A reverse (5′-GCTTAGTCAGTCTTTACATTCGC-3′) (SEQ. ID 12), tmRNA Primer #B forward (5′-GCAAACGACGAAAACTACGCTTTAGC-3′) (SEQ. ID 13), tmRNA Primer #B reverse (5′-CGGACGGACACGCCACTAAC-3′) (SEQ. ID 14), tmRNA Primer #C forward (5′-GCAAACGACGAAAACTACGCTTTAGC-3′) (SEQ. ID 15), tmRNA Primer #C reverse (5′-CCTACATCCTCGGTACTACATGC-3′) (SEQ. ID 16), tmRNA Primer #D forward (5′-GGGATTTGCGAAACCCAAGGTGC-3′) (SEQ. ID 17), tmRNA Primer #D reverse (5′-GTTTTAACGCTTCAACCCCAGGC-3′) (SEQ. ID 18), tmRNA Primer #E forward (5′-GGGATTTGCGAAACCCAAGGTGC-3′) (SEQ. ID 19), tmRNA Primer #E reverse (5′-GCTTAGTCAGTCTTTACATTCGC-3′) (SEQ. ID 20), tmRNA Primer #F forward (5′-GGGATTTGCGAAACCCAAGGTGC-3′) (SEQ. ID 21), tmRNA Primer #F reverse (5′-CGGACGGACACGCCACTAAC-3′) (SEQ. ID 22), tmRNA Primer #G forward (5′-GGGATTTGCGAAACCCAAGGTGC-3′) (SEQ. ID 23), tmRNA Primer #G reverse (5′-CCTACATCCTCGGTACTACATGC-3′) (SEQ. ID 24). The tmRNA fragments of 218 base pairs (nucleotides 97 to 315 using K. pneumoniae str. Kp52.145 as a reference) were amplified by primer pair #A, 183 base pairs by primer pair #B, 240 base pairs by primer pair #C, 221 base pairs by primer pair #D, 293 base pairs by primer pair #E, 258 base pairs by primer pair #F and 315 base pairs by primer pair #G.

The real time fluorescence signal versus cycle number is presented in FIGS. 8 and 9 to demonstrate the sensitivity tests using norfloxacin and tetracycline. In this example, the reduction of rRNA and tmRNA in response to the antibiotic was demonstrated compared to without antibiotic (Non-treated) culture.

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Patent 2024

Example 1

In the following examples, the method to ensure the growth of bacteria in whole blood sample after partial lysis of blood cells and enrichment of the blood culture was described. Klebsiella pneumoniae species was used as an example. The blood cells in the K. pneumoniae spiked whole blood were partially lysed by a lysing reagent, and most of the supernatant was removed after centrifugation. The remaining minimal volume of the liquid containing harvested microbial cells was enriched with TSB growth medium and pre-incubated at 37° C. to allow the bacteria in the mixture multiply.

K. pneumoniae cells were spiked into 2 mL of EDTA-treated whole blood at 100 CFU/mL concentrations. Blood cell lysing reagent of 200 uL, containing 40 mg/mL Saponin, 10 mg/mL SPS and 1% PPG, was added to the blood. The content was thoroughly mixed by inverting followed by vortexing at low speed for 10 sec, centrifugation at 12,000 rpm for 2 min and removal of 1.8 mL of the supernatant, leaving 200 μL of the liquid and the sedimented remaining blood cells and the microbial cells. One wash cycle was performed by adding 1.8 mL of TSB medium to the remaining liquid supernatant of 200 uL, mixing by inverting, centrifugation at 12,000 rpm for 2 min and removal of 1.8 mL of the supernatant, 200 μL of the liquid and the sedimented remaining blood cells and the microbial cells. The remaining minimal volume of the liquid was enriched with 3.8 mL TSB growth medium, mixed thoroughly by inverting and pre-incubated at 37° C. for 2 hr to allow the bacteria in the mixture multiply. At 0, 1 and 2 hr time point of pre-incubation, 1 mL each of the culture was subjected to blood sample pretreatment procedure which includes blood cell lysis step followed by 4 wash cycles was performed as described above.

K. pneumoniae cells in the resulting pretreated sample was lysed by electrical lysis and the cell lysate of 1 uL containing nominal 1 cell was subjected to RT-PCR. The rRNA primers used in this example are rRNA Primer #2 (enterob4) forward (5′-ACAAGCGGTGGAGCATGTGG-3′) (SEQ. ID 1) and rRNA Primer #2 (enterob4) reverse (5′-GCGGGACTTAACCCAACATTTCAC-3′) (SEQ. ID 2). The 16S rRNA fragments of 166 base pairs were amplified by rRNA primer pair #2. The tmRNA primers used in this example are tmRNA Primer #C forward (5′-GCAAACGACGAAAACTACGCTTTAGC-3′) (SEQ. ID 3) and tmRNA Primer #C reverse (5′-CCTACATCCTCGGTACTACATGC-3′) (SEQ. ID 4). The tmRNA fragments of 240 base pairs (nucleotides 97 to 337 using K. pneumoniae str. Kp52.145 as a reference) were amplified by tmRNA primer pair #C. The real time fluorescence signal versus cycle number is presented in FIG. 3 to show the growth of bacteria in the enriched blood culture.

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Patent 2024
Rifampicin was used as the treatment to stop the transcription [35 (link)]. S. aureus ST398 strain and its derivatives were cultured overnight, diluted to 1/100, grown for 5 h at 37°C, and incubated with 20 mg/ml rifampicin. About 8 ml of each strain was collected before and at 5, 10, 20, 30 min after adding rifampicin. These samples were centrifuged, the pellets were frozen in liquid nitrogen then stored at −80°C. Total RNA was extracted. cDNAs preparations and qRT-PCR experiments were performed as previously described. The tmRNA was used for normalization.
Publication 2024

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More about "TmRNA"

Transfer-messenger RNA, also known as tmRNA or SsrA RNA, is a unique bacterial RNA molecule that plays a crucial role in protein synthesis and quality control.
This multifunctional RNA serves as both a tRNA and an mRNA, helping to rescue stalled ribosomes and tag incomplete or aberrant proteins for degradation. tmRNA is involved in the trans-translation process, which is a bacterial mechanism for recycling stalled ribosomes and ensuring the proper disposal of incomplete or defective proteins.
The tmRNA molecule acts as a 'resume tape,' providing the stalled ribosome with an mRNA-like template to resume translation and append a peptide tag to the incomplete protein, marking it for proteolytic degradation.
In addition to its role in protein quality control, tmRNA has been studied for its potential applications in various areas of molecular biology and biotechnology.
Researchers have utilized techniques like Northern blotting (using Hybond-N+ membranes and Rapid-hyb buffer), in vitro transcription (with the MEGAscript T7 Transcription Kit), and qPCR (with Power SYBR Green PCR Master Mix) to investigate tmRNA structure, function, and expression.
Furthermore, the study of tmRNA has benefited from advancements in next-generation sequencing, such as the use of the NEBNext® UltraTM RNA Library Prep Kit for Illumina® and the Agilent 2100 Bioanalyzer for quality control.
These tools have enabled researchers to delve deeper into the complex world of bacterial RNA, including the identification and characterization of tmRNA and other regulatory RNAs.
By understanding the intricacies of tmRNA and its role in bacterial protein synthesis and quality control, scientists can develop more effective strategies for studying and manipulating bacterial systems, paving the way for advancements in areas like antimicrobial development, biotechnology, and synthetic biology.
Discover how PubCompare.ai's AI-driven platform can help you optimize your tmRNA research by locating, comparing, and identifying the most reproducible and accurate protocols from the scientific literature, preprints, and patents.