universal 16s rrna primers  (ATCC)

Journal: Infection and Drug Resistance

Article Title: High-throughput and Efficient Assay for Central Nervous System Infection with Targeted Nanopore Sequencing Technology

doi: 10.2147/IDR.S540638

Figure Lengend Snippet: Workflow of tNPS-based experimental and bioinformatics analysis for detecting pathogens of CNS infection. ( A ) Genomic DNA was first extracted from samples using boiling method. Second, the target regions of genes related to 17 high-priority in CNS infections (seven bacteria, one fungus, and nine viruses), the full-length gene regions of 16S rRNA and ITS, and three internal control plasmids were then amplified by multiplex PCR in a bacterial-fungal tube and a viral tube. Third, the amplicons were mixed and pooled for library preparation. Fourth, the library was subjected to nanopore sequencing to generate long-read sequence data for bioinformatics analysis. The turnaround time was approximately 8 h. ( B ) MinKNOW first collected real-time data, which were converted to FASTQ format (raw reads) by Guppy. Second, Q_score filtered the raw reads into clean reads with base quality values >9. Third, using Bowtie 2, the human reads were removed by matching human genome reference sequence (GRCH38.p14). Kraken 2 was followed to map these reads to the core_nt database for species classification, with the parameter “--minimum-hit-groups” set to 3 to improve accuracy. Bracken also estimated the abundance of each taxon at the species level (with –l S), using a read length of 300 bp (the shortest read length in the dataset) and a threshold of 10 reads (with –t 10) to filter out low-abundance species and reduce noise. The results of bioinformatics analysis were finally visualized by Pavian and GraphPad.

Article Snippet: In addition, the assay results from Zymo confirmed the utility of 16S rRNA/ITS universal primers for the detection of other potential bacteria and fungi, as reported by the literature.

Techniques: Infection, Bacteria, Control, Amplification, Multiplex Assay, Nanopore Sequencing, Sequencing

Journal: Nature Microbiology

Article Title: Learning ecosystem-scale dynamics from microbiome data with MDSINE2

doi: 10.1038/s41564-025-02112-6

Figure Lengend Snippet: a , Input data are measurements of total bacterial concentration (for example, 16S rRNA gene quantitative polymerase chain reaction (qPCR)) and measurements of taxa abundances (for example, 16S rRNA gene amplicon sequencing). Measurements should be obtained from studies in which the microbiome undergoes perturbations, providing effective information for inference. b , MDSINE2 infers dynamical systems models from data with the option of automatically learning interaction modules, or groups of taxa that share the same interactions with other modules and perturbations. This is a more compact representation that is more readily interpretable than learning interactions among all microbes. c , Example microbial interaction networks for the same number of taxa without module learning ( i ) and with module learning ( ii ). d , MDSINE is fully Bayesian and propagates error throughout the model ( i ), providing estimates of uncertainty for all variables (for example, latent trajectory along with measurements and their uncertainty ( ii ), and indicator and interaction strengths for ecological interactions ( iii )). Prob., probability. e – g , The software provides a variety of tools for analysis and visualization of the inferred dynamical system, including analyses of taxonomic composition and phylogeny of modules ( e ), formal analyses of ecosystem stability and interaction motifs ( f ), and keystoneness (quantitative impact on the ecosystem when modules are removed) ( g ).

Article Snippet: Briefly, for amplicon sequencing, the v4 region of the 16S rRNA gene was PCR amplified using 515F and 806R primers , 5’-[Illumina adaptor]-[unique bar code]-[sequencing primer pad]-[linker]-[primer]: (fwd primer): AATGATACGGCGACCACCGAGATCTACAC-NNNNNNNN-TATGGTAATT-GT-GTGCCAGCMGCCGCGGTAA (rev primer): CAAGCAGAAGACGGCATACGAGAT-NNNNNNNN-AGTCAGTCAG-CC-GGACTACHVGGGTWTCTAAT Following PCR of the v4 region, 250-bp paired-end reads were generated on an Illumina MiSeq with the following custom primers with index primer, ATTAGAWACCCBDGTAGTCC-GG-CTGACTGACT: 5’-[sequencing primer pad]-[linker]-[primer] Read 1: TATGGTAATT-GT-GTGCCAGCMGCCGCGGTAA 5’-[primer]-[linker]-[sequencing primer pad] Read 2: AGTCAGTCAG-CC-GGACTACHVGGGTWTCTAAT Total bacterial concentration estimation was performed with qPCR using universal 16S rRNA primers : 1048F: GTG STG CAY GGY TGT CGT CA 1175R: ACG TCR TCC MCA CCT TCC TC with a standard curve prepared from dilutions of Bacteroides fragilis (ATCC 51477).

Techniques: Concentration Assay, Real-time Polymerase Chain Reaction, Amplification, Sequencing, Software

Journal: The Journal of nutrition

Article Title: Protective Role of Dietary Short-Chain Fatty Acid Propionate against Autoimmune Responses and Pathology of Systemic Lupus Erythematosus in MRL-lpr Mice

doi: 10.1016/j.tjnut.2025.06.031

Figure Lengend Snippet: Propionate supplementation altered gut microbiome composition. Fecal bacterial DNA was isolated from CON and PA-treated MRL-lpr mice, and 16S rRNA sequencing was performed to evaluate the microbiome composition in each mouse. (A) The variety of organisms in a community is termed as α diversity, (B) different mouse strains showed distinct microbiome patterns (β diversity), (C) taxa relative abundance at the genus level. (D) Bar graph for the relative abundance of each genus. Results are mean ± SEM; n = 5, ** P < 0.01. CON, control; DNA, deoxyribonucleic acid; PA, propionate; SEM, standard error of the mean; 16S rRNA, 16S ribosomal ribonucleic acid; MRL-lpr, MRL/MpJ-Fas lpr .

Article Snippet: Sequencing libraries for each isolate were generated using universal 16S rRNA V3-V4 region primers in accordance with the Quick-16S Plus NGS library prep kit protocol (Zymo Research), including positive and negative controls for nucleic acid extractions and library preparation.

Techniques: Isolation, Sequencing, Control

Journal: bioRxiv

Article Title: Mosquito host background influences microbiome-ZIKV interactions in field and laboratory-reared Aedes aegypti

doi: 10.1101/2025.02.02.636091

Figure Lengend Snippet: Two Ae. aegypti lines reared in the insectaries of UTMB, Galveston (N=97) and Rio Grande Valley (RGV) (N=125), were offered a bloodmeal (red) spiked with ZIKV (yellow). Additionally, laboratory-reared mosquitoes were offered an uninfected bloodmeal (unexposed, pink). Ten days post bloodmeal (PBM) infection was assessed and mosquitoes were classified in exposed (ZIKV was not detected) (green) or infected (ZIKV was detected) (blue). Infection rate was assessed (right) and statistical difference is shown as * (Chi-square, p<0.05) ( A ). Relative abundance of bacterial 16S rRNA was measured in Galveston ( B ) and RGV ( C ) mosquitoes. Alpha diversity (Shannon diversity index) of the microbiome was assessed in Galveston ( D ) and RGV ( E ) mosquitoes. Statistical differences are shown as **** ( p <0.0001), ** ( p <0.01), * ( p <0.05) and ns (non-significant) (Wilcoxon Rank Test). Beta diversity of the microbiome was assessed in Galveston ( F ) and RGV ( G ) mosquitoes. p values show results of PERMANOVA analysis of Bray-Curtis dissimilarity. Subsequent pairwise testing of beta diversity indicated in the RGV group, there were statistically significant differences between both unexposed vs. exposed and unexposed vs. infected (both p <0.003).

Article Snippet: Sequencing libraries for each isolate were generated using universal 16S rRNA V3-V4 region primers following Illumina 16S rRNA metagenomic sequencing library protocols [ ].

Techniques: Infection

Journal: bioRxiv

Article Title: Mosquito host background influences microbiome-ZIKV interactions in field and laboratory-reared Aedes aegypti

doi: 10.1101/2025.02.02.636091

Figure Lengend Snippet: Field collected Ae. aegypti mosquitoes were collected from three locations in Texas; Austin (N=113), Galveston (N=40) and Brownsville (N=19), and offered a ZIKV infected blood meal. infection was assessed and mosquitoes were classified in exposed (ZIKV was not detected, green) or infected (ZIKV was detected, blue). Infection rate was assessed (right) and statistical difference is shown as * (Chi-square, p <0.05) ( A ). Relative abundance of bacterial 16S rRNA in Austin ( B ), Galveston ( C ) and Brownsville ( D ) mosquitoes. Alpha diversity (Shannon diversity index) of the microbiome in Austin ( E ), Galveston ( F ) and Brownsville ( G ) mosquitoes. Statistical differences are shown as ** ( p <0.01) and ns (non-significant) (Wilcoxon rank test). Beta diversity of the microbiome in Austin ( H ), Galveston ( I ) and Brownsville ( J ) mosquitoes. Pairwise PERMANOVA was used for statistical analysis of the Bray-Curtis dissimilarity distance of microbiomes (bottom right of panel).

Article Snippet: Sequencing libraries for each isolate were generated using universal 16S rRNA V3-V4 region primers following Illumina 16S rRNA metagenomic sequencing library protocols [ ].

Techniques: Infection