Vaginal microbial profiling in a preterm birth high-risk cohort using shallow shotgun metagenomics

Preterm birth (PTB) is a significant health problem globally, with an estimate of 15 million cases annually. Approximately 10% of neonates born early will die prematurely, while a subset will develop severe life-long morbidities. Unfortunately, preterm birth's syndromic nature has evaded prevention strategies, and it continues to impose a high burden on healthcare systems and families. The role of vaginal bacteria in triggering biomolecular causes of PTB has been recognised for years. However, translating this knowledge to practical diagnostic and therapeutic strategies has remained elusive. New techniques in high-throughput sequencing have improved our understanding of the nature and role of the vaginal microbiome during pregnancy. Several multi-ethnic and multi-geographical studies into the vaginal microbiome have identified five distinct bacterial profiles termed community state types (CSTs), one of which is positively associated with dysbiosis and increased risk of PTB. In a small pilot study of first-trimester vaginal microbial DNA obtained from pregnant women at high-risk of PTB, we compared the CST profiles generated using standard 16S amplicon sequencing with shallow shotgun metagenomics (SSM). Both methods identified the presence of the five CSTs as has been reported previously, although the metagenomic data showed greater taxonomic resolution and more accurate CST assignation. These findings suggest that SSM is a cost-effective and potentially superior alternative to 16S sequencing for vaginal microbiome analysis

Fecal sample collection methods and time of day impact microbiome composition and short chain fatty acid concentrations

Associations between the human gut microbiome and health outcomes continues to be of great interest, although fecal sample collection methods which impact microbiome studies are sometimes neglected. Here, we expand on previous work in sample optimization, to promote high quality microbiome data. To compare fecal sample collection methods, amplicons from the bacterial 16S rRNA gene (V4) and fungal (ITS2) region, as well as short chain fatty acid (SCFA) concentrations were determined in fecal material over three timepoints. We demonstrated that spot sampling of stool results in variable detection of some microbial members, and inconsistent levels of SCFA; therefore, sample homogenization prior to subsequent analysis or subsampling is recommended. We also identify a trend in microbial and metabolite composition that shifts over two consecutive stool collections less than 25 h apart. Lastly, we show significant differences in bacterial composition that result from collecting stool samples in OMNIgene·Gut tube (DNA Genotec) or Stool Nucleic Acid Collection and Preservation Tube (NORGEN) compared to immediate freezing. To assist with planning fecal sample collection and storage procedures for microbiome investigations with multiple analyses, we recommend participants to collect the first full bowel movement of the day and freeze the sample immediately after collection

DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea

<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 214.1.  Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r214 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well.  </em></strong></p> <p> </p> <p>This Version was primarily updated because we have recently updated the RefSeq+RDP database and also included mitochondrial and eukaryotic 16S rRNA sequences. Also because I decided to include the required formats to be able to use the addSpecies command in DADA2. This command searches the database at 100% identity and has the flexibility to either get the best hit or multiple hits to your amplicon. I recommend it if you are using a single or 2 region amplicons of the 16S rRNA gene.</p> <p>These two combined bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>RefSeq+RDP: This database contains 22433 bacterial, 1055 archaea and 99 eukaryotic full lengths16S rRNA gene sequences.  It was compiled by <strong>Paul Greenfield </strong>on the <strong>06/11/2020</strong> from predominantly the NCBI RefSeq 16S rRNA database (https://www.ncbi.nlm.nih.gov/refseq/targetedloci/16S_process/) and was supplemented with extra sequences from the RDP database (https://rdp.cme.msu.edu/misc/resources.jsp).</li> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 46891 bacteria and 2812 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted.  The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 19/12/2023. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence ID be it NCBI/RDP or GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>Bash script can be provided on request

Regulation of Epidermal Growth Factor Receptor Signaling and Erlotinib Sensitivity in Head and Neck Cancer Cells by miR-7

<div><p>Elevated expression and activity of the epidermal growth factor receptor (EGFR)/protein kinase B (Akt) signaling pathway is associated with development, progression and treatment resistance of head and neck cancer (HNC). Several studies have demonstrated that microRNA-7 (miR-7) regulates EGFR expression and Akt activity in a range of cancer cell types via its specific interaction with the EGFR mRNA 3′-untranslated region (3′-UTR). In the present study, we found that miR-7 regulated EGFR expression and Akt activity in HNC cell lines, and that this was associated with reduced growth <em>in vitro</em> and <em>in vivo</em> of cells (HN5) that were sensitive to the EGFR tyrosine kinase inhibitor (TKI) erlotinib (Tarceva). miR-7 acted synergistically with erlotinib to inhibit growth of erlotinib-resistant FaDu cells, an effect associated with increased inhibition of Akt activity. Microarray analysis of HN5 and FaDu cell lines transfected with miR-7 identified a common set of downregulated miR-7 target genes, providing insight into the tumor suppressor function of miR-7. Furthermore, we identified several target miR-7 mRNAs with a putative role in the sensitization of FaDu cells to erlotinib. Together, these data support the coordinate regulation of Akt signaling by miR-7 in HNC cells and suggest the therapeutic potential of miR-7 alone or in combination with EGFR TKIs in this disease.</p> </div

miR-7 regulates EGFR expression and Akt signaling in HNC cell lines.

<p>(A) Western blotting analysis of EGFR, P-EGFR, Akt and P-Akt levels in HN5 (left panel) and FaDu (center panel) and SCC-25 (right panel) cells 3 d after transfection with miR-7 or miR-NC precursor molecules or vehicle (LF2000) only. β-actin is included as a loading control. (B) RT-qPCR analysis of EGFR mRNA expression in HN5 cells 24 h after transfection with miR-7 or miR-NC precursor molecules. Data was normalized to GAPDH mRNA expression and expressed relative to miR-NC-transfected cells. (C) Luciferase reporter assay with FaDu cells co-transfected with miR-7 or miR-NC precursor molecules, a firefly luciferase full-length EGFR mRNA 3′-UTR reporter plasmid, and a <i>Renilla</i> luciferase reporter plasmid. Firefly luciferase activity was assessed 24 h post-transfection, normalized to <i>Renilla</i> luciferase measurements, and data was expressed relative to vehicle (LF2000) only-transfected cells. Error bars represent standard deviations. All data are representative of three independent experiments. *, p<0.001, miR-7 vs miR-NC.</p

Coordinate regulation of EGFR/Akt signaling by miR-7 in HNC cells.

<p>Schematic representation of molecules in the EGFR/Akt signaling pathway that are inhibited by miR-7 in HNC. IPA software was used to map common miR-7-downregulated genes (shown in green) in FaDu and HN5 cells onto the canonical PI3K/Akt pathway. The density of shading represents the fold-change downregulation of a gene by miR-7. Blue circles indicate that a gene is a predicted or validated target of miR-7 by IPA analysis. Several genes belonging to the PI3K/Akt pathway that were downregulated by miR-7 in HN5 cells only are shaded in light blue.</p

Synergistic inhibition of cell growth and EGFR/Akt signaling in erlotinib-resistant FaDu cells by miR-7 and erlotinib.

<p>(A) Cell titre analysis of FaDu cells that were transfected with vehicle only (LF2000), miR-7, or miR-NC for 3 d, and then treated with erlotinib (7.5 µM) or vehicle (DMSO) for a further 4 d. Data is expressed relative to vehicle-transfected, vehicle-treated FaDu cells (LF2000 minus erlotinib, first column). (B) Western blotting analysis of EGFR, P-EGFR, Akt and P-Akt levels in FaDu cells that were transfected with vehicle only (LF2000), or miR-7 or miR-NC for 3 d and then treated ± erlotinib (7.5 µM) for 24 h. β-actin is included as a loading control. (C) Densitometry analysis of P-Akt levels from western blotting between FaDu cells transfected with miR-NC or miR-7 and then treated with erlotinib (7.5 µM) for 24 h. Data is shown relative to miR-NC-transfected cells. Error bars represent standard deviations. All data are representative of three independent experiments. *, p<0.05, miR-7 minus erlotinib vs miR-NC minus erlotinib, and LF2000 plus erlotinib vs LF2000 minus erlotinib; **, p<0.01, miR-7 plus erlotinib vs miR-NC plus erlotinib. † indicates synergy between miR-7 and erlotinib as defined by the Bliss additivism model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047067#pone.0047067-Bliss1" target="_blank">[34]</a>.</p

Microarray analysis of miR-7-downregulated genes in HN5 and FaDu cells.

<p>(A) Volcano plots representing array probes between HN5 (left) and FaDu (right) cells 24 h after transfection with miR-7 or miR-NC precursor molecules. Assigning a cut off of ±1.5-fold change (miR-7 vs miR-NC) and p<0.05, significantly downregulated probes are in green and significantly upregulated probes are in red. (B) Cluster analysis of miR-7-downregulated genes in HN5 and FaDu cells, where green and red shading corresponds to downregulated and upregulated genes, respectively. (C) Venn diagram of miR-7-downregulated genes in HN5 and FaDu cells. (D) Scatter plot of miR-7-downregulated genes common to HN5 and FaDu cells (R² = 0.435, p<0.001).</p

A functional miR-7 target signature in HNC cells.

<p>miR-7-downregulated genes common to both HN5 and FaDu cells (103) were assigned to annotated cancer-associated processes using IPA software. These included “cell cycle”, “cell movement”, “cell proliferation”, “cell development”, “tumorigenesis”, “protein synthesis”, “angiogenesis” and “cell death”. Official gene symbols are used for each miR-7-downregulated gene and a blue circle indicates that a gene is a predicted or validated target of miR-7 by IPA analysis.</p

miR-7 inhibits HNC cell growth <i>in vitro</i>.

<p>(A) Colorimetric cell titre assay of HN5 cells 5 d after transfection in 96-well plates with miR-7 or miR-NC precursor molecules, or vehicle (LF2000) only. (B) Graphical representation of cell titre assay from (A). Data represents the relative number of viable HN5 cells normalized to LF2000-treated HN5 cells. (C) TaqMan RT-qPCR analysis of miR-7 expression in HN5 clones with stable expression of miR-7 (clone 39) or miR-NC (clone 2). Data was normalized to U44 snRNA expression and expressed relative to HN5 miR-NC clone 2. (D) Western blotting analysis of EGFR, Akt and P-Akt levels in HN5 clones with stable expression of miR-7 (clone 39) or miR-NC (clone 2). β-actin is included as a loading control. (E) Manual cell counting of HN5 cells with stable expression of miR-7 (clone 39) or miR-NC (clone 2). Data is expressed relative to miR-NC. (F) Clonogenicity assay of HN5 cells with stable expression of miR-7 (clone 39) or miR-NC (clone 2) 10 d after cells were seeded in 10 cm dishes. Error bars represent standard deviations. All data are representative of three independent experiments. *, p<0.01, miR-7 vs miR-NC; **, p<0.005, miR-7 vs miR-NC.</p