The targeted sequencing of the 16S rRNA gene is one of the most frequently employed techniques in the field of microbial ecology, with the bacterial communities of a wide variety of niches in the human body have been characterised in this way. This is performed by targeting one or more hypervariable (V) regions within the 16S rRNA gene in order to produce an amplicon suitable in size for next generation sequencing. To date, all technical research has focused on the ability of different V regions to accurately resolve the composition of bacterial communities. We present here an underreported artefact associated with 16S rRNA gene sequencing, namely the off-target amplification of human DNA. By analysing 16S rRNA gene sequencing data from a selection of human sites we highlighted samples susceptible to this off-target amplification when using the popular primer pair targeting the V3–V4 region of the gene. The most severely affected sample type identified (breast tumour samples) were then re-analysed using the V1–V2 primer set, showing considerable reduction in off target amplification. Our data indicate that human biopsy samples should preferably be amplified using primers targeting the V1–V2 region. It is shown here that these primers result in on average 80% less human genome aligning reads, allowing for more statistically significant analysis of the bacterial communities residing in these samples.

The effect of hydralazine treatment on 3 murine tumours (RIF-I, KHT and 16/C) was monitored using 31P-NMR. Changes in the 31p-NMR spectrum are compared with measurements of radiobiological hypoxic fraction (RHF) in the RIF-1 and KHT. Hydralazine is known to reduce temporarily blood flow in experimental tumours, and thus cause a transient increase in the RHF to 100% (in RIF-1 and KHT). This correlates with a decline in energy status as measured by 31p-NMR (i.e. there was an increase in Pi in all three tumours). Time-course data from the RIF-I and KHT tumours show that maintenance of anaesthesia prolongs the hypoxia induced by hydralazine.

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The relationship between bacterial communities and their host is being extensively investigated for the potential to improve the host's health. Little is known about the interplay between the microbiota of parasites and the health of the infected host. Using nematode coinfection of lambs as a proof-of-concept model, the aim of this study was to characterise the microbiomes of nematodes and that of their host, enabling identification of candidate nematode-specific microbiota member(s) that could be exploited as drug development tools or for targeted therapy. Deep sequencing techniques were used to elucidate the microbiomes of different life stages of two parasitic nematodes of ruminants, Haemonchus contortus and Teladorsagia circumcincta, as well as that of the co-infected ovine hosts, pre-and post-infection. Bioinformatic analyses demonstrated significant differences between the composition of the nematode and ovine microbiomes. The two nematode species also differed significantly. Data indicated a shift in the constitution of the larval nematode microbiome after exposure to the ovine microbiome, and in the ovine intestinal microbial community over time as a result of helminth co-infection. Several bacterial species were identified in nematodes that were absent from their surrounding abomasal environment, the most significant of which included Escherichia coli/Shigella. The ability to purposefully infect nematode species with engineered E. coli was demonstrated in vitro, validating the concept of using this bacterium as a nematode-specific drug development tool and/or drug delivery vehicle. To our knowledge, this is the first description of the concept of exploiting a parasite's microbiome for drug development and treatment purposes.

Background Formalin-fixed, paraffin-embedded (FFPE) tissue is the gold standard in pathology tissue storage, representing the largest collections of patient material. Their reliable use for DNA analyses could open a trove of potential samples for research and are currently being recognised as a viable source material for bacterial analysis. There are several key features which limit bacterial-related data generation from this material: (i) DNA damage inherent to the fixing process, (ii) low bacterial biomass that increases the vulnerability to contamination and exacerbates the host DNA effects and (iii) lack of suitable DNA extraction methods, leading to data bias. The development and systematic use of reliable standards is a key priority for microbiome research. More than perhaps any other sample type, FFPE material urgently requires the development of standards to ensure the validity of results and to promote reproducibility. Results To address these limitations and concerns, we have developed the Protoblock as a biological standard for FFPE tissue-based research and method optimisation. This is a novel system designed to generate bespoke mock FFPE ‘blocks’ with a cell content that is user-defined and which undergoes the same treatment conditions as clinical FFPE tissues. The ‘Protoblock’ features a mix of formalin-fixed cells, of known number, embedded in an agar matrix which is solidified to form a defined shape that is paraffin embedded. The contents of various Protoblocks populated with mammalian and bacterial cells were verified by microscopy. The quantity and condition of DNA purified from blocks was evaluated by qPCR, 16S rRNA gene amplicon sequencing and whole genome sequencing. These analyses validated the capability of the Protoblock system to determine the extent to which each of the three stated confounding features impacts on eventual analysis of cellular DNA present in FFPE samples. Conclusion The Protoblock provides a representation of biological material after FFPE treatment. Use of this standard will greatly assist the stratification of biological variations detected into those legitimately resulting from experimental conditions, and those that are artefacts of the processed nature of the samples, thus enabling users to relate the outputs of laboratory analyses to reality.

Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.

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