Biology subjects

Artuyants, A.

Publications and source records attributed to Artuyants, A..

4 recordsLinked to original sources

bioRxiv2025

Evaluating sequencing strategies for endometrial microbiome profiling in endometrial cancer: a comparative study of short- and long-read 16S rRNA approaches

BackgroundEndometrial cancer (EC) is the most common gynaecological malignancy globally, with rising incidence and notable disparities in outcomes. In New Zealand, EC rates have increased significantly, particularly among M[a]ori and Pacific women, who face higher risks of advanced disease and poorer outcomes. Microbial dysbiosis has been implicated in EC pathogenesis, but characterising the uterine microbiome is challenging due to low microbial biomass and high contamination risk. AimsThis study aimed to pilot a protocol that could inform the preparation of a larger cohort trial. Short-read Illumina MiSeq and long-read Oxford Nanopore Technologies (ONT) 16S rRNA gene sequencing were investigated to profile the uterine microbiome in people with EC. Methods and ResultsUterine and vaginal swabs were analysed to assess platform performance in terms of DNA recovery, sequencing success, diversity metrics, and taxonomic resolution. The impact of sample freezing or immediate lysis prior to DNA extraction was also evaluated. ONT sequencing provided enhanced species-level resolution and improved detection of low-abundance taxa but showed variable performance in low-yield samples. Freezing prior to cell DNA extraction modestly increased bacterial 16S copy numbers and improved community consistency. Contamination was a problem across both platforms, particularly in low-biomass samples, but can be minimised during data analysis. ConclusionThis study provides practical guidance for sequencing platform selection and sample handling in uterine microbiome research. Our findings support future efforts to elucidate microbial contributions to EC pathogenesis and highlight the importance of rigorous contamination control. Importantly, this is the first presentation of a New Zealand cohort and contributes valuable data from an underrepresented population and informs future research in diverse clinical settings.

cancer biology↗

bioRxiv2023

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

bioengineering↗

bioRxiv2022

Investigating the Consistency of Extracellular Vesicle Production from Breast Cancer Subtypes Using CELLine Adherent Bioreactors

Extracellular vesicle (EV) research has grown rapidly in recent years, largely due to the potential use of EVs as liquid biopsy biomarkers or therapeutics. However, in-depth characterisation and validation of EVs produced using conventional in vitro cultures can be challenging due to the large area of cell monolayers and volumes of culture media required. To overcome this obstacle, multiple bioreactor designs have been tested for EV production with varying success, but the consistency of EVs produced over time in these systems has not been reported previously. In this study, we demonstrate that several breast cancer cell lines of different subtypes can be cultured simultaneously in space, resource, and time efficient manner using CELLine AD 1000 systems, allowing the consistent production of vast amounts of EVs for downstream experimentation. We report an improved workflow used for inoculating, maintaining, and monitoring the bioreactors, their EV production, and the characterisation of the EVs produced. Lastly, our proteomic analyses of the EVs produced throughout the lifetime of the bioreactors show that core EV-associated proteins are relatively consistent, with few minor variations over time, and that tracking the production of EVs may be a convenient method for indirectly monitoring the bioreactors health. These findings will aid future studies requiring the simultaneous production of large amounts of EVs from several cell lines of different subtypes of a disease and other EV biomanufacturing applications.

cancer biology↗

bioRxiv2018

The protozoan Trichomonas vaginalis targets bacteria with laterally-acquired NlpC/P60 peptidoglycan hydrolases

Trichomonas vaginalis is a human eukaryotic pathogen and the causative agent of trichomoniasis, the most prevalent non-viral sexually transmitted infection worldwide. This extracellular protozoan parasite is intimately associated with the human vaginal mucosa and microbiota but key aspects of the complex interactions between the parasite and the vaginal bacteria remain elusive. We report that T. vaginalis has acquired, by lateral gene transfer from bacteria, genes encoding peptidoglycan hydrolases of the NlpC/P60 family. Two of the T. vaginalis enzymes were active against bacterial peptidoglycan, retaining the active site fold and specificity as DL-endopeptidases. The endogenous NlpC/P60 genes are transcriptionally up regulated in T. vaginalis when in the presence of bacteria. The over-expression of an exogenous copy produces a remarkable phenotype where the parasite is capable of competing out bacteria from mixed cultures, consistent with the biochemical activity of the enzyme in vitro. Our study highlights the relevance of the interactions of this eukaryotic pathogen with bacteria, a poorly understood aspect on the biology of this important human parasite.\n\nAuthor summaryTrichomonas vaginalis is a protozoan parasite that causes a very common sexually transmitted disease known as trichomoniasis. This extracellular parasite resides in the vagina where it is in close association with the mucosa and the local microbiota. Very little is known about the nature of the parasite-bacteria interactions. Here, we report that this parasite had acquired genes from bacteria which retained their original function producing active enzymes capable of degrading peptidoglycan, a polymer that is chemically unique to the cell envelope of bacteria. Our results indicate that these enzymes help the parasite compete out bacteria in mixed cultures. These observations suggest that these enzymes may be critical for the parasite to establish infection in the vagina, a body site that is densely colonised with bacteria. Our study further highlights the importance of understanding the interactions between pathogens and microbiota, as the outcomes of these interactions are increasingly understood to have important implications on health and disease.

microbiology↗