Biology subjects

Abramova, A.

Publications and source records attributed to Abramova, A..

3 recordsLinked to original sources

bioRxiv2023

Metagenomic assemblies tend to break around antibiotic resistance genes

BackgroundAssembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs), and inform interventions to prevent further spread of resistant bacteria. However, ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies. ResultsThe results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample. ConclusionsOur study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database might be necessary, at least as a complementary strategy, to get accurate ARG abundance and diversity measures.

bioinformatics↗

bioRxiv2023

ZeptoCTC - Sensitive Protein Analysis of True Single Cell Lysates using Reverse Phase Protein Arrays (RPPA)

Circulating Tumor Cells (CTCs) are commonly analyzed through genomic profiling, which does not capture posttranslational and functional alterations of encoded proteins. To address this limitation, we developed ZeptoCTC, a single-cell protein analysis workflow that combines established technologies for single-cell isolation and sensitive Reverse Phase Protein Array (RPPA) analysis to assess multiple protein expression and activation in individual CTCs. The workflow involves single cell labeling, isolation, lysis, and printing of the true single cell lysates onto a ZeptoChip using a modified micromanipulator CellCelectorTM. Subsequently, the printed lysates undergo fluorescence immunoassay RPPA protein detection using a ZeptoReader followed by signal quantification with Image J software. ZeptoCTC was successfully optimized, beginning with the measurement of EpCAM protein expression--a standard marker for CTC detection. As expected, mean fluorescence signals for EpCAM levels were significantly higher in single MCF-7 cells compared to MDA-MB-231 cells. Next, Capivasertib-treated MCF-7 cells exhibited an approximately 2-fold increase in the pAkt/Akt ratio compared to non-treated control cells. This finding was consistent with a co-performed western blot analysis of pooled MCF-7 cells. Application of ZeptoCTC to the analysis of single CTCs derived from a metastasized breast cancer (MBC) patient indicated a significantly higher level of pAkt, accompanied by a corresponding increase in pErk level when compared to patient-matched WBC. Finally, the current workflow successfully indicated the detectable pAkt and Akt signal difference in CTCs from two MBC patients: one with an Akt1 wild-type genotype, and the other harboring approximately 80% Akt1(E17K) mutated CTCs. The mutated CTCs revealed clearly elevated pAkt levels (1.8-fold), along with an even more strongly elevated total Akt (3.4-fold) when compared to the respective signals measured in wild-type CTCs. In conclusion, ZeptoCTC is a highly sensitive method for measuring the expression and phosphorylation of treatment-relevant proteins in key cancer-driving signaling pathways from true single cell samples.

molecular biology↗

bioRxiv2022

Meta-analysis reveals the global picture of antibiotic resistance gene prevalence across environments

The environment is an important component in the emergence and transmission of antimicrobial resistance (AMR). Despite that, little effort has been made to monitor AMR outside of clinical and veterinary settings. Partially, this is caused by a lack of comprehensive reference data for the vast majority of environments. To enable monitoring to detect deviations from the normal background resistance levels in the environment, it is necessary to establish a baseline of AMR in a variety of settings. In an attempt to establish this baseline level, we here performed a comprehensive literature survey, identifying 150 scientific papers containing relevant qPCR data on antimicrobial resistance genes (ARGs) in environments associated with potential routes for AMR dissemination. The collected data included 1594 samples distributed across 30 different countries and 12 sample types, in a time span from 2001 to 2020. We found that for most ARGs, the typically reported abundances in human impacted environments fell in an interval from 10-5 to 10-3 copies per 16S rRNA, roughly corresponding to one ARG copy in a thousand bacteria. Altogether these data represent a comprehensive overview of the occurrence and levels of ARGs in different environments, providing background data for risk assessment models within current and future AMR monitoring frameworks.

microbiology↗