Biology subjects

Kazakova, E. M.

Publications and source records attributed to Kazakova, E. M..

3 recordsLinked to original sources

bioRxiv2024

Study on the mechanism of action of the Pt(IV) complex with lonidamine ligands by ultrafast chemical proteomics

Platinum (II) complexes such as cisplatin among the few others are well-known and approved for clinical use as anticancer metal-based drugs. In spite of their successful and wide acceptance, the respective chemotherapy is associated with severe side effects and the ability of tumors to quickly develop resistance. To overcome these drawbacks the novel strategy is considered, which is based on the use of platinum complexes with bioactive ligands attached to act in synergy with platinum and further improve its pharmacological properties. Among the recently introduced such multi-action prodrugs is Pt(IV) complex with two lonidamine ligands, the latter selectively inhibiting hexokinase and, thus, the glycolysis in cancer cells. While platinum based multi-action prodrugs are exhibiting increased levels of activity towards cancer cells and, thus, considered as potent to overcome the resistance to cisplatin, there is a crucial need to uncover their mechanism of action by revealing all possibly affected processes and targets across the whole cellular proteomes. These are the challenging tasks in proteomics requiring high-throughput analysis of hundreds of samples for just a single drug-to-proteome system. In this work we performed these analyses for 8-azaguanine and experimental Pt(IV)-lonidamine complex applied to ovarian cancer cell line A2780, using both mechanism- and compound-centric chemical proteomics approaches based on ultrafast expression proteomics and thermal proteome profiling, respectively. Analysis of data obtained for Pt(IV)-lonidamine complex revealed regulation of proteins involved in glucose metabolic process associated with lonidamine further supporting the multi-action mechanism of this prodrug action.

pharmacology and toxicology↗

bioRxiv2024

A modified decision tree improves generalization across multiple brains proteomic data sets and reveals the role of ferroptosis in Alzheimer disease

Low generalization to the patient cohort and variety of experimental conditions in the proteomic search for disease biomarkers are among the main reasons for the bumpy road of quantitative proteomics from discovery stage to clinical validation. Only a small fraction of biomarkers discovered so far by proteomic analysis reaches clinical trials. Here, we presented a machine learning-based workflow for proteomics data analysis, which partially solves some of these issues. In particular, we used a customized decision tree model, which was regulated using a newly introduced parameter, min_cohorts_leaf, that resulted in better generalization of trained models. Further, we analyzed the trend of feature importances curve as a function of min_cohorts_leaf parameter and found that it could be used for accurate feature selection to obtain a list of proteins with significantly improved generalization. Finally, we demonstrated that the recently introduced DirectMS1 search algorithm for protein identification and quantitation provides a simple, yet, a highly efficient solution for the problem of combining multiple data sets obtained using different experimental settings. The developed workflow was tested using five published LC-MS/MS data sets obtained in the large consortia studies of Alzheimers disease brain samples. The selected data sets consist of 535 files in total analyzed using label-free single-shot data-dependent or data-independent acquisitions. Using the proposed modified ExtraTrees model we found that the expressions of two proteins involved in ferroptosis Serotransferrin TRFE and DNA repair nuclease/redox regulator APEX1, are important for explaining a lack of dementia for patients with the presence of neuritic plaques and neurofibrillary tangles.

molecular biology↗

bioRxiv2024

Quantitative assessment of strain isolates and microbiomes using fast MS/MS-free metaproteomics

BACKGROUNDMicrobial communities play a crucial role in human health and environmental regulation, but present an especial challenge for the analytical science due to their diversity and dynamic range. Tandem mass spectrometry provides functional insights on microorganisms life cycle, but still lacks throughput and sensitivity. MALDI TOF is widely used for ultrafast identification of species, but does not assess their functional activity. Development of ultrafast mass spectrometry methods and bioinformatic approaches applicable for both accurate identification and functional assessment of microbial communities based on their protein content is of high interest. RESULTSWe show for the first time that both identity and functional activity of microorganisms and their communities can be accurately determined in experiments as short as 7 minutes per sample, using the basic Orbitrap MS configuration without peptide fragmentation. The approach was validated using strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. Our new bioinformatic algorithm identifies the bacterial species with an accuracy of 95 %, when no prior information on the sample is available. Microbiome composition was resolved at the genus level with the mean difference between the actual and identified components of 12 %. For mock microbiomes, Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed the accurate assessment of functional activity of strain isolates, compared with the standard label-free and label-based approaches. SIGNIFICANCEOur approach makes microbial proteomics fast, functional and insightful using the Orbitrap instruments even without employing peptide fragmentation technology. The approach can be applied to any microorganisms and can take a niche in routine functional assessment of microbial pathogens and consortiums in clinical diagnostics together with MALDI TOF MS and 16S rRNA gene sequencing.

microbiology↗