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Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
A nanosized drug complex was explored to improve the efficiency of cancer chemotherapy, complementing it with nanodelivery and photodynamic therapy. For this, nanomolar amounts of a non-covalent nanocomplex of Doxorubicin (Dox) with carbon nanoparticle C60 fullerene (C60) were applied in 1:1 and 2:1 molar ratio, exploiting C60 both as a drug-carrier and as a photosensitizer. The fluorescence microscopy analysis of human leukemic CCRF-CEM cells, in vitro cancer model, treated with nanocomplexes showed Dox’s nuclear and C60’s extranuclear localization. It gave an opportunity to realize a double hit strategy against cancer cells based on Dox’s antiproliferative activity and C60’s photoinduced pro-oxidant activity. When cells were treated with 2:1 C60-Dox and irradiated at 405 nm the high cytotoxicity of photo-irradiated C60-Dox enabled a nanomolar concentration of Dox and C60 to efficiently kill cancer cells in vitro. The high pro-oxidant and pro-apoptotic efficiency decreased IC50 16, 9 and 7 × 103-fold, if compared with the action of Dox, non-irradiated nanocomplex, and C60’s photodynamic effect, correspondingly. Hereafter, a strong synergy of therapy arising from the combination of C60-mediated Dox delivery and C60 photoexcitation was revealed. Our data indicate that a combination of chemo- and photodynamic therapies with C60-Dox nanoformulation provides a promising synergetic approach for cancer treatment.
Quantification of nitroaromatic explosives in contaminated soil using MALDI-TOF mass spectrometry
(2019)
Contamination from various sources is a global environmental and health threat, with mining and military activities in particular having spread nitroaromatic compounds, such as 2,4,6-trinitrotoluene and its degradation products and by-products, to the soil. The investigation and monitoring of large contaminated areas requires new detection methods since the established ones are expensive and time-consuming. Hence, we established a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI--TOF MS) method using 1,5-diaminonaphthalene as the matrix substance and an internal standard for quantification. Analyzing standard substances, we found specific signals for radical and fragment ions of different nitrotoluenes and nitrobenzenes with good reproducibility and detection limits down to 0.25 ng/μL. The analysis of soil sample extracts from a former production site showed clear signals for 2,4,6-trinitrotoluene and the primary degradation products aminodinitrotoluenes. Furthermore, quantification gave results comparable to those obtained by conventional liquid chromatography--tandem mass spectrometry analysis. The MALDI-TOF MS method has a comparatively lower reproducibility, with relative standard deviations of 6% to 20% for multiple measurements of standard solutions and soil sample extracts. Nevertheless, a comparison of both methods revealed the advantages of MALDI-TOF MS analysis of explosive-contaminated areas with regard to costs, time, and handling. Finally, our MALDI-TOF MS method fulfills all the needs for high sample throughput and can therefore be a valuable screening tool for explosive-contaminated areas.
Complexation with C60 Fullerene Increases Doxorubicin Efficiency against Leukemic Cells In Vitro
(2019)
Conventional anticancer chemotherapy is limited because of severe side effects as well as a quickly evolving multidrug resistance of the tumor cells. To address this problem, we have explored a C60 fullerene-based nanosized system as a carrier for anticancer drugs for an optimized drug delivery to leukemic cells.
Here, we studied the physicochemical properties and anticancer activity of C60 fullerene noncovalent complexes with the commonly used anticancer drug doxorubicin. C60-Doxorubicin complexes in a ratio 1:1 and 2:1 were characterized with UV/Vis spectrometry, dynamic light scattering, and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The obtained analytical data indicated that the 140-nm complexes were stable and could be used for biological applications. In leukemic cell lines (CCRF-CEM, Jurkat, THP1 and Molt-16), the nanocomplexes revealed ≤ 3.5 higher cytotoxic potential in comparison with the free drug in a range of nanomolar concentrations. Also, the intracellular drug’s level evidenced C60 fullerene considerable nanocarrier function.
The results of this study indicated that C60 fullerene-based delivery nanocomplexes had a potential value for optimization of doxorubicin efficiency against leukemic cells.
Complexation with C60 Fullerene Increases Doxorubicin Efficiency against Leukemic Cells In Vitro
(2019)
Conventional anticancer chemotherapy is limited because of severe side effects as well as a quickly evolving multidrug resistance of the tumor cells. To address this problem, we have explored a C60 fullerene-based nanosized system as a carrier for anticancer drugs for an optimized drug delivery to leukemic cells.
Here, we studied the physicochemical properties and anticancer activity of C60 fullerene noncovalent complexes with the commonly used anticancer drug doxorubicin. C60-Doxorubicin complexes in a ratio 1:1 and 2:1 were characterized with UV/Vis spectrometry, dynamic light scattering, and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The obtained analytical data indicated that the 140-nm complexes were stable and could be used for biological applications. In leukemic cell lines (CCRF-CEM, Jurkat, THP1 and Molt-16), the nanocomplexes revealed ≤ 3.5 higher cytotoxic potential in comparison with the free drug in a range of nanomolar concentrations. Also, the intracellular drug’s level evidenced C60 fullerene considerable nanocarrier function.
The results of this study indicated that C60 fullerene-based delivery nanocomplexes had a potential value for optimization of doxorubicin efficiency against leukemic cells.
Leishmania species are protozoan parasites and the causative agents of leishmaniasis, a vector borne disease that imposes a large health burden on individuals living mainly in tropical and subtropical regions. Different Leishmania species are responsible for the distinct clinical patterns, such as cutaneous, mucocutaneous, and visceral leishmaniasis, with the latter being potentially fatal if left untreated. For this reason, it is important to perform correct species identification and differentiation. Fourier transform infrared spectroscopy (FTIR) is an analytical spectroscopic technique increasingly being used as a potential tool for identification of microorganisms for diagnostic purposes. By employing mid-infrared (MIR) spectral data, it is not only possible to assess the chemical structures but also to achieve differentiation supported by multivariate statistic analysis. This work comprises a pilot study on differentiation of Leishmania species of the Old World (L. major, L. tropica, L. infantum, and L. donovani) as well as hybrids of distinct species by using vibrational spectroscopic fingerprints. Films of intact Leishmania parasites and their deoxyribonucleic acid (DNA) were characterized comparatively with respect to their biochemical nature and MIR spectral patterns. The strains’ hyperspectral datasets were multivariately examined by means of variance-based principal components analysis (PCA) and distance-based hierarchical cluster analysis (HCA). With the implementation of MIR spectral datasets we show that a phenotypic differentiation of Leishmania at species and intra-species level is feasible. Thus, FTIR spectroscopy can be further exploited for building up spectral databases of Leishmania parasites in view of high-throughput analysis of clinical specimens.
The presented dataset describes the quantification of carbon nanoparticle C60 fullerene accumulated in mitochondria of human leukemic cells treated with nanostructure. Firstly, the high performance liquid chromatography–electro spray ionization–mass spectrometry (HPLC-ESI-MS) method was developed for quantitative analysis of pristine C60 fullerene. Then, human leukemic cells were incubated with C60 fullerene, homogenized and subjected to the differential centrifugation to retrieve mitochondrial fraction. The C60 fullerene content was quantified by HPLC-ESI-MS in extracts of cellular fractions.
This data article refers to the research article “C60 Fullerene Accumulation in Human Leukemic Cells and Perspectives of LED-mediated Photodynamic Therapy” by Grebinyk et al.
The growing demand for cost-effective nucleic acid detection assays leads to an increasing number of different isothermal amplification reaction methods. However, all of the most efficient methods suffer from highly complex assay conditions due to the use of complicated primer sets and/or auxiliary enzymes. The present study describes the application of a new linker moiety that can be incorporated between a primer and a secondary target binding site which can act both as a block to polymerase extension as well as a hinge for refolding. This novel “hinge-primer” approach results in an efficient regeneration of the primer binding site and thus improves the strand-displacement and amplification process under isothermal conditions. Our investigations revealed that the reaction with forward and reverse hinge-primer including an abasic site is very efficient. The assay complexity can be reduced by combining the hinge-primer with a corresponding linear primer. Furthermore, the reaction speed can be increased by reducing the length of the amplified target sequence. We tested the sensitivity down to 104 copies and found a linear correlation between reaction time and input copy number. Our approach overcomes the usually cumbersome primer-design and extends the range of isothermal amplification methods using a polymerase with strand-displacement activity.

