570 Biowissenschaften; Biologie
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Processing biological materials with 3-dimensional (3D) printers has attracted increasing interest in several research areas. At the same time, off-the-shelf solutions are increasingly available in a wider variety to meet the current market demand. However, most of the available bioprinters are closed source, thus, modifications are quite challenging or require an avoidable consultation process with the manufacturer. Furthermore, the entry prices for basic machines amount to several thousands of euros. Whereas, high-end 3D bio printers with a vast array of features are available for several hundred thousand euros. Due to the immense potential of this tool in the field of biotechnology it is important to extend the availability of this technology for research purposes in terms of adaptability and price. This ongoing work focuses on open-source 3D printer heads with the ability to extrude biological materials. The print heads include several techniques to process low as well as high viscose biomaterials such as agar and gelatin. Additionally, obstacles such as continuous substrate tempering or integration into existing 3D printers are addressed. The work presented is open source and thus freely adaptable to any user’s specific needs. Our goal is to process a diverse range of biomaterials with different print techniques.
The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.
Glycolysis is one of the primordial pathways of metabolism, playing a pivotal role in energy metabolism and biosynthesis. Glycolytic enzymes are known to form transient multi-enzyme assemblies. Here we examine the wider protein-protein interactions of plant glycolytic enzymes and reveal a moonlighting role for specific glycolytic enzymes in mediating the co-localization of mitochondria and chloroplasts. Knockout mutation of phosphoglycerate mutase or enolase resulted in a significantly reduced association of the two organelles. We provide evidence that phosphoglycerate mutase and enolase form a substrate-channelling metabolon which is part of a larger complex of proteins including pyruvate kinase. These results alongside a range of genetic complementation experiments are discussed in the context of our current understanding of chloroplast-mitochondrial interactions within photosynthetic eukaryotes.
The isoquinoline quaternary alkaloid Berberine possesses a variety of pharmacological properties that suggests its promising application for an anticancer delivery system design utilizing its ability to intercalate DNA. In the current work, we have investigated the effects of Berberine on the human T cell leukemia cell line in vitro. Fluorescent microscopy of leukemic cells revealed Berberine nuclear localization. The results showed that Berberine inhibited leukemic cell growth in a time- and dose-dependent manner, that was associated with reactive oxygen species production intensification and caspase 3/7 activity increase with followed apoptosis induction. Berberine was used as a toxic and phototoxic agent for triple system synthesis along with DNA as a carrier and nanosilver as a plasmonic accelerator of Berberine electronic transitions and high energy emission absorbent centers. The proposed method allows to obtain the complex of DNA with Berberine molecules and silver nanoparticles. The optical properties of free components as well as their various combinations, including the final triple system DNA-Nanosilver-Berberine, were investigated. Obtained results support the possibility to use the triple system DNA-Nanosilver-Berberine as an alternative therapeutic agent for cancer treatment.
The isoquinoline quaternary alkaloid Berberine possesses a variety of pharmacological properties that suggests its promising application for an anticancer delivery system design utilizing its ability to intercalate DNA.In the current work we have investigated the effects of Berberine on the human T-cell leukemia cell line in vitro.Fluorescent microscopy of leukemic cells revealed Berberine nuclear localization. The results showed that Berberine inhibited leukemic cell growth in a time-and dose-dependent manner, that was associated with reactive oxygen species production intensification and caspase 3/7 activity increase with followed apoptosis induction.Berberine was used as a toxic and phototoxic agent for triple system synthesis along with DNA as a carrier and nanosilver as a plasmonic accelerator of Berberine electronic transitions and high energy emission absorbent centers.The proposed method allows to obtain the complex of DNA with Berberine molecules and silver nanopoarticles. The optical properties of free components as well as their various combinations, including the final triple system DNA-Nanosilver-Berberine, were investigated. Obtained results support the possibility to use the triple system DNA-Nanosilver-Berberine as an alternative therapeutic agent for cancer treatment.
Most biochemical reactions depend on the pH value of the aqueous environment and some are strongly favoured to occur in an acidic environment. A non-invasive control of pH to tightly regulate such reactions with defined start and end points is a highly desirable feature in certain applications, but has proven difficult to achieve so far. We report a novel optical approach to reversibly control a typical biochemical reaction by changing the pH and using acid phosphatase as a model enzyme. The reversible photoacid G-acid functions as a proton donor, changing the pH rapidly and reversibly by using high power UV LEDs as an illumination source in our experimental setup. The reaction can be tightly controlled by simply switching the light on and off and should be applicable to a wide range of other enzymatic reactions, thus enabling miniaturization and parallelization through non-invasive optical means.
The use of explosives has led to a widespread distribution of 2,4,6-trinitrotoluene (TNT) and its by- and degradation products in the soil on former production and testing sites. The investigation of those large contaminated sites is so far based on a few selected soil samples, due to high costs of conventional HPLC and GC analysis, although huge differences in concentrations can already be found in small areas and different collection depths. We introduce a novel high-throughput screening system for those areas, which combines a smartphone-based collection of GPS data and soil characteristics with a fast MALDI-TOF MS quantification of explosives in soil sample extracts and finally a heatmap visualization of the explosives’ spread in soil and an analysis of correlation between concentrations and soil characteristics. The analysis of a 400 m2 area presented an extensive contamination with TNT and lower concentrations of the degradation and by-products aminodinitrotoluenes (ADNT) and dinitrotoluenes (DNT) next to a former production facility for TNT. The contamination decreased in deeper soil levels and depended on the soil type. Pure humus samples showed significantly lower contaminations compared to sand and humus/sand mixtures, which is likely to be caused by an increased binding potential of the humic material. No correlation was found between the vegetation and the concentration of explosives. Since the results were obtained and visualized within several hours, the MALDI-TOF MS based comprehensive screening and heatmap analysis might be valuable for a fast and high-throughput characterization of contaminated areas.
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.
Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.