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Fiorentino, G.

Publications and source records attributed to Fiorentino, G..

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A hyperthermoactive-Cas9 editing tool reveals the role of a unique arsenite methyltransferase in the arsenic resistance system of Thermus thermophilus HB27

Arsenic detoxification systems can be found in a wide range of organisms, from bacteria to man. In a previous study, we discovered an arsenic-responsive transcriptional regulator in the thermophilic bacterium Thermus thermophilus HB27 (TtSmtB). Here, we characterize the arsenic resistance system of T. thermophilus in more detail. We employed TtSmtB-based pull-down assays with protein extracts from cultures treated with arsenate and arsenite to obtain an S-adenosyl-Lmethionine (SAM)-dependent arsenite methyltransferase (TtArsM). In vivo and in vitro analyses were performed to shed light on this new component of the arsenic resistance network and its peculiar catalytic mechanism. Heterologous expression of TtarsM in Escherichia coli resulted in arsenite detoxification at mesophilic temperatures. Although TtArsM does not contain a canonical arsenite binding site, the purified protein does catalyze SAM- dependent arsenite methylation with formation of monomethylarsenite (MMAs) and dimethylarsenite (DMAs). In addition, in vitro analyses confirmed the unique interaction between TtArsM and TtSmtB. Next, a highly efficient ThermoCas9- based genome-editing tool was developed to delete the TtArsM-encoding gene on the T. thermophilus genome and to confirm its involvement in the arsenite detoxification system. Finally, the TtarsX efflux pump gene in the T. thermophilus {Delta}TtarsM genome was substituted by a gene encoding a stabilized yellow fluorescent protein (sYFP) to create a sensitive genome-based bioreporter system for the detection of arsenic ions. ImportanceWe here describe the discovery of an unknown protein by using a proteomic approach with a functionally related protein as bait. Remarkably, we successfully obtained a novel type of enzyme through the interaction with a transcription regulator, controlling the expression of this enzyme. Employing this strategy, we isolated TtArsM, the first thermophilic prokaryotic arsenite methyltransferase, as a new enzyme of the arsenic resistance mechanism in T. thermophilus HB27. The atypical arsenite binding site of TtArsM categorizes the enzyme as the first member of a new arsenite methyltransferase type, exclusively present in the Thermus genus. The enzyme methylates arsenite producing MMAs and DMAs. Furthermore, we developed an hyperthermophilic Cas9-based genome-editing tool, active up to 65{degrees}C. The tool allowed us to perform highly efficient, marker-free modifications (either gene deletion or insertion) in the T. thermophilus genome. With these modifications, we confirmed the critical role of TtArsM in the arsenite detoxification system and developed a sensitive whole cell bioreporter for arsenic ions. We anticipate that the developed tool can be easily adapted for editing the genomes of other thermophilic bacteria, significantly boosting fundamental and metabolic engineering in hyperthermophilic microorganisms.

biochemistry

Harnessing cytoplasmic particles movement of the human early embryo analysed by advanced imaging and artificial intelligence to predict development to blastocyst stage

Research QuestionProgress in artificial intelligence (AI) and advanced image analysis offers unique opportunities to develop novel embryo assessment approaches. In this study, we tested the hypothesis that such technologies can extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst. DesignIn a proof-of principle study, an artificial neural network (ANN) approach was undertaken to assess retrospectively 230 human preimplantation embryos. After ICSI, embryos were subjected to time-lapse monitoring for 44 hours. For comparison as a standard embryo assessment methodology, a single senior embryologist assessed each embryo to predict development to blastocyst stage (BL) based on a single picture frame taken at 42 hours of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest (NoBL), cytoplasm movement velocity (CMV) was recorded by time-lapse monitoring during the first 44 hours of culture and analysed with a Particle Image Velocimetry (PIV) algorithm to extract quantitative information. Three main AI approaches, the k-Nearest Neighbor (k-NN), the Long-Short Term Memory Neural Network (LSTM-NN) and the hybrid ensemble classifier (HyEC) were employed to classify the two embryo classes. ResultsBlind operator assessment classified each embryo in terms of ability of development to blastocyst, reaching a 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. After integration of results from AI models together with the blind operator classification, the performance metrics improved significantly, with a 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score. ConclusionsThe present study suggests the possibility to predict human blastocyst development at early cleavage stages by detection of CMV and AI analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.

developmental biology