Stefania Vaga

Bio: Stefania Vaga is an academic researcher from Francis Crick Institute. The author has contributed to research in topics: Stem cell & Neural stem cell. The author has an hindex of 3, co-authored 6 publications receiving 300 citations. Previous affiliations of Stefania Vaga include King's College London.

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Abstract: Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

Id4 promotes the elimination of the pro-activation factor ascl1 to maintain quiescence of adult hippocampal stem cells

Abstract: Quiescence is essential for the long-term maintenance of adult stem cells but how stem cells maintain quiescence is poorly understood. Here, we show that neural stem cells (NSCs) in the adult mouse hippocampus actively transcribe the pro-activation factor Ascl1 regardless of their activated or quiescent states. We found that the inhibitor of DNA binding protein Id4 is enriched in quiescent NSCs and that elimination of Id4 results in abnormal accumulation of Ascl1 protein and premature stem cell activation. Accordingly, Id4 and other Id proteins promote elimination of Ascl1 protein in NSC cultures. Id4 sequesters Ascl1 heterodimerization partner E47, promoting Ascl1 protein degradation and stem cell quiescence. Our results highlight the importance of non-transcriptional mechanisms for the maintenance of NSC quiescence and reveal a role for Id4 as a quiescence-inducing factor, in contrast with its role of promoting the proliferation of embryonic neural progenitors.

Id4 eliminates the pro-activation factor Ascl1 to maintain quiescence of adult hippocampal stem cells

Abstract: SUMMARY Quiescence is essential for the long-term maintenance of adult stem cells and tissue homeostasis. However, how stem cells maintain quiescence is still poorly understood. Here we show that stem cells in the dentate gyrus of the adult hippocampus actively transcribe the pro-activation factor Ascl1 regardless of their activation state. We found that the inhibitor of DNA binding protein Id4 suppresses Ascl1 activity in neural stem cell cultures. Id4 sequesters Ascl1 heterodimerisation partner E47, promoting Ascl1 protein degradation and neural stem cell quiescence. Accordingly, elimination of Id4 from stem cells in the adult hippocampus results in abnormal accumulation of Ascl1 protein and premature stem cell activation. We also found that multiple signalling pathways converge on the regulation of Id4 to reduce the activity of hippocampal stem cells. Id4 therefore maintains quiescence of adult neural stem cells, in sharp contrast with its role of promoting the proliferation of embryonic neural progenitors.

Id4 Eliminates the Pro-Activation Factor Ascl1 to Maintain Quiescence of Adult Hippocampal Stem Cells

Abstract: Quiescence is essential for the long-term maintenance of adult stem cells and tissue homeostasis. However, how stem cells maintain quiescence is still poorly understood. Here we show that stem cells in the dentate gyrus of the adult hippocampus actively transcribe the pro-activation factor Ascl1 regardless of their activation state. We found that the inhibitor of DNA binding protein Id4 suppresses Ascl1 activity in neural stem cell cultures. Id4 sequesters Ascl1 heterodimerisation partner E47, promoting Ascl1 protein degradation and neural stem cell quiescence. Accordingly, elimination of Id4 from stem cells in the adult hippocampus results in abnormal accumulation of Ascl1 protein and premature stem cell activation. We also found that multiple signalling pathways converge on the regulation of Id4 to reduce the activity of hippocampal stem cells. Id4 therefore maintains quiescence of adult neural stem cells, in sharp contrast with its role of promoting the proliferation of embryonic neural progenitors.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Abstract: Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

Abstract: A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.

3D bioprinting of cells, tissues and organs.

Abstract: 3D bioprinting has emerged as a promising new approach for fabricating complex biological constructs in the field of tissue engineering and regenerative medicine. It aims to alleviate the hurdles of conventional tissue engineering methods by precise and controlled layer-by-layer assembly of biomaterials in a desired 3D pattern. The 3D bioprinting of cells, tissues, and organs Collection at Scientific Reports brings together a myriad of studies portraying the capabilities of different bioprinting modalities. This Collection amalgamates research aimed at 3D bioprinting organs for fulfilling demands of organ shortage, cell patterning for better tissue fabrication, and building better disease models.