High—throughput and automated screening for COVID-19

The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at

Engineering xylose metabolism for production of polyhydroxybutyrate in the non-model bacterium Burkholderia sacchari

Abstract Background Despite its ability to grow and produce high-value molecules using renewable carbon sources, two main factors must be improved to use Burkholderia sacchari as a chassis for bioproduction at an industrial scale: first, the lack of molecular tools to engineer this organism and second, the inherently slow growth rate and poly-3-hydroxybutyrate [P(3HB)] production using xylose. In this work, we have addressed both factors. Results First, we adapted a set of BglBrick plasmids and showed tunable expression in B. sacchari. Finally, we assessed growth rate and P(3HB) production through overexpression of xylose transporters, catabolic or regulatory genes. Overexpression of xylR significantly improved growth rate (55.5% improvement), polymer yield (77.27% improvement), and resulted in 71% of cell dry weight as P(3HB). Conclusions These values are unprecedented for P(3HB) accumulation using xylose as a sole carbon source and highlight the importance of precise expression control for improving utilization of hemicellulosic sugars in B. sacchari

In Silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

There is pressing urgency to better understand the immunological underpinnings of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in order to identify potential therapeutic targets and drugs that allow treating patients effectively. To fill in this gap, we performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. As results, the high-confidence protein interactome network was conformed by 1,588 nodes between immune system proteins and human proteins physically associated with SARS-CoV-2. Subsequently, we screened all these nodes in ACE2 and TMPRSS2 co-expressing cells according to the Alexandria Project, finding 75 potential therapeutic targets significantly overexpressed (Z score > 2) in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs for 25 of the 75 aforementioned proteins. On one hand, we obtained 45 approved drugs, 16 compounds under investigation, and 35 experimental compounds with the highest area under the receiver operating characteristic (AUROCs) for 15 immune system proteins. On the other hand, we obtained 4 approved drugs, 9 compounds under investigation, and 16 experimental compounds with the highest multi-target affinities for 9 immune system proteins. In conclusion, computational structure-based drug discovery focused on immune system proteins is imperative to select potential drugs that, after being effectively analyzed in cell lines and clinical trials, these can be considered for treatment of complex symptoms of COVID-19 patients, and for co-therapies with drugs directly targeting SARS-CoV-2