Modifying the Replication of Geminiviral Vectors Reduces Cell Death and Enhances Expression of Biopharmaceutical Proteins in Nicotiana benthamiana Leaves

Plants are a promising platform to produce biopharmaceutical proteins, however, the toxic nature of some proteins inhibits their accumulation. We previously created a replicating geminiviral expression system based on bean yellow dwarf virus (BeYDV) that enables very high-level production of recombinant proteins. To study the role of replication in this system, we generated vectors that allow separate and controlled expression of BeYDV Rep and RepA proteins. We show that the ratio of Rep and RepA strongly affects the efficiency of replication. Rep, RepA, and vector replication all elicit the plant hypersensitive response, resulting in cell death. We find that a modest reduction in expression of Rep and RepA reduces plant leaf cell death which, despite reducing the accumulation of viral replicons, increases target protein accumulation. A single nucleotide change in the 5′ untranslated region (UTR) reduced Rep/RepA expression, reduced cell death, and enhanced the production of monoclonal antibodies. We also find that replicating vectors achieve optimal expression with lower Agrobacterium concentrations than non-replicating vectors, further reducing cell death. Viral UTRs are also shown to contribute substantially to cell death, while a native plant-derived 5′ UTR does not

Evaluation of a toxoid fusion protein vaccine produced in plants to protect poultry against necrotic enteritis

Background Necrotic enteritis (NE) is caused by type A strains of the bacterium Clostridium perfringens. Total global economic losses to the poultry industry due to NE is estimated to be over two billion dollars annually. Traditionally, NE has been effectively controlled by inclusion of antibiotics in the diet of poultry. However, recent concerns regarding the impact of this practice on increasing antibiotic resistance in human pathogens have led us to consider alternative approaches, such as vaccination, for controlling this disease. NE strains of C. perfringens produce two major toxins, a-toxin and NetB. Immune responses against either toxin can provide partial protection against NE. Methods We have developed a fusion protein combining a non-toxic carboxyl-terminal domain of a-toxin (PlcC) and an attenuated, mutant form of NetB (NetB-W262A) for use as a vaccine antigen to immunize poultry against NE. We utilized a DNA sequence that was codon-optimized for Nicotiana benthamiana to enable high levels of expression. The 6-His tagged PlcC-NetB fusion protein was synthesized in N. benthamiana using a geminiviral replicon transient expression system, purified by metal affinity chromatography, and used to immunize broiler birds. Results Immunized birds produced a strong serum IgY response against both the plant produced PlcC-NetB protein and against bacterially produced His-PlcC and His-NetB. Immunized birds were significantly protected against a subsequent in-feed challenge with virulent C. perfringens when treated with the fusion protein. These results indicate that a plant-produced PlcC-NetB toxoid is a promising vaccine candidate for controlling NE in poultry

MedPerf : Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform

5’ and 3’ untranslated regions strongly enhance performance of geminiviral replicons in Nicotiana benthamiana leaves

We previously reported a recombinant protein production system based on a geminivirus replicon that yields high levels of vaccine antigens and monoclonal antibodies in plants. The bean yellow dwarf virus (BeYDV) replicon generates massive amounts of DNA copies, which engage the plant transcription machinery. However, we noticed a disparity between transcript level and protein production, suggesting that mRNAs could be more efficiently utilized. In this study, we systematically evaluated genetic elements from human, viral, and plant sources for their potential to improve the BeYDV system. The tobacco extensin terminator enhanced transcript accumulation and protein production compared to other commonly used terminators, indicating that efficient transcript processing plays an important role in recombinant protein production. Evaluation of human-derived 5’ untranslated regions (UTRs) indicated that many provided high levels of protein production, supporting their cross-kingdom function. Among the viral 5’ UTRs tested, we found the greatest enhancement with the tobacco mosaic virus omega leader. An analysis of the 5’ UTRs from the Arabidopsis thaliana and Nicotinana benthamiana photosystem I K genes found that they were highly active when truncated to include only the near upstream region, providing a dramatic enhancement of transgene production that exceeded that of the tobacco mosaic virus omega leader. The tobacco Rb7 matrix attachment region (MAR) inserted downstream from the gene of interest provided significant enhancement, which was correlated with a reduction in plant cell death. Evaluation of agrobacterium strains found that EHA105 enhanced protein production and reduced cell death compared to LBA4301 and GV3101. We used these improvements to produce Norwalk virus capsid protein at >20% total soluble protein, corresponding to 1.8mg/g leaf fresh weight, more than twice the highest level ever reported in a plant system. We also produced the monoclonal antibody rituximab at 1mg/g leaf fresh weight

Federated benchmarking of medical artificial intelligence with MedPerf

Funder: NIH (U01CA242871) NIH (U24CA189523)AbstractMedical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.</jats:p

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages