Search

BACKGROUNDCOVID-19 convalescent plasma (CCP) virus-specific antibody levels that translate into recipient posttransfusion antibody levels sufficient to prevent disease progression are not defined.METHODSThis secondary analysis correlated donor and recipient antibody levels to hospitalization risk among unvaccinated, seronegative CCP recipients within the outpatient, double-blind, randomized clinical trial that compared CCP to control plasma. The majority of COVID-19 CCP arm hospitalizations (15/17, 88%) occurred in this unvaccinated, seronegative subgroup. A functional cutoff to delineate recipient high versus low posttransfusion antibody levels was established by 2 methods: (i) analyzing virus neutralization-equivalent anti-Spike receptor-binding domain immunoglobulin G (anti-S-RBD IgG) responses in donors or (ii) receiver operating characteristic (ROC) curve analysis.RESULTSSARS-CoV-2 anti-S-RBD IgG antibody was volume diluted 21.3-fold into posttransfusion seronegative recipients from matched donor units. Virus-specific antibody delivered was approximately 1.2 mg. The high-antibody recipients transfused early (symptom onset within 5 days) had no hospitalizations. A CCP-recipient analysis for antibody thresholds correlated to reduced hospitalizations found a statistical significant association between early transfusion and high antibodies versus all other CCP recipients (or control plasma), with antibody cutoffs established by both methods-donor-based virus neutralization cutoffs in posttransfusion recipients (0/85 [0%] versus 15/276 [5.6%]; P = 0.03) or ROC-based cutoff (0/94 [0%] versus 15/267 [5.4%]; P = 0.01).CONCLUSIONIn unvaccinated, seronegative CCP recipients, early transfusion of plasma units in the upper 30% of study donors antibody levels reduced outpatient hospitalizations. High antibody level plasma units, given early, should be reserved for therapeutic use.TRIAL REGISTRATIONClinicalTrials.gov NCT04373460.FUNDINGDepartment of Defense (W911QY2090012); Defense Health Agency; Bloomberg Philanthropies; the State of Maryland; NIH (3R01AI152078-01S1, U24TR001609-S3, 1K23HL151826NIH); the Mental Wellness Foundation; the Moriah Fund; Octapharma; the Healthnetwork Foundation; the Shear Family Foundation; the NorthShore Research Institute; and the Rice Foundation.

UNLABELLED: Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of an ongoing pandemic that has infected over 36 million and killed over 1 million people. Informed implementation of government public health policies depends on accurate data on SARS-CoV-2 immunity at a population scale. We hypothesized that detection of SARS-CoV-2 salivary antibodies could serve as a noninvasive alternative to serological testing for monitoring of SARS-CoV-2 infection and seropositivity at a population scale. We developed a multiplex SARS-CoV-2 antibody immunoassay based on Luminex technology that comprised 12 CoV antigens, mostly derived from SARS-CoV-2 nucleocapsid (N) and spike (S). Saliva and sera collected from confirmed coronavirus disease 2019 (COVID-19) cases and from the pre-COVID-19 era were tested for IgG, IgA, and IgM to the antigen panel. Matched saliva and serum IgG responses (n = 28) were significantly correlated. The salivary anti-N IgG response resulted in the highest sensitivity (100%), exhibiting a positive response in 24/24 reverse transcription-PCR (RT-PCR)-confirmed COVID-19 cases sampled at >14 days post-symptom onset (DPSO), whereas the salivary anti-receptor binding domain (RBD) IgG response yielded 100% specificity. Temporal kinetics of IgG in saliva were consistent with those observed in blood and indicated that most individuals seroconvert at around 10 DPSO. Algorithms employing a combination of the IgG responses to N and S antigens result in high diagnostic accuracy (100%) by as early as 10 DPSO. These results support the use of saliva-based antibody testing as a noninvasive and scalable alternative to blood-based antibody testing.

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH3). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

Todays Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrows products and processes. On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the worlds grand challenges.

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

A high-quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrect predictions of translated sequence, inaccurate splice isoforms, and missing genes. Here, we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 3529 new proteins compared to the hamster RefSeq protein annotation and 2256 novel translational events (e.g., alternative splices, mutations, and novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 119 type-C retroviruses, thus enabling future efforts to eliminate retroviruses to reduce the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate resource for CHO cell line engineering, by facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

Mammalian cell culture processes are widely utilized for biotherapeutics production, disease diagnostics, and biosensors, and hence, should be optimized to support robust cell growth and viability. However, toxic by-products accumulate in cultures due to inefficiencies in metabolic activities and nutrient utilization. In this study, we applied comprehensive 13C stable-isotope tracing of amino acids and glucose to two Immunoglobulin G (IgG) producing Chinese Hamster Ovary (CHO) cell lines to identify secreted by-products and trace their origins. CHO cells were cultured in media formulations missing a single amino acid or glucose supplemented with a 13C-tracer of the missing substrate, followed by gas chromatography-mass spectrometry (GC-MS) analysis to track labeled carbon flows and identify by-products. We tracked the sources of all secreted by-products and verified the identity of 45 by-products, majority of which were derived from glucose, leucine, isoleucine, valine, tyrosine, tryptophan, methionine, and phenylalanine. In addition to by-products identified previously, we identified several metabolites including 2-hydroxyisovaleric acid, 2-aminobutyric acid, L-alloisoleucine, ketoisoleucine, 2-hydroxy-3-methylvaleric acid, desmeninol, and 2-aminobutyric acid. When added to CHO cell cultures at different concentrations, certain metabolites inhibited cell growth while others including 2-hydroxy acids, surprisingly, reduced lactate accumulation. In vitro enzymatic analysis indicated that 2-hydroxy acids were metabolized by lactate dehydrogenase suggesting a possible mechanism for lowered lactate accumulation, e.g., competitive substrate inhibition. The 13C-labeling assisted metabolomics pipeline developed and the metabolites identified will serve as a springboard to reduce undesirable by-products accumulation and alleviate inefficient substrate utilization in mammalian cultures used for biomanufacturing and other applications through altered media formulations and pathway engineering strategies. [ABSTRACT FROM AUTHOR]

Accurate and complete genome sequences are essential in biotechnology to facilitate genome-based cell engineering efforts. The current genome assemblies for Cricetulus griseus, the Chinese hamster, are fragmented and replete with gap sequences and misassemblies, consistent with most short-read-based assemblies. Here, we completely resequenced C. griseus using single molecule real time sequencing and merged this with Illumina-based assemblies. This generated a more contiguous and complete genome assembly than either technology alone, reducing the number of scaffolds by >28-fold, with 90% of the sequence in the 122 longest scaffolds. Most genes are now found in single scaffolds, including up- and downstream regulatory elements, enabling improved study of noncoding regions. With >95% of the gap sequence filled, important Chinese hamster ovary cell mutations have been detected in draft assembly gaps. This new assembly will be an invaluable resource for continued basic and pharmaceutical research.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Convalescent plasma is a leading treatment for coronavirus disease 2019 (COVID-19), but there is a paucity of data identifying its therapeutic efficacy. Among 126 potential convalescent plasma donors, the humoral immune response was evaluated using a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus neutralization assay with Vero-E6- TMPRSS2 cells; a commercial IgG and IgA ELISA to detect the spike (S) protein S1 domain (EUROIMMUN); IgA, IgG, and IgM indirect ELISAs to detect the full-length S protein or S receptor-binding domain (S-RBD); and an IgG avidity assay. We used multiple linear regression and predictive models to assess the correlations between antibody responses and demographic and clinical characteristics. IgG titers were greater than either IgM or IgA titers for S1, full-length S, and S-RBD in the overall population. Of the 126 plasma samples, 101 (80%) had detectable neutralizing antibody (nAb) titers. Using nAb titers as the reference, the IgG ELISAs confirmed 95%-98% of the nAb-positive samples, but 20%-32% of the nAb-negative samples were still IgG ELISA positive. Male sex, older age, and hospitalization for COVID-19 were associated with increased antibody responses across the serological assays. There was substantial heterogeneity in the antibody response among potential convalescent plasma donors, but sex, age, and hospitalization emerged as factors that can be used to identify individuals with a high likelihood of having strong antiviral antibody responses., Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), emerged in Wuhan, China, in December 2019. Following the rapid, global spread of SARS-CoV-2 [...]

Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.