Biology subjects

Betenbaugh, M.

Publications and source records attributed to Betenbaugh, M..

12 recordsLinked to original sources

bioRxiv2026

Multi-Objective Bayesian Optimization for Data-Efficient Bioprocess Development

Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive when extended across multiple parameters and clones. To address this challenge, we developed a multi-objective Bayesian Optimization (BO) framework that identifies optimal process conditions efficiently in grouped recommendations, which is well suited for experimental workflows in bioprocess development. The model integrates continuous variables such as pH, DO, temperature, and feed rate with categorical identifiers to enable knowledge transfer across clones and scales, optimizing titer, glycan profile, and charge variants. We validated the framework through in-silico benchmarks on analytic functions, retrospective cross-validation on historical CHO datasets, and forward experimental validation in small-scale bioreactors. Across these tests, our algorithm consistently outperformed Latin Hypercube Sampling (LHS) and Random Search baselines, achieving superior performance under a limited experimental budget. The framework improved titer by up to 37% under single-objective optimization. In the multi-objective setting, it increased titer by 25% while simultaneously reducing overall glycan-profile error by a factor of seven, demonstrating the ability to optimize multiple biologically coupled objectives simultaneously. Through comprehensive in-silico and experimental validation, this study establishes a framework that enables adaptive, AI-guided process development and improves decision-making across multiple objectives, clones, and scales while minimizing experimental runs in process development and optimization workflows.

bioengineering↗

bioRxiv2026

Tailored Cell Cycle Modulation Enhances AAV Manufacturing: Balancing Arrest with Adaptive Stress Responses

Recombinant adeno-associated virus (rAAV) vectors show therapeutic potential, but their biomanufacturing is limited by low yields and high costs. Host cell-cycle modulation is emerging as a promising strategy to enhance rAAV production. Two G2/M phase-arresting small molecules, ABT-751, a microtubule inhibitor, and helenalin, a thiol-reactive sesquiterpene lactone, were applied post-transfection in HEK293 cells to evaluate how cell-cycle arrest and stress pathways influence rAAV yields. ABT-751 induced G2/M arrest with minimal cytotoxicity, leading to a near five-fold increase in rAAV vector genomes across multiple serotypes and production platforms. Helenalin caused G2/M arrest, yet suppressed rAAV production. Comparative transcriptomic profiling (RNA-Seq) revealed that helenalin altered expression of a widespread set of genes (4,579) compared to control, characterized by rampant p53, ferroptosis, and endoplasmic reticulum dysregulation that overflowed into unfolded protein response with CHOP induction and apoptosis. ABT-751 elicited a more moderate, targeted response (1,895 differentially expressed genes) in a similar subset of pathways, including compensatory mechanisms mitigating oxidative stress. Together, these findings indicate that cell-cycle arrest alone is insufficient to improve rAAV yield. Indeed, tailored cell-cycle modulation, coupled with balanced activation of cellular stress pathways, can enhance rAAV manufacturing efficiency, facilitating more scalable and cost-effective gene therapy production strategies for the future.

bioengineering↗

bioRxiv2026

A continuous viral vaccine biomanufacturing platform utilizing multiple bioreactor configurations

Scalable, continuous biomanufacturing processes have grown in importance to meet demand for smaller bioreactor sizes, lowered production costs, and improved quality attributes. The Sf9/recombinant baculovirus (rBV) expression system demonstrates promise for virus-like particle (VLP) vaccine and gene therapy production. Here, we present a continuous rBV platform integrating an infection plug flow reactor (PFR) between stirred tank growth (gCSTR) and production (pCSTR) bioreactors. Cell expansion in the gCSTR included a ramp-up stage followed by continuous growth, reaching a steady state of 5x106 cells/mL and >90% viability. Peclet number-fit tracer studies confirmed near-ideal plug flow in the PFR, yielding a 10 h residence time and progressive infection as measured by gp64 signaling. Finally, a pCSTR with a residence time of 48 h exhibited sustained recombinant protein production. An integrated pilot cascade incorporating all reactors ran continuously for 5 days, maintaining stable CSTR cell densities and a measurable increase in infected cell diameter from 14.5 m to 16.1 m. Western blotting and EM of [~]100 nm VLPs in pCSTR effluent demonstrated platform success. Digital twin mechanistic models across four distinct stages of bioreactor operation and Hill-type relationships for rBV infection kinetics predicted cell growth and death for a 7-day run, demonstrating promise for designing continuous systems in silico and building a quantitative framework for scale-up and optimization. Our multi-stage reactor configuration represents a cell host- and product-agnostic production scheme, particularly for processes prone to product heterogeneity, and paves the way towards a true end-to-end continuous platform for myriad modalities in the future.

bioengineering↗

bioRxiv2025

Characterizing the Effects of Protein Glycosylation Perturbation on Phosphorylation Signaling

Protein glycosylation and phosphorylation constitute two pervasive regulatory layers in mammalian cells, yet the effects that protein glycosylation play in phosphorylation signaling remain poorly understood. Here we show that controlled perturbation of N-linked glycan biosynthesis through glycoengineering fundamentally rewires phosphorylation signaling networks in human cells. Using comprehensive proteomics approaches, we simultaneously profiled the global proteome, glycoproteome, and phosphoproteome in engineered HEK293 cells designed to eliminate core fucosylation while enhancing sialylation and reducing GlcNAc branching complexity. Glycoengineering emerged as the dominant source of molecular variation across all datasets, with over 9,800 intact glycopeptides identified of which 3,400 are significantly altered, establishing a remodeled baseline cellular state. Upon serum stimulation, engineered cells not only exhibited markedly decreased phosphorylation responses compared to wild-type cells, but comprehensively re-wired to prefer signaling away from canonical EGFR/mTOR growth pathways. These findings establish a systematic framework for targeting glycosylation-phosphorylation regulation and nominate glycan-dependent signaling nodes as potential therapeutic vulnerabilities in glycosylation-remodeled disease states.

cell biology↗

bioRxiv2025

Comparing Kinetic versus Stoichiometric Priorities in Hybrid Models of CHO Metabolism

Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Current mechanistic models rely primarily on either flux balance analysis (FBA), estimating intracellular fluxes while assuming steady state, or kinetic modeling, capturing dynamic behavior but typically for a limited number of reactions. Dynamic FBA (dFBA) integrates both approaches in a hybrid framework, but challenges remain in integrating the two formats to describe bioprocesses. In this study, we first enhanced an existing dynamic CHO-metabolism model by incorporating 13C-labeled data to refine kinetic expressions and stoichiometric constraints of amino acid pathways, including the asparagine-aspartate network and serine biosynthesis. We next evaluated the impact of prioritizing either stoichiometry, through the pseudo steady state assumption (PSSA), or the kinetic expressions of fluxes. Comparing error and predictive performance for both models for two industrially relevant fed-batch CHO culture conditions involving varying initial concentrations of nutrients and three feed streams, demonstrated that the kinetic-oriented model (KOM) yielded superior predictions for viable cell density (VCD), antibody production, and a range of amino acids and metabolites compared to the stoichiometric oriented model (SOM). Indeed, the KOM was able to predict production-to-consumption shifts of lactate and alanine, fluctuating levels of ammonia based on reversible kinetic expressions, and amino acids like asparagine and the serine-glycine pool. The KOM also provided better predictions for a third case including lactate-supplemented (LS) feed; however, slight parameter adjustments helped to improve model fidelity, likely due to the impact of high lactate on kinetic expressions of antibody (directly) and VCD (indirectly). In summary, our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics such as transient shifts for key metabolites like lactate, alanine, and ammonia, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.

systems biology↗

bioRxiv2025

Inferring the Metabolic Objectives of Mammalian Cells via Inverse Modeling of Fluxomics and Metabolomics

Metabolism reflects evolutionary priorities that govern how cells allocate resources. In mammalian cells, metabolic objectives are layered and context-dependent, making it difficult to pinpoint the priorities that underlie observed phenotypes. Here, we introduce ObjFind-M, an inverse optimization framework that infers reaction-level metabolic objectives in mammalian cells directly from fluxomic and metabolomic data. Using Chinese hamster ovary (CHO) cells as a data-rich mammalian cell system, ObjFind-M consistently identifies mitochondrial ATP synthase as the central metabolic driver, supported by key TCA cycle and electron transport chain nodes. Priorities adjust with cellular state, favoring glycolysis-TCA coupling in the growth phase and shifting toward oxidative phosphorylation and redox balance when proliferative activity slows. High recombinant protein producing CHO cells emphasize citrate shuttling and beta-oxidation, linking energy supply with biosynthetic capacity for protein secretion. Benchmarking against conventional objectives demonstrates that maximizing ATP production most accurately reproduces experimental fluxes. By quantifying metabolic objectives directly from data rather than assuming them a priori, ObjFind-M provides a framework for identifying reaction-level strategies that shape cellular decision making. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/677837v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@5c507aorg.highwire.dtl.DTLVardef@a7b544org.highwire.dtl.DTLVardef@6db0d4org.highwire.dtl.DTLVardef@17464e2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIObjFind-M, an inverse FBA framework, infers reaction-level metabolic objectives from fluxomic and metabolomic data. C_LIO_LIATP synthase (Complex V) emerges as the top reaction, supported by NADH-generating TCA reactions such as the -ketoglutarate dehydrogenase complex. C_LIO_LICHO cells suppress formation of toxic or wasteful byproducts while emphasizing energy-efficient flux routing. C_LIO_LIGrowth vs. non-growth phases reveal shifts between biosynthesis and oxidative phosphorylation. C_LIO_LIHigh recombinant protein producers emphasize citrate shuttling and beta-oxidation, linking energy with secretory capacity. C_LIO_LIATP demand maximization provides the most accurate generalizable FBA objective C_LI

systems biology↗

bioRxiv2025

DIA-PASEF Proteomic Profiling Reveals MpkA-Dependent Iron Stress Responses and Siderophore Biosynthesis in Aspergillus nidulans

Filamentous faungi play essential roles in biotechnology as producers of valuable bioproducts and, conversely, as opportunistic pathogens. Aspergillus nidulans is a widely used model organism for fungal genetics and cell biology; however, comprehensive proteomic references for this species remain limited. In this study, we applied a data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF) approach to enable efficient and in-depth proteome profiling of A. nidulans. Leveraging ion mobility-based ion cloud information from DDA-PASEF experiments, we developed DIA-PASEF methods that identified 3,904 proteins across biological triplicates grown in rich medium. Compared to prior studies, this represents an increase in protein identifications of more than 140% and was achieved with more than five-fold reduction in analysis time. We employed this newly developed DIA-PASEF methodology to conduct both proteomic and phosphoproteomic analyses under iron-depleted conditions in an MpkA protein-kinase deficient mutant ({Delta}mpkA). The {Delta}mpkA strain exhibited expression of approximately 500 additional proteins and occupancy of over 1,800 additional phosphosites relative to a control. Differentially expressed and phosphorylated proteins increased by more than an order of magnitude in the {Delta}mpkA mutant across both iron-replete and iron-deplete conditions. Gene Ontology (GO) enrichment analysis revealed broader and distinct biological processes under iron-depleted conditions, highlighting adaptive responses specific to iron limitation and MAPK pathway disruption. This work establishes a high-coverage proteomic resource for A. nidulans and provides novel insights into fungal stress responses and signaling network perturbation. Importantly, high-throughput proteomic profiling reveals that limited iron availability and MAPK pathway disruption increases siderophore biosynthesis.

systems biology↗

bioRxiv2025

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) struggle to achieve rapid and highly accurate monitoring of small molecule critical process parameters and critical quality attributes. While Raman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), is constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. Herein, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary "ON/OFF" signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of single-molecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1, a chemically-defined, serum-free complete media for mammalian cell culture. The obtained highly accurate and reproducible results demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

biophysics↗

bioRxiv2024

Investigating Subpopulation Dynamics in Clonal CHO-K1 Cells with Single-Cell RNA Sequencing

Chinese Hamster Ovary (CHO) cells are used to produce monoclonal antibodies and other biotherapeutics at industrial scale. Despite their ubiquitous nature in the biopharmaceutical industry, little is known about the behaviors of individual transfected clonal CHO cells. Most CHO cells are assessed on their ability to produce the protein of interest over time, known as their stability. But these CHO cells have primarily been studied in bulk, working under the assumption that these bulk samples are identical because of genetic clonality across the sample; however, this does not address other forms of cellular heterogeneity in these ostensibly clonal cells. It is possible these variable stability phenotypes reflect heterogeneity within the clonal samples. In this study, we performed single-cell RNA sequencing on two clonal CHO-K1 cell populations with different stability phenotypes over a 90 day culture period. Our data showed that the instability of the unstable clone was due in part to the emergence of a low-producing subpopulation in the aged samples. This low-producing subpopulation did not exhibit markers of cellular stress which were expressed in the higher-producing populations. Further multiomic investigation should be performed to better characterize this heterogeneity.

genomics↗

bioRxiv2021

Sex differences in lung imaging and SARS-CoV-2 antibody responses in a COVID-19 golden Syrian hamster model

In the ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), more severe outcomes are reported in males compared with females, including hospitalizations and deaths. Animal models can provide an opportunity to mechanistically interrogate causes of sex differences in the pathogenesis of SARS-CoV-2. Adult male and female golden Syrian hamsters (8-10 weeks of age) were inoculated intranasally with 105 TCID50 of SARS-CoV-2/USA-WA1/2020 and euthanized at several time points during the acute (i.e., virus actively replicating) and recovery (i.e., after the infectious virus has been cleared) phases of infection. There was no mortality, but infected male hamsters experienced greater morbidity, losing a greater percentage of body mass, developing more extensive pneumonia as noted on chest computed tomography, and recovering more slowly than females. Treatment of male hamsters with estradiol did not alter pulmonary damage. Virus titers in respiratory tissues, including nasal turbinates, trachea, and lungs, and pulmonary cytokine concentrations, including IFN{beta} and TNF, were comparable between the sexes. However, during the recovery phase of infection, females mounted two-fold greater IgM, IgG, and IgA responses against the receptor-binding domain of the spike protein (S-RBD) in both plasma and respiratory tissues. Female hamsters also had significantly greater IgG antibodies against whole inactivated SARS-CoV-2 and mutant S-RBDs, as well as virus neutralizing antibodies in plasma. The development of an animal model to study COVID-19 sex differences will allow for a greater mechanistic understanding of the SARS-CoV-2 associated sex differences seen in the human population. ImportanceMen experience more severe outcomes from COVID-19 than women. Golden Syrian hamsters were used to explore sex differences in the pathogenesis of a human clinical isolate of SARS-CoV-2. After inoculation, male hamsters experienced greater sickness, developed more severe lung pathology, and recovered more slowly than females. Sex differences in disease could not be reversed by estradiol treatment in males and were not explained by either virus replication kinetics or the concentrations of inflammatory cytokines in the lungs. During the recovery period, antiviral antibody responses in the respiratory tract and plasma, including to newly emerging SARS-CoV-2 variants, were greater in females than male hamsters. Greater lung pathology during the acute phase combined with reduced antiviral antibody responses during the recovery phase of infection in males than females illustrate the utility of golden Syrian hamsters as a model to explore sex differences in the pathogenesis of SARS-CoV-2 and vaccine-induced immunity and protection. One Sentence SummaryFollowing SARS-CoV-2 infection, male hamsters experience worse clinical disease and have lower antiviral antibody responses than females.

microbiology↗

bioRxiv2018

Proteogenomic annotation of the Chinese hamster reveals extensive novel translation events and endogenous retroviral elements

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 incorrectly predictions of translated sequence, incorrect 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 4,333 new proteins compared to the hamster RefSeq protein annotation and 2,503 novel translational events (e.g., alternative splices, mutations, 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 131 type-C retroviruses, thus enabling future efforts to eliminate retroviruses by reducing the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate platform for guiding CHO cell line engineering, including facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

bioinformatics↗

bioRxiv2018

Comprehensive Glycoproteomic Analysis of Chinese Hamster Ovary Cells

The Chinese hamster ovary (CHO) cell line is a major expression system for the production of therapeutic proteins, the majority of which are glycoproteins, such as antibodies and erythropoietin (EPO). The characterization of the glycosylation profiles is critical to understand the important role of glycosylation on therapeutic glycoproteins from CHO cells. In this study, a large scale glycoproteomic workflow was established and applied to CHO-K1 cells expressing EPO. The workflow includes enrichment of intact glycopeptides from CHO-K1 cell lysate and medium using hydrophilic enrichment, fractionation of the obtained intact glycopeptides (IGPs) by basic reversed phase liquid chromatography (bRPLC), analyzing the glycopeptides using LC-MS/MS, and annotating the results by GPQuest 2.0. A total of 10,338 N-linked glycosite-containing IGPs were identified, representing 1,162 unique glycosites in 530 glycoproteins, including 71 unique atypical N-linked IGPs on 18 atypical N-glycosylation sequons with an overrepresentation of the N-X-C motifs. Moreover, we compared the glycoproteins from CHO cell lysate with those from medium using the in-depth N-linked glycoproteome data. The obtained large scale glycoproteomic data from intact N-linked glycopeptides in this study is complementary to the genomic, proteomic, and N-linked glycomic data previously reported for CHO cells. Our method has the potential to accelerate the production of recombinant therapeutic glycoproteins.

biochemistry↗