Biology subjects

Tat, J.

Publications and source records attributed to Tat, J..

7 recordsLinked to original sources

bioRxiv2026

SEC-seq reveals translation-focused metabolic strategies for high IgG productivity in clonal CHO cells

Chinese hamster ovary (CHO) cells are the dominant host for therapeutic protein production, yet intra- and inter-clonal heterogeneity in manufacturing phenotypes, and the underlying metabolic and secretory circuitry, remain poorly defined at single-cell resolution. Here, we apply secretion encoded single-cell sequencing (SEC-seq) to simultaneously measure transcriptomes and secreted IgG in single-cells from a parental production cell line and five CHO clones, each varying in cell-specific productivity. IgG mRNA and recombinant protein secretion are only moderately correlated across single cells, indicating that transcription alone does not explain intra-clonal secretion heterogeneity. By integrating SEC-seq with single-cell metabolic and secretory task scoring, we find that CHO cells accommodating recombinant protein expression burden have more active translation-associated pathways and suppressed energy-intensive endogenous secreted protein processing. Three high-secreting clones converge on this translation-focused state but differ in their subpopulation composition and energy/redox programs coupled to IgG output: one highly productive clone shows a low-growth, glycolytic, NAD/one-carbon-associated and UPR-activated program; a second shows increased oxidative phosphorylation and fatty-acid {beta}-oxidation, and a third shows higher lipid-uptake with modest central carbon metabolism. Genes such as Aldoa, Ndufab1, Acsl5, Mthfd2 showed clone-specific correlations with IgG, linking glycolysis, mitochondrial respiration, fatty-acid metabolism, and redox to secretion. Together, these results demonstrate that SEC-seq can resolve IgG-coupled metabolic-secretory wiring within and between CHO clones, providing a framework to identify subpopulation and circuit features to engineer or select for improved recombinant protein production.

systems biology↗

bioRxiv2024

A reconstruction of the mammalian secretory pathway identifies mechanisms regulating antibody production

The secretory pathway processes >30% of mammalian proteins, orchestrating their synthesis, modification, trafficking, and quality control. However, its complexity-- spanning multiple organelles and dependent on coordinated protein interactions--limits our ability to decipher how protein secretion is controlled in biomedical and biotechnological applications. To advance such research, we present secRecon--a comprehensive reconstruction of the mammalian secretory pathway, comprising 1,127 manually curated genes organized within an ontology of 77 secretory process terms, annotated with functional roles, subcellular localization, protein interactions, and complex composition. Using secRecon to integrate multi-omics data, we identified distinct secretory topologies in antibody-producing plasma cells compared to CHO cells. Genes within proteostasis, translocation, and N-glycosylation are deficient in CHO cells, highlighting them as potential engineering targets to boost secretion capacity. Applying secRecon to single-cell transcriptomics and SEC-seq data, we uncovered secretory pathway signatures underlying secretion diversity among IgG-secreting plasma cells. Different transcriptomic clusters had unique secretory phenotypes characterized by variations in the unfolded protein response (UPR), endoplasmic reticulum-associated degradation (ERAD), and vesicle trafficking pathways. Additionally, we discovered specific secretory machinery genes as new markers for plasma cell differentiation. These findings demonstrate secRecon can identify mechanisms regulating protein secretion and guide diverse studies in biomedical research and biotechnology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/623668v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@f9f865org.highwire.dtl.DTLVardef@122db7forg.highwire.dtl.DTLVardef@1788a1aorg.highwire.dtl.DTLVardef@b2b2bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of the production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity but cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic characterization approach combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product production in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

systems biology↗

bioRxiv2023

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

systems biology↗

bioRxiv2022

Schedule-dependent production of stereotyped sequences of actions

Concatenating actions into automatic routines is evolutionarily advantageous as it allows organisms to efficiently use time and energy under predictable conditions. However, over reliance on inflexible behaviors can be life-threatening in a changing environment and can become pathological in disease states such as obsessive-compulsive disorder (OCD) and substance use disorder (SUD). Understanding the conditions under which stereotypical sequences of actions are produced is crucial to studying how these behaviors can become maladaptive. Here, we investigated the ability of operant conditioning schedules and contingencies to promote reproducible sequences of five lever presses. We found that signaling reinforcer delivery with a visual cue was effective at increasing learning rates but resulted in mice pressing the lever in fast succession until the cue turned on, rather than pressing it five times. We also found that requiring mice to collect their reinforcer between sequences had little effect on both rate of behavior and on quantitative metrics of reproducibility such as interresponse interval (IRI) variance, and that a training strategy that directly reinforced sequences with low variance IRIs was not more effective than a traditional fixed ratio schedule at promoting reproducible action execution. Together, our findings provide insights into the parameters of behavioral training that promote reproducible sequences and serve as a roadmap to investigating the neural substrates of automatic behaviors.

animal behavior and cognition↗

bioRxiv2022

Dopamine release at the time of a predicted aversive outcome causally controls the trajectory and expression of conditioned behavior

BackgroundThe inability to predict when aversive stimuli will and will not occur in is a hallmark of anxiety and stress disorders. Dopamine release in the nucleus accumbens (NAc) is sufficient and necessary for aversive learning and has been linked to both anxiety and stress disorder symptomatology. Thus, understanding how dopamine controls associative learning in response to aversive stimuli is critical to understanding the role of dopamine in behavior in health and disease. MethodsUsing an optical dopamine sensor combined with in-vivo fiber photometry in the NAc core of male and female C57BL/6J mice (N=38), we recorded dopamine responses to expected and omitted aversive outcomes during learning. We derived predictions from a theory-driven model of associative learning (Kutlu-Calipari-Schmajuk, KCS model) and tested the causality of these predictions using optogenetics. ResultsDopamine release was evoked by the predicted omission of aversive stimuli in a fashion that cannot be explained by dopamine as a reward-based prediction signal. The magnitude of the dopamine response during omissions scaled with predictions about the probability of their occurrence; however, dopamine did not track the associative value of predictive cues. Finally, we showed that the observed effects are causal to learned behavior and can only be explained by dopamine signaling the perceived saliency of predicted aversive events. ConclusionsWe elucidate the role of NAc core dopamine signaling in aversive learning in a theory-based and stimulus-specific fashion and offer potential avenues for understanding the neural mechanisms involved in anxiety and stress disorders.

neuroscience↗

bioRxiv2019

A novel multidimensional reinforcement task in mice elucidates sex-specific behavioral strategies

BackgroundSex is a critical biological variable in the neuropathology of psychiatric disease, and in many cases, women represent a vulnerable population. It has been hypothesized that sex differences in neuropsychiatric disorders are manifestations of differences in basic reward processing. However, preclinical models often present rewards in isolation, ignoring that ethologically, reward seeking requires the consideration of potential aversive outcomes.\n\nMethodsWe developed a Multidimensional Cue Outcome Action Task (MCOAT) to dissociate motivated action from cue learning and valence. Mice are trained in a series of operant tasks. In phase 1, mice acquire positive and negative reinforcement in the presence of discrete discriminative stimuli. In phase 2, both discriminative stimuli are presented concurrently allowing us to parse innate behavioral strategies based on reward seeking and shock avoidance. Phase 3 is punished responding where a discriminative stimulus predicts that nose-poking for sucrose occurs concurrently with footshock, allowing for the assessment of how positive and negative outcomes are relatively valued.\n\nResultsFemales prioritize avoidance of negative outcomes over seeking positive, while males have the opposite strategy. In cases where rules are uncertain, males and females employ different strategies, with females demonstrating bias for shock avoidance.\n\nConclusionsThe MCOAT has broad utility for neuroscience research where pairing this task with recording and manipulation techniques will allow for the definition of the discrete information encoded within cellular populations. Ultimately, we show that making conclusions from unidimensional data leads to inaccurate generalizations about sex-specific behaviors that do not accurately represent ground truth.

animal behavior and cognition↗