Biology subjects

Lee, T. S.

Publications and source records attributed to Lee, T. S..

18 recordsLinked to original sources

bioRxiv2025

Predictive genome-wide CRISPR-mediated gene downregulation for enhanced bioproduction

CRISPR interference (CRISPRi) has emerged as a valuable tool for redirecting metabolic flux to enhance bioproduction. However, conventional approaches for identifying target genes for CRISPRi-mediated downregulation have largely relied on heuristic methods and trial and error, which are labor-intensive and time-consuming. Additionally, the ability to achieve multigene knockdowns is limited by the constraints of molecular cloning techniques required for building multiplexed CRISPRi systems. In this study, we describe two novel methodologies FluxRETAP (Flux-REaction TArget Prioritization, a Genome-Scale Modeling Technique) and VAMMPIRE (a Versatile Assembly Method for MultiPlexing CRISPRi-mediated downREgulation). FluxRETAP accurately identified gene targets whose knockdown led to substantial increase of isoprenol titers, outperforming traditional heuristic selection. The use of VAMMPIRE enabled accurate and position-independent assembly of CRISPRi constructs containing up to five sgRNA arrays. The integration of FluxRETAP and VAMMPIRE has the potential to advance metabolic engineering by rapidly identifying CRISPRi-mediated knockdowns and knockdown combinations that enhance bioproduction titers, with potential applicability to other microbial systems.

synthetic biology↗

bioRxiv2025

Biosensor-Driven Strain Engineering Reveals Key Cellular Processes for Maximizing Isoprenol Production in Pseudomonas putida

Synthetic biology tools have accelerated the generation of simple mutants, but combinatorial testing remains challenging. High-throughput methods struggle translating from proof-of-principle molecules to advanced bioproducts. We address this challenge with a biosensor-driven strategy for enhanced isoprenol production in Pseudomonas putida, a key precursor for sustainable aviation fuel and platform chemicals. This biosensor leverages P. putidas native response to short-chain alcohols via a previously uncharacterized hybrid histidine kinase signaling cascade. Refactoring the biosensor for a conditional growth-based selection enabled identification of competing cellular processes with a [~]16,500-member CRISPRi-library. An iterative combinatorial strain engineering approach yielded an integrated P. putida strain producing [~]900 mg/L isoprenol in glucose minimal medium, a 36-fold increase. Ensemble -omics analysis revealed metabolic rewiring, including amino acid accumulation as key drivers of enhanced production. Techno-economic analysis elucidated the path to economic viability and confirmed the benefits of adding amino acids outweigh the additional costs. This study establishes a robust biosensor driven approach for optimizing other heterologous pathways, accelerating microbial cell factory development. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/643695v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@533605org.highwire.dtl.DTLVardef@ff98fforg.highwire.dtl.DTLVardef@9592fdorg.highwire.dtl.DTLVardef@1e52993_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Manifold Transform by Recurrent Cortical Circuit EnhancesRobust Encoding of Familiar Stimuli

A ubiquitous phenomenon observed along the ventral stream of the primate hierarchical visual system is the suppression of neural responses to familiar stimuli at the population level. The observation of the suppression of the neural response in the early visual cortex (V1 and V2) to familiar stimuli of size that are multiple times larger in size than the receptive fields of individual neurons reflects the plausible development of recurrent circuits for encoding these global stimuli. In this work, we investigated the neural mechanisms of familiarity suppression and showed that an excitatory recurrent neural circuit, consisting of neurons with small and local receptive fields, can develop to encode specific global familiar stimuli robustly as a result of familiarity training. This Hebbian learning based model attributes the observed familiarity suppression effect to the sparsification of the population neural code for the familiar stimuli due to the formation of image-specific local excitatory circuits and competitive normalization among neurons, leading to the paradoxical neural response suppression to the familiar stimuli at the population level. We explored the computational implications of the proposed circuit by relating it to the sparse manifold transform. The recurrent circuit, by linking spatially co-occurring visual features together, compresses the dimensions of irrelevant variations of a familiar image in the neural response manifold relative to the dimensions for discriminating different familiar stimuli. The computation can be considered as a globally non-linear but locally linear manifold transform that orthogonalizes the slow modes of network dynamics relative to the subspace of irrelevant stimulus variations, resulting in increased robustness of the global stimulus representation against noises and other irrelevant perturbations. These results provide testable predictions for neurophysiological experiments. Author summaryIn this research, we explored how the brain can become more efficient at processing familiar visual information. When we repeatedly see something, our brains response to it changes. In response to familiar stimuli, neurons across the different visual areas of the mammalian visual system become more selective and their overall activities decrease. We developed a computational model to investigate why this happens and what functional advantages these mechanisms might provide. We discovered that familiarity leads to the development of a more efficient and robust neural representation of what we see. It allows us to rapidly and robustly recognize our friends face despite changes in lighting conditions, view angle, or facial expression. Our model showed that through repeated exposure, the brains neural circuits, even in the early stages of visual processing, rapidly adapt and organize themselves to focus on important and consistent features in our visual environment while becoming less sensitive to irrelevant variations, and distractions.

neuroscience↗

bioRxiv2025

Evolution guided tolerance engineering of Pseudomonas putida KT2440 for production of the sustainable aviation fuel precursor isoprenol

Isoprenol (3-methyl-3-buten-1-ol) is a sustainable aviation fuel precursor and commodity chemical which can be microbially synthesized from renewable carbon streams. Its production has been demonstrated in Pseudomonas putida KT2440 but its titers, rates, and yields have yet to reach commercially viable levels, potentially due to its inherent toxicity. We hypothesized that utilization of Tolerization Adaptive Laboratory Evolution (TALE) would generate P. putida hosts more tolerant to isoprenol and suitable for enhanced production phenotypes. Here, we performed a comprehensive TALE campaign using three strains, the wild-type and two strains lacking subsets of known isoprenol catabolism and transport functions in quadruplicate independently evolved lineages. Several evolved clones from each starting strain displayed robust growth (up to 0.2 h-1) at 8 g/L of isoprenol, where starting strains could not grow. Whole genome resequencing of the 12 independent strain lineages identified convergent mutations. Reverse engineering four commonly mutated regions individually (gnuR, ttgB-PP_1394, PP_3024-PP_5558, PP_1695) each resulted in a partial recovery of the tolerance phenotypes observed in the evolved strains. Additionally, a proteomics-guided deletion of the master motility regulator, fleQ, in an evolved clone alleviated the tolerance vs. production trade-off, restoring isoprenol titers and consumption to levels observed in the starting strains. Collectively, this work demonstrated that an integrated strategy of laboratory evolution and rational engineering was effective to develop robust biofuel production hosts with minimized product toxicity.

microbiology↗

bioRxiv2025

Single-molecule tweezers decoding hidden dimerization patterns of membrane proteins within lipid bilayers

Dimerization of transmembrane (TM) proteins is an essential biological process within cellular membranes, playing a key role in diverse pathophysiological pathways and serving as a promising therapeutic target. Although often simplified as a two-state transition from freely diffusing monomers to fully formed dimers, the dimerization process after monomer diffusion--the post-diffusion dimerization--is likely more complex due to intricate inter-residue interactions. Here, we introduce a single-molecule tweezer platform to map detailed profiles of the post-diffusion transitions in TM protein dimerization. This approach captures reversible dimerization events of a single TM dimer, revealing hidden intermediate states that emerge following the quiescent phase of monomer diffusion. Profiling the post-diffusion intermediates, kinetics, and energy landscapes--integrated with molecular dynamics simulations--uncovers the dimerization pathway, the effects of residue interactions and lipid bilayers, and the kinetic and energetic contributions of distinct dimerization domains. Furthermore, this platform characterizes selective and localized modulations via peptide binding, underscoring its potential to elucidate the mechanisms of action of TM dimer-targeting drugs at single-molecule resolution.

biophysics↗

bioRxiv2024

Alternate routes to acetate tolerance lead to varied isoprenol production from mixed carbon sources in Pseudomonas putida

Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. While this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here we examined growth and isoprenol production in a Pseudomonas putida strain pre-tolerized ("PT") background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon source. We found that PT displays impaired growth in minimal medium containing acetate, and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, while a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains, and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol. ImportanceAcetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential of being used together with sugars, especially in microbes with versatile catabolism such as P. putida. However, the use of mixed carbon streams necessitates additional optimization. Further, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study we generate acetate tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, though independent phenotypes can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.

microbiology↗

bioRxiv2023

Familiarity training enhance straightening of neural trajectory for video prediction

Predictive processing in the visual system is pivotal for efficient sensory-driven behaviors. Previous research has shown that the visual system transforms sequential inputs into straighter temporal trajectories. However, the specific role of neural straightening in predictive processing, especially in how learning reshapes this phenomenon for enhanced prediction, remains unclear. To address this, we analyzed V2 population activity in macaques during familiarity training with video stimuli. Our findings reveal that repeated exposure to the same movies significantly enhances neural straightening, indicating a critical role of learning in refining neural trajectories for prediction. In parallel, our studies with the deep predictive network model, Pred-Net, demonstrated similar enhancements in neural straightening in response to familiar movies. This underscores a strong association between neural straightening and predictive coding. Together, our results provide novel insights into the adaptive mechanisms of the visual cortex, enriching our understanding of how learning shapes neural path-ways for efficient prediction.

neuroscience↗

bioRxiv2023

Adopting orphan receptors: group IX/X transition metals as potential ligands of zebrafish Tlr4 homologs

TLR4 is the prototype immune receptor and central to infection defence via detecting lipopolysaccharide (LPS). Surprisingly, the impacts of LPS upon the TLR4 homologs in zebrafish, an important animal model, are equivocal and the function of TLR4 homologs across all fishes remains debatable. Recent work suggests zebrafish Tlr4 mediates ototoxic responses to a platinum-based chemotherapeutic. This prompts our hypothesis that Tlr4 detects group IX/X transition metals and thus has conserved roles with human TLR4 mediating allergic responses to nickel. Here, we use the larval zebrafish lateral line model to demonstrate (sub-)micromolar Ni, Co and Pt are ototoxic in a dose-dependent manner. TLR4 homologs are required for this toxicity because Tlr4 knockdown via CRISPR significantly reduced the metals impacts by [~]50%. Moreover, zebrafish Tlr4 was sufficient to mediate inflammatory responses to metals when expressed in a human cell line. These data are consistent with the notion that mediating responses to transition metals was a function of TLR4 homologs in the last common ancestor of fish and mammals, and begins to resolve the function(s) of TLR4 homologs in the zebrafish animal model of disease.

zoology↗

bioRxiv2023

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane), and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

synthetic biology↗

bioRxiv2022

A highly active Burkholderia polyketoacyl-CoA thiolase for production of triacetic acid lactone

Triacetic acid lactone (TAL) is a platform chemical biosynthesized primarily through decarboxylative Claisen condensation by type III polyketide synthase 2-pyrone synthase (2-PS). However, this reaction suffers from intrinsic energy inefficiency and feedback inhibition by and competition for malonyl-CoA. TAL production through non-decarboxylative Claisen condensation by polyketoacyl-CoA thiolase alleviates many of these disadvantages. We discovered five more thiolases with TAL production activity by exploring homologs of a previously reported polyketoacyl-CoA thiolase, BktB, from Cupriavidus necator. Among them, the BktB homolog from Burkholderia sp. RF2-non_BP3 has [~] 30 times higher in vitro and in vivo TAL production activity and led to [~]10 times higher TAL titer than 2-PS when expressed in Escherichia coli, achieving a titer of 2.8 g/L in fed-batch fermentations. This discovery of a novel polyketoacyl-CoA thiolase with superior TAL production activity paves the way for realization of total biomanufacturing of TAL.

bioengineering↗

bioRxiv2020

A spiking neural circuit model for learning multi-sensory integration

Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

neuroscience↗

bioRxiv2020

Development of natural scene representation in primary visual cortex requires early postnatal experience

The development of the visual system is known to be shaped by early-life experience. To identify response properties that contribute to enhanced natural scene representation, we performed calcium imaging of excitatory neurons in the primary visual cortex (V1) of awake mice raised in three different conditions (standard-reared, dark-reared, and delayed-visual experience) and compared neuronal responses to natural scene features relative to simpler grating stimuli that varied in orientation and spatial frequency. We assessed population selectivity in V1 using decoding methods and found that natural scene discriminability increased by 75% between the ages of 4 to 6 weeks. Both natural scene and grating discriminability were higher in standard-reared animals compared to those raised in the dark. This increase in discriminability was accompanied by a reduction in the number of neurons that responded to low-spatial frequency gratings. At the same time there was an increase in neuronal preference for natural scenes. Light exposure restricted to a 2-4 week window during adulthood did not induce improvements in natural scene nor in grating stimulus discriminability. Our results demonstrate that experience reduces the number of neurons required to effectively encode grating stimuli and that early visual experience enhances natural scene discriminability by directly increasing responsiveness to natural scene features.

neuroscience↗

bioRxiv2020

Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits

The brain performs probabilistic inference to interpret the external world, but the underlying neuronal mechanisms remain not well understood. The stimulus structure of natural scenes exists in a high-dimensional feature space, and how the brain represents and infers the joint posterior distribution in this rich, combinatorial space is a challenging problem. There is added difficulty when considering the neuronal mechanics of this representation, since many of these features are computed in parallel by distributed neural circuits. Here, we present a novel solution to this problem. We study continuous attractor neural networks (CANNs), each representing and inferring a stimulus attribute, where attractor coupling supports sampling-based inference on the multivariate posterior of the high-dimensional stimulus features. Using perturbative analysis, we show that the dynamics of coupled CANNs realizes Langevin sampling on the stimulus feature manifold embedded in neural population responses. In our framework, feedforward inputs convey the likelihood, reciprocal connections encode the stimulus correlational priors, and the internal Poisson variability of the neurons generate the correct random walks for sampling. Our model achieves high-dimensional joint probability representation and Bayesian inference in a distributed manner, where each attractor network infers the marginal posterior of the corresponding stimulus feature. The stimulus feature can be read out simply with a linear decoder based only on local activities of each network. Simulation experiments confirm our theoretical analysis. The study provides insight into the fundamental neural mechanisms for realizing efficient high-dimensional probabilistic inference.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

neuroscience↗

bioRxiv2019

Emergence of opposite neurons in a firing-rate model of multisensory integration

Opposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.

neuroscience↗

bioRxiv2018

Concurrent Multisensory Integration and Segregation with Complementary Congruent and Opposite Neurons

Our brain perceives the world by exploiting multiple sensory modalities to extract information about various aspects of external stimuli. If these sensory cues are from the same stimulus of interest, they should be integrated to improve perception; otherwise, they should be segregated to distinguish different stimuli. In reality, however, the brain faces the challenge of recognizing stimuli without knowing in advance whether sensory cues come from the same or different stimuli. To address this challenge and to recognize stimuli rapidly, we argue that the brain should carry out multisensory integration and segregation concurrently with complementary neuron groups. Studying an example of inferring heading-direction via visual and vestibular cues, we develop a concurrent multisensory processing neural model which consists of two reciprocally connected modules, the dorsal medial superior temporal area (MSTd) and the ventral intraparietal area (VIP), and that at each module, there exists two distinguishing groups of neurons, congruent and opposite neurons. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, both optimally as described by Bayesian inference. The two groups of neurons provide complementary information which enables the neural system to assess the validity of cue integration and, if necessary, to recover the lost information associated with individual cues without re-gathering new inputs. Through this process, the brain achieves rapid stimulus perception if the cues come from the same stimulus of interest, and differentiates and recognizes stimuli based on individual cues with little time delay if the cues come from different stimuli of interest. Our study unveils the indispensable role of opposite neurons in multisensory processing and sheds light on our understanding of how the brain achieves multisensory processing efficiently and rapidly.\n\nSignificance StatementOur brain perceives the world by exploiting multiple sensory cues. These cues need to be integrated to improve perception if they come from the same stimulus and otherwise be segregated. To address the challenge of recognizing whether sensory cues come from the same or different stimuli that are unknown in advance, we propose that the brain should carry out multisensory integration and segregation concurrently with two different neuron groups. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, and the interplay between them achieves rapid stimulus recognition without information loss. We apply our model to the example of inferring heading-direction based on visual and vestibular cues and reproduce the experimental data successfully.

neuroscience↗

bioRxiv2018

Convolutional neural network models of V1 responses to complex patterns

In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized linear models. We then systematically dissected different components of the CNN and found two key factors that made CNNs outperform other models: thresholding nonlinearity and convolution. In addition, we fitted our data using a pre-trained deep CNN via transfer learning. The deep CNNs higher layers, which encode more complex patterns, outperformed lower ones, and this result was consistent with our earlier work on the complexity of V1 neural code. Our study systematically evaluates the relative merits of different CNN components in the context of V1 neuron modeling.

neuroscience↗

bioRxiv2018

Large-scale two-photon imaging revealed super-sparse population codes in V1 superficial layer of awake monkeys

Efficient coding has been proposed as a general principle for the sensory systems. The efficient coding hypothesis predicts that neuronal population responses should be sparse, but limited by the measurement techniques, the precise estimates of the population sparseness of visual cortical neurons are still uncertain. Here, we employed large-scale two-photon calcium imaging to examine the neuronal population activities in V1 superficial layers of awake macaques in response to a large set of natural images. We found that only 0.5% of these neurons on average responded strongly to any given natural image with response strength above half of their individual peak responses, which is more than tenfold sparse over those reported by early studies. We further showed that these sparse population activities contain sufficient information for discriminating images with high accuracy. This study provided the first accurate measure of sparseness in V1 neuronal population responses, which support super-sparse neural codes in primates.

neuroscience↗