Biology subjects

Lin, Y. T.

Publications and source records attributed to Lin, Y. T..

7 recordsLinked to original sources

bioRxiv2026

Complete biosynthesis of penicillin G in Nicotiana benthamiana

Commercial penicillin production has relied on microbial fermentation for more than 80 years. Here, we engineered the plant, Nicotiana benthamiana, to produce penicillin G in its leaves by transient expression of up to seven fungal biosynthetic genes. Remarkably, all recombinant proteins localize to the analogous subcellular compartments without engineering signal peptide sequences or post-translational modification sites. Although non-ribosomal peptide synthetases occur widely in fungi and bacteria to produce a plethora of specialized metabolites, their evolutionary distribution does not extend to plants. Our results now open a new metabolic frontier for natural product synthesis, and offer possibilities to address global health concerns through an alternative biotechnology platform for fungal-derived pharmaceutical production.

synthetic biology↗

bioRxiv2025

Mobility-enhanced virus vectors enable meristem genome editing in model and crop plants

CRISPR/Cas9 gene editing revolutionized genetics, but its application is often hampered in non-model plants that are recalcitrant or less amenable to standard plant transformation and regeneration methods. Harnessing viruses to convey guide RNAs (gRNAs) directly to the meristem promises to overcome those limitations and accelerate the generation of edited lines in diverse crops. With several RNA viruses, delivery of gRNAs to the meristem is enhanced with the addition of mobile RNA elements. We hypothesized that incorporating distinct RNA secondary structures as candidate mobility factors in the widely used Tobacco rattle virus (TRV) could propel virus delivery for enhanced meristem editing in non-model species. To test this, we engineered TRV vectors to deliver gRNAs targeting visible marker genes, with each virus incorporating unique mobility factors. We determined optimal virus construction for multiplexed meristem editing by first delivering each virus to Nicotiana benthamiana plants harboring the Cas9 transgene. Strikingly different phenotypes were observed among virus treatments, which were confirmed to represent distinct somatic and heritable editing events. We further tested our hypothesis by leveraging these results to edit pennycress (Thlaspi arvense), an emerging oilseed crop. Our results demonstrated successful virus delivery of meristem editing to this non-model plant, underscoring the potential of this approach to deliver targeted genome modifications in diverse crops. One sentence summaryIncorporating RNA mobility factors in TRV affects meristem editing in model plants and crops.

plant biology↗

bioRxiv2025

Proteolytic activation of diverse antiviral defense modules in prokaryotes

Linked protease-effector modules are widespread in prokaryotic antiviral defense, yet the mechanisms of most remain poorly understood. Here we show that four of the most prevalent modules--metallo-{beta}-lactamase (MBL)-fold hydrolase, /{beta}-hydrolase, Pepco, and EACC1--form latent death effectors that are unleashed by site-specific proteolysis. Genetic, biochemical, and structural analyses reveal novel modes of effector licensing. MBL and /{beta}-hydrolase are zymogens activated by cleavage at two distinct sites, and upon proteolysis, MBL becomes a Zn2+-dependent double-stranded DNA nuclease. In contrast, Pepco and EACC1 act as pore-forming toxins via distinct mechanisms: Pepco constitutively oligomerizes into a denaturation-resistant beta barrel that is activated by cleavage after a specific isoleucine in its C-terminal tail, whereas EACC1 monomers assemble into large membrane pores following removal of an autoinhibitory domain. All four modules are fused to diverse sensors to detect a wide range of phage signals, and EACC1-DnaK chaperone fusions suggest a convergence between defense and general stress responses. These findings establish proteolysisgated activation as a dominant, modular logic for anti-phage defense and reveal parallels with eukaryotic innate immunity.

molecular biology↗

bioRxiv2022

Gene expression noise accelerates the evolution of a biological oscillator

Gene expression is a biochemical process, where stochastic binding and unbinding events naturally generate fluctuations and cell-to-cell variability in gene dynamics. These fluctuations typically have destructive consequences for proper biological dynamics and function (e.g., loss of timing and synchrony in biological oscillators). Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms. We used computer simulations to evolve two mechanistic models of a biological oscillator at different levels of gene expression noise. We first show that gene expression noise induces oscillatory-like dynamics in regions of parameter space that cannot oscillate in the absence of noise. We then demonstrate that these noise-induced oscillations generate a fitness landscape whose gradient robustly and quickly guides evolution by mutation towards robust and self-sustaining oscillation. These results suggest that noise can help dynamical systems evolve or learn new behavior by revealing cryptic dynamic phenotypes outside the bifurcation point. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/485207v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@fdbc9forg.highwire.dtl.DTLVardef@6a8b08org.highwire.dtl.DTLVardef@cdbc7borg.highwire.dtl.DTLVardef@1116812_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

A common pattern of influenza A virus single cell gene expression heterogeneity governs the innate antiviral response to infection

Viral infection outcomes are governed by the complex and dynamic interplay between the infecting virus population and the host response. It is increasingly clear that both viral and host cell populations are highly heterogeneous, but little is known about how this heterogeneity influences infection dynamics or viral pathogenicity. To dissect the interactions between influenza A virus (IAV) and host cell heterogeneity, we examined the combined host and viral transcriptomes of thousands of individual, single virion-infected cells. We observed complex patterns of viral gene expression and the existence of multiple distinct host transcriptional responses to infection at the single cell level. Our analyses reveal that viral NS segment gene expression diverges from that of the rest of the viral genome within a subset of infected cells, and that this unique pattern of NS segment expression can play a dominant role in shaping the host cell response to infection. Finally, we show that seasonal human H1N1 and H3N2 strains differ significantly in patterns of host anti-viral gene transcriptional heterogeneity at the single cell level. Altogether, these data reveal a common pattern of viral gene expression heterogeneity across human IAV subtypes that can serve as a major determinant of antiviral gene activation.

microbiology↗

bioRxiv2018

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to statistical inference is the most complete method for model selection and uncertainty quantification of kinetic parameters from single-cell data. This approach is impractical because current numerical algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piece-wise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We validated our method of accelerated Bayesian inference on several synthetic data sets. Our results show that kinetic parameters can be reasonably constrained for modestly sampled data sets, if the model is known a priori. If the model is unknown,the Bayesian evidence can be used to rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics, e.g., Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), often used for model selection.

molecular biology↗

bioRxiv2017

High Cooperativity In Negative Feedback Can Amplify Noisy Gene Expression

Burst-like synthesis of protein is a significant source of cell-to-cell variability in protein levels. Negative feedback is a common example of a regulatory mechanism by which such stochasticity can be controlled. Here we consider a specific kind of negative feedback, which makes bursts smaller in the excess of protein. Increasing the strength of the feedback may lead to dramatically different outcomes depending on a key parameter, the noise load, which is defined as the squared coefficient of variation the protein exhibits in the absence of feedback. Combining stochastic simulation with asymptotic analysis, we identify a critical value of noise load: for noise loads smaller than critical, the coefficient of variation remains bounded with increasing feedback strength; contrastingly, if the noise load is larger than critical, the coefficient of variation diverges to infinity in the limit of ever greater feedback strengths. Interestingly, high-cooperativity feedbacks have lower critical noise loads, implying that low-cooperativity feedbacks in burst size can be preferable for noisy proteins. Finally, we discuss our findings in the context of previous results on the impact of negative feedback in burst size and burst frequency on gene-expression noise.

systems biology↗