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1. An optimal regulation of fluxes dictates microbial growth in and out of steady state

   https://doi.org/10.7554/eLife.84878  

   Chure, Griffin ; Cremer, Jonas ( March 2023 , eLife)

   Bitbol, Anne-Florence ; Walczak ; Aleksandra M (Ed.)

   Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with ≈ 60 data sets from Escherichia coli establishes this regulatory mechanism’s biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments. 

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2. Modulation of stochastic gene expression by nuclear export processes

   https://doi.org/10.1109/CDC45484.2021.9683294  

   Smith, Madeline ; Soltani, Mohammad ; Kulkarni, Rahul ; Singh, Abhyudai ( December 2021 , 2021 60th IEEE Conference on Decision and Control (CDC))

   Inside mammalian cells, single genes are known to be transcribed in stochastic bursts leading to the synthesis of nuclear RNAs that are subsequently exported to the cytoplasm to create mRNAs. We systematically characterize the role of export processes in shaping the extent of random fluctuations (i.e. noise) in the mRNA level of a given gene. Using the method of Partitioning of Poisson arrivals, we derive an exact analytical expression for the noise in mRNA level assuming that the nuclear retention time of each RNA is an independent and identically distributed random variable following an arbitrary distribution. These results confirm recent experimental/theoretical findings that decreasing the nuclear export rate buffers the noise in mRNA level, and counterintuitively, decreasing the noise in the nuclear retention time enhances the noise in the mRNA level. Next, we further generalize the model to consider a dynamic extrinsic disturbance that affects the nuclear-to-cytoplasm export. Our results show that noise in the mRNA level varies non-monotonically with the disturbance timescale. More specifically, high- and low-frequency external disturbances have little impact on the mRNA noise level, while noise is amplified at intermediate frequencies. In summary, our results systematically uncover how the coupling of bursty transcription with nuclear export can both attenuate or amplify noise in mRNA levels depending on the nuclear retention time distribution and the presence of extrinsic fluctuations. 

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3. Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

   https://doi.org/10.1093/bioinformatics/btac423  

   Cui, Elvis Han ; Song, Dongyuan ; Wong, Weng Kee ; Li, Jingyi Jessica ; Mathelier, ed., Anthony ( June 2022 , Bioinformatics)

   Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.

   Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene’s expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.

   The Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM.

   Supplementary data are available at Bioinformatics online.

    

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4. Mitochondrial mRNA localization is governed by translation kinetics and spatial transport

   https://doi.org/10.1371/journal.pcbi.1010413  

   Arceo, Ximena G. ; Koslover, Elena F. ; Zid, Brian M. ; Brown, Aidan I. ( August 2022 , PLOS Computational Biology)

   Allard, Jun (Ed.)

   For many nuclear-encoded mitochondrial genes, mRNA localizes to the mitochondrial surface co-translationally, aided by the association of a mitochondrial targeting sequence (MTS) on the nascent peptide with the mitochondrial import complex. For a subset of these co-translationally localized mRNAs, their localization is dependent on the metabolic state of the cell, while others are constitutively localized. To explore the differences between these two mRNA types we developed a stochastic, quantitative model for MTS-mediated mRNA localization to mitochondria in yeast cells. This model includes translation, applying gene-specific kinetics derived from experimental data; and diffusion in the cytosol. Even though both mRNA types are co-translationally localized we found that the steady state number, or density, of ribosomes along an mRNA was insufficient to differentiate the two mRNA types. Instead, conditionally-localized mRNAs have faster translation kinetics which modulate localization in combination with changes to diffusive search kinetics across metabolic states. Our model also suggests that the MTS requires a maturation time to become competent to bind mitochondria. Our work indicates that yeast cells can regulate mRNA localization to mitochondria by controlling mitochondrial volume fraction (influencing diffusive search times) and gene translation kinetics (adjusting mRNA binding competence) without the need for mRNA-specific binding proteins. These results shed light on both global and gene-specific mechanisms that enable cells to alter mRNA localization in response to changing metabolic conditions. 

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5. Regulation of LRRK2 mRNA stability by ATIC and its substrate AICAR through ARE ‐mediated mRNA decay in Parkinson's disease

   https://doi.org/10.15252/embj.2022113410  

   Liu, Qinfang ; Zhu, Dong ; Li, Naren ; Chen, Shifan ; Hu, Liang ; Yu, Jianzhong ; Xiong, Yulan ( June 2023 , The EMBO Journal)

   Mutations in LRRK2 are the most common genetic causes of Parkinson's disease (PD). While the enzymatic activity of LRRK2 has been linked to PD, previous work has also provided support for an important role of elevated LRRK2 protein levels, independent of enzymatic activity, in PD pathogenesis. However, the mechanisms underlying the regulation of LRRK2 protein levels remain unclear. Here, we identify a role for the purine biosynthesis pathway enzyme ATIC in the regulation of LRRK2 levels and toxicity. AICAr, the precursor of ATIC substrate, regulates LRRK2 levels in a cell‐type‐specific mannerin vitroand in mouse tissue. AICAr regulates LRRK2 levels through AUF1‐mediated mRNA decay. Upon AICAr treatment, the RNA binding protein AUF1 is recruited to the AU‐rich elements (ARE) of LRRK2 mRNA leading to the recruitment of the decapping enzyme complex DCP1/2 and decay of LRRK2 mRNA. AICAr suppresses LRRK2 expression and rescues LRRK2‐induced dopaminergic neurodegeneration and neuroinflammation in PDDrosophilaand mouse models. Together, this study provides insight into a novel regulatory mechanism of LRRK2 protein levels and function via LRRK2 mRNA decay that is distinct from LRRK2 enzymatic functions.

    

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