Feedback between stochastic gene networks and population dynamics enables cellular decision-making.

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.

MOTIVATION: Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively.

Patterns of interdivision time correlations reveal hidden cell cycle factors.

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.

On the interplay of temporal resolution power and spatial suppression in their prediction of psychometric intelligence.

As a measure of the brain's temporal fine-tuning capacity, temporal resolution power (TRP) explained repeatedly a substantial amount of variance in psychometric intelligence. Recently, spatial suppression, referred to as the increasing difficulty in quickly perceiving motion direction as the size of the moving stimulus increases, has attracted particular attention, when it was found to be positively related to psychometric intelligence. Due to the conceptual similarities of TRP and spatial suppression, the present study investigated their mutual interplay in the relation to psychometric intelligence in 273 young adults to better understand the reasons for these relationships. As in previous studies, psychometric intelligence was positively related to a latent variable representing TRP but, in contrast to previous reports, negatively to latent and manifest measures of spatial suppression. In a combined structural equation model, TRP still explained a substantial amount of variance in psychometric intelligence while the negative relation between spatial suppression and intelligence was completely explained by TRP. Thus, our findings confirmed TRP to be a robust predictor of psychometric intelligence but challenged the assumption of spatial suppression as a representation of general information processing efficiency as reflected in psychometric intelligence. Possible reasons for the contradictory findings on the relation between spatial suppression and psychometric intelligence are discussed.

A systematic dissection of determinants and consequences of snoRNA-guided pseudouridylation of human mRNA.

RNA can be extensively modified post-transcriptionally with >170 covalent modifications, expanding its functional and structural repertoire. Pseudouridine (Ψ), the most abundant modified nucleoside in rRNA and tRNA, has recently been found within mRNA molecules. It remains unclear whether pseudouridylation of mRNA can be snoRNA-guided, bearing important implications for understanding the physiological target spectrum of snoRNAs and for their potential therapeutic exploitation in genetic diseases. Here, using a massively parallel reporter based strategy we simultaneously interrogate Ψ levels across hundreds of synthetic constructs with predesigned complementarity against endogenous snoRNAs. Our results demonstrate that snoRNA-mediated pseudouridylation can occur on mRNA targets. However, this is typically achieved at relatively low efficiencies, and is constrained by mRNA localization, snoRNA expression levels and the length of the snoRNA:mRNA complementarity stretches. We exploited these insights for the design of snoRNAs targeting pseudouridylation at premature termination codons, which was previously shown to suppress translational termination. However, in this and follow-up experiments in human cells we observe no evidence for significant levels of readthrough of pseudouridylated stop codons. Our study enhances our understanding of the scope, 'design rules', constraints and consequences of snoRNA-mediated pseudouridylation.

Adult Attachment and Personality as Predictors of Jealousy in Romantic Relationships.

Functional relationships between romantic jealousy and traits, such as neuroticism or adult attachment styles, are well-known. For the first time, we conducted a joint analysis of the Big Five traits and attachment dimensions as predictors of jealousy, which considered gender differences as well as differences in infidelity experiences and relationship status. In 847 participants, path modeling showed that higher neuroticism, lower agreeableness, and lower openness predicted higher romantic jealousy. The attachment dimensions "anxiety" and "depend" partly mediated the effect of neuroticism and fully mediated the effect of agreeableness on romantic jealousy. The direct and indirect relationships did not differ as a function of gender, relationship status, and infidelity experiences. These findings contribute to a better understanding of individual differences in romantic jealousy from a personality perspective.

Generation of a µ-1,2-hydroperoxo FeIIIFeIII and a µ-1,2-peroxo FeIVFeIII Complex.

µ-1,2-Peroxo-diferric intermediates (P) of non-heme diiron enzymes are proposed to convert upon protonation either to high-valent active species or to activated P' intermediates via hydroperoxo-diferric intermediates. Protonation of synthetic µ-1,2-peroxo model complexes occurred at the µ-oxo and not at the µ-1,2-peroxo bridge. Here we report a stable µ-1,2-peroxo complex {FeIII(µ-O)(µ-1,2-O2)FeIII} using a dinucleating ligand and study its reactivity. The reversible oxidation and protonation of the µ-1,2-peroxo-diferric complex provide µ-1,2-peroxo FeIVFeIII and µ-1,2-hydroperoxo-diferric species, respectively. Neither the oxidation nor the protonation induces a strong electrophilic reactivity. Hence, the observed intramolecular C-H hydroxylation of preorganized methyl groups of the parent µ-1,2-peroxo-diferric complex should occur via conversion to a more electrophilic high-valent species. The thorough characterization of these species provides structure-spectroscopy correlations allowing insights into the formation and reactivities of hydroperoxo intermediates in diiron enzymes and their conversion to activated P' or high-valent intermediates.

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations.

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation-including static extrinsic noise-exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

Catalytic H2O2 Activation by a Diiron Complex for Methanol Oxidation.

In nature, C-H bond oxidation of CH4 involves a peroxo intermediate that decays to the high-valent active species of either a "closed" {FeIV(µ-O)2FeIV} core or an "open" {FeIV(O)(µ-O)FeIV(O)} core. To mimic and to obtain more mechanistic insight in this reaction mode, we have investigated the reactivity of the bioinspired diiron complex [(susan){Fe(OH)(µ-O)Fe(OH)}]2+ [susan = 4,7-dimethyl-1,1,10,10-tetrakis(2-pyridylmethyl)-1,4,7,10-tetraazadecane], which catalyzes CH3OH oxidation with H2O2 to HCHO and HCO2H. The kinetics is faster in the presence of a proton. 18O-labeling experiments show that the active species, generated by a decay of the initially formed peroxo intermediate [(susan){FeIII(µ-O)(µ-O2)FeIII}]2+, contains one reactive oxygen atom from the µ-oxo and another from the µ-peroxo bridge of its peroxo precursor. Considering an FeIVFeIV active species, a "closed" {FeIV(µ-O)2FeIV} core explains the observed labeling results, while a scrambling of the terminal and bridging oxo ligands is required to account for an "open" {FeIV(O)(µ-O)FeIV(O)} core.

Computation of Single-Cell Metabolite Distributions Using Mixture Models.

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.

MOTIVATION: Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. RESULTS: Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method's likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The R package 'bayNorm' is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Branch site bulge conformations in domain 6 determine functional sugar puckers in group II intron splicing.

Although group II intron ribozymes are intensively studied the question how structural dynamics affects splicing catalysis has remained elusive. We report for the first time that the group II intron domain 6 exists in a secondary structure equilibrium between a single- and a two-nucleotide bulge conformation, which is directly linked to a switch between sugar puckers of the branch site adenosine. Our study determined a functional sugar pucker equilibrium between the transesterification active C2'-endo conformation of the branch site adenosine in the 1nt bulge and an inactive C3'-endo state in the 2nt bulge fold, allowing the group II intron to switch its activity from the branching to the exon ligation step. Our detailed NMR spectroscopic investigation identified magnesium (II) ions and the branching reaction as regulators of the equilibrium populations. The tuneable secondary structure/sugar pucker equilibrium supports a conformational selection mechanism to up- and downregulate catalytically active and inactive states of the branch site adenosine to orchestrate the multi-step splicing process. The conformational dynamics of group II intron domain 6 is also proposed to be a key aspect for the directionality selection in reversible splicing.

Bounding the stationary distributions of the chemical master equation via mathematical programming.

The stochastic dynamics of biochemical networks are usually modeled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals, and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the nonunique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schlögl's model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerization model with multiple stationary distributions.

Stochastic modelling reveals mechanisms of metabolic heterogeneity.

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

Eukaryotic Translation Elongation is Modulated by Single Natural Nucleotide Derivatives in the Coding Sequences of mRNAs.

RNA modifications are crucial factors for efficient protein synthesis. All classes of RNAs that are involved in translation are modified to different extents. Recently, mRNA modifications and their impact on gene regulation became a focus of interest because they can exert a variety of effects on the fate of mRNAs. mRNA modifications within coding sequences can either directly or indirectly interfere with protein synthesis. In order to investigate the roles of various natural occurring modified nucleotides, we site-specifically introduced them into the coding sequence of reporter mRNAs and subsequently translated them in HEK293T cells. The analysis of the respective protein products revealed a strong position-dependent impact of RNA modifications on translation efficiency and accuracy. Whereas a single 5-methylcytosine (m5C) or pseudouridine () did not reduce product yields, N¹-methyladenosine (m¹A) generally impeded the translation of the respective modified mRNA. An inhibitory effect of 2'O-methlyated nucleotides (Nm) and N6-methyladenosine (m6A) was strongly dependent on their position within the codon. Finally, we could not attribute any miscoding potential to the set of mRNA modifications tested in HEK293T cells.

Intrinsic and extrinsic noise of gene expression in lineage trees.

Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

Cell size control driven by the circadian clock and environment in cyanobacteria.

How cells maintain their size has been extensively studied under constant conditions. In the wild, however, cells rarely experience constant environments. Here, we examine how the 24-h circadian clock and environmental cycles modulate cell size control and division timings in the cyanobacterium Synechococcus elongatus using single-cell time-lapse microscopy. Under constant light, wild-type cells follow an apparent sizer-like principle. Closer inspection reveals that the clock generates two subpopulations, with cells born in the subjective day following different division rules from cells born in subjective night. A stochastic model explains how this behavior emerges from the interaction of cell size control with the clock. We demonstrate that the clock continuously modulates the probability of cell division throughout day and night, rather than solely applying an on-off gate to division, as previously proposed. Iterating between modeling and experiments, we go on to identify an effective coupling of the division rate to time of day through the combined effects of the environment and the clock on cell division. Under naturally graded light-dark cycles, this coupling narrows the time window of cell divisions and shifts divisions away from when light levels are low and cell growth is reduced. Our analysis allows us to disentangle, and predict the effects of, the complex interactions between the environment, clock, and cell size control.

Translation of non-standard codon nucleotides reveals minimal requirements for codon-anticodon interactions.

The precise interplay between the mRNA codon and the tRNA anticodon is crucial for ensuring efficient and accurate translation by the ribosome. The insertion of RNA nucleobase derivatives in the mRNA allowed us to modulate the stability of the codon-anticodon interaction in the decoding site of bacterial and eukaryotic ribosomes, allowing an in-depth analysis of codon recognition. We found the hydrogen bond between the N1 of purines and the N3 of pyrimidines to be sufficient for decoding of the first two codon nucleotides, whereas adequate stacking between the RNA bases is critical at the wobble position. Inosine, found in eukaryotic mRNAs, is an important example of destabilization of the codon-anticodon interaction. Whereas single inosines are efficiently translated, multiple inosines, e.g., in the serotonin receptor 5-HT2C mRNA, inhibit translation. Thus, our results indicate that despite the robustness of the decoding process, its tolerance toward the weakening of codon-anticodon interactions is limited.

Sources, propagation and consequences of stochasticity in cellular growth.

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.

Sex Differences in Visual Motion Processing.

The importance of sex as a biological variable has recently been emphasized by major funding organizations [1] and within the neuroscience community [2]. Critical sex-based neural differences are indicated by, for example, conditions such as autism spectrum disorder (ASD) that have a strong sex bias with a higher prevalence among males [51, 3]. Motivated by this broader context, we report a marked sex difference in a visual motion perception task among neurotypical adults. Motion duration thresholds [4, 5]-the minimum duration needed to accurately perceive motion direction-were considerably shorter for males than females. We replicated this result across three laboratories and 263 total participants. This type of enhanced performance has previously been observed only in special populations including ASD, depression, and senescence [6-8]. The observed sex difference cannot be explained by general differences in speed of visual processing, overall visual discrimination abilities, or potential motor-related differences. We also show that while individual differences in motion duration thresholds are associated with differences in fMRI responsiveness of human MT+, surprisingly, MT+ response magnitudes did not differ between males and females. Thus, we reason that sex differences in motion perception are not captured by an MT+ fMRI measure that predicts within-sex individual differences in perception. Overall, these results show how sex differences can manifest unexpectedly, highlighting the importance of sex as a factor in the design and analysis of perceptual and cognitive studies.