A Post-Transcriptional Feedback Mechanism for Noise Suppression and Fate Stabilization.

Diverse biological systems utilize fluctuations ("noise") in gene expression to drive lineage-commitment decisions. However, once a commitment is made, noise becomes detrimental to reliable function, and the mechanisms enabling post-commitment noise suppression are unclear. Here, we find that architectural constraints on noise suppression are overcome to stabilize fate commitment. Using single-molecule and time-lapse imaging, we find that-after a noise-driven event-human immunodeficiency virus (HIV) strongly attenuates expression noise through a non-transcriptional negative-feedback circuit. Feedback is established through a serial cascade of post-transcriptional splicing, whereby proteins generated from spliced mRNAs auto-deplete their own precursor unspliced mRNAs. Strikingly, this auto-depletion circuitry minimizes noise to stabilize HIV's commitment decision, and a noise-suppression molecule promotes stabilization. This feedback mechanism for noise suppression suggests a functional role for delayed splicing in other systems and may represent a generalizable architecture of diverse homeostatic signaling circuits.

Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-native folding intermediates.

The series of RNA folding events that occur during transcription can critically influence cellular RNA function. Here, we present reconstructing RNA dynamics from data (R2D2), a method to uncover details of cotranscriptional RNA folding. We model the folding of the Escherichia coli signal recognition particle (SRP) RNA and show that it requires specific local structural fluctuations within a key hairpin to engender efficient cotranscriptional conformational rearrangement into the functional structure. All-atom molecular dynamics simulations suggest that this rearrangement proceeds through an internal toehold-mediated strand-displacement mechanism, which can be disrupted with a point mutation that limits local structural fluctuations and rescued with compensating mutations that restore these fluctuations. Moreover, a cotranscriptional folding intermediate could be cleaved in vitro by recombinant E. coli RNase P, suggesting potential cotranscriptional processing. These results from experiment-guided multi-scale modeling demonstrate that even an RNA with a simple functional structure can undergo complex folding and processing during synthesis.

Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Boom-bust population dynamics drive rapid genetic change.

Increasing environmental threats and more extreme environmental perturbations place species at risk of population declines, with associated loss of genetic diversity and evolutionary potential. While theory shows that rapid population declines can cause loss of genetic diversity, populations in some environments, like Australia's arid zone, are repeatedly subject to major population fluctuations yet persist and appear able to maintain genetic diversity. Here, we use repeated population sampling over 13 y and genotype-by-sequencing of 1903 individuals to investigate the genetic consequences of repeated population fluctuations in two small mammals in the Australian arid zone. The sandy inland mouse (Pseudomys hermannsburgensis) experiences marked boom-bust population dynamics in response to the highly variable desert environment. We show that heterozygosity levels declined, and population differentiation (FST) increased, during bust periods when populations became small and isolated, but that heterozygosity was rapidly restored during episodic population booms. In contrast, the lesser hairy-footed dunnart (Sminthopsis youngsoni), a desert marsupial that maintains relatively stable population sizes, showed no linear declines in heterozygosity. These results reveal two contrasting ways in which genetic diversity is maintained in highly variable environments. In one species, diversity is conserved through the maintenance of stable population sizes across time. In the other species, diversity is conserved through rapid genetic mixing during population booms that restores heterozygosity lost during population busts.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Fluctuating hydrodynamics and the Rayleigh-Plateau instability.

The Rayleigh-Plateau instability occurs when surface tension makes a fluid column become unstable to small perturbations. At nanometer scales, thermal fluctuations are comparable to interfacial energy densities. Consequently, at these scales, thermal fluctuations play a significant role in the dynamics of the instability. These microscopic effects have previously been investigated numerically using particle-based simulations, such as molecular dynamics (MD), and stochastic partial differential equation-based hydrodynamic models, such as stochastic lubrication theory. In this paper, we present an incompressible fluctuating hydrodynamics model with a diffuse-interface formulation for binary fluid mixtures designed for the study of stochastic interfacial phenomena. An efficient numerical algorithm is outlined and validated in numerical simulations of stable equilibrium interfaces. We present results from simulations of the Rayleigh-Plateau instability for long cylinders pinching into droplets for Ohnesorge numbers of Oh = 0.5 and 5.0. Both stochastic and perturbed deterministic simulations are analyzed and ensemble results show significant differences in the temporal evolution of the minimum radius near pinching. Short cylinders, with lengths less than their circumference, were also investigated. As previously observed in MD simulations, we find that thermal fluctuations cause these to pinch in cases where a perturbed cylinder would be stable deterministically. Finally, we show that the fluctuating hydrodynamics model can be applied to study a broader range of surface tension-driven phenomena.

The temporal structure of REM sleep shows minute-scale fluctuations across brain and body in mice and humans.

Rapid eye movement sleep (REM) is believed to have a binary temporal structure with "phasic" and "tonic" microstates, characterized by motoric activity versus quiescence, respectively. However, we observed in mice that the frequency of theta activity (a marker of rodent REM) fluctuates in a nonbinary fashion, with the extremes of that fluctuation correlating with phasic-type and tonic-type facial motricity. Thus, phasic and tonic REM may instead represent ends of a continuum. These cycles of brain physiology and facial movement occurred at 0.01 to 0.06 Hz, or infraslow frequencies, and affected cross-frequency coupling and neuronal activity in the neocortex, suggesting network functional impact. We then analyzed human data and observed that humans also demonstrate nonbinary phasic/tonic microstates, with continuous 0.01 to 0.04-Hz respiratory rate cycles matching the incidence of eye movements. These fundamental properties of REM can yield insights into our understanding of sleep health.

Fluctuations in local shear-fault energy produce unique and dominating strengthening in metastable complex concentrated alloys.

Local chemical short-range ordering (SRO) and spatial fluctuations of planar fault energy are important features of multi-element and metastable complex concentrated alloys (CCAs). Arising from them, dislocations in such alloys are distinctively wavy in both static and migrating conditions; yet, such effects on strength have remained unknown. In this work, molecular dynamics simulations are used to show that the wavy configurations of dislocations and their jumpy motion in a prototypic CCA of NiCoCr are due to the local fluctuations of the energy of SRO shear-faulting that accompanies dislocation motion, with the dislocation getting pinned at sites of hard atomic motifs (HAMs) associated with high local shear-fault energies. Unlike the global averaged shear-fault energy which in general will subdue on successive dislocation passes, the local fluctuations in the fault energy always remain in a CCA, thus offering a strength contribution that is unique in such alloys. Analysis of the magnitude of this form of dislocation resistance shows that this is dominating over contributions due to elastic misfit of alloying elements and is in good agreement with strengths predicted from molecular dynamics simulations and experiments. This work has unfolded the physical basis of strength in CCAs, which is important for the development of these alloys into useful structural materials.

Universal abundance fluctuations across microbial communities, tropical forests, and urban populations.

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

Galvanization of Protein-Protein Interactions in a Dynamic Zinc Interactome.

The presence of Zn2+ at protein-protein interfaces modulates complex function, stability, and introduces structural flexibility/complexity, chemical selectivity, and reversibility driven in a Zn2+-dependent manner. Recent studies have demonstrated that dynamically changing Zn2+ affects numerous cellular processes, including protein-protein communication and protein complex assembly. How Zn2+-involved protein-protein interactions (ZPPIs) are formed and dissociate and how their stability and reactivity are driven in a zinc interactome remain poorly understood, mostly due to experimental obstacles. Here, we review recent research advances on the role of Zn2+ in the formation of interprotein sites, their architecture, function, and stability. Moreover, we underline the importance of zinc networks in intersystemic communication and highlight bioinformatic and experimental challenges required for the identification and investigation of ZPPIs.

Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations. Recently, researchers have "unfurled" traditional FC matrices in "edge cofluctuation time series" which measure timepoint-by-timepoint cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture moment-to-moment fluctuations in networks related to attention. In two independent fMRI datasets examining young adults of both sexes in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest-based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.

Rigorous Progress in Coarse-Graining.

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Multiple sclerosis in Denmark (1950-2023): mean age, sex distribution, incidence and prevalence.

With rising life expectancy and advancements in disease management, we expect the multiple sclerosis population is getting older. However, evidence supporting this hypothesis remains sparse. Our study aimed to determine whether the mean age of the Danish multiple sclerosis population has increased and to analyse the developments in sex distribution, incidence, and prevalence, all of which affect age composition. We conducted a cohort study by linking nationwide data from the Danish Multiple Sclerosis Registry to the Population Statistics Registry, the Danish Cause of Death Registry, and the Historical Migration Registry. We included all living patients with a confirmed multiple sclerosis diagnosis who lived in Denmark on the 1st of January each year from 1950 to 2023. We calculated the mean and median age, age distribution, sex distribution, incidence, and prevalence of the Danish MS population annually from 1950 to 2023. We included 28,145 individuals with multiple sclerosis. The mean age of the Danish multiple sclerosis population increased until the late 1970s to around 52.5 years, where it stabilised until 1990. The mean age experienced a slight decline to 51.2 years in 2005, followed by a subsequent rise to its peak of 54.2 years in 2023. In 1975, females comprised 58.7% of the multiple sclerosis population, increasing to 65.7% by 2000 and 68.5% in 2023. The incidence of multiple sclerosis remained stable at around 3.5 per 100,000 until 1975 and steadily increased by more than 2.5 times in 2000 to 11.4 per 100,000. Despite fluctuations, it remained relatively stable from 2000 until 2022, showing a slight decrease in 2022 compared to the previous two decades. Both overall and sex-specific prevalence exhibited an upward trend, particularly among females. Our study demonstrates that the mean age of the Danish multiple sclerosis population has increased, although not as decisively as expected. The female proportion has grown in tandem with prevalence, while incidence appears to have stabilised in recent decades after years of increase. Denmark's robust registry data and universal healthcare system offer a unique opportunity for reliable epidemiological analysis. Our results establish a benchmark for future demographic studies in the field of multiple sclerosis.

Aggregate fluctuations in adaptive production networks.

To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study network resilience and the consequences of reshoring of supply chains. In our model, firms exit due to exogenous shocks or the propagation of shocks through the network, while firms can replace suppliers they have lost due to exit subject to switching costs and search frictions. Applying our model to a large international firm-level production network dataset, we find that restricting buyer-supplier links via reshoring policies reduces output and increases volatility and that volatility can be amplified through network adaptivity.

Anomalous metals: From "failed superconductor" to "failed insulator".

Resistivity saturation is found on both superconducting and insulating sides of an "avoided" magnetic-field-tuned superconductor-to-insulator transition (H-SIT) in a two-dimensional In/InOx composite, where the anomalous metallic behavior cuts off conductivity or resistivity divergence in the zero-temperature limit. The granular morphology of the material implies a system of Josephson junctions (JJs) with a broad distribution of Josephson coupling EJ and charging energy EC, with an H-SIT determined by the competition between EJ and EC. By virtue of self-duality across the true H-SIT, we invoke macroscopic quantum tunneling effects to explain the temperature-independent resistance where the "failed superconductor" side is a consequence of phase fluctuations and the "failed insulator" side results from charge fluctuations. While true self-duality is lost in the avoided transition, its vestiges are argued to persist, owing to the incipient duality of the percolative nature of the dissipative path in the underlying random JJ system.

The development of single molecule force spectroscopy: from polymer biophysics to molecular machines.

The advent of single-molecule force spectroscopy represents the introduction of forces, torques, and displacements as controlled variables in biochemistry. These methods afford the direct manipulation of individual molecules to interrogate the forces that hold together their structure, the forces and torques that these molecules generate in the course of their biochemical reactions, and the use of force, torque, and displacement as tools to investigate the mechanisms of these reactions. Because of their microscopic nature, the signals detected in these experiments are often dominated by fluctuations, which, in turn, play an important role in the mechanisms that underlie the operation of the molecular machines of the cell. Their direct observation and quantification in single-molecule experiments provide a unique window to investigate those mechanisms, as well as a convenient way to investigate fundamental new fluctuation theorems of statistical mechanics that bridge the equilibrium and non-equilibrium realms of this discipline. In this review we have concentrated on the developments that occurred in our laboratory on the characterization of biopolymers and of molecular machines of the central dogma. Accordingly, some important areas like the study of cytoskeletal motors have not been included. While we adopt at times an anecdotal perspective with the hope of conveying the personal circumstances in which these developments took place, we have made every effort, nonetheless, to include the most important developments that were taking place at the same time in other laboratories.

GPCR Signaling: A Study of the Interplay Between Structure, Energy, and Function.

G protein-coupled receptors (GPCRs) exemplify sophisticated allosteric communication, transducing extracellular signals through ligand-induced structural rearrangements that resonate through the molecular scaffold. Despite extensive study, the biophysical underpinnings of how conformational changes spread remain unclear. This work employs a novel physics-based framework to characterize the role of energy dissipation in directing intramolecular signaling pathways. By modeling each residue as a network of coupled oscillators, we generate a localization landscape depicting the vibrational energy distribution throughout the protein scaffold. Quantifying directional energy flux between residues reveals distinct pathways for energy and information transfer, illuminating sequences of allosteric communication. Our analysis of CB1 and CCR5 crystal structures unveils an anisotropic pattern of energy dissipation aligning with key functional dynamics, such as activation-related conformational changes. These anisotropic patterns of vibrational energy flow constitute pre-configured channels for allosteric signaling. Elucidating the relationship between structural topology and energy dissipation patterns provides key insights into the thermodynamic drivers of conformational signaling. This methodology significantly advances our mechanistic understanding of allostery in GPCRs and presents a broadly applicable approach for rationally dissecting allosteric communication pathways, with potential implications for structure-based drug design targeting these critical receptors.

Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects.

BACKGROUND: Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown. MATERIAL AND METHODS: A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed. RESULTS: Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications. CONCLUSION: Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.

Cerebrovascular adaptations to habitual resistance exercise with aging.

Resistance training (RT) is associated with improved metabolism, bone density, muscular strength, and lower risk of osteoporosis, sarcopenia, and cardiovascular disease. Although RT imparts many physiological benefits, cerebrovascular adaptations to chronic RT are not well defined. Participation in RT is associated with greater resting peripheral arterial diameters, improved endothelial function, and general cardiovascular health, whereas simultaneously linked to reductions in central arterial compliance. Rapid blood pressure fluctuations during resistance exercise, combined with reduced arterial compliance, could lead to cerebral microvasculature damage and subsequent cerebral hypoperfusion. Reductions in cerebral blood flow (CBF) accompany normal aging, where chronic reductions in CBF are associated with changes in brain structure and function, and increased risk of neurodegeneration. It remains unclear whether reductions in arterial compliance with RT relate to subclinical cerebrovascular pathology, or if such adaptations require interpretation in the context of RT specifically. The purpose of this narrative review is to synthesize literature pertaining to cerebrovascular adaptations to RT at different stages of the life span. This review also aims to identify gaps in the current understanding of the long-term impacts of RT on cerebral hemodynamics and provide a mechanistic rationale for these adaptations as they relate to aging, cerebral vasculature, and overall brain health.

Impact of Electric Field Magnitude in the Left Dorsolateral Prefrontal Cortex on Changes in Intrinsic Functional Connectivity Using Transcranial Direct Current Stimulation: A Randomized Crossover Study.

This study investigated whether the electric field magnitude (E-field) delivered to the left dorsolateral prefrontal cortex (L-DLPFC) changes resting-state brain activity and the L-DLPFC resting-state functional connectivity (rsFC), given the variability in tDCS response and lack of understanding of how rsFC changes. Twenty-one healthy participants received either 2 mA anodal or sham tDCS targeting the L-DLPFC for 10 min. Brain imaging was conducted before and after stimulation. The fractional amplitude of low-frequency fluctuation (fALFF), reflecting resting brain activity, and the L-DLPFC rsFC were analyzed to investigate the main effect of tDCS, main effect of time, and interaction effects. The E-field was estimated by modeling tDCS-induced individual electric fields and correlated with fALFF and L-DLPFC rsFC. Anodal tDCS increased fALFF in the left rostral middle frontal area and decreased fALFF in the midline frontal area (FWE p < 0.050), whereas sham induced no changes. Overall rsFC decreased after sham (positive and negative connectivity, p = 0.001 and 0.020, respectively), with modest and nonsignificant changes after anodal tDCS (p = 0.063 and 0.069, respectively). No significant differences in local rsFC were observed among the conditions. Correlations were observed between the E-field and rsFC changes in the L-DLPFC (r = 0.385, p = 0.115), left inferior parietal area (r = 0.495, p = 0.037), and right lateral visual area (r = 0.683, p = 0.002). Single-session tDCS induced resting brain activity changes and may help maintain overall rsFC. The E-field in the L-DLPFC is associated with rsFC changes in both proximal and distally connected brain regions to the L-DLPFC.