Jay M. Newby

A collection of identical and independent rare event first passage times is considered. The problem of finding the fastest out of [Formula: see text] such events to occur is called an extreme first passage time. The rare event times are singular and limit to infinity as a positive parameter scaling the noise magnitude is reduced to zero. In contrast, previous work has shown that the mean of the fastest event time goes to zero in the limit of an infinite number of walkers. The combined limit is studied. In particular, the mean time and the most likely path taken by the fastest random walker are investigated. Using techniques from large deviation theory, it is shown that there is a distinguished limit where the mean time for the fastest walker can take any positive value, depending on a single proportionality constant. Furthermore, it is shown that the mean time and most likely path can be approximated using the solution to a variational problem related to the single-walker rare event.

Diatoms are ancestrally photosynthetic microalgae. However, some underwent a major evolutionary transition, losing photosynthesis to become obligate heterotrophs. The molecular and physiological basis for this transition is unclear. Here, we isolate and characterize new strains of non-photosynthetic diatoms from the coastal waters of Singapore. These diatoms occupy diverse ecological niches and display glucose-mediated catabolite repression, a classical feature of bacterial and fungal heterotrophs. Live-cell imaging reveals deposition of secreted extracellular polymeric substance (EPS). Diatoms moving on pre-existing EPS trails (runners) move faster than those laying new trails (blazers). This leads to cell-to-cell coupling where runners can push blazers to make them move faster. Calibrated micropipettes measure substantial single-cell pushing forces, which are consistent with high-order myosin motor cooperativity. Collisions that impede forward motion induce reversal, revealing navigation-related force sensing. Together, these data identify aspects of metabolism and motility that are likely to promote and underpin diatom heterotrophy.

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis without solving a full virus load dynamic model. We validate our model on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics, which shows an excellent fit. Our virus-load function offers a new way to analyse patient virus load data, and it can be used as input to higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis without solving a full virus load dynamic model. We validate our model on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics, which shows an excellent fit. Our virus-load function offers a new way to analyse patient virus load data, and it can be used as input to higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

The makings of anisosomes: Phase separation of proteins within the cell can produce a liquid-inside-a-liquid phase resembling oil droplets in water. Yu et al. now report that an RNA-binding protein called TDP-43, in which mutation and aggregation are linked to amyotrophic lateral sclerosis and frontotemporal dementia, phase separates into complex droplets, which they named anisosomes. This process occurred when TDP-43 lost its ability to bind RNA through disease-causing mutation or posttranslational acetylation. Anisosomes have spherical shells of TDP-43 (with properties of a liquid crystal) surrounding centers of the protein chaperone HSP70. Chaperone activity was required to maintain liquidity. Anisosomes formed in neurons in vivo when proteasome activity was inhibited and were converted into aggregates when adenosine triphosphate (ATP) levels fell. Science , this issue p. eabb4309

Liquid–liquid phase separation is an emerging mechanism for intracellular organization. This work presents a mathematical model to examine molecular mechanisms that yield phase-separated droplets composed of different RNA–protein complexes. Using a Cahn–Hilliard diffuse interface model with a Flory–Huggins free energy scheme, we explore how multiple (here two, for simplicity) protein–RNA complexes (species) can establish a heterogeneous droplet field where droplets with single or multiple species phase separate and evolve during coarsening. We show that the complex–complex de-mixing energy tunes whether the complexes co-exist or form distinct droplets, while the transient binding kinetics dictate both the timescale of droplet formation and whether distinct species phase separate into droplets simultaneously or sequentially. For specific energetics and kinetics, a field of droplets driven by the formation of only one protein–RNA complex will emerge. Slowly, the other droplet species will accumulate inside the preformed droplets of the other species, allowing them to occupy the same droplet space. Alternatively, unfavorable species mixing creates a parasitic relationship: the slow-to-form protein–RNA complex will accumulate at the surface of a competing droplet species, siphoning off the free protein as it is released. Once this competing protein–RNA complex has sufficiently accumulated on the droplet surface, it can form a new droplet that is capable of sharing an interface with the first complex droplet but is not capable of mixing. These results give insights into a wide range of phase-separation scenarios and heterogeneous droplets that coexist but do not mix within the nucleus and the cytoplasm of cells.

The structure of the cytosol across different length scales is a debated topic in cell biology. Here we present tools to measure the physical state of the cytosol by analyzing the 3D motion of nanoparticles expressed in cells. We find evidence that the physical structure of the cytosol is a fundamental source of variability in biological systems.

The RNA binding protein TDP-43 naturally phase separates within cell nuclei and forms cytoplasmic aggregates in age-related neurodegenerative diseases. Here we show that acetylation-mediated inhibition of TDP-43 binding to RNA produces co-de-mixing of acetylated and unmodified TDP-43 into symmetrical, intranuclear spherical annuli whose shells and cores have liquid properties. Shells are anisotropic, like liquid crystals. Consistent with our modelling predictions that annulus formation is driven by components with strong self-interactions but weak interaction with TDP-43, the major components of annuli cores are identified to be HSP70 family proteins, whose chaperone activity is required to maintain liquidity of the core. Proteasome inhibition, mimicking reduction in proteasome activity during aging, induces TDP-43-containing annuli in neurons in rodents. Thus, we identify that TDP-43 phase separation is regulated by acetylation, proteolysis, and ATPase-dependent chaperone activity of HSP70. One Sentence Summary Acetylation of TDP-43 drives its phase separation into spherical annuli that form a liquid-inside-a-liquid-inside-a-liquid.