Robustness and evolvability of heterogeneous cell populations

Coupling Cell Size Regulation and Proliferation Dynamics of C. glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

A fitness trade-off explains the early fate of yeast aneuploids with chromosome gains

The early development of aneuploidy from an accidental chromosome missegregation shows contrasting effects. On the one hand, it is associated with significant cellular stress and decreased fitness. On the other hand, it often carries a beneficial effect and provides a quick (but typically transient) solution to external stress. These apparently controversial trends emerge in several experimental contexts, particularly in the presence of duplicated chromosomes. However, we lack a mathematical evolutionary modeling framework that comprehensively captures these trends from the mutational dynamics and the trade-offs involved in the early stages of aneuploidy. Here, focusing on chromosome gains, we address this point by introducing a fitness model where a fitness cost of chromosome duplications is contrasted by a fitness advantage from the dosage of specific genes. The model successfully captures the experimentally measured probability of emergence of extra chromosomes in a laboratory evolution setup. Additionally, using phenotypic data collected in rich media, we explored the fitness landscape, finding evidence supporting the existence of a per-gene cost of extra chromosomes. Finally, we show that the substitution dynamics of our model, evaluated in the empirical fitness landscape, explains the relative abundance of duplicated chromosomes observed in yeast population genomics data. These findings lay a firm framework for the understanding of the establishment of newly duplicated chromosomes, providing testable quantitative predictions for future observations.

Meta-Dynamic Network Modelling for Biochemical Networks

ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.

Effects of aneuploidy on cell behaviour and function

Aneuploidy, a genomic alternation characterized by deviations in the copy number of chromosomes, affects organisms from early development through to aging. Although it is a main cause of human pregnancy loss and a hallmark of cancer, how aneuploidy affects cellular function has been elusive. The last two decades have seen rapid advances in the understanding of the causes and consequences of aneuploidy at the molecular and cellular levels. These studies have uncovered effects of aneuploidy that can be beneficial or detrimental to cells and organisms in an environmental context-dependent and karyotype-dependent manner. Aneuploidy also imposes general stress on cells that stems from an imbalanced genome and, consequently, also an imbalanced proteome. These insights provide the fundamental framework for understanding the impact of aneuploidy in genome evolution, human pathogenesis and drug resistance.

Computational modeling of dynein motor proteins at work

Along with various experimental methods, a combination of theoretical and computational methods is essential to explore different length-scale and time-scale processes in the biological system. The functional mechanism of a dynein, an ATP-fueled motor protein, working in a multiprotein complex, involves a wide range of length/time-scale events. It generates mechanical force from chemical energy and moves on microtubules towards the minus end direction while performing a large number of biological processes including ciliary beating, intracellular material transport, and cell division. Like in the cases of other conventional motor proteins, a combination of experimental techniques including X-crystallography, cryo-electron microscopy, and single molecular assay have provided a wealth of information about the mechanochemical cycle of a dynein. Dyneins have a large and complex structural architecture and therefore, computational modeling of different aspects of a dynein is extremely challenging. As the process of dynein movement involves varying length and timescales, it demands, like in experiments, a combination of computational methods covering such a wide range of processes for the comprehensive investigation of the mechanochemical cycle. In this review article, we will summarize how the use of state-of-the-art computational methods can provide a detailed molecular understanding of the mechanochemical cycle of the dynein. We implemented all-atom molecular dynamics simulations and hybrid quantum-mechanics/molecular-mechanics simulations to explore the ATP hydrolysis mechanisms at the primary ATPase site (AAA1) of dynein. To investigate the large-scale conformational changes we employed coarse-grained structure-based molecular dynamics simulations to capture the domain motions. Here we explored the conformational changes upon binding of ATP at AAA1, nucleotide state-dependent regulation of the mechanochemical cycle, and inter-head coordination by inter-head tension. Additionally, implementing a phenomenological theoretical model we explore the force-dependent detachment rate of a motorhead from the microtubule and the principle of multi-dynein cooperation during cargo transport.

Application of Animal Models in Cancer Research: Recent Progress and Future Prospects

Animal models refers to the animal experimental objects and related materials that can simulate human body established in medical research. As the second-largest disease in terms of morbidity and mortality after cardiovascular disease, cancer has always been the focus of human attention all over the world, which makes it a research hotspot in the medical field. At the same time, more and more animal models have been constructed and used in cancer research. With the deepening of research, the construction methods of cancer animal models are becoming more and more diverse, including chemical induction, xenotransplantation, gene programming, and so on. In recent years, patient-derived xenotransplantation (PDX) model has become a research hotspot because it can retain the microenvironment of the primary tumor and the basic characteristics of cells. Animal models can be used not only to study the biochemical and physiological processes of the occurrence and development of cancer in objects but also for the screening of cancer drugs and the exploration of gene therapy. In this paper, several main tumor animal models and the application progress of animal models in tumor research are systematically reviewed. Finally, combined with the latest progress and development trend in this field, the future research of tumor animal model was prospected.

Generally Stressed Out Bacteria: Environmental Stress Response Mechanisms in Gram-Positive Bacteria

The ability to monitor the environment for toxic chemical and physical disturbances is essential for bacteria that live in dynamic environments. The fundamental sensing mechanisms and physiological responses that allow bacteria to thrive are conserved even if the molecular components of these pathways are not. The bacterial general stress response (GSR) represents a conceptual model for how one pathway integrates a wide range of environmental signals, and how a generalized system with broad molecular responses is coordinated to promote survival likely through complementary pathways. Environmental stress signals such as heat, osmotic stress, and pH changes are received by sensor proteins that through a signaling cascade activate the sigma factor, SigB, to regulate over 200 genes. Additionally, the GSR plays an important role in stress priming that increases bacterial fitness to unrelated subsequent stressors such as oxidative compounds. While the GSR response is implicated during oxidative stress, the reason for its activation remains unknown and suggests crosstalk between environmental and oxidative stress sensors and responses to coordinate antioxidant functions. Systems levels studies of cellular responses such as transcriptomes, proteomes, and metabolomes of stressed bacteria and single-cell analysis could shed light into the regulated functions that protect, remediate, and minimize damage during dynamic environments. This perspective will focus on fundamental stress sensing mechanisms and responses in Gram-positive bacterial species to illustrate their commonalities at the molecular and physiological levels; summarize exciting directions; and highlight how system-level approaches can help us understand bacterial physiology.

AXL Targeting Abrogates Autophagic Flux and Induces Immunogenic Cell Death in Drug-Resistant Cancer Cells

INTRODUCTION

Acquired cancer therapy resistance evolves under selection pressure of immune surveillance and favors mechanisms that promote drug resistance through cell survival and immune evasion. AXL receptor tyrosine kinase is a mediator of cancer cell phenotypic plasticity and suppression of tumor immunity, and AXL expression is associated with drug resistance and diminished long-term survival in a wide range of malignancies, including NSCLC.

METHODS

We aimed to investigate the mechanisms underlying AXL-mediated acquired resistance to first- and third-generation small molecule EGFR tyrosine kinase inhibitors (EGFRi) in NSCLC.

RESULTS

We found that EGFRi resistance was mediated by up-regulation of AXL, and targeting AXL reduced reactivation of the MAPK pathway and blocked onset of acquired resistance to long-term EGFRi treatment in vivo. AXL-expressing EGFRi-resistant cells revealed phenotypic and cell signaling heterogeneity incompatible with a simple bypass signaling mechanism, and were characterized by an increased autophagic flux. AXL kinase inhibition by the small molecule inhibitor bemcentinib or siRNA mediated AXL gene silencing was reported to inhibit the autophagic flux in vitro, bemcentinib treatment blocked clonogenicity and induced immunogenic cell death in drug-resistant NSCLC in vitro, and abrogated the transcription of autophagy-associated genes in vivo. Furthermore, we found a positive correlation between AXL expression and autophagy-associated gene signatures in a large cohort of human NSCLC (n = 1018).

CONCLUSION

Our results indicate that AXL signaling supports a drug-resistant persister cell phenotype through a novel autophagy-dependent mechanism and reveals a unique immunogenic effect of AXL inhibition on drug-resistant NSCLC cells.

Adaptation, fitness landscape learning and fast evolution

We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on  fitness we prove that such model organisms  are capable, to some extent, to recognize the fitness landscape. That fitness landscape learning sharply reduces the number of mutations needed for adaptation. Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype.  We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to  many constraints associated with a hard environment, if that environment becomes harder step by step. Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.

Adaptation, fitness landscape learning and fast evolution

We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on  fitness we prove that such model organisms  are capable, to some extent, to recognize the fitness landscape. That fitness landscape learning sharply reduces the number of mutations needed for adaptation. Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype.  We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to  many constraints associated with a hard environment, if that environment becomes harder step by step. Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.