搜索结果：Biochimica et biophysica acta. Molecular and cell biology of lipids

Petal senescence is reviewed, with the main emphasis on gene expression in relation to physiological functions. Autophagy seems to be the major mechanism for large-scale degradation of macromolecules, but it is still unclear if it contributes to cell death. Depending on the species, petal senescence is controlled by ethylene or is independent of this hormone. EIN3-like (EIL) transcription factors are crucial in ethylene-regulated senescence. The presence of adequate sugar levels in the cell delays senescence and prevents an increase in the levels of EIL mRNA and the subsequent up-regulation of numerous senescence-associated genes. A range of other transcription factors and regulators are differentially expressed in ethylene-sensitive and ethylene-insensitive petal senescence. Ethylene-independent senescence is often delayed by cytokinins, but it is still unknown whether these are natural regulators. A role for caspase-like enzymes or metacaspases has as yet not been established in petal senescence, and a role for proteins released by organelles such as the mitochondrion has not been shown. The synthesis of sugars, amino acids, and fatty acids, and the degradation of nucleic acids, proteins, lipids, fatty acids, and cell wall components are discussed. It is claimed that there is not enough experimental support for the widely held view that a gradual increase in cell leakiness, resulting from gradual plasma membrane degradation, is an important event in petal senescence. Rather, rupture of the vacuolar membrane and subsequent rapid, complete degradation of the plasma membrane seems to occur. This review recommends that more detailed analysis be carried out at the level of cells and organelles rather than at that of whole petals.

helper T (Th) cells are central to the balance between immune tolerance and immune rejection. Th cells adopt distinct functions to maintain tolerance and clear pathogens. Dysregulation of Th cell function often leads to maladies, including autoimmunity, inflammatory disease, cancer, and infection. Regulatory T (Treg) and Th17 cells are critical Th cell types involved in immune tolerance, homeostasis, pathogenicity, and pathogen clearance. It is therefore critical to understand how Treg and Th17 cells are regulated in health and disease. Cytokines are instrumental in directing Treg and Th17 cell function. The evolutionarily conserved TGF-β (transforming growth factor-β) cytokine superfamily is of particular interest because it is central to the biology of both Treg cells that are predominantly immunosuppressive and Th17 cells that can be proinflammatory, pathogenic, and immune regulatory. How TGF-β superfamily members and their intricate signaling pathways regulate Treg and Th17 cell function is a question that has been intensely investigated for two decades. Here, we introduce the fundamental biology of TGF-β superfamily signaling, Treg cells, and Th17 cells and discuss in detail how the TGF-β superfamily contributes to Treg and Th17 cell biology through complex yet ordered and cooperative signaling networks.

Background: Secondary cell walls (SCWs) form the architecture of terrestrial plant biomass. They reinforce tracheary elements and strengthen fibres to permit upright growth and the formation of forest canopies. The cells that synthesize a strong, thick SCW around their protoplast must undergo a dramatic commitment to cellulose, hemicellulose and lignin production. Scope: This review puts SCW biosynthesis in a cellular context, with the aim of integrating molecular biology and biochemistry with plant cell biology. While SCWs are deposited in diverse tissue and cellular contexts including in sclerenchyma (fibres and sclereids), phloem (fibres) and xylem (tracheids, fibres and vessels), the focus of this review reflects the fact that protoxylem tracheary elements have proven to be the most amenable experimental system in which to study the cell biology of SCWs. Conclusions: SCW biosynthesis requires the co-ordination of plasma membrane cellulose synthases, hemicellulose production in the Golgi and lignin polymer deposition in the apoplast. At the plasma membrane where the SCW is deposited under the guidance of cortical microtubules, there is a high density of SCW cellulose synthase complexes producing cellulose microfibrils consisting of 18-24 glucan chains. These microfibrils are extruded into a cell wall matrix rich in SCW-specific hemicelluloses, typically xylan and mannan. The biosynthesis of eudicot SCW glucuronoxylan is taken as an example to illustrate the emerging importance of protein-protein complexes in the Golgi. From the trans-Golgi, trafficking of vesicles carrying hemicelluloses, cellulose synthases and oxidative enzymes is crucial for exocytosis of SCW components at the microtubule-rich cell membrane domains, producing characteristic SCW patterns. The final step of SCW biosynthesis is lignification, with monolignols secreted by the lignifying cell and, in some cases, by neighbouring cells as well. Oxidative enzymes such as laccases and peroxidases, embedded in the polysaccharide cell wall matrix, determine where lignin is deposited.

We generated a computational approach to analyze the biomechanics of epithelial cell aggregates, either island or stripes or entire monolayers, that combines both vertex and contact-inhibition-of-locomotion models to include both cell-cell and cell-substrate adhesion. Examination of the distribution of cell protrusions (adhesion to the substrate) in the model predicted high order profiles of cell organization that agree with those previously seen experimentally. Cells acquired an asymmetric distribution of basal protrusions, traction forces and apical aspect ratios that decreased when moving from the edge to the island center. Our in silico analysis also showed that tension on cell-cell junctions and apical stress is not homogeneous across the island. Instead, these parameters are higher at the island center and scales up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence. Without formally being a 3-dimensional model, our approach has the minimal elements necessary to reproduce the distribution of cellular forces and mechanical crosstalk as well as distribution of principal stress in cells within epithelial cell aggregates. By mak

The understanding of molecular cell biology requires insight into the structure and dynamics of networks that are made up of thousands of interacting molecules of DNA, RNA, proteins, metabolites, and other components. One of the central goals of systems biology is the unraveling of the as yet poorly characterized complex web of interactions among these components. This work is made harder by the fact that new species and interactions are continuously discovered in experimental work, necessitating the development of adaptive and fast algorithms for network construction and updating. Thus, the "reverse-engineering" of networks from data has emerged as one of the central concern of systems biology research. A variety of reverse-engineering methods have been developed, based on tools from statistics, machine learning, and other mathematical domains. In order to effectively use these methods, it is essential to develop an understanding of the fundamental characteristics of these algorithms. With that in mind, this chapter is dedicated to the reverse-engineering of biological systems. Specifically, we focus our attention on a particular class of methods for reverse-engineering, namely th

A cell undergoes many genetic and epigenetic changes as it transitions to malignancy. Malignant transformation is also accompanied by a progressive loss of tissue homeostasis and perturbations in tissue architecture that ultimately culminates in tumor cell invasion into the parenchyma and metastasis to distant organ sites. Increasingly, cancer biologists have begun to recognize that a critical component of this transformation journey involves marked alterations in the mechanical phenotype of the cell and its surrounding microenvironment. These mechanical differences include modifications in cell and tissue structure, adaptive force-induced changes in the environment, altered processing of micromechanical cues encoded in the extracellular matrix (ECM), and cell-directed remodeling of the extracellular stroma. Here, we review critical steps in this "force journey," including mechanical contributions to tissue dysplasia, invasion of the ECM, and metastasis. We discuss the biophysical basis of this force journey and present recent advances in the measurement of cellular mechanical properties in vitro and in vivo. We end by describing examples of molecular mechanisms through which tumor cells sense, process and respond to mechanical forces in their environment. While our understanding of the mechanical components of tumor growth, survival and motility remains in its infancy, considerable work has already yielded valuable insight into the molecular basis of force-dependent tumor pathophysiology, which offers new directions in cancer chemotherapeutics.

Non-prescriptional use of medicinal herbs among cancer patients is common around the world. The alleged anti-cancer effects of most herbal extracts are mainly based on studies derived from in vitro or in vivo animal experiments. The current information suggests that these herbal extracts exert their biological effect either through cytotoxic or immunomodulatory mechanisms. One of the active compounds responsible for the immune effects of herbal products is in the form of complex polysaccharides known as beta-glucans. beta-glucans are ubiquitously found in both bacterial or fungal cell walls and have been implicated in the initiation of anti-microbial immune response. Based on in vitro studies, beta-glucans act on several immune receptors including Dectin-1, complement receptor (CR3) and TLR-2/6 and trigger a group of immune cells including macrophages, neutrophils, monocytes, natural killer cells and dendritic cells. As a consequence, both innate and adaptive response can be modulated by beta-glucans and they can also enhance opsonic and non-opsonic phagocytosis. In animal studies, after oral administration, the specific backbone 1-->3 linear beta-glycosidic chain of beta-glucans cannot be digested. Most beta-glucans enter the proximal small intestine and some are captured by the macrophages. They are internalized and fragmented within the cells, then transported by the macrophages to the marrow and endothelial reticular system. The small beta-glucans fragments are eventually released by the macrophages and taken up by other immune cells leading to various immune responses. However, beta-glucans of different sizes and branching patterns may have significantly variable immune potency. Careful selection of appropriate beta-glucans is essential if we wish to investigate the effects of beta-glucans clinically. So far, no good quality clinical trial data is available on assessing the effectiveness of purified beta-glucans among cancer patients. Future effort should direct at performing well-designed clinical trials to verify the actual clinical efficacy of beta-glucans or beta-glucans containing compounds.

Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving single-cell foundation models. First, we scaled the pre-training data to a diverse collection of 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained the \model family of models comprising six transformer-based state-of-the-art single-cell foundation models with 70 million, 160 million, and 400 million parameters. We vetted our models on several downstream evaluation tasks, including identifying the underlying disease state of held-out donors not seen during training, distinguishing between diseased and healthy cells for disease conditions and

Oxidative stress is induced by a wide range of environmental factors including UV stress, pathogen invasion (hypersensitive reaction), herbicide action and oxygen shortage. Oxygen deprivation stress in plant cells is distinguished by three physiologically different states: transient hypoxia, anoxia and reoxygenation. Generation of reactive oxygen species (ROS) is characteristic for hypoxia and especially for reoxygenation. Of the ROS, hydrogen peroxide (H(2)O(2)) and superoxide (O(2)(.-)) are both produced in a number of cellular reactions, including the iron-catalysed Fenton reaction, and by various enzymes such as lipoxygenases, peroxidases, NADPH oxidase and xanthine oxidase. The main cellular components susceptible to damage by free radicals are lipids (peroxidation of unsaturated fatty acids in membranes), proteins (denaturation), carbohydrates and nucleic acids. Consequences of hypoxia-induced oxidative stress depend on tissue and/or species (i.e. their tolerance to anoxia), on membrane properties, on endogenous antioxidant content and on the ability to induce the response in the antioxidant system. Effective utilization of energy resources (starch, sugars) and the switch to anaerobic metabolism and the preservation of the redox status of the cell are vital for survival. The formation of ROS is prevented by an antioxidant system: low molecular mass antioxidants (ascorbic acid, glutathione, tocopherols), enzymes regenerating the reduced forms of antioxidants, and ROS-interacting enzymes such as SOD, peroxidases and catalases. In plant tissues many phenolic compounds (in addition to tocopherols) are potential antioxidants: flavonoids, tannins and lignin precursors may work as ROS-scavenging compounds. Antioxidants act as a cooperative network, employing a series of redox reactions. Interactions between ascorbic acid and glutathione, and ascorbic acid and phenolic compounds are well known. Under oxygen deprivation stress some contradictory results on the antioxidant status have been obtained. Experiments on overexpression of antioxidant production do not always result in the enhancement of the antioxidative defence, and hence increased antioxidative capacity does not always correlate positively with the degree of protection. Here we present a consideration of factors which possibly affect the effectiveness of antioxidant protection under oxygen deprivation as well as under other environmental stresses. Such aspects as compartmentalization of ROS formation and antioxidant localization, synthesis and transport of antioxidants, the ability to induce the antioxidant defense and cooperation (and/or compensation) between different antioxidant systems are the determinants of the competence of the antioxidant system.

Decades of research in skeletal muscle physiology have provided multiscale insights into the structural and functional complexity of this important anatomical tissue, designed to accomplish the task of generating contraction, force and movement. Skeletal muscle can be viewed as a biomechanical device with various interacting components including the autonomic nerves for impulse transmission, vasculature for efficient oxygenation, and embedded regulatory and metabolic machinery for maintaining cellular homeostasis. The "omics" revolution has propelled a new era in muscle research, allowing us to discern minute details of molecular cross-talk required for effective coordination between the myriad interacting components for efficient muscle function. The objective of this review is to provide a systems-level, comprehensive mapping the molecular mechanisms underlying skeletal muscle structure and function, in health and disease. We begin this review with a focus on molecular mechanisms underlying muscle tissue development (myogenesis), with an emphasis on satellite cells and muscle regeneration. We next review the molecular structure and mechanisms underlying the many structural components of the muscle: neuromuscular junction, sarcomere, cytoskeleton, extracellular matrix, and vasculature surrounding muscle. We highlight aberrant molecular mechanisms and their possible clinical or pathophysiological relevance. We particularly emphasize the impact of environmental stressors (inflammation and oxidative stress) in contributing to muscle pathophysiology including atrophy, hypertrophy, and fibrosis. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Models of Systems Properties and Processes > Cellular Models.

A rigorous understanding of how multicellular behaviors arise from the actions of single cells requires quantitative frameworks that bridge the gap between genetic circuits, the arrangement of cells in space, and population-level behaviors. Here, we provide such a framework for a ubiquitous class of multicellular systems - namely, "secrete-and-sense cells" that communicate by secreting and sensing a signaling molecule. By using formal, mathematical arguments and introducing the concept of a phenotype diagram, we show how these cells tune their degrees of autonomous and collective behavior to realize distinct single-cell and population-level phenotypes; these phenomena have biological analogs, such as quorum sensing or paracrine signaling. We also define the "entropy of population," a measurement of the number of arrangements that a population of cells can assume, and demonstrate how a decrease in the entropy of population accompanies the formation of ordered spatial patterns. Our conceptual framework ties together diverse systems, including tissues and microbes, with common principles.

The crawling motility of many eukaryotic cells is driven by filamentous actin (F-actin), and regulated by a network of signaling proteins and lipids (including small GTPases). The tangle of positive and negative feedback loops gives rise to various experimentally observed dynamic patterns (``actin waves''). Here we consider a recent prototypical model for actin waves in which F-actin exerts negative feedback onto a GTPase. Guided by recent numerical PDE bifurcation analysis in Hughes (2025) and Hughes et al (2026), we explore cell shapes and motility associated with polar, oscillatory, and traveling waves solutions of a mass-conserved partial differential equation (PDE) model. We use Morpheus (cellular Potts) simulations to investigate the implications of such regimes of behavior on the shapes and motion of cells, and on transitions between modes of behavior. The model demonstrates various cell states, including resting (spatially uniform GTPase), polar cells (static ``zones'' of GTPase), and traveling waves along the cell edge. In some parameter regimes, such states can coexist, so that cells can transition from one behavior to another in response to noisy stimuli.

We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdos-Renyi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for understanding cell biology. However, a comprehensive evaluation of LLMs' performance on language-driven single-cell analysis tasks still remains unexplored. Motivated by this challenge, we introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data and encompasses three hierarchical levels of single-cell analysis tasks: cell type annotation (cell-level), drug response prediction (drug-level), and perturbation analysis (gene-level). Going beyond this, we systematically evaluate the performance across 14 open-source and closed-source LLMs ranging from 160M to 671B on CellVerse. Remarkably, the experimental results reveal: (1) Existing specialist models (C2S-Pythia) fail to make reasonable decisions across all sub-tasks within CellVerse, while generalist models such as Qwen, Llama, GPT, and DeepSeek family models exhibit preliminary understanding capabilities within the realm of cell biology. (2) The performance of current LLMs falls short

The last decade has witnessed a rapid growth in understanding of the pivotal roles of mechanical stresses and physical forces in cell biology. As a result an integrated view of cell biology is evolving, where genetic and molecular features are scrutinized hand in hand with physical and mechanical characteristics of cells. Physics of liquid crystals has emerged as a burgeoning new frontier in cell biology over the past few years, fueled by an increasing identification of orientational order and topological defects in cell biology, spanning scales from subcellular filaments to individual cells and multicellular tissues. Here, we provide an account of most recent findings and developments together with future promises and challenges in this rapidly evolving interdisciplinary research direction.

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor reoccurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows to profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a whole-system understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones.

Nearly all cell models explicitly or implicitly deal with the biophysical constraints that must be respected for life to persist. Despite this, there is almost no systematicity in how these constraints are implemented, and we lack a principled understanding of how cellular dynamics interact with them and how they originate in actual biology. Computational cell biology will only overcome these concerns once it treats the life-death boundary as a central concept, creating a theory of cellular viability. We lay the foundation for such a development by demonstrating how specific geometric structures can separate regions of qualitatively similar survival outcomes in our models, offering new global organizing principles for cell fate. We also argue that idealized models of emergent individuals offer a tractable way to begin understanding life's intrinsically generated limits.

In a recent paper, Wilmes et al. demonstrated a qualitative integration of omics data streams to gain a mechanistic understanding of cyclosporine A toxicity. One of their major conclusions was that cyclosporine A strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells exposed in vitro. We pursue here the analysis of those data with a quantitative integration of omics data with a differential equation model of the Nrf2 pathway. That was done in two steps: (i) Modeling the in vitro pharmacokinetics of cyclosporine A (exchange between cells, culture medium and vial walls) with a minimal distribution model. (ii) Modeling the time course of omics markers in response to cyclosporine A exposure at the cell level with a coupled PK-systems biology model. Posterior statistical distributions of the parameter values were obtained by Markov chain Monte Carlo sampling. Data were well simulated, and the known in vitro toxic effect EC50 was well matched by model predictions. The integration of in vitro pharmacokinetics and systems biology modeling gives us a quantitative insight into mechanisms of cyclosporine A oxidative-stress