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Biology subjects

Pan, M.

Publications and source records attributed to Pan, M..

At least 91 records · Page 5Linked to original sources

Energy dependence of signalling dynamics and robustness in bacterial two component systems

One of the best known ways bacteria cells understand and respond to the environment are through Two-Component Systems (TCS). These signalling systems are highly diverse in function and can detect a range of physical stimuli including molecular concentrations and temperature, with a range of responses including chemotaxis and anaerobic energy production. TCS exhibit a range of different molecular structures and energy costs, and multiple types co-exist in the same cell. TCSs that incur relatively high energy cost are abundant in biology, despite strong evolutionary pressure to efficiently spend energy. We are motivated to discern what benefits, if any, the more energetically expensive variants had for a cell. We seek to answer this question by modelling energy flow through two variants of TCS. This was accomplished using bond graphs, a physics-based modelling framework that accurately models energy transfer through different physical domains. Our analysis demonstrates that energy availability can affect a cells signal sensitivity, noise filtering effectiveness, and the stimulus level where cell response is maximal. We also found that these properties are determined not by the molecular parameters themselves, but the reaction rate parameters that govern the reaction systems as a whole. This suggests possible connections between the molecular structure and evolutionary purpose of any two-component system. This opens the door to new synthetic circuit design in systems biology, and we propose new hypotheses about this link between structure and purpose that could be experimentally verified. Author summaryTwo-component systems are the main way many bacteria sense and respond to their environment. They exist in such well-studied bacteria as E. coli where they have been shown to detect a range of stimuli including nutrients, temperature, acidity, and pressure. Two-component systems are ubiquitous in bacteria yet have a deceptively simple structure. Knowing how they operate and the purpose of variations in signalling structure is helpful to our understanding of cellular biology and the design of synthetic biological circuits. Critical unanswered questions remain about the energy usage and functional benefits of these systems. We sought to improve our understanding of two-component systems by applying a physics-based modelling framework. We found that tracking energy flow through the cell reveals new energy-dependent behaviour in signalling sensitivity, noise filtering, and maximal cell response. We also found that these properties are not strictly dependent on the molecular properties themselves, but from the configuration of the reaction system as a whole.

biophysics↗

Making waves: Comparative analysis of gene drive spread characteristics in a continuous space model

With their ability to rapidly increase in frequency, gene drives can be used to modify or suppress target populations after an initial release of drive-containing individuals. Recent advances in this field have revealed many possibilities for different types of drives, and several of these have been realized in experimental demonstrations. These drives all have unique advantages and disadvantages related to their ease of construction, confinement, and capacity to act as a modification or suppression system. While many properties of these drives have been explored in modelling studies, assessment of these drives in continuous space environments has been limited, often focusing on outcomes rather than fundamental properties. Here, we conduct a comparative analysis of many different gene drive types that have the capacity to form a wave of advance against wild-type alleles in one-dimensional continuous space. We evaluate the drive wave speed as a function of drive performance and ecological parameters, which reveals substantial differences between drive performance in panmictic versus spatial environments. In particular, we find that suppression drive waves are uniquely vulnerable to fitness costs and undesired CRISPR cleavage activity that can form resistance alleles in embryos by maternal deposition. Some drives, though, retain robust characteristics even with widely varying performance characteristics. To gain a better understanding of drive waves, we compare panmictic performance of drives across the full range of drive frequencies. We find that rates of wild-type allele removal in panmictic setting is correlated with drive wave speed, though this is also affected by a range of other factors. Overall, our results provide a useful resource for understanding the performance of drives in continuous spatial environments, which may be most representative of potential drive deployment in many relevant scenarios.

genetics↗

Energy-based Advection Modelling Using Bond Graphs

Advection, the transport of a substance by the flow of a fluid, is a key process in biological systems. The energy-based bond graph approach to modelling chemical transformation within reaction networks is extended to include transport and thus advection. The approach is illustrated using a simple model of advection via circulating flow and by a simple pharmacokinetic model of anaesthetic gas uptake. This extension provides a physically-consistent framework for linking advective flows with the fluxes associated with chemical reactions within the context of physiological systems in general and the human physiome in particular.

systems biology↗

SBML to bond graphs: from conversion to composition

The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausibile or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/493355v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16f5998org.highwire.dtl.DTLVardef@1630beborg.highwire.dtl.DTLVardef@1a37309org.highwire.dtl.DTLVardef@1d582df_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA framework to convert suitable SBML models of biochemical networks into bond graphs is developed. C_LIO_LIThe framework is applied here to two interconnecting models of metabolism pathways. C_LIO_LIWe automatically integrate the generated bond graph modules. C_LIO_LIWe qualitatively illustrate the functionality of the composed model. C_LI

bioengineering↗

De novo lipogenesis fuels adipocyte autophagosome membrane dynamics

Autophagy is a homeostatic degradative process for cell components that enables stress resilience and can determine cellular fate and function. However, lipid sources for the rapid membrane expansions of autophagosomes, the workhorses of autophagy, are poorly understood. Here, we identify de novo lipogenesis (DNL) as a critical source of fatty acids (FA) to fuel autophagosome dynamics in adipocytes. Adipocyte fatty acid synthase (Fasn) deficiency markedly impairs autophagy, evident by autophagosome accumulation, and severely compromises degradation of the autophagic substrate p62. Autophagy dependence on FA produced by Fasn is not fully alleviated by exogenous FA in cultured adipocytes even though lipid droplet size is restored. Imaging studies reveal that Fasn colocalizes with nascent autophagosomes, while loss of Fasn decreases certain membrane phosphoinositides known to be required for autophagosome assembly. Together, our studies highlight a newly appreciated function for adipocyte DNL in autophagosome membrane formation and provide evidence that localized FA synthesis contributes to autophagosome dynamics.

molecular biology↗

Network Thermodynamics of Biological Systems: A Bond Graph Approach

Edmund Crampin (1973-2021) was at the forefront of Systems Biology research and his work will influence the field for years to come. This paper brings together and summarises the seminal work of his group in applying energy-based bond graph methods to biological systems. In particular, this paper: (a) motivates the need to consider energy in modelling biology; (b) introduces bond graphs as a methodology for achieving this; (c) describes extensions to modelling electrochemical transduction; (d) outlines how bond graph models can be constructed in a modular manner and (e) describes stoichiometric approaches to deriving fundamental properties of reaction networks. These concepts are illustrated using a new bond graph model of photosynthesis in chloroplasts.

systems biology↗

Distinct bile salt hydrolase substrate preferences dictate C. difficile pathogenesis

Summary paragraphBile acids (BAs) mediate the crosstalk between human and microbial cells and influence intestinal diseases including Clostridioides difficile infection (CDI). While bile salt hydrolases (BSHs) shape the BA pool by deconjugating conjugated BAs, the basis for their substrate preferences and impact on C. difficile remain elusive. Here, we survey the diversity of Lactobacillus BSHs and unravel the structural basis of their substrate preference. We show that leveraging BSH activity and specificity is an effective strategy to prevent C. difficile growth in clinically relevant CDI models. A range of non-canonical conjugated BAs is also identified, comprising unique BSH substrates that also inhibit C. difficile spore germination. These findings establish BSHs as intestinal enzymes essential to BA homeostasis and colonization resistance against C. difficile. One sentence summaryBile salt hydrolase activity inhibits C. difficile by shaping the conventional and non-canonical conjugated bile acid pools

microbiology↗

Paradoxical activation of SREBP1c and de novo lipogenesis by hepatocyte-selective ACLY depletion in obese mice.

Hepatic steatosis associated with high fat diets, obesity and type 2 diabetes is thought to be the major driver of severe liver inflammation, fibrosis, and cirrhosis. Cytosolic acetyl-coenzyme A (AcCoA), a central metabolite and substrate for de novo lipogenesis (DNL), is produced from citrate by ATP-citrate lyase (ACLY) and from acetate through AcCoA synthase short chain family member 2 (ACSS2). However, the relative contributions of these two enzymes to hepatic AcCoA pools and DNL rates in response to high fat feeding is unknown. We report here that hepatocyte-selective depletion of either ACSS2 or ACLY caused similar 50% decreases in liver AcCoA levels in obese mice, showing that both pathways contribute to generation of this DNL substrate. Unexpectedly however, the hepatocyte ACLY depletion in obese mice paradoxically increased total DNL flux measured by D2O incorporation into palmitate, while in contrast ACSS2 depletion had no effect. The increase in liver DNL upon ACLY depletion was associated with increased expression of nuclear sterol regulatory element-binding protein 1c (SREBP1c) and of its target DNL enzymes. This upregulated DNL enzyme expression explains the increased rate of palmitate synthesis in ACLY depleted livers. Furthermore, this increased flux through DNL may also contribute to the observed depletion of AcCoA levels due to its increased conversion to Malonyl CoA (MalCoA) and palmitate. Together, these data indicate that in HFD fed obese mice, hepatic DNL is not limited by its immediate substrates AcCoA or MalCoA, but rather by activities of DNL enzymes.

molecular biology↗

Connectome-wide association analysis identified significant brain functional alteration in adults with attention-deficit/hyperactivity disorder

Adults with attention-deficit/hyperactivity disorder (ADHD), as an extreme-phenotype of ADHD, is still facing problems of inconsistency and undeciphered mechanisms for its neuropathology. To address this matter, our present study performed connecotome-wide voxel-based analyses with the resting-state fMRI data of 84 adults with ADHD and 89 healthy controls. We found that functional connectivity patterns of the left precuneus and the left middle temporal significantly altered in ADHD populations serving as potential neural biomarkers to distinguish ADHD with healthy controls, with subsequent seed-based analysis revealing the dysfunction of functional connections both intra- and inter-default mode and attention networks, among which middle temporal gyrus plays the key role of bridge linking the default mode and attention networks. After cognitive behavioral therapy, two of these ADHD-altered functional connections ameliorated accompanied with improvement of ADHD core symptoms. Additionally, imaging genetic analyses also revealed close relationships between the observed brain functional alterations and ADHD-risk genes. Taken together, our findings suggested the interference of default mode on attention networks in adults with ADHD, which would be severing as a potential biomarker for both ADHD pathogenesis and treatment effects.

neuroscience↗

Glutamine deficiency in solid tumors confers resistance to ribosomal RNA synthesis inhibitors

Ribosome biogenesis involves the processing of precursor ribosomal RNAs (pre-rRNAs) and sequential assembly with ribosomal proteins. Here we report that nutrient deprivation severely impairs pre-rRNA processing and leads to the accumulation of unprocessed rRNAs. Upon nutrient restoration, the accumulated pre-rRNAs are processed into mature rRNAs that are utilized for ribosome biogenesis. Failure to accumulate pre-rRNAs under nutrient deprivation leads to perturbed ribosome assembly during nutrient restoration and subsequent apoptosis via uL5/uL18-mediated activation of p53. Restoration of glutamine alone activates p53 by triggering uL5/uL18 translation. Induction of uL5/uL18 protein synthesis by glutamine was dependent on the translation factor eukaryotic elongation factor 2 (eEF2), which was in turn dependent on Raf/MEK/ERK signalling. Depriving cells of glutamine prevents the activation of p53 by rRNA synthesis inhibitors. Our data reveals a mechanism that cancer cells can exploit to suppress p53-mediated apoptosis during fluctuations in environmental nutrient availability.

cancer biology↗

Population-level asymmetry of the cerebral cortex: reproducibility, lifespan changes, heritability, and individual differences

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the genetic and later influences that shape them, such as age. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in 7 datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry correlates phenotypically and genetically in specific regions, and is low-moderately heritable (max h2SNP [~]19%). In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises in early life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

neuroscience↗

A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each models components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models. Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.

bioengineering↗

Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

bioinformatics↗

Metabolomic and transcriptomic analyses reveal the effects of grafting on anthocyanin synthesis in grapevine

The grafting has been commonly used in viticulture, which joints the scion from a cultivar with the stem of a rootstock. Grafting has crucial impacts on various phenotypes of the cultivar including berry metabolome and berry coloring, however, the genetics and regulation mechanisms are largely unexplored. In this study, we analyzed the phenotypic, metabolomic and transcriptomic profiles at three stages (45, 75 and105 days after flowering) of the Crimson Seedless (Vitis vinifera, CS) cultivar grafted to four rootstocks (three heterografting: CS/101-14MG, CS/SO4, CS/110R and one self-grafting CS/CS) with an own-rooted grafting-free Crimson Seedless (CS) as a control. All the heterografting plants had a significant influence on berry reddening as early as ~45 days after flowering. The grafting of rootstocks promoted anthocyanin synthesis and accumulation in grape berries. The metabolomic features showed that Cyanidin 3-O-glucoside, Delphinidin 3-O-glucosid, Malvidin 3-O-glucoside, Peonidin 3-O-glucoside and Petunidin 3-O-glucoside were the pigments responsible for the purplish-red color peels. Transcriptomic analyses revealed that the anthocyanins biosynthetic related genes from the upstream (phenylalanine ammonia-lyase) to the downstream (anthocyanidin 3-O-glucosyltransferase and anthocyanidin synthase) were upregulated with the accumulations of anthocyanins in CS/101-14MG, CS/SO4 and CS/110R. At the same time, all these genes were also highly expressed and more anthocyanin was accumulated in CS/CS samples compared to CS samples, suggesting that self-grafting rootstocks might also have promoted berry reddening in grapevine. Our results provide global transcriptomic and metabolomic features in berry coloring regulation under different grafting conditions for improving the berry quality in grapevine production.

plant biology↗

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity. Author summaryThe biochemistry within a cell is complex, being composed of numerous biomolecules and reactions. In order to develop fully detailed mathematical models of cells, smaller submodels need to be constructed and connected together. Software and standards can assist in this endeavour, but challenges remain in ensuring submodels are both consistent with each other and consistent with the fundamental conservation laws of physics. In this paper, we propose a new approach using bond graphs from engineering. In this approach, connections between models are defined using physical conservation laws. We show that this approach is compatible with current software approaches in the field, and can therefore be readily used to incorporate physical consistency into existing model integration methodologies. We illustrate the utility of this approach in streamlining the development of models for a signalling network (the MAPK cascade) and a metabolic network (the glycolysis pathway). The advantage of this approach is that models can be developed in a scalable manner while also ensuring consistency with the laws of physics, enhancing the range of data available to train models. This approach can be used to quickly construct detailed and accurate models of cells, facilitating future advances in biotechnology and personalised medicine.

systems biology↗

Structural Insights Into the Initiation and Elongation of Ubiquitination by Ubr1

The N-end rule pathway was one of the first ubiquitin (Ub)-dependent degradation pathways to be identified. Ubr1, a single-chain E3 ligase, targets proteins bearing a destabilizing residue at the N-terminus (N-degron) for rapid K48-linked ubiquitination and proteasome-dependent degradation. How Ubr1 catalyses the initiation of ubiquitination on the substrate and elongation of the Ub chain in a linkage-specific manner through a single E2 ubiquitin-conjugating enzyme (Ubc2) remains unknown. Here, we report the cryo-electron microscopy structures of two complexes representing the initiation and elongation intermediates of Ubr1 captured using chemical approaches. In these two structures, Ubr1 adopts different conformations to facilitate the transfer of Ub from Ubc2 to either an N-degron peptide or a monoubiquitinated degron. These structures not only reveal the architecture of the Ubr1 complex but also provide mechanistic insights into the initiation and elongation steps of ubiquitination catalysed by Ubr1.

biophysics↗

Analysing and simulating energy-based models in biology using BondGraphTools

Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

systems biology↗

Modular Dynamic Biomolecular Modelling: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data.

Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli (E. coli) and published experimental data.

systems biology↗