Genome-wide identification of replication fork stalling/pausing sites and the interplay between RNA Pol II transcription and DNA replication progression.

BACKGROUND: DNA replication progression can be affected by the presence of physical barriers like the RNA polymerases, leading to replication stress and DNA damage. Nonetheless, we do not know how transcription influences overall DNA replication progression. RESULTS: To characterize sites where DNA replication forks stall and pause, we establish a genome-wide approach to identify them. This approach uses multiple timepoints during S-phase to identify replication fork/stalling hotspots as replication progresses through the genome. These sites are typically associated with increased DNA damage, overlapped with fragile sites and with breakpoints of rearrangements identified in cancers but do not overlap with replication origins. Overlaying these sites with a genome-wide analysis of RNA polymerase II transcription, we find that replication fork stalling/pausing sites inside genes are directly related to transcription progression and activity. Indeed, we find that slowing down transcription elongation slows down directly replication progression through genes. This indicates that transcription and replication can coexist over the same regions. Importantly, rearrangements found in cancers overlapping transcription-replication collision sites are detected in non-transformed cells and increase following treatment with ATM and ATR inhibitors. At the same time, we find instances where transcription activity favors replication progression because it reduces histone density. CONCLUSIONS: Altogether, our findings highlight how transcription and replication overlap during S-phase, with both positive and negative consequences for replication fork progression and genome stability by the coexistence of these two processes.

Critical review of single-cell mechanotyping approaches for biomedical applications.

Accurate mechanical measurements of cells has the potential to improve diagnostics, therapeutics and advance understanding of disease mechanisms, where high-resolution mechanical information can be measured by deforming individual cells. Here we evaluate recently developed techniques for measuring cell-scale stiffness properties; while many such techniques have been developed, much of the work examining single-cell stiffness is impacted by difficulties in standardization and comparability, giving rise to large variations in reported mechanical moduli. We highlight the role of underlying mechanical theories driving this variability, and note opportunities to develop novel mechanotyping devices and theoretical models that facilitate convenient and accurate mechanical characterisation. Moreover, many high-throughput approaches are confounded by factors including cell size, surface friction, natural population heterogeneity and convolution of elastic and viscous contributions to cell deformability. We nevertheless identify key approaches based on deformability cytometry as a promising direction for further development, where both high-throughput and accurate single-cell resolutions can be realized.

BondGraphs.jl: composable energy-based modelling in systems biology.

SUMMARY: BondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multiscale, and multiphysics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. AVAILABILITY AND IMPLEMENTATION: BondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl.

Sub-wavelength acoustic stencil for tailored micropatterning.

Acoustofluidic devices are ideal for biomedical micromanipulation applications, with high biocompatibility and the ability to generate force gradients down to the scale of cells. However, complex and designed patterning at the microscale remains challenging. In this work we report an acoustofluidic approach to direct particles and cells within a structured surface in arbitrary configurations. Wells, trenches and cavities are embedded in this surface. Combined with a half-wavelength acoustic field, together these form an 'acoustic stencil' where arbitrary cell and particle arrangements can be reversibly generated. Here a bulk-wavemode lithium niobate resonator generates multiplexed parallel patterning via a multilayer resonant geometry, where cell-scale resolution is accomplished via structured sub-wavelength microfeatures. Uniquely, this permits simultaneous manipulation in a unidirectional, device-spanning single-node field across scalable ∼cm2 areas in a microfluidic device. This approach is demonstrated via patterning of 5, 10 and 15 µm particles and 293-F cells in a variety of arrangements, where these activities are enabling for a range of cell studies and tissue engineering applications via the generation of highly complex and designed acoustic patterns at the microscale.

InsP3R-RyR channel crosstalk augments sarcoplasmic reticulum Ca2+ release and arrhythmogenic activity in post-MI pig cardiomyocytes.

Ca2+ transients (CaT) underlying cardiomyocyte (CM) contraction require efficient Ca2+ coupling between sarcolemmal Ca2+ channels and sarcoplasmic reticulum (SR) ryanodine receptor Ca2+ channels (RyR) for their generation; reduced coupling in disease contributes to diminished CaT and arrhythmogenic Ca2+ events. SR Ca2+ release also occurs via inositol 1,4,5-trisphosphate receptors (InsP3R) in CM. While this pathway contributes negligeably to Ca2+ handling in healthy CM, rodent studies support a role in altered Ca2+ dynamics and arrhythmogenic Ca2+ release involving InsP3R crosstalk with RyRs in disease. Whether this mechanism persists in larger mammals with lower T-tubular density and coupling of RyRs is not fully resolved. We have recently shown an arrhythmogenic action of InsP3-induced Ca2+ release (IICR) in end stage human heart failure (HF), often associated with underlying ischemic heart disease (IHD). How IICR contributes to early stages of disease is however not determined but highly relevant. To access this stage, we chose a porcine model of IHD, which shows substantial remodelling of the area adjacent to the infarct. In cells from this region, IICR preferentially augmented Ca2+ release from non-coupled RyR clusters that otherwise showed delayed activation during the CaT. IICR in turn synchronised Ca2+ release during the CaT but also induced arrhythmogenic delayed afterdepolarizations and action potentials. Nanoscale imaging identified co-clustering of InsP3Rs and RyRs, thereby allowing Ca2+-mediated channel crosstalk. Mathematical modelling supported and further delineated this mechanism of enhanced InsP3R-RyRs coupling in MI. Our findings highlight the role of InsP3R-RyR channel crosstalk in Ca2+ release and arrhythmia during post-MI remodelling.

IP3R activity increases propensity of RyR-mediated sparks by elevating dyadic [Ca2＋].

Calcium (Ca2＋) plays a critical role in the excitation contraction coupling (ECC) process that mediates the contraction of cardiomyocytes during each heartbeat. While ryanodine receptors (RyRs) are the primary Ca2＋ channels responsible for generating the cell-wide Ca2＋ transients during ECC, Ca2＋ release, via inositol 1,4,5-trisphosphate (IP3) receptors (IP3Rs) are also reported in cardiomyocytes to elicit ECC-modulating effects. Recent studies suggest that the localization of IP3Rs at dyads grant their ability to modify the occurrence of Ca2＋ sparks (elementary Ca2＋ release events that constitute cell wide Ca2＋ releases associated with ECC) which may underlie their modulatory influence on ECC. Here, we aim to uncover the mechanism by which dyad-localized IP3Rs influence Ca2＋ spark dynamics. To this end, we developed a mathematical model of the dyad that incorporates the behaviour of IP3Rs, in addition to RyRs, to reveal the impact of their activity on local Ca2＋ handling and consequent Ca2＋ spark occurrence and its properties. Consistent with published experimental data, our model predicts that the propensity for Ca2＋ spark formation increases in the presence of IP3R activity. Our simulations support the hypothesis that IP3Rs elevate Ca2＋ in the dyad, sensitizing proximal RyRs towards activation and hence Ca2＋ spark formation. The stochasticity of IP3R gating is an important aspect of this mechanism. However, dyadic IP3R activity lowers the Ca2＋ available in the junctional sarcoplasmic reticulum (JSR) for release, thus resulting in Ca2＋ sparks with similar durations but lower amplitudes.

CardioVinci: building blocks for virtual cardiac cells using deep learning.

Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.

Effects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy: an in silico study.

Diabetic cardiomyopathy is a leading cause of heart failure in diabetes. At the cellular level, diabetic cardiomyopathy leads to altered mitochondrial energy metabolism and cardiomyocyte ultrastructure. We combined electron microscopy (EM) and computational modelling to understand the impact of diabetes-induced ultrastructural changes on cardiac bioenergetics. We collected transverse micrographs of multiple control and type I diabetic rat cardiomyocytes using EM. Micrographs were converted to finite-element meshes, and bioenergetics was simulated over them using a biophysical model. The simulations also incorporated depressed mitochondrial capacity for oxidative phosphorylation (OXPHOS) and creatine kinase (CK) reactions to simulate diabetes-induced mitochondrial dysfunction. Analysis of micrographs revealed a 14% decline in mitochondrial area fraction in diabetic cardiomyocytes, and an irregular arrangement of mitochondria and myofibrils. Simulations predicted that this irregular arrangement, coupled with the depressed activity of mitochondrial CK enzymes, leads to large spatial variation in adenosine diphosphate (ADP)/adenosine triphosphate (ATP) ratio profile of diabetic cardiomyocytes. However, when spatially averaged, myofibrillar ADP/ATP ratios of a cardiomyocyte do not change with diabetes. Instead, average concentration of inorganic phosphate rises by 40% owing to lower mitochondrial area fraction and dysfunction in OXPHOS. These simulations indicate that a disorganized cellular ultrastructure negatively impacts metabolite transport in diabetic cardiomyopathy. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.

Role of actin filaments and cis binding in cadherin clustering and patterning.

Cadherins build up clusters to maintain intercellular contact through trans and cis (lateral) bindings. Meanwhile, interactions between cadherin and the actin cytoskeleton through cadherin/F-actin linkers can affect cadherin dynamics by corralling and tethering cadherin molecules locally. Despite many experimental studies, a quantitative, mechanistic understanding of how cadherin and actin cytoskeleton interactions regulate cadherin clustering does not exist. To address this gap in knowledge, we developed a coarse-grained computational model of cadherin dynamics and their interaction with the actin cortex underlying the cell membrane. Our simulation predictions suggest that weak cis binding affinity between cadherin molecules can facilitate large cluster formation. We also found that cadherin movement inhibition by actin corralling is dependent on the concentration and length of actin filaments. This results in changes in cadherin clustering behaviors, as reflected by differences in cluster size and distribution as well as cadherin monomer trajectory. Strong cadherin/actin binding can enhance trans and cis interactions as well as cadherin clustering. By contrast, with weak cadherin/actin binding affinity, a competition between cadherin-actin binding and cis binding for a limited cadherin pool leads to temporary and unstable cadherin clusters.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome Project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science.

Paradoxes of Hymenoptera flight muscles, extreme machines.

In the Carboniferous, insects evolved flight. Intense selection drove for high performance and approximately 100 million years later, Hymenoptera (bees, wasps and ants) emerged. Some species had proportionately small wings, with apparently impossible aerodynamic challenges including a need for high frequency flight muscles (FMs), powered exclusively off aerobic pathways and resulting in extreme aerobic capacities. Modern insect FMs are the most refined and form large dense blocks that occupy 90% of the thorax. These can beat wings at 200 to 230 Hz, more than double that achieved by standard neuromuscular systems. To do so, rapid repolarisation was circumvented through evolution of asynchronous stimulation, stretch activation, elastic recoil and a paradoxically slow Ca2+ reuptake. While the latter conserves ATP, considerable ATP is demanded at the myofibrils. FMs have diminished sarcoplasmic volumes, and ATP is produced solely by mitochondria, which pack myocytes to maximal limits and have very dense cristae. Gaseous oxygen is supplied directly to mitochondria. While FMs appear to be optimised for function, several unusual paradoxes remain. FMs lack any significant equivalent to the creatine kinase shuttle, and myofibrils are twice as wide as those of within cardiomyocytes. The mitochondrial electron transport systems also release large amounts of reactive oxygen species (ROS) and respiratory complexes do not appear to be present at any exceptional level. Given that the loss of the creatine kinase shuttle and elevated ROS impairs heart function, we question how do FM shuttle adenylates at high rates and tolerate oxidative stress conditions that occur in diseased hearts?

Multimodal imaging reveals membrane skeleton reorganisation during reticulocyte maturation and differences in dimple and rim regions of mature erythrocytes.

The red blood cell (RBC) is remarkable in its ability to deform as it passages through the vasculature. Its deformability derives from a spectrin-actin protein network that supports the cell membrane and provides strength and flexibility, however questions remain regarding the assembly and maintenance of the skeletal network. Using scanning electron microscopy (SEM) and atomic force microscopy (AFM) we have examined the nanoscale architecture of the cytoplasmic side of membrane discs prepared from reticulocytes and mature RBCs. Immunofluorescence microscopy was used to probe the distribution of spectrin and other membrane skeleton proteins. We found that the cell surface area decreases by up to 30% and the spectrin-actin network increases in density by approximately 20% as the reticulocyte matures. By contrast, the inter-junctional distance and junctional density increase only by 3-4% and 5-9%, respectively. This suggests that the maturation-associated reduction in surface area is accompanied by an increase in spectrin self-association to form higher order oligomers. We also examined the mature RBC membrane in the edge (rim) and face (dimple) regions of mature RBCs and found the rim contains about 1.5% more junctional complexes compared to the dimple region. A 2% increase in band 4.1 density in the rim supports these structural measurements.

Cortical tension initiates the positive feedback loop between cadherin and F-actin.

Adherens junctions physically link two cells at their contact interface via extracellular binding between cadherin molecules and intracellular interactions between cadherins and the actin cytoskeleton. Cadherin and actomyosin cytoskeletal dynamics are regulated reciprocally by mechanical and chemical signals, which subsequently determine the strength of cell-cell adhesions and the emergent organization and stiffness of the tissues they form. However, an understanding of the integrated system is lacking. We present a new mechanistic computational model of intercellular junction maturation in a cell doublet to investigate the mechanochemical cross talk that regulates adherens junction formation and homeostasis. The model couples a two-dimensional lattice-based simulation of cadherin dynamics with a reaction-diffusion representation of the reorganising actomyosin network through its regulation by Rho signalling at the intracellular junction. We demonstrate that local immobilization of cadherin induces cluster formation in a cis-less-dependent manner. We then recapitulate the process of cell-cell contact formation. Our model suggests that cortical tension applied on the contact rim can explain the ring distribution of cadherin and actin filaments (F-actin) on the cell-cell contact of the cell doublet. Furthermore, we propose and test the hypothesis that cadherin and F-actin interact like a positive feedback loop, which is necessary for formation of the ring structure. Different patterns of cadherin distribution were observed as an emergent property of disturbances of this positive feedback loop. We discuss these findings in light of available experimental observations on underlying mechanisms related to cadherin/F-actin binding and the mechanical environment.

Periodic Rayleigh streaming vortices and Eckart flow arising from traveling-wave-based diffractive acoustic fields.

Recent studies have demonstrated that periodic time-averaged acoustic fields can be produced from traveling surface acoustic waves (SAWs) in microfluidic devices. This is caused by diffractive effects arising from a spatially limited transducer. This permits the generation of acoustic patterns evocative of those produced from standing waves, but instead with the application of a traveling wave. While acoustic pressure fields in such systems have been investigated, acoustic streaming from diffractive fields has not. In this work we examine this phenomenon and demonstrate the appearance of geometry-dependent acoustic vortices, and demonstrate that periodic, identically rotating Rayleigh streaming vortices result from the imposition of a traveling SAW. This is also characterized by a channel-spanning flow that bridges between adjacent vortices along the channel top and bottom. We find that the channel dimensions determine the types of streaming that develops; while Eckart streaming has been previously presumed to be a distinguishing feature of traveling-wave actuation, we show that Rayleigh streaming vortices also results. This has implications for microfluidic actuation, where traveling acoustic waves have applications in microscale mixing, separation, and patterning.

Respiration mask waveguide optimisation for maximised speech intelligibility.

Both the scarcity and environmental impact of disposable face masks, as in the COVID-19 pandemic, have instigated the recent development of reusable masks. Such face masks reduce transmission of infectious agents and particulates, but often impact a user's ability to be understood when materials, such as silicone or hard polymers, are used. In this work, we present a numerical optimisation approach to optimise waveguide topology, where a waveguide is used to transmit and direct sound from the interior of the mask volume to the outside air. This approach allows acoustic energy to be maximised according to specific frequency bands, including those most relevant to human speech. We employ this method to convert a resuscitator mask, made of silicone, into respiration personal protective equipment (PPE) that maximises the speech intelligibility index (SII). We validate this approach experimentally as well, showing improved SII when using the fabricated device. Together, this design represents a unique and effective approach to utilize and adapt available apparatus to filter air while improving the ability to communicate effectively, including in healthcare settings.

Unconventional acoustic approaches for localized and designed micromanipulation.

Acoustic fields are ideal for micromanipulation, being biocompatible and with force gradients approaching the scale of single cells. They have accordingly found use in a variety of microfluidic devices, including for microscale patterning, separation, and mixing. The bulk of work in acoustofluidics has been predicated on the formation of standing waves that form periodic nodal positions along which suspended particles and cells are aligned. An evolving range of applications, however, requires more targeted micromanipulation to create unique patterns and effects. To this end, recent work has made important advances in improving the flexibility with which acoustic fields can be applied, impressively demonstrating generating arbitrary arrangements of pressure fields, spatially localizing acoustic fields and selectively translating individual particles in ways that are not achievable via traditional approaches. In this critical review we categorize and examine these advances, each of which open the door to a wide range of applications in which single-cell fidelity and flexible micromanipulation are advantageous, including for tissue engineering, diagnostic devices, high-throughput sorting and microfabrication.

A toolbox for generating scalable mitral valve morphometric models.

The mitral valve is a complex anatomical structure, whose shape is key to several traits of its function and disease, being crucial for the success of surgical repair and implantation of medical devices. The aim of this study was to develop a parametric, scalable, and clinically useful model of the mitral valve, enabling the biomechanical evaluation of mitral repair techniques through finite element simulations. MATLAB was used to parameterize the valve: the annular boundary was sampled from a porcine mitral valve mesh model and landmark points and relevant boundaries were selected for the parameterization of leaflets using polynomial fitting. Several geometric parameters describing the annulus, leaflet shape and papillary muscle position were implemented and used to scale the model according to patient dimensions. The developed model, available as a toolbox, allows for the generation of a population of models using patient-specific dimensions obtained from medical imaging or averaged dimensions evaluated from empirical equations based on the Golden Proportion. The average model developed using this framework accurately represents mitral valve shapes, associated with relative errors reaching less than 10% for annular and leaflet length dimensions, and less than 24% in comparison with clinical data. Moreover, model generation takes less than 5 min of computing time, and the toolbox can account for individual morphological variations and be employed to evaluate mitral valve biomechanics; following further development and validation, it will aid clinicians when choosing the best patient-specific clinical intervention and improve the design process of new medical devices.

EGFRvIII Promotes Cell Survival during Endoplasmic Reticulum Stress through a Reticulocalbin 1-Dependent Mechanism.

Reticulocalbin 1 (RCN1) is an endoplasmic reticulum (ER)-residing protein, involved in promoting cell survival during pathophysiological conditions that lead to ER stress. However, the key upstream receptor tyrosine kinase that regulates RCN1 expression and its potential role in cell survival in the glioblastoma setting have not been determined. Here, we demonstrate that RCN1 expression significantly correlates with poor glioblastoma patient survival. We also demonstrate that glioblastoma cells with expression of EGFRvIII receptor also have high RCN1 expression. Over-expression of wildtype EGFR also correlated with high RCN1 expression, suggesting that EGFR and EGFRvIII regulate RCN1 expression. Importantly, cells that expressed EGFRvIII and subsequently showed high RCN1 expression displayed greater cell viability under ER stress compared to EGFRvIII negative glioblastoma cells. Consistently, we also demonstrated that RCN1 knockdown reduced cell viability and exogenous introduction of RCN1 enhanced cell viability following induction of ER stress. Mechanistically, we demonstrate that the EGFRvIII-RCN1-driven increase in cell survival is due to the inactivation of the ER stress markers ATF4 and ATF6, maintained expression of the anti-apoptotic protein Bcl-2 and reduced activity of caspase 3/7. Our current findings identify that EGFRvIII regulates RCN1 expression and that this novel association promotes cell survival in glioblastoma cells during ER stress.

EM-stellar: benchmarking deep learning for electron microscopy image segmentation.

MOTIVATION: The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high-resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. RESULTS: We present EM-stellar, a platform that is hosted on Google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics. AVAILABILITY AND IMPLEMENTATION: EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https://github.com/cellsmb/em-stellar). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.