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Pathogenic variation in DNA mismatch repair (MMR) gene MLH1 is associated with Lynch syndrome (LS), an autosomal dominant hereditary cancer. Of the 3798 MLH1 germline variants collected in the ClinVar database, 38.7% (1469) were missense variants, of which 81.6% (1199) were classified as Variants of Uncertain Significance (VUS) due to the lack of functional evidence. Further determination of the impact of VUS on MLH1 function is important for the VUS carriers to take preventive action. We recently developed a protein structure-based method named “Deep Learning-Ramachandran Plot-Molecular Dynamics Simulation (DL-RP-MDS)” to evaluate the deleteriousness of MLH1 missense VUS. The method extracts protein structural information by using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, then combines the variation data with an unsupervised learning model composed of auto-encoder and neural network classifier to identify the variants causing significant change in protein structure. In this report, we applied the method to classify 447 MLH1 missense VUS. We predicted 126/447 (28.2%) MLH1 missense VUS were deleterious. Our study demonstrates that DL-RP-MDS is able to classify the missense VUS based solely on their impact on protein structure.

Abstract Three-dimensional (3D) printing has emerged as a powerful tool for material, food, and life science research and development, where the technology’s democratization necessitates the advancement of open-source platforms. Herein, we developed a hackable, multi-functional, and modular extrusion 3D printer for soft materials, nicknamed Printer.HM. Multi-printhead modules are established based on a robotic arm for heterogeneous construct creation, where ink printability can be tuned by accessories such as heating and UV modules. Software associated with Printer.HM were designed to accept geometry inputs including computer-aided design models, coordinates, equations, and pictures, to create prints of distinct characteristics. Printer.HM could further perform versatile operations, such as liquid dispensing, non-planar printing, and pick-and-place of meso-objects. By ‘mix-and-match’ software and hardware settings, Printer.HM demonstrated printing of pH-responsive soft actuators, plant-based functional hydrogels, and organ macro-anatomical models. Integrating affordability and open design, Printer.HM is envisaged to democratize 3D printing for soft, biological, and sustainable material architectures.

The mRNA vaccines (RVs) can reduce the severity and mortality of severe acute respiratory syndrome coronavirus (SARS-CoV-2). However, almost only the inactivated vaccines (IVs) but no RVs had been used in mainland China until most recently, and the relaxing of its anti-pandemic strategies in December 2022 increased concerns about new outbreaks. In comparison, many of the citizens in Macao Special Administrative Region of China received three doses of IV (3IV) or RV (3RV), or 2 doses of IV plus one booster of RV (2IV+1RV). By the end of 2022, we recruited 147 participants with various vaccinations in Macao and detected antibodies (Abs) against the spike (S) protein and nucleocapsid (N) protein of the virus as well as neutralizing antibodies (NAb) in their serum. We observed that the level of anti-S Ab or NAb was similarly high with both 3RV and 2IV+1RV but lower with 3IV. In contrast, the level of anti-N Ab was the highest with 3IV like that in convalescents, intermediate with 2IV+1RV, and the lowest with 3RV. Whereas no significant differences in the basal levels of cytokines related to T-cell activation were observed among the various vaccination groups before and after the boosters. No vaccinees reported severe adverse events. Since Macao took one of the most stringent non-pharmaceutical interventions in the world, this study possesses much higher confidence in the vaccination results than many other studies from highly infected regions. Our findings suggest that the heterologous vaccination 2IV+1RV outperforms the homologous vaccinations 3IV and 3RV as it induces not only anti-S Ab (to the level as with 3RV) but also anti-N antibodies (via the IV). It combines the advantages of both RV (to block the viral entry) and IV (to also intervene the subsequent pathological processes such as intracellular viral replication and interference with the signal transduction and hence the biological functions of host cells).

Reduction of the rapid delayed rectifier potassium current (IKr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Summary: Functional classification of genetic variants is a key for their clinical applications in patient care. However, abundant variant data generated by the next-generation DNA sequencing technologies limit the use of experimental methods for their classification. Here, we developed a protein structure and deep learning (DL)-based system for genetic variant classification, DL-RP-MDS, which comprises two principles: 1) Extracting protein structural and thermodynamics information using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, 2) combining those data with an unsupervised learning model of auto-encoder and a neural network classifier to identify the statistical significance patterns of the structural changes. We observed that DL-RP-MDS provided higher specificity than over 20 widely used in silico methods in classifying the variants of three DNA damage repair genes: TP53, MLH1, and MSH2. DL-RP-MDS offers a powerful platform for high-throughput genetic variant classification. The software and online application are available at https://genemutation.fhs.um.edu.mo/DL-RP-MDS/.

Abstract Nicotinamide, the amide form of Vitamin B3, is a common nutrient supplement that plays important role in human fetal development. Nicotinamide has been widely used in clinical treatments, including the treatment of diseases during pregnancy. However, its impacts during embryogenesis have not been fully understood. In this study, we show that nicotinamide plays multiplex roles in mesoderm differentiation of human embryonic stem cells (hESCs). Nicotinamide promotes cardiomyocyte fate from mesoderm progenitor cells, and suppresses the emergence of other cell types. Independent of its functions in PARP and Sirtuin pathways, nicotinamide modulates differentiation through kinase inhibition. A KINOMEscan assay identifies 14 novel nicotinamide targets among 468 kinase candidates. We demonstrate that nicotinamide promotes cardiomyocyte differentiation through p38 MAP kinase inhibition. Furthermore, we show that nicotinamide enhances cardiomyocyte survival as a Rho-associated protein kinase (ROCK) inhibitor. This study reveals nicotinamide as a pleiotropic molecule that promotes the derivation and survival of cardiomyocytes, and it could become a useful tool for cardiomyocyte production for regenerative medicine. It also provides a theoretical foundation for physicians when nicotinamide is considered for treatments for pregnant women.

Current spread hampers the efficacy of neuromodulation, while existing animal, in vitro and in silico models have failed to give patient-centric insights. Here the authors employ 3D printing and machine learning to advance clinical predictions of current spread for cochlear implant patients.

Automated patch-clamp platforms are widely used and vital tools in both academia and industry to enable high-throughput studies such as drug screening. A leak current to ground occurs whenever the seal between a pipette and cell (or internal solution and cell in high-throughput machines) is not perfectly insulated from the bath (extracellular) solution. Over 1 GΩ seal resistance between pipette and bath solutions is commonly used as a quality standard for manual patch work. With automated platforms it can be difficult to obtain such a high seal resistance between the intra- and extra-cellular solutions. One suggested method to alleviate this problem is using an F− containing internal solution together with a Ca2+ containing external solution — so that a CaF2 crystal forms when the two solutions meet which ‘plugs the holes’ to enhance the seal resistance. However, we observed an unexpected nonlinear-in-voltage and time-dependent current using these solutions on an automated patch-clamp platform. We performed manual patch-clamp experiments with the automated patch-clamp solutions, but no biological cell, and observed the same nonlinear time-dependent leak current. The current could be completely removed by washing out F− ions to leave a conventional leak current that was linear and not time-dependent. We therefore conclude fluoride ions interacting with the CaF2 crystal are the origin of the nonlinear time-dependent leak current. The consequences of such a nonlinear and time-dependent leak current polluting measurements should be considered carefully if it cannot be isolated and subtracted.

Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. These models provide an abstract description of the underlying conformational changes of the ion channels. However, due to the abstracted conformation states and assumptions for the rates of transition between them, there are differences between the models and reality—termed model discrepancy or misspecification. In this paper, we demonstrate the feasibility of using a mechanistically-inspired neural network differential equation model, a hybrid non-parametric model, to model ion channel kinetics. We apply it to the hERG potassium ion channel as an example, with the aim of providing an alternative modelling approach that could alleviate certain limitations of the traditional approach. We compare and discuss multiple ways of using a neural network to approximate extra hidden states or alternative transition rates. In particular we assess their ability to learn the missing dynamics, and ask whether we can use these models to handle model discrepancy. Finally, we discuss the practicality and limitations of using neural networks and their potential applications.

Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system’s behaviour changes over time. A key problem in time series modelling is inference; determining properties of the underlying system based on observed time series. For both statistical and mechanistic models, inference involves finding parameter values, or distributions of parameters values, which produce outputs consistent with observations. A wide variety of inference techniques are available and different approaches are suitable for different classes of problems. This variety presents a challenge for researchers, who may not have the resources or expertise to implement and experiment with these methods. PINTS (Probabilistic Inference on Noisy Time Series — https://github.com/pints-team/pints) is an open-source (BSD 3-clause license) Python library that provides researchers with a broad suite of non-linear optimisation and sampling methods. It allows users to wrap a model and data in a transparent and straightforward interface, which can then be used with custom or pre-defined error measures for optimisation, or with likelihood functions for Bayesian inference or maximum-likelihood estimation. Derivative-free optimisation algorithms — which work without harder-to-obtain gradient information — are included, as well as inference algorithms such as adaptive Markov chain Monte Carlo and nested sampling, which estimate distributions over parameter values. By making these statistical techniques available in an open and easy-to-use framework, PINTS brings the power of these modern methods to a wider scientific audience. Funding statement: M.C., G.R.M. and D.J.G. acknowledge support from the UK Biotechnology and Biological Sciences Research Council [BBSRC grant number BB/P010008/1]; M.R., S.G. and D.J.G. gratefully acknowledge research support from the UK Engineering and Physical Sciences Research Council Cross-Disciplinary Interface Programme [EPSRC grant number EP/I017909/1]; C.L.L. acknowledges support from the Clarendon Scholarship Fund, the EPSRC and the UK Medical Research Council (MRC) [EPSRC grant number EP/L016044/1]; B.L. acknowledges support from the UK Engineering and Physical Sciences Research Council [EPSRC grant number EP/F500394/1]; and S.G. and G.R.M. acknowledge support from the Wellcome Trust & Royal Society [Wellcome Trust grant numbers 101222/Z/13/Z and 212203/Z/18/Z].

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

The mRNA vaccines (RVs) can reduce the severity and mortality of severe acute respiratory syndrome coronavirus (SARS-CoV-2). However, almost only the inactivated vaccines (IVs) but no RVs had been used in mainland China until most recently, and the relaxing of its anti-pandemic strategies in December 2022 increased concerns about new outbreaks. In comparison, many of the citizens in Macao Special Administrative Region of China received three doses of IV (3IV) or RV (3RV), or 2 doses of IV plus one booster of RV (2IV+1RV). By the end of 2022, we recruited 147 participants with various vaccinations in Macao and detected antibodies (Abs) against the spike (S) protein and nucleocapsid (N) protein of the virus as well as neutralizing antibodies (NAb) in their serum. We observed that the level of anti-S Ab or NAb was similarly high with both 3RV and 2IV+1RV but lower with 3IV. In contrast, the level of anti-N Ab was the highest with 3IV like that in convalescents, intermediate with 2IV+1RV, and the lowest with 3RV. Whereas no significant differences in the basal levels of cytokines related to T-cell activation were observed among the various vaccination groups before and after the boosters. No vaccinees reported severe adverse events. Since Macao took one of the most stringent non-pharmaceutical interventions in the world, this study possesses much higher confidence in the vaccination results than many other studies from highly infected regions. Our findings suggest that the heterologous vaccination 2IV+1RV outperforms the homologous vaccinations 3IV and 3RV as it induces not only anti-S Ab (to the level as with 3RV) but also anti-N antibodies (via the IV). It combines the advantages of both RV (to block the viral entry) and IV (to also intervene the subsequent pathological processes such as intracellular viral replication and interference with the signal transduction and hence the biological functions of host cells). [ABSTRACT FROM AUTHOR]

The mRNA vaccines (RVs) can reduce the severity and mortality of severe acute respiratory syndrome coronavirus (SARS-CoV-2). However, almost only the inactivated vaccines (IVs) but no RVs had been used in mainland China until most recently, and the relaxing of its anti-pandemic strategies in December 2022 increased concerns about new outbreaks. In comparison, many of the citizens in Macao Special Administrative Region of China received three doses of IV (3IV) or RV (3RV), or 2 doses of IV plus one booster of RV (2IV+1RV). By the end of 2022, we recruited 147 participants with various vaccinations in Macao and detected antibodies (Abs) against the spike (S) protein and nucleocapsid (N) protein of the virus as well as neutralizing antibodies (NAb) in their serum. We observed that the level of anti-S Ab or NAb was similarly high with both 3RV and 2IV+1RV but lower with 3IV. In contrast, the level of anti-N Ab was the highest with 3IV like that in convalescents, intermediate with 2IV+1RV, and the lowest with 3RV. Whereas no significant differences in the basal levels of cytokines related to T-cell activation were observed among the various vaccination groups before and after the boosters. No vaccinees reported severe adverse events. Since Macao took one of the most stringent non-pharmaceutical interventions in the world, this study possesses much higher confidence in the vaccination results than many other studies from highly infected regions. Our findings suggest that the heterologous vaccination 2IV+1RV outperforms the homologous vaccinations 3IV and 3RV as it induces not only anti-S Ab (to the level as with 3RV) but also anti-N antibodies (via the IV). It combines the advantages of both RV (to block the viral entry) and IV (to also intervene the subsequent pathological processes such as intracellular viral replication and interference with the signal transduction and hence the biological functions of host cells). [ABSTRACT FROM AUTHOR]

Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study, we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters. However, the origin of the observed variability was not identified. Here, we study the source of variability by constructing a model that describes not just ion current dynamics, but the entire voltage-clamp experiment. The experimental artefact components of the model include: series resistance, membrane and pipette capacitance, voltage offsets, imperfect compensations made by the amplifier for these phenomena, and leak current. In this model, variability in the observations can be explained by either cell properties, measurement artefacts, or both. Remarkably, by assuming that variability arises exclusively from measurement artefacts, it is possible to explain a larger amount of the observed variability than when assuming cell-specific ion current kinetics. This assumption also leads to a smaller number of model parameters. This result suggests that most of the observed variability in patch-clamp data measured under the same conditions is caused by experimental artefacts, and hence can be compensated for in post-processing by using our model for the patch-clamp experiment. This study has implications for the question of the extent to which cell-cell variability in ion channel kinetics exists, and opens up routes for better correction of artefacts in patch-clamp data. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-movingaverage models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. [ABSTRACT FROM AUTHOR]

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance.We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. [ABSTRACT FROM AUTHOR]