Machine learning in drug delivery

For decades, drug delivery scientists have been performing trial-and-error experimentation to manually sample parameter spaces and optimize release profiles through rational design. To enable this approach, scientists spend much of their career learning nuanced drug-material interactions that drive system behavior. In relatively simple systems, rational design criteria allow us to fine tune release profiles and enable efficacious therapies. However, as materials and drugs become increasingly sophisticated and their interactions have non-linear and compounding effects, the field is suffering the Curse of Dimensionality which prevents us from comprehending complex structure-function relationships. In the past, we have embraced this complexity by implementing high-throughput screens to increase the probability of finding ideal compositions. However, this brute force method was inefficient and led many to abandon these fishing expeditions. Fortunately, methods in data science including artificial intelligence / machine learning (AI/ML) are providing ideal analytical tools to model this complex data and ascertain quantitative structure-function relationships. In this Oration, I speak to the potential value of data science in drug delivery with particular focus on polymeric delivery systems. Here, I do not suggest that AI/ML will simply replace mechanistic understanding of complex systems. Rather, I propose that AI/ML should be yet another useful tool in the lab to navigate complex parameter spaces. The recent hype around AI/ML is breathtaking and potentially over inflated, but the value of these methods is poised to revolutionize how we perform science. Therefore, I encourage readers to consider adopting these skills and applying data science methods to their own problems. If done successfully, I believe we will all realize a paradigm shift in our approach to drug delivery.

Perspectives on computational modeling of biological systems and the significance of the SysMod community

Motivation

In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems.

Results

In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences.

Availability and implementation

Additional information about SysMod is available at https://sysmod.info.

Building multiscale models with PhysiBoSS, an agent-based modeling tool

Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms - coupled with a graphical interface - is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

Imidacloprid-induced lung injury in mice: Activation of the PI3K/AKT/NF-κB signaling pathway via TLR4 receptor engagement

Significant impairment of pulmonary function has been demonstrated through long-term exposure to neonicotinoid insecticides, such as imidacloprid (IMI). However, the underlying mechanisms of lung injury induced by IMI remain unclear. In this study, a mouse model of IMI-induced pulmonary injury was established, and the toxicity and lung damage were assessed through mouse body weight, organ index, hematological parameters, and histopathological analysis of lung tissues. Furthermore, metabolomics and transcriptomics techniques were employed to explore the mechanistic aspects. Results from the toxicity assessments indicated that mouse body weight was significantly reduced by IMI, organ index was disturbed, and hematological parameters were disrupted, resulting in pulmonary injury. The mechanistic experimental results indicate that the differences in metabolites and gene expression in mouse lungs could be altered by IMI. Validation of the results through combined analysis of metabolomics and transcriptomics revealed that the mechanism by which IMI induces lung injury in mice might be associated with the activation of the TLR4 receptor, thereby activating the PI3K/AKT/NF-κB signaling pathway to induce inflammation in mouse lungs. This study provided valuable insights into the mechanisms underlying IMI-induced pulmonary damage, potentially contributing to the development of safer pest control strategies. The knowledge gained served as a robust scientific foundation for the prevention and treatment of IMI-related pulmonary injuries.

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the fields of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.

Piezo regulates epithelial topology and promotes precision in organ size control

Mechanosensitive Piezo channels regulate cell division, cell extrusion, and cell death. However, systems-level functions of Piezo in regulating organogenesis remain poorly understood. Here, we demonstrate that Piezo controls epithelial cell topology to ensure precise organ growth by integrating live-imaging experiments with pharmacological and genetic perturbations and computational modeling. Notably, the knockout or knockdown of Piezo increases bilateral asymmetry in wing size. Piezo's multifaceted functions can be deconstructed as either autonomous or non-autonomous based on a comparison between tissue-compartment-level perturbations or between genetic perturbation populations at the whole-tissue level. A computational model that posits cell proliferation and apoptosis regulation through modulation of the cutoff tension required for Piezo channel activation explains key cell and tissue phenotypes arising from perturbations of Piezo expression levels. Our findings demonstrate that Piezo promotes robustness in regulating epithelial topology and is necessary for precise organ size control.

Immunoregulatory role of platelet derivatives in the macrophage-mediated immune response

Background

Macrophages are innate immune cells that display remarkable phenotypic heterogeneity and functional plasticity. Due to their involvement in the pathogenesis of several human conditions, macrophages are considered to be an attractive therapeutic target. In line with this, platelet derivatives have been successfully applied in many medical fields and as active participants in innate immunity, cooperation between platelets and macrophages is essential. In this context, the aim of this review is to compile the current evidence regarding the effects of platelet derivatives on the phenotype and functions of macrophages to identify the advantages and shortcomings for feasible future clinical applications.

Methods

A total of 669 articles were identified during the systematic literature search performed in PubMed and Web of Science databases.

Results

A total of 27 articles met the inclusion criteria. Based on published findings, platelet derivatives may play an important role in inducing a dynamic M1/M2 balance and promoting a timely M1-M2 shift. However, the differences in procedures regarding platelet derivatives and macrophages polarization and the occasional lack of information, makes reproducibility and comparison of results extremely challenging. Furthermore, understanding the differences between human macrophages and those derived from animal models, and taking into account the peculiarities of tissue resident macrophages and their ontogeny seem essential for the design of new therapeutic strategies.

Conclusion

Research on the combination of macrophages and platelet derivatives provides relevant information on the function and mechanisms of the immune response.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Molecular Engineering of Functional SiRNA Agents

Synthetic biology constitutes a scientific domain focused on intentional redesign of organisms to confer novel functionalities or create new products through strategic engineering of their genetic makeup. Leveraging the inherent capabilities of nature, one may address challenges across diverse sectors including medicine. Inspired by this concept, we have developed an innovative bioengineering platform, enabling high-yield and large-scale production of biological small interfering RNA (BioRNA/siRNA) agents via bacterial fermentation. Herein, we show that with the use of a new tRNA fused pre-miRNA carrier, we can produce various forms of BioRNA/siRNA agents within living host cells. We report a high-level overexpression of nine target BioRNA/siRNA molecules at 100% success rate, yielding 3-10 mg of BioRNA/siRNA per 0.25 L of bacterial culture with high purity (>98%) and low endotoxin (<5 EU/μg RNA). Furthermore, we demonstrate that three representative BioRNA/siRNAs against GFP, BCL2, and PD-L1 are biologically active and can specifically and efficiently silence their respective targets with the potential to effectively produce downstream antiproliferation effects by PD-L1-siRNA. With these promising results, we aim to advance the field of synthetic biology by offering a novel platform to bioengineer functional siRNA agents for research and drug development.

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides in modifier-like DNA sequences. With a convolutional neural network structure, PDCNN employs dual convolutional and fully connected layers. The cross-entropy loss function iteratively updates using a gradient descent algorithm, enhancing prediction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis with traditional methods and existing models demonstrates PDCNN's robust feature extraction capability. It outperforms advanced machine learning methods in identifying DNA enhancers, presenting an effective method with broad implications for genomics, biology, and medical research.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

In silico transcriptome screens identify epidermal growth factor receptor inhibitors as therapeutics for noise-induced hearing loss

Noise-induced hearing loss (NIHL) is a common sensorineural hearing impairment that lacks U.S. Food and Drug Administration-approved drugs. To fill the gap in effective screening models, we used an in silico transcriptome-based drug screening approach, identifying 22 biological pathways and 64 potential small molecule treatments for NIHL. Two of these, afatinib and zorifertinib [epidermal growth factor receptor (EGFR) inhibitors], showed efficacy in zebrafish and mouse models. Further tests with EGFR knockout mice and EGF-morpholino zebrafish confirmed their protective role against NIHL. Molecular studies in mice highlighted EGFR's crucial involvement in NIHL and the protective effect of zorifertinib. When given orally, zorifertinib was found in the perilymph with favorable pharmacokinetics. In addition, zorifertinib combined with AZD5438 (a cyclin-dependent kinase 2 inhibitor) synergistically prevented NIHL in zebrafish. Our results underscore the potential for in silico transcriptome-based drug screening in diseases lacking efficient models and suggest EGFR inhibitors as potential treatments for NIHL, meriting clinical trials.

Numerical modeling of senile plaque development under conditions of limited diffusivity of amyloid-β monomers

This paper introduces a new model to simulate the progression of senile plaques, focusing on scenarios where concentrations of amyloid beta (Aβ) monomers and aggregates vary between neurons. Extracellular variations in these concentrations may arise due to limited diffusivity of Aβ monomers and a high rate of Aβ monomer production at lipid membranes, requiring a substantial concentration gradient for diffusion-driven transport of Aβ monomers. The dimensionless formulation of the model is presented, which identifies four key dimensionless parameters governing the solutions for Aβ monomer and aggregate concentrations, as well as the radius of a growing Aβ plaque within the control volume. These parameters include the dimensionless diffusivity of Aβ monomers, the dimensionless rate of Aβ monomer production, and the dimensionless half-lives of Aβ monomers and aggregates. A dimensionless parameter is then introduced to evaluate the validity of the lumped capacitance approximation. An approximate solution is derived for the scenario involving large diffusivity of Aβ monomers and dysfunctional protein degradation machinery, resulting in infinitely long half-lives for Aβ monomers and aggregates. In this scenario, the concentrations of Aβ aggregates and the radius of the Aβ plaque depend solely on a single dimensionless parameter that characterizes the rate of Aβ monomer production. According to the approximate solution, the concentration of Aβ aggregates is linearly dependent on the rate of monomer production, and the radius of an Aβ plaque is directly proportional to the cube root of the rate of monomer production. However, when departing from the conditions of the approximate solution (e.g., finite half-lives), the concentrations of Aβ monomers and aggregates, along with the plaque radius, exhibit complex dependencies on all four dimensionless parameters. For instance, under physiological half-life conditions, the plaque radius reaches a maximum value and stabilizes thereafter.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Exploring the safety of cannabidiol (CBD): A comprehensive in vitro evaluation of the genotoxic and mutagenic potential of a CBD isolate and extract from Cannabis sativa L

Cannabidiol (CBD), a naturally occurring cyclic terpenoid found in Cannabis sativa L., is renowned for its diverse pharmacological benefits. Marketed as a remedy for various health issues, CBD products are utilized by patients as a supplementary therapy or post-treatment failure, as well as by healthy individuals seeking promised advantages. Despite its widespread use, information regarding potential adverse effects, especially genotoxic properties, is limited. The present study is focused on the mutagenic and genotoxic activity of a CBD isolate (99.4 % CBD content) and CBD-rich Cannabis sativa L extract (63.6 % CBD content) in vitro. Both CBD samples were non-mutagenic, as determined by the AMES test (OECD 471) but exhibited cytotoxicity for HepG2 cells (∼IC50(4 h) 26 µg/ml, ∼IC50(24 h) 6-8 µg/ml, MTT assay). Noncytotoxic concentrations induced upregulation of genes encoding metabolic enzymes involved in CBD metabolism, and CBD oxidative as well as glucuronide metabolites were found in cell culture media, demonstrating the ability of HepG2 cells to metabolize CBD. In this study, the CBD samples were found non-genotoxic. No DNA damage was observed with the comet assay, and no influence on genomic instability was observed with the cytokinesis block micronucleus and the γH2AX and p-H3 assays. Furthermore, no changes in the expression of genes involved in genotoxic stress response were detected in the toxicogenomic analysis, after 4 and 24 h of exposure. Our comprehensive study contributes valuable insights into CBD's safety profile, paving the way for further exploration of CBD's therapeutic applications and potential adverse effects.

RNA-binding proteins in bone pathophysiology

Bone remodelling is a highly regulated process that maintains mineral homeostasis and preserves bone integrity. During this process, intricate communication among all bone cells is required. Indeed, adapt to changing functional situations in the bone, the resorption activity of osteoclasts is tightly balanced with the bone formation activity of osteoblasts. Recent studies have reported that RNA Binding Proteins (RBPs) are involved in bone cell activity regulation. RBPs are critical effectors of gene expression and essential regulators of cell fate decision, due to their ability to bind and regulate the activity of cellular RNAs. Thus, a better understanding of these regulation mechanisms at molecular and cellular levels could generate new knowledge on the pathophysiologic conditions of bone. In this Review, we provide an overview of the basic properties and functions of selected RBPs, focusing on their physiological and pathological roles in the bone.

Diesel exhaust particle extract elicits an oxPAPC-like transcriptomic profile in macrophages across multiple mouse strains

Air pollution is a prominent cause of cardiopulmonary illness, but uncertainties remain regarding the mechanisms mediating those effects as well as individual susceptibility. Macrophages are highly responsive to particles, and we hypothesized that their responses would be dependent on their genetic backgrounds. We conducted a genome-wide analysis of peritoneal macrophages harvested from 24 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP). Cells were treated with a DEP methanol extract (DEPe) to elucidate potential pathways that mediate acute responses to air pollution exposures. This analysis showed that 1247 genes were upregulated and 1383 genes were downregulated with DEPe treatment across strains. Pathway analysis identified oxidative stress responses among the most prominent upregulated pathways; indeed, many of the upregulated genes included antioxidants such as Hmox1, Txnrd1, Srxn1, and Gclm, with NRF2 (official gene symbol: Nfe2l2) being the most significant driver. DEPe induced a Mox-like transcriptomic profile, a macrophage subtype typically induced by oxidized phospholipids and likely dependent on NRF2 expression. Analysis of individual strains revealed consistency of overall responses to DEPe and yet differences in the degree of Mox-like polarization across the various strains, indicating DEPe × genetic interactions. These results suggest a role for macrophage polarization in the cardiopulmonary toxicity induced by air pollution.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Molecular causality in the advent of foundation models

Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Spatial heterogeneity in tumor adhesion qualifies collective cell invasion

Collective cell invasion (CCI), a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal invasion. Here, we constitute a tumor core of two separate cell subsets showing distinct intra- and inter-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). We observe that low and high inter-subset cell adhesion favors invasion of high-HI and low-HI intermingled populations with distinct intra-subset cell-cell adhesion strengths, respectively. In addition, for explored values of cell-ECM adhesion strengths, populations with high HI values collectively invade better than those with lower HI values. We then asked how spatial invasion is regulated by progressively intermingled cellular subsets that are epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and invasion. An exception to this relationship is seen for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively invade better than their relatively higher-HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies collective invasion, when either cell-cell or cell-ECM adhesion type is varied but results in an invasive dispersion when both adhesion types are simultaneously altered.

Gene Regulatory Programs that Specify Age-Related Differences during Thymocyte Development

T cell development is fundamental to immune system establishment, yet how this development changes with age remains poorly understood. Here, we construct a transcriptional and epigenetic atlas of T cell developmental programs in neonatal and adult mice, revealing the ontogeny of divergent gene regulatory programs and their link to age-related differences in phenotype and function. Specifically, we identify a gene module that diverges with age from the earliest stages of genesis and includes programs that govern effector response and cell cycle regulation. Moreover, we reveal that neonates possess more accessible chromatin during early thymocyte development, likely establishing poised gene expression programs that manifest later in thymocyte development. Finally, we leverage this atlas, employing a CRISPR-based perturbation approach coupled with single-cell RNA sequencing as a readout to uncover a conserved transcriptional regulator, Zbtb20, that contributes to age-dependent differences in T cell development. Altogether, our study defines transcriptional and epigenetic programs that regulate age-specific differences in T cell development.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Integration analysis of transcriptome and proteome profiles brings new insights of somatic embryogenesis of two eucalyptus species

BACKGROUND

Somatic embryogenesis (SE) is recognized as a promising technology for plant vegetative propagation. Although previous studies have identified some key regulators involved in the SE process in plant, our knowledge about the molecular changes in the SE process and key regulators associated with high embryogenic potential is still poor, especially in the important fiber and energy source tree - eucalyptus.

RESULTS

In this study, we analyzed the transcriptome and proteome profiles of E. camaldulensis (with high embryogenic potential) and E. grandis x urophylla (with low embryogenic potential) in SE process: callus induction and development. A total of 12,121 differentially expressed genes (DEGs) and 3,922 differentially expressed proteins (DEPs) were identified in the SE of the two eucalyptus species. Integration analysis identified 1,353 (131 to 546) DEGs/DEPs shared by the two eucalyptus species in the SE process, including 142, 13 and 186 DEGs/DEPs commonly upregulated in the callus induction, maturation and development, respectively. Further, we found that the trihelix transcription factor ASR3 isoform X2 was commonly upregulated in the callus induction of the two eucalyptus species. The SOX30 and WRKY40 TFs were specifically upregulated in the callus induction of E. camaldulensis. Three TFs (bHLH62, bHLH35 isoform X2, RAP2-1) were specifically downregulated in the callus induction of E. grandis x urophylla. WGCNA identified 125 and 26 genes/proteins with high correlation (Pearson correlation > 0.8 or < -0.8) with ASR3 TF in the SE of E. camaldulensis and E. grandis x urophylla, respectively. The potential target gene expression patterns of ASR3 TF were then validated using qRT-PCR in the material.

CONCLUSIONS

This is the first time to integrate multiple omics technologies to study the SE of eucalyptus. The findings will enhance our understanding of molecular regulation mechanisms of SE in eucalyptus. The output will also benefit the eucalyptus breeding program.

An updated model of shoot apical meristem regulation by ERECTA family and CLAVATA3 signaling pathways in Arabidopsis

The shoot apical meristem (SAM) gives rise to the aboveground organs of plants. The size of the SAM is relatively constant due to the balance between stem cell replenishment and cell recruitment into new organs. In angiosperms, the transcription factor WUSCHEL (WUS) promotes stem cell proliferation in the central zone of the SAM. WUS forms a negative feedback loop with a signaling pathway activated by CLAVATA3 (CLV3). In the periphery of the SAM, the ERECTA family receptors (ERfs) constrain WUS and CLV3 expression. Here, we show that four ligands of ERfs redundantly inhibit the expression of these two genes. Transcriptome analysis confirmed that WUS and CLV3 are the main targets of ERf signaling and uncovered new ones. Analysis of promoter reporters indicated that the WUS expression domain mostly overlaps with the CLV3 domain and does not shift along the apical-basal axis in clv3 mutants. Our three-dimensional mathematical model captured gene expression distributions at the single-cell level under various perturbed conditions. Based on our findings, CLV3 regulates cellular levels of WUS mostly through autocrine signaling, and ERfs regulate the spatial expression of WUS, preventing its encroachment into the peripheral zone.

Integrated sequence-based genomic, transcriptomic, and methylation characterization of the susceptibility to tuberculosis in monozygous twins

Background

Tuberculosis (TB) is a complex disease with a spectrum of outcomes for more than six decades; however, the genomic and epigenetic mechanisms underlying the highly heritable susceptibility to TB remain unclear.

Methods

Integrated sequence-based genomic, transcriptomic, and methylation analyses were conducted to identity the genetic factors associated with susceptibility to TB in two pairs of Mongolian monozygous twins. In this study, whole-genome sequencing was employed to analyze single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), and copy number variations (CNVs). Gene expression was assessed through RNA sequencing, and methylation patterns were examined using the Illumina Infinium Methylation EPIC BeadChip. The gene-gene interaction network was analyzed using differentially expressed genes.

Results

Our study revealed no significant difference in SNP and InDel profiles between participants with and without TB. Genes with CNVs were involved in human immunity (human leukocyte antigen [HLA] family and interferon [IFN] pathway) and the inflammatory response. Different DNA methylation patterns and mRNA expression profiles were observed in genes participating in immunity (HLA family) and inflammatory responses (IFNA, interleukin 10 receptor [IL-10R], IL-12B, Toll-like receptor, and IL-1B).

Conclusions

The results of this study suggested that susceptibility to TB is associated with transcriptional and epigenetic alternations of genes involved in immune and inflammatory responses. The genes in the HLA family (HLA-A, HLA-B, and HLA-DRB1) and IFN pathway (IFN-α and IFN-γ) may play major roles in susceptibility to TB.

Canalization reduces the nonlinearity of regulation in biological networks

Biological networks, such as gene regulatory networks, possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated into biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased, and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we compare published Boolean biological network models with different ensembles of null models and show that the abundance of canalization in biological networks can almost completely explain their recently postulated high approximability. Moreover, an analysis of random N-K Kauffman models reveals a strong dependence of approximability on the dynamical robustness of a network.

PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks

BACKGROUND

In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks.

RESULTS

Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose.

CONCLUSIONS

PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability.

Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies

BACKGROUND

Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting.

METHODS

We sought to bridge this gap by linking preclinical data in a porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation.

RESULTS

The mathematical model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time.

CONCLUSIONS

This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.

Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.

Recent Advances in Molecular and Genetic Research on Uveal Melanoma

Uveal melanoma (UM), a distinct subtype of melanoma, presents unique challenges in its clinical management due to its complex molecular landscape and tendency for liver metastasis. This review highlights recent advancements in understanding the molecular pathogenesis, genetic alterations, and immune microenvironment of UM, with a focus on pivotal genes, such as GNAQ/11, BAP1, and CYSLTR2, and delves into the distinctive genetic and chromosomal classifications of UM, emphasizing the role of mutations and chromosomal rearrangements in disease progression and metastatic risk. Novel diagnostic biomarkers, including circulating tumor cells, DNA and extracellular vesicles, are discussed, offering potential non-invasive approaches for early detection and monitoring. It also explores emerging prognostic markers and their implications for patient stratification and personalized treatment strategies. Therapeutic approaches, including histone deacetylase inhibitors, MAPK pathway inhibitors, and emerging trends and concepts like CAR T-cell therapy, are evaluated for their efficacy in UM treatment. This review identifies challenges in UM research, such as the limited treatment options for metastatic UM and the need for improved prognostic tools, and suggests future directions, including the discovery of novel therapeutic targets, immunotherapeutic strategies, and advanced drug delivery systems. The review concludes by emphasizing the importance of continued research and innovation in addressing the unique challenges of UM to improve patient outcomes and develop more effective treatment strategies.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction

Introduction

Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction.

Methods

By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy.

Results

By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset.

Conclusion

In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Targeting the RAS upstream and downstream signaling pathway for Cancer treatment

Cancer often involves the overactivation of RAS/RAF/MEK/ERK (MAPK) and PI3K-Akt-mTOR pathways due to mutations in genes like RAS, RAF, PTEN, and PIK3CA. Various strategies are employed to address the overactivation of these pathways, among which targeted therapy emerges as a promising approach. Directly targeting specific proteins, leads to encouraging results in cancer treatment. For instance, RTK inhibitors such as imatinib and afatinib selectively target these receptors, hindering ligand binding and reducing signaling initiation. These inhibitors have shown potent efficacy against Non-Small Cell Lung Cancer. Other inhibitors, like lonafarnib targeting Farnesyltransferase and GGTI 2418 targeting geranylgeranyl Transferase, disrupt post-translational modifications of proteins. Additionally, inhibition of proteins like SOS, SH2 domain, and Ras demonstrate promising anti-tumor activity both in vivo and in vitro. Targeting downstream components with RAF inhibitors such as vemurafenib, dabrafenib, and sorafenib, along with MEK inhibitors like trametinib and binimetinib, has shown promising outcomes in treating cancers with BRAF-V600E mutations, including myeloma, colorectal, and thyroid cancers. Furthermore, inhibitors of PI3K (e.g., apitolisib, copanlisib), AKT (e.g., ipatasertib, perifosine), and mTOR (e.g., sirolimus, temsirolimus) exhibit promising efficacy against various cancers such as Invasive Breast Cancer, Lymphoma, Neoplasms, and hematological malignancies. This review offers an overview of small molecule inhibitors targeting specific proteins within the RAS upstream and downstream signaling pathways in cancer.

Effect of alternating nicotinamide phosphoribosyltransferase expression levels on mitophagy in Alzheimer's disease mouse models

AD is the abbreviation for Alzheimer's Disease, which is a neurodegenerative disorder that features progressive dysfunction in cognition. Previous research has reported that mitophagy impairment and mitochondrial dysfunction have been crucial factors in the AD's pathogenesis. More recently, literature has emerged which offers findings suggesting that the nicotinamide adenine dinucleotide (short for NAD+) augmentation eliminates the defective mitochondria and restores mitophagy. Meanwhile, as an enzyme which is rate-limiting, the Nicotinamide phosphoribosyltransferase, or NAMPT, is part of the salvage pathway of NAD+ synthesis. Therefore, the aim of the research project has been to produce proof for how the NAMPT-NAD +-silent information-regulated transcription factors1/3 (short for SIRT1/3) axis function in mediating mitophagy in APP/PS1 mice aged six months. The results revealed that the NAMPT-NAD+-SIRT1/3 axis in the APP/PS1 mice's hippocampus was considerably declined. Surprisingly, P7C3 (an NAMPT activator) noticeably promoted the NAD+-SIRT1/3 axis, improved mitochondrial structure and function, enhanced mitophagy activity along with the ability of learning and memory. While FK866 (an NAMPT inhibitor) reversed the decreased NAD+-SIRT1/3 axis, and even exacerbated Aβ plaque deposition level in the APP/PS1 mice's hippocampus. The findings observed in this study indicate two main points: avoiding downregulation of the NAMPT activity can prevent AD-related mitophagy impairment; on the other hand, NAMPT characterizes a potential therapeutic intervention regarding AD pathogenesis.

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model

Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.

Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes

The shape of a cell as defined by its membrane can be closely associated with its physiological state. For example, the irregular shapes of cancerous cells and elongated shapes of neuron cells often reflect specific functions, such as cell motility and cell communication. However, it remains unclear whether and which cell shape descriptors can characterize different cellular physiological states. In this study, 12 geometric shape descriptors for a three-dimensional (3D) object were collected from the previous literature and tested with a public dataset of ~400,000 independent 3D cell regions segmented based on fluorescent labeling of the cell membranes in Caenorhabditis elegans embryos. It is revealed that those shape descriptors can faithfully characterize cellular physiological states, including (1) cell division (cytokinesis), along with an abrupt increase in the elongation ratio; (2) a negative correlation of cell migration speed with cell sphericity; (3) cell lineage specification with symmetrically patterned cell shape changes; and (4) cell fate specification with differential gene expression and differential cell shapes. The descriptors established may be used to identify and predict the diverse physiological states in numerous cells, which could be used for not only studying developmental morphogenesis but also diagnosing human disease (e.g., the rapid detection of abnormal cells).

Bayesian approach enabled objective comparison of multiple human iPSC-derived Cardiomyocytes' Proarrhythmia sensitivities

The one-size-fits-all approach has been the mainstream in medicine, and the well-defined standards support the development of safe and effective therapies for many years. Advancing technologies, however, enabled precision medicine to treat a targeted patient population (e.g., HER2+ cancer). In safety pharmacology, computational population modeling has been successfully applied in virtual clinical trials to predict drug-induced proarrhythmia risks against a wide range of pseudo cohorts. In the meantime, population modeling in safety pharmacology experiments has been challenging. Here, we used five commercially available human iPSC-derived cardiomyocytes growing in 384-well plates and analyzed the effects of ten potential proarrhythmic compounds with four concentrations on their calcium transients (CaTs). All the cell lines exhibited an expected elongation or shortening of calcium transient duration with various degrees. Depending on compounds inhibiting several ion channels, such as hERG, peak and late sodium and L-type calcium or IKs channels, some of the cell lines exhibited irregular, discontinuous beating that was not predicted by computational simulations. To analyze the shapes of CaTs and irregularities of beat patterns comprehensively, we defined six parameters to characterize compound-induced CaT waveform changes, successfully visualizing the similarities and differences in compound-induced proarrhythmic sensitivities of different cell lines. We applied Bayesian statistics to predict sample populations based on experimental data to overcome the limited number of experimental replicates in high-throughput assays. This process facilitated the principal component analysis to classify compound-induced sensitivities of cell lines objectively. Finally, the association of sensitivities in compound-induced changes between phenotypic parameters and ion channel inhibitions measured using patch clamp recording was analyzed. Successful ranking of compound-induced sensitivity of cell lines was in lined with visual inspection of raw data.

Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms

Parameter optimization (PO) methods to determine the ionic current composition of experimental cardiac action potential (AP) waveform have been developed using a computer model of cardiac membrane excitation. However, it was suggested that fitting a single AP record in the PO method was not always successful in providing a unique answer because of a shortage of information. We found that the PO method worked perfectly if the PO method was applied to a pair of a control AP and a model output AP in which a single ionic current out of six current species, such as IKr, ICaL, INa, IKs, IKur or IbNSC was partially blocked in silico. When the target was replaced by a pair of experimental control and IKr-blocked records of APs generated spontaneously in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), the simultaneous fitting of the two waveforms by the PO method was hampered to some extent by the irregular slow fluctuations in the Vm recording and/or sporadic alteration in AP configurations in the hiPSC-CMs. This technical problem was largely removed by selecting stable segments of the records for the PO method. Moreover, the PO method was made fail-proof by running iteratively in identifying the optimized parameter set to reconstruct both the control and the IKr-blocked AP waveforms. In the lead potential analysis, the quantitative ionic mechanisms deduced from the optimized parameter set were totally consistent with the qualitative view of ionic mechanisms of AP so far described in physiological literature.

Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer

To delineate the mechanisms by which the ERK1 and ERK2 mitogen-activated protein kinases support mutant KRAS-driven cancer growth, we determined the ERK-dependent phosphoproteome in KRAS-mutant pancreatic cancer. We determined that ERK1 and ERK2 share near-identical signaling and transforming outputs and that the KRAS-regulated phosphoproteome is driven nearly completely by ERK. We identified 4666 ERK-dependent phosphosites on 2123 proteins, of which 79 and 66%, respectively, were not previously associated with ERK, substantially expanding the depth and breadth of ERK-dependent phosphorylation events and revealing a considerably more complex function for ERK in cancer. We established that ERK controls a highly dynamic and complex phosphoproteome that converges on cyclin-dependent kinase regulation and RAS homolog guanosine triphosphatase function (RHO GTPase). Our findings establish the most comprehensive molecular portrait and mechanisms by which ERK drives KRAS-dependent pancreatic cancer growth.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Gene Expression Analysis of Yeast Strains with a Nonsense Mutation in the eRF3-Coding Gene Highlights Possible Mechanisms of Adaptation

In yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 (Sup45) and eRF3 (Sup35), which are essential for viability. Previous studies have revealed that presence of nonsense mutations in these genes leads to amplification of mutant alleles (sup35-n and sup45-n), which appears to be necessary for the viability of such cells. However, the mechanism of this phenomenon remained unclear. In this study, we used RNA-Seq and proteome analysis to reveal the complete set of gene expression changes that occur during cellular adaptation to the introduction of the sup35-218 nonsense allele. Our analysis demonstrated significant changes in the transcription of genes that control the cell cycle: decreases in the expression of genes of the anaphase promoting complex APC/C (APC9, CDC23) and their activator CDC20, and increases in the expression of the transcription factor FKH1, the main cell cycle kinase CDC28, and cyclins that induce DNA biosynthesis. We propose a model according to which yeast adaptation to nonsense mutations in the translation termination factor genes occurs as a result of a delayed cell cycle progression beyond the G2-M stage, which leads to an extension of the S and G2 phases and an increase in the number of copies of the mutant sup35-n allele.

Neutrophil Extracellular Traps: A Crucial Factor in Post-Surgical Abdominal Adhesion Formation

Post-surgical abdominal adhesions, although poorly understood, are highly prevalent. The molecular processes underlying their formation remain elusive. This review aims to assess the relationship between neutrophil extracellular traps (NETs) and the generation of postoperative peritoneal adhesions and to discuss methods for mitigating peritoneal adhesions. A keyword or medical subject heading (MeSH) search for all original articles and reviews was performed in PubMed and Google Scholar. It included studies assessing peritoneal adhesion reformation after abdominal surgery from 2003 to 2023. After assessing for eligibility, the selected articles were evaluated using the Critical Appraisal Skills Programme checklist for qualitative research. The search yielded 127 full-text articles for assessment of eligibility, of which 7 studies met our criteria and were subjected to a detailed quality review using the Critical Appraisal Skills Programme (CASP) checklist. The selected studies offer a comprehensive analysis of adhesion pathogenesis with a special focus on the role of neutrophil extracellular traps (NETs) in the development of peritoneal adhesions. Current interventional strategies are examined, including the use of mechanical barriers, advances in regenerative medicine, and targeted molecular therapies. In particular, this review emphasizes the potential of NET-targeted interventions as promising strategies to mitigate postoperative adhesion development. Evidence suggests that in addition to their role in innate defense against infections and autoimmune diseases, NETs also play a crucial role in the formation of peritoneal adhesions after surgery. Therefore, therapeutic strategies that target NETs are emerging as significant considerations for researchers. Continued research is vital to fully elucidate the relationship between NETs and post-surgical adhesion formation to develop effective treatments.

Serum and Urine Metabolites and Kidney Function

Key Points

Background

Metabolites represent a read-out of cellular processes underlying states of health and disease.

Methods

We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.

Results

At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5).

Conclusions

Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation

Background

With a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD).

Methods

Phenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis.

Results

Included in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased.

Conclusion

H. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1.

Improved reference quality genome sequence of the plastic-degrading greater wax moth, Galleria mellonella

Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research. This paper presents an improved genome (99.3% completed lepidoptera_odb10 BUSCO; genome mode) for G. mellonella. This 472 Mb genome is in 221 contigs with an N50 of 6.4 Mb and contains 13,604 protein-coding genes. Genes that code for known and putative polyethylene-degrading enzymes and their similarity to proteins found in other Lepidoptera are highlighted. An analysis of secretory proteins more likely to be involved in the plastic catabolic process has also been carried out.