Smad4 regulates TGF-β1-mediated hedgehog activation to promote epithelial-to-mesenchymal transition in pancreatic cancer cells by suppressing Gli1 activity

Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-β1-triggered EMT in PCCs (PANC-1 and BxPc-3). In addition, overexpression and shRNA techniques were used to evaluate the role of Smad4 in TGF-β1-treated PCCs. Our data showed that TGF-β1 promoted PCC invasion and infiltration via Smad2/3-dependent EMT. Hedgehog-Gli signaling axis in PCCs was activated upon TGF-β1 stimulation. Inhibition of hedgehog with cyclopamine effectively antagonized TGF-β1-induced EMT, thereby suggesting that the hedgehog signaling may act as a downstream cascade signaling of TGF-β1. As a key protein that assists the nuclear translocation of Smad2/3, Smad4 was highly expressed in PANC-1 cells, but not in BxPc-3 cells. Conversely, Gli1 expression was low in PANC-1 cells, but high in BxPc-3 cells. Furthermore, knockdown of Smad4 in PANC-1 cells by shRNA inhibited TGF-β1-mediated EMT and collagen deposition. Overexpression of Smad4 did not affect TGF-β1-mediated EMT due to the lack of significant increase in nuclear expression of Smad4. Importantly, Gli1 activity was upregulated by Smad4 knockdown in PANC-1 cells and downregulated by Smad4 overexpression in BxPc-3 cells, indicating that Gli1 may be a negative target protein downstream of Smad4. Thus, Smad4 regulates TGF-β1-mediated hedgehog activation to promote EMT in PCCs by suppressing Gli1 activity.

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Using methylation data to improve transcription factor binding prediction

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

CFSA: Comparative flux sampling analysis as a guide for strain design

Genome-scale metabolic models of microbial metabolism have extensively been used to guide the design of microbial cell factories, still, many of the available strain design algorithms often fail to produce a reduced list of targets for improved performance that can be implemented and validated in a step-wise manner. We present Comparative Flux Sampling Analysis (CFSA), a strain design method based on the extensive comparison of complete metabolic spaces corresponding to maximal or near-maximal growth and production phenotypes. The comparison is complemented by statistical analysis to identify reactions with altered flux that are suggested as targets for genetic interventions including up-regulations, down-regulations and gene deletions. We applied CFSA to the production of lipids by Cutaneotrichosporon oleaginosus and naringenin by Saccharomyces cerevisiae identifying engineering targets in agreement with previous studies as well as new interventions. CFSA is an easy-to-use, robust method that suggests potential metabolic engineering targets for growth-uncoupled production that can be applied to the design of microbial cell factories.

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Regulation of AreA on lipid biosynthesis under different nitrogen sources and C/N ratios in the model oleaginous fungus Mucor circinelloides

Mucor circinelloides has been exploited as model filamentous fungi for studies of genetic manipulation of lipogenesis. It is widely recognized that lipid accumulation is increased when there is a lack of nitrogen source in oleaginous microorganism. Nitrogen metabolism in filamentous fungi is a complex process that can be regulated by the global nitrogen regulator AreA. In this study, we cultivated the areA-knockout and -overexpression strains obtained in our previous study, using 20 different nitrogen sources. It emerged that the disruption of AreA in M. circinelloides reduced its sensitivity to nitrogen availability, resulting in increased lipid synthesis. Specially, the areA-knockout strain was unable to fully utilize many nitrogen sources but the ammonium and glutamate. We continued to investigate lipid production at different molar C/N ratios using glucose as sole carbon source and ammonium sulfate as sole nitrogen source, of which the high C/N ratios activate high lipid accumulation. By comparing the experimental results with transcriptional analysis, we were able to identify the optimal process conditions suitable for lipid accumulation and potential targets for future metabolic engineering.

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.

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.

Inflammatory gene regulation by Cdc42 in airway epithelial cells

Cytokine release from airway epithelial cells is a key immunological process that coordinates an immune response in the lungs. We propose that the Rho GTPase, Cdc42, regulates both transcription and trafficking of cytokines, ultimately affecting the essential process of cytokine release and subsequent inflammation in the lungs. Here, we examined the pro-inflammatory transcriptional profile that occurs in bronchial epithelial cells (BEAS-2B) in response to TNF-α using RNA-Seq and differential gene expression analysis. To interrogate the role of Cdc42 in inflammatory gene expression, we used a pharmacological inhibitor of Cdc42, ML141, and determined changes in the transcriptomic profile induced by Cdc42 inhibition. Our results indicated that Cdc42 inhibition with ML141 resulted in a unique inflammatory phenotype concomitant with increased gene expression of ER stress genes, Golgi membrane and vesicle transport genes. To further interrogate the inflammatory pathways regulated by Cdc42, we made BEAS-2B knockdown strains for the signaling targets TRIB3, DUSP5, SESN2 and BMP4, which showed high differential expression in response to Cdc42 inhibition. Depletion of DUSP5 and TRIB3 reduced the pro-inflammatory response triggered by Cdc42 inhibition as shown by a reduction in cytokine transcript levels. Depletion of SESN2 and BMP4 did not affect cytokine transcript level, however, Golgi fragmentation was reduced. These results provide further evidence that in airway epithelial cells, Cdc42 is part of a signaling network that controls inflammatory gene expression and secretion by regulating Golgi integrity. Summary sentence:We define the Cdc42-regulated gene networks for inflammatory signaling in airway epithelial cells which includes regulation of ER stress response and vesicle trafficking pathways.

Bioprobe-RNA-seq-microRaman system for deep tracking of the live single-cell metabolic pathway chemometrics

The integration between RNA-sequencing and micro-spectroscopic techniques has recently profiled the advanced transcriptomic discoveries on the cellular level. In the current study, by combining the sensation approach (including bio-molecules structural evaluation, high throughput next-generation sequencing (HT-NGS), and confocal Raman microscopy) the functionality on the single live cancer cells' ferroptosis and apoptosis signaling pathways is visualized. Our study reveals a hydrophobic tunnel by phycocyanin-isoprene molecule (PC-SIM) electrostatic charge within hepatoma cells (HepG2) that activates the ferritin light chain (FTL) and caspase-8 associate protein (CASP8AP2) ferroptosis responsible genes. Moreover, this research proves that PC-vanillin (VAN) stimulation induces the actin-binding factor profilin-1 (PFN1), subsequently in situ tracking its expression at 1139.75 cm-1 microRaman wavenumber. While PC-thymol (THY) induces the lysophospholipase-2 (LYPLA2) (p-value = 0.009) and acetylneuraminate-9-O-acetyltransferase (CASD1) (p-value = 0.022) at 1143.19 cm-1. Our findings establish a new concept to promote the cross-disciplinary use of instant cellular-based detection technology for intermediary evaluating the signaling cellular transcriptome.

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.

Protective effects and mechanism of Sangyu granule on acetaminophen-induced liver injury in mice

ETHNOPHARMACOLOGICAL RELEVANCE

The Sang Yu granule (SY), a traditional Chinese medicine prescription of Xijing Hospital, was developed based on the Guanyin powder in the classical prescription "Hong's Collection of Proven Prescriptions" and the new theory of modern Chinese medicine. It has been proved to have a certain therapeutic effect on drug-induced liver injury (DILI), but the specific mechanism of action is still unclear.

AIM OF STUDY

Aim of the study was to explore the effect of SangYu granule on treating drug-induced liver injury induced by acetaminophen in mice.

MATERIALS AND METHODS

The chemical composition of SY, serum, and liver tissue was analyzed using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. To assess hepatic function, measurements were taken using kits for total bile acids, as well as serum AST, ALT, and ALP activity. Concentrations of IL-1β and TNF-α in serum were quantified using ELISA kits. Transcriptome Sequencing Analysis and 2bRAD-M microbial diversity analysis were employed to evaluate gene expression variance in liver tissue and fecal microbiota diversity among different groups, respectively. Western blotting was performed to observe differences in the activation levels of FXR, SHP, CYP7A1 and PPARα in the liver, and the levels of FXR and FGF-15 genes and proteins in the ileum of mice. Additionally, fecal microbiota transplantation (FMT) experiments were conducted to investigate the potential therapeutic effect of administering the intestinal microbial suspension from mice treated with SY on drug-induced liver injury.

RESULTS

SY treatment exhibited significant hepatoprotective effects in mice, effectively ameliorating drug-induced liver injury while concurrently restoring intestinal microbial dysbiosis. Furthermore, SY administration demonstrated a reduction in the concentration of total bile acids, the expression of FXR and SHP proteins in the liver was up-regulated, CYP7A1 protein was down-regulated, and the expressions of FXR and FGF-15 proteins in the ileum were up-regulated. However, no notable impact on PPARα was observed. Furthermore, results from FMT experiments indicated that the administration of fecal suspensions derived from mice treated with SY did not yield any therapeutic benefits in the context of drug-induced liver injury.

CONCLUSION

The aforementioned findings strongly suggest that SY exerts a pronounced ameliorative effect on drug-induced liver injury through its ability to modulate the expression of key proteins involved in bile acid secretion, thereby preserving hepato-enteric circulation homeostasis.

Comparing genome-scale metabolic models of the non-resistant Enterococcus faecalis ATCC 19433 and the multi-resistant Enterococcus faecalis V583

Enterococcus faecalis is a versatile lactic acid bacterium with a large variety of implications for humans. While some strains of this species are pathobionts being resistant against most of the common antibiotics, other strains are regarded as biological protectants or even probiotics. Accordingly, E. faecalis strains largely differ in the size and content of their accessory genome. In this study, we describe the genome-scale metabolic network reconstruction of E. faecalis ATCC 19433, a non-resistant human-associated strain. A comparison of the genome-scale metabolic model (GSM) of E. faecalis ATCC 19433 with a previously published GSM of the multi-resistant pathobiontic E. faecalis V583 reveals high similarities in the central metabolic abilities of these two human associated strains. This is reflected, e.g., in the identical amino acid auxotrophies. The ATCC 19433 strain, however, has a 14.1 % smaller genome than V583 and lacks the multiple antibiotic resistance genes and genes involved in capsule formation. Based on the measured metabolic fluxes at different growth rates, the energy demand at zero growth was calculated to be about 40 % lower for the ATCC 19433 strain compared to V583. Furthermore, the ATCC 19433 strain seems less prone to the depletion of amino acids utilizable for energy metabolism. This might hint at a lower overall energy demand of the ATCC 19433 strain as compared to V583.

Minimal cellular automaton model with heterogeneous cell sizes predicts epithelial colony growth

Regulation of cell proliferation is a crucial aspect of tissue development and homeostasis and plays a major role in morphogenesis, wound healing, and tumor invasion. A phenomenon of such regulation is contact inhibition, which describes the dramatic slowing of proliferation, cell migration and individual cell growth when multiple cells are in contact with each other. While many physiological, molecular and genetic factors are known, the mechanism of contact inhibition is still not fully understood. In particular, the relevance of cellular signaling due to interfacial contact for contact inhibition is still debated. Cellular automata (CA) have been employed in the past as numerically efficient mathematical models to study the dynamics of cell ensembles, but they are not suitable to explore the origins of contact inhibition as such agent-based models assume fixed cell sizes. We develop a minimal, data-driven model to simulate the dynamics of planar cell cultures by extending a probabilistic CA to incorporate size changes of individual cells during growth and cell division. We successfully apply this model to previous in-vitro experiments on contact inhibition in epithelial tissue: After a systematic calibration of the model parameters to measurements of single-cell dynamics, our CA model quantitatively reproduces independent measurements of emergent, culture-wide features, like colony size, cell density and collective cell migration. In particular, the dynamics of the CA model also exhibit the transition from a low-density confluent regime to a stationary postconfluent regime with a rapid decrease in cell size and motion. This implies that the volume exclusion principle, a mechanical constraint which is the only inter-cellular interaction incorporated in the model, paired with a size-dependent proliferation rate is sufficient to generate the observed contact inhibition. We discuss how our approach enables the introduction of effective bio-mechanical interactions in a CA framework for future studies.

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions

Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling, in particular, provides relevant biological insights into the spatial and temporal evolution of tumors. Here, we develop a novel agent-based model (ABM) to predict the consequences of intercellular interactions. Furthermore, we leverage our prior work that predicts the transitions of CD8+ T cells from a naïve state to a terminally differentiated state using Boolean modeling. Given the details incorporated to predict T cell state, we apply the integrated Boolean-ABM framework to study how the properties of CD8+ T cells influence the composition and spatial organization of tumors and the efficacy of an immune checkpoint blockade. Overall, we present a mechanistic understanding of tumor evolution that can be leveraged to study targeted immunotherapeutic strategies.

DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals

Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.

Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network

BACKGROUND

Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge.

OBJECTIVES

To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network.

METHODS

Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model.

RESULTS

We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%.

CONCLUSION

Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.

Modelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro

Drug resistance is one of the biggest challenges in the fight against cancer. In particular, in the case of glioblastoma, the most lethal brain tumour, resistance to temozolomide (the standard of care drug for chemotherapy in this tumour) is one of the main reasons behind treatment failure and hence responsible for the poor prognosis of patients diagnosed with this disease. In this work, we combine the power of three-dimensional in vitro experiments of treated glioblastoma spheroids with mathematical models of tumour evolution and adaptation. We use a novel approach based on internal variables for modelling the acquisition of resistance to temozolomide that was observed in experiments for a group of treated spheroids. These internal variables describe the cell's phenotypic state, which depends on the history of drug exposure and affects cell behaviour. We use model selection to determine the most parsimonious model and calibrate it to reproduce the experimental data, obtaining a high level of agreement between the in vitro and in silico outcomes. A sensitivity analysis is carried out to investigate the impact of each model parameter in the predictions. More importantly, we show how the model is useful for answering biological questions, such as what is the intrinsic adaptation mechanism, or for separating the sensitive and resistant populations. We conclude that the proposed in silico framework, in combination with experiments, can be useful to improve our understanding of the mechanisms behind drug resistance in glioblastoma and to eventually set some guidelines for the design of new treatment schemes.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Short preconditioning with TGFβ of equine adipose tissue-derived mesenchymal stem cells predisposes towards an anti-fibrotic secretory phenotype: A possible tool for treatment of endometrosis in mares

Endometrosis in mares is a disease resulting from chronic inflammation characterized by peri glandular fibrosis. There is no effective treatment so far, which opens the door for exploring the use of stem cells as a candidate. Transforming growth factor beta (TGFβ) is crucial for the establishment and progression of fibrosis in mare's endometrosis. We aimed to develop regenerative approaches to treat endometrosis by using mesenchymal stem cells (MSC), for which understanding the effect of TGFβ on exogenous MSC is crucial. We isolated and characterized equine adipose MSC from six donors. Cells were pooled and exposed to 10 ng/ml of TGFβ for 0, 4, and 24 h, after which cells were analyzed for proliferation, migration, mesodermal differentiation, expression of fibrosis-related mRNAs, and prostaglandin E2 secretion. At 24 h of exposition to TGFβ, there was a progressive increase in the contraction of the monolayer, leading to nodular structures, while cell viability did not change. Exposure to TGFβ impaired adipogenic and osteogenic differentiation after 4 h of treatment, which was more marked at 24 h, represented by a decrease in Oil red and Alizarin red staining, as well as a significant drop (p < 0.05) in the expression of key gene regulators of differentiation processes (PPARG for adipose and RUNX2 for osteogenic differentiation). TGFβ increased chondrogenic differentiation as shown by the upsurge in size of the resulting 3D cell pellet and intensity of Alcian Blue staining, as well as the significant up-regulation of SOX9 expression (p < 0.05) at 4 h, which reached a maximum peak at 24 h (p < 0.01), indicative of up-regulation of glycosaminoglycan synthesis. Preconditioning MSC with TGFβ led to a significant increase (p < 0.05) in the expression of myofibroblast gene markers aSMA, COL1A1, and TGFβ at 24 h exposition time. In contrast, the expression of COL3A1 did not change with respect to the control but registered a significant downregulation compared to 4 h (p < 0.05). TGFβ also affected the expression of genes involved in PGE2 synthesis and function; COX2, PTGES, and the PGE2 receptor EP4 were all significantly upregulated early at 4 h (p < 0.05). Cells exposed to TGFβ showed a significant upregulation of PGE2 secretion at 4 h compared to untreated cells (p < 0.05); conversely, at 24 h, the PGE2 values decreased significantly compared to control cells (p < 0.05). Preconditioning MSC for 4 h led to an anti-fibrotic secretory phenotype, while a longer period (24 h) led to a pro-fibrotic one. It is tempting to propose a 4-h preconditioning of exogenous MSC with TGFβ to drive them towards an anti-fibrotic phenotype for cellular and cell-free therapies in fibrotic diseases such as endometrosis of mares.

The identification of biomarkers for Alzheimer's disease using a systems biology approach based on lncRNA-circRNA-miRNA-mRNA ceRNA networks

In addition to being the most prevalent form of neurodegeneration among the elderly, AD is a devastating multifactorial disease. Currently, treatments address only its symptoms. Several clinical studies have shown that the disease begins to manifest decades before the first symptoms appear, indicating that studying early changes is crucial to improving early diagnosis and discovering novel treatments. Our study used bioinformatics and systems biology to identify biomarkers in AD that could be used for diagnosis and prognosis. The procedure was performed on data from the GEO database, and GO and KEGG enrichment analysis were performed. Then, we set up a network of interactions between proteins. Several miRNA prediction tools including miRDB, miRWalk, and TargetScan were used. The ceRNA network led to the identification of eight mRNAs, four circRNAs, seven miRNAs, and seven lncRNAs. Multiple mechanisms, including the cell cycle and DNA replication, have been linked to the promotion of AD development by the ceRNA network. By using the ceRNA network, it should be possible to extract prospective biomarkers and therapeutic targets for the treatment of AD. It is possible that the processes involved in DNA cell cycle and the replication of DNA contribute to the development of Alzheimer's disease.

Activating adaptive immunity by bispecific, T-cell engager antibodies bridging infected and immune-effector cells is a promising novel therapy for chronic hepatitis B

Bispecific antibodies (bsAbs) are engineered immunoglobulins that combine two different antigen-binding sites in one molecule. BsAbs can be divided into two molecular formats: IgG-like and non-IgG-like antibodies. Structural elements of each format have implications for engaging the immune system. T cell engager antibodies (TCEs) are bsAbs designed to engage T cells with target cells. TCEs can be applied not only in cancer but also in infectious disease therapy to activate T-cell responses. In this review, we focus on current literature on the design and use of bsAbs as an innovative strategy to enhance adaptive antiviral immune responses. We summarized the novel T cell-related immunotherapies with a focus on TCEs, that are developed for the treatment of chronic hepatitis B. Chronic infection with the hepatitis B virus (HBV) had a death toll of 1.1 million humans in 2022, mainly due to liver cirrhosis and hepatocellular carcinoma developing in the more than 250 million humans chronically infected. A curative treatment approach for chronic hepatitis B is lacking. Combining antiviral therapy with immune therapies activating T-cell responses is regarded as the most promising therapeutic approach to curing HBV and preventing the sequelae of chronic infection. Attracting functionally intact T cells that are not HBV-specific and, therefore, have not yet been exposed to regulatory mechanisms and activating those at the target site in the liver is a very interesting therapeutic approach that could be achieved by TCEs. Thus, TCEs redirecting T cells toward HBV-positive cells represent a promising strategy for treating chronic hepatitis B and HBV-associated hepatocellular carcinoma.

Risk of Dementia and Alzheimer's Disease Associated With Antidiabetics: A Bayesian Network Meta-Analysis

INTRODUCTION

Dementia risk is substantially elevated in patients with diabetes. However, evidence on dementia risk associated with various antidiabetic regimens is still limited. This study aims to comprehensively investigate the risk of dementia and Alzheimer's disease (AD) associated with various antidiabetic classes.

METHODS

Cochrane Central Register of Controlled Trials, Embase, MEDLINE (PubMed), and Scopus were searched from inception to March 2024 (PROSPERO CRD 42022365927). Observational studies investigating dementia and AD incidences after antidiabetic initiation were identified. Bayesian network meta-analysis was performed to determine dementia and AD risks associated with antidiabetics. Preferred Reporting Items for Systematic Reviews-Network Meta-Analyses (PRISMA-NMA) guidelines were followed. Statistical analysis was performed and updated in November 2023 and March 2024, respectively.

RESULTS

A total of 1,565,245 patients from 16 studies were included. Dementia and AD risks were significantly lower with metformin and sodium glucose co-transporter-2 inhibitors (SGLT2i). Metformin displayed the lowest risk of dementia across diverse antidiabetics, whereas α-glucosidase inhibitors demonstrated the highest risk. SGLT2i exhibited the lowest dementia risk across second-line antidiabetics. Dementia risk was significantly higher with dipeptidyl peptidase-4 inhibitor (DPP4i), metformin, sulfonylureas, and thiazolidinediones (TZD) compared to SGLT2i in the elderly (≥75 years). Dementia risk associated with metformin was substantially lower, regardless of diabetic complication status or baseline A1C.

DISCUSSION

Metformin and SGLT2i demonstrated lower dementia risk than other antidiabetic classes. Patient-specific factors may affect this relationship and cautious interpretation is warranted as metformin is typically initiated at an earlier stage with fewer complications. Hence, further large-scaled clinical trials are required.

Novel Therapies for the Treatment of Cardiovascular Disease

Over the last decade, randomized clinical trials of several pharmacologic agents have demonstrated a reduction in cardiovascular mortality and other important secondary outcomes. Angiotensin-Neprilysin Inhibitors and Sodium-Glucose Co-transporter 2 inhibitors have now become pillars in the treatment of heart failure. Ivabradine is a negative chronotropic agent used as an adjunctive therapy in patients with heart failure. Two new hypertension therapies, zilebresiran and aprocitentan, are currently in investigational stages. Finally, mavacamten has emerged as a pharmacologic treatment for hypertrophic obstructive cardiomyopathy. Practitioners must be familiar with the indications and side effects of newer therapies as they are now frequently prescribed.

Dosage Optimization: A Regulatory Perspective for Developing Oncology Drugs

Optimized dosages provide a secure foundation for the development of well-tolerated and effective oncology drugs. Project Optimus, an initiative within the Oncology Center of Excellence, strives to reform the dosage optimization and dosage selection paradigm in oncology. This initiative stems from the availability of targeted drugs and from the demand for more tolerable dosages from patients, caregivers, and advocates. Reforming dosage optimization for oncology drugs requires a paradigm shift from the one employed for cytotoxic chemotherapy to one that understands and considers the unique attributes of targeted therapy, immunotherapy, and cellular therapy. Limited characterization of dosage during drug development may result in (1) patients not staying on a therapy long-term due to poor tolerability, (2) failure to establish positive benefit-risk in clinical trials for regulatory approval (3) withdrawal of drugs from the market (4) removal of indications of drugs from the market. Timely access to drugs is important for all patients with cancer, so it is vital to iteratively analyze all nonclinical and clinically relevant data at each stage of development and leverage quantitative approaches, innovative trial designs, and regulatory support to arrive at dosages with favorable benefit-risk. This review highlights the key challenges and opportunities to embracing this new paradigm and realizing the full potential of new oncology therapies.

Metformin's dual impact on Gut microbiota and cardiovascular health: A comprehensive analysis

Cardiovascular diseases (CVD) cause significant global morbidity, mortality and public health burden annually. CVD alters richness, diversity, and composition of Gut microbiota along with RAS and histopathological differences. Present study explores Metformin role in mitigating doxorubicin induced cardiovascular toxicity/remodeling. Animals were divided into 4 groups with n=6: Group I (N. Control) free access to diet and water; Group II (MET. Control) on oral Metformin (250 mg/kg) daily; Group III (DOX. Control) alternate day intraperitoneal Doxorubicin (3 mg/kg) totaling 18 mg/kg; Group IV (DOX. MET. Control) received both daily oral Metformin (250 mg/kg) and alternate day Doxorubicin (3 mg/kg). Gut microbial analysis was made from stool before animals were sacrificed for biochemical and histopathological analysis. Significant alterations were observed in ɑ and β-diversity with new genus from Firmicutes, specifically Clostridia_UCG-014, Eubacterium ruminantium, and Tunicibacter, were prevalent in both the DOX. Control and DOX.MET groups. Proteobacteria, represented by Succinivibrio, were absent in all groups. Additionally, Parabacteroides from the Bacteroidia phylum was absent in all groups except the N. control. In the DOX.MET Control group, levels of Angiotensin II ( 7.75± 0.49 nmol/min, p<0.01) and Renin (2.60±0.26 ng/ml/hr) were significantly reduced. Conversely, levels of CK-MB, Fibrinogen, Troponin, CRP ( p < 0.0001), and TNFɑ (p < 0.05) were elevated. Histopathological examination revealed substantial cardiac changes, including Fibrinogen and fat deposition and eosinophilic infiltration, as well as liver damage characterized by binucleated cells and damaged hepatocytes, along with altered renal tissues in the DOX.MET.Control group. The findings suggest that MET. significantly modifies gut microbiota, particularly impacting the Firmicutes and Proteobacteria phyla. The reduction in Angiotensin II levels, alongside increased inflammatory markers and myocardial damage, highlights the complex interactions and potential adverse effects associated with MET therapy on cardiovascular health.

Origins of the problematic E in SEIR epidemic models

During the COVID-19 pandemic, over one thousand papers were published on "Susceptible-Exposed-Infectious-Removed" (SEIR) epidemic computational models. The English word "exposed" in its vernacular and public health usage means a state of having been in contact with an infectious individual, but not necessarily infected. In contrast, the term "exposed" in SEIR modeling usage typically stands for a state of already being infected but not yet being infectious to others, a state more properly termed "latently infected." In public health language, "exposed" means possibly infected, yet in SEIR modeling language, "exposed" means already infected. This paper retraces the conceptual and mathematical origins of this terminological disconnect and concludes that epidemic modelers should consider using the "SLIR" notational short-hand (L for Latent) instead of SEIR.

Interfacing data science with cell therapy manufacturing: where we are and where we need to be

Although several cell-based therapies have received FDA approval, and others are showing promising results, scalable, and quality-driven reproducible manufacturing of therapeutic cells at a lower cost remains challenging. Challenges include starting material and patient variability, limited understanding of manufacturing process parameter effects on quality, complex supply chain logistics, and lack of predictive, well-understood product quality attributes. These issues can manifest as increased production costs, longer production times, greater batch-to-batch variability, and lower overall yield of viable, high-quality cells. The lack of data-driven insights and decision-making in cell manufacturing and delivery is an underlying commonality behind all these problems. Data collection and analytics from discovery, preclinical and clinical research, process development, and product manufacturing have not been sufficiently utilized to develop a "systems" understanding and identify actionable controls. Experience from other industries shows that data science and analytics can drive technological innovations and manufacturing optimization, leading to improved consistency, reduced risk, and lower cost. The cell therapy manufacturing industry will benefit from implementing data science tools, such as data-driven modeling, data management and mining, AI, and machine learning. The integration of data-driven predictive capabilities into cell therapy manufacturing, such as predicting product quality and clinical outcomes based on manufacturing data, or ensuring robustness and reliability using data-driven supply-chain modeling could enable more precise and efficient production processes and lead to better patient access and outcomes. In this review, we introduce some of the relevant computational and data science tools and how they are being or can be implemented in the cell therapy manufacturing workflow. We also identify areas where innovative approaches are required to address challenges and opportunities specific to the cell therapy industry. We conclude that interfacing data science throughout a cell therapy product lifecycle, developing data-driven manufacturing workflow, designing better data collection tools and algorithms, using data analytics and AI-based methods to better understand critical quality attributes and critical-process parameters, and training the appropriate workforce will be critical for overcoming current industry and regulatory barriers and accelerating clinical translation.

A multi-biomarker micronucleus assay using imaging flow cytometry

Genetic toxicity testing assesses the potential of compounds to cause DNA damage. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development aiding the identification of promising drugs that have low-risk potential for causing genetic damage contributing to cancer risk in humans. Despite this, in vitro tests generate a high number of misleading positives, the consequences of which can lead to unnecessary animal testing and/or the abandonment of promising drug candidates. Understanding chemical Mode of Action (MoA) is vital to identifying the true genotoxic potential of substances and, therefore, the risk translation into the clinic. Here we demonstrate a simple, robust protocol for staining fixed, human-lymphoblast p53 proficient TK6 cells with antibodies against ɣH2AX, p53 and pH3S28 along with DRAQ5™ DNA staining that enables analysis of un-lysed cells via microscopy approaches such as imaging flow cytometry. Here, we used the Cytek® Amnis® ImageStream®X Mk II which provides a high-throughput acquisition platform with the sensitivity of flow cytometry and spatial morphological information associated with microscopy. Using the ImageStream manufacturer's software (IDEAS® 6.2), a masking strategy was developed to automatically detect and quantify micronucleus events (MN) and characterise biomarker populations. The gating strategy developed enables the generation of a template capable of automatically batch processing data files quantifying cell-cycle, MN, ɣH2AX, p53 and pH3 populations simultaneously. In this way, we demonstrate how a multiplex system enables DNA damage assessment alongside MN identification using un-lysed cells on the imaging flow cytometry platform. As a proof-of-concept, we use the tool chemicals carbendazim and methyl methanesulphonate (MMS) to demonstrate the assay's ability to correctly identify clastogenic or aneugenic MoAs using the biomarker profiles established.

ECM Microenvironment in Vascular Homeostasis: New Targets for Atherosclerosis

Alterations in vascular extracellular matrix (ECM) components, interactions, and mechanical properties influence both the formation and stability of atherosclerotic plaques. This review discusses the contribution of the ECM microenvironment in vascular homeostasis and remodeling in atherosclerosis, highlighting Cartilage oligomeric matrix protein (COMP) and its degrading enzyme ADAMTS7 as examples, and proposes potential avenues for future research aimed at identifying novel therapeutic targets for atherosclerosis based on the ECM microenvironment.

Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

Less is more: Exploring neoadjuvant immunotherapy as a de-escalation strategy in head and neck squamous cell carcinoma treatment

Head and neck squamous cell carcinoma (HNSCC) constitutes a significant global cancer burden, given its high prevalence and associated mortality. Despite substantial progress in survival rates due to the enhanced multidisciplinary approach to treatment, these methods often lead to severe tissue damage, compromised function, and potential toxicity. Thus, there is an imperative need for novel, effective, and minimally damaging treatment modalities. Neoadjuvant treatment, an emerging therapeutic strategy, is designed to reduce tumor size and curtail distant metastasis prior to definitive intervention. Currently, neoadjuvant chemotherapy (NACT) has optimized the treatment approach for a subset of HNSCC patients, yet it has not produced a noticeable enhancement in overall survival (OS). In the contemporary cancer therapeutics landscape, immunotherapy is gaining traction at an accelerated pace. Notably, neoadjuvant immunotherapy (NAIT) has shown promising radiological and pathological responses, coupled with encouraging efficacy in several clinical trials. This potentially paves the way for a myriad of possibilities in treatment de-escalation of HNSCC, which warrants further exploration. This paper reviews the existing strategies and efficacies of neoadjuvant immune checkpoint inhibitors (ICIs), along with potential de-escalation strategies. Furthermore, the challenges encountered in the context of the de-escalation strategies of NAIT are explored. The aim is to inform future research directions that strive to improve the quality of life (QoL) for patients battling HNSCC.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Food-breastmilk combinations alter the colonic microbiome of weaning infants: an in silico study

The introduction of solid foods to infants, also known as weaning, is a critical point for the development of the complex microbial community inhabiting the human colon, impacting host physiology in infancy and later in life. This research investigated in silico the impact of food-breastmilk combinations on growth and metabolite production by colonic microbes of New Zealand weaning infants using the metagenome-scale metabolic model named Microbial Community. Eighty-nine foods were individually combined with breastmilk, and the 12 combinations with the strongest influence on the microbial production of short-chain fatty acids (SCFAs) and branched-chain fatty acids (BCFAs) were identified. Fiber-rich and polyphenol-rich foods, like pumpkin and blackcurrant, resulted in the greatest increase in predicted fluxes of total SCFAs and individual fluxes of propionate and acetate when combined, respectively, with breastmilk. Identified foods were further combined with other foods and breastmilk, resulting in 66 multiple food-breastmilk combinations. These combinations altered in silico the impact of individual foods on the microbial production of SCFAs and BCFAs, suggesting that the interaction between the dietary compounds composing a meal is the key factor influencing colonic microbes. Blackcurrant combined with other foods and breastmilk promoted the greatest increase in the production of acetate and total SCFAs, while pork combined with other foods and breastmilk decreased the production of total BCFAs.IMPORTANCELittle is known about the influence of complementary foods on the colonic microbiome of weaning infants. Traditional in vitro and in vivo microbiome methods are limited by their resource-consuming concerns. Modeling approaches represent a promising complementary tool to provide insights into the behavior of microbial communities. This study evaluated how foods combined with other foods and human milk affect the production of short-chain fatty acids and branched-chain fatty acids by colonic microbes of weaning infants using a rapid and inexpensive in silico approach. Foods and food combinations identified here are candidates for future experimental investigations, helping to fill a crucial knowledge gap in infant nutrition.

Single-mode termination of phage transcriptions, disclosing bacterial adaptation for facilitated reinitiations

Bacterial and bacteriophage RNA polymerases (RNAPs) have divergently evolved and share the RNA hairpin-dependent intrinsic termination of transcription. Here, we examined phage T7, T3 and SP6 RNAP terminations utilizing the single-molecule fluorescence assays we had developed for bacterial terminations. We discovered the phage termination mode or outcome is virtually single with decomposing termination. Therein, RNAP is displaced forward along DNA and departs both RNA and DNA for one-step decomposition, three-dimensional diffusion and reinitiation at any promoter. This phage displacement-mediated decomposing termination is much slower than readthrough and appears homologous with the bacterial one. However, the phage sole mode of termination contrasts with the bacterial dual mode, where both decomposing and recycling terminations occur compatibly at any single hairpin- or Rho-dependent terminator. In the bacterial recycling termination, RNA is sheared from RNA·DNA hybrid, and RNAP remains bound to DNA for one-dimensional diffusion, which enables facilitated recycling for reinitiation at the nearest promoter located downstream or upstream in the sense or antisense orientation. Aligning with proximity of most terminators to adjacent promoters in bacterial genomes, the shearing-mediated recycling termination could be bacterial adaptation for the facilitated reinitiations repeated at a promoter for accelerated expression and coupled at adjoining promoters for coordinated regulation.

Artificial intelligence in cosmetic dermatology

BACKGROUND

Cosmetic dermatology is a growing field as more patients are seeking treatments for esthetic concerns. Traditionally, practitioners and patients utilize their own perceptions, current beauty standards, and manual observation to determine their satisfaction with cosmetic interventions. Artificial intelligence (AI) can be introduced into cosmetic dermatology to provide objective data-driven recommendations to both dermatologists and patients.

OBJECTIVE

The purpose of this paper is to compose a unified review that illustrates the various facets of artificial intelligence and formulate a hypothesis regarding the new implications of artificial intelligence in cosmetic dermatology specifically.

METHODS

A comprehensive search on PubMed was conducted to identify the available information related to AI in cosmetic dermatology. The search was conducted using a combination of keywords including "cosmetic dermatology" and "artificial intelligence."

RESULTS

The current literature indicates that AI models offer personalized, efficient, and result-driven outputs that can enhance cosmetic outcomes, patient satisfaction, and overall experience.

CONCLUSION

Artificial intelligence integration in cosmetic dermatology shows a promising future, offering the ability to analyze vast data sets and deliver a tailored patient experience. By incorporating AI into cosmetic dermatology, there is an opportunity to balance evidence-based decision-making with the artistic human touch of cosmetic dermatologists.

A graph theoretical approach to experimental prioritization in genome-scale investigations

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

Low-Dose Radiation Induces Alterations in Fatty Acid and Tyrosine Metabolism in the Mouse Hippocampus: Insights from Integrated Multiomics

In recent years, there has been a drastic surge in neurological disorders with sporadic cases contributing more than ever to their cause. Radiation exposure through diagnostic or therapeutic routes often results in neurological injuries that may lead to neurodegenerative pathogenesis. However, the underlying mechanisms regulating the neurological impact of exposure to near-low doses of ionizing radiation are not known. In particular, the neurological changes caused by metabolomic reprogramming have not yet to be elucidated. Hence, in the present study, C57BL/6 mice were exposed to a single whole-body X-ray dose of 0.5 Gy, and 14 days post-treatment, the hippocampus was subjected to metabolomic analysis. The hippocampus of the IR animals showed significant alterations in 15 metabolites, which aligned with altered tyrosine, phenylalanine, and alpha-linolenic acid metabolism and the biosynthesis of unsaturated fatty acids. Furthermore, a multiomics interaction network comprising metabolomics and RNA sequencing data analysis provided insights into gene-metabolite interactions. Tyrosine metabolism was revealed to be the most altered, which was demonstrated by the interaction of several crucial genes and metabolites. The present study revealed the regulation of low-dose radiation-induced neurotoxicity at the metabolomic level and its implications for the pathogenesis of neurological disorders. Present study provides novel insights into metabolomic pathways altered following near-low-dose IR exposure and its link withneurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Identifying key genes against rutin on human colorectal cancer cells via ROS pathway by integrated bioinformatic analysis and experimental validation

Colorectal cancer (CRC) poses a significant global health challenge, characterized by substantial prevalence variations across regions. This study delves into the therapeutic potential of rutin, a polyphenol abundant in fruits, for treating CRC. The primary objectives encompass identifying molecular targets and pathways influenced by rutin through an integrated approach combining bioinformatic analysis and experimental validation. Employing Gene Set Enrichment Analysis (GSEA), the study focused on identifying potential differentially expressed genes (DEGs) associated with CRC, specifically those involved in regulating reactive oxygen species, metabolic reprogramming, cell cycle regulation, and apoptosis. Utilizing diverse databases such as GEO2R, CTD, and Gene Cards, the investigation revealed a set of 16 targets. A pharmacological network analysis was subsequently conducted using STITCH and Cytoscape, pinpointing six highly upregulated genes within the rutin network, including TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Gene Ontology (GO) analysis predicted functional categories, shedding light on rutin's potential impact on antioxidant properties. KEGG pathway analysis enriched crucial pathways like metabolic and ROS signaling pathways, HIF1a, and mTOR signaling. Diagnostic assessments were performed using UALCAN and GEPIA databases, evaluating mRNA expression levels and overall survival for the identified targets. Molecular docking studies confirmed robust binding associations between rutin and biomolecules such as TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Experimental validation included inhibiting colorectal cell HT-29 growth and promoting cell growth with NAC through MTT assay. Flow cytometric analysis also observed rutin-induced G1 phase arrest and cell death in HT-29 cells. RT-PCR demonstrated reduced expression levels of target biomolecules in HT-29 cells treated with rutin. This comprehensive study underscores rutin's potential as a promising therapeutic avenue for CRC, combining computational insights with robust experimental evidence to provide a holistic understanding of its efficacy.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Structural basis for FN3K-mediated protein deglycation

Protein glycation is a universal, non-enzymatic modification that occurs when a sugar covalently attaches to a primary amine. These spontaneous modifications may have deleterious or regulatory effects on protein function, and their removal is mediated by the conserved metabolic kinase fructosamine-3-kinase (FN3K). Despite its crucial role in protein repair, we currently have a poor understanding of how FN3K engages or phosphorylates its substrates. By integrating structural biology and biochemistry, we elucidated the catalytic mechanism for FN3K-mediated protein deglycation. Our work identifies key amino acids required for binding and phosphorylating glycated substrates and reveals the molecular basis of an evolutionarily conserved protein repair pathway. Additional structural-functional studies revealed unique structural features of human FN3K as well as differences in the dimerization behavior and regulation of FN3K family members. Our findings improve our understanding of the structure of FN3K and its catalytic mechanism, which opens new avenues for therapeutically targeting FN3K.

Sweetened caffeine drinking revealed behavioral rhythm independent of the central circadian clock in male mice

Caffeine consumption is associated with the evening chronotype, and caffeine administration in mice results in prolonged period of the circadian rhythm in locomotor activity. However, as caffeine is bitter, sweetened caffeine is preferred by humans and mice; yet, its impact on the circadian clock has not been explored. In this study, mice were provided with freely available sweetened caffeine to investigate its effects on behavioral rhythms and peripheral clocks. Mice that freely consumed sweetened caffeine shifted from nocturnal to diurnal activity rhythms. In addition to the light-dark entrained behavioral rhythm component, some animals exhibited free-running period longer than 24-h. Intraperitoneal administration of caffeine at the beginning of the light phase also acutely induced diurnal behavior. The behavioral rhythms with long period (26-30 h) due to sweetened caffeine were observed even in mice housed under constant light or with a lesioned central circadian clock located in the suprachiasmatic nucleus of the hypothalamus; however, the rhythmicity was unstable. PER2::LUCIFERASE rhythms in peripheral tissues, such as the kidney, as measured via in vivo whole-body imaging during caffeine consumption, showed reduced amplitude and desynchronized phases among individuals. These results indicate that consumption of sweetened caffeine induces diurnal and long-period behavioral rhythms irrespective of the central clock, causing desynchronization of the clock in the body.

Understanding the neurobiology of social behavior through exploring brain-wide dynamics of neural activity

Social behavior is highly complex and adaptable. It can be divided into multiple temporal stages: detection, approach, and consummatory behavior. Each stage can be further divided into several cognitive and behavioral processes, such as perceiving social cues, evaluating the social and non-social contexts, and recognizing the internal/emotional state of others. Recent studies have identified numerous brain-wide circuits implicated in social behavior and suggested the existence of partially overlapping functional brain networks underlying various types of social and non-social behavior. However, understanding the brain-wide dynamics underlying social behavior remains challenging, and several brain-scale dynamics (macro-, meso-, and micro-scale levels) need to be integrated. Here, we suggest leveraging new tools and concepts to explore social brain networks and integrate those different levels. These include studying the expression of immediate-early genes throughout the entire brain to impartially define the structure of the neuronal networks involved in a given social behavior. Then, network dynamics could be investigated using electrode arrays or multi-channel fiber photometry. Finally, tools like high-density silicon probes and miniscopes can probe neural activity in specific areas and across neuronal populations at the single-cell level.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Multi-omic analysis reveals VEGFR2, PI3K, and JNK mediate the small molecule induction of human iPSC-derived cardiomyocyte proliferation

Mammalian hearts lose their regenerative potential shortly after birth. Stimulating the proliferation of preexisting cardiomyocytes is a potential therapeutic strategy for cardiac damage. In a previous study, we identified 30 compounds that induced the bona-fide proliferation of human iPSC-derived cardiomyocytes (hiPSC-CM). Here, we selected five active compounds with diverse targets, including ALK5 and CB1R, and performed multi-omic analyses to identify common mechanisms mediating the cell cycle progression of hiPSC-CM. Transcriptome profiling revealed the top enriched pathways for all compounds including cell cycle, DNA repair, and kinesin pathways. Functional proteomic arrays found that the compounds collectively activated multiple receptor tyrosine kinases including ErbB2, IGF1R, and VEGFR2. Network analysis integrating common transcriptomic and proteomic signatures predicted that MAPK/PI3K pathways mediated compound responses. Furthermore, VEGFR2 negatively regulated endoreplication, enabling the completion of cell division. Thus, in this study, we applied high-content imaging and molecular profiling to establish mechanisms linking pro-proliferative agents to mechanisms of cardiomyocyte cell cycling.

Subregion-specific transcriptomic profiling of rat brain reveals sex-distinct gene expression impacted by adolescent stress

Stress during adolescence clearly impacts brain development and function. Sex differences in adolescent stress-induced or exacerbated emotional and metabolic vulnerabilities could be due to sex-distinct gene expression in hypothalamic, limbic, and prefrontal brain regions. However, adolescent stress-induced whole-genome expression changes in key subregions of these brain regions were unclear. In this study, female and male adolescent Sprague Dawley rats received one-hour restraint stress daily from postnatal day (PD) 32 to PD44. Corticosterone levels, body weights, food intake, body composition, and circulating adiposity and sex hormones were measured. On PD44, brain and blood samples were collected. Using RNA-sequencing, sex-specific differences in stress-induced differentially expressed (DE) genes were identified in subregions of the hypothalamus, limbic system, and prefrontal cortex. Canonical pathways reflected well-known sex-distinct maladies and diseases, substantiating the therapeutic potential of the DE genes found in the current study. Thus, we proposed specific sex distinct, adolescent stress-induced transcriptional changes found in the current study as examples of the molecular bases for sex differences witnessed in stress induced or exacerbated emotional and metabolic disorders. Future behavioral studies and single-cell studies are warranted to test the implications of the DE genes identified in this study in sex-distinct stress-induced susceptibilities.