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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Functionalized azirine based scaffolds as endothelin inhibitors for the selective anti-angiogenic activity

Anti-angiogenic therapy has long been used as an adjunct therapy for the resolution of tumor burden. The current findings describe the synthesis of novel marine-based azirine-containing compounds that exhibit anti-angiogenic mediated anti-tumor activity. Azirine-2-carboxylate inhibited HUVEC-mediated tubulogenesis without causing cell death in a dose-dependent manner. Ex-vivo CAM, in-vivo Matrigel implantation, and ear angiogenesis experiments have all shown that azirine-2-carboxylate effectively inhibits angiogenesis. Furthermore, azirine-2-carboxylate inhibits the migration of ECs without disrupting the preformed tubule network. Azirine-2-carboxylate had adequate intramuscular systemic exposure and inhibited tumor growth in a xenograft mouse model. DARTS analysis, competitive binding assay, and gene expression investigations revealed that azirine-2-carboxylate inhibits endothelin-1-mediated angiogenesis. Overall, the discovery of azirine-2-carboxylate demonstrated a potent inhibition of angiogenesis targeting ET1 and a possible application in anti-angiogenic therapy.

Dynamic analysis of a drug resistance evolution model with nonlinear immune response

Recent studies have utilized evolutionary mechanisms to impede the emergence of drug-resistant populations. In this paper, we develop a mathematical model that integrates hormonal treatment, immunotherapy, and the interactions among three cell types: drug-sensitive cancer cells, drug-resistant cancer cells and immune effector cells. Dynamical analysis is performed, examining the existence and stability of equilibria, thereby confirming the model's interpretability. Model parameters are calibrated using available prostate cancer data and literature. Through bifurcation analysis for drug sensitivity under different immune effector cells recruitment responses, we find that resistant cancer cells grow rapidly under weak recruitment response, maintain at a low level under strong recruitment response, and both may occur under moderate recruitment response. To quantify the competitiveness of sensitive and resistant cells, we introduce the comprehensive measures R1 and R2, respectively, which determine the outcome of competition. Additionally, we introduce the quantitative indicators CIE1 and CIE2 as comprehensive measures of the immune effects on sensitive and resistant cancer cells, respectively. These two indicators determine whether the corresponding cancer cells can maintain at a low level. Our work shows that the immune system is an important factor affecting the evolution of drug resistance and provides insights into how to enhance immune response to control resistance.

Glucose-6-phosphate dehydrogenase alleviates epileptic seizures by repressing reactive oxygen species production to promote signal transducer and activator of transcription 1-mediated N-methyl-d-aspartic acid receptors inhibition

The pathogenesis of epilepsy remains unclear; however, a prevailing hypothesis suggests that the primary underlying cause is an imbalance between neuronal excitability and inhibition. Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme in the pentose phosphate pathway, which is primarily involved in deoxynucleic acid synthesis and antioxidant defense mechanisms and exhibits increased expression during the chronic phase of epilepsy, predominantly colocalizing with neurons. G6PD overexpression significantly reduces the frequency and duration of spontaneous recurrent seizures. Furthermore, G6PD overexpression enhances signal transducer and activator of transcription 1 (STAT1) expression, thus influencing N-methyl-d-aspartic acid receptors expression, and subsequently affecting seizure activity. Importantly, the regulation of STAT1 by G6PD appears to be mediated primarily through reactive oxygen species signaling pathways. Collectively, our findings highlight the pivotal role of G6PD in modulating epileptogenesis, and suggest its potential as a therapeutic target for epilepsy.

Factors affecting protein recovery during Hsp40 affinity profiling

The identification and quantification of misfolded proteins from complex mixtures is important for biological characterization and disease diagnosis, but remains a major bioanalytical challenge. We have developed Hsp40 Affinity Profiling as a bioanalytical approach to profile protein stability in response to cellular stress. In this assay, we ectopically introduce the Hsp40 FlagDNAJB8H31Q into cells and use quantitative proteomics to determine how protein affinity for DNAJB8 changes in the presence of cellular stress, without regard for native clients. Herein, we evaluate potential approaches to improve the performance of this bioanalytical assay. We find that although intracellular crosslinking increases recovery of protein interactors, this is not enough to overcome the relative drop in DNAJB8 recovery. While the J-domain promotes Hsp70 association, it does not affect the yield of protein association with DNAJB8 under basal conditions. By contrast, crosslinking and J-domain ablation both substantially increase relative protein interactor recovery with the structurally distinct Class B Hsp40 DNAJB1 but are completely compensated by poorer yield of DNAJB1 itself. Cellular thermal stress promotes increased affinity between DNAJB8H31Q and interacting proteins, as expected for interactions driven by recognition of misfolded proteins. DNAJB8WT does not demonstrate such a property, suggesting that under stress misfolded proteins are handed off to Hsp70. Hence, we find that DNAJB8H31Q is still our most effective recognition element for the recovery of destabilized client proteins following cellular stress.

Selective inhibition of HDAC6 by N-acylhydrazone derivative reduces the proliferation and induces senescence in carcinoma hepatocellular cells

Hepatocellular carcinoma (HCC) is a significant contributor to cancer-related deaths globally. Systemic therapy is the only treatment option for HCC at an advanced stage, with limited therapeutic response. In this study, we evaluated the antitumor potential of four N-acylhydrazone (NAH) derivatives, namely LASSBio-1909, 1911, 1935, and 1936, on HCC cell lines. We have previously demonstrated that the aforementioned NAH derivatives selectively inhibit histone deacetylase 6 (HDAC6) in lung cancer cells, but their effects on HCC cells have not been explored. Thus, the present study aimed to evaluate the effects of NAH derivatives on the proliferative behavior of HCC cells. LASSBio-1911 was the most cytotoxic compound against HCC cells, however its effects were minimal on normal cells. Our results showed that LASSBio-1911 inhibited HDAC6 in HCC cells leading to cell cycle arrest and decreased cell proliferation. There was also an increase in the frequency of cells in mitosis onset, which was associated with disturbing mitotic spindle formation. These events were accompanied by elevated levels of CDKN1A mRNA, accumulation of CCNB1 protein, and sustained ERK1 phosphorylation. Furthermore, LASSBio-1911 induced DNA damage, resulting in senescence and/or apoptosis. Our findings indicate that selective inhibition of HDAC6 may provide an effective therapeutic strategy for the treatment of advanced HCC, including tumor subtypes with integrated viral genome. Further, in vivo studies are required to validate the antitumor effect of LASSBio-1911 on liver cancer.

Mutagenesis and fluorescence-activated cell sorting of oleaginous Saccharomyces cerevisiae and the multi-omics analysis of its high lipid accumulation mechanisms

Acquiring lipid-producing strains of Saccharomyces cerevisiae is necessary for producing high-value palmitoleic acid. This study sought to generate oleaginous S. cerevisiae mutants through a combination of zeocin mutagenesis and fluorescence-activated cell sorting, and then to identify key mutations responsible for enhanced lipid accumulation by multi-omics sequencing. Following three consecutive rounds of mutagenesis and sorting, a mutant, MU310, with the lipid content of 44%, was successfully obtained. Transcriptome and targeted metabolome analyses revealed that a coordinated response involving fatty acid precursor biosynthesis, nitrogen metabolism, pentose phosphate pathway, ethanol conversion, amino acid metabolism and fatty acid β-oxidation was crucial for promoting lipid accumulation. The carbon fluxes of acetyl-CoA and NADPH in lipid biosynthesis were boosted in these pathways. Certain transcriptional regulators may also play significant roles in modulating lipid biosynthesis. Results of this study provide high-quality resource for palmitoleic acid production and deepen the understanding of lipid synthesis in yeast.

Machine learning: an advancement in biochemical engineering

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

Gene expression profiling and the isocitrate dehydrogenase mutational landscape of temozolomide‑resistant glioblastoma

Glioblastoma multiforme (GBM) is an aggressive brain cancer that occurs more frequently than other brain tumors. The present study aimed to reveal a novel mechanism of temozolomide resistance in GBM using bioinformatics and wet lab analyses, including meta-Z analysis, Kaplan-Meier survival analysis, protein-protein interaction (PPI) network establishment, cluster analysis of co-expressed gene networks, and hierarchical clustering of upregulated and downregulated genes. Next-generation sequencing and quantitative PCR analyses revealed downregulated [tyrosine kinase with immunoglobulin and epidermal growth factor homology domains 1 (TIE1), calcium voltage-gated channel auxiliary subunit α2Δ1 (CACNA2D1), calpain 6 (CAPN6) and a disintegrin and metalloproteinase with thrombospondin motifs 6 (ADAMTS6)] and upregulated [serum amyloid (SA)A1, SAA2, growth differentiation factor 15 (GDF15) and ubiquitin specific peptidase 26 (USP26)] genes. Different statistical models were developed for these genes using the Z-score for P-value conversion, and Kaplan-Meier plots were constructed using several patient cohorts with brain tumors. The highest number of nodes was observed in the PPI network was for ADAMTS6 and TIE1. The PPI network model for all genes contained 35 nodes and 241 edges. Immunohistochemical staining was performed using isocitrate dehydrogenase (IDH)-wild-type or IDH-mutant GBM samples from patients and a significant upregulation of TIE1 (P<0.001) and CAPN6 (P<0.05) protein expression was demonstrated in IDH-mutant GBM in comparison with IDH-wild-type GBM. Structural analysis revealed an IDH-mutant model demonstrating the mutant residues (R132, R140 and R172). The findings of the present study will help the future development of novel biomarkers and therapeutics for brain tumors.

Clinical Pharmacology Strategies for Bispecific Antibody Development: Learnings from FDA-Approved Bispecific Antibodies in Oncology

Bispecific antibodies, by enabling the targeting of more than one disease-associated antigen or engaging immune effector cells, have both advantages and challenges compared with a combination of two different biological products. As of December 2023, there are 11 U.S. Food and Drug Administration-approved BsAb products on the market. Among these, 9 have been approved for oncology indications, and 8 of these are CD3 T-cell engagers. Clinical pharmacology strategies, including dose-related strategies, are critical for bispecific antibody development. This analysis reviewed clinical studies of all approved bispecific antibodies in oncology and identified dose-related perspectives to support clinical dose optimization and regulatory approvals, particularly in the context of the Food and Drug Administration's Project Optimus: (1) starting doses and dose ranges in first-in-human studies; (2) dose strategies including step-up doses or full doses for recommended phase 2 doses or dose level(s) used for registrational intent; (3) restarting therapy after dose delay; (4) considerations for the introduction of subcutaneous doses; (5) body weight vs. flat dosing strategy; and (6) management of immunogenicity. The learnings arising from this review are intended to inform successful strategies for future bispecific antibody development.

New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives

In recent years, synthetic lethality has been recognized as a solid paradigm for anticancer therapies. The discovery of a growing number of synthetic lethal targets has led to a significant expansion in the use of synthetic lethality, far beyond poly(ADP-ribose) polymerase inhibitors used to treat BRCA1/2-defective tumors. In particular, molecular targets within DNA damage response have provided a source of inhibitors that have rapidly reached clinical trials. This Perspective focuses on the most recent progress in synthetic lethal targets and their inhibitors, within and beyond the DNA damage response, describing their design and associated therapeutic strategies. We will conclude by discussing the current challenges and new opportunities for this promising field of research, to stimulate discussion in the medicinal chemistry community, allowing the investigation of synthetic lethality to reach its full potential.

Characterization of cuproptosis signature in clear cell renal cell carcinoma by single cell and spatial transcriptome analysis

Cuproptosis is a novel type to regulate cell death with copper-dependent manner, and has been reported to involve in the occurrence and development of various malignant tumors. However, the association between cuproptosis and the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remained unclear. To address this question, we integrated the single cell RNA sequencing (scRNA-seq) datasets of ccRCC across different stages, systematically examined the distinctive expression patterns of cuproptosis-related genes (CRGs) within the TME of ccRCC, and explored the crucial signatures using the spatial transcriptome sequencing (ST-seq) dataset. The cuproptosis activities reduced in cancer tissues along with the ccRCC development, and recovered after therapy. We identified HILPDA+ ccRCC1 subtype, characterized with hypoxia, as cuproptosis susceptible cells associated with a better prognosis. The main co-expression modules of HILPDA+ ccRCC1 subtype highlighted the role in anion transport, response to oxygen species and PD-L1-PD-1 pathway. Furthermore, the immunosuppressive cells might interact with HILPDA+ ccRCC1 subtype via HAVCR2-LGALS9, C3-C3AR1, HLA-A-CD8B and HLA-C-CD8A axises to shape the cuproptosis-related TME landscape. In summary, we anticipate that this study will offer valuable insights and potential strategies of cuproptosis for therapy of ccRCC.

Evaluating computational efforts and physiological resolution of mathematical models of cardiac tissue

Computational techniques have significantly advanced our understanding of cardiac electrophysiology, yet they have predominantly concentrated on averaged models that do not represent the intricate dynamics near individual cardiomyocytes. Recently, accurate models representing individual cells have gained popularity, enabling analysis of the electrophysiology at the micrometer level. Here, we evaluate five mathematical models to determine their computational efficiency and physiological fidelity. Our findings reveal that cell-based models introduced in recent literature offer both efficiency and precision for simulating small tissue samples (comprising thousands of cardiomyocytes). Conversely, the traditional bidomain model and its simplified counterpart, the monodomain model, are more appropriate for larger tissue masses (encompassing millions to billions of cardiomyocytes). For simulations requiring detailed parameter variations along individual cell membranes, the EMI model emerges as the only viable choice. This model distinctively accounts for the extracellular (E), membrane (M), and intracellular (I) spaces, providing a comprehensive framework for detailed studies. Nonetheless, the EMI model's applicability to large-scale tissues is limited by its substantial computational demands for subcellular resolution.

Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence.

IMPORTANCE

This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

Galectin-3 protects distal convoluted tubules in rhabdomyolysis-induced kidney injury

Advanced glycation endproducts (AGEs) contribute to cellular damage of various pathologies, including kidney diseases. Acute kidney injury (AKI) represents a syndrome seldom characterized by a single, distinct pathophysiological cause. Rhabdomyolysis-induced acute kidney injury (RIAKI) constitutes roughly 15% of AKI cases, yet its underlying pathophysiology remains poorly understood. Using a murine model of RIAKI induced by muscular glycerol injection, we observed elevated levels of AGEs and the AGE receptor galectin-3 (LGALS3) in the kidney. Immunofluorescence localized LGALS3 to distal nephron segments. According to transcriptomic profiling via next-generation sequencing, RIAKI led to profound changes in kidney metabolism, oxidative stress, and inflammation. Cellular stress was evident in both proximal and distal tubules, as shown by kidney injury markers KIM-1 and NGAL. However, only proximal tubules exhibited overt damage and apoptosis, as detected by routine morphology, active Caspase-3, and TUNEL assay, respectively. In vitro, distal convoluted tubule (DCT) cells challenged with AGEs underwent apoptosis, which was markedly enhanced by Lgals3 siRNA treatment. Thus, in RIAKI, the upregulation of LGALS3 may protect the distal nephron from AGE-mediated damage, while proximal tubules lacking LGALS3 stay at risk. Thus, stimulating LGALS3 in the proximal nephron, if achievable, may attenuate RIAKI.

A possible path to persistent re-entry waves at the outlet of the left pulmonary vein

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, often evolving from paroxysmal episodes to persistent stages over an extended timeframe. While various factors contribute to this progression, the precise biophysical mechanisms driving it remain unclear. Here we explore how rapid firing of cardiomyocytes at the outlet of the pulmonary vein of the left atria can create a substrate for a persistent re-entry wave. This is grounded in a recently formulated mathematical model of the regulation of calcium ion channel density by intracellular calcium concentration. According to the model, the number of calcium channels is controlled by the intracellular calcium concentration. In particular, if the concentration increases above a certain target level, the calcium current is weakened to restore the target level of calcium. During rapid pacing, the intracellular calcium concentration of the cardiomyocytes increases leading to a substantial reduction of the calcium current across the membrane of the myocytes, which again reduces the action potential duration. In a spatially resolved cell-based model of the outlet of the pulmonary vein of the left atria, we show that the reduced action potential duration can lead to re-entry. Initiated by rapid pacing, often stemming from paroxysmal AF episodes lasting several days, the reduction in calcium current is a critical factor. Our findings illustrate how such episodes can foster a conducive environment for persistent AF through electrical remodeling, characterized by diminished calcium currents. This underscores the importance of promptly addressing early AF episodes to prevent their progression to chronic stages.

mTOR in metabolic homeostasis and disease

The ability to maintain cellular metabolic homeostasis is critical to life, in which mTOR plays an important role. This kinase integrates upstream nutrient signals and performs essential functions in physiology and metabolism by increasing metabolism and suppressing autophagy. Thus, dysregulation of mTOR activity leads to diseases, especially metabolic diseases such as cancer, type 2 diabetes and neurological disorders. Therefore, inhibition of overactivated mTOR becomes a rational approach to treat a variety of metabolic diseases. In this review, we discuss how mTOR responds to upstream signals and how mTOR regulates metabolic processes, including protein, nucleic acid, and lipid metabolism. Furthermore, we discuss the possible causes and consequences of dysregulated mTOR signaling activity, and summarize relevant applications, such as inhibition of mTOR activity to treat these diseases. This review will advance our comprehensive knowledge of the association between mTOR and metabolic homeostasis, which has significant ramifications for human health.

Ala-Cys-Cys-Ala dipeptide dimer alleviates problematic cysteine and cystine levels in media formulations and enhances CHO cell growth and metabolism

Cysteine and cystine are essential amino acids present in mammalian cell cultures. While contributing to biomass synthesis, recombinant protein production, and antioxidant defense mechanisms, cysteine poses a major challenge in media formulations owing to its poor stability and oxidation to cystine, a cysteine dimer. Due to its poor solubility, cystine can cause precipitation of feed media, formation of undesired products, and consequently, reduce cysteine bioavailability. In this study, a highly soluble cysteine containing dipeptide dimer, Ala-Cys-Cys-Ala (ACCA), was evaluated as a suitable alternative to cysteine and cystine in CHO cell cultures. Replacing cysteine and cystine in basal medium with ACCA did not sustain cell growth. However, addition of ACCA at 4 mM and 8 mM to basal medium containing cysteine and cystine boosted cell growth up to 15% and 27% in CHO-GS and CHO-K1 batch cell cultures respectively and led to a proportionate increase in IgG titer. 13C-Metabolic flux analysis revealed that supplementation of ACCA reduced glycolytic fluxes by 20% leading to more efficient glucose metabolism in CHO-K1 cells. In fed-batch cultures, ACCA was able to replace cysteine and cystine in feed medium. Furthermore, supplementation of ACCA at high concentrations in basal medium eliminated the need for any cysteine equivalents in feed medium and increased cell densities and viabilities in fed-batch cultures without any significant impact on IgG charge variants. Taken together, this study demonstrates the potential of ACCA to improve CHO cell growth, productivity, and metabolism while also facilitating the formulation of cysteine- and cystine-free feed media. Such alternatives to cysteine and cystine will pave the way for enhanced biomanufacturing by increasing cell densities in culture and extending the storage of highly concentrated feed media as part of achieving intensified bioproduction processes.

Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism

Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications

A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.

Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.

Synthesis and evaluation of sulfonamide derivatives of quinoxaline 1,4-dioxides as carbonic anhydrase inhibitors

A series of sulfonamide-derived quinoxaline 1,4-dioxides were synthesized and evaluated as inhibitors of carbonic anhydrases (CA) with antiproliferative potency. Overall, the synthesized compounds demonstrated good inhibitory activity against four CA isoforms. Compound 7g exhibited favorable potency in inhibiting a CA IX isozyme with a K i value of 42.2 nM compared to the reference AAZ (K i = 25.7 nM). Nevertheless, most of the synthesized compounds have their highest activity against CA I and CA II isoforms over CA IX and CA XII. A molecular modeling study was used for an estimation of the binding mode of the selected ligand 7g in the active site of CA IX. The most active compounds (7b, 7f, 7h, and 18) exhibited significant antiproliferative activity against MCF-7, Capan-1, DND-41, HL60, and Z138 cell lines, with IC50 values in low micromolar concentrations. Moreover, derivatives 7a, 7e, and 8g showed similar hypoxic cytotoxic activity and selectivity compared to tirapazamine (TPZ) against adenocarcinoma cells MCF-7. The structure-activity relationships analysis revealed that the presence of a halogen atom or a sulfonamide group as substituents in the phenyl ring of quinoxaline-2-carbonitrile 1,4-dioxides was favorable for overall cytotoxicity against most of the tested cancer cell lines. Additionally, the presence of a carbonitrile fragment in position 2 of the heterocycle also had a positive effect on the antitumor properties of such derivatives against the majority of cell lines. The most potent derivative, 3-trifluoromethylquinoxaline 1,4-dioxide 7h, demonstrated higher or close antiproliferative activity compared to the reference agents, such as doxorubicin, and etoposide, with an IC50 range of 1.3-2.1 μM. Analysis of the obtained results revealed important patterns in the structure-activity relationship. Moreover, these findings highlight the potential of selected lead sulfonamides on the quinoxaline 1,4-dioxide scaffold for further in-depth evaluation and development of chemotherapeutic agents targeting carbonic anhydrases.

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.

T cell exhaustion and senescence for ovarian cancer immunotherapy

Ovarian cancer is a common gynecological malignancy, and its treatment remains challenging. Although ovarian cancer may respond to immunotherapy because of endogenous immunity at the molecular or T cell level, immunotherapy has so far not had the desired effect. The functional status of preexisting T cells is an indispensable determinant of powerful antitumor immunity and immunotherapy. T cell exhaustion and senescence are two crucial states of T cell dysfunction, which share some overlapping phenotypic and functional features, but each status possesses unique molecular and developmental signatures. It has been widely accepted that exhaustion and senescence of T cells are important strategies for cancer cells to evade immunosurveillance and maintain the immunosuppressive microenvironment. Herein, this review summarizes the phenotypic and functional features of exhaust and senescent T cells, and describes the key drivers of the two T cell dysfunctional states in the tumor microenvironment and their functional roles in ovarian cancer. Furthermore, we present a summary of the molecular machinery and signaling pathways governing T cell exhaustion and senescence. Possible strategies that can prevent and/or reverse T cell dysfunction are also explored. An in-depth understanding of exhausted and senescent T cells will provide novel strategies to enhance immunotherapy of ovarian cancer through redirecting tumor-specific T cells away from a dysfunctional developmental trajectory.

Oxidative stress modulating nanomaterials and their biochemical roles in nanomedicine

Many pathological conditions are predominantly associated with oxidative stress, arising from reactive oxygen species (ROS); therefore, the modulation of redox activities has been a key strategy to restore normal tissue functions. Current approaches involve establishing a favorable cellular redox environment through the administration of therapeutic drugs and redox-active nanomaterials (RANs). In particular, RANs not only provide a stable and reliable means of therapeutic delivery but also possess the capacity to finely tune various interconnected components, including radicals, enzymes, proteins, transcription factors, and metabolites. Here, we discuss the roles that engineered RANs play in a spectrum of pathological conditions, such as cancer, neurodegenerative diseases, infections, and inflammation. We visualize the dual functions of RANs as both generator and scavenger of ROS, emphasizing their profound impact on diverse cellular functions. The focus of this review is solely on inorganic redox-active nanomaterials (inorganic RANs). Additionally, we deliberate on the challenges associated with current RANs-based approaches and propose potential research directions for their future clinical translation.

Biomarkers associated with survival in patients with platinum-refractory urothelial carcinoma treated with paclitaxel

BACKGROUND

Taxane- based chemotherapy is widely used in patients with platinum- and immunotherapy refractory, metastatic urothelial carcinoma (mUC). Outcomes are poor and biomarkers associated with outcome are lacking. We aim to identify cancer hallmarks associated with survival in patients receiving paclitaxel.

METHODS

Whole-transcriptome profiles were generated for a subset of patients enrolled in a randomised phase II study investigating paclitaxel and pazopanib in platinum refractory mUC (PLUTO, EudraCT 2011-001841-34). Estimates of gene expression were calculated and input into the Almac proprietary analysis pipeline and signature scores were calculated using ClaraT V3.0.0. Ten key gene signatures were assessed: Immuno-Oncology, Epithelial to Mesenchymal Transition, Angiogenesis, Proliferation, Cell Death, Genome Instability, Energetics, Inflammation, Immortality and Evading Growth. Hazard ratios were calculated using Cox regression model and Kaplan-Meier methods were used to estimate progression free survival (PFS) and overall survival (OS).

RESULTS

38 and 45 patients treated with paclitaxel or pazopanib were included. Patients with high genome instability expression treated with paclitaxel had significantly improved survival with a HR of 0.29 (95% CI: 0.14-0.61, p=0.001) and HR 0.34 (95% CI: 0.17-0.69, p=0.003) for PFS and OS, respectively. Similarly, patients with high evading growth suppressor expression treated with paclitaxel had improved PFS and OS with a HR of 0.35 (95% CI: 0.19-0.77, p=0.007) and HR 0.46 (95% CI: 0.23-0.91, p=0.026), respectively. No other gene signatures had significant impact on outcome. In both paclitaxel and pazopanib cohorts, angiogenesis activation was associated with worse PFS and OS, and VEGF targeted therapy did not improve outcomes.

CONCLUSION

High Genome-instability and Evading-growth suppressor biologies are associated with improved survival in patients with platinum refractory mUC receiving paclitaxel. These may refine mUC risk stratification and guide treatment decision in the future.

NFκB dynamics-dependent epigenetic changes modulate inflammatory gene expression and induce cellular senescence

Upregulation of nuclear factor κB (NFκB) signaling is a hallmark of aging and a major cause of age-related chronic inflammation. However, its effect on cellular senescence remains unclear. Here, we show that alteration of NFκB nuclear dynamics from oscillatory to sustained by depleting a negative feedback regulator of NFκB pathway, NFκB inhibitor alpha (IκBα), in the presence of tumor necrosis factor α (TNFα) promotes cellular senescence. Sustained NFκB activity enhanced inflammatory gene expression through increased NFκB-DNA binding and slowed the cell cycle. IκBα protein was decreased under replicative or oxidative stress in vitro. Furthermore, a decrease in IκBα protein and an increase in DNA-NFκB binding at the transcription start sites of age-associated genes in aged mouse hearts suggested that nuclear NFκB dynamics may play a critical role in the progression of aging. Our study suggests that nuclear NFκB dynamics-dependent epigenetic changes regulated over time in a living system, possibly through a decrease in IκBα, enhance the expression of inflammatory genes to advance the cells to a senescent state.

Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

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.

Evolving scientific discovery by unifying data and background knowledge with AI Hilbert

The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. However, this does not include experimental data within the discovery process, which may be inefficient. We propose a solution to this problem when all axioms and scientific laws are expressible as polynomials and argue our approach is widely applicable. We model notions of minimal complexity using binary variables and logical constraints, solve polynomial optimization problems via mixed-integer linear or semidefinite optimization, and prove the validity of our scientific discoveries in a principled manner using Positivstellensatz certificates. We demonstrate that some famous scientific laws, including Kepler's Law of Planetary Motion and the Radiated Gravitational Wave Power equation, can be derived in a principled manner from axioms and experimental data.

Association of COPD with adverse outcomes in heart failure patients with preserved ejection fraction

We performed a systematic review and meta-analysis to detect the impact of chronic obstructive pulmonary disease (COPD) on the prognosis of heart failure patients with preserved ejection fraction (HFpEF). We systematically screened eligible literature from three electronic databases, PubMed, EMBASE and Cochrane Library, up to April 2023. Two researchers participated in data collection independently. Risk ratios (RRs) from included studies with 95% confidence intervals (CIs) were pooled in the Review Manager version 5.40 software using a random-effects model for analysis. A total of 11 studies (3 post hoc analyses of RCTs and 8 observational studies) with 18 602 participants were included in this meta-analysis. After pooling all the data from eligible studies, our results indicated that COPD was associated with an increased risk of hospitalization (RR = 1.66, 95% CI, 1.47-1.87, P < 0.00001), mortality (RR = 1.62, 95% CI, 1.34-1.95, P < 0.00001), and the composition of hospitalization or mortality (RR = 1.84, 95% CI, 1.35-2.51, P < 0.001) in patients with HFpEF. In a subgroup analysis, the risks of cardiovascular-related mortality (RR = 1.59, 95% CI, 1.30-1.93, P < 0.00001) and post-discharge mortality risk (RR = 2.57, 1.34-4.93, P < 0.01) were increased in HFpEF patients comorbid with COPD, and these associations were also detected in HF-caused hospitalization (RR = 1.64, 95% CI, 1.44-1.87, P < 0.00001). Evidence from existing studies supported that COPD was an independent prognostic risk factor for patients with HFpEF. Developing rapid clinical diagnostic indicators and early use of novel drugs such as SGLT-2 and ARNI may improve the prognosis of this population, deserving further study.

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.

Immune cell landscapes are associated with high-grade serous ovarian cancer survival

High-grade serous ovarian cancer (HGSOC) is an aggressive disease known to develop resistance to chemotherapy. We investigated the prognostic significance of tumor cell states and potential mechanisms underlying chemotherapy resistance in HGSOC. Transcriptome deconvolution was performed to address cellular heterogeneity. Kaplan-Meier survival curves were plotted to illustrate the outcomes of patients with varying cellular abundances. The association between gene expression and chemotherapy response was tested. After adjusting for surgery status and grading, several cell states exhibited a significant correlation with patient survival. Cell states can organize into carcinoma ecotypes (CE). CE9 and CE10 were proinflammatory, characterized by higher immunoreactivity, and were associated with favorable survival outcomes. Ratios of cell states and ecotypes had better prognostic abilities than a single cell state or ecotype. A total of 1265 differentially expressed genes were identified between samples with high and low levels of C9 or CE10. These genes were partitioned into three co-expressed modules, which were associated with tumor cells and immune cells. Pogz was identified to be linked with immune cell genes and the chemotherapy response of paclitaxel. Collectively, the survival of HGSOC patients is correlated with specific cell states and ecotypes.

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.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations

The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets-characterized by sparse and irregularly timed measurements-poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model-informed drug development.

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.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.