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In recent years, mostly spanning the past decade, the concept of immunometabolism has ushered with a novel perspective on carcinogenesis, tumor progression, and tumor response to therapy. It has become clear that the metabolic state of immune cells plays a significant role in shaping their antitumor or protumor activities within the cancer microenvironment. Consequently, the examination of tumor metabolic heterogeneity, including an exploration of immunometabolism, proves indispensable for enhancing prognostic tools and advancing the quest for personalized treatments. Here we have delved into how metabolic reprogramming profoundly influences the acquisition and maintenance of functional states, spanning from effector and cytotoxic profiles to regulatory and immunosuppressive phenotypes in both innate and adaptive immunity. These alterations wield considerable influence over tumor evolution and affect the outcome of cancer. Furthermore, we explore some of the cellular signaling mechanisms that underpin the metabolic and phenotypic flexibility of immune cells in response to external stimuli. Show less

Ferroptosis, the non-apoptotic, iron-dependent form of cell death is an unavoidable outcome and byproduct of cellular metabolism. Reactive oxygen species generation during metabolic activities transcends to ­Fe2+-induced lipid peroxidation, leading to ferroptosis. Cancer cells being highly metabolic are more prone to ferroptosis. However, their neoplastic nature enables them to bypass ferroptosis and become ferroptosis-resistant. The capability of cancer cells to reprogram its metabolic activities is one of its finest abilities to abort oxidative damage, and hence ferroptosis. Moreover, the reprogrammed metabolism of cancer cells, also associates with the radical trapping antioxidant Show less

Ferroptosis, the non-apoptotic, iron-dependent form of cell death is an unavoidable outcome and byproduct of cellular metabolism. Reactive oxygen species generation during metabolic activities transcends to ­Fe2+-induced lipid peroxidation, leading to ferroptosis. Cancer cells being highly metabolic are more prone to ferroptosis. However, their neoplastic nature enables them to bypass ferroptosis and become ferroptosis-resistant. The capability of cancer cells to reprogram its metabolic activities is one of its finest abilities to abort oxidative damage, and hence ferroptosis. Moreover, the reprogrammed metabolism of cancer cells, also associates with the radical trapping antioxidant Show less

Nucleotide excision repair (NER) is a universal cut-and-paste DNA repair mechanism that corrects bulky DNA lesions such as those caused by UV radiation, environmental mutagens, and some chemotherapy drugs. In this review, we focus on the human transcription/DNA repair factor TFIIH, a key player of the NER pathway in eukaryotes. This 10-subunit multiprotein complex notably verifies the presence of a lesion and opens the DNA around the damage via its XPB and XPD subunits, two proteins identified in patients suffering from Xeroderma Pigmentosum syndrome. Isolated as a class II gene transcription factor in the late 1980s, TFIIH is a prototypic molecular machine that plays an essential role in both DNA repair and transcription initiation and harbors a DNA helicase, a DNA translocase, and kinase activity. More recently, TFIIH subunits have been identified as participating in other cellular processes, including chromosome segregation during mitosis, maintenance of mitochondrial DNA integrity, and telomere replication. Show less

Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug−drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

Abstract Background: Chemoresistance remains a significant barrier in colorectal cancer (CRC) treatment, particularly with oxaliplatin, a key first-line chemotherapy agent. This study is therefore aimed to develop an in vitro model of oxaliplatin-resistant HCT116 cells and investigate the associated morphological and molecular adaptations to identify potential dysregulated survival and apoptotic pathways that are playing a role in developing chemoresistance. Methods: HCT116 CRC cells were cultured, and resistance was induced by stepwise oxaliplatin exposure, with recovery phases between treatments. Morphological changes during resistance development were observed using phase-contrast microscopy. Flow cytometry was performed to analyze cell cycle distribution and apoptotic cell populations. Gene expression of key survival pathways, including Wnt/β-catenin, NFκB, and PI3K/Akt, was assessed using qPCR to identify molecular alterations associated with resistance. Results: Parental HCT116 cells displayed cuboidal morphology, high proliferation (Ki-67 positivity), and epithelial characteristics (moderate E-cadherin and ALDH1 expression). Resistant cells exhibited increased morphological heterogeneity, cell detachment, and features of epithelial-mesenchymal transition (EMT). Flow cytometry revealed a significant reduction in the sub-G1 population and an increase in the G2/M phase, suggesting diminished apoptosis and cell cycle dysregulation in oxaliplatin resistant HCT116 cells as compared to wild type. Gene expression analysis showed a marked upregulation of β-catenin, NFκB, and PI3K/Akt pathway components, coupled with increased expression of EMT markers (Snail, Vimentin) and drug efflux genes (ABCG2, ABCC1) in oxaliplatin resistant cells as compared to wild type HCT116 cells suggesting a role of these survival pathways and genes in induction of resistance. Conclusion: Oxaliplatin resistance in HCT116 CRC cells is characterized by morphological plasticity, altered cell cycle regulation, and activation of survival pathways, including Wnt/β-catenin, NFκB, and PI3K/Akt. These findings highlight the complexity of chemoresistance and provide a basis for identifying novel targets to enhance treatment efficacy in CRC. Citation Format: Aliza Jafri, Sheerien Rajput, Areeb Ahmed, Azhar Hussain Rajabali, Kulsoom Ghias. Identification of signaling and apoptotic pathway alterations in oxaliplatin resistant HCT116 colorectal cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl₁₎:Abstract nr 4417. Show less

Solvent effects play a critical role in ionic chemical reactions and have been a research topic for a long time. The solvent molecules in the first solvation shell of the solute are the most important solvating species. Consequently, manipulation of the structure of this shell can be used to control the reactivity and selectivity of ionic reactions. In this work, we report extensive experimental and insightful computational studies of the effects of adding diverse fluorinated bulky alcohols with different solvation abilities to the fluorination reaction of alkyl bromides with potassium fluoride promoted by 18-crown-6. We found that adding a stoichiometric amount of these alcohols to the acetonitrile solution has an important effect on the kinetics and selectivity. The most effective alcohol was 2-trifluoromethyl-2-propanol (TBOH-F3), and the use of 3 equiv of this alcohol to fluorinate a primary alkyl bromide led to a 78% fluorination yield in just 6 h of reaction time at a mild temperature of 82 °C, with 8% of E2 yield. The more challenging secondary alkyl bromide substrate obtained 44% fluorination yield and 56% E2 yield at 18 h of reaction time. More fluorinated alcohols with six or more fluorine atoms have resulted in relatively acidic alcohols, leading to large amounts of the corresponding ethers of these alcohols as side products. The widely used hexafluoroisopropanol (HFIP) was the least effective one for monofluorination, indicating that both acidity and bulkiness are important features of the alcohols for promoting fluorination using KF salt. Nevertheless, the ether of HFIP can be easily formed with the substrate, generating a highly fluorinated ether product. Theoretical calculations predict ΔG ^‡ in close agreement with the experiments and explain the higher selectivity induced by the fluorinated bulky alcohols in relation to the use of crown ether alone. Show less

Platinum-based drugs are a mainstay in chemotherapy, with traditional forms exerting their work directly on DNA. In recent years, it has been observed that platinum complexes had the potential to induce immunogenic cell death (ICD) and effectively trigger antitumor immune responses. Herein, to obtain novel platinum complexes with chemo-immunological properties, a series of Pt(ΙΙ)-N-heterocyclic carbene (Pt(ΙΙ)-NHC) complexes derived from 4,5-diarylimidazoles were synthesized. Among them, the dominant complex 3f was proved to exhibit better anti-liver cancer capacity compared to cisplatin and oxaliplatin. Complex 3f showed the ability to cause DNA damage by binding to DNA. In addition, it triggered intracellular reactive oxygen species (ROS) generation, affected the function of mitochondria, and blocked cells in G0/G1 phase, ultimately induced apoptosis in liver cancer cells. Furthermore, complex 3f activated endoplasmic reticulum stress (ERS) which promoted the release of damage-associated molecular patterns (DAMPs), induced ICD and dendritic cells (DCs) maturation. Interestingly, complex 3f also upregulated PD-L1, consequently converted "cold tumors" into "hot tumors". Overall, complex 3f had the potential to be regarded as a promising chemoimmunotherapy for the treatment of liver cancer. Show less

Ewing sarcoma (EwS) cell line culture largely relies on standard techniques, which do not recapitulate physiological conditions. Here, we report on a feasible and cost-efficient EwS cell culture technique with increased physiological relevance employing an advanced medium composition, reduced fetal calf serum, and spheroidal growth. Improved reflection of the transcriptional activity related to proliferation, hypoxia, and differentiation in EwS patient tumors was detected in EwS cells grown in this refined in vitro condition. Moreover, transcriptional signatures associated with the oncogenic activity of the EwS-specific FET::ETS fusion transcription factors in the refined culture condition were shifted from proliferative toward metabolic gene signatures. The herein-presented EwS cell culture technique with increased physiological relevance provides a broadly applicable approach for enhanced in vitro modeling relevant to advancing EwS research and the validity of experimental results. Show less

Background/Objectives: The origin of genes and genetics is the story of the coevolution of translation systems and the genetic code. Remarkably, the history of the origin of life on Earth was inscribed and preserved in the sequences of tRNAs. Methods: Sequence logos demonstrate the patterning of pre-life tRNA sequences. Results: The pre-life type I and type II tRNA sequences are known to the last nucleotide with only a few ambiguities. Type I and type II tRNAs evolved from ligation of three 31 nt minihelices of highly patterned and known sequence followed by closely related 9 nt internal deletion(s) within ligated acceptor stems. The D loop 17 nt core was a truncated UAGCC repeat. The anticodon and T 17 nt stem-loop-stems are homologous sequences with 5 nt stems and 7 nt U-turn loops that were selected in pre-life to resist ribozyme nucleases and to present a 3 nt anticodon with a single wobble position. The 7 nt T loop in tRNA was selected to interact with the D loop at the "elbow". The 5'-acceptor stem was based on a 7 nt truncated GCG repeat. The 3'-acceptor stem was based on a complementary 7 nt CGC repeat. In pre-life, ACCA-Gly was a primitive adapter molecule ligated to many RNAs, including tRNAs, to synthesize polyglycine. Conclusions: Analysis of sequence logos of tRNAs from an ancient Archaeon substantiates how the pre-life to life transition occurred on Earth. Polyglycine is posited to have aggregated complex molecular assemblies, including minihelices, tRNAs, cooperating molecules, and protocells, leading to the first life on Earth. Show less

The preparation of a new series of Ir(III) tetrazolato complexes with the general formula [Ir(C^N)2(N^N)]0/+, where the ancillary ligand (N^N) is represented in turn by 2-pyridyltetrazolato (PTZ-), 2-pyrazinyltetrazolato (PYZ-) or 2-pyridyl 5-trifluoromethyl tetrazolato (PTZ-CF3-), is described herein. The design of the cyclometalated (C^N) ligands, namely 2-phenylisonicotinonitrile (ppyCN) and 2-(2,4-difluorophenyl)isonicotinonitrile (F2ppy-CN), features the well-known ppy- or F2ppy core, with the introduction of one electron-withdrawing cyano (-CN) group at the para position of the pyridyl ring. The photophysical and electrochemical properties of the new Ir(III) cyclometalated complexes have been investigated and the resulting data suggest how the (C^N) ligands significantly rule the luminescence behavior of the new complexes. Further blue or red shifting of the emission profiles of the neutral complexes was observed upon their conversion into cationic species through the regioselective addition of a methyl moiety to the coordinated tetrazolato ring. Lastly, neutral [Ir(F2ppy-CN)2(PTZ)] was used as an emissive phosphor for the fabrication of an OLED-type device. Show less

Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug-drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 38 million PubMed abstracts and 6.6 Show less

Abstract Neuroblastoma, a transcriptionally driven pediatric malignancy, exhibits a remarkable clinical and biological heterogeneity. Two major subtypes, the adrenergic and mesenchymal, are differentially governed by a subset of transcription factors that comprise the core regulatory circuit (CRC). The former subtype is often associated with MYCN amplification and is particularly aggressive and therapy-resistant, underscoring the need for novel targets. Here, we identify the multifunctional non-POU domain-containing octamer-binding (NONO) protein as a guardian of individual CRC genes, thereby contributing to survival of neuroblastoma cells with different MYCN copy numbers. Intracellular oxidation in response to auranofin, an inhibitor of thioredoxin reductase 1, rapidly down-regulated the amounts of NONO mRNA and protein in MYCN -amplified Kelly cell line. Conversely, NONO knockdown with RNA interference (siNONO) also triggered intracellular oxidation. These effects were less pronounced in the SK-N-AS cell line carrying a single MYCN copy, as well as in non-malignant HS5 fibroblasts. In Kelly cells, siNONO attenuated auranofin-induced activation of CRC genes HAND2 and PHOX2B . In line with preferential effects on NONO abundance, the Kelly cells were more sensitive than single MYCN copy counterparts to combinations of a sublethal concentration of auranofin with siNONO. Importantly, MYCN -amplified cells demonstrated a significantly suppressed clonogenic survival 14 days after transient exposure to these combinations compared with each agent alone; HS5 fibroblasts were largely spared. Our findings 1) establish NONO as a redox sensor, a non-trivial role for transcriptional proteins, and 2) justify the strategy of therapeutic targeting of MYCN -amplified tumors vulnerable to oxidative stress. Key points NONO, a master regulator of the core regulatory circuit (CRC) in MYCN -amplified neuroblastoma, is rapidly down-regulated by auranofin-induced intracellular oxidation. NONO knockdown synergizes with auranofin in triggering individual CRC gene deregulation and lethal oxidative stress preferentially in MYCN -amplified cells. Show less

Ferroptosis suppressor protein 1 (FSP1) has emerged as a critical regulator of ferroptosis, an iron-dependent form of programmed cell death with significant therapeutic potential in cancer treatment. Despite rapidly expanding research, current knowledge on FSP1 remains fragmented across various tumor types and experimental contexts. The aim of this review is to systematically integrate the latest evidence regarding the molecular structure, biological functions, and regulatory mechanisms controlling FSP1 expression, emphasizing its involvement in tumor progression and resistance to therapy. Readers can expect comprehensive coverage of FSP1's structural characteristics, enzymatic roles, transcriptional and post-transcriptional regulation, and its pathological significance in hepatocellular carcinoma, colorectal cancer, pancreatic cancer, gastric cancer, breast cancer, lung cancer, and leukemia. We further evaluate emerging therapeutic strategies targeting FSP1 aimed at overcoming resistance and improving clinical outcomes. Relevant studies were systematically identified by searching PubMed, Web of Science, and Embase databases, focusing particularly on the recent and impactful literature to guide future research directions. Show less

In this Review, Emerling and colleagues summarize the roles of phosphatidylinositol 4-kinases (PI4Ks) and phosphatidylinositol phosphate kinases (PIPKs) in cancer. They highlight the altered expression of these kinases in tumours and discuss ongoing efforts in developing therapies targeting these lesser-studied phosphoinositide kinase families. Show less

BACKGROUND: Drug repositioning is a pivotal strategy in pharmaceutical research, offering accelerated and cost-effective therapeutic discovery. However, biomedical information relevant to drug repositioning is often complex, dispersed, and underutilized due to limitations in traditional extraction methods, such as reliance on annotated data and poor generalizability. Large language models (LLMs) show promise but face challenges such as hallucinations and interpretability issues. OBJECTIVE: This study proposed long chain-of-thought for drug repositioning knowledge extraction (LCoDR-KE), a lightweight and domain-specific framework to enhance LLMs' accuracy and adaptability in extracting structured biomedical knowledge for drug repositioning. METHODS: A domain-specific schema defined 11 entities (eg, drug, disease) and 18 relationships (eg, treats, is biomarker of). Following the established schema architecture, we constructed automatic annotation based on 10,000 PubMed abstracts via chain-of-thought prompt engineering. A total of 1000 expert-validated abstracts were curated into a drug repositioning corpus, a high-quality specialized corpus, while the remaining entries were allocated for model training purposes. Then, the proposed LCoDR-KE framework combined supervised fine-tuning of the Qwen2.5-7B-Instruct model with reinforcement learning and dual-reward mechanisms. Performance was evaluated against state-of-the-art models (eg, conditional random fields, Bidirectional Encoder Representations From Transformers, BioBERT, Qwen2.5, DeepSeek-R1, OpenBioLLM-70B, and model variants) using precision, recall, and F1-score. In addition, the convergence of the training method was assessed by analyzing performance progression across iteration steps. RESULTS: LCoDR-KE achieved an entity F1 of 81.46% (eg, drug 95.83%, disease 90.52%) and triplet F1 of 69.04%, outperforming traditional models and rivaling larger LLMs (DeepSeek-R1: entity F1=84.64%, triplet F1=69.02%). Ablation studies confirmed the contributions of supervised fine-tuning (8.61% and 20.70% F1 drop if removed) and reinforcement learning (6.09% and 14.09% F1 drop if removed). The training process demonstrated stable convergence, validated through iterative performance monitoring. Qualitative analysis of the model's chain-of-thought outputs showed that LCoDR-KE performed structured and schema-aware reasoning by validating entity types, rejecting incompatible relations, enforcing constraints, and generating compliant JSON. Error analysis revealed 4 main types of mistakes and challenges for further improvement. CONCLUSIONS: LCoDR-KE enhances LLMs' domain-specific adaptability for drug repositioning by offering an open-source drug repositioning corpus and a long chain-of-thought framework based on a lightweight LLM model. This framework supports drug discovery and knowledge reasoning while providing scalable, interpretable solutions applicable to broader biomedical knowledge extraction tasks. Show less

In nature, life is inherently dissipative. Cells continuously consume energy (such as ATP) to sustain homeostasis, drive metabolism, and respond dynamically to environmental cues. Inspired by this principle, we develop a synthetic protocell system that exhibits dissipative behavior and initiates metabolic-like processes. Our design features synthetic vesicles formed from a cationic surfactant, which undergo a fuel-driven transformation into coacervate protocells via liquid-liquid phase separation. Dissipation is achieved through alkaline phosphatase (ALP)-catalyzed ATP hydrolysis, driving the reverse transition from coacervates back to vesicles. The distinct physicochemical properties and internal organization of vesicle and coacervate protocells enable us to design functional regulators capable of producing secondary signals, such as fluorescence and enzymatic products. This work offers a strategy for engineering enzymatic reaction-regulated dissipative behaviors of protocell systems that emulate key aspects of cellular metabolism, representing a step toward synthetic life-like systems with dynamic behavior and functional complexity. Show less

In the healthcare industry, the ever-increasing volume of clinical trial data presents challenges for ensuring drug safety and detecting adverse drug reactions (ADRs). This study aims to address the challenge of accurately detecting Serious Adverse Events (SAEs) in pharmacovigilance, a critical component in ensuring drug safety during and after clinical trials. The key problem lies in the underreporting and delayed detection of Adverse Drug Reactions (ADRs) due to the heterogeneous nature of medical data, class imbalance, and the limited scope of traditional monitoring techniques. This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. The objective is to outperform traditional signal detection methods and provide interpretable predictions to aid clinicians in real-time. By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. Through meticulous feature engineering and the application of techniques to address data imbalance, our model demonstrates improved accuracy and interpretability in predicting ADRs. The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes.•The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data.•Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model's performance. Among the models tested, the CNN model with BERT achieved the highest accuracy, providing valuable insights into the potential of deep learning for enhancing pharmacovigilance practices.•These findings suggest that an inclusion of diverse clinical data when supplied to advanced ML and NLP techniques can significantly improve the detection of ADRs, leading to better alignment with the fundamental principles of Good Clinical Practice (GCP). Show less

Geological structures known as alkaline hydrothermal vents (AHVs) likely displayed dynamic energy characteristics analogous to cellular chemiosmosis and contained iron-oxyhydroxide green rusts in the early Earth. Under specific conditions, those minerals could have acted as non-enzymatic catalysts in the development of early bioenergetic chemiosmotic energy systems while being integrated into the membrane of AHV-produced organic vesicles. Here, we show that the simultaneous addition of two probable AHV components, namely nickel and amino acids, impacts green rust's physico-chemical properties, especially those required for its incorporation in lipid vesicle's membranes, such as decreasing the mineral size to the nanometer scale and increasing its hydrophobicity. These results suggest that such hydrophobic nano green rusts could fit into lipid vesicle membranes and could have functioned as a primitive, inorganic precursor to modern chemiosmotic metalloenzymes, facilitating both electron and proton transport in early life-like systems. Show less

Conformer generation is crucial for computational chemistry tasks such as structure-based modeling and property prediction. Although reliable methods exist for organic molecules, coordination complexes remain challenging due to their diverse coordination geometries, ligand types, and stereochemistry. Current tools often lack the flexibility and reliability required for these systems. Here, we introduce MetalloGen, a novel algorithm designed for the automated generation of 3D conformers of mononuclear coordination complexes. MetalloGen accepts either SMILES strings or molecular graph representations as input and enables the generation of reliable conformers, including those with multiple polyhapto ligands, which are typically inaccessible to conventional conformer generators. To rigorously assess MetalloGen's performance, we benchmarked it on three distinct data sets: a curated collection of experimentally determined structures from the Cambridge Structural Database, the MOR41 benchmark set encompassing a wide range of organometallic reactions and complex ligand environments, and three catalytic reactions. Across all test sets, MetalloGen consistently reproduced appropriate geometries with high fidelity and demonstrated robust stereochemical control, even for challenging cases involving multiple polyhapto ligands. The versatility and reliability of MetalloGen make it a valuable tool for more accurate and efficient computational investigations in inorganic and organometallic chemistry. Show less

A branch of the nucleotide excision repair (NER) pathway, transcription-coupled repair (TCR or TC-NER) specifically operates on the template DNA strand of actively transcribed genes. Initiated by stalling of elongating RNA polymerase complexes at damaged sites, TC-NER has historically been viewed as "accelerated repair", arguably necessary for the maintenance of vital transcription function. Conversely, the conventional "global genome" (GG-NER) mechanism, operating throughout the genome, is usually regarded as a much slower process, even though it has long been found that differences in repair kinetics between transcribed DNA and the rest of the genome are not manifested for all structural types of DNA damage. Considering that damage detection is the rate-limiting step of overall repair reactions in most cases and that the mechanisms of the initial recognition of modified DNA structure are fundamentally different between TC-NER and GG-NER, it is suggestive to attribute the observed kinetic differences to different damage spectra recognized by the two pathways. This review summarizes current knowledge on the differential requirements of TC-NER and GG-NER towards specific damage types, based on their structural rather than spatial characteristics, and highlights some common features of DNA modifications repaired preferentially or exclusively by TC-NER, while evading other repair mechanisms. Show less

Mitochondria are associated with cellular energy metabolism, proliferation, and mode of death. Damage to mitochondrial DNA (mtDNA) greatly affects mitochondrial function by interfering with energy production and the signaling pathway. Monofunctional trinuclear platinum complex MTPC demonstrates different actions on the mtDNA of cancerous and normal cells. It severely impairs the integrity and function of mitochondria in the human lung cancer A549 cells, such as dissipating mitochondrial membrane potential, decreasing the copy number of mtDNA, interfering in nucleoid proteins and polymerase gamma gene, reducing adenosine triphosphate (ATP), and inducing mitophagy, whereas it barely affects the mtDNA of the human kidney 2 (HK-2) cells. Moreover, MTPC promotes the release of mtDNA into the cytosol and stimulates the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway, thus showing the potential to trigger antitumor immunity. MTPC displays significant cytotoxicity against A549 cells, while it exhibits weak toxicity toward HK-2 cells, therefore displaying great advantage to overcome the lingering nephrotoxicity of platinum anticancer drugs. Discrepant effects of a metal complex on mitochondria of different cells mean that targeting mitochondria has special significance in cancer therapy. Show less

PT-112 is a novel small molecule exhibiting promising clinical activity in patients with solid tumors. PT-112 kills malignant cells by inhibiting ribosome biogenesis while promoting the emission of immunostimulatory signals. Accordingly, PT-112 is an authentic immunogenic cell death (ICD) inducer and synergizes with immune checkpoint inhibitors in preclinical models of mammary and colorectal carcinoma. Moreover, PT-112 monotherapy has led to durable clinical responses, some of which persisting after treatment discontinuation. Mitochondrial outer membrane permeabilization (MOMP) regulates the cytotoxicity and immunogenicity of various anticancer agents. Here, we harnessed mouse mammary carcinoma TS/A cells to test whether genetic alterations affecting MOMP influence PT-112 activity. As previously demonstrated, PT-112 elicited robust antiproliferative and cytotoxic effects against TS/A cells, which were preceded by the ICD-associated exposure of calreticulin (CALR) on the cell surface, and accompanied by the release of HMGB1 in the culture supernatant. TS/A cells responding to PT-112 also exhibited eIF2α phosphorylation and cytosolic mtDNA accumulation, secreted type I IFN, and exposed MHC Class I molecules as well as the co-inhibitory ligand PD-L1 on their surface. Acute cytotoxicity and HMGB1 release caused by PT-112 in TS/A cells were influenced by MOMP competence. Conversely, PT-112 retained antiproliferative effects and its capacity to drive type I IFN secretion as well as CALR, MHC Class I and PD-L1 exposure on the cell surface irrespective of MOMP defects. These data indicate a partial involvement of MOMP in the mechanisms of action of PT-112, suggesting that PT-112 is active across various tumor types, including malignancies with MOMP defects. Show less