Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of pr Show more
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation – and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less
Background Metabolic adaptations can allow cancer cells to survive DNA-damaging chemotherapy. This unmet clinical challenge is a potential vulnerability of cancer. Accordingly, there is an intense se Show more
Background Metabolic adaptations can allow cancer cells to survive DNA-damaging chemotherapy. This unmet clinical challenge is a potential vulnerability of cancer. Accordingly, there is an intense search for mechanisms that modulate cell metabolism during anti-tumor therapy. We set out to define how colorectal cancer CRC cells alter their metabolism upon DNA replication stress and whether this provides opportunities to eliminate such cells more efficiently. Methods We incubated p53-positive and p53-negative permanent CRC cells and short-term cultured primary CRC cells with the topoisomerase-1 inhibitor irinotecan and other drugs that cause DNA replication stress and consequently DNA damage. We analyzed pro-apoptotic mitochondrial membrane depolarization and cell death with flow cytometry. We evaluated cellular metabolism with immunoblotting of electron transport chain (ETC) complex subunits, analysis of mitochondrial mRNA expression by qPCR, MTT assay, measurements of oxygen consumption and reactive oxygen species (ROS), and metabolic flux analysis with the Seahorse platform. Global metabolic alterations were assessed using targeted mass spectrometric analysis of extra- and intracellular metabolites. Results Chemotherapeutics that cause DNA replication stress induce metabolic changes in p53-positive and p53-negative CRC cells. Irinotecan enhances glycolysis, oxygen consumption, mitochondrial ETC activation, and ROS production in CRC cells. This is connected to increased levels of electron transport chain complexes involving mitochondrial translation. Mass spectrometric analysis reveals global metabolic adaptations of CRC cells to irinotecan, including the glycolysis, tricarboxylic acid cycle, and pentose phosphate pathways. P53-proficient CRC cells, however, have a more active metabolism upon DNA replication stress than their p53-deficient counterparts. This metabolic switch is a vulnerability of p53-positive cells to irinotecan-induced apoptosis under glucose-restricted conditions. Conclusion Drugs that cause DNA replication stress increase the metabolism of CRC cells. Glucose restriction might improve the effectiveness of classical chemotherapy against p53-positive CRC cells. Graphical Abstract The topoisomerase-1 inhibitor irinotecan and other chemotherapeutics that cause DNA damage induce metabolic adaptations in colorectal cancer (CRC) cells irrespective of their p53 status. Irinotecan enhances the glycolysis and oxygen consumption in CRC cells to deliver energy and biomolecules necessary for DNA repair and their survival. Compared to p53-deficient cells, p53-proficient CRC cells have a more active metabolism and use their intracellular metabolites more extensively. This metabolic switch creates a vulnerability to chemotherapy under glucose-restricted conditions for p53-positive cells.
Supplementary Information The online version contains supplementary material available at 10.1186/s40170-022-00286-9. Show less
Summary The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed increased phosphorylation of AKT1/mTO Show more
Summary The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed increased phosphorylation of AKT1/mTOR/4EBP1 and levels of p21 in FOLFOX-resistant CRC cells. We have identified a small molecule, NSC49L, that stimulates protein phosphatase 2A (PP2A) activity, downregulates the AKT1/mTOR/4EBP1-axis, and inhibits p21 translation. We have provided evidence that NSC49L- and TRAIL-mediated sensitization is synergistically induced in p21-knockdown CRC cells, which is reversed in p21-overexpressing cells. p21 binds with procaspase 3 and prevents the activation of caspase 3. We have shown that TRAIL induces apoptosis through the activation of caspase 3 by NSC49L-mediated downregulation of p21 translation, and thereby cleavage of procaspase 3 into caspase 3. NSC49L does not affect global protein synthesis. These studies provide a mechanistic understanding of NSC49L as a PP2A agonist, and how its combination with TRAIL sensitizes FOLFOX-resistant CRC cells. Show less
Abstract TFIIH is a 10‐subunit complex that regulates RNA polymerase II (pol II) transcription but also serves other important biological roles. Although much remains unknown about TFIIH function in Show more
Abstract TFIIH is a 10‐subunit complex that regulates RNA polymerase II (pol II) transcription but also serves other important biological roles. Although much remains unknown about TFIIH function in eukaryotic cells, much progress has been made even in just the past few years, due in part to technological advances (e.g. cryoEM and single molecule methods) and the development of chemical inhibitors of TFIIH enzymes. This review focuses on the major cellular roles for TFIIH, with an emphasis on TFIIH function as a regulator of pol II transcription. We describe the structure of TFIIH and its roles in pol II initiation, promoter‐proximal pausing, elongation, and termination. We also discuss cellular roles for TFIIH beyond transcription (e.g. DNA repair, cell cycle regulation) and summarize small molecule inhibitors of TFIIH and diseases associated with defects in TFIIH structure and function. Show less
Abstract Significance: Mitochondria are the energetic, metabolic, redox, and information signaling centers of the cell. Substrate pressure, mitochondrial network dynamics, and cristae morphology Show more
Abstract Significance: Mitochondria are the energetic, metabolic, redox, and information signaling centers of the cell. Substrate pressure, mitochondrial network dynamics, and cristae morphology state are integrated by the protonmotive force Δ p or its potential component, Δ Ψ , which are attenuated by proton backflux into the matrix, termed uncoupling. The mitochondrial uncoupling proteins (UCP1–5) play an eminent role in the regulation of each of the mentioned aspects, being involved in numerous physiological events including redox signaling. Recent Advances: UCP2 structure, including purine nucleotide and fatty acid (FA) binding sites, strongly support the FA cycling mechanism: UCP2 expels FA anions, whereas uncoupling is achieved by the membrane backflux of protonated FA. Nascent FAs, cleaved by phospholipases, are preferential. The resulting Δ p dissipation decreases superoxide formation dependent on Δ p . UCP-mediated antioxidant protection and its impairment are expected to play a major role in cell physiology and pathology. Moreover, UCP2-mediated aspartate, oxaloacetate, and malate antiport with phosphate is expected to alter metabolism of cancer cells. Critical Issues: A wide range of UCP antioxidant effects and participations in redox signaling have been reported; however, mechanisms of UCP activation are still debated. Switching off/on the UCP2 protonophoretic function might serve as redox signaling either by employing/releasing the extra capacity of cell antioxidant systems or by directly increasing/decreasing mitochondrial superoxide sources. Rapid UCP2 degradation, FA levels, elevation of purine nucleotides, decreased Mg 2+ , or increased pyruvate accumulation may initiate UCP-mediated redox signaling. Future Directions: Issues such as UCP2 participation in glucose sensing, neuronal (synaptic) function, and immune cell activation should be elucidated. Antioxid. Redox Signal. 29, 667–714. Show less
Whereas cisplatin and carboplatin kill cancer cells by inducing DNA damage, another platinum derivative, oxaliplatin, induces cell death by triggering ribosome biogenesis stress.