Funct Integr Genomics. 2025 Jan 6;25(1):8. doi: 10.1007/s10142-024-01514-9.

ABSTRACT

Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored. This study leverages a high-throughput swarm-based deep neural network (SDNN) and transcriptomic data-an approach that enhances predictive accuracy and robustness through collective intelligence-to augment, model, and enhance the understanding of the TP53 pathway in AML cohorts. Our integrative systems biology approach identified SPAG5 as a uniquely downregulated driver in adult AML, underscoring its potential as a novel therapeutic target. The interaction of SPAG5 with key hub genes such as MDM2 and CDK1 not only reinforces its role in tumour suppression through negative regulation but also highlights its potential in moderating the phenotypic and genomic alterations associated with AML progression. This study of the role and interaction dynamics of SPAG5 sets the stage for future research aimed at developing targeted and personalized treatment approaches for AML, utilizing the capabilities of genetic interventions.

PMID:39762615 | DOI:10.1007/s10142-024-01514-9

Cell Death Differ. 2025 Jan 6. doi: 10.1038/s41418-024-01431-1. Online ahead of print.

ABSTRACT

Cellular senescence contributes to a variety of pathologies associated with aging and is implicated as a cellular state in which cancer cells can survive treatment. Reported senolytic drug treatments act through varying molecular mechanisms, but heterogeneous efficacy across the diverse contexts of cellular senescence indicates a need for predictive biomarkers of senolytic activity. Using multi-parametric analyses of commonly reported molecular features of the senescent phenotype, we assayed a variety of models, including malignant and nonmalignant cells, using several triggers of senescence induction and found little univariate predictive power of these traditional senescence markers to identify senolytic drug sensitivity. We sought to identify novel drug targets in senescent cells that were insensitive to frequently implemented senolytic therapies, such as Navitoclax (ABT-263), using quantitative mass spectrometry to measure changes in the senescent proteome, compared to cells which acquire an acute sensitivity to ABT-263 with senescence induction. Inhibition of the antioxidant GPX4 or the Bcl-2 family member MCL-1 using small molecule compounds in combination with ABT-263 significantly increased the induction of apoptosis in some, but not all, previously insensitive senescent cells. We then asked if we could use BH3 profiling to measure differences in mitochondrial apoptotic priming in these models of cellular senescence and predict sensitivity to the senolytics ABT-263 or the combination of dasatinib and quercetin (D + Q). We found, despite being significantly less primed for apoptosis overall, the dependence of senescent mitochondria on BCL-XL was significantly correlated to senescent cell killing by both ABT-263 and D + Q, despite no significant changes in the gene or protein expression of BCL-XL. However, our data caution against broad classification of drugs as globally senolytic and instead provide impetus for context-specific senolytic targets and propose BH3 profiling as an effective predictive biomarker.

PMID:39762561 | DOI:10.1038/s41418-024-01431-1

Nat Chem Biol. 2025 Jan 6. doi: 10.1038/s41589-024-01814-y. Online ahead of print.

NO ABSTRACT

PMID:39762537 | DOI:10.1038/s41589-024-01814-y

Inflamm Res. 2025 Jan 7;74(1):2. doi: 10.1007/s00011-024-01968-4.

ABSTRACT

BACKGROUND: Traditional Chinese medicine (TCM) is a valuable resource for drug discovery and has demonstrated excellent efficacy in treating inflammatory diseases. This study aimed to develop a universal gene signature-based strategy for high-throughput discovery of anti-inflammatory drugs, especially Traditional Chinese medicine (TCM).

METHODS: The disease gene signature of liposaccharide-stimulated THP-1 cells and drug gene signatures of 655 drug candidates were established via sequencing. Anti-inflammatory drugs were screened based on similarities between drug gene signatures and the reversed disease gene signature.

RESULTS: Through screening, 83 potential anti-inflammatory drugs were identified. The efficacy of the TCM formula Biyun Powder, along with individual TCMs, Centipedea Herba, Kaempferiae Rhizoma, and Schizonepetae Spica Carbonisata, was verified in vitro or in vivo. Mechanistically, they exerted anti-inflammatory effects by inhibiting the nuclear factor-kappa B pathway. Kaempferol and luteolin were identified as bioactive IκB kinase-β inhibitors in Kaempferiae Rhizoma and Schizonepetae Spica Carbonisata, respectively.

CONCLUSION: We developed a universal gene signature-based approach for the high-throughput discovery of anti-inflammatory drugs that is applicable to compounds and to TCM herbs/formulae and established a workflow (screening, validation of efficacy, and identification of the mechanism of action and bioactive compounds) that can serve as a research template for high-throughput drug research.

PMID:39762416 | DOI:10.1007/s00011-024-01968-4

Nat Metab. 2025 Jan 6. doi: 10.1038/s42255-024-01181-x. Online ahead of print.

ABSTRACT

Nutrient sensors allow cells to adapt their metabolisms to match nutrient availability by regulating metabolic pathway expression. Many such sensors are cytosolic receptors that measure intracellular nutrient concentrations. One might expect that inducing the metabolic pathway that degrades a nutrient would reduce intracellular nutrient levels, destabilizing induction. However, in the galactose-responsive (GAL) pathway of Saccharomyces cerevisiae, we find that induction is stabilized by flux sensing. Previously proposed mechanisms for flux sensing postulate the existence of metabolites whose concentrations correlate with flux. The GAL pathway flux sensor uses a different principle: the galactokinase Gal1p both performs the first step in GAL metabolism and reports on flux by signalling to the GAL repressor, Gal80p. Both Gal1p catalysis and Gal1p signalling depend on the concentration of the Gal1p-GAL complex and are therefore directly correlated. Given the simplicity of this mechanism, flux sensing is probably a general feature throughout metabolic regulation.

PMID:39762390 | DOI:10.1038/s42255-024-01181-x

Nat Metab. 2025 Jan 6. doi: 10.1038/s42255-024-01182-w. Online ahead of print.

NO ABSTRACT

PMID:39762389 | DOI:10.1038/s42255-024-01182-w

Pharmacol Res. 2025 Jan 4:107581. doi: 10.1016/j.phrs.2025.107581. Online ahead of print.

ABSTRACT

Sepsis is a life-threatening organ failure resulting from a poorly regulated infection response. Organ dysfunction includes hepatic involvement, weakening the immune system due to excretory liver failure, and metabolic dysfunction, increasing the death risk. Although experimental studies correlated excretory liver functionality with immune performance and survival rates in sepsis, the proteins and pathways involved remain unclear. This study identified protein kinase C-α (PKCα) as a novel target for managing excretory liver function during sepsis. Using a preclinical murine sepsis model, we found that both PKCα knockout and the use of a PKCα-inhibitor midostaurin successfully restored liver function without hindering the host's response or ability to clear the pathogen, highlighting PKCα's vital role in excretory liver failure. In septic animals, both approaches significantly boosted survival rates. Midostaurin is the clinically approved active pharmaceutical ingredient in Rydapt, approved for the adjuvant treatment of FTL3-mutated AML. Here, it reduced plasma bile acids and related inflammation in those patients, opening a translational avenue for therapeutics in sepsis. Conclusively, our research underscores the significance of PKCα in controlling excretory liver function during inflammation. This suggests that targeting this protein could restore liver function without compromising the immune system, thereby decreasing sepsis mortality and supporting the recent paradigm that the liver is a hub for the host response to infection that might, in the future, result in novel host-directed therapies supporting the current state-of-the-art intensive care medicine in patients with sepsis-associated liver failure.

PMID:39761839 | DOI:10.1016/j.phrs.2025.107581

Arch Biochem Biophys. 2025 Jan 4:110288. doi: 10.1016/j.abb.2025.110288. Online ahead of print.

ABSTRACT

The peptidoglycan biosynthetic pathway involves a series of enzymatic reactions in which UDP-N-acetylglucosamine-enolpyruvate reductase (MurB) plays a crucial role in catalyzing the conversion of UDP-N-acetylglucosamine-enolpyruvate (UNAGEP) to UDP-N-acetylmuramic acid. This reaction relies on NADPH and FAD and, since MurB is not found in eukaryotes, it is an attractive target for the development of antimicrobials. MurB from Brucella ovis, the causative agent of brucellosis in sheep, is characterized here. The FAD cofactor in B. ovis MurB is reduced to the hydroquinone state without semiquinone stabilization with an estimated Eox/hq of -260 mV. MurB from B. ovis catalyzes the oxidation of NADPH in a slow process that is positively influenced by the presence of the second product, UNAGEP. The crystallographic structure of the MurBox:UNAGEP complex confirms its folding into three domains and the binding of UNAGEP, positioning its enolpyruvyl group for hydride transfer from FAD. MurB shows a complex thermal unfolding pathway that is influenced by UNAGEP and NADP+, confirming its ability to bind both molecules. Molecular dynamics (MD) simulations predict that the nicotinamide of NADP+ is more stable at the active site than the enolpyruvyl of UNAGEP, and suggests that MurB can simultaneously accommodate NADPH and UNAGEP in the substrate channel, increasing overall protein-ligand flexibility. Sequence and evolutionary analyses show that MurB from B. ovis conserves all motifs predicted to be involved in catalysis within the Type IIa family.

PMID:39761724 | DOI:10.1016/j.abb.2025.110288

Proc Natl Acad Sci U S A. 2025 Jan 14;122(2):e2411962122. doi: 10.1073/pnas.2411962122. Epub 2025 Jan 6.

ABSTRACT

Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR. In the three SRs, 185/4,662, 122/1,741, and 45/66 articles have been selected and considered for full-text screening by two independent reviewers. Due to technical variations and the inability of the LLMs to classify all records, the LLM's considered sample sizes were smaller. However, on average, the 18 LLMs classified 4,294 (min 4,130; max 4,329), 1,539 (min 1,449; max 1,574), and 27 (min 22; max 37) of the titles and abstracts correctly as either included or excluded for the three SRs, respectively. Additional analysis revealed that the definitions of the inclusion criteria and conceptual designs significantly influenced the LLM performances. In conclusion, LLMs can reduce one reviewer´s workload between 33% and 93% during title and abstract screening. However, the exact formulation of the inclusion and exclusion criteria should be refined beforehand for ideal support of the LLMs.

PMID:39761403 | DOI:10.1073/pnas.2411962122

PLoS Genet. 2025 Jan 6;21(1):e1011540. doi: 10.1371/journal.pgen.1011540. Online ahead of print.

ABSTRACT

Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed. We extracted putative cancer driver missense mutations from the Cancer Hotspots database and annotated them as germline variants, including presence/absence and classification in ClinVar. We trained two supervised learning models (logistic regression and random forest) to predict variant classifications of germline missense variants in ClinVar using Cancer Hotspot data (training dataset). The performance of each model was evaluated with an independent test dataset generated in part from searching public and private genome-wide sequencing datasets from ~1.5 million individuals. Of the 2,447 cancer mutations, 691 corresponding germline variants had been previously classified in ClinVar: 426 (61.6%) as likely pathogenic/pathogenic, 261 (37.8%) as uncertain significance, and 4 (0.6%) as likely benign/benign. The odds ratio for a likely pathogenic/pathogenic classification in ClinVar was 28.3 (95% confidence interval: 24.2-33.1, p < 0.001), compared with all other germline missense variants in the same 216 genes. Both supervised learning models showed high correlation with pathogenicity assessments in the training dataset. There was high area under precision-recall curve values (0.847 and 0.829) and area under the receiver-operating characteristic curve values (0.821 and 0.774) for logistic regression and random forest models, respectively, when applied to the test dataset. With the use of cancer and germline datasets and supervised learning techniques, our study shows that cancer mutation data can be leveraged to improve the interpretation of germline missense variation potentially causing rare Mendelian disorders.

PMID:39761285 | DOI:10.1371/journal.pgen.1011540

Iran J Nurs Midwifery Res. 2024 Nov 20;29(6):726-730. doi: 10.4103/ijnmr.ijnmr_109_23. eCollection 2024 Nov-Dec.

ABSTRACT

BACKGROUND: Recent evidence suggests a negative correlation between physical activity and the incidence and severity of noncommunicable chronic diseases like cardiovascular disease, diabetes, and respiratory infections. This study explores the potential influence of physical activity levels on the mortality rate and coronavirus disease (COVID-19) recovery.

MATERIALS AND METHODS: This descriptive analytical cross-sectional study evaluated 175 Polymerase Chain Reaction (PCR)-confirmed COVID-19 patients admitted to Baqiyatallah Hospital. The participants' hospitalization data and physical activity levels were assessed. The Mann-Whitney U test explored the association between physical activity and COVID-19 outcomes.

RESULTS: Findings revealed that COVID-19 patients had a mean (SD) physical activity score of 6.55 (1.76) out of a possible 15. The mean scores for physical activity in work, sport, and leisure environments were 2.69 (0.49), 1.37 (1.45), and 2.49 (0.59) out of 5, respectively. Surviving patients exhibited significantly higher sports-specific and overall physical activity levels than those who succumbed to the disease. A Man-Whitney U test results noted statistically significant relationship between total and sports-specific physical activity, hospitalization (interquartile range (IQR) 2.3-3.06, p = 0.020 and IQR 2.5-3, p = 0.010, respectively), and mortality (IQR 0.44-2.75, p = 0.020 and IQR 1.47-2.97, p = 0.020).

CONCLUSIONS: In summary, increased total physical activity, particularly in a sports environment, appears to be linked with reduced COVID-19 hospitalization and mortality rates.

PMID:39759909 | PMC:PMC11694590 | DOI:10.4103/ijnmr.ijnmr_109_23

JAMIA Open. 2025 Jan 3;8(1):ooae155. doi: 10.1093/jamiaopen/ooae155. eCollection 2025 Feb.

ABSTRACT

OBJECTIVES: The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

MATERIALS AND METHODS: The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The protocol of this review including the eligibility criteria, the search strategy data extraction sheet and methods, was published prior to the actual review with International Registered Report Identifier of DERR1-10.2196/47105.

RESULTS: The initial search resulted in 4975 studies from 2 databases and 7 studies from manual search. Eleven articles were selected for data extraction based on the eligibility criteria. We found that while guidelines generally excel in scope, purpose, and editorial independence, there is significant variability in applicability and the rigor of guideline development. Well-established initiatives such as TRIPOD+AI, DECIDE-AI, SPIRIT-AI, and CONSORT-AI have shown high quality, particularly in terms of stakeholder involvement. However, applicability remains a prominent challenge among the guidelines. The result also showed that the reproducibility, ethical, and environmental aspects of AI in medicine still need attention from both medical and AI communities.

DISCUSSION: Our work highlights the need for working toward the development of integrated and comprehensive reporting guidelines that adhere to the principles of Findability, Accessibility, Interoperability and Reusability. This alignment is essential for fostering a cultural shift toward transparency and open science, which are pivotal milestone for sustainable digital health research.

CONCLUSION: This review evaluates the current reporting guidelines, discussing their advantages as well as challenges and limitations.

PMID:39759773 | PMC:PMC11700560 | DOI:10.1093/jamiaopen/ooae155

iScience. 2024 Nov 28;27(12):111502. doi: 10.1016/j.isci.2024.111502. eCollection 2024 Dec 20.

ABSTRACT

Transforming growth factor β (TGF-β) is abundantly present in the tumor microenvironment, contributing to cancer progression. However, the regulatory mechanism by which TGF-β affects vascular endothelial cells (ECs) in the tumor microenvironment is not well understood. Herein, we generated tamoxifen-inducible TGF-β type II receptor (TβRII) knockout mice, specifically targeting ECs (TβRIIiΔEC), by crossbreeding TβRII-floxed mice with Pdgfb-icreER mice. We established tumor-bearing mice by transplanting Lewis lung carcinoma (LLC) cells. TβRIIiΔEC mice exhibited increased tumor angiogenesis with fragile new blood vessels, increased bleeding, and hypoxia compared to control mice. Consequently, the compromised tumor microenvironment precipitated a notable surge in circulating tumor cells. Paradoxically, lung metastasis showed a significant decline. This intriguing discrepancy was explained by a reduction in the engraftment between cancer cells and ECs. Disruption of TGF-β signaling downregulated CD44 on ECs, hindering cancer cell adhesion. These findings highlight TGF-β's role in promoting metastasis by modulating EC function.

PMID:39758992 | PMC:PMC11699617 | DOI:10.1016/j.isci.2024.111502

Bioinform Adv. 2024 Dec 30;5(1):vbae184. doi: 10.1093/bioadv/vbae184. eCollection 2025.

ABSTRACT

MOTIVATION: Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system is a ground-breaking genome editing tool, which has revolutionized cell and gene therapies. One of the essential components involved in this system that ensures its success is the design of an optimal single-guide RNA (sgRNA) with high on-target cleavage efficiency and low off-target effects. This is challenging as many conditions need to be considered, and empirically testing every design is time-consuming and costly. In silico prediction using machine learning models provides high-performance alternatives.

RESULTS: We present CrisprBERT, a deep learning model incorporating a Bidirectional Encoder Representations from Transformers (BERT) architecture to provide a high-dimensional embedding for paired sgRNA and DNA sequences and Bidirectional Long Short-term Memory networks for learning, to predict the off-target effects of sgRNAs utilizing only the sgRNAs and their paired DNA sequences. We proposed doublet stack encoding to capture the local energy configuration of the Cas9 binding and applied the BERT model to learn the contextual embedding of the doublet pairs. Our results showed that the new model achieved better performance than state-of-the-art deep learning models regarding single split and leave-one-sgRNA-out cross-validations as well as independent testing.

AVAILABILITY AND IMPLEMENTATION: The CrisprBERT is available at GitHub: https://github.com/OSsari/CrisprBERT.

PMID:39758829 | PMC:PMC11696696 | DOI:10.1093/bioadv/vbae184

Bioinform Adv. 2024 Nov 5;5(1):vbae172. doi: 10.1093/bioadv/vbae172. eCollection 2025.

ABSTRACT

MOTIVATION: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.

RESULTS: Building upon the similarity between human and mouse mitochondrial metabolism, we present mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is able to model mouse and human mitochondrial metabolism. To demonstrate this, using an adapted E-Flux algorithm, we integrated proteomic data from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue, as well as transcriptomic data from in vitro differentiated human brown adipocytes and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research in cardiac disease and diabetes in both mouse and human contexts.

AVAILABILITY AND IMPLEMENTATION: The MitoMammal Jupyter Notebook is available at: https://gitlab.com/habermann_lab/mitomammal.

PMID:39758828 | PMC:PMC11696703 | DOI:10.1093/bioadv/vbae172

iScience. 2024 Dec 2;28(1):111525. doi: 10.1016/j.isci.2024.111525. eCollection 2025 Jan 17.

ABSTRACT

Pleural mesothelioma is a highly chemotherapy-resistant cancer. Approximately 50% of mesotheliomas do not express argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme in arginine biosynthesis, making arginine depletion with pegylated arginine deiminase (ADI-PEG20) an attractive therapeutic strategy. We investigated whether combinatory treatment composed of ADI-PEG20 and polyamine inhibitors constitutes a promising novel therapeutic strategy to overcome ADI-PEG20 resistance in mesothelioma patients. Treatment of ADI-PEG20-resistant cell lines with a range of different polyamine inhibitors demonstrated that ADI-PEG20-resistant cell lines were highly sensitive to the spermidine-analog GC7. We observed a synergistic effect of GC7 and ADI-PEG20 in both ADI-PEG20-sensitive and ADI-PEG20-resistant cell lines. Metabolomic analysis revealed that sensitivity to GC7 is due to inhibition of the Tricarboxylic (TCA) cycle. Significantly, combination of GC7 and ADI-PEG20 prevented the emergence of resistant cells in vitro. Taken together, we have identified the therapeutic potential of combinatorial treatment of ADI-PEG20 with GC7 for mesothelioma management.

PMID:39758821 | PMC:PMC11699351 | DOI:10.1016/j.isci.2024.111525

iScience. 2024 Jul 10;27(8):110482. doi: 10.1016/j.isci.2024.110482. eCollection 2024 Aug 16.

ABSTRACT

A major obstacle to exploiting industrial flue gas for microalgae cultivation is the unfavorable acidic environment. We previously identified three upregulated genes in the low-pH-adapted model diatom Phaeodactylum tricornutum: ferredoxin (PtFDX), cation/proton antiporter (PtCPA), and HCO3 - transporter (PtSCL4-2). Here, we individually overexpressed these genes in P. tricornutum to investigate their respective roles in resisting acidic stress (pH 5.0). The genetic modifications enabled positive growths of transgenic strains under acidic stress that completely inhibited the growth of the wild-type strain. Physiological studies indicated improved photosynthesis and reduced oxidative stress in the transgenic strains. Transcriptomes of the PtCPA- and PtSCL4-2-overexpressing transgenics showed widespread upregulation of various transmembrane transporters, which could help counteract excessive external protons. This work highlights ion/electron carrier genes' role in enhancing diatom resistance to acidic stress, providing insights into phytoplankton adaptation to ocean acidification and a strategy for biological carbon capture and industrial flue gas CO2 utilization.

PMID:39758278 | PMC:PMC11700652 | DOI:10.1016/j.isci.2024.110482

Mol Biomed. 2025 Jan 6;6(1):2. doi: 10.1186/s43556-024-00239-2.

ABSTRACT

Cancer remains a leading cause of mortality globally and a major health burden, with chemotherapy often serving as the primary therapeutic option for patients with advanced-stage disease, partially compensating for the limitations of non-curative treatments. However, the emergence of chemotherapy resistance significantly limits its efficacy, posing a major clinical challenge. Moreover, heterogeneity of resistance mechanisms across cancer types complicates the development of universally effective diagnostic and therapeutic approaches. Understanding the molecular mechanisms of chemoresistance and identifying strategies to overcome it are current research focal points. This review provides a comprehensive analysis of the key molecular mechanisms underlying chemotherapy resistance, including drug efflux, enhanced DNA damage repair (DDR), apoptosis evasion, epigenetic modifications, altered intracellular drug metabolism, and the role of cancer stem cells (CSCs). We also examine specific causes of resistance in major cancer types and highlight various molecular targets involved in resistance. Finally, we discuss current strategies aiming at overcoming chemotherapy resistance, such as combination therapies, targeted treatments, and novel drug delivery systems, while proposing future directions for research in this evolving field. By addressing these molecular barriers, this review lays a foundation for the development of more effective cancer therapies aimed at mitigating chemotherapy resistance.

PMID:39757310 | DOI:10.1186/s43556-024-00239-2

Int J Biol Macromol. 2025 Jan 3:139232. doi: 10.1016/j.ijbiomac.2024.139232. Online ahead of print.

ABSTRACT

Transcriptional regulation of sterol biosynthetic genes is mediated by conserved sterol-regulatory element binding proteins (SREBPs) in human pathogenic fungi, however, its homolog in S. cerevisiae regulate filamentous growth during stress conditions. These pseudohyphal growths might be associated with the expression of MEP2 gene in response to ammonium limitation. Hitherto, there is limited literature available for Hms1p and precisely how it establishes interaction with DNA. Though DNA and Hms1p mutual interaction was predicted computationally, however, the structural details regarding how they establish interaction still remains elusive. Here, we resolved the crystal structure of Hms1pDBD-DNA complex at a nominal resolution of 2.77 Å. The structure highlighted several residues (Hms1pHis3/Asn4/Glu7/Tyr10/Arg11) could specifically recognize the core signature sequence in the promoter DNA fragment, which was validated by biochemical assays. Comparative analysis of Hms1p with other basic helix-loop-helix (bHLH) transcriptional regulators reflected that residues (His, Glu and Arg) are highly conserved. Despite distinct core signature sequences, these conserved residues in different bHLH proteins could specifically recognize and bind their corresponding promoter DNA fragment. Collectively, these results could pinpoint critical residues (Hms1pHis3/Asn4/Glu7/Tyr10/Arg11) for the binding interface with the signature sequence of MEP2 promoter DNA fragment.

PMID:39756762 | DOI:10.1016/j.ijbiomac.2024.139232

Lancet Oncol. 2025 Jan;26(1):e34-e45. doi: 10.1016/S1470-2045(24)00395-4.

ABSTRACT

Technological innovations in genomics and related fields have facilitated large sequencing efforts, supported new biological discoveries in cancer, and spawned an era of liquid biopsy biomarkers. Despite these advances, precision oncology has practical constraints, partly related to cancer's biological diversity and spatial and temporal complexity. Advanced imaging technologies are being developed to address some of the current limitations in early detection, treatment selection and planning, drug delivery, and therapeutic response, as well as difficulties posed by drug resistance, drug toxicity, disease monitoring, and metastatic evolution. We discuss key areas of advanced imaging for improving cancer outcomes and survival. Finally, we discuss practical challenges to the broader adoption of precision imaging in the clinic and the need for a robust translational infrastructure.

PMID:39756454 | DOI:10.1016/S1470-2045(24)00395-4