Proc Natl Acad Sci U S A. 2024 Jul 9;121(28):e2410274121. doi: 10.1073/pnas.2410274121. Epub 2024 Jun 27.

NO ABSTRACT

PMID:38935582 | DOI:10.1073/pnas.2410274121

J Cell Biol. 2024 Oct 7;223(10):e202403115. doi: 10.1083/jcb.202403115. Epub 2024 Jun 27.

ABSTRACT

Regulated cell shape change requires the induction of cortical cytoskeletal domains. Often, local changes to plasma membrane (PM) topography are involved. Centrosomes organize cortical domains and can affect PM topography by locally pulling the PM inward. Are these centrosome effects coupled? At the syncytial Drosophila embryo cortex, centrosome-induced actin caps grow into dome-like compartments for mitoses. We found the nascent cap to be a collection of PM folds and tubules formed over the astral centrosomal MT array. The localized infoldings require centrosome and dynein activities, and myosin-based surface tension prevents them elsewhere. Centrosome-engaged PM infoldings become specifically enriched with an Arp2/3 induction pathway. Arp2/3 actin network growth between the infoldings counterbalances centrosomal pulling forces and disperses the folds for actin cap expansion. Abnormal domain topography with either centrosome or Arp2/3 disruption correlates with decreased exocytic vesicle association. Together, our data implicate centrosome-organized PM infoldings in coordinating Arp2/3 network growth and exocytosis for cortical domain assembly.

PMID:38935075 | DOI:10.1083/jcb.202403115

Brief Bioinform. 2024 May 23;25(4):bbae309. doi: 10.1093/bib/bbae309.

ABSTRACT

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

PMID:38935070 | DOI:10.1093/bib/bbae309

mSystems. 2024 Jun 27:e0135823. doi: 10.1128/msystems.01358-23. Online ahead of print.

ABSTRACT

The alarming rise of antibiotic-resistant bacterial infections is driving efforts to develop alternatives to conventional antibiotics. In this context, antimicrobial peptides (AMPs) have emerged as promising candidates for their ability to target a broad range of microorganisms. However, the development of AMPs with optimal potency, selectivity, and/or stability profiles remains a challenge. To address it, computational tools for predicting AMP properties and designing novel peptides have gained increasing attention. PyAMPA is a novel platform for AMP discovery. It consists of five modules, namely AMPScreen, AMPValidate, AMPSolve, AMPMutate, and AMPOptimize, that allow high-throughput proteome inspection, candidate screening, and optimization through point-mutation and genetic algorithms. The platform also offers additional tools for predicting and evaluating AMP properties, including antimicrobial and cytotoxic activity, and peptide half-life. By providing innovative and accessible inroads into AMP motifs in proteomes, PyAMPA will enable advances in AMP development and potential translation into clinically useful molecules. PyAMPA is available at: https://github.com/SysBioUAB/PyAMPA.

IMPORTANCE: This paper introduces PyAMPA, a new bioinformatics platform designed for the discovery and optimization of antimicrobial peptides (AMPs). It addresses the urgent need for new antimicrobials due to the rise of antibiotic-resistant infections. PyAMPA, with its five predictive modules -AMPScreen, AMPValidate, AMPSolve, AMPMutate and AMPOptimize, enables high-throughput screening of proteomes to identify potential AMP motifs and optimize them for clinical use. Its unique approach, combining prediction, design, and optimization tools, makes PyAMPA a robust solution for developing new AMP-based therapies, offering a significant advance in combatting antibiotic resistance.

PMID:38934543 | DOI:10.1128/msystems.01358-23

ACS Synth Biol. 2024 Jun 27. doi: 10.1021/acssynbio.3c00572. Online ahead of print.

ABSTRACT

As the availability of data sets increases, meta-analysis leveraging aggregated and interoperable data types is proving valuable. This study leveraged a meta-analysis workflow to identify mutations that could improve robustness to reactive oxygen species (ROS) stresses using an industrially important melatonin production strain as an example. ROS stresses often occur during cultivation and negatively affect strain performance. Cellular response to ROS is also linked to the SOS response and resistance to pH fluctuations, which is important to strain robustness in large-scale biomanufacturing. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations across 59 experiments to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 unique conditions. Mutant oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute fitness in ROS stress. Across these genes, 259 total mutations were inspected in conjunction with transcriptomics from 46 iModulon experiments. Ten mutations were chosen for reintroduction based on mutation clustering and coinciding transcriptional changes as evidence of fitness impact. Strains with mutations reintroduced into oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress and reduced SOS response, all of which are related to ROS. Additionally, new evidence was generated toward understanding the function of ygfZ, an uncharacterized gene. This meta-analysis approach utilized aggregated and interoperable multiomics data sets to identify mutations conferring industrially relevant phenotypes with the least drawbacks, describing an approach for data-driven strain engineering to optimize microbial cell factories.

PMID:38934464 | DOI:10.1021/acssynbio.3c00572

Fundam Res. 2021 Dec 30;2(1):2-13. doi: 10.1016/j.fmre.2021.12.007. eCollection 2022 Jan.

ABSTRACT

To enable diverse functions and precise regulation, an RNA sequence often folds into complex yet distinct structures in different cellular states. Probing RNA in its native environment is essential to uncovering RNA structures of biological contexts. However, current methods generally require large amounts of input RNA and are challenging for physiologically relevant use. Here, we report smartSHAPE, a new RNA structure probing method that requires very low amounts of RNA input due to the largely reduced artefact of probing signals and increased efficiency of library construction. Using smartSHAPE, we showcased the profiling of the RNA structure landscape of mouse intestinal macrophages upon inflammation, and provided evidence that RNA conformational changes regulate immune responses. These results demonstrate that smartSHAPE can greatly expand the scope of RNA structure-based investigations in practical biological systems, and also provide a research paradigm for the study of post-transcriptional regulation.

PMID:38933905 | PMC:PMC11197792 | DOI:10.1016/j.fmre.2021.12.007

Fundam Res. 2022 Aug 9;2(6):894-902. doi: 10.1016/j.fmre.2022.07.011. eCollection 2022 Nov.

ABSTRACT

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous. Biomarkers related to multiple biological processes involved in cell growth, proliferation, and disease occurrence have been identified in recent years with the development of immunology, molecular biology, and genomics technologies. Biomarkers are capable of reflecting normal physiological processes, pathological processes, and the response to therapeutic intervention; as such, they play vital roles in disease diagnosis, prevention, drug response, and other aspects of biomedicine. The discovery of valuable biomarkers has become a focal point of current research. Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules (e.g., genes/proteins) for disease state diagnosis, characterization, and treatment. Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases, only a small number of these candidates have been properly validated for use in patient treatment. The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions, and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established. This approach promises to be more stable and reliable in diagnosing disease states. Furthermore, the newly-emerged dynamic network biomarkers (DNBs) based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states, thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

PMID:38933388 | PMC:PMC11197705 | DOI:10.1016/j.fmre.2022.07.011

Fundam Res. 2023 Jun 12;3(5):727-737. doi: 10.1016/j.fmre.2023.06.001. eCollection 2023 Sep.

ABSTRACT

RNA molecules serve a wide range of functions that are closely linked to their structures. The basic structural units of RNA consist of single- and double-stranded regions. In order to carry out advanced functions such as catalysis and ligand binding, certain types of RNAs can adopt higher-order structures. The analysis of RNA structures has progressed alongside advancements in structural biology techniques, but it comes with its own set of challenges and corresponding solutions. In this review, we will discuss recent advances in RNA structure analysis techniques, including structural probing methods, X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and small-angle X-ray scattering. Often, a combination of multiple techniques is employed for the integrated analysis of RNA structures. We also survey important RNA structures that have been recently determined using various techniques.

PMID:38933295 | PMC:PMC11197651 | DOI:10.1016/j.fmre.2023.06.001

J Med Virol. 2024 Jun;96(6):e29769. doi: 10.1002/jmv.29769.

ABSTRACT

Integration of the human papillomavirus (HPV) genome into the cellular genome is a key event that leads to constitutive expression of viral oncoprotein E6/E7 and drives the progression of cervical cancer. However, HPV integration patterns differ on a case-by-case basis among related malignancies. Next-generation sequencing technologies still face challenges for interrogating HPV integration sites. In this study, utilizing Nanopore long-read sequencing, we identified 452 and 108 potential integration sites from the cervical cancer cell lines (CaSki and HeLa) and five tissue samples, respectively. Based on long Nanopore chimeric reads, we were able to analyze the methylation status of the HPV long control region (LCR), which controls oncogene E6/E7 expression, and to identify transcriptionally-active integrants among the numerous integrants. As a proof of concept, we identified an active HPV integrant in between RUNX2 and CLIC5 on chromosome 6 in the CaSki cell line, which was supported by ATAC-seq, H3K27Ac ChIP-seq, and RNA-seq analysis. Knockout of the active HPV integrant, by the CRISPR/Cas9 system, dramatically crippled cell proliferation and induced cell senescence. In conclusion, identifying transcriptionally-active HPV integrants with Nanopore sequencing can provide viable targets for gene therapy against HPV-associated cancers.

PMID:38932482 | DOI:10.1002/jmv.29769

Viruses. 2024 Jun 9;16(6):935. doi: 10.3390/v16060935.

ABSTRACT

The HIV envelope glycoprotein (Env) is a trimeric protein that facilitates viral binding and fusion with target cells. As the sole viral protein on the HIV surface, Env is important both for immune responses to HIV and in vaccine designs. Targeting Env in clinical applications is challenging due to its heavy glycosylation, high genetic variability, conformational camouflage, and its low abundance on virions. Thus, there is a critical need to better understand this protein. Flow virometry (FV) is a useful methodology for phenotyping the virion surface in a high-throughput, single virion manner. To demonstrate the utility of FV to characterize Env, we stained HIV virions with a panel of 85 monoclonal antibodies targeting different regions of Env. A broad range of antibodies yielded robust staining of Env, with V3 antibodies showing the highest quantitative staining. A subset of antibodies tested in parallel on viruses produced in CD4+ T cell lines, HEK293T cells, and primary cells showed that the cellular model of virus production can impact Env detection. Finally, in addition to being able to highlight Env heterogeneity on virions, we show FV can sensitively detect differences in Env conformation when soluble CD4 is added to virions before staining.

PMID:38932227 | DOI:10.3390/v16060935

Viruses. 2024 Jun 1;16(6):895. doi: 10.3390/v16060895.

ABSTRACT

This review accompanies the Special Issue on the subject of physical virology, which features work presented at the recent Gordon Research Conference (GRC) on this topic [...].

PMID:38932189 | DOI:10.3390/v16060895

Viruses. 2024 May 24;16(6):837. doi: 10.3390/v16060837.

ABSTRACT

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

PMID:38932131 | DOI:10.3390/v16060837

Viruses. 2024 May 22;16(6):821. doi: 10.3390/v16060821.

ABSTRACT

When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and genome rearrangements. Attenuation typically involves the reduction in virus replication, due to direct effects on viral structural and replicative machinery or viral factors that antagonize host defense or cause disease. However, attenuation must balance reduced replication and immunogenic antigen expression. In the present study, we explored a new approach in order to discover attenuating mutations. Specifically, we used protein structure modeling and computational methods to identify amino acid substitutions in the RSV nonstructural protein 1 (NS1) predicted to cause various levels of structural perturbation. Twelve different mutations predicted to alter the NS1 protein structure were introduced into infectious virus and analyzed in cell culture for effects on viral mRNA and protein expression, interferon and cytokine expression, and caspase activation. We found the use of structure-based machine learning to predict amino acid substitutions that reduce the thermodynamic stability of NS1 resulted in various levels of loss of NS1 function, exemplified by effects including reduced multi-cycle viral replication in cells competent for type I interferon, reduced expression of viral mRNAs and proteins, and increased interferon and apoptosis responses.

PMID:38932114 | DOI:10.3390/v16060821

Pharmaceuticals (Basel). 2024 Jun 17;17(6):795. doi: 10.3390/ph17060795.

ABSTRACT

BACKGROUND: Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features.

METHODS: This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023.

RESULTS: The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects.

CONCLUSIONS: This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

PMID:38931462 | DOI:10.3390/ph17060795

Nutrients. 2024 Jun 8;16(12):1805. doi: 10.3390/nu16121805.

ABSTRACT

Lipid functions can be influenced by genetics, age, disease states, and lifestyle factors, particularly dietary patterns, which are crucial in diabetes management. Lipidomics is an expanding field involving the comprehensive exploration of lipids from biological samples. In this cross-sectional study, 396 participants from a Mediterranean region, including individuals with type 1 diabetes (T1D), type 2 diabetes (T2D), and non-diabetic individuals, underwent lipidomic profiling and dietary assessment. Participants completed validated food frequency questionnaires, and lipid analysis was conducted using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC/MS). Multiple linear regression models were used to determine the association between lipid features and dietary patterns. Across all subjects, acylcarnitines (AcCa) and triglycerides (TG) displayed negative associations with the alternate Healthy Eating Index (aHEI), indicating a link between lipidomic profiles and dietary habits. Various lipid species (LS) showed positive and negative associations with dietary carbohydrates, fats, and proteins. Notably, in the interaction analysis between diabetes and the aHEI, we found some lysophosphatidylcholines (LPC) that showed a similar direction with respect to aHEI in non-diabetic individuals and T2D subjects, while an opposite direction was observed in T1D subjects. The study highlights the significant association between lipidomic profiles and dietary habits in people with and without diabetes, particularly emphasizing the role of healthy dietary choices, as reflected by the aHEI, in modulating lipid concentrations. These findings underscore the importance of dietary interventions to improve metabolic health outcomes, especially in the context of diabetes management.

PMID:38931159 | DOI:10.3390/nu16121805

Nutrients. 2024 Jun 7;16(12):1800. doi: 10.3390/nu16121800.

ABSTRACT

Gut microbiota might affect the severity and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to characterize gut dysbiosis and clinical parameters regarding fibrosis stages assessed by magnetic resonance elastography. This study included 156 patients with MASLD, stratified into no/mild fibrosis (F0-F1) and moderate/severe fibrosis (F2-F4). Fecal specimens were sequenced targeting the V4 region of the 16S rRNA gene and analyzed using bioinformatics. The genotyping of PNPLA3, TM6SF2, and HSD17B13 was assessed by allelic discrimination assays. Our data showed that gut microbial profiles between groups significantly differed in beta-diversity but not in alpha-diversity indices. Enriched Fusobacterium and Escherichia_Shigella, and depleted Lachnospira were found in the F2-F4 group versus the F0-F1 group. Compared to F0-F1, the F2-F4 group had elevated plasma surrogate markers of gut epithelial permeability and bacterial translocation. The bacterial genera, PNPLA3 polymorphisms, old age, and diabetes were independently associated with advanced fibrosis in multivariable analyses. Using the Random Forest classifier, the gut microbial signature of three genera could differentiate the groups with high diagnostic accuracy (AUC of 0.93). These results indicated that the imbalance of enriched pathogenic genera and decreased beneficial bacteria, in association with several clinical and genetic factors, were potential contributors to the pathogenesis and progression of MASLD.

PMID:38931155 | DOI:10.3390/nu16121800

Molecules. 2024 Jun 9;29(12):2747. doi: 10.3390/molecules29122747.

ABSTRACT

Currently, an estimated 537 million individuals are affected by type 2 diabetes mellitus (T2DM), the occurrence of which is invariably associated with complications. Glucose-lowering therapy remains the main treatment for alleviating T2DM. However, conventional antidiabetic agents are fraught with numerous adverse effects, notably elevations in blood pressure and lipid levels. Recently, the use of traditional Chinese medicines (TCMs) and their constituents has emerged as a preferred management strategy aimed at curtailing the progression of diabetes and its associated complications with fewer adverse effects. Increasing evidence indicates that gut microbiome disturbances are involved in the development of T2DM and its complications. This regulation depends on various metabolites produced by gut microbes and their interactions with host organs. TCMs' interventions have demonstrated the ability to modulate the intestinal bacterial microbiota, thereby restoring host homeostasis and ameliorating metabolic disorders. This review delves into the alterations in the gut microbiota and metabolites in T2DM patients and how TCMs treatment regulates the gut microbiota, facilitating the management of T2DM and its complications. Additionally, we also discuss prospective avenues for research on natural products to advance diabetes therapy.

PMID:38930814 | DOI:10.3390/molecules29122747

Microorganisms. 2024 Jun 18;12(6):1225. doi: 10.3390/microorganisms12061225.

ABSTRACT

Periodontal disease (PerioD) is a chronic inflammatory disease of dysbiotic etiology. Animal models and few human data showed a relationship between oral bacteria and gut dysbiosis. However, the effect of periodontal inflammation and subgingival dysbiosis on the gut is unknown. We hypothesized that periodontal inflammation and its associated subgingival dysbiosis contribute to gut dysbiosis even in subjects free of known gut disorders. We evaluated and compared elderly subjects with Low and High periodontal inflammation (assessed by Periodontal Inflamed Surface Area (PISA)) for stool and subgingival derived bacteria (assayed by 16S rRNA sequencing). The associations between PISA/subgingival dysbiosis and gut dysbiosis and bacteria known to produce short-chain fatty acid (SCFA) were assessed. LEfSe analysis showed that, in Low PISA, species belonging to Lactobacillus, Roseburia, and Ruminococcus taxa and Lactobacillus zeae were enriched, while species belonging to Coprococcus, Clostridiales, and Atopobium were enriched in High PISA. Regression analyses showed that PISA associated with indicators of dysbiosis in the gut mainly reduced abundance of SCFA producing bacteria (Radj = -0.38, p = 0.03). Subgingival bacterial dysbiosis also associated with reduced levels of gut SCFA producing bacteria (Radj = -0.58, p = 0.002). These results suggest that periodontal inflammation and subgingival microbiota contribute to gut bacterial changes.

PMID:38930608 | DOI:10.3390/microorganisms12061225

Microorganisms. 2024 May 27;12(6):1079. doi: 10.3390/microorganisms12061079.

ABSTRACT

Elizabethkingia anophelis MSU001, isolated from Anopheles stephensi in the laboratory, was characterized by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-ToF/MS), biochemical testing, and genome sequencing. Average nucleotide identity analysis revealed 99% identity with the type species E. anophelis R26. Phylogenetic placement showed that it formed a clade with other mosquito-associated strains and departed from a clade of clinical isolates. Comparative genome analyses further showed that it shared at least 98.6% of genes with mosquito-associated isolates (except E. anophelis As1), while it shared at most 88.8% of common genes with clinical isolates. Metabolites from MSU001 significantly inhibited growth of E. coli but not the mosquito gut symbionts Serratia marcescens and Asaia sp. W12. Insect-associated E. anophelis carried unique glycoside hydrolase (GH) and auxiliary activities (AAs) encoding genes distinct from those of clinical isolates, indicating their potential role in reshaping chitin structure and other components involved in larval development or formation of the peritrophic matrix. Like other Elizabethkingia, MSU001 also carried abundant genes encoding two-component system proteins (51), transcription factor proteins (188), and DNA-binding proteins (13). E. anophelis MSU001 contains a repertoire of antibiotic resistance genes and several virulence factors. Its potential for opportunistic infections in humans should be further evaluated prior to implementation as a paratransgenesis agent (by transgenesis of a symbiont of the vector).

PMID:38930461 | DOI:10.3390/microorganisms12061079

J Pers Med. 2024 Jun 15;14(6):639. doi: 10.3390/jpm14060639.

ABSTRACT

Early-onset colorectal cancer (EOCRC), defined as colorectal cancer in individuals under 50 years of age, has shown an alarming increase in incidence worldwide. We report a case of a twenty-four-year-old female with a strong family history of colorectal cancer (CRC) but without an identified underlying genetic predisposition syndrome. Two years after primary surgery and adjuvant chemotherapy, the patient developed new liver lesions. Extensive diagnostic imaging was conducted to investigate suspected liver metastases, ultimately leading to a diagnosis of focal nodular hyperplasia. The young age of the patient has prompted comprehensive genomic and transcriptomic profiling in order to identify potential oncogenic drivers and inform further clinical management of the patient. Besides a number of oncogenic mutations identified in the patient's tumour sample, including KRAS G12D, TP53 R248W and TTN L28470V, we have also identified a homozygous deletion of 24.5 MB on chromosome 8. A multivariate Cox regression analysis of this patient's mutation profile conferred a favourable prognosis when compared with the TCGA COADREAD database. Notably, the identified deletion on chromosome 8 includes the WRN gene, which could contribute to the patient's overall positive response to chemotherapy. The complex clinical presentation, including the need for emergency surgery, early age at diagnosis, strong family history, and unexpected findings on surveillance imaging, necessitated a multidisciplinary approach involving medical, radiation, and surgical oncologists, along with psychological support and reproductive medicine specialists. Molecular profiling of the tumour strongly indicates that patients with complex mutational profile and rare genomic rearrangements require a prolonged surveillance and personalised informed interventions.

PMID:38929861 | DOI:10.3390/jpm14060639