bims-tumhet 2025-07-13 papers

bims-tumhet

Biomed News

on Tumor heterogeneity

Issue of 2025–07–13
fifteen papers selected by
Sergio Marchini, Humanitas Research

Droplet Digital PCR Assay for Detection and Monitoring of Universally Methylated Circulating Tumor DNA in Sarcoma Patients.
High-coverage allele-resolved single-cell DNA methylation profiling reveals cell lineage, X-inactivation state, and replication dynamics.
Genome-wide extraction of differentially methylated DNA regions using adapter-anchored proximity primers.
Utility Of Fallopian Tube Brush Cytology and Cell-blocks As a Screening Tool for Epithelial Ovarian Cancer in Patients Undergoing Gynaecological Surgeries for Benign and Malignant Indications: Attempt at a Classification System.
Genetic mutation predicts survival after immunotherapy for ovarian cancer.
Spatial joint profiling of DNA methylome and transcriptome in mammalian tissues.
Re: Long-term follow-up of PAOLA-1 and the evolving landscape of post-PARP treatment strategies in ovarian cancer.
What the VAF? A guide to the interpretation of variant allele fraction, percent mosaicism, and copy number in cancer.
DiffMethylTools: a toolbox of the detection, annotation and visualization of differential DNA methylation.
Development and Validation of a Histology-Specific Natural History Model of Ovarian Cancer.
Therapeutic targeting of mismatch repair-deficient cancers.
Circulating tumor DNA: a biomarker for oncology drug development in phase I clinical trials?
Predicting time-to-first cancer diagnosis across multiple cancer types.
The biological and evolutionary consequences of competition between DNA sequences that benefit the cell and DNA sequences that benefit themselves.
Interactions Between the Innate and Adaptive Immune Responses.

Clin Cancer Res. 2025 Jul 08.

Droplet Digital PCR Assay for Detection and Monitoring of Universally Methylated Circulating Tumor DNA in Sarcoma Patients.

Elisabeth Ashton, Valérie Taly, Camille Tlemsani, Pascaline Boudou-Rouquette, Sixtine de Percin, Johanna Noël, Antoine Gaudet-Chardonnet, Francesco Sullo, Beatrice Parfait, Justine Abdelli, Thomas Bruneau, Frederique Larousserie, Virginie Audard, Sandra Larrede, Benoit Blanchet, Lorraine Waechter, Francois Goldwasser, Pierre Laurent-Puig, Jerome Alexandre, Guillaume Beinse.

PURPOSE: No universal circulating biomarker exists for soft tissue (STS) and bone sarcoma (BS). We report the translational relevance of a droplet digital PCR (ddPCR) assay allowing universal, specific and dynamic detection of sarcoma-related hypermethylated circulating tumor DNA (ctDNA).
EXPERIMENTAL DESIGN: In-silico analysis (TCGA/GEO datasets, n=8330) identified hypermethylated DNA positions in STS/BS, unmethylated in non-sarcoma tissues or white blood cells releasing circulating plasma cell-free DNA (cfDNA). A ddPCR assay following bisulfite conversion of cfDNA was developed. The methylation signature performances were evaluated in independent in-silico cohorts (TCGA/GEO, n=1342). The ddPCR assay was applied to cfDNA from healthy donors, patients with metastatic STS (METASARC cohort, n=49, 13 histotypes), and patients with STS/BS treated with neoadjuvant chemotherapy (NEOSARC cohort, n=42, 10 histotypes).
RESULTS: A ddPCR assay targeting seven methylated genomic positions distinguished sarcoma samples versus non-neoplastic mesenchymal and endothelial/liver tissues (AUC=0.95; in silico validation set). Sensitivity allowed methylated DNA detection at 1:1000 dilution in genomic DNA, with a methylated allele frequency of 0.06%. CtDNA was positively detected in 45% of METASARC (22/49) and 74% of NEOSARC (31/42) patients, across all histotypes. CtDNA detection correlated with poor overall survival in METASARC patients with STS (p=0.039). Increasing ctDNA during neoadjuvant chemotherapy was associated with poor outcomes in NEOSARC (composite criteria with poor histological response, radiological progression or relapse within 6 months; p=0.0095).
CONCLUSIONS: This sensitive ddPCR assay for universally methylated ctDNA enables precise detection, prognostication, and real-time monitoring of tumor burden in patients with high-grade and advanced sarcoma, regardless of histotype or origin.

DOI: https://doi.org/10.1158/1078-0432.CCR-25-0134
Nat Commun. 2025 Jul 08. 16(1): 6273

High-coverage allele-resolved single-cell DNA methylation profiling reveals cell lineage, X-inactivation state, and replication dynamics.

Nathan J Spix, Walid Abi Habib, Zhouwei Zhang, Emily Eugster, Hsiao-Yun Milliron, David Sokol, KwangHo Lee, Paula A Nolte, Jamie L Endicott, Kelly F Krzyzanowski, Toshinori Hinoue, Jacob Morrison, Benjamin K Johnson, Wanding Zhou, Hui Shen, Peter W Laird.

DNA methylation patterns at crucial short sequence features, such as enhancers and promoters, may convey key information about cell lineage and state. The need for high-resolution single-cell DNA methylation profiling has therefore become increasingly apparent. Existing single-cell whole-genome bisulfite sequencing (scWGBS) studies have both methodological and analytical shortcomings. Inefficient library generation and low CpG coverage mostly preclude direct cell-to-cell comparisons and necessitate the use of cluster-based analyses, imputation of methylation states, or averaging of DNA methylation measurements across large genomic bins. Such summarization methods obscure the interpretation of methylation states at individual regulatory elements and limit our ability to discern important cell-to-cell differences. We report an improved scWGBS method, single-cell Deep and Efficient Epigenomic Profiling of methyl-C (scDEEP-mC), which offers efficient generation of high-coverage libraries. scDEEP-mC allows for cell type identification, genome-wide profiling of hemi-methylation, and allele-resolved analysis of X-inactivation epigenetics in single cells. Furthermore, we combine methylation and copy-number data from scDEEP-mC to identify single, actively replicating cells and profile DNA methylation maintenance dynamics during and after DNA replication. These analyses unlock further avenues for exploring DNA methylation regulation and dynamics and illustrate the power of high-complexity, highly efficient scWGBS library construction as facilitated by scDEEP-mC.

DOI: https://doi.org/10.1038/s41467-025-61589-1
bioRxiv. 2025 Jun 30. pii: 2025.06.29.660377. [Epub ahead of print]

Genome-wide extraction of differentially methylated DNA regions using adapter-anchored proximity primers.

Farzaneh Darbeheshti, Hayet Radia Zeggar, Hamzeh Salmani, Yibin Liu, Ruolin Liu, Viktor A Adalsteinsson, G Mike Makrigiorgos.

The epigenetic deregulation of CpG islands (CGIs) plays a crucial role in cancer initiation and progression. CGIs comprise 1-2% of the human genome and are rich in differentially methylated regions (DMRs) that can serve as cancer biomarkers in clinical samples and liquid biopsies. Focusing epigenetic sequencing on CpG-rich sequences, including CGIs and avoiding non-informative regions, offers an efficient and sensitive approach for cancer identification and tracking, especially within samples containing excess of unaltered, normal DNA. To this end, we have developed Adaptor-anchored Methylation amplification via Proximity Primers (aMAPP), a versatile PCR-based enrichment method. aMAPP employs specially designed primers to selectively enrich either methylated or unmethylated CpGs, depending on the upstream methylation conversion method employed. aMAPP achieves high coverage of genome-wide CGIs and detects hundreds of DMRs in tumor samples compared to adjacent normal tissue using ultra-low depth sequencing (∼300,000 reads). It enables tracing of aberrant methylation down to allelic frequency 0.01% in dilutions of tumor DNA and in cell-free DNA samples, can be applied using picogram amounts of DNA, and can be adapted to enrich either small panels of cancer-specific DMRs, or the majority (>90%) of genomic CGIs and CpGs. aMAPP offers a simple, cost-effective, and highly sensitive approach for capturing the epigenetic footprint of genome-wide CpGs and identifying aberrantly methylated or un-methylated genomic regions.

DOI: https://doi.org/10.1101/2025.06.29.660377
Acta Cytol. 2025 Jul 08. 1-31

Utility Of Fallopian Tube Brush Cytology and Cell-blocks As a Screening Tool for Epithelial Ovarian Cancer in Patients Undergoing Gynaecological Surgeries for Benign and Malignant Indications: Attempt at a Classification System.

Meenakshi Rao, Garima Yadav, Pratibha Singh, Navdeep Kaur Ghuman, Shashank Shekhar, Meenakshi Gothwal, Priyanka Kathuria, Anju G.

INTRODUCTION: Fallopian tube cytology is an evolving and as yet not well-established field. Through this study, we aimed to establish the utility of fallopian tube brush cytology by stratification into cytological diagnostic categories.
METHODS: Cytological specimens were collected using an endobrush from the fimbrial end of the tubes at the time of gynaecological surgeries, and LBC preparation (Liquid-based cy-tology slides prepared by SurePath technique) and cell blocks were prepared. Smears were stratified into: Unsatisfactory/Non-diagnostic (ND), Benign, Atypical, Suspicious of Malig-nancy (SOM), and Malignant. Correlation with histopathology was done, and the Risk of Malignancy (ROM) was calculated for each category. Negative Predictive Value (NPV) and positive Predictive Value (PPV) were calculated. Diagnostic accuracy was calculated.
RESULTS: A total of 392 tubal cytology specimens of 225 patients were collected. 8.2% (n=32) of the specimens were Unsatisfactory/Non-Diagnostic (ND), 87% (n=343) were Benign, 2.6% (n=10) were Atypical, 0.8% (n=3) were SOM, and 1% (n=4) were Malignant. All the cases in the SOM and malignant categories were serous carcinomas on histopathology. Of the ten atypical cases, all were non-malignant on histopathology: two were Serous Tubal In-traepithelial Lesions (STILs) and negative for Serous Tubal Intraepithelial Carcinoma (STIC), four showed salpingitis, and four showed normal histology. ROM for non-diagnostic, benign, and atypical categories was 0%. ROM for the malignant category, as well as the SOM category, was 100%. NPV for the benign category, benign and atypical categories, was 100%. PPV for the malignant category, as well as the malignant and SOM catego-ries, was 100%. Cellblocks were prepared for all cases, and the grey zone categories of atypical and SOM were reduced from 13 to 8. The diagnostic accuracy was 91.3% without and 99.4% with consideration of the Non-Diagnostic category.
CONCLUSION: Fallopian tube brush cytology shows excellent concordance with the follow-up histopathology in all categories, barring the ND category. Excellent concordance with histo-pathology was seen in cases of the benign category, which comprised the majority of the samples (87.5%). Although excellent concordance was also seen in the other categories with the final histopathology, the number of samples in these categories was less for a definite conclusion. Cell block preparation, though useful, especially in the grey zone categories, did not offer statistically significant results. Another important finding was that not even a single case of incidental STIC was found. This finding raises questions on the accepted current rou-tine practice of preventive salpingectomy for all in the correct setting.

DOI: https://doi.org/10.1159/000546944
Nature. 2025 Jul 08.

Genetic mutation predicts survival after immunotherapy for ovarian cancer.

Sarah Adams.



Keywords:  Cancer; Genetics; Medical research; Therapeutics

DOI:  https://doi.org/10.1038/d41586-025-02053-4
bioRxiv. 2025 Jul 04. pii: 2025.07.01.662607. [Epub ahead of print]

Spatial joint profiling of DNA methylome and transcriptome in mammalian tissues.

Chin Nien Lee, Hongxiang Fu, Angelysia Cardilla, Wanding Zhou, Yanxiang Deng.

The spatial resolution of omics dynamics is fundamental to understanding tissue biology. Spatial profiling of DNA methylation, which is a canonical epigenetic mark extensively implicated in transcriptional regulation, remains an unmet demand. Here, we introduce a method for whole genome spatial co-profiling of DNA methylation and transcriptome of the same tissue section at near single-cell resolution. Applying this technology to mouse embryogenesis and postnatal brain resulted in rich DNA-RNA bimodal tissue maps. These maps revealed the spatial context of known methylation biology and its interplay with gene expression. The two modalities' concordance and distinction in spatial patterns highlighted a synergistic molecular definition of cell identity in spatial programming of mammalian development and brain function. By integrating spatial maps of mouse embryos at two different developmental stages, we reconstructed the dynamics of both epigenome and transcriptome underlying mammalian embryogenesis, revealing details in sequence, cell type, and region-specific methylation-mediated transcriptional regulation. This method extends the scope of spatial omics to DNA cytosine methylation for a more comprehensive understanding of tissue biology over development and disease.

DOI: https://doi.org/10.1101/2025.07.01.662607
Ann Oncol. 2025 Jul 07. pii: S0923-7534(25)00831-2. [Epub ahead of print]

Re: Long-term follow-up of PAOLA-1 and the evolving landscape of post-PARP treatment strategies in ovarian cancer.

Martina Parenza Arenhardt, Angélica Nogueira-Rodrigues.

DOI: https://doi.org/10.1016/j.annonc.2025.06.016
Mol Cytogenet. 2025 Jul 08. 18(1): 13

What the VAF? A guide to the interpretation of variant allele fraction, percent mosaicism, and copy number in cancer.

Adam C Smith, Hubert Tsui, Sila Usta, Jose-Mario Capo-Chichi.

The evolution of techniques used to identify structural variants (SVs) and copy number variants (CNVs) in genomes have seen significant development in the last decade. With the growing use of more technologies including chromosomal microarray, genome sequencing and genome mapping in clinical cytogenetics laboratories, reporting the frequency of SVs and CNVs has increased the complexity of genomic results. In conventional testing (e.g. karyotype or FISH) individual cells are analyzed and abnormalities are reported at the single cell level directly as a proportion of the analyzed cells. Whereas for bulk genome assays structural and sequence changes are often reported as variant allele frequencies and fractional copy number states. The International System of Cytogenomic Nomenclature (ISCN) recommends converting these values into a "proportion of the sample", which requires different calculations and underlying assumptions based on the data type. This review illustrates how the different methods of interpreting and reporting data are performed and identifies challenges in the conversion of these values to a proportion of the sample. We stress the need for careful interpretation of data with consideration for factors that may alter how proportions are reported including overlapping SVs and CNVs or regions with acquired homozygosity. We also demonstrate, using validation data of SVs and CNVs tested by multiple techniques how results are largely consistent across methodologies, but can show dramatic differences in rare circumstances. This review focuses on illustrating many of the challenges with aligning reporting using different techniques and their underlying assumptions. As hematologic disease classifications start to incorporate numeric limits (e.g. VAF defining thresholds), it is important for laboratory geneticists, pathologists and clinicians to appreciate the differences in methodologies, potential pitfalls and the nuances when comparing bulk genome analyses to the more conventional single cell techniques.

DOI: https://doi.org/10.1186/s13039-025-00718-3
bioRxiv. 2025 Jul 05. pii: 2025.07.01.662655. [Epub ahead of print]

DiffMethylTools: a toolbox of the detection, annotation and visualization of differential DNA methylation.

Houssemeddine Derbel, Evan Kinnear, Justin Wong, Qian Liu.

DNA methylation is a compulsory and fundamental epigenetic mechanism, and its significant changes (i.e., differential methylation) regulate gene expression, cell-type specification and disease progression without altering the underlying DNA sequence. Differential methylation biomarkers were widely used as inputs for various downstream investigations, and differential methylation could be detected via existing statistical tools by comparing two groups of methyomes (i.e. whole-genome methylation profiles). However, few toolboxes were available to integrate robust detection, annotation and visualization of differential methylation to efficiently streamline methylation investigation. Also, differential methylation detected via tools has poor reproducibility and no tools were tested on long-read methylomes. To address these issues, we introduced DiffMethylTools, an end-to-end solution to eliminate analytical and computational difficulties for differential methylation dissection. Comparison on six datasets including three long-read methylomes demonstrated that DiffMethylTools achieved overall better detection performance of differential methylation than existing tools like MethylKit, DSS, MethylSig, and bsseq. Besides, DiffMethylTools supported versatile input formats for seamless transition from upstream methylation detection tools, and offered diverse annotations and visualizations to facilitate downstream investigations. DiffMethylTools therefore offered a robust, interpretable, and user-friendly solution for differential methylation investigation, benefiting the dissection of methylation's roles in human disease studies.

DOI: https://doi.org/10.1101/2025.07.01.662655
Am J Obstet Gynecol. 2025 Jul 04. pii: S0002-9378(25)00459-4. [Epub ahead of print]

Development and Validation of a Histology-Specific Natural History Model of Ovarian Cancer.

Sayaka Ishizawa, Maryam Eghbalizarch, Renu S Nargund, Seyyed Mostafa Mousavi Janbeh Sarayi, Jiangong Niu, Mehdi Hemmati, Maddie Tumbarello, Andrew J Schaefer, Karen Lu, Sharon H Giordano, Larissa A Meyer, Iakovos Toumazis.

   BACKGROUND: Ovarian cancer is the second leading cause of death from gynecologic cancers, yet no effective screening program exists for the general population. Past screening trials evaluated the effectiveness of annual ovarian cancer screening and concluded that it does not yield significant mortality reduction. Future investments on ovarian cancer screening trials would require convincing preliminary evidence on the effectiveness of interventions of interest. Simulation modeling offers an effective, fast, cost-efficient, and safe approach to gain insights on the effectiveness of interventions, that is increasingly being used to inform guidelines for cancer screening programs. Models that simulate the natural progression of diseases in the absence of any intervention, commonly referred to as natural history models (NHMs), are the cornerstone of such analyses because they provide a reference point for evaluating interventions. Currently, no histology-specific NHM exists for ovarian cancer despite significant differences among subtypes.
OBJECTIVE: Develop and validate a histology-specific ovarian cancer NHM.
STUDY DESIGN: We developed NHMs for the most common histological subtypes of epithelial ovarian cancer: high-grade serous carcinoma, low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, endometrioid carcinoma, carcinosarcoma, and not otherwise specified. Each NHM simulates the natural progression of ovarian cancer from disease's onset until death from any cause. We modeled ovarian cancer progression as a state-transition model comprising of 13 mutually exclusive and collectively exhaustive health states. We informed the model input parameters using observed, nationally representative estimates, whenever possible. Unobserved parameters (e.g., preclinical transitions) were estimated through calibration to histology-specific data from the Surveillance, Epidemiology, and End Results (SEER) registry. We validated the NHMs on the control arms of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the United Kingdom Collaborative Trial on Ovarian Cancer Screening (UKCTOCS) trials, in terms of ovarian cancer incidence and mortality rates, and stage distribution at diagnosis. Differences between observed and estimated outcomes were assessed using traditional statistical tests.
RESULTS: The calibrated NHMs reproduced the observed SEER data (range of weighted root mean square error (RMSE) across histological subtypes: 0.0081 to 0.0185) as well as individual calibration targets; survival after diagnosis, stage distribution at diagnosis, and age distribution at diagnosis (ranges of RMSE across histological subtypes: 0.0029 to 0.0204, 0.0005 to 0.0203, and 0.0637 to 0.0816, respectively). The NHMs reproduced PLCO's observed incidence and mortality rates, and stage at diagnosis (p-value=0.411 for incidence, p-value=0.195 for mortality, and p-value=0.200 for stage distribution at diagnosis) and UKCTOCS's observed ovarian cancer incidence (p-value=0.607) and mortality (p-value = 0.624) rates. The average duration of the preclinical phase ranges between 1-3 years, which partly explains screening's failure to yield mortality reduction. Moreover, across all subtypes considered stage II ovarian cancer is a transient state with significantly shorter average duration as compared to other stages.
CONCLUSION: The NHMs accurately describe the histology-specific natural progression of ovarian cancer and provide important insights into the natural history of the disease. The developed models may be used to evaluate the impact of future and emerging ovarian cancer interventions, thus providing valuable insights to decision-makers and policy-makers.

Keywords:  Markov model; PLCO; UKCTOCS; model calibration; model validation; natural history model; ovarian cancer; screening; simulation study

DOI:  https://doi.org/10.1016/j.ajog.2025.06.063
Nat Rev Clin Oncol. 2025 Jul 10.

Therapeutic targeting of mismatch repair-deficient cancers.

Paul Johannet, Benoit Rousseau, Carol Aghajanian, Michael B Foote, Luis A Diaz.

DNA mismatch repair (MMR) is one of many evolutionarily conserved processes that act as guardians of genomic integrity. MMR proteins recognize errors that occur during DNA replication and initiate countermeasures to rectify those mistakes. MMR deficiency (MMRd) therefore leads to a dramatic accumulation of mutations. The MMRd genomic signature is characterized by a high frequency of single-base substitutions as well as insertions and/or deletions that preferentially occur in short nucleotide repeat sequences known as microsatellites. This accumulation leads to a phenomenon termed microsatellite instability, which accordingly serves as a marker of underlying MMRd. MMRd is associated with hereditary cancer syndromes such as Lynch syndrome and constitutional MMRd as well as with sporadic tumour development across a variety of tissues. High baseline immune cell infiltration is a characteristic feature of MMRd/microsatellite instability-high tumours, as is the upregulation of immune checkpoints. Importantly, the molecular profile of MMRd tumours confers remarkable sensitivity to immune-checkpoint inhibitors (ICIs). Many patients with MMRd disease derive durable clinical benefit when treated with these agents regardless of the primary tumour site. Nevertheless, a substantial subset of these patients will fail to respond to ICI, and increasing research is focused on identifying the factors that confer resistance. In this Review, we begin by discussing the biological function of the MMR machinery as well as the genomic sequelae of MMRd before then examining the clinical implications of MMRd with a specific focus on cancer predisposition, diagnostic approaches, therapeutic strategies and potential mechanisms of resistance to ICIs.

DOI: https://doi.org/10.1038/s41571-025-01054-6
Expert Rev Mol Diagn. 2025 Jul 09. 1-9

Circulating tumor DNA: a biomarker for oncology drug development in phase I clinical trials?

Riccardo Asnaghi, Renato M Marsicano, Valeria Fuorivia, Giulia Malvezzi, Julian D Etessami, Davide Izzo, Carmine Valenza, Giuseppe Curigliano, Dario Trapani.

   INTRODUCTION: Circulating tumor DNA (ctDNA) is a noninvasive and promising biomarker for cancer diagnosis, prognosis, and therapeutic monitoring, offering significant potential for real-time insights into tumor dynamics when compared to traditional tissue-based biopsies. Phase I oncology clinical trials, which primarily focus on assessing the safety, pharmacodynamics, and early activity of novel cancer therapies, might find in the unique biological characteristics of ctDNA, a valuable biomarker to boost the efficiency of testing novel agents.
AREAS COVERED: This review explores the utility of ctDNA as a biomarker in phase I trials, discussing its biological and technical features, clinical relevance, current limitations, and future potential in advancing early clinical drug development.
EXPERT OPINION: Despite being an emerging field in phase I trials, ctDNA analysis has proved to be a remarkable tool for patient inclusion, optimal biological dose determination, and early response assessment. However, several challenges hinder its systematic adoption in early trials, including assay variability, biological and anatomical differences across cancer types, and, most notably, the lack of standardization. Systematic implementation of ctDNA in phase I trials could facilitate the development of robust, reproducible noninvasive biomarker models, which can then be further validated in phae II/III trials.

Keywords:  biomarker; ctDNA; liquid biopsy; optimal biological dose; phase I clinical trials

DOI:  https://doi.org/10.1080/14737159.2025.2531065
Sci Rep. 2025 Jul 09. 15(1): 24790

Predicting time-to-first cancer diagnosis across multiple cancer types.

Kien Lau, Gregory R Hart, Jun Deng.

Cancer causes over 10 million deaths annually worldwide, with 40.5% of Americans expected to be diagnosed in their lifetime. Early detection is critical; for liver cancer, survival rates improve from 4 to 37% when caught early. However, predicting time to first cancer diagnosis is challenging due to its complex and multifactorial nature. We developed predictive models using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for training and the UK Biobank for evaluation to estimate time-to-first cancer diagnosis for high-incidence cancers, including lung, liver, and bladder cancers. Utilizing Cox proportional hazards models with elastic net regularization, survival decision trees, and random survival forests, we used 46 sex-agnostic demographic, clinical, and behavioral features. The Cox model achieved a C-index of 0.813 for lung cancer, surpassing non-parametric machine learning methods in accuracy and interpretability. Cancer-specific models consistently outperformed non-specific cancer models, as shown by time-dependent AUC analyses. Scaled Cox coefficients revealed novel insights, including BMI's inverse association with lung cancer risk. Our findings offer interpretable, accurate tools for personalized cancer risk assessment, improving early detection and bridging computational advances with clinical practice.

DOI: https://doi.org/10.1038/s41598-025-08790-w
Nucleic Acids Res. 2025 Jul 08. pii: gkaf589. [Epub ahead of print]53(13):

The biological and evolutionary consequences of competition between DNA sequences that benefit the cell and DNA sequences that benefit themselves.

Arnold J Bendich, Scott O Rogers.

Mobile genetic elements (e.g. transposons and introns) are genomic parasites, DNA sequences that can proliferate within host genomes. In prokaryotes, these elements are minimized by purifying selection and host-cell defenses. In eukaryotes, however, these parasitic sequences may comprise most of the genome. This extreme imbalance in eukaryotes is maintained by a metabolically quiescent germline through which the genomic parasites are transmitted to the next generation via gametes and spread throughout the population via sexual reproduction. Some eukaryotes avoid the threat posed by genomic parasites by eliminating them from somatic cells, whereas others retain the parasites in somatic cells and attempt to suppress them by epigenetic silencing mechanisms. Here, we review the evidence for the ongoing competition between host and parasite for genome occupancy. We conclude that defenses against mobile genetic elements vary greatly among organisms, and this variation accounts for the enormous range in genome size among organisms.

DOI: https://doi.org/10.1093/nar/gkaf589
Adv Exp Med Biol. 2025 ;1476 297-308

Interactions Between the Innate and Adaptive Immune Responses.

Andrew J T George, Ahmad Al Aiyan, Basel K Al-Ramadi, Uday Kishore.

  The adaptive and innate immune responses of vertebrates should not be considered separate independent systems but as interacting components of one system that provide complementary information to direct an inflammatory and immune response appropriately. In this chapter, we will examine two examples, the role of dendritic cell and T cell interactions and how complement functions with the humoral arm of the adaptive immune response.The innate immune response recognises molecules that are expressed on all pathogens, or damaged cells. It is commonly considered a rapid arm of the immune system, which responds swiftly in an antigen non-specific manner. This is in contrast to adaptive immunity, which is antigen specific and can take days to weeks to become effective due to the need for clonal expansion of antigen-specific T and B cells.The interaction of the dendritic cell with the naïve T cell is one of the central interactions of the immune system, as it provides the sole way in which the naïve T cell can be activated, thus initiating the T cell response. The outcome of this interaction is dependent on the phenotype of the dendritic cell, which is in itself a consequence of the interactions that the dendritic cell has had with molecules in its environment. For example, if the dendritic cell has been activated by molecules released from pathogens or damaged cells, then it will be activated and express co-stimulatory molecules that activate the T cell. It will also secrete cytokines that direct the differentiation of the T cell in a particular direction. This interaction can, therefore, be considered to have innate components, which provide context for the immune response, and adaptive components that provide specificity.The complement system contributes to defence against pathogens through interaction of molecules with components of cell surfaces that lead to activation of cascade in the presence of microbes. However, the complement system also contributes to humoral immunity through the classical pathway of activation, and through its role in B cell activation, affinity maturation and memory.This divide of the immune system into innate and adaptive responses can give the impression of two parallel immune systems, which sometimes interact. This is false. It is perhaps better to think about immune responses having both innate and adaptive components. Parts of the response recognise antigen in a specific manner, and other parts are activated by the context, the environment (presence of pathogens, damage, etc.). It is the coordination of these two components that are vital for effective immunological responses.

Keywords:  Adaptive immunity; Complement; Dendritic cell; Innate immunity; Pattern recognising receptors

DOI:  https://doi.org/10.1007/978-3-031-85340-1_12