Mahek Khatri

Meta-analysis of epigenome-wide association studies of schizophrenia demonstrates that sex-stratified analyses significantly outperform conventional models at detecting methylation patterns.

Schizophrenia is a complex psychiatric disorder primarily characterized by disoriented identity, thoughts, behaviour, emotions and ideas.¹ Although it is not as common as other psychiatric disorders, people with schizophrenia are 2 to 3 times more likely to die early than an average person.² Owing to its high heritability, several genes have been linked to the disorder. However, most of these are found in non-coding, suggesting that epigenetic regulation plays a key role in the disorder.³ Epigenetic mechanisms, such as DNA methylation, influence gene expression without altering the DNA sequence.⁴ DNA methylation is highly dynamic, influenced by factors such as age and sex, with widespread differences observed in the methylation patterns between males and females.⁵ In their meta-analysis of epigenome-wide association studies (EWAS) of schizophrenia,  Tesfaye et al. report that sex-stratified analysis improves the discovery of schizophrenia-associated epigenetic markers. The sex-stratified analysis identified 20 differentially methylated regions (DMRs), significantly more than the sex-adjusted method, which missed several key markers⁶. These findings underscore the importance of considering sex differences in epigenetic studies of schizophrenia.

The differences in male and female methylation patterns can be seen across several tissues, such as blood, the brain, and the buccal tissue and in these, females exhibit a pattern of global increase in methylation in comparison to males.⁷ These DMRs between the two are enriched in the genes associated with psychiatric disorders like schizophrenia.⁵ In this meta-analysis, Tesfaye et al. conducted a meta-analysis of four cohorts accounting for sequences from whole blood samples of over 3,000 individuals with and without schizophrenia. They systematically compared two approaches: the traditional sex-adjusted model and the sex-stratified model. The models were adjusted for confounding factors like age and smoking scores in both models, and sex was included as a covariate for the sex-adjusted models only.

The study reported that the sex-stratified analysis identified 20 DMRs associated with schizophrenia, while the sex-specific model only detected one and four DMRs in males and females, respectively. This remained true even after applying a more conservative p-value, where the sex-stratified model identified ten DMRs while the sex-adjusted model could only identify one. For the entire sample, half of the top 200 DMRs identified were common for the sex-stratified and sex-adjusted analysis. Moreover, most of the identified DMRs had at least one protein-coding gene.

Notably, further analysis and comparison of the four models revealed that the sex-stratified model exhibited greater statistical power than the conventional sex-adjusted model. This finding was further validated by calculating the poly-methylation score, which also confirmed the increased power of the sex-stratified model. The study further highlighted differences in methylation patterns between sexes. For example, the gene METTL8 showed changes specific in men, while VGLL4 and SLC9A10 exhibited alterations unique to women. This difference in methylation specific to schizophrenia may potentially explain some of the observed clinical differences between sexes.

By demonstrating that sex-specific DNA methylation patterns improve schizophrenia risk prediction, this study advances precision psychiatry, highlighting the need for sex-stratified approaches in research. It challenges the traditional one-size-fits-all model, uncovering sex-specific molecular mechanisms that may explain clinical differences and improve targeted treatments. The findings also suggest potential biomarkers for early detection and reinforce the role of epigenetic regulation in schizophrenia. Beyond schizophrenia, this approach could be extended to other psychiatric and neurological disorders with sex-biased characteristics.

However, this study also raises important questions and highlights gaps that warrant further investigation. One significant limitation of the study is the reliance on peripheral blood for DNA methylation analysis. While blood-derived data is accessible and practical, it may not fully reflect methylation patterns in the brain, the primary site of pathology in schizophrenia.³ This raises questions about the direct relevance of the findings to brain-specific mechanisms. Future research should validate these results in brain tissues or employ advanced computational methods to infer brain methylation from blood data. Another challenge is the cross-sectional design of the study, which analyzes methylation at a single point in time. This approach limits the ability to infer causality or track changes associated with disease progression. Longitudinal studies that monitor individuals over time could provide critical insights into the temporal dynamics of these epigenetic changes. Additionally, the study predominantly analyzed samples of European ancestry, which may limit the applicability of its findings to other populations. Epigenetic changes and their associations with schizophrenia could vary across ancestries due to genetic and environmental differences. Expanding analyses to include diverse populations is essential to ensure broader generalizability and equity in research.

This study underscores the importance of integrating sex-specific analyses into epigenetic research. By recognizing the unique biological differences between men and women, researchers can uncover hidden complexities in diseases like schizophrenia and develop more effective, tailored treatments. Future research should aim to validate findings in brain tissues to strengthen the biological relevance of blood-based methylation markers, conduct longitudinal studies to clarify the causal relationship between DNA methylation and schizophrenia, expand analyses to include diverse populations and account for environmental factors to improve generalizability.

Ultimately, Tesfaye et al.’s work highlights a critical paradigm shift in psychiatric epigenomics. By embracing sex-stratified approaches, researchers can unlock new avenues for understanding schizophrenia’s molecular underpinnings and move closer to realizing the promise of precision psychiatry.

References

1.         Magwai, T. et al. DNA Methylation and Schizophrenia: Current Literature and Future Perspective. Cells 10, 2890 (2021).

2.         Schizophrenia. https://www.who.int/news-room/fact-sheets/detail/schizophrenia.

3.         Mendizabal, I. et al. Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol. 20, 135 (2019).

4.         Dupont, C., Armant, D. R. & Brenner, C. A. Epigenetics: Definition, Mechanisms and Clinical Perspective. Semin. Reprod. Med. 27, 351–357 (2009).

6.         Maschietto, M. et al. Sex differences in DNA methylation of the cord blood are related to sex-bias psychiatric diseases. Sci. Rep. 7, 44547 (2017).

7.         Tesfaye, M. et al. Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia. Mol. Psychiatry 29, 2467–2477 (2024).

8.         Yousefi, P. et al. Sex differences in DNA methylation assessed by 450 K BeadChip in newborns. BMC Genomics 16, 911 (2015).

Kayla Krolikowski

Combining optical genome mapping and karyotyping offers a promising method to improve diagnostic precision and reduces the diagnostic odyssey that patients must experience.

Many patients with genetic disorders face a diagnostic odyssey, a prolonged search for answers that delays treatment and complicates genetic counselling. With 60% of cases still undiagnosed, there is a critical need for more precise diagnostic tools in clinics¹. Optical Genome Mapping (OGM), developed by Bionano’s Stratys™ system, has the potential to end this odyssey¹. OGM visualizes the human genome’s structure by using fluorescent labels to detect structural variants, which are changes in DNA affecting the copy number, location or order of genes¹. By analyzing the pattern and spacing of labels through software, OGM identifies these variations with high precision (Figure 1)². A recent study by Yin et al., suggests combining OGM with karyotyping, a traditional method to analyze a complete set of chromosomes to detect abnormalities in number or structure, will be able to alleviate the diagnostic odyssey for patients¹.

Prior to the introduction of OGM the detection of structural variants relied on low-resolution techniques, like karyotyping, but they were unable to identify many genomic changes¹. Fluorescence in situ hybridization (FISH), improved resolution by using fluorescent probes to bind specific DNA sequences for analysis, but it struggled to detect cryptic translocations and inversions¹. Similarly chromosomal microarrays could detect gains or losses of DNA segments, but were unable to identify balanced structural variations, where no genetic material is gained or lost¹. These limitations highlighted the need for OGM, which offers higher accuracy in detecting structural variants and delivers faster results¹.

In their study, Yin et al., examined families that had a history of infertility, chromosomal problems in their children’s genomes, repeated miscarriages or intellectual disabilities, which were the reasons for seeking genetic counselling¹. The findings revealed that OGM successfully identified two cryptic translocations that karyotyping had missed and confirmed four additional translocations that were suspected but not confirmed¹. One of these cryptic translocations disrupted the Argonaute 2 (AGO2) gene, which plays a key role in controlling how genes are turned on or off^1,3. This change is associated with Lessel–Kreienkamp syndrome, a condition with significant implications for genetic counselling and family planning¹. Beyond detecting structural variants, OGM provides details regarding the specific location and orientation of the DNA segments, which is important to diagnose a genetic condition¹. However, OGM failed to identify centric fusions, where two chromosomes fuse at the center¹. This suggests that additional complementary diagnostic tools could be used with OGM in the clinic, to achieve a comprehensive genetic analysis.

To assess the utility of OGM, researchers compared the diagnostic yield of the tools both individually and in combination. The study found that OGM alone was the most effective tool but combining it with karyotyping resulted in an increased diagnostic yield of 51%¹. This highlights OGM’s potential to help shorten the diagnostic odyssey faced by patients and their families. However, what was not studied in combination with OGM was long-read sequencing, which has the potential to further enhance the diagnostics of genetic diseases⁴. A separate study by Sund et al., demonstrated the integration of OGM and long-read sequencing allowing for the characterization of a complex breakpoint that was unable to be detected through long-read sequencing alone⁵. While long-read sequencing could complement OGM, its high cost and data interpretation challenges limit its practicality in clinical settings⁵. These findings support the idea for further research to determine the optimal combination of diagnostic tools. At present, current evidence suggests integration of both karyotyping and OGM to address patients individualized needs¹.

Despite the potential of OGM, its clinical utility is limited by the lack of a comprehensive database with control samples analyzed using this technology⁶. The current Saphyr derived database comprises of only a small number of samples from the general population and lacks coverage of larger structural variants^6,7. This leads to a bias in variant frequency and limits its applicability in an international setting, resulting in structural variants that are not representive of the entire population⁷. A large, comprehensive database would reduce these biases and improve structural variant classification. Establishing an initiative to systematically sample the general population using OGM, along with continuously expanding a public database, would enhance the understanding of common structural variations and provide appropriate control data for diagnosis and research.

The application of OGM in clinical settings will have broader implications not only within genetic diagnostics, but within the healthcare system. By reducing the prevalence of undiagnosed genetic disorders, OGM could expedite accurate diagnoses, and ultimately minimize unnecessary testing and lower healthcare costs¹. From the patient perspective, this translates to alleviate the emotional and financial burdens on patients and families, improving their quality of life.

To be able to successfully incorporate karyotyping with OGM in clinical practice requires further research to assess their combined long-term impacts. Studies on the cost-effectiveness and clinical outcomes of OGM are essential to secure funding for its adoption. Additionally, the Canadian College of Medical Geneticists will play a role in establishing guidelines for clinical implementation. With continued research, expanded databases, and clinical validation, OGM in combination with karyotyping can revolutionize genetic diagnostics, shorten diagnostic odysseys, and ultimately improve patient outcomes.

References

1. Yin, K. et al. Optical genome mapping to decipher the chromosomal aberrations in families seeking for preconception genetic counseling. Sci Rep 15, 2614 (2025).
2. Bionano: Transforming the Way the World Sees the Genome. https://bionano.com/.
3. PubChem. AGO2 – argonaute RISC catalytic component 2 (human). https://pubchem.ncbi.nlm.nih.gov/gene/AGO2/human.
4. Olivucci, G. et al. Long read sequencing on its way to the routine diagnostics of genetic diseases. Front. Genet. 15, 1374860 (2024).
5. Sund, K. L. et al. Long‐read sequencing and optical genome mapping identify causative gene disruptions in noncoding sequence in two patients with neurologic disease and known chromosome abnormalities. American J of Med Genetics Pt A 194, e63818 (2024).
6. Schrauwen, I. et al. Optical genome mapping unveils hidden structural variants in neurodevelopmental disorders. Sci Rep 14, 11239 (2024).
7. Dremsek, P. et al. Optical Genome Mapping in Routine Human Genetic Diagnostics-Its Advantages and Limitations. Genes (Basel) 12, 1958 (2021).

Yael Kvint

Scientists have published a groundbreaking article detailing their promising progress on the development of a comprehensive catalogue of structural variation in the human genome using Nanopore long-read sequencing technology.

Capturing the variation within the human genome and cataloguing rare variants has always been a cornerstone of genomics research. These efforts are valuable, as they enable discoveries that can be directly translated into clinical practice to improve the diagnosis and treatment of genetic conditions. One large initiative to do this was the 1000 Genomes Project (1KGP) in 2015, which mapped patterns of human genetic variation on an unprecedented scale¹. While revolutionary, the project’s reliance on short-read sequencing technology limited its full capacity to reveal the full scope of our genetic variation. Specifically, identification of structural variations (such as insertions, deletions, duplications, rearrangements, and expansions) and large chromosomal rearrangements (especially in highly repetitive regions) presented as a major challenge at the time¹.

Now, almost a decade later, scientists have taken the 1KGP to the next level by utilizing cutting-edge, long-read Nanopore sequencing to unlock a more nuanced understanding of SVs (structural variants) in the human genome². Unlike short-read sequencing, which reads DNA in small fragments (around 100-300 base pairs), long-read sequencing (LRS) technologies can capture much longer stretches of DNA – sometimes even tens of thousands of bases in a single read. This ongoing global effort, also known as the “1KGP Oxford Nanopore Technologies Sequencing Consortium” involves genomic experts from multiple countries and institutions. By re-sequencing the first 100 samples from the original project, they have begun assembling a new high-quality catalogue of both simple and complex genetic variants². Early findings indicate that LRS technology excels in uncovering previously challenging SVs, including those implicated in common recessive disorders.

To build this comprehensive variant catalogue, the researchers began by resequencing a diverse set of 100 DNA samples from the original 1KGP project². This was done using Oxford Nanopore LRS technology, which involves threading a single DNA molecule through a synthetic nanopore (Figure 1A). The team then analyzed the sequencing data by employing computational tools to identify both SVs and SNVs (smaller nucleotide variants). To ensure comprehensive variant detection, the researchers utilized two distinct pipelines for genome assembly and variant annotation (Figure 1B): an alignment-based internal pipeline (which has been traditionally used in the past) and an assembly-based Napu-pipeline – a newer approach optimized for SV detection³.

Using both pipelines demonstrated high 98% accuracy in identifying SNVs (single nucleotide variants)². Better yet, structural variant calling showed a remarkable improvement, with an average of 24,543 high-confidence SVs identified per genome – far surpassing the 2,100 SVs identified in the original 1KGP¹. Overall, this enhanced sensitivity enabled the discovery of 349 SVs affecting 236 unique genes linked to disease phenotypes. Notably, 123 exons (protein-coding regions) in these genes harboured variants annotated as pathogenic or likely pathogenic by submitters in ClinVar. Many of these SVs were extremely rare and only found in one sample, highlighting the potential of this technology to uncover highly personalized genomic insights. This could ultimately help patients make more informed health decisions based on their unique genetic profiles.

Beyond the overall improvements in variant detection, several SVs were identified in key medically relevant genes. For example, a large deletion spanning the HBB, HBD, and HBG1 genes was detected², a locus associated with beta thalassemia – one of the most common recessive disorders worldwide⁴. Additionally, rare SVs were identified on the X chromosome², which has been notoriously difficult to sequence due to large repetitive regions⁵. One such SV was found in the RPGR gene, which is associated with a few X-linked ocular disorders². As well, novel common insertions were also found across all 100 samples, indicating that the reference genome should be updated. Since the reference genome serves as a baseline for genetic comparisons, updating it with diverse SVs could improve representation across different populations and improve disease risk assessments.

A key implication of this study is the genuine potential for LRS being used to provide a highly accurate picture of an individual’s genome. No two human genomes are 100% identical, which makes it essential to establish reliable methods for identifying a large variety of variants. Oxford Nanopore sequencing has already shown its value in personalized SV detection back in 2018, where it was used for a case study in a 12 year old boy with glycogen storage disease⁶. This technology was able to uncover a substantially large deletion that had been missed by short-read whole-exome sequencing. The discovery eventually allowed the family to use pre-implantation genetic diagnosis (PGD) to avoid passing down the deletion variant to their next child. This study underscores that such technologies could transform patient care through not just personalized detection, but also allow for informed, early intervention during family planning.

Looking beyond the study’s core findings, the broader potential of LRS is becoming increasingly clearer. Apart from improving diagnostics, LRS enables proactive strategies for not just families, but also for optimizing medication prescriptions. SVs are now known to be prevalent in pharmacogenes, which affect how individuals are able to metabolize medications, and can subsequently effect treatment outcomes⁷.  LRS implementation could also reduce genetic testing costs by allowing a single, comprehensive genome sequence to be used across multiple clinical applications. Combined with other similar initiatives using LRS to analyze 1KGP samples for SVs – such as Schloissnig et al’s project⁸ – these efforts will bring us closer to a comprehensive and reliable catalog of human genetic variation for personalized genome analysis. Ultimately, this progress would pave a path for a future where genomic-driven personalized treatment plans become the pillar of global healthcare.

References

1.         The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

2.         Gustafson, J. A. et al. High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation. Genome Res. 34, 2061–2073 (2024).

3.         Wu, L., Yavas, G., Hong, H., Tong, W. & Xiao, W. Direct comparison of performance of single nucleotide variant calling in human genome with alignment-based and assembly-based approaches. Sci. Rep. 7, 10963 (2017).

4.         Cao, A. & Galanello, R. Beta-thalassemia. Genet. Med. 12, 61–76 (2010).

5.         Ross, M. T. et al. The DNA sequence of the human X chromosome. Nature 434, 325–337 (2005).

6.         Miao, H. et al. Long-read sequencing identified a causal structural variant in an exome-negative case and enabled preimplantation genetic diagnosis. Hereditas 155, 32 (2018).

7.         Sherman, C. A., Claw, K. G. & Lee, S. Pharmacogenetic analysis of structural variation in the 1000 genomes project using whole genome sequences. Sci. Rep. 14, 22774 (2024).

8.         Schloissnig, S. et al. Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project. Preprint at https://doi.org/10.1101/2024.04.18.590093 (2024).

Amy Li

What drives the mysterious loss of the Y chromosome in aging men? A recent study explores the key genetic and environmental factors behind mosaic loss of chromosome Y, using novel bioinformatics and sequencing techniques across diverse populations to reveal its genetic mechanisms and potential health impacts.

For decades, mosaic loss of chromosome Y (mLOY)—the loss of the Y chromosome in some cells—was thought to be a natural part of aging in men. However, recent research has revealed that it is not only the most common genetic alteration in men but also linked to cancer¹. In a study published in The American Journal of Human Genetics, Jakubek and colleagues analyzed thousands of whole genomes from diverse populations to better understand mLOY and its clinical implications². Their findings pinpoint at-risk populations and genomic regions tied to mLOY susceptibility, marking a significant step forward in aging science and reshaping how we assess disease risks in aging men.

While chromosome Y (chrY) is known for determining male sex, its structure has gradually degraded over time. Despite losing over a thousand genes, chrY does not seem to affect cell survival significantly³, which may explain why mLOY persists. mLOY is a part of a broader genetic condition called mosaicism, where genetically distinct cell populations exist within the body. In mLOY, some cells lose chrY while others retain it, with varying effects depending on the cell type. In blood cells, mLOY has been linked to an increased disease risk in aging men (Figure 1)⁴. Traditional research on mLOY has often focused on homogenous populations and narrow detection methods, limiting our understanding of mLOY. To address these gaps, Jakubek and colleagues developed a new computational tool to more accurately detect mLOY across diverse cohorts, advancing the study of this complex genetic event.

Building on their new tool, Jakubek and colleagues conducted a comprehensive study to investigate mLOY prevalence and patterns across diverse populations. They began by analyzing thousands of ethnically diverse males from the United States (Figure 2A) using whole genome data from blood samples (Figure 2B)². Custom-built software detected mLOY by analyzing coverage differences of genetic variants (alleles) in pseudo-autosomal region 1 (PAR1) (Figure 2C)². This region is shared between the X and Y chromosomes and is known to exhibit allelic imbalance in mLOY cases. Therefore, the combination of sample diversity and precise detection allowed the authors to identify significant mLOY marker differences across ethnicities, setting the stage for further investigation into these patterns.

The authors uncovered a strong link between mLOY and genetic ancestry. Even after adjusting for smoking status, individuals of European ancestry showed higher mLOY levels than Hispanic American and African American (AA) individuals², suggesting that ancestry-specific genetic factors influence chrY gene expression and mLOY levels. Supporting this, the authors identified four key genetic regions linked to mLOY risk. Notably, variants in TCL1A and BCL2L1—genes on chromosomes 14 and 20 respectively—were twice as common in AA individuals compared to those of European ancestry, potentially offering protection against mLOY². These protective variants may counteract mLOY’s effects, possibly through loss-of-function mutations. By identifying ancestry-specific protective variants, this study highlights the importance of incorporating diverse populations into genetic research to uncover mechanisms that may be missed in predominantly European cohorts. This research could also prompt further studies on how mLOY contributes to age-related diseases such as cardiovascular disease, potentially leading to ancestry-informed screening. Lastly, the findings emphasize that prioritizing diversity in genomic studies is not only a matter of equity but also a critical pathway to advancing scientific discovery and improving health outcomes for all populations.

Beyond mLOY, these variants also hold therapeutic potential. Genome editing strategies targeting protective variants might help treat diseases associated with mLOY, such as cancer and Alzheimer’s^1,6. Interestingly though, TCL1A overexpression is associated with leukemia⁷, highlighting the delicate balance between protective and risk variants. Given the complexity of using these variants for disease diagnosis, future studies should explore how environmental factors might interact with them, such as in an environment-wide association study on mLOY prevalence among European individuals. Furthermore, understanding the gene and environment interactions in mLOY could guide therapeutic interventions, advancing precision medicine.

In addition to protective variants, the authors identified non-coding variants lost in mLOY that influence disease risk. Some of these variants—associated with increased CD99 expression, a gene in PAR1—were retained on the X chromosome². This suggests that the loss of CD99 expression on chrY is compensated by its increased expression on the X chromosome². Hence, these findings reveal the cell survival mechanisms during mLOY and CD99’s potential role in conferring disease. Additionally, reduced CD99 expression has also been observed in leukocytes—white blood cells of the immune system—with mLOY⁸, reinforcing the connection between CD99 and mLOY². These findings position CD99 expression and its associated variants as potential prognosis biomarkers for conditions related to mLOY. Moreover, if CD99 levels can be easily detected in the blood, it could enable early cancer detection in individuals with mLOY. Future research should investigate how methylation impacts CD99 expression and whether its modulation could reduce mLOY-associated disease risks. Integrating proteomic, transcriptomic, and single-cell analyses data will also be informative in clarifying CD99’s molecular role and its downstream effects in mLOY-affected cells.

Jakubek and colleagues’ computational pipeline and analysis of mLOY triggers will enhance future studies in detecting mLOY events. Their exploration of genetic ancestry, variants, and gene expression deepened the understanding of mLOY dynamics and its potential as a cancer biomarker. While further studies in other national biobanks are needed, studying mLOY not only reveals what is lost in our genome but also uncovers new pathways to understanding disease—reminding us that every loss can lead to new discoveries.

References

1.         Wright, D. J. et al. Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility. Nat. Genet. 49, 674–679 (2017).

2.         Jakubek, Y. A. et al. Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood. Am. J. Hum. Genet. (2025) doi:10.1016/j.ajhg.2024.12.014.

3.         Guo, X. et al. Mosaic loss of human Y chromosome: what, how and why. Hum. Genet. 139, 421–446 (2020).

4.         Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood. Nature 575, 652–657 (2019).

5.         Gutiérrez-Hurtado, I. A. et al. Loss of the Y Chromosome: A Review of Molecular Mechanisms, Age Inference, and Implications for Men’s Health. Int. J. Mol. Sci. 25, 4230 (2024).

6.         Dumanski, J. P. et al. Mosaic Loss of Chromosome Y in Blood Is Associated with Alzheimer Disease. Am. J. Hum. Genet. 98, 1208–1219 (2016).

7.         Virgilio, L. et al. Identification of the TCL1 gene involved in T-cell malignancies. Proc. Natl. Acad. Sci. U. S. A. 91, 12530–12534 (1994).

8.         Mattisson, J. et al. Leukocytes with chromosome Y loss have reduced abundance of the cell surface immunoprotein CD99. Sci. Rep. 11, 15160 (2021).

9.         Liu, J. et al. Lrp5 and Lrp6 are required for maintaining self-renewal and differentiation of hematopoietic stem cells. FASEB J. 33, 5615–5625 (2019).

Michelle Mariaprabhu

Key findings from a deep-sequencing study provide an understanding of the role of thymidine metabolism and its association with telomere lengths in stem cells.

Since the discovery of the four bases of DNA in the 1950s, the scientific community has unraveled exorbitant amounts of information about the role of these nucleotides. In the 2020s, we are now beginning to understand how the nucleoside thymidine, may hold the key to regulating telomere length. Could this discovery unlock new treatments for telomere-associated disorders, and possibly the key to aging? Extensive research has been conducted to find associations with shortening telomeres in diseases, aging, and environmental factors, to uncover novel therapies to minimize cellular decline. Continuing in the path of uncovering the role of genetics in aging, Mannherz and Agarwal revealed that disruptions in thymidine metabolism could affect telomere maintenance, leading to adverse effects in important biological processes¹.

Telomeres are regions of repetitive nucleotide sequences found at the ends of chromosomes and are crucial in protecting the chromosome tips from shortening or further degradation, acting as a protective barrier to the internal regions².The length of a telomere is slightly shortened with each DNA replication event, leading to aging tissues and ultimately cell death, once degraded to a critical length. Telomerase is a key enzyme in regulating telomere length, effectively working in conjunction with several subunits to maintain chromosomal integrity². In essence, telomeres are vital in keeping the genome intact, and from further dysfunction, due to degradation.

 Unlike standard nucleotides, thymidine is a nucleoside, comprised of a thymine base and a sugar, and carries distinct structural characteristics and specific roles in cellular processes³. Mannherz and Agarwal investigated a previously understudied area of genetics by visualizing the effects of thymidine metabolism on telomere length. By using an already existing genome-wide guide RNA (sgRNA), a short sequence of RNA typically used in guiding Cas9 proteins, they aimed to find genes associated with telomere length⁴. Through integrating a highly specific screening method, the CRISPR/Cas9 gene-editing technique, genes associated with regulating telomere length were then identified in human cell lines expressing this specific system, as explained in Figure 1^1,5,6.

 Utilization of the CRISPR/Cas9 system allowed the researchers to perform an overall quality control check, pinpointing the specific genes that play a role in telomere length. Once introduced to the mutations, telomere lengths were measured after 49 days in culture through flow-FISH, a technique that integrates both fluorescence and genetic material to identify the number of repetitive elements in DNA, a key identifier of telomeres¹. These results were then sorted and categorized into the shortest and longest telomeric groups. Further sequencing of the genomic DNA identified genes found in the groups, resulting in enriched sgRNA targeting genes that regulate thymidine metabolism¹. This result provided the first key insight into the role of thymidine metabolism on telomeric length, prompting follow-up validation studies on genes found in this specific pathway.

By performing a knock-out study, which observe the effects of specific genes that are deactivated, two main results were established: a knockout of TK1 or TYMS, genes vital in thymidine synthesis, resulted in shortened telomeres, supporting the hypothesis that disrupting genes involved in thymidine production would negatively affect telomeric length¹. Conversely, the knock-out of SAMHD1, a gene found in the degradation of thymidine triphosphate, resulted in a longer telomere length, further supporting the hypothesis of the association between telomere length and thymidine production^1,7.

To better understand the role of thymidine in telomere-associated disorders, the researchers introduced thymidine into stem cells extracted from individuals with disorders that stem from telomere dysfunction. This showed an increase in telomere length, further highlighting the positive role of thymidine in alleviating telomere biology disorders (TBDs) ^1,8. It is important to note that this finding was only observed in cells with active telomerase activity, pointing to the importance of the enzyme’s function in maintaining telomerase homeostasis.

The findings of Mannherz and Agarwal can open doors for distinguishing several therapeutic areas for patients with TBDs. Currently, the loss of normal tissue function in patients with TBDs is typically treated through serious interventions, in the form of organ transplants⁸. However, by creating a strong foundation for the role of thymidine in telomere length, this study provided a robust blueprint for the future of precision medicine in patients with TBDs⁹. These findings also provide the opportunity for further research in animal models and can eventually be used in clinical trials to implement strategies to combat TBDs.

Translating the findings of Mannherz and Agarwal into bridging the gap of genetics in aging is possible through prompting follow-up studies. Potential strategies include inhibiting SAMDH1, or supplementing thymidine in patients with TBDs to restore normal cellular function, further leading to actionable therapies based on the resulting output. Additionally, personalized treatments targeting genes associated with the thymidine metabolism pathway could have the potential to inhibit the proliferation of certain cancer cells in individuals. Further testing could be performed to understand the environmental factors that could play a part in telomere length, by performing a longitudinal controlled cohort study, to minimize confounding factors and to better understand the nucleoside’s role in telomere length. However, additional studies also need to be conducted to understand the role of all nucleosides, as the association found in this study does not directly lead to a promised treatment. To answer our earlier question, have we yet discovered the elixir for immortality? Not yet, but the role of thymidine potentially has the key to unlocking a new era of treatments for telomere-related diseases and, hopefully, reversing cellular aging.

References

1. Mannherz, W. & Agarwal, S. Thymidine nucleotide metabolism controls human telomere length. Nat. Genet. 55, 568–580 (2023).

2. Shammas, M.A. Telomeres, lifestyle, cancer, and aging. Curr. Opin. Clin. Nutr. Metab. Care 14, 28–34 (2011).

3. Young, D.W., Tollin, P. & Wilson, H.R. The crystal and molecular structure of thymidine. Acta Crystallogr. Sect. B 25, 1423–1432 (1969).

4. Cui, Y., Xu, J., Cheng, M., Liao, X. & Peng, S. Review of CRISPR/Cas9 sgRNA design tools. Interdiscip. Sci. Comput. Life Sci. 10, 455–465 (2018).

5. Mathiowetz, A.J., Roberts, M.A., Morgens, D.W., Olzmann, J.A., & Li, Z. Protocol for performing pooled CRISPR-Cas9 loss-of-function screens. STAR Protoc. 4, 102201 (2023).

6. Li, W., et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol 15, 554 (2014).

7. Schneider C.,, et al. SAMHD1 is a biomarker for cytarabine response and a therapeutic target in acute myeloid leukemia. Nat. Med. 23, 250–255 (2017).

8. Kam M.LW., Nguyen T.T.T & Ngeow J.Y.Y. Telomere biology disorders. NPJ Genom. Med. 6, 36 (2021).

9. Niewisch, M. R. & Savage, S. A. An update on the biology and management of dyskeratosis congenita and related telomere biology disorders. Expert Rev. Hematol. 12, 1037–1052 (2019).

Putri Ramadani

A novel technique, Ss-STARR-seq (Silencer screening STARR-seq), maps silencer elements across the human genome with unprecedented precision, opening the potential to reshape our understanding of gene regulation and to discover groundbreaking innovations in medicine.

Like a symphony of gene regulation, silencers are the yin to the enhancers’ yang. While enhancers boost gene activity, silencers serve as brakes, preventing certain genes from being turned on when they shouldn’t be¹. Once thought to be part of ‘junk DNA’, these non-coding regions are now recognized for their critical roles in controlling gene expression. A remarkable achievement in silencer research came from Zhu et al. through Ss-STARR-seq (Silencer Screening Self-Transcribing Active Regulatory Region Sequencing). They successfully mapped over 300,000 silencer regions across three human cell lines K562, LNCaP, and 293T ².

How history was made

The discovery journey of silencers started in 1985 with the studies of yeast³. Over time, researchers identified silencers in human, such as CD4 (Cluster of Differentiation 4), a gene that is important for immune system, and NRSE (Neuron Restrictive Silencer Element), DNA element that controls gene expression in the nervous system². Advancements were followed by the discoveries of methods like MPRAs (Massively Parallel Reporter Assays) and ReSE that successfully identified more than 2,000 silencers, representing approximately 1% of the human genome^4,5. Despite current advances, large-scale silencer maps remain limited to very few cell lines in humans. In addition, different groups reported a lack of consistency among silencer maps. For instance, in the HepG2 (human liver cancer cell line), two independent sets of silencers containing ~4000 elements each shared only four silencers in common⁶. On another hand, predictive methods using histone modifications or protein binding, such as PRC2 ChIA-PET and H3K27me3 ChIP-seq, were also reported to have biases^7,8. These inconsistencies highlighted the need for a more comprehensive and accurate approach, which is where Ss-STARR-seq comes in.

How Ss-STARR-seq works

You might wonder how Ss-STARR-seq works. Ss-STARR-seq builds on existing STARR-seq method, but with key improvements that allow for direct and quantitative measurement of silencer activity across the entire human genome. The research was done in three human cell lines K562 (a human cell line of a patient with chronic myeloid leukemia), LNCaP (a human prostate cancer cell line), and 293T (a human embryonic kidney cell line). Step by step process of Ss-STARR-seq is described with the following workflow (Figure 1):

1. Data collection: DNA sequences from human cell lines K562, LNCaP, and 293T are fragmented into ~300bp segments.
2. Input library construction: DNA fragments are inserted to a Ss-STARR-seq vector downstream of a strong promoter (hPGK) to examine silencer activity.
3. Cell transfection: The input library is introduced into human cell lines.
4. Output library extraction: Output RNA is extracted, reverse-transcribed into cDNA (Complementary DNA), amplified by PCR, and then sequenced.
5. Analysis: Computational tools (CRADLE software) compare the original DNA fragments to the final RNA output, identifying silencers.
6. Result identification: The identification of more than 300,000 silencers across the three cell lines.
7. Validation: Validation of silencers through lab tests like luciferase assays.

Using this method, Zhu et al. successfully identified 134,171 silencers in K562 cells, 137,753 in LNCaP, and 125,307 in 293T, achieving 90% genome coverage with high reproducibility².

Biological Roles and Epigenetic Landscape of Silencers

Using Ss-STARR-seq, silencers were revealed to demonstrate crucial roles in fine-tuning gene expression, in terms of redundancy, specificity, and cell-type dependence. Many silencers function redundantly, meaning knocking out just one has little effect, but disabling multiple silencers can significantly alter gene expression. For instance, in PTEN (tumor suppressor gene), knocking out a single silencer did nothing, but removing several silencers reactivated PTEN and hindered cell proliferation. Despite redundancy, silencers exhibit specific effects depending on the target gene and context. Most silencers (~83-94%) are specific to cell types. In the cell lines K562, LNCaP, and 293T, silencers were substantially unique to each cell type, with only little overlap. It is confirmed by experimental tests resulting that silencers that were active in one cell type were inactive in another².

In terms of epigenetic landscape, silencers show higher levels of DNA methylation compared to enhancers, suggesting distinct regulatory roles. Fascinatingly, Ss-STARR-seq detected silencer activity even in the absence of methylation, suggesting that other factors like chromatin structure may play a role².

Dual Functionality

Another intriguing finding from the study is that silencers exhibit context-dependent dual functionality. They can act as enhancers or insulators depending on the cell type. For example, some silencers in K562 might function as enhancers in LNCaP cells due to differences in transcription factor binding and chromatin states². This flexibility underlines the complexity of regulatory elements and their dependence on cellular context than previously thought.

Therapeutic Potential and Future Directions

Zhu et al. have solved a piece of the puzzle in our understanding of gene regulation. However, further study is needed to validate functional mechanisms and improve the generalizability of findings in different cell types using this method. This is important because the ability to map silencers across the human genome could revolutionize medicine, particularly in cancer research. For instance, silencers knockouts in PTEN were found to inhibit leukemia cell proliferation, making silencers promising therapeutic targets². Other than that, applying Ss-STARR-seq to more different cell types and species can help establish comprehensive silencer atlases. Finally, the datasets obtained from the study are valuable resource for training machine learning models to predict silencer activity and shed light on their regulatory mechanism. The discovery of silencers raises exciting questions: How might future discoveries in silencer research revamp our understanding of gene regulation and disease? And what other gems are hidden within this so-called “junk DNA”?

References

1.         Segert, J. A., Gisselbrecht, S. S. & Bulyk, M. L. Transcriptional Silencers: Driving Gene Expression with the Brakes On. Trends Genet. TIG 37, 514–527 (2021).

2.         Zhu, X. et al. Uncovering the whole genome silencers of human cells via Ss-STARR-seq. Nat. Commun. 16, 723 (2025).

3.         Brand, A. H., Breeden, L., Abraham, J., Sternglanz, R. & Nasmyth, K. Characterization of a “silencer” in yeast: A DNA sequence with properties opposite to those of a transcriptional enhancer. Cell 41, 41–48 (1985).

4.         Pang, B. & Snyder, M. P. Systematic identification of silencers in human cells. Nat. Genet. 52, 254–263 (2020).

5.         Doni Jayavelu, N., Jajodia, A., Mishra, A. & Hawkins, R. D. Candidate silencer elements for the human and mouse genomes. Nat. Commun. 11, 1061 (2020).

6.         Huang, D. & Ovcharenko, I. Enhancer–silencer transitions in the human genome. Genome Res. 32, 437–448 (2022).

7.         Cai, Y. et al. H3K27me3-rich genomic regions can function as silencers to repress gene expression via chromatin interactions. Nat. Commun. 12, 719 (2021).

8.         Ngan, C. Y. et al. Chromatin interaction analyses elucidate the roles of PRC2-bound silencers in mouse development. Nat. Genet. 52, 264–272 (2020).

Olivia Tesolin

As clinical practices shift toward genome-first approaches, compelling evidence highlights the potential benefits of opportunistic screening for a broad range of secondary findings that may uncover clinically actionable information and prompt change in management, shaping the future of clinical practice and health policy. See article ¹.

The human genome holds an immense amount of information, and advances in sequencing technologies provide access to more of it. This information can help in guiding precision medicine, but with thousands of genes and complex interactions, much of the information is not yet fully understood². Despite gaps in our understanding of the full genome, clinical practice is increasingly shifting toward genome-first approaches to maximize the available genetic information.  As a result, understanding the clinical utility of returning results has become increasingly important. Genetic tests ordered by clinicians often reveal more than initially intended – known as secondary findings (SFs) – and the question becomes, should this additional information be returned to patients? A new study by Mighton and colleagues explores the impact of opportunistic screening, which involves actively searching for SFs when genomic sequencing is already being performed¹. Their findings suggest reporting a broad range of SFs has clinical utility and provides patients with valuable information, potentially shaping future guidelines and policies in medical genomics¹.

Whole exome sequencing (WES), which encompasses the protein-coding regions of the entire human genome, can answer a range of clinical questions; although it is ordered for a specific clinical indication, individuals may also choose to receive SFs unrelated to the primary purpose of genetic testing (Figure 1). The same is true for whole genome sequencing (WGS), which covers the entire human genome. The expanding use of WES and WGS in a clinical setting has led to significant debate over whether SFs should be actively searched for and analyzed³. Understanding the clinical utility of returning such information, involves determining its impact on diagnosis, management, psychological, and healthcare costs which is essential in shaping policy and guiding practice⁴. Specifically, determining the potential benefits and harms of reporting SFs and striking a balance between them to ensure that genetic testing leads to meaningful clinical outcomes and aids in informed decision-making⁴.

Mighton et al. evaluated the yield of SFs, impact on clinical management and the consistency between SFs and an individual’s phenotype¹. WES was performed on 139 adult cancer patients who chose to learn about SFs¹. The SFs were categorized into medically actionable disease risk, early onset neurodegenerative disease risk, mendelian disease risk, carrier status for recessive conditions, pharmacogenomic (PGx) variants, and risk variants for common or multifactorial disease (Figure 1)¹. Interestingly, at least one SF was returned in all participants meaning the SF uncovered could potentially affect their health in ways unrelated to the original reason for testing¹. The most frequent SF results returned were from PGx variants, carrier status, and risk variants for common or multifactorial diseases (Figure 1)¹. These findings can offer insights into an individual’s drug response, potential genetic conditions they may carry, or their susceptibility to certain diseases. Importantly, the return of SFs across all categories prompted a change in clinical management in roughly 28.1% of participants¹. This means that individuals had further visits to their physicians, further investigation, or treatment changes guided by their results.

This work supports the potential benefits of screening a broad range of SFs. However, the American College of Medical Genetics (ACMG) current policy statement on reporting SFs in clinical WES and WGS suggests analysis be contained to a medically actionable gene list⁵. Here, the authors show the benefits of extending the analysis of SFs from these selected genetic disorders to incorporate other risks and variants (Figure 1). Further, the identification and reporting of SFs can serve to provide other important information and may allow for early detection, management, and monitoring of disease.

Most recent guidelines from the Canadian College of Medical Geneticists (CCMG) and European Society of Human Genetics (ESHG) suggest a more cautious approach, where genetic analysis should be linked only to the primary indication of the genetic test, suggesting that further evidence is needed to inform policy and practice^6,7. However, these recommendations are routinely re-evaluated as knowledge and understanding of disease and the utility of clinical WES and WGS improves⁶.

Despite the many benefits that opportunistic screening for SFs could have, challenges in variant interpretation continue to add to the complexity of genomic medicine. Many variants exhibit complex expression patterns, which can modify the severity of the phenotype, complicating risk assessment and patient management¹. The authors mention performing large-scale sequencing studies to expand the sample size with linked phenotypic data to improve the understanding of genotype-phenotype correlations¹. This helps clarify which genetic findings are clinically meaningful and improves accuracy of risk predictions, diagnosis, and management.

Additionally, reporting both primary findings and SFs once a genetic test is complete can overwhelm both the patient and provider⁸. Researchers suggest a comprehensive genomic reporting structure to relay information and communicate all clinically significant results, including SFs, to facilitate clinical management⁸. Adoption of comprehensive reporting strategies would further enhance the clinical utility of returning various SFs by informing disease surveillance, management, and/or family planning⁸. Furthermore, information on psychosocial outcomes, personal utility, cascade effects on family members, and health costs are needed to inform decision-making, clinical practice, and new policies around SFs¹.

Mighton and colleagues’ paper serves as a valuable step toward demonstrating the potential benefits of opportunistic screening for a broad range of SFs. Future advances in genomic sequencing methods can reveal a vast amount of clinically significant information that could impact an individual’s health. Establishing and updating guidelines for SFs is an important stepping stone of integrating precision medicine. Therefore, continued work in building evidence and implementation strategies is needed to inform clinical practice and policy.

References

1.         Mighton, C. et al. Opportunistic genomic screening has clinical utility: An interventional cohort study. Genetics in Medicine 27, (2025).

2.         Saelaert, M., Mertes, H., Moerenhout, T., De Baere, E. & Devisch, I. Criteria for reporting incidental findings in clinical exome sequencing – a focus group study on professional practices and perspectives in Belgian genetic centres. BMC Medical Genomics 12, 123 (2019).

3.         Woudstra, A., Dondorp, W. & de Wert, G. Stakeholder views on opportunistic genomic screening in the Netherlands: a qualitative study. Eur J Hum Genet 29, 949–956 (2021).

4.         Hayeems, R. Z. et al. Clinical utility of genomic sequencing: a measurement toolkit. npj Genom. Med. 5, 1–11 (2020).

5.         Miller, D. T. et al. ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in Medicine 25, (2023).

6.         Boycott, K. et al. The clinical application of genome-wide sequencing for monogenic diseases in Canada: Position Statement of the Canadian College of Medical Geneticists. J Med Genet 52, 431–437 (2015).

7.         de Wert, G. et al. Opportunistic genomic screening. Recommendations of the European Society of Human Genetics. Eur J Hum Genet 29, 365–377 (2021).

8.         Sam, J. et al. A comprehensive genomic reporting structure for communicating all clinically significant primary and secondary findings. Hum Genet 141, 1875–1885 (2022).

Erika Tvaskis

The length of a cancer patient’s 3’ untranslated regions reveals molecular mechanisms in cancer and can aid in prognosis prediction.

Cancer remains one of the leading causes of death worldwide, necessitating further advances in research, risk assessment, and treatment. One key mechanism influencing cancer progression is alternative polyadenylation (APA), a form of post-transcriptional regulation that generates diverse mRNA isoforms through the cleavage of 3’ untranslated regions (3’UTRs) and addition of adenine bases^1,2. While shortened 3’UTRs have been implicated in various cancers, Gao, Shaw, and Amos specifically investigated APA profiles in lung adenocarcinoma (LUAD) patients¹. Their findings reveal distinct APA profiles in LUAD, with profound implications for cancer pathology, prognosis, and potential therapeutic interventions.

Most eukaryotic precursor-mRNAs undergo polyadenylation, a process that impacts their stability, translation efficiency, nuclear exportability, and localization^3,4. This involves cleavage of the precursor-mRNA at a specific polyadenylation site within the 3’UTR, which includes a conserved upstream AAUAAA hexamer, a CA dinucleotide cleavage site, and a downstream U- or GU-rich region⁴. The exact location where cleavage occurs, referred to as the poly(A) site, is then extended by the addition of approximately 250 adenine bases^2,4. However, 70% of human genes contain several polyadenylation sites, enabling the selection between proximal and distal poly(A) sites to generate diverse mRNA transcripts and further regulate gene expression (Figure 1A)¹. Increased cleavage at proximal poly(A) sites, leading to shortened 3’UTRs, have been associated with tumor proliferation and poor prognosis^1,2,4.

The authors set out to compare the amount of proximal versus distal poly(A) sites in RNA samples from healthy individuals and LUAD patients. Linear regression analysis revealed that while most genes did not demonstrate changes in APA in the tumour samples, those that did significantly exhibited shortened 3’UTRs rather than lengthened ones, aligning with previous cancer research (Figure 1B)^1,4. Among the genes with shortened 3’UTRs, several were LUAD growth-promoting genes, including: TAZ, CD47, SKP2, SPP1, and PARVA¹. This particular trend of shortening the 3’UTRs is seen across various cancers, and is problematic as it hijacks the body’s natural mechanisms for regulating protein expression^1,4. 3’UTRs harbour a significant amount of binding sites for microRNA (miRNA) and RNA-binding proteins (RBPs), which upon binding, can degrade mRNA transcripts^1,4. By reducing the length of 3’UTRs, these binding sites are lost, reducing the possibility of degradation, and allowing for increased expression of genes that go on to promote cell proliferation (Figure 1B)⁴. Additionally, by reducing the amount of miRNA binding sites on 3’UTR shortened transcripts, this frees up miRNAs to target tumor suppressor genes, further enhancing tumor growth⁶.

The authors further explored whether the change in APA was driven by in cis mechanisms, such as direct disruptions of poly(A) sites, or in trans mechanisms involving polyadenylation factors. Since mutations in 3’UTRs accounted for less than 3% of APA events, this led Gao, Shaw, and Amos to investigate polyadenylation factors, the machinery responsible for both polyadenylation and APA. Differential gene expression analysis revealed that polyadenylation factors were significantly universally increased in LUAD tumors compared to healthy samples¹. Cleavage Stimulation Factor Subunit 2 and 3 (CSTF2 and CSTF3) as well as Cleavage and Polyadenylation Specificity Factor Subunit 3 (CPSF3) were specifically looked into². Their expression levels demonstrated a proportional relationship with the number of 3’UTR shortening events per LUAD tumor sample¹. This indicates that an increased APA machinery availability could allow for quicker access to precursor-mRNA, leading to proximal poly(A) sites being selected for at a higher rate, highlighting a potential therapeutic target.

Cohort characterization revealed that LUAD patients with lengthened 3’UTRs live significantly longer. Gao, Shaw, and Amos explored whether APA events could aid in predicting survival outcomes¹. Patients were grouped into cohorts based on their APA profiles by identifying 3’UTR genes with high variance and applying hierarchal clustering. This analysis produced 4 cohorts, each with distinct patterns of APA events. Surprisingly, cohort 2, characterized by greater distal poly(A) site usage compared to the other groups, lived significantly longer than those in cohort 3 and 4¹. While still having shorter 3’UTRs compared to healthy individuals, the distinct cluster of lengthened 3’UTRs in cohort 2 patients exhibits a seemingly protective effect, and investigating the mechanism behind this can deepen our understanding of cancer pathology. Additionally, enhancing the expression of these transcripts could potentially serve as a complementary treatment strategy to prolong survival.

By investigating LUAD specifically, the authors confirm that it follows the same trend of 3’UTR shortening, while simultaneously addressing the small sample sizes and lack of cohort characterization in previous studies. Since 3’UTR shortening is common across cancer types and is associated with tumor aggressiveness and poor prognosis, this study has broad implications for enhancing prognostic accuracy and identifying potential therapeutic targets^1,4. Accurate prognosis predictions are crucial for patients and their families, and integrating APA-based cohort classification could improve the inaccurate LUAD survival estimates provided by physicians, and importantly, set a clinical standard for other malignancies⁷. Additionally, the observed correlation between polyadenylation factor levels and 3’UTR shortening events suggest that targeting specific polyadenylation factors could lead to 3’UTR length retention. Cohort 2 – characterized by its lengthened 3’UTR profile – lived significantly longer, indicating that preserving longer 3’UTRs may be the key in enhancing survival outcomes in LUAD and other cancers¹. However, further research in the LUAD-specific context is needed, as grouping all the subtypes together may have masked more nuanced APA patterns¹. Overall, this study gives rise to new areas of research in APA, improved prognostic capabilities, and highlights potential therapeutic targets that have broad applicability.

References

1. Gao, Y., Shaw, V. R. & Amos, C. I. Alternative polyadenylation shapes the molecular and clinical features of lung adenocarcinoma. Hum. Mol. Genet. 34, 1–10 (2025).
2. Xu, S., Curry-Hyde, A., Sytnyk, V. & Janitz, M. RNA polyadenylation patterns in the human transcriptome. Gene 816, 146133 (2022).
3. Tang, P. & Zhou, Y. Alternative polyadenylation regulation: insights from sequential polyadenylation. Transcription 13, 89–95 (2022).
4. Zhang, Y. et al. Alternative polyadenylation: methods, mechanism, function, and role in cancer. J. Exp. Clin. Cancer Res. 40, 51–7 (2021).
5. Biorender. BioRender App. (n.d.). https://app.biorender.com/
6. Park, H. J. et al. 3′ UTR shortening represses tumor-suppressor genes in trans by disrupting ceRNA crosstalk. Nat. Genet. 50, 783–789 (2018).
7. Zhang, W. et al. A novel APA-based prognostic signature may predict the prognosis of lung adenocarcinoma in an East Asian population. iScience 26, 108068 (2023).

Isabella Vessio

Advances in genome-wide studies of copy number variants (CNVs) have established novel links between rare germline CNVs and endometrial cancer, uncovering new genetic risk factors and improving diagnostic potential.

Investigating copy number variants (CNVs) offers a powerful approach to identifying critical loci and novel genetic variants that may contribute to the underlying causes of various cancers, including endometrial. Endometrial cancer, caused by abnormal uterine lining growth, is the sixth most common cancer in females¹. With the incidence and mortality rates of endometrial cancer rising—particularly in younger women— enabling early diagnosis is essential for better prognoses and limiting metastasis^1,2. Well-established risk factors include obesity, high body mass index, hypertension, and sex hormone levels². Diagnosis often relies on symptoms in post-menopausal women; however, current techniques lack specificity and require invasive, repetitive endometrial sampling¹. Early detection is crucial for expanding treatment options, making understanding the underlying disease mechanisms important.

While genetic factors contribute to 5-10% of cases, less than one-third of familial endometrial cancer has been linked to a genetic mechanism³. Current understanding is attributed to mismatch repair genes associated with DNA repair and Lynch syndrome, a genetic condition that increases the risk of various cancers, including colorectal and endometrial³. While previous research identifies single nucleotide polymorphisms (SNPs) linked to cancer predisposition, Stylianou and colleagues used the largest endometrial CNV dataset to elucidate rare variants implicated in cancer susceptibility⁴. Copy number variants, structural changes over 1 kb that alter the number of DNA copies, can be inherited or de novo⁵. The team conducted three CNV-based genome-wide association studies (GWAS), finding more CNV burden in cases than controls⁴. A recurrent deletion involves several of 141 candidate genes linked to endometrial cancer⁴. This research identified novel candidate genes, loci, and gene dosage mechanisms, providing vital insights for risk prediction, early diagnosis, and intervention for high-risk patients⁴.

Exploring rare CNVs linked to genomic regions and associated genes provides a foundation for understanding the molecular basis of endometrial cancer. Data was sourced from the Endometrial Cancer Association Consortium and Breast Cancer Association Consortium, encompassing cases and controls of European ancestry⁴. The authors implemented CamCNV, a data analysis method capable of detecting rare copy number variants, to identify CNVs from an SNP array⁴. CNVs were investigated for overlap with protein-coding genes, exons and intergenic regions⁴. Approximately five CNVs were identified per individual, with a larger CNV burden observed in genes and exons⁴. This suggests that disrupting exons may contribute to endometrial cancer susceptibility through a loss of function mechanism. To explore this, researchers used three GWAS models to investigate CNVs and cancer risk: one focused on deletions, another on duplications, and the third on loss-of-function, which disrupts gene functionality⁴. 

Four genes, LPCAT1, TERT, MSH2 and SLC6A, each with identified links to cancer progression, showed significant association in CNV-GWAS⁴. Given MSH2‘s established link to Lynch Syndrome, investigating other genes could highlight novel connections to endometrial cancer³. The genes are in close proximity (Figure 1) and may be inherited together, known as linkage disequilibrium⁶. TERT encodes a catalytic subunit essential for maintaining telomere ends⁴. Links between upregulated TERT expression and epithelioid trophoblastic tumour (ETT) development have been found, whereby these rare uterine tumours are linked to copy number gains in chromosome 5⁶. Genomic deletions promote LPCAT1-TERT fusion transcripts in ETTs, which can enhance tumour metastasis, potentially leading to endometrial cancer due to proximity⁶. LPCAT1 encodes an enzyme that adds an acyl group to lipid molecules, contributing to energy production and regulating lipid droplet size⁶. Similarly, SLC6A is a solute carrier transporter, a protein that facilitates the movement of biological molecules across cell membranes⁷. While overexpression of solute carrier transporters is implicated in cancer, no direct link to these genes has been established⁷. Proposed linkage mechanisms remain poorly understood, emphasizing the need for experimentation on these genes and endometrial cancer risk.

A recurrent 593 kb deletion involving twenty-five candidate genes is associated with 16p11.2 proximal deletion syndrome, a condition caused by the loss of part of chromosome 16. This deletion occurs within the 16p11.2 region, promoting the loss of numerous genes (Figure 2)^4,8. Chromosome 16p contains repetitive regions due to segmental duplications, which makes it more prone to rearrangements and structural variation⁹. This deletion drives 16p11.2 proximal deletion syndrome, associated with clinical features like obesity (Figure 3) — the major risk factor for endometrial cancer¹. The authors identified a significantly higher frequency of this deletion in endometrial cases than controls⁴. This suggests that the deletion may promote this phenotype, making the twenty-five deleted genes strong candidates as biomarkers linked to increased cancer susceptibility⁹. While research has established links between this disorder and neuroblastoma¹⁰, no correlations are made with endometrial cancer.

Gene dosage effects—changes in gene copy number — observed in the 593 kb deletion may contribute to endometrial cancer susceptibility. The authors investigated the gene dosage of candidate genes, seeking correlations between copy number and expression in normal and endometrial tissue⁴. Sixteen genes showed a positive correlation between endometrial expression levels and gene dosage, indicating that this deletion alters gene expression⁴. These findings demonstrate how variations in copy number can drive phenotypic changes that increase susceptibility to the disease.

Stylianou and colleagues provide a greater understanding of the molecular mechanisms behind endometrial cancer, influencing further research. The study demonstrates the potential for CNV-based GWAS to identify novel candidate genes and loci not identified using SNP arrays. This underscores the value of implementing CNV research to uncover unknown risk factors or genetic candidates of unique cancers. A significant challenge is overcoming the limitations of CNV analysis, such as limited statistical power^3,4,10. Validating these candidate genes, through fluorescent in situ hybridization or next-generation sequencing, could enhance diagnostic potential^3,4,10. Replication in larger, more diverse cohorts could broaden the applicability of these findings^2,4,10. Continued advancements in CNV research and integration into cancer genomics can unveil critical insights, encouraging the development of effective strategies in risk assessment, prevention, and early diagnosis for endometrial and high-risk cancers.

References

1. Crosbie, E. J. et al. Endometrial cancer. Lancet 399, 1412–1428 (2022).
2. D’Agostino, E. et al. Molecular characterization as new driver in prognostic signatures and therapeutic strategies for endometrial cancer. Cancer Treat. Rev. 126, 102723 (2024).
3. Dugo, E. et al. Copy number variations in endometrial cancer: from biological significance to clinical utility. Int. J. Gynecol. Cancer 34, 1089–1097 (2024).
4. Stylianou, C. E. et al. Germline copy number variants and endometrial cancer risk. Hum. Genet. 143, 1481–1498 (2024).
5. Shlien, A. & Malkin, D. Copy number variations and cancer. Genome Med. 1, 62 (2009).
6. Oliver, G. R. et al. LPCAT1-TERT fusions are uniquely recurrent in epithelioid trophoblastic tumors and positively regulate cell growth. PLoS One 16, e0250518 (2021).
7. Hossen, Md. S., Islam, M. S. U., Yasin, M., Ibrahim, M. & Das, A. A review on the role of human solute carriers transporters in cancer. Health Sci. Rep. 8, e70343 (2025).
8. Zufferey, F. et al. A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders. J. Med. Genet. 49, 660–668 (2012).
9. Auwerx, C., Kutalik, Z. & Reymond, A. The pleiotropic spectrum of proximal 16p11.2 CNVs. Am. J. Hum. Genet. 111, 2309–2346 (2024).
10. Egolf, L. E. et al. Germline 16p11.2 microdeletion predisposes to neuroblastoma. Am. J. Hum. Genet. 105, 658–668 (2019).

Paula Zachcial

A mouse model reveals the mechanistic underpinnings of neurodevelopmental deficits in congenital heart disease, revolutionizing future treatment opportunities for multiple patient groups.

The idea that diseases can involve multiple organ systems is not a novel finding. The human body is extremely interconnected, and isolation of both everyday and pathogenic phenomena to a single area is rare. Congenital heart disease (CHD) is no exception, posing increased risk of neurodevelopmental deficits through ones lifespan¹. Of particular interest is Hypoplastic Left Heart Syndrome (HLHS), a severe form of CHD characterized by an underdevelopment of the structures present on the left side of the heart².

HLHS, a previously fatal disease in infancy, has tremendously increased its survival rate due to improvements in modern medicine which has allowed for the development of a three part surgical treatment plan³. Interestingly, this reality has brought a shocking new challenge to patients: an evolving number of neurodevelopmental issues³. As more of the HLHS population survives into adulthood, the problem concerning these patients shifts to mitigating existing and developing neural deficits. The barrier remains that these mechanisms are seldom fully characterized. In their most recent work, Gabriel et al.  set out to investigate the behavioural and cellular workings behind these neurodevelopmental intricacies in HLHS. They discover cellular mechanisms and epigenetic pathway modulations that, not only, hold promise for future treatment of CHD, but also other common neurodevelopmental disorders.

The researchers conducted a series of anatomical, behavioural, chemical and histological tests on the Ohia HLHS model, a mice mutant with both copies of both Pcdha9 and Sap130 genes knocked out⁴. These genes encode products that, while important in heart development, are gaining traction with their importance in brain development. Thus, feeding the theory that importantly expressed genes in the heart are just as highly expressed in the brain⁴. Pcdha9 encodes a cell adhesion protein thought to guide connection patterning in the brain⁴. In contrast, Sap130 encodes a chromatin modifier, a protein that can alter chemical groups on chromatin that, in this case, act to repress transcription, allowing for proper brain development⁴.

When subjected to anatomical testing, half of the Ohia mice exhibited both HLHS and a form of head defect. Overwhelmingly, these mice showed a smaller than normal brain volume, formally termed microcephaly, ranging from moderate to severe appearance, particularly in forebrain regions. In fact, those with severe microcephaly also showed reduction of internal higher order structures, such as those involved in sensation and decision making⁴ (Figure 1). Through further imaging methods, it was found that this loss of volume results from a cell division defect preventing expansion in the developing brain. In mice and humans, the brain develops in a series of six layers, each of which goes on to play different roles within the brain⁵. Within these mice, this cellular defect disrupts layers II- IV which are fundamental layers that later form sensory and cross-brain communication systems⁵. Thus, these changes set a scene for a dysregulated neural environment through an aberrant development system.

The scientists also aimed to characterize gene expression, identifying a slew of differentially expressed genes (DEGs) within Ohia mice through investigating brain tissue transcripts. To analyze these results, they utilized a type of pathway visualization method where they searched for commonalities in the downstream effects of the identified genes. This analysis revealed the important cellular signaling pathways such as: cyclic AMP response element binding protein (CREB), repressor element 1 silencing transcription factor (REST), and nitric oxide (NO) pathways as more highly affected within the Ohia mice. These are pathways currently being investigated in their relation to autism, ADHD, seizures as well as several neurodegenerative and neuropsychiatric conditions in humans^6,7. This is primarily due to their impact on neuron development, attention and memory^6,7.

These DEGs also showed large overlap with genes that are downregulated in response to methylation, most of which are direct targets of Sap130, as identified by a DNA- target binding pulldown assay. Notably, pathway enrichment software consistently identified these cellular states as those linked to autism, schizophrenia, intellectual disability, and seizures⁴ (Figure 1). Thus, these authors have provided a link to human-defined versions of brain disorders. Importantly, lending hope that these findings can overcome the main limitation of this study, that it was not conducted in humans at all, to have real implications for humans. Hopefully, allowing for the pathway commonalities to facilitate widespread treatment development for these disorders.

This phenomenal work by Gabriel et al highlights two major realities: the same genes that are causing HLHS are posing a detriment to brain health, and that neurodevelopment has a dynamic nature that can be modulated through, not solely genetic, but rather epigenetic means. While these may seem grim, they open the door for many therapeutic targets for HLHS, the broader CHD population, and various neurodevelopmental and neuropsychiatric populations. Understanding molecular mechanisms can lead to the development of drugs that improve neurodevelopmental outcomes for many HLHS and CHD patients alike. More importantly, it allows for the exploration of the epigenome as a therapeutic target through lifestyle, pharmacological means or, as is gaining popularity, direct epigenome editing based⁸ opportunities both for CHD-related deficiencies and implicated brain related disorders. While far from clinical implementation, this paper by Gabriel et al. marks a great step forward into the investigation of newly apparent challenges within the CHD community and holds promise for tackling these issues in a way that may revolutionize therapy for not only CHD but other neurodevelopmental disorders.

References

1.             Goldberg, C. S., Mussatto, K., Licht, D. & Wernovsky, G. Neurodevelopment and quality of life for children with hypoplastic left heart syndrome: current knowns and unknowns. Cardiol. Young 21, 10.1017/S104795111100165X (2011).

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