Thomas Barbazuk

The last decade of genetic medicine has seen a paradigm shift from the investigation of genetic constitution to a focus on active gene expression. The human genome is now understood to be incredibly plastic, as gene expression is not one-dimensional enough to fit into the confines of classical Mendelian genetics. The inception of transcriptomics, the analysis of actively transcribed mRNA molecules in the cell, has allowed for a tangible understanding of genetic dysregulation in the context of human disease. More specifically, the cutting-edge ability to analyze gene expression in individual cells has allowed us to disentangle the expression profiles found in complex tissues. This technology has recently been utilized to decode the cellular heterogeneity of endometriosis, a uterine dysregulation of tissue that is endemic in 10% of individuals with female-assigned reproductive systems. Endometriosis is characterized by endometrial-like tissue (resembling the inner lining of the uterus) proliferating outside of the uterine cavity ¹’². In addition to causing chronic pain, infertility, and inconsistencies in menstrual cycles, endometriosis has been observed to significantly raise an individual’s risk of epithelial ovarian cancer¹’³. Fonseca et al at Cedars-Sinai used single-cell RNA sequencing methods to construct a cellular atlas of endometriosis in an effort to characterize the molecular hallmarks that set aberrant endometrial tissues apart¹.

Endometriosis is generally characterized into three subtypes:  ovarian endometriosis/endometrioma (fig. 1), superficial peritoneal endometriosis (superficial deposits on the lining of the abdominal wall) and deep infiltrating

endometriosis (characterized by lesions that infiltrate 5mm or more under the peritoneal surface)¹’⁴. Numerous and diverse tissue samples were taken from the sample population allowing for a comprehensive analysis of the disease. All these tissues were analyzed using a high-throughput single-cell RNA sequencing method called droplet based scRNA seq:  a method that earns its name by utilizing microfluidics to encapsulate individual cells in nanoliter droplet emulsions⁵’⁶’⁷.

With over 9.2 billion reads sequenced, 373,851/432,751 fully profiled individual cells were taken through analysis after stringent quality control¹. The sequence data was segregated into different groups by analyzing the expression levels of cell-specific markers, leading to 114 distinctly different cell clusters¹. The transcript data was compared across tissue types to deeply investigate the differences in tissue composition between the samples. Analysis confirmed that eutopic endometrium tissues were enriched for epithelial cells and endothelial cells in relation to control tissues. There was a 7-fold depletion in epithelial cells in endometrioma tissues, accompanied by an enrichment in immune cells such as B and plasma cells. Ectopic endometrial tissues were also particularly rich in mast cells and killer T cells. These results affirm our understanding of the aggressive immune response that can accompany the disease, as well as the primary cell types present in dysregulated tissues.

Further stringency in the grouping of the transcript sequence data was accomplished by conducting a principal component analysis (PCA) of the genetic profiles of each tissue type. Cluster 1 was primarily composed of endometriosis, with cluster 2 containing the majority of the endometrioma and eutopic endometrium samples. Unaffected ovarian tissues consistently clustered in group 3. These results reiterate the distinctly different tissue landscapes across different forms of endometriosis. This is a crucial observation, as it presents the possibility that different forms of endometriosis demand the development of different treatments and thus should not necessarily be categorized as the same disease between patients. Furthermore, it was observed that ectopic endometrial tissue and eutopic ovarian tissue from separate ovaries within the same patient grouped to their respective PCA clusters.

The molecular consequence of somatic mutation within endometrial tissues and how such mutations facilitate aberrant cancer expression is relatively poorly understood. It was previously known that somatic mutations within the cancer driver genes ARID1A and KRAS are associated of endometriosis, but the in vivo transcriptional result was especially unclear¹. The in vivo transcriptional behaviors of these cancer genes were investigated and better characterized in the study by using immunohistochemical and PCR methods. Heterogenous ARID1A expression was observed, indicating a heterozygous loss-of-function mutation within endometrioma and peritoneal lesion samples. KRAS mutations at codon 12 were also observed within these tissues (fig. 2).

Endometrial tissues strongly differed in expression from unaffected tissues, suggesting aberrant expression and hormone regulation with the disease tissues. Differential gene expression analysis of the tissues examined showed that these tissues go through extensive expression remodeling in conjunction with different stages of the menstrual cycle. There were inverse relationships in gene expression with healthy and disease tissues in concordance with the luteal and follicular phases of the menstrual cycle, suggesting a greater relationship between hormone regulation and disease phenotype.

Endometriosis has remained poorly characterized despite its prevalence in the human population. This study addressed the complexity of endometrial disease with a robust analysis of mRNA transcripts in the context of multiple tissue types, hormonal regulation, and somatic mutation. This was a landmark study in characterizing how the molecular profiles of endometrial type epithelium and stroma differ in expression by locale, affirming the growing body of literature that endometriomas and peritoneal lesions exhibit two distinct disease entities. Specific genes were identified to be upregulated within ectopic endometrial tissues, providing a promising outlook for non-invasive screening assuming these biomarkers can be detected in a blood test. Dysregulation of innate immunity was also observed in the ectopic endometrial tissues studied. Continued large-scale cellular analyses of endometrial tissues is crucial, as the distinction between endometrioma and peritoneal endometriosis that was detected in this study needs to be explored due to different treatment and diagnostic demands.

Works cited:

1.         Fonseca, M. A. S. et al. Single-cell transcriptomic analysis of endometriosis. Nat. Genet. (2023) doi:10.1038/s41588-022-01254-1.

2.         Amro, B. et al. New Understanding of Diagnosis, Treatment and Prevention of Endometriosis. Int. J. Environ. Res. Public. Health 19, 6725 (2022).

3.         Kheil, M. H., Sharara, F. I., Ayoubi, J. M., Rahman, S. & Moawad, G. Endometrioma and assisted reproductive technology: a review. J. Assist. Reprod. Genet. 39, 283–290 (2022).

4.         Rolla, E. Endometriosis: advances and controversies in classification, pathogenesis, diagnosis, and treatment. F1000Research 8, F1000 Faculty Rev-529 (2019).

5.         Salomon, R. et al. Droplet-based single cell RNAseq tools: a practical guide. Lab. Chip 19, 1706–1727 (2019).

6.         Zhang, X. et al. Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems. Mol. Cell 73, 130-142.e5 (2019).

7.         De Rop, F. V. et al. Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads. eLife 11, e73971 (2022).

8.         Endometriosis: Causes, Symptoms, Diagnosis & Treatment. Cleveland Clinic https://my.clevelandclinic.org/health/diseases/10857-endometriosis.

Lise Cinq-Mars

The SickKids Cancer Sequencing (KiCS) program is advancing pediatric cancer care by performing somatic-germline and tumor sequencing to personalize therapeutic efforts.

The KiCS group at SickKids, headed by Dr. Anita Villani, Dr. David Malkin, and Dr. Adam Shlien¹, recently came out with a study that investigated 300 pediatric patients that were previously treated and had poor cancer prognosis, rare tumors, or were suspected of cancer predisposition.  There has been almost no improvement in treating patients who have relapsed metastatic or treatment-refractory diseases in about 40 years. The KiCS group aimed to identify new, targeted approaches that improved the prognosis of pediatric oncology patients (Figure 1). Through in-depth somatic-germline and tumor sequencing, they came up with two major findings: there is a notable proportion of high mutation burden in relapsed pediatric cancers, and pediatric cancers tend to have a prevalence of defects in the homologous recombination repair pathway. This work suggests that analysis of every child and young adult with cancer is likely a realistic goal and should be readily incorporated into clinical care².

The KiCS program identified at least one clinically actionable finding in 56% of participants². 54% of patients who had tumor analysis done were found to have variants that were therapeutically targetable². Interestingly, out of all the cancers examined, Central Nervous System (CNS) tumors had the highest amount (61%) of targetable findings². Most of these findings were targets of MEK-ERK inhibitors, PARP inhibitors, immune checkpoint inhibitors and cell cycle inhibitors. MEK-ERK inhibitors have been found to be extremely effective in suppressing tumor growth in MAPK dependant cancers³. PARP is an enzyme that aids in DNA repair so inhibiting the enzyme can push cancer cells towards death⁴. Of the 69 patients that needed a new therapeutic option, 60% were treated with a matching targeting agent². Additionally, germline sequencing identified pathogenic/likely pathogenic variants in cancer predisposition genes in 15% of participants². Most of these variants were not found in genes typically associated with pediatric cancers but in genes involved in Homologous Recombination (HR) and mismatch repair (MMR) pathways.

Villani, et al., wanted to investigate this involvement of the HR pathway more and decided to compare Whole Genome Sequencing (WGS) of 293 available pediatric tumor samples to a group of adult control tumor samples. They found that Signature 3 (a BRCAness mutational signal which requires a minimum of 100 mutations) was detected in 23.1% of samples with no known HR mutations and only 13.3% of adult samples². They also found that Signature 3 was significantly higher in KiCS samples with previously identified somatic mutations in the HR pathway and highest in tumors from the previously stated group of KiCS patients (pathogenic/likely pathogenic variants associated with the HR pathway in their germline)². This notable increase suggests that mutations in the HR pathway could be a driver of pediatric cancers.

Tumor sequencing shows that it is possible to identify the evolution of tumors by taking multiple samples at different points in cancer treatment. Villani, et al., found that tumor genomes showed massive changes over the course of cancer and that most mutations could be identified from a sample at only one time point. This information was important for treatment as physicians were able to determine if the relapse was from an older, previously identified cancer strain, or if it was a completely new “relapse”².

It is clear from their work that there is a high mutational burden in relapsed pediatric cancers. Specifically, tumors previously treated with chemotherapy and radiation tend to have a significantly higher number of mutations when compared to pre-therapy biopsies. The high number of defects in the HR repair pathway suggests that PARP inhibitors might be useful in treatment.

Additionally, the KiCS group made it very clear that germline sequencing is a critical part of the sequencing effort. Understanding what patients are already predisposed to can influence therapeutic options and advise any family history and planning options. A major interest of this study was to sequence participants’ germlines on an 864-gene cancer panel. These patients had not previously had cancer but were suspected of being highly predisposed. Villani, et al., found that the panel identified only 12% of these patients to have a pathogenic variant. Given that 88% of patients were still unaccounted for, the KiCS group suggests that other, yet-to-be-discovered mechanisms must be at play. They suggest continuing to look for novel genes, variants in regulatory regions, epigenetic changes, and multiple gene interactions resulting from several low penetrant variants in a common pathway.

In terms of setting a new standard in pediatric oncology care, the KiCS program suggests that NGS-based tumor analysis can provide more robust assessments than the current molecular/cytogenic testing standard. It would also cut down on the time required to perform sequential assays, meaning that patient oncology teams would have a therapeutic plan devised in a fraction of the time. This approach is flexible, adaptable, and inexpensive. This work is the first of its kind in Canadian precision oncology. It will open the doors and lead the way for what cancer care should look like. The KiCS program shows that careful clinical annotation of variants can significantly accelerate biological insights. Cancer genomics is just getting started and it’s amazing to see such impressive results in such a small cohort. We look forward to seeing what Dr. Villani and the team at KiCS comes out with next!

References

1. The Hospital for Sick Children. SickKids study demonstrates how comprehensive genetic sequencing informs a new standard of cancer care. https://www.sickkids.ca/en/news/archive/2023/sickkids-study-demonstrates-how-comprehensive-genetic-sequencing-informs-a-new-standard-of-cancer-care/ (2023)
2. Villani, A. et al. The clinical utility of integrative genomics in childhood cancers extends beyond targetable mutations. Nat. Cancer. 10.1038/s43018-022-00474-y (2022)
3. Merchant, M. et al. Combined MEK and ERK inhibition overcomes therapy-mediated pathway reactivation in RAS mutant tumors. PLoS One. 12, 10 (2017).
4. National Cancer Institute. PARP Inhibitor. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/parp-inhibitor

Teresa Brooke-Lynn Coe

Single-nuclei RNA sequencing has been established as a reliable and scalable method of analyzing single cell transcriptomes in frozen, clinically obtained tumor samples, allowing large-scale clinical application of single cell analyses and superior patient experiences.

Single-cell RNA sequencing (scRNA-seq) has been an invaluable player in cancer research for over a decade. Tumor landscapes are complex, thus scRNA-seq enables gene expression analysis of individual cell populations within those environments, helping to identify rare cellular populations and associated non-cancerous cells like immune cells¹. This allows for unbridled insights into tumor progression, metastasis, and drug resistance¹. However, scRNA-seq research does not always translate to the clinical world – in fact, it is falling fatally behind. There is a deep need in clinical cancer genomics for reliable and scalable single cell characterization. Unfortunately, scRNA-seq cannot achieve this due to the clinical impracticality of large, fresh tissue samples that require immediate processing¹. A recently published article by Wang et al. (2023) sought to establish a multimodal method in which single cell transcriptomics could be reliably assessed from frozen clinical samples using single-nuclei RNA sequencing (snRNA-seq) thereby overcoming the practical issues that make scRNA-seq irreconcilable with clinical work.

Single cell transcriptomics is a subset of genomics that examines gene expression levels for individual cells in a population via quantification of messenger RNA (mRNA)². Two common methods for this are scRNA-seq and snRNA-seq, which assess expression using cellular and nuclear mRNA, respectively³. While analogous in most technical aspects, snRNA-seq holds significant advantages in clinical settings as it does not require large amounts of fresh tissue samples like scRNA-seq does^1,3.

Clinical tumor sample collection is typically done via needle biopsy, providing small quantities of sample which are frozen after initial analysis¹. However, frozen tissue samples cannot be adequately dissociated before scRNA-seq^4,5. The dissociation step of scRNA-seq protocol is important as it releases and isolates individual cells from solid tissue samples⁵. With fresh samples, the intense enzymatic and mechanical processing can affect cell quantity and quality, and potentially result in the exclusion of rare cell populations (Figure 1)^4,5. Moreover, the temperature of dissociation causes transcriptional machinery to remain active, thus mechanical assaults can create stress-response signals called artifacts, potentially biasing results^4-6.

Due to their increased membrane rigidity, nuclei are more tolerant to mechanical isolation than whole cells, thus snRNA-seq can be performed from frozen samples (figure 1)^3,7. Likewise, snRNA-seq can be done on smaller tissue samples more akin to those obtained clinically¹. This overcomes logistical processing hurdles exposed by scRNA-seq while also correcting downstream data processing issues. However, snRNA-seq has its own concerns – nuclei have lower RNA amounts than whole cells, thus obtaining enough data to identify and classify cell types introduces different challenges³. Further validation of snRNA-seq’s ability to accurately establish cellular expression is vital to its clinical implementation.

Wang and colleagues yearned to adapt a framework that produced reliable and comparable data using snRNA-seq that could be easily implemented into clinical practice¹. To do this, the researchers tested scRNA-seq and snRNA-seq protocols on a variety of tumor tissue samples (using matched fresh and frozen samples). In accordance with previous work^3,4, snRNA-seq reliably performed on par with scRNA-seq in isolating cells, classifying cell types, and producing high-quality data. Notably, when assessing fragile tumor samples, snRNA-seq reproducibly out-performed scRNA-seq – isolating more high-quality nuclei and reducing stress-response signals due to snRNA-seq tolerance to dissociation. Thus, the snRNA-seq method was able to provide consistently accurate data using frozen sample and 1000-fold less starting material.

To test the effectiveness of adequately tracking cellular differentiation in response to treatment, Wang et al. (2023) tested past clinical trial samples. These samples were long term frozen samples (some archived up to ten years). Here, snRNA-seq was able to assess diversity in tumor cell populations and associated immune cell populations with excellent technical quality. This protocol was also able to track cellular sub-types involved in resistance to the drug when assessing samples captured at different times of treatment. Additionally, the researchers were able to show changes in chromosomal copy number that occurred after treatment in resistant cells. Whole genome sequencing confirmed these results. Although the copy number results are notable, it does require further study for reliability as a potential standard in clinical practice.

By tracking cellular transcriptomic differences related to drug resistance in long-term frozen samples, the authors clearly demonstrated the potential for clinical impact. Validation of this technique on small, long term frozen samples shows that snRNA-seq is adequate to perform on small and/or frozen clinical samples, negating the need for immediate processing that is so incompatible with clinical workflows. By utilizing samples that would likely be collected regardless, this method provides a less intensive way of achieving valuable single cell genomic results so that clinicians can better assess their patient’s personal treatment needs over the course of treatment. Moreover, to assess frozen samples and still achieve reproducible, high-quality of data opens doors for broad, coordinated, multi-institutional genomic cancer studies capable of producing more widely applicable results on complicate aspect of cancer, like drug treatments and resistance targets¹.

A potentially interesting application of this technique is for exploring metastasis. Since scRNA-seq has been proven a powerful tool for studying metastasis⁸, it would be intriguing to demonstrate the efficacy of this snRNA-seq method in detecting metastatic expression markers. Not only would this help establish metastatic markers via archived sample studies, but it provides a methodology that would help identify those markers in clinical spaces which could have profound effects on patient outcomes.

Additionally, cancer-cell reference atlases are becoming popular to synthesize genomic data as a means of aiding clinical identification of cell type and potential evolutionary pathways⁶. Here, researchers demonstrated the capability of this method to effectively evaluate single cell genomics in long-term archival samples. This creates a hugely advantageous way to add large amounts of data into such digital archives, generating comprehensive, versatile, and clinically valuable reference atlases.

Although, there are many high-impact transcriptomic tools in development, the translation of these methodologies into clinical practice is lacking due to incompatible practical requirements. There is a need to rethink how these tools can be adapted for clinical spaces and enhance patient experiences. The potential of the multimodal snRNA-seq framework outlined by Wang et al. (2023) to produce broadly applicable data and highly personal medicine illustrates just how technical advances in the field of genomics need to be implemented in clinical spaces.

References:

1. Wang, Y. et al. Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens. Nature Genetics 55, 19–25 (2023).
2. Kanter, I. & Kalisky, T. Single cell transcriptomics: Methods and applications. Frontiers in Oncology 5, (2015).
3. Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nature Medicine 26, 792–802 (2020).
4. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLOS ONE 13, (2018).
5. Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biology 21, (2020).
6. Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: Charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).
7. Lammerding, J. Mechanics of the nucleus. Comprehensive Physiology 783–807 (2011). doi:10.1002/cphy.c100038
8. Han, Y. et al. Single-cell sequencing: A promising approach for uncovering the mechanisms of tumor metastasis. Journal of Hematology & Oncology 15, (2022).

Kamalika Bhandari Deka

Obese patients with maladaptive behaviour such as anxiety, are found to carry a rare serotonin receptor variant that explains the link between maladaptive behaviour and weight gain.

Severe early-onset obesity (SEOO) has been potentially linked to manifestation of behavioural issue such as depression in adulthood.¹ Patients prescribed with selective serotonin reuptake inhibitors (SSRI) or antidepressants complained of weight gain.² Studies have found the non-specificity of SSRI drugs is the causal factor.³ Therefore, the need to understand the role of serotonin receptor on mood and food intake is important for developing targeted therapy.

A recent study by He et al, exploring the role of serotonin 2C receptor on weight regulation and behaviour was published.⁴ Using exome analysis, 13 rare variants were found in HTR2C, a gene encoding for serotonin 2C receptor. The individuals harbouring HTR2C variant reportedly had maladaptive behaviour and extreme unsatisfied food-consuming drive (also called hyperphagia). Additionally, using mouse model it was demonstrated that specific serotonin receptor activating drug, such as Lorcaserin, are better suited to treat obese patients carrying the variant.

The study cohort consisted of 2548 individuals of European ancestry diagnosed with SEOO and 1117 ancestry-matched controls. The rare variants identified on HTR2C gene were found in 19 unrelated individuals. Sixteen heterozygous (variant on one allele, here on one of the X-chromosome) XX females and 3 hemizygous (variant only on X-chromosome) XY males carried a variant.

To understand the molecular consequence of the rare variants, cellular functional assays were done. One of the variants leads to replacement of the amino acid, Phenylalanine, which leads to form an altered serotonin receptor. This receptor is unable to bind with neurotransmitters (fig.1) such as serotonin.⁵ By studying the plasma membrane expression, He and team found this newly formed protein loses the ability to bind to serotonin.⁴ This illustrates that indeed a mutated gene is impairing the normal functional ability of serotonin receptors in such patients. The inability to bind to serotonin can impact many normal physiological pathways that includes mood changes and satiety.¹

Mouse model studies have shown that serotonin receptor plays a major role in mediating the action of serotonin on appetite suppression through the melanocortin pathway.^6,7 The activation of Proopiomelanocortin (POMC) neurons found on the hypothalamus region is necessary to induce satiety (Fig.1). POMC activation is dependent on serotonin receptors.⁶ To study the human HTR2C variant’s impact on satiety, a CRISPR-Cas9 approach was done in a knock-in (KI) mouse model. The POMC neurons in KI-mice displayed reduced activity indicating a deficient melanocortin signalling.⁴ This finding can be beneficial in studying the impaired melanocortin pathway in human.

Serotonin receptor drugs or melanocortin receptor (MC4R) drugs that activate serotonin receptors (also called agonists) have shown efficacy in treating SEOO caused by other genetic variations affecting the melanocortin pathway.⁸ A serotonin receptor drug, Lorcaserin, has shown promise to be a substitute for the non-specific SSRI drugs.⁸ The association of impaired melanocortin pathway due to the rare variantwith MC4R drugs was explored. Comparable hunger control was observed in the KI-mouse when dosed with the medication(fig.2).⁴ These observations are indicative that similar MC4R drugs can be used to repair the melanocortin signalling, which is impaired in severely obese patients due to damaged POMC neural activity. Currently, obese patients with other rare variants in different genes acting on the POMC activity are being included in clinical trials of various MC4R drugs.⁸ Therefore, it is projected that patients carrying HTR2C variants with impaired POMC neural activity can also benefit from such trials.

Both the male and female KI-mice, harbouring the rare variant displayed uncontrolled hunger drive. The behavioural aspects studied in such mice also showed increased offences, reduce sociability and reduced risk assessment ability. The results are comparable with other such obesity mouse model studies although particularly the serotonin 2C receptor was not studied in them.^9,10 Similarly, the individuals in this cohort carrying the HTR2C variant also reported early-onset anxiety, a maladaptive behaviour. This is an association highlight the influential nature of our mood which creates a vicious cycle of uncontrolled eating habits.

The study associated the role of biological gender in developing such maladaptive behaviour and obesity. It has been seen that female KI-mouse carrying the rare variant in one allele (heterozygote) displayed similar but less pronounced behavioural and hyperphagic effect as the male mouse. This suggests that the penetrance of a heterozygous rare HTR2C variants is variable. Additionally, most probands carrying HTR2C variants were female, suggesting a possible X-linked inheritance pattern. As the gene is found in X-chromosome, it is speculated to be because of gene-dosage effect. However, authors expressed further exploration to understand the roles of other genetic and environmental factors influencing the full effect of this variant.  

Predominantly, it has been assumed that anxiety in obese female was caused due to societal pressure and stigma. However, this study puts forward a new biological perspective on the associated symptoms of obesity and maladaptive behaviour. The study shows impaired serotonin signalling in females carrying HTR2C variant causes co-occurrence of such symptoms.

Often SEOO patients are reported to have maladaptive behaviour.¹ This study has provided evidence for a shared mechanistic origin between SEOO and maladaptive behaviour.⁴ Through the study, He et al., have emphasised that a systematic psychological analysis of maladaptive behaviour in SEOO patients can be useful in guiding them towards genetic testing and finding an actionable variant. ⁴

An elaborate functional study has been performed by He and team. Improving the power and effect size of this association further, would require larger studies of thousands along with control samples. An important missing link  between mood and hunger has been established through this study. Population studies can solidify this association across diverse group of population.  Although animal studies are not truly capable of emulating the complex and social environment we live in, they do provide a solid foundation for human disease research. These observations can be utilised to broaden the clinical effectiveness of MC4R drugs. Through this study, it can be accentuated that targeting and understanding the underlying genetic mechanisms regulating serotonin receptor can play a huge role in the development of psychiatric medications that are effective and causes no chronic weight gain.

References

1. Mills, J. K. & Andrianopoulos, G. D. The relationship between childhood

onset obesity and psychopathology in adulthood. Journal of Psychology: Interdisciplinary and Applied 127, 547–551 (1993).

2. Brian, H. & Bouwer, C. D. Neuropharmacology of paradoxic weight gain with selective serotonin reuptake inhibitors. Clinical neuropharmacology 23, 90-97 (2000).

3. Marston, O. J., Garfield, A. S. & Heisler, L. K. Role of central serotonin and melanocortin systems in the control of energy balance. Eur. J.Pharmacol. 660, 70–79 (2011).

4. He, Y. et al. Human loss-of-function variants in the serotonin 2C receptor associated with obesity and maladaptive behavior. Nat Med 28, 2537–2546 (2022).

5. Peng, Y. et al. 5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology. Cell 172, 719-730 (2018).

6. Berglund, E. D. et al. Serotonin 2C receptors in pro-opiomelanocortin neurons regulate energy and glucose homeostasis. J. Clin. Invest. 123, 5061–5070 (2013).

7. D’Agostino, G. et al. Nucleus of the Solitary Tract Serotonin 5-HT2C Receptors Modulate Food Intake. Cell Metab 28, 619-630 (2018).

8. Clément, K. et al. Efficacy and safety of setmelanotide, an MC4R agonist, in individuals with severe obesity due to LEPR or POMC deficiency: single-arm, open-label, multicentre, phase 3 trials. Lancet Diabetes Endocrinol 8, 960–970 (2020).

9. Walf, A. A. & Frye, C. A. The use of the elevated plus maze as an assay of anxiety-related behavior in rodents. Nat Protoc 2, 322–328 (2007).

10. Haller, J., Mikics, É., Halász, J. & Tóth, M. Mechanisms differentiating normal from abnormal aggression: Glucocorticoids and serotonin. In Eur. J. Pharmacol. 526 89–100 (2005).

Solomiya Hnatovska

Study uncovers causal relationship between the breakdown of the nuclear lamina and uncontrolled overexpression of CGI- genes, potentially explaining the chronic inflammation symptoms that are associated with aging.

It is well established that the 3D organization of chromatin is important to normal gene expression. In healthy cells, heterochromatin is found tethered to the inner walls of the nucleus by nuclear lamina proteins, while the more actively expressed regions loop out into the center of the nucleus. Unraveling of heterochromatin is seen in aging tissue and has long been believed to contribute to aging associated degenerative changes, however, the underlying mechanism has remained elusive^1,2. Lee et al, have made important strides in our understanding of this mechanism using mouse models and meta-analyses of human and mouse data. They discovered that increased expression of a specific group of genes lacking CpG islands is the missing link between the chromatin unraveling and inflammation associated with aging³.

CpG islands are regions of high CG nucleotide density which are known to affect the regulation of genes if found in their promotors. CpG island lacking genes (CGI- genes) make up 40% of genes, are expressed in a tissue specific manner, and are silenced through association with heterochromatin^4,5. These are unlike the other 60% of genes which contain CpG islands (CGI+ genes), are broadly expressed across tissues and are silenced through a different mechanism, called polycomb inactivation^4,5. See figure 1 for a visualization of this CGI-mediated dual mode form of gene regulation. Given that CGI- genes are silenced by association with heterochromatin, the authors hypothesized that disorganization of nuclear lamina proteins, which occurs during aging, would disproportionately affect CGI- gene expression. Indeed, they found a 33.7% increase in expression of the CGI- genes with age in mouse liver and brain tissue, compared to only 9.5% increased expression of CGI+ genes. They found similar gene expression changes, accompanied by loss of heterochromatin markers, in mouse lines with disrupted and knocked out nuclear lamina proteins. These results indicate that, at least in mice, specifically the CGI- genes are overexpressed during aging, due to the nuclear lamina proteins becoming disorganized and releasing the heterochromatin to unravel.

Having determined that nuclear lamina disruption and heterochromatin unraveling lead to CGI- gene upregulation, the researchers wanted to find out what effect this has on known hallmarks of aging. One poorly understood but prominent marker of aging is ‘Senescent Associated Secretory Phenotype’ (SASP). SASP is a phenotype associated with chronic inflammation that occurs with age where non-immune cells begin to secrete pro-inflammatory proteins into the extracellular space⁶. Thus, the researchers chose to investigate whether CGI- gene upregulation might explain what causes SASP.

Initial findings showed that older mice with particularly high expression of CGI- genes showed signs of liver damage, local inflammation, and an increased expression of genes encoding inflammatory markers. While this critical finding supports the hypothesis that CGI- gene misexpression contributes to inflamm-aging, further investigations using human data were essential to confirm this link exists in humans. Through meta-analysis of both mouse and human data they found that a large proportion of mis-expressed CGI- genes encode secreted extracellular or transmembrane proteins, many of which have previously been implicated in SASP. This was a major finding as the mechanism underlying SASP was previously unknown.

Having found evidence associating the misexpression of CGI- genes with SASP and other markers of aging, the authors turned to testing potential applications of their findings. They suggest using the misexpression of CGI- genes as a marker of aging when testing the effectiveness of treatments for aging and aging related diseases. They found that administration of treatments such as caloric restriction to healthy aged mice, significantly reduces their misexpression of CGI- genes. Through another meta-analysis of both mouse and human data they found that individuals with age related diseases, such as Alzheimer’s Disease (AD) show significantly increased expression of CGI- genes relative to healthy age matched individuals. Given the current lack of therapeutic targets for complex diseases such as AD, this is a significant finding as it supports the potential of CGI- gene expression as a therapeutic target. Thus, misexpression of CGI- genes may be not only be a marker for aging, but also a potential target for treatments of age-related diseases.

This study shows how aging associated inflammation in both mice and humans may be explained by underlying uncontrolled overexpression of CGI- genes. Misexpression of CGI- genes is likely caused by disorganization of the 3D chromatin conformation that occurs with aging. While these findings improve our understanding of mechanisms of aging, further research is needed to resolve what causes the initial breaking down of the nuclear lamina and how misexpression of CGI- genes contributes to systemic aging and age-related diseases. Nevertheless, this research is an essential steppingstone to future studies investigating the potential to target this mechanism of aging and reduce the inflammatory processes associated with aging.

References

1.         Scaffidi, P. & Misteli, T. Lamin A-dependent nuclear defects in human aging. Science 312, 1059–1063 (2006).

2.         Chandra, T. et al. Global reorganization of the nuclear landscape in senescent cells. Cell Rep. 10, 471–483 (2015).

3.         Lee, J.-Y. et al. Misexpression of genes lacking CpG islands drives degenerative changes during aging. Sci. Adv. 7, eabj9111 (2021).

4.         Deaton, A. M. & Bird, A. CpG islands and the regulation of transcription. Genes Dev. 25, 1010–1022 (2011).

5.         Lee, J.-Y. et al. Conserved dual-mode gene regulation programs in higher eukaryotes. Nucleic Acids Res. 49, 2583–2597 (2021).

6.         Childs, B. G. et al. Senescent cells: an emerging target for diseases of ageing. Nat. Rev. Drug Discov. 16, 718–735 (2017).

Vivian Hong

Genome-wide association studies (GWAS) find genetic associations between severe COVID-19 and systemic lupus erythematosus (SLE) – identifying the TYK2 locus to have a significant negative local genetic correlation in terms of disease severity.

The coronavirus disease-19 (COVID-19) pandemic has garnered much interest in understanding the genetics involved in the response to viral infection¹. COVID-19 is caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), leading to a range of mild to severe symptoms that include respiratory, gastrointestinal, and cardiac problems². To investigate genetic loci that are associated with severe COVID-19, many studies conduct genome-wide association studies (GWAS), which can detect variants that are significantly correlated with the phenotype of interest across the whole genomes of a large selection of individuals^1,3.

Recent research using GWAS has shown that the clinical variability of COVID-19 outcomes may be partly attributed to variations in an individual’s genetics^1,3. These studies have identified several genetic loci involved in response pathways of host immunity that appear to be highly associated with COVID-19 outcome heterogeneity^1,3. Variants that upregulate factors required in immune response pathways were found to have protective effects against COVID-19¹. Conversely, variants that decrease the expression of essential factors in the immune pathways may lead to more severe phenotypes¹. As a result, comparing autoimmune diseases (associated with an overactive immune system that attacks its own host) to severe COVID-19 (associated with a weaker viral immune response system) could contribute to understanding the genetic mechanisms of the COVID-19 pathogenicity¹. One autoimmune disease that has been analyzed with COVID-19 is systemic lupus erythematosus (SLE), which is a chronic illness that can cause systemic inflammation across multiple organs⁴.  In Wang et al’s study, a series of genetic association analyses between severe COVID-19 and SLE identified TYK2 as a significant locus that had reciprocal effects on the two diseases¹. 

In order to compare the genetics of severe COVID-19 and SLE, the authors used SLE data from three previously published European GWAS meta-analysis studies and COVID-19 association European datasets obtained from GenOMICC¹. They conducted a genome-wide genetic correlation analysis between the two diseases by calculating the cross-trait linkage disequilibrium score regression (LDSR) using the GWAS result summary statistics¹. LDSR can provide the genetic correlation values between different phenotypes⁵. The results showed that there was indeed a significant genetic correlation between COVID-19 and SLE¹.

To identify the regions that contributed to the observed correlation, Wang et al. performed local genetic correlation analyses¹. This was done using an approach called p-Hess, which quantifies the correlation of the two tested traits that is due to genetic variation at a genetic locus⁶. The analyses found fifteen significant loci with positive and negative correlations¹. Of those genetic loci, the TYK2 gene was shown to be the most significantly correlated, displaying a negative relationship in disease severity between COVID-19 and SLE¹.

To further support their results, the authors performed TYK2 locus-wide association tests to show significant alleles associated with SLE and COVID-19¹. Wang et al. found that the significantly associated TYK2 alleles had reciprocal effects in terms of disease outcome¹. Some TYK2 alleles are found to confer risk for SLE while being protective against COVID-19, and vice-versa¹.

The TYK2 gene encodes for a protein kinase that is involved in the host autoimmunity response pathways by helping promote immune factor signalling and production, such as type 1 interferon (IFN-I)^1,7. Mutations that affect TYK2 function may lead to dysregulation in IFN signalling during immune responses¹. Previous studies have shown that SLE patients are associated with upregulated TYK2 leading to overactive immune systems^1,7.

For the TYK2 alleles that are found to confer SLE risk and COVID-19 protectiveness, the mutation had either led to increased gene expression or protein activity¹. The increased interferon production due to TYK2 activity may help enhance the host defence system against viral infection^1,7. Additionally, the authors suggest that the identified SLE risk alleles may also be involved in intracellular viral sensing pathways where the immune system loses tolerance in sensing foreign molecules¹. This may lead to autoimmune attacks, but may also provide extra protection against foreign viruses¹. Thus, Wang et al. suggest that these reasons might be why those SLE risk alleles may be protective against COVID-19¹. Contrastingly, the TYK2 alleles that are SLE protective and confer risk for COVID-19 were found to have either decreased gene expression or protein activity (e.g., impaired phosphorylation)¹. This likely leads to a decrease in IFN response signals and the corresponding downstream factors¹. The downregulated immune pathway could result in poor viral clearance and severe COVID-19 symptoms, but safe against SLE^1,8. Therefore, Wang et al. show why the TYK2 alleles may have opposing effects on COVID-19 and SLE severity.

Figure 1. Proper and downregulated immune response to viral infection lead to different clinical COVID-19 outcomes. The proper pathway shows normal IFN signalling cascade to viral infection, allowing for effective viral clearance and protective immune response. The dysregulated pathway showed downregulated IFN response that leads to poor viral clearance and acute respiratory distress syndrome (ARDS), which is a severe COVID-19 outcome. Adapted from⁸.

Overall, these studies suggest that there are alleles that predispose an individual for an autoimmune disease but provide more protection against viral infection¹. There appears to be a delicate balance between calibrating our immune system to fight against viruses (and other foreign agents) and increasing our risk of developing autoimmune diseases. Although this study has provided some insight into the underlying mechanisms behind the shared genetic effects of COVID-19 and SLE, further studies need to be conducted with a larger and more representative dataset. The study performed analyses primarily using limited datasets from European ancestry¹. Moreover, functional studies on the identified significantly associated loci in the future could help researchers better understand the host immune response system and disease pathogenicity, which could contribute to the development of possible therapeutic treatments that rescue gene dysregulation in the immune pathways for COVID-19 and other immune-related diseases. 

References

1. Wang, Y. et al. COVID-19 and systemic lupus erythematosus genetics: A balance between autoimmune disease risk and protection against infection. PLoS Genet 18(11), e1010253 (2022). https://doi.org/10.1371/journal.pgen.1010253.  
2. Ofner, M. et al. COVID-19 signs, symptoms and severity of disease: A clinician guide. Government of Canada (2022). https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection/guidance-documents/signs-symptoms-severity.html#shr-pg0.
3. Pairo-Castineira, E. et al. Genetic mechanisms of critical illness in COVID-19. Nature 591, 92–98 (2021). https://doi.org/10.1038/s41586-020-03065-y.
4. Fava, A and Petri, M. Systemic lupus erythematosus: Diagnosis and clinical management. J Autoimmun 96, 1-13 (2019). https://doi.org/j.jaut.2018.11.001.
5. Byun, J. et al. Shared genomic architecture between COVID-19 severity and numerous clinical and physiologic parameters revealed by LD score regression analysis. Sci Rep 12(1), 1891 (2022). https://doi.org/10.1038/s41598-022-05832-5.
6. Shi, H., Mancuso, N., Spendlove, S., Pasaniuc, B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. Am J Hum Genet 101(5), 737-751 (2017). https://doi.org/10.1016/j.ajhg.2017.09.022.
7. Shao, W.H., Cohen, P.L. The role of tyrosine kinases in systemic lupus erythematosus and their potential as therapeutic targets. Expert Rev Clin Immunol 10(5), 573-82 (2014). https://doi.org/10.1586/1744666X.2014.893827.
8. Spihlman, A.P., Gadi, N., Wu, S.C., Moulton, V.R. COVID-19 and Systemic Lupus Erythematosus: Focus on Immune Response and Therapeutics. Front Immunol 11, 589474 (2020). https://doi.org/fimmu.2020.589474. 

Kevin Navarro Hernandez

Newest research has found that varicose veins have overlapping genetic correlations with other health conditions that could lead to more complications. These findings are of great importance for developing potential treatment targets.

Varicose veins are a manifestation of chronic venous disease characterized for being dilated veins that do not transport blood efficiently¹. Varicose veins have been known to lead to cardiovascular consequences such as venous thromboembolism (VTE) and peripheral artery disease (PAD) (figure 1)². At the moment there are limited medical therapies available to treat them and little is known about the pathways leading to these outcomes. For most, varicose veins represent more of an asymptomatically aesthetic issue, rather than a disease. Recent studies are emphasizing the importance of understanding it’s genomic pattern because of related health complications.

Understanding the genetics of varicose veins is important for the development of new treatment targets and therapies. Data obtained from the Veterans Affairs Million Veteran Program and four other biobanks suggest that there is genetic overlap between varicose veins and multiple factor inheritance traits². The findings of Levin et al., contribute towards the goal of targeted therapies that could improve health and/or prevent future health complications.

This study focused on conducting the largest multi-ancestry genome-wide association study (GWAS) of varicose veins (figure 1)². The four ancestral populations included African, East-Asian, European, and Hispanic. For the purpose of this study, they used data from 49,765 individuals with varicose veins and 1,334,301 disease-free controls². The studies revealed 139 loci linked to varicose veins from which 8,366 DNA sequence variants/mutations were identified.

Levin and his team identified variants with the given names of: rs6025 and rs62033413 respectfully. The first variant is associated with chronic venous disorders. In this study, they estimated genetic correlations between varicose veins, anthropometric traits, and other vascular traits. This was done to understand the shared genetic factors and its pattern to assess any possible genetic risks. The second variant mentioned above is associated with increased BMI and weight. Despite the fact that BMI is not inherited, there seems to be a correlation between the presence of this variants and an increased accumulation of weight. Measures of body composition such as fat mass and BMI were positively correlated across vascular traits as well². This is evidence of the presence of genetic overlap between varicose veins and these other outcomes.

Proteome-wide MR (a test that involves protein functions) was performed to find proteins which could have a potential risk influence of varicose veins outcomes but that could also mean treatment targets². The targeted proteins where those in close relationship with venous vasculature. From 714 circulating proteins, 35 had a significant high-confidence genetic association with risk of varicose veins². These results were enriched for proteins involved in ECM structure, collagen binding, cell adhesion, and anatomical morphogenesis.

Colocalization was also performed to corroborate the MR findings². There were only 26 proteins from the 35 previously discussed, which were available for colocalization. The results showed evidence of colocalization for 10. This helped narrow the number of proteins that may represent a risk factor. Examples of these proteins included ECM proteins such as fibrillin and Asporin. Increased levels of asporin in ECM proteoglycan was associated with increased risk of varicose veins. An ECM secreted protein called Periostin (POSTN) which function is to bind integrins for migration support and adhesion of epithelial cells were associated with decreased risk of varicose veins when found in higher circulating concentrations. This was a very peculiar finding, since it could be a great treatment target.

While the molecular and genetic basis of varicose veins remains uncertain, epidemiologic studies have identified risk factors linked to this phenotype. To prove their findings, researchers used many different mathematic calculations and statistics as their fundamental evidence of genetic significance. The correlations mentioned in the paper are of statistical significance, which make them targets of relevant importance for further research. Identifying circulating proteins was achievable with MR and confirmed with colocalization. The findings in this study could potentially define disease-causing effectors and new treatment targets.

Michael G. Levin and colleagues’ work has given a lead in the research and future discoveries of therapeutic developments for varicose veins. This study has set the pace for further research in many proteins which play a role in venous vasculature. As some proteins were found to have an association with increased risk of varicose veins when in high concentrations, others were found to cause the opposite effect. Targeting these proteins and having a better understanding of their pathways and genetic overlap with varicose veins, could represent potential treatments that will not require invasive surgical procedures. The variants identified in this study are of great importance as well, since it proves the genetic overlap that was hypothesized. These could be genetic targets for future genetic therapies that could not only help with the varicose veins, but also prevent VTE and PAD phenotypes. It would be interesting to also put more attention in the SNPs found which were associated with BMI and weight. If there is a predisposition to high BMI due to these variants which also have an association with varicose veins, it would be a point of focus for people to start thinking about their diets and make healthy decision to prevent undesirable health-related outcomes.

Reference

1. Fukaya, E., Flores, A. M., Lindholm, D., Gustafsson, S., Zanetti, D., Ingelsson, E., & Leeper, N. J. Clinical and Genetic Determinants of Varicose Veins: Prospective, Community-Based Study of ≈500 000 Individuals. Circulation, 2869–2880 (2018). https://doi.org/10.1161/CIRCULATIONAHA.118.035584
2. Levin, M.G., Huffman, J.E., Verma, A. et al. Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease. Nat Cardiovasc Res 2, 44–57 (2023). https://doi.org/10.1038/s44161-022-00196-5

Mailoan Panchalingam

Radiotherapy has shown to be associated with an increased burden of small deletions within post-treatment cancer genomes resulting in the partial or complete loss of sensitivity to further radiation treatment, thus leading to poorer survival outcomes for patients.

Radiotherapy, also commonly known as radiation therapy (RT) has been used for nearly a century as a primary method to treat cancer patients². RT has been shown to effectively treat a variety of cancer types including breast, prostate and lung cancer by inducing targeted DNA damage to tumour cells⁴. The mechanism behind RT is that ionizing radiation is directed towards tumour cells resulting in DNA damage such as double-stranded breaks (DSBs)⁴. This causes the cell-cycle to be impacted, halting tumour cell proliferation⁴. This would lead to apoptosis and other forms of programmed cell-death among cancerous cells⁴. The extensive DNA damage resulting from radiation treatment is then sensed by cellular machinery leading to the initiation of DNA repair mechanisms through error-prone methods such as classical non-homologous end joining (c-NHEJ). One of the major known caveats of RT for cancer patients, is that effective repair of radiation-induced DNA damage can result in poorer survival outcomes, through the introduction of new mutations within the post-treatment cancer genome. The article published by Kocakavuk et al., 2021 describes how RT is highly associated with an increased burden of small deletions that are a result of effective DNA repair mechanisms (c-NHEJ) subsequently leading to decreased treatment efficiency and lower survival outcomes.

The study conducted by Kocakavuk et al., utilized two major datasets that were the Glioma Longitudinal Analysis Consortium (GLASS) and the Hartwig Medical Foundation (HMF) in which comprised of glioma and metastatic tumours respectively. A total of 3883 tumour samples were whole genome sequenced (WGS) within this study between both datasets to determine any common mutational signatures that were present within the post-RT-treated tumour DNA.

Kocakavuk and colleagues had initially discovered in this study that there is an increased small-deletion burden that is heavily associated with RT. They were able to attribute scores to these “mutational burdens” in both the recurrent RT-treated gliomas and gliomas not treated with RT. The results showed that recurrent RT-treated gliomas had a mutational burden of 0.68 new small deletions per megabase, while recurrent non-treated gliomas had a mutational burden of 0.19 deletions per megabase. This indicated that radiation-treated tumours indeed have an increased mutational burden, specifically consisting of small deletions. From a clinical perspective, quantifying these increased RT-small deletion burdens can serve as an effective method for monitoring RT intervention among cancer patients. Through examining RT-deletion mutational burdens, clinicians can alter or modify existing treatment interventions to ensure a patient does not lose full sensitivity or efficacy of radiotherapy.

The researchers of this study then go onto describe some of the characteristics of these specific deletion signatures associated with RT. They had identified that the small deletions associated with RT had a length of ~5-15 bp and tumours treated with either palliative or curative RT had differing deletion lengths. This essentially supports the concept of cumulative radiation treatment dosage can impact the size of these acquired mutational burdens. The finding made here can also serve in clinical importance for physicians, as RT treatment dosage can be modified based on if a patient has already received either palliative or curative RT to reduce the development of acquired deletions. Furthermore Kocakavuk et al.,2021 also identified distinct mutation signatures involving these RT-associated small deletions. In particular they identified an indel signature, ID8 which is a deletion signature, that is greater than 5 bp and the result of effective DSB repair through c-NHEJ. The findings made here had proven that effective DNA DSB repair can result in the formation of these RT-associated mutational signatures that can later impact the efficacy of RT treatment.

The study specifies a RT-associated acquired homozygous CDKN2A deletion that can result in increased chromosome losses, and is also significantly associated with poorer survival outcomes. These findings highly correlate with a previous study in which focusses on CDKN2A functioning. CDKN2A is a tumour suppressor gene and is shown to have even slight mutations within the gene that can result in impaired cell-cycle functioning leading to tumour progression¹. Therefore, entire deletions of this gene would likely be highly detrimental for a patient’s prognosis. The study then comes back to illustrate that an intermediate or high ID8 deletion burden is heavily associated with poor survival while a lower ID8 burden has stronger survival outcomes as seen in Figure 1. The authors consolidate their findings to establish that accumulating a high number of these RT-associated small deletions, was able to characterize a tumour in which initially responded to RT therapy. Though at some-point, these tumours lost all or most of the treatment sensitivity once acquiring the deletion signatures.

The figure above illustrates the relationship between RT-associated ID8 deletions and survival time among the metastatic tumour sample cohort obtained from the HMF dataset. The samples from this dataset were separated into 3 categories based on the degree of their deletion burden (low, intermediate and high) and the dotted line for each category represents the median survival time.

The significant findings made by Kocakavuk and colleagues suggest that effective DNA repair resulting in increased mutational deletion burden due to recurrent RT treatment can lead to loss of sensitivity to RT. This further highlights the need for improved therapeutic options. For example, inhibitors of poly (ADP-ribose) polymerase also known as PARP inhibitors are drugs that directly target DNA repair mechanisms and inhibit them⁵. A recent study has shown that PARP inhibitors can enhance radiosensitivity within glioma cells through increased DNA damage and turnover by repair systems being blocked⁵. Overall, based on the results of this study, clinical reassessment of tumour DNA for these acquired small deletions associated with RT may reveal the degree of sensitivity to RT treatment. This could open further options and lead to alternate treatment interventions that may improve patient prognosis.

Reference List:

1. Casula, M., Paliogiannis, P., Ayala, F., De Giorgi, V., Stanganelli, I., Mandalà, M., Colombino, M., Manca, A., Sini, M. C., Caracò, C., Ascierto, P. A., Satta, R. R., Melanoma Unit of Sassari (MUS), Lissia, A., Cossu, A., Palmieri, G., & Italian Melanoma Intergroup (IMI) (2019). Germline and somatic mutations in patients with multiple primary melanomas: a next generation sequencing study. BMC cancer, 19(1), 772. 

• Gianfaldoni, S., Gianfaldoni, R., Wollina, U., Lotti, J., Tchernev, G., & Lotti, T. (2017). An Overview on Radiotherapy: From Its History to Its Current Applications in Dermatology. Open access Macedonian journal of medical sciences, 5(4), 521–525.

• Kocakavuk, E., Anderson, K. J., Varn, F. S., Johnson, K. C., Amin, S. B., Sulman, E. P., Lolkema, M. P., Barthel, F. P., & Verhaak, R. G. W. (2021). Radiotherapy is associated with a deletion signature that contributes to poor outcomes in patients with cancer. Nature genetics, 53(7), 1088–1096. 

• Liu, Y. P., Zheng, C. C., Huang, Y. N., He, M. L., Xu, W. W., & Li, B. (2021). Molecular mechanisms of chemo- and radiotherapy resistance and the potential implications for cancer treatment. MedComm, 2(3), 315–340. 

• Sim, H. W., Galanis, E., & Khasraw, M. (2022). PARP Inhibitors in Glioma: A Review of Therapeutic Opportunities. Cancers, 14(4), 1003. 

Pooja Kiran Ravi

The largest structural variant analysis on Parkinson’s disease paves way for a different approach to complex genetic diseases. Hints at new variants that might increase risk for Parkinson’s.

Structural Variants (SVs) deserve to have a spotlight shine on them. Structural variants involve large sections of the chromosome being rearranged through insertions, deletions, duplications, translocations and inversion¹(Figure 1). Despite representing a significant portion of variants and the fact that they can also induce functional change across cells and tissue population types², they are still vastly understudied. In the recent past, research for Parkinson’s Disease (PD) predominantly looked only at SNVs³(Single Nucleotide Variants). This is with good reason because though SVs are easy to pin point in theory, it is difficult to distinguish them with current sequencing methods especially with short read sequencing alone. Even when they are mapped and identified without errors, different SVs could be nested or it might be difficult to differentiate between one type of SV and another⁴.

But given the complex nature of PD, it seems short sighted to focus on only one aspect of DNA variants. It does make sense to look at SNVs and SVs together so that it provides a more cohesive outlook of diseases. While there are a number of studies focus on SNVs, GWAS (Genome Wide Association Study) with SVs – particularly in PD – are still far and few in between. One such hidden gem paper is the GWAS analysis by Billingsley et. al⁵ which casts a large net in its screening for PD SVs. The outcome of the experiments shows how vastly unexplored the landscape of SV investigations are. The pipeline, which comprised of 227,357 SVs was able to show that several of them could be associated with an increased risk of PD. If one such thorough analysis of SVs could show us the other facets of a complex disease, imagine the potential if this blue print was followed through and combined with research that also looked at SNVs. The information it would provide us would allow us to take a peek into the dark side of the moon that are complex diseases.

This study utilized ten different cohorts for their analysis and their clinical and demographic characteristics have been described in table 1 in the supplementary attachment⁵. For identifying and filtering SVs, the GATK-SV algorithm pipeline was used. The GATK-SV algorithm is a workflow that helps to bring together the data generated from Illumina Whole Genome Sequencing to detect SVs⁴. Under GATK-SV, different tools were used to call different SVs to account for the variability across the pipeline. After cleaning up the data, the final dataset comprised of 366,555 SV calls which were then cross verified with their GWAS.

Billingsley et al., tried to find rare PD variants from their GWAS by using long-read sequencing and confirm this data against the variant dataset generated. While they were able to identify significant SV association signals, there was a possibility that these hits were false-positives. The loci implicated in the SV associated with these false positive signals, were a large known inversion that was filtered out earlier in the pipeline but could not be validated using either short or long read sequencing showing us the possible pitfalls to avoid while filtering for false positive SVs. Billingsley et. al theorize three possible SVs, in accordance with their research, within the associated risk loci. All three were deletions in the Alu gene mobile elements which have been recently shown to have a huge functional impact. The nature of this finding implies that future studies need to focus on stringent filtering of SVs to avoid the same pitfalls. According to the Billingsley et al., one possible variant of interest which is a 2kb deletion in gene LRRN4 could be a strong candidate for causal variant at the PD risk locus at chromosome 20 but further complex genetic analysis might be required to confirm it.

At multiple points throughout this study, it was clear that while the theory behind the SV analysis pipeline was solid, it doesn’t hold up when there are so many limitations to short-read sequencing as most SVs do not show up on short-read sequencing alone. One solution to this problem is population scale long-read sequencing which is still underway for many neurodegenerative diseases⁶. Some complex SVs might require non-sequencing-based approaches like optical mapping⁷. These new techniques are still underway but if combined with a workflow pipeline like Billingsley et al., the gap in information could be shortened until it eventually closes.

While the Billingsley et al. paper is the largest GWAS Structural Variant Analysis in PD completed so far, it is not as large scale as other GWAS which hints at the promise such analysis can hold. The biggest wall currently is the lack of data for similar studies involving structural variants and it can only be overcome by more focus on the field. But hopefully, with this paper as a map, many more explorers can follow them so that we can finally see what lies beyond our known genomic universe.

References:

1.        Types of DNA variant: structural variation | Garvan Institute of Medical Research. https://www.garvan.org.au/research/kinghorn-centre-for-clinical-genomics/learn-about-genomics/dna-base/collection1/structural-variation.

2.        Hurles, M. E., Dermitzakis, E. T. & Tyler-Smith, C. The functional impact of structural variation in humans. Trends Genet 24, 238 (2008).

3.        Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18, 1091–1102 (2019).

4.        Mahmoud, M. et al. Structural variant calling: The long and the short of it. Genome Biol 20, (2019).

5.        Billingsley, K. J. et al. Genome-Wide Analysis of Structural Variants in Parkinson Disease. Ann Neurol (2023) doi:10.1002/ANA.26608.

6.        de Coster, W., Weissensteiner, M. H. & Sedlazeck, F. J. Towards population-scale long-read sequencing. Nature Reviews Genetics 2021 22:9 22, 572–587 (2021).

7.        Ebert, P. et al. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science (1979) 372, (2021).

Kajeetha Sarvananthan

A multiomics study utilized whole-genome sequencing, RNA sequencing and immune cell assays in combination with machine learning modelling to differentiate young critical COVID-19 patients from non-critical patients and healthy individuals, in the absence of co-morbidities. ADAM9, a gene that indirectly modulates NK T cell function is a top driver gene and possible therapeutic target for critical COVID-19 response.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of novel coronavirus disease (COVID-19)1. COVID-19 is thought to be asymptomatic or mild in healthy individuals while elderly individuals or those with pre-existing co-morbidities seem to be at a greater risk of severe disease1. At present, there remains a major lack of information about the characteristics and risk factors associated with severe disease among young adult patients with COVID-191. On top of this, there is a need for understanding the mechanisms underlying COVID-19 and establishing biomarkers, to be able to identify patients who will progress to severe disease1. Carpito et al. have been one of the first groups to identify driver genes and a signature of impaired immune response of critical COVID-19 in young, comorbidity-free patients2.

A cohort of 72 patients under the age of 50 were recruited from a university hospital network in northeast France for a repeatable comprehensive multi-omics COVID-19 study 2. These patients had no pre-existing comorbidities2. The median age of the patients was 40 with 25 of them being non-critical (recruited from the non-critical ward) and 45 considered to be critical patients. Critical patients were in the intensive care unit (ICU) with severe or moderate acute respiratory distress syndrome (ARDS), requiring invasive mechanical ventilation or high flow nasal oxygen2.

Previous COVID-19 studies have shown that asymptomatic patients display a higher T cell response compared to symptomatic patients3. In this study, a common pattern that the researchers elicited in their proteomics investigation was that critical patients had a stronger activation in B cell response while T cell response was impaired with severe skewing towards helper T cell 17(TH17)2. TH17 has been found to be over-activated in the peripheral blood of other severe COVID-19 patients with a hyper- inflammatory state in their lungs5. These findings add to the screening potential of T cell count as a biomarker for disease state and an area for targeted therapy to mitigate a poor antiviral and anti- inflammatory immune response seen in some COVID-19 patients4.

In hopes of solidifying and correlating these proteomic assays to a specific genetic profile, RNA- sequencing was coupled with machine learning algorithms to differentiate between the gene expression of critical and non-critical COVID-19 patients2. 600 of the most informative genes were selected and used

for structural casual modeling (SCM)2. A structural causal model in genetics is a model that shows how individual genetic factors cause disease6.  Carapito et al.’s model used differential gene expression to depict a possible signal transduction cascade for COVID-19 (figure 1)2. The genes on the far left of this

diagram is predicted to have the highest degree of influence on COVID-19 response and expected to exert the greatest effect on the expression of downstream genes2. ADAM9, RAB10, MCEMP1, MS4A4A and GCLM were the top five driver genes and all five of these were significantly up regulated in critical patients.2

ADAM9(a disintegrin and a metalloproteinase 9) is transcribed by the gene with the greatest degree of causal influence in the SCM2,7. ADAM9 transcripts are alternatively spliced to have a membrane bound and secreted isoform2. Higher concentrations of the secreted form were found in the

serum of critical COVID-19 patients compared to non-critical2.Consistent with this finding, soluble forms

of major histocompatibility complex class I- related chain A (MICA), a protein that’s cleaved by ADAM9 had an increased concentration in the plasma of critical patients compared to non-critical and healthy controls2. MICA is a ligand of natural killer (NK) T cells and is expressed on the cell surface of several different types of cells when they are under stress (e.g a viral infection)7. Increasing evidence has shown that membrane-bound MICA can be cleaved by ADAM9 to release soluble MICA and this prevents NK cells from targeting cells under stress (figure 2).7 An increase of ADAM9 could be the reason for reduced NK T cells in the critical COVID-19 patients and impaired recognition of infected cells due to a lack of membrane-bound MICA.

Whilst protein levels can indicate severity of a disease state, single nucleotide polymorphisms (SNPs) near a disease-causing gene can be used as a biomarker to predict risk for a disease8. rs784020 was an SNP that was found near ADAM9 more often in the critical patients with a higher abundance of ADAM9 transcripts2. Targeting this variant’s products would explore its therapeutic efficacy in attenuating COVID-19 severity which was exactly what the researchers sought out to do by targeting

ADAM9 transcripts2. ADAM9 was silenced using small interfering RNA (siRNA) in enzyme cells prior to infecting the cells with SARs-CoV-22. The amount of intracellular virus and released virus was successfully lowered when ADAM9 was silenced, confirming that targeting ADAM9 can reduce COVID-

19 viral load.

This study highlights that monitoring T cell changes could have important implications for the diagnosis and treatment of severe COVID-19 patients and possibly for patients of other viral infections4. The algorithms designed for structural causal modelling was shown to be repeatable with a second patient cohort not only indicating the likelihood that driver genes such as ADAM9 do play a role in critical COVID-19 patients but, also the future practicality of such algorithms to distinguish between other diseases and healthy populations2. ADAM9-blocking antibodies are currently in the preclinical stages for treating patients with advanced solid tumors8. Such blocking-antibodies for ADAM9 should be studied

for repurposing abilities to treat severe COVID-19 which can further prove its potential as a therapeutic target for severe viral infection.

References

1.     Deng, M. et al. Obesity as a potential predictor of disease severity in young Covid‐19 patients: A

retrospective study. Obesity 28, 1815–1825 (2020).

2.   Han, H. et al. Descriptive, retrospective study of the clinical characteristics of asymptomatic COVID-19 patients. mSphere 5, (2020).

3.   Carapito, R. et al. Identification of driver genes for severe forms of covid-19 in a deeply phenotyped young patient cohort. (2021).

4.   Zhang, L., Li, R., Song, G., Scholes, G. D. & She, Z.-S. Impairment of T cells’ antiviral and anti- inflammation immunities may be critical to death from covid-19. Royal Society Open Science 8, (2021).

5.   Pourgholaminejad, A., Pahlavanneshan, S. & Basiri, M. Covid‐19 immunopathology with emphasis on th17 response and cell‐based immunomodulation therapy: Potential targets and challenges. Scandinavian Journal of Immunology 95,(2022).

6.   Chen, Z., Liu, K. & Jiao, W. A genetic algorithm for causal discovery based on structural causal model. Artificial Intelligence 39–54 (2022).

7.   Chou, C.-W., Huang, Y.-K., Kuo, T.-T., Liu, J.-P. & Sher, Y.-P. An overview of adam9: Structure, activation, and regulation in human diseases. International Journal of Molecular Sciences 21, 7790 (2020).

8.     Shastry, B. S. SNPs in disease gene mapping, medicinal drug development and evolution. Journal of

Human Genetics 52,871–880 (2007).

9.   Scribner, J. A. et al. Preclinical evaluation of IMGC936, a next-generation Maytansinoid-based antibody– drug conjugate targeting ADAM9-expressing tumors. Molecular Cancer Therapeutics 21, 1047–1059          (2022).