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Nupur Biswas, PhD

Sr. Scientist - Omics

Lifespan or healthspan which is your goal?

Published on: January 31, 2024

Lifespan refers to the time a person lives. On the other hand, healthspan refers to the time during which a person lives a healthy life without any chronic disease and age related disorders. However, defining healthy life is a challenge as good health is a subjective parameter. Different people have different feelings of goodness. Also, good health may be reversible. It is also questionable onset of how many diseases and what type of diseases will mark the end of good health. Hence, measuring healthspan is often a challenge [1]. Despite that, Garmany et al. measured a gap of 9.2 years between lifespan and healthspan [2]. The major obstacle to overcome this gap is the progressive decline in physiological functions. It demands the optimization of physiological functions throughout life [3]. Considering healthspan as the age of first onset of a list of common diseases which include cancer, cardiac disorders, diabetes and others. Based on the UK Biobank data, Zenin et al. found that disease incidence increases exponentially with age. They also observed a strong correlation of healthspan with diabetes and cardiac diseases and identified 16 genetic loci associated with the end of healthspan caused by those diseases [4]. Among the interventions for expanding healthspan, diet and exercise comes first [5]. However, pharmacological interventions are also being explored. Our knowledge of biological pathways save identified several pharmacological targets which include the sirtuin family of proteins and the mammalian target of rapamycin (mTOR) complexes [6]. In conclusion, there exists a gap of almost a decade between lifespan and healthspan which need to be reduced. Apart from genetic factors, lifestyle and diet need to be explored inorder to reduce the gap. References: 1. Kaeberlein M. How healthy is the healthspan concept? Geroscience. 2018 Aug;40(4):361-364. doi: 10.1007/s11357-018-0036-9. Epub 2018 Aug 6. PMID: 30084059; PMCID: PMC6136295. 2. Zenin A, Tsepilov Y, Sharapov S, Getmantsev E, Menshikov LI, Fedichev PO, Aulchenko Y. Identification of 12 genetic loci associated with human healthspan. Commun Biol. 2019 Jan 30;2:41. doi: 10.1038/s42003-019-0290-0. PMID: 30729179; PMCID: PMC6353874 3. Physiological geroscience: targeting function to increase healthspan and achieve optimal longevity, Douglas R. Seals, Jamie N. Justice, Thomas J. LaRocca, The Journal of Physiology. 4. Zenin A, Tsepilov Y, Sharapov S, Getmantsev E, Menshikov LI, Fedichev PO, Aulchenko Y. Identification of 12 genetic loci associated with human healthspan. Commun Biol. 2019 Jan 30;2:41. doi: 10.1038/s42003-019-0290-0. PMID: 30729179; PMCID: PMC6353874. 5. Wickramasinghe K, Mathers JC, Wopereis S, Marsman DS, Griffiths JC. From lifespan to healthspan: the role of nutrition in healthy ageing. Journal of Nutritional Science. 2020;9:e33. doi:10.1017/jns.2020.26 6. Increasing Healthspan: Prosper and Live Long. EBioMedicine. 2015 Nov 7;2(11):1559. doi: 10.1016/j.ebiom.2015.11.015. PMID: 26870762; PMCID: PMC4740330.

Longevity: How far can we stretch it?

Published on: January 03, 2024

The basic question of longevity is how long can a human live. Increasing number of centenarians indicate increase of average lifespan. Till date, based on the available and reliable documents, the longest living person was of age 122 years. The question is what can be the maximum lifespan of human. Is maximum lifespan fixed or flexible? For model organisms, maximum lifespan is flexible. It is affected by genetic and pharmacological interventions. However, for humans experiment is challenging. Analysis of demographic data reveals that the maximum reported age at death (MRAD) increased till 1994. After that it started to decrease. According to de Beer et al. maximum human lifespan is fixed and subject to natural constraints [1, 2,3]. The reason is genomic but not clear yet. Stretching this maximum lifespan is possible either by scientific breakthrough in aging research or by upgrading medical facilities to the centenarians who can beat 122 [4]. Another associated concern is although lifespan is increasing, increase in healthspan is lagging behind. Measuring healthspan is itself a challenge. It can be increased by exploring biological pathways linked to longevity [5]. References: 1. De Beer J, Bardoutsos A, Janssen F. Maximum human lifespan may increase to 125 years. Nature. 2017 Jun 28; 546(7660):E16-E17. DOI: 10.1038/nature22792. PMID: 28658213. 2. Vaupel JW, Villavicencio F, Bergeron-Boucher MP. Demographic perspectives on the rise of longevity. Proc Natl Acad Sci U S A. 2021 Mar 2;118(9):e2019536118. DOI: 10.1073/pnas.2019536118. PMID: 33571137. 3. Gavrilova NS, Gavrilov LA. Are We Approaching a Biological Limit to Human Longevity? J Gerontol A Biol Sci Med Sci. 2020 May 22;75 (6):1061-1067. DOI: 10.1093/gerona/glz164. PMID: 31276575. 4. Blagosklonny MV. No limit to maximal lifespan in humans: how to beat a 122-year-old record. Oncoscience. 2021 Dec 1;8:110-119. DOI: 10.18632/oncoscience.547. PMID: 34869788. 5. Smulders L, Deelen J. Genetics of human longevity: From variants to genes to pathways. J Intern Med. 2023 Nov 8. DOI: 10.1111/joim.13740. Epub ahead of print. PMID: 37941149.

Longevity: determining factors.

Published on: November 29, 2023

Achievement of long life is desired throughout the history of mankind. Aging is characterized by progressive loss of physiological integrity. Lopez-Otin et al. defined nine hallmarks of aging [1]. The hallmarks satisfy the following criteria of age-associated manifestations, which can accelerate aging by experimentally accentuating them, and provide an opportunity to control aging by therapeutic interventions. The nine hallmarks are genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and intercellular communication [1]. Genomic instability is the resultant of accumulation of DNA damage over the lifetime. Telomere attrition refers to shortening of telomere every time the cell divides [2]. Epigenetic alterations include induced epigenetic changes which contribute to aging. Loss of proteostasis means failure to repair and removal of unfolded proteins. Deregulated nutrient-sensing is a consequence of failure of signaling pathways and insulin sensitive proteins. Mitochondrial dysfunction refers to perturbed mitochondrial functions due to several reasons including DNA mutation, reduced mitochondriogenesis, reduced mitophagy and others. Cellular senescence is also affected with aging. Stem cell exhaustion refers to multiple diseases. Altered intercellular communication also result in aging related conditions like inflammaging [1]. However, in the year 2023, three new hallmarks are identified which include disabled macroautophagy, chronic inflammation, and dysbiosis [3]. With the identified hallmarks, researchers are now looking for biomarkers of aging which can control the rate of aging [45]. References: 1. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell, 153(6), 1194-217 (2013). 2. Liu, J., Wang, L., Wang, Z., and Liu, J-P. Roles of telomere biology in cell senescence, replicative and chronological ageing. Cells, 8 (1), 54 (2019). 3. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell, 186(2), 243-278 (2023). 4. Keshavarz, M., Xie, K., Schaaf, K. et al. Targeting the “hallmarks of aging” to slow aging and treat age-related disease: fact or fiction?. Mol Psychiatry 28, 242–255 (2023). 5. Guerville, F., De Souto Barreto, P., Ader, I. et al. Revisiting the Hallmarks of Aging to Identify Markers of Biological Age. J Prev Alzheimers Dis 7, 56–64 (2020).

Gene transfer extends lifespan of mice.

Published on: November 08, 2023

Original author: Zhang, Z., et al., 2023 (doi: 10.1038/s41586-023-06463-0)

Longer lifespan accompanied by healthy lifespan is always desired. Naked mole rats are the longest living rodents. They evolved efficient anti-aging and anti-cancer defense. Naked mole-rat tissues are highly enriched for high molecular weight hyaluronic acid (HMW-HA). HMW-HA improves tissue homeostasis, and shows anti-inflammatory and antioxidant properties. HMM-HA is responsible for cancer resistance in the naked mole-rat. Current study explores whether anti-cancer and potential anti-aging effects of HMW-HA can be recapitulated in other species. Methodology Researchers generated a mouse model overexpressing naked mole-rat hyaluronan synthase 2 (HAS2) gene, henceforth called nmrHAS2 mouse. HA was isolated from tissues and purified. Pulse field gel electrophoresis was used for checking the molecular weight. Methylation data generated using Illumina. RNAseq was done by Illumina HiSeq 4000. Results Compared to the control mice overexpression of nmrHAS2 mRNA were detected in multiple tissues of nmrHAS2. Higher HA level was found in muscle, kidney and intestines and lower HA levels were observed at the breakdown sites – liver and spleen. The nmrHAS2 mice showed lower cancer incidence. 57% nmrHAS2 mice died of cancer whereas 70% of control mice died of cancer. Difference was further amplified for the older aged mice. The nmrHAS2 mice have increased lifespan. They showed an increase of 4.4% in median lifespan and an increase of 12.2% in the maximum lifespan. No change on body weight was observed. Epigenetic age was compared to chronological age to quantify age acceleration. Methylation age of CreER mice was close to their chronological age but nmrHAS2 mice showed decrease in age acceleration. nmrHAS2 mice exhibit a younger biological age. 145 CpG sites that gain methylation during aging showed lower methylation in nmrHAS2. 20 CpG sites that lose methylation during aging showed higher methylation in nmrHAS2 mice. Researchers used Frailty Index (FI) to quantify the quality of health considering different parameters like body weight, grip strength, and mobility. They observed FI score of old nmrHAS2 mice was substantially lower. It implies nmrHAS2 mice have an improved healthspan. Also they maintained a better grip strength and connectivity density in bones. RNAseq analysis was performed on different tissues. nmrHAS2 mice showed fewer transcriptome changes during aging. Transcriptome of nmrHAS2 mice is less perturbed during aging. nmrHAS2 mice liver showed reduced expression of inflammatory-related genes and higher expression of genes involved in normal liver function such as nutrient metabolism. Macrophage cells overexpressing mHAS2 or nmrHAS2 show lower levels of pro-inflammatory cytokines and higher levels of anti-inflammatory cytokines. The anti-inflammatory effect arose from the production of high molecular weight hyaluronic acid. Conclusion In the tissues of the naked mole-rat, HMW-HA is abundant and contributes to cancer resistance and possibly longevity. This evolutionary adaptation, unique to the naked mole-rat, can be exported to other species. It hypothesize that if we were able to simultaneously attenuate HA degradation in nmrHAS2 mice we would achieve a greater lifespan extension. Impact of the research The evolutionary adaptations found in long-lived species can be exported and adapted to the benefit human health.

Role of air pollutants in lung cancer.

Published on: June 21, 2023

Original author: Hill, W., Lim, E.L., et al., 2023 (doi: 10.1038/s41586-023-05874-3)

Lung cancer in never-smokers (LCINS) is the eighth most common cause of cancer death in the UK. Such patients bear distinct clinical and molecular characteristics compared to lung cancer in smokers which include EGFR mutations. The current study explores the relationship between air pollutants and EGFR-driven mutations in the context of lung adenocarcinoma (LUAD). Particulate matter (PM) of size  2.5 m (PM2.5) can travel deep into the lungs. The current study proposes that the air pollutants may promote inflammatory changes in the lung tissue microenvironment that permit pre-existing mutated clones to expand, consistent with the two-stage carcinogenesis model of initiation and promotion. Methodology: Researchers used data from three countries to explore different ranges of PM2.5 air pollution and ethnicities. They analyzed 3 years and 20 years of PM2.5 cumulative exposure. They used data from PEACE, TRACERx, UK Biobank and NDRS datasets for the England population. They also used South Korea dataset and Chang Gung Research Database of Taiwan. They performed mutational profiles by ddPCR, MiSeq, Duplex-Seq, whole genome sequencing, RNA sequencing. They also used mice models. Results: Researchers found that the frequency of EGFR-driven lung cancer cases was significantly higher after 3 years of high air pollutant exposure compared with low exposure. However, this association was not so strong for 20 years of exposure. It means 3 years of high PM2.5 exposure is enough for EGFR-driven lung cancer to arise. An analysis including all participants, irrespective of EGFR status, showed that PM2.5 levels were associated with lung cancer incidence. Smoking and high PM2.5 status may have a combined effect on lung cancer. Researchers used genetically engineered mouse models of LUAD and induced expression of oncogenic human EGFR (L858R) in mouse lungs. Mice were exposed to PM or PBS (control) for 3 weeks and tumor burden was assessed after 10 weeks. An increase in the number of pre-invasive neoplasias was observed in PM-exposed mice. Exposure to PM before induction of EGFR (L858R) also increased the number of pre-invasive neoplasias. The spatial analysis of clonal dynamics showed that the fraction of EGFR (L858R) cells grew into clusters and their numbers increased within clusters after the PM-exposed period. PM increases number of EGFR mutant cells and also the proliferation rate of mutant cells within early lesions. Whole genome sequencing revealed no enhancement of mutagenesis on exposure to PM. After exposure to PM, the proportion of interstitial macrophages and PD-L1 expression increased, irrespective of EGFR status. It leads to the hypothesis that transient PM exposure is associated with enhanced and sustained lung macrophage infiltration beyond the period of PM exposure. RNA-seq data analysis showed that EGFR mutation is responsible for 38% variance of gene expression difference between control and mutated mice exposed to PM. In mutated mice, PM exposure led to an upregulation of genes known to regulate macrophage recruitment, including those that encode interleukin-1β (IL-1β). AT2 cells are known as the probable cell of origin of lung adenocarcinoma. EGFR (L858R) mutated AT2 cells are transcriptionally reprogrammed to this progenitor cell state following PM exposure. Researchers isolated AT2 cells from mice, induced EGFR (L858R) expression, and exposed them in vivo to either PM or PBS. They observed PM exposed macrophages enhanced organoid formation efficiency. It indicates that PM-induced inflammation arises from macrophages. On the other hand, treatment of EGFR mutant AT2 cells in IL-1 resulted with larger organoids. Also, treatment of PM exposed mutated mice with anti-IL-1 antibody attenuated EGFR driven LUAD formation. It establishes PM exposed macrophages can induce a progenitor-like state in EGFR mutant AT2 cells. However, it is also observed that EGFR mutations can also be present in normal lung tissue of patients. Also, 16% (3 out of 19) of healthy people bear EGFR mutations in their lung tissues. Anthracosis is known to act as a surrogate marker of exposure to air pollution. An association between anthracosis and increased variant allele frequencies of EGFR mutations was also observed. Apart from EGFR mutation status, the current study also explored KRAS mutation status. It reported that KRAS mutant clones may be more highly selected than EGFR mutant clones in healthy lungs of ever-smokers. Also, there was a significant correlation between age and mutation count. Discussion: It provides a balanced study of cohorts considering sex, geographically and ethnically distinct populations. It reveals PM exposure contributes to the geographical disparities of EGFR driven lung cancer. Conclusion: Researchers have performed a comprehensive analysis and proposed that PM can trigger the expansion of preexisting mutant lung cells through an inflammatory axis which can be treated. The temporal analysis shows that three years of PM2.5 exposure is sufficient to develop EGFR driven LUAD. Impact of the research: It reveals PM2.5 induces an altered progenitor state in EGFR mutant cells through the macrophage release of IL-1β, which promotes lung cancer. It provides a public health mandate to control air pollution specifically to the urban area.

Circular RNAs in Diagnosis of Astrocytoma.

Published on: March 01, 2023

Original author: Peiyao Li, Zihao Xu, et. al. (2022) (DOI: 10.1093/clinchem/hvab254)

Astrocytoma is a type of brain tumor associated with astrocytes. Grade IV astrocytoma is commonly known as glioblastoma. Molecular-based diagnosis is challenging as it requires brain surgery for obtaining tissue samples/specimens from the tumor site. On the other hand, circular RNA (circRNAs) are abundant in the brain tissues, and their expression level changes with neural development. They are carried by exosomes which can cross the blood-brain barrier. CircRNAs are also found in biofluids, which are easily accessible, and hence a potential candidate for liquid biopsy. The current research article looks for the possible circRNAs as biomarkers of high-grade astrocytoma (HGA). Methodology Cells were collected from grade IV glioma patients. CircRNAs in tumor cells and tumor cell-derived exosomes were compared in three pairs. CircRNAs from the tissues and serum of high-grade glioma (HGA) patients were compared with tissues and serum of healthy individuals.​ Exosomes were isolated following protocols. CircRNAs were isolated, amplified, and sequenced. The reads were aligned with the reference genome and compared with the circbase database to identify the known and predicted circRNAs. Results This study shows cells are more populated with circRNAs compared to cell-derived exosomes. Twenty-six highly expressed circRNAs coexisted in all three tumor cells and 12 circRNAs were commonly enriched in three tumor cell-derived exosomes. 11 higher level HGA cell-derived exosome circRNAs were included in the HGA cell circRNAs. There are five higher-level cell circRNAs across all samples. Among them, three circRNAs were highly expressed in both HGA cells and exosomes. Randomly chosen circRNAs show low expression levels in tumor tissues compared to the tissues from healthy individuals and eight circRNAs show significant differences in the expression levels. Among them, four are associated with overall survival, and hence can be used as biomarkers.​ Serum exosome circRNAs from HGA patients and healthy individuals were compared. Thirteen (nine up and four down) circRNAs were significantly different and three of them had significantly different expression levels. Any two of these can distinguish normal and HGA patients and can be used as biomarkers for liquid biopsy. Discussion Down-regulation of most of the differentially expressed circRNAs was observed in HGA tissues and HGA serum exosomes compared to their normal counterpart. Most of the reduced circRNAs are involved in glioma formation and development. This study identifies three serum exosome circRNAs that could form a panel of noninvasive liquid biomarkers for the precise screening of HGA.​ There are several challenges. The current study aims to develop circRNAs research technology. It is difficult to accurately extract the sequence and length of intronic circRNA through software. Conclusion This study characterizes HGA cell circRNAs and exosome circRNAs. A serum exosome panel of 3 circRNAs was identified as biomarkers. Tissue circRNAs can serve as tissue biopsy targets for monitoring HGA prognosis. Impact of the research This study identifies novel issues in the fields of HGA, exosome, and circRNA research, providing new directions for future studies. It enhances the potential of liquid biopsy which may help diagnose cancers at sites where accessing tissue is difficult.

Identifying candidate Parkinson's disease genes by integrating genomic data.

Published on: October 19, 2022

Original author: Demis A. Kia et al. JAMA Neurol. (2021) (DOI: 10.1001/jamaneurol.2020.5257)

Parkinson’s disease (PD) is a neurological disorder observed mostly in elderly individuals. The symptoms include tremors, muscle stiffness, slow motion, and loss of balance. It is caused when neurons of the basal ganglia region of the brain fail to produce the neurotransmitter dopamine which regulates movement. The genomic analysis has identified more than 40 loci associated with PD risk. The causal genes corresponding to each locus and their roles in PD are not clear. Methodology To identify the candidate genes researchers have used Genome-Wide Association Studies (GWASs) along with Quantitative Trait Loci (QTL). It provides associations of an individual's genotype with gene expression (eQTLs), splicing, or methylation. The data was obtained from several sources like International Parkinson’s Disease Genomics Consortium, the UK Brain Expression Consortium Braineac dataset, and the Genotype-Tissue Expression (GTEx) Consortium dataset. The GWAS data contained 8055803 genotyped and imputed variants in 26035 PD patients and 403190 controls. The genetic colocalization analysis was done using the Coloc R package which probes whether two phenotypes share common genetic causal variant(s) in each region. For integrating GWAS and QTL data, the Transcriptome-wide association study (TWAS) was done. It probes associations between gene expression and complex diseases or traits. Results Researchers found out of 515 genes within 1 Mb of a significant PD risk variant, 470 were expressed at a detectable level across different datasets. They proceeded with these genes. Coloc analysis showed out of 470 genes, 9 in Braineac and 27 in GTEx have strong evidence for colocalization in at least one brain region. TWAS analysis showed 61 genes were found to be significantly associated with PD risk. Five genes (WDR6, CD38, GPNMB, RAB29, and TMEM163) were replicated among Coloc and TWAS analysis.​ Braineac dataset was used for exon-level eQTL data. Colocalization analysis found 25 genes having strong evidence for colocalization in at least one exon in at least one region of the brain. For 15 genes the evidence suggested that the association is due to an exon-level splicing event. A combination of colocalization and TWAS analysis identifies six genes (ZRANB, PCGF3, NEK1, NUPL2, GALC, and CTSB) having a putative splicing effect.​ It provided 11 candidate genes. Analysis of methylation data showed 3 genes (GPNMB, TMEM163, and CTSB) overlapped with expression and splicing analysis. Cell-type–specific expression of candidate genes was studied. Although no single cell type dominated, glial cell types dominate over neurons. WGCNA analysis provided NUPL2, TMEM163, and ZRANB3 were the most relevant genes.​ Researchers further constructed a network of 11 candidate genes. Candidate genes are related to proteins that are also relevant for Mendelian forms of PD and Parkinsonism. It implies disease-specific interaction between candidate genes with known risk genes. Pathway enrichment analysis was done with a core composed of candidate genes and Mendelian genes. It showed enrichment of proteins involved in or regulating the ERBB receptor tyrosine-protein kinase signaling pathways. Discussion Researchers have performed a comprehensive analysis by integrating QTL and GWAS data and found 11 candidate genes. It is the key strength of the study. The biological roles of those genes were also explored. CD38 regulates neuroinflammation and it showed enrichment in the astrocyte region. It shows the existence of a group of genes and proteins – associated with the ERBB signaling pathways. It increases the risk of developing sporadic and familial PD.​ The current study has some limitations also. For example, it considers only cis-QTL and excludes trans-QTL which corresponds to distant genes of different chromosomes. Impact of the research It applied a new methodology in the context of PD. It identified hitherto unknown PD risk genes and validated the biological functions of the genes. Conclusion In conclusion, it combines GWAS with QTL data to discover 11 candidate genes whose changes in expression, splicing, or methylation are associated with the risk of PD. Protein-protein interaction network analysis highlights the functional pathways and cell types where these genes have important roles.

Identification of new Genetic Clusters in Glioblastoma Multiforme: EGFR status and ADD3 losses influence prognosis.

Published on: June 24, 2022

Original author: L. Navarro et al. Cell, vol 9, 2429 (2020) (DOI: 10.3390/cells9112429)

Glioblastoma (GB) multiforme, IDH wild-type (GB-IDHwt) is the most frequent brain tumor with poor survival rate. Glioblastoma with mutant IDH variant shows good prognosis. The highly genetic heterogeneity is responsible for absence of effective treatment of GB. Among the genetic features, amplification of both wild type and mutant EGFR is a signature event of different types of cancers including glioblastomas. EGFR amplification is often associated with a deletion of exon 2-7 of the EGFR gene. This mutant variant is called EGFR variant III. The current manuscript aims to characterize frequent alterations in EGFR via its amplification or the presence of EGFR variant III with associated somatic copy number alterations (SCNA) in primary GB samples. Also, the importance of ADD3 and EGFR variant III as genetic biomarkers is being explored. Samples Samples were obtained from 128 surgically treated GB-IDHwt patients prior to any chemotherapy or radiotherapy. GB samples were also accessed from TCGA for further validation of the correlation between SCNA and EGFR amplification. Results & discussion 1. The multiplex ligation-dependent probe amplification (MLPA) method is used to characterize multiple genes at the same time. It is a variation of the multiplex polymerase chain reaction that permits the amplification of multiple targets with only a single primer pair. EGFR amplification was observed in almost 70% of the samples. However, no significant association between EGFR amplification and survival were observed. Also, there was no association between with age and sex. 2. On the other hand EGFRvIII shows higher presence in women but no association with age, tumor location, and size. 3. High genetic heterogeneity was observed in the samples. EGFR appears as the most altered gene, followed by CDKN2A, TIMP3, MEN1, CDKN2B, MVP, PTEN, MTAP, and ADD3. Among them, only ADD3 showed an association with the overall survival period. 4. Depending on the EGFR amplification status, genes MSH6, CDKN2A, MTAP, and JAG1 showed different gain/loss. Also, EGFR variant III samples varied widely from wild type EGFR samples in SCNAs. 5. 91 samples were considered for the classification algorithm. Among them, 18 were not classified. Remaining 73 samples were classified among three clusters depending on the frequency of alterations. 6. Different clusters point to different pathways. Cluster2 showed the highest frequency of SCNAs in CDKN2A, MEN1, EGFR, TIMP3, PTEN, MTAP, MVP, SMARCA4, ADD3, MSH6, JAG1, SPG11 and DOCK8. Cluster2 also showed an abundance of EGFR variant III samples with ADD3 losses. It shows more association with ‘regulation of cell surface adhesion’ and ‘cell-matrix adhesion’ processes. On the other hand, cluster3 shows an association with ‘regulation of cell cycle phase transition’. Cluster1 is the less altered cluster, not affected by EGFR. Impact of the research The impact of this manuscript is two-fold. It sets out MLPA as an advantageous methodology for probing multiple genes avoiding the cost of the NGS method. The major impact of this study is, in spite of highly genetic heterogeneity, the cluster analysis has allowed a categorization based on the frequency of alterations. This analysis was also validated on the TCGA data. It highlights the importance of EGFR variant III along with ADD3 SCNAs as prognostic markers in the case of GB. The current study also showed the presence of a group of GB-IDHwt samples where EGFR alterations are not noticed. So the association of EGFR and GB-IDHwt is not linear. It strongly validates the necessity of genetically diagnosed personalized treatments in GB patients.

​​Somatic mutations in Alzheimer’s disease neurons.​

Published on: September 14, 2022

Original author: Michael B. Miller et al. Nature, 604, 714 (2022) (DOI: 10.1038/s41586-022-04640-1)

Alzheimer’s disease (AD) is a common age-associated neurodegenerative disorder. It is associated with the deposition of amyloid-beta oligomers and tau proteins. However, the underneath cellular dysfunction is still not well understood. In the current paper, researchers explore genomic mutations in AD-associated neurons at the single-cell level. Through single cell whole genome sequencing (scWGS) the researchers have compared somatic mutations in AD patients and neurotypical control individuals. Methodology scWGS was performed on pyramidal neurons collected from two regions of the brain, the prefrontal cortex (PFC) and the CA1 subfield of the hippocampus region. Cells were collected during post-mortem from 8 AD patients and 18 neurotypical control individuals of different age groups. For scWGS, whole genome amplification was done using the multiple displacement amplification (MDA) method. Mutational signatures, defined in earlier studies, were analyzed to identify underneath specific processes causing somatic mutations in neurons collected from AD patients. Results & discussion It is observed that for both regions of the brain, sSNVs accumulated with age. However, the amount of accumulation was more in the case of AD patients. AD neurons showed an increase in 'signature C' compared to controls but ‘signature A’ increased in all the samples. Moreover, the ‘signature C’ burden showed higher variation between neurons compared to that of ‘signature A’. It means ‘signature A’ represents age-related mutations and ‘signature C’ is from irregular ‘calamitous’ events. DNA oxidation also may be responsible for excess sSNVs because higher oxidized nucleotides were observed in AD neurons. Interestingly, no somatic mutation was observed in classic AD risk genes (APP, PSEN1, PSEN2, and APOE). The ‘signature A’ mutations were correlated with gene expression values but not ‘signature C’ mutations, as ‘signature A’ mutations occurred during transcription. The 'signature C' inversely correlated with expression values. There are multiple consequences of somatic mutations, including neuronal dysfunction, direct impairment of transcription, protein stability alterations, and neoantigen creation. As somatic mutations accumulate in the genome, the likelihood of two deleterious exonic alterations in the same gene, producing a knockout cell, increases exponentially. The mathematical model suggests that dysfunctional neurons would be markedly more abundant in AD resulting in compromised neuronal functions. To rule out any artifact of the genome amplification method, apart from MDA, researchers followed primary template-directed amplification (PTA) for a subset of samples. PTA-based scWGS also confirmed enhanced sSNVs in AD neurons and mutational signatures were also similar to the MDA method. It also suggested that PTA-detected sSNVs represented double-stranded somatic mutations. In the end, the researchers tried to correlate the role of sSNVs in the pathogenesis of AD. Amyloid beta peptide (Aβ) initiates a cascade of events. It induces the conversion of tau proteins to neurofibrillary tangles and the accumulation of reactive oxidative species (ROS) molecules. Somatic mutations develop due to the damage caused by ROS and/or other mutagens. NER pathway conducts the repair mechanism. However, the accelerated accumulation of oxidized nucleotides overwhelms the repair pathway. Impact of the research This article reports accumulation pattern of sSNVs in AD neurons differs from normal aging neurons. No somatic mutation was observed in known AD-associated genes including APP, the precursor of Aβ. Amyloid beta aggregation causes lipid peroxidation and oxidative stress. Aβ oligomers outside neurons spur tau neurofibrillary tangles and reactive oxygen species inside the neuron. This continuous process damages DNA and overcomes the repair mechanisms. As a result, single-nucleotide variations occur and persist as somatic mutations, leading to neuron death. Conclusion This research concludes that AD patients accumulate more sSNVs compared to their normal counterparts. Previously known AD risk genes do not show any mutation and copy number changes. Aβ aggregation plays an important role in disease pathogenesis. Aβ induces the conversion of tau proteins to neurofibrillary tangles and the accumulation of (ROS) molecules. Somatic mutations develop due to the damage caused by ROS and/or other mutagens which further lead to neuron death.

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