Forward-reverse mutation cycles in cancer cell lines under chemical treatments.

Spontaneous forward-reverse mutations were reported by us earlier in clinical samples from various types of cancers and in HeLa cells under normal culture conditions. To investigate the effects of chemical stimulations on such mutation cycles, the present study examined single nucleotide variations (SNVs) and copy number variations (CNVs) in HeLa and A549 cells exposed to wogonin-containing or acidic medium. In wogonin, both cell lines showed a mutation cycle during days 16-18. In acidic medium, both cell lines displayed multiple mutation cycles of different magnitudes. Genomic feature colocalization analysis suggests that CNVs tend to occur in expanded and unstable regions, and near promoters, histones, and non-coding transcription sites. Moreover, phenotypic variations in cell morphology occurred during the forward-reverse mutation cycles under both types of chemical treatments. In conclusion, chemical stresses imposed by wogonin or acidity promoted cyclic forward-reverse mutations in both HeLa and A549 cells to different extents.

Developmental dyslexia susceptibility genes DNAAF4, DCDC2, and NRSN1 are associated with brain function in fluently reading adolescents and young adults.

Reading skills and developmental dyslexia, characterized by difficulties in developing reading skills, have been associated with brain anomalies within the language network. Genetic factors contribute to developmental dyslexia risk, but the mechanisms by which these genes influence reading skills remain unclear. In this preregistered study (https://osf.io/7sehx), we explored if developmental dyslexia susceptibility genes DNAAF4, DCDC2, NRSN1, and KIAA0319 are associated with brain function in fluently reading adolescents and young adults. Functional MRI and task performance data were collected during tasks involving written and spoken sentence processing, and DNA sequence variants of developmental dyslexia susceptibility genes previously associated with brain structure anomalies were genotyped. The results revealed that variation in DNAAF4, DCDC2, and NRSN1 is associated with brain activity in key language regions: the left inferior frontal gyrus, middle temporal gyrus, and intraparietal sulcus. Furthermore, NRSN1 was associated with task performance, but KIAA0319 did not yield any significant associations. Our findings suggest that individuals with a genetic predisposition to developmental dyslexia may partly employ compensatory neural and behavioral mechanisms to maintain typical task performance. Our study highlights the relevance of these developmental dyslexia susceptibility genes in language-related brain function, even in individuals without developmental dyslexia, providing valuable insights into the genetic factors influencing language processing.

Effects of genetic variation on the structure of RNA and protein.

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data.

The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC$^2$, Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC$^2$ takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC$^2$ can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC$^2$ shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC$^2$ also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC$^2$ software is available at https://github.com/ucasdp/BiTSC2.

Co-evolution of vaginal microbiome and cervical cancer.

BACKGROUND: Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models. METHODS: This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs). RESULTS: In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set. CONCLUSIONS: The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma.

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

Genomic analyses of a new baculovirus isolated from the wheat armyworm, Mythimna sequax (Franclemont) (Lepidoptera: Noctuidae).

We report the genomic analysis of a novel alphabaculovirus, Mythimna sequax nucleopolyhedrovirus isolate CNPSo-98 (MyseNPV-CNPSo-98), obtained from cadavers of the winter crop pest, Mythimna sequax Franclemont (Lepidoptera: Noctuidae). The insects were collected from rice fields in Southern Brazil in the 1980's and belongs to the 'EMBRAPA-Soja' Virus Collection. High-throughput sequencing reads of DNA from MyseNPV occlusion bodies and assembly of the data yielded an AT-rich circular genome contig of 148,403 bp in length with 163 annotated opening reading frames (ORFs) and four homologous regions (hrs). Phylogenetic inference based on baculovirus core protein sequence alignments indicated that MyseNPV-CNPSo-98 is a member of Alphabaculovirus genus that clustered with other group II noctuid-infecting baculoviruses, including viruses isolated from Helicoverpa armigera and Mamestra spp. The genomes of the clade share strict collinearity and high pairwise nucleotide identity, with a common set of 149 genes, evolving under negative selection, except a bro gene. Branch lengths and Kimura-2-parameter pairwise nucleotide distances indicated that MyseNPV-CNPSo-98 represents a distinct lineage that may not be classified in any of the currently listed species in the genus.

Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer's disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.

Chromosome instability region analysis and identification of the driver genes of the epithelial ovarian cancer cell lines A2780 and SKOV3.

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole-exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single-nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

Traceability of SARS-CoV-2 transmission through quasispecies analysis.

During COVID-19 pandemic, consensus genomic sequences were used for rapidly monitor the spread of the virus worldwide. However, less attention was paid to intrahost genetic diversity. In fact, in the infected host, SARS-CoV-2 consists in an ensemble of replicating and closely related viral variants so-called quasispecies. Here we show that intrahost single nucleotide variants (iSNVs) represent a target for contact tracing analysis. Our data indicate that in the acute phase of infection, in highly likely transmission links, the number of viral particles transmitted from one host to another (bottleneck size) is large enough to propagate iSNVs among individuals. Furthermore, we demonstrate that, during SARS-CoV-2 outbreaks when the consensus sequences are identical, it is possible to reconstruct the transmission chains by genomic investigations of iSNVs. Specifically, we found that it is possible to identify transmission chains by limiting the analysis of iSNVs to only three well-conserved genes, namely nsp2, ORF3, and ORF7.

Isothermal exponential amplification reactions triggered by circular templates (cEXPAR) targeting miRNA.

BACKGROUND: Isothermal exponential amplification reaction (EXPAR) is an emerging amplification technique that is most frequently used to amplify microRNA (miRNA). However, EXPAR also exhibits non-specific background amplification in the absence of the targeted sequence, which limits the attainable assay sensitivity of EXPAR. METHODS AND RESULTS: A novel modified isothermal EXPAR based on circular amplification templates (cEXPAR) was developed in this study. The circular template consists of two same linear fragments that complement the target sequence, and these two linear fragments are separated by two nicking agent recognition sequences (NARS). Compared with the linear structure template, this circular template allows DNA or RNA fragments to be randomly paired with two repeated sequences and can be successfully amplified. This reaction system developed in this study could rapidly synthesize short oligonucleotide fragments (12-22 bp) through simultaneous nicking and displacement reactions. Highly sensitive chain reactions can be specifically triggered by as low as a single copy of target molecule, and non-specific amplification can be effectively eliminated in this optimized system. Moreover, the proposed approach applied to miRNA test can discriminate single-nucleotide variations between miRNAs. CONCLUSION: The newly developed cEXPAR assay provides a useful alternative tool for rapid, sensitive, and highly specific detection of miRNAs.

Electrochemical biosensors for analysis of DNA point mutations in cancer research.

Cancer is a genetic disease induced by mutations in DNA, in particular point mutations in important driver genes that lead to protein malfunctioning and ultimately to tumorigenesis. Screening for the most common DNA point mutations, especially in such genes as TP53, BRCA1 and BRCA2, EGFR, KRAS, or BRAF, is crucial to determine predisposition risk for cancer or to predict response to therapy. In this review, we briefly depict how these genes are involved in cancer, followed by a description of the most common techniques routinely applied for their analysis, including high-throughput next-generation sequencing technology and less expensive low-throughput options, such as real-time PCR, restriction fragment length polymorphism, or high resolution melting analysis. We then introduce benefits of electrochemical biosensors as interesting alternatives to the standard methods in terms of cost, speed, and simplicity. We describe most common strategies involved in electrochemical biosensing of point mutations, relying mostly on PCR or isothermal amplification techniques, and critically discuss major challenges and obstacles that, until now, prevented their more widespread application in clinical settings.

Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID 19, continues to evolve since its first emergence in December 2019. Using the complete sequences of 1,932 SARS-CoV-2 genomes, various clustering analyses consistently identified six types of the strains. Independent of the dendrogram construction, 13 signature variations in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5' untranslated region (UTR) were identified and provided a direct interpretation for the six types (types I to VI). The six types of the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228 and 38,248 SARS-CoV-2 genomes which became available later. To date, type VI, characterized by the four signature SNVs C241T (5'UTR), C3037T (nsp3 F924F), C14408T (nsp12 P4715L), and A23403G (Spike D614G), with strong allelic associations, has become the dominant type. Since C241T is in the 5' UTR with uncertain significance and the characteristics can be captured by the other three strongly associated SNVs, we focus on the other three. The increasing frequency of the type VI haplotype 3037T-14408T-23403G in the majority of the submitted samples in various countries suggests a possible fitness gain conferred by the type VI signature SNVs. The fact that strains missing one or two of these signature SNVs fail to persist implies possible interactions among these SNVs. Later SNVs such as G28881A, G28882A, and G28883C have emerged with strong allelic associations, forming new subtypes. This study suggests that SNVs may become an important consideration in SARS-CoV-2 classification and surveillance.

Whole genome sequencing reveals candidate genes involving in PAS resistance in M. Tuberculosis isolated from patients in Thailand.

Para-amino salicylic acid (PAS) was first reported by Lehmann in 1946 and used for tuberculosis treatment. However, due to its adverse effects, it is now used only as a second line anti-tuberculosis drug for treatment of multidrug resistant or extensively drug resistant M. tuberculosis. The structure of PAS is similar to para-amino benzoic acid (pABA), an intermediate metabolite in the folate synthesis pathway. The study has identified mutations in genes in folate pathway and their intergenic regions for their possibilities in responsible for PAS resistance. Genomic DNA from 120 PAS-resistant and 49 PAS-sensitive M. tuberculosis isolated from tuberculosis patients in Thailand were studied by whole genome sequencing. Twelve genes in the folate synthesis pathway were investigated for variants associated with PAS resistance. Fifty-one SNVs were found in nine genes and their intergenic regions (pabC, pabB, folC, ribD, thyX, dfrA, thyA, folK, folP). Functional correlation test confirmed mutations in RibD, ThyX, and ThyA are responsible for PAS resistance. Detection of mutation in thyA, folC, intergenic regions of thyX, ribD, and double deletion of thyA dfrA are proposed for determination of PAS resistant M. tuberculosis.

CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

BACKGROUND: Although both copy number variations (CNVs) and single nucleotide variations (SNVs) detected by single-cell RNA sequencing (scRNA-seq) are used to study intratumor heterogeneity and detect clonal groups, a software that integrates these two types of data in the same cells is unavailable. RESULTS: We developed Clonal Architecture with Integration of SNV and CNV (CAISC), an R package for scRNA-seq data analysis that clusters single cells into distinct subclones by integrating CNV and SNV genotype matrices using an entropy weighted approach. The performance of CAISC was tested on simulation data and four real datasets, which confirmed its high accuracy in sub-clonal identification and assignment, including subclones which cannot be identified using one type of data alone. Furthermore, integration of SNV and CNV allowed for accurate examination of expression changes between subclones, as demonstrated by the results from trisomy 8 clones of the myelodysplastic syndromes (MDS) dataset. CONCLUSIONS: CAISC is a powerful tool for integration of CNV and SNV data from scRNA-seq to identify clonal clusters with better accuracy than obtained from a single type of data. CAISC allows users to interactively examine clonal assignments.

Pharmacogenomics of in vitro response of the NCI-60 cancer cell line panel to Indian natural products.

BACKGROUND: Indian natural products have been anecdotally used for cancer treatment but with limited efficacy. To better understand their mechanism, we examined the publicly available data for the activity of Indian natural products in the NCI-60 cell line panel. METHODS: We examined associations of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 75 compounds derived from Indian plant-based natural products. We analyzed expression measures for annotated transcripts, lncRNAs, and miRNAs, and protein-changing single nucleotide variants in cancer-related genes. We also examined the similarities between cancer cell line response to Indian natural products and response to reference anti-tumor compounds recorded in a U.S. National Cancer Institute (NCI) Developmental Therapeutics Program database. RESULTS: Hierarchical clustering based on cell line response measures identified clustering of Phyllanthus and cucurbitacin products with known anti-tumor agents with anti-mitotic mechanisms of action. Curcumin and curcuminoids mostly clustered together. We found associations of response to Indian natural products with expression of multiple genes, notably including SLC7A11 involved in solute transport and ATAD3A and ATAD3B encoding mitochondrial ATPase proteins, as well as significant associations with functional single nucleotide variants, including BRAF V600E. CONCLUSION: These findings suggest potential mechanisms of action and novel associations of in vitro response with gene expression and some cancer-related mutations that increase our understanding of these Indian natural products.

Detection of five common variants of ABO gene by a triplex probe-based fluorescence-melting-curve-analysis.

Current studies have suggested that the ABO blood group system is associated with several clinical conditions. For large-scale genotyping of ABO alleles, we developed a triplex fluorescence melting curve analysis (FMCA) to determine five single nucleotide variants (SNVs), c.261delG, c.796C>A, c.802G>A and c.803G>C and c.1061delC, responsible for common ABO phenotypes using dual-labeled self-quenched (TaqMan) probes in a single tube. We accurately determined c.796C>A, c.802G>A, and c.803G>C genotypes using a FAM-labeled probe, c.261delG using a CAL Fluor Orange 560- labeled probe, and c.1061delC using a Cy5-labeled probe. The present genotyping results of five SNVs in 214 subjects of the 1000 Genomes Project were in full agreement with those of the database sequence. The predicted ABO phenotypes using combinations of these five SNVs by this method in 288 Japanese subjects were in complete agreement with those by hemagglutination assay, although we did not find any A2 (alleles containing c.1061delC) or O.02 (alleles containing c.802G>A) alleles. The present triplex probe-based FMCA is a valid and credible method for a considerably accurate large-scale determination of ABO allele genotypes and estimation of phenotypes.

The Genomic Landscape of Corticotroph Tumors: From Silent Adenomas to ACTH-Secreting Carcinomas.

Corticotroph cells give rise to aggressive and rare pituitary neoplasms comprising ACTH-producing adenomas resulting in Cushing disease (CD), clinically silent ACTH adenomas (SCA), Crooke cell adenomas (CCA) and ACTH-producing carcinomas (CA). The molecular pathogenesis of these tumors is still poorly understood. To better understand the genomic landscape of all the lesions of the corticotroph lineage, we sequenced the whole exome of three SCA, one CCA, four ACTH-secreting PA causing CD, one corticotrophinoma occurring in a CD patient who developed Nelson syndrome after adrenalectomy and one patient with an ACTH-producing CA. The ACTH-producing CA was the lesion with the highest number of single nucleotide variants (SNV) in genes such as USP8, TP53, AURKA, EGFR, HSD3B1 and CDKN1A. The USP8 variant was found only in the ACTH-CA and in the corticotrophinoma occurring in a patient with Nelson syndrome. In CCA, SNV in TP53, EGFR, HSD3B1 and CDKN1A SNV were present. HSD3B1 and CDKN1A SNVs were present in all three SCA, whereas in two of these tumors SNV in TP53, AURKA and EGFR were found. None of the analyzed tumors showed SNV in USP48, BRAF, BRG1 or CABLES1. The amplification of 17q12 was found in all tumors, except for the ACTH-producing carcinoma. The four clinically functioning ACTH adenomas and the ACTH-CA shared the amplification of 10q11.22 and showed more copy-number variation (CNV) gains and single-nucleotide variations than the nonfunctioning tumors.

Diagnostic and clinical utility of next-generation sequencing in children born with multiple congenital anomalies in the China neonatal genomes project.

Multiple congenital anomalies (MCAs) at birth have emerged as an important cause of neonatal morbidity and mortality. This study aimed to investigate the genetic causes and characteristics of clinical outcomes in a large cohort of neonates with MCAs. Clinical exome sequencing/exome sequencing/genome sequencing were undertaken from December 1, 2016 to December 1, 2019 to detect single nucleotide variations (SNVs) and copy number variations (CNVs) simultaneously in individuals who met the inclusion criteria. A total of 588 neonates with MCAs were enrolled. One hundred sixty-one patients received diagnosis, with 71 CNVs and 90 SNVs detected, the overall diagnostic rate being 27.38%. Cardiovascular malformation was the most common anomaly (60%) and accounted for the top symptomatic proportion in both CNVs and SNVs. As the number of involved system increased from 2 to 3-4, and then to ≥5, the overall diagnostic rate increased gradually from 23.1% to 30.5%, and then to 52.2%, respectively. Patients who received genetic diagnoses were offered better clinical management or were referred to the specific disease clinic. In conclusion, this large cohort study demonstrates that both CNVs and SNVs contribute to the genetic causes of MCAs, and earlier genetic assertion may lead to better clinical management for patients.

Brain cell somatic gene recombination and its phylogenetic foundations.

A new form of somatic gene recombination (SGR) has been identified in the human brain that affects the Alzheimer's disease gene, amyloid precursor protein (APP). SGR occurs when a gene sequence is cut and recombined within a single cell's genomic DNA, generally independent of DNA replication and the cell cycle. The newly identified brain SGR produces genomic complementary DNAs (gencDNAs) lacking introns, which integrate into locations distinct from germline loci. This brief review will present an overview of likely related recombination mechanisms and genomic cDNA-like sequences that implicate evolutionary origins for brain SGR. Similarities and differences exist between brain SGR and VDJ recombination in the immune system, the first identified SGR form that now has a well-defined enzymatic machinery. Both require gene transcription, but brain SGR uses an RNA intermediate and reverse transcriptase (RT) activity, which are characteristics shared with endogenous retrotransposons. The identified gencDNAs have similarities to other cDNA-like sequences existing throughout phylogeny, including intron-less genes and inactive germline processed pseudogenes, with likely overlapping biosynthetic processes. gencDNAs arise somatically in an individual to produce multiple copies; can be functional; appear most frequently within postmitotic cells; have diverse sequences; change with age; and can change with disease state. Normally occurring brain SGR may represent a mechanism for gene optimization and long-term cellular memory, whereas its dysregulation could underlie multiple brain disorders and, potentially, other diseases like cancer. The involvement of RT activity implicates already Food and Drug Administration-approved RT inhibitors as possible near-term interventions for managing SGR-associated diseases and suggest next-generation therapeutics targeting SGR elements.