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PubMed2026

A digital PCR-based platform for rapid assessment of chloroplast stress adaptation in microalgal metabolic engineering.

Microalgae rapidly adjust their chloroplast physiology in response to environmental stress, and these adaptive responses are closely associated with cellular fitness and metabolic performance. However, conventional assessments of stress adaptation primarily rely on growth characteristics, pigment accumulation, or physiological measurements, which often require extended cultivation periods and may not capture early molecular responses. In this study, we introduce a digital PCR (dPCR)-based platform for rapid assessment of chloroplast stress adaptation in microalgae. The platform quantifies the chloroplast-to-nuclear genome copy number ratio (C/N ratio) using multiplex dPCR and utilizes this metric as a molecular indicator of chloroplast acclimation. As a proof-of-concept, the assay was applied to the halotolerant microalga Dunaliella salina cultivated under different salinity stress conditions. Distinct temporal changes in the C/N ratio were observed across salinity treatments, indicating dynamic chloroplast genome remodeling during stress adaptation. The assay enabled sensitive detection of chloroplast responses at early cultivation stages, prior to the appearance of clear phenotypic differences. These findings demonstrate that chloroplast-to-nuclear genome quantification by dPCR provides a rapid and reproducible approach for monitoring chloroplast stress adaptation in microalgae. The proposed platform offers a practical molecular tool for strain evaluation, cultivation optimization, and stress-response studies, and may support future applications in microalgal biotechnology and industrial production systems.

Microalgae↗

PubMed2024

Common genetic variants associated with urinary phthalate levels in children: A genome-wide study.

INTRODUCTION: Phthalates, or dieters of phthalic acid, are a ubiquitous type of plasticizer used in a variety of common consumer and industrial products. They act as endocrine disruptors and are associated with increased risk for several diseases. Once in the body, phthalates are metabolized through partially known mechanisms, involving phase I and phase II enzymes. OBJECTIVE: In this study we aimed to identify common single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) associated with the metabolism of phthalate compounds in children through genome-wide association studies (GWAS). METHODS: The study used data from 1,044 children with European ancestry from the Human Early Life Exposome (HELIX) cohort. Ten phthalate metabolites were assessed in a two-void pooled urine collected at the mean age of 8 years. Six ratios between secondary and primary phthalate metabolites were calculated. Genome-wide genotyping was done with the Infinium Global Screening Array (GSA) and imputation with the Haplotype Reference Consortium (HRC) panel. PennCNV was used to estimate copy number variants (CNVs) and CNVRanger to identify consensus regions. GWAS of SNPs and CNVs were conducted using PLINK and SNPassoc, respectively. Subsequently, functional annotation of suggestive SNPs (p-value < 1E-05) was done with the FUMA web-tool. RESULTS: We identified four genome-wide significant (p-value < 5E-08) loci at chromosome (chr) 3 (FECHP1 for oxo-MiNP_oh-MiNP ratio), chr6 (SLC17A1 for MECPP_MEHHP ratio), chr9 (RAPGEF1 for MBzP), and chr10 (CYP2C9 for MECPP_MEHHP ratio). Moreover, 115 additional loci were found at suggestive significance (p-value < 1E-05). Two CNVs located at chr11 (MRGPRX1 for oh-MiNP and SLC35F2 for MEP) were also identified. Functional annotation pointed to genes involved in phase I and phase II detoxification, molecular transfer across membranes, and renal excretion. CONCLUSION: Through genome-wide screenings we identified known and novel loci implicated in phthalate metabolism in children. Genes annotated to these loci participate in detoxification, transmembrane transfer, and renal excretion.

Humans↗

PubMed2026

sWGS Identifies a Copy-Number-High Subset of TP53-mutated Multiple-Classifier Endometrial Carcinomas With Adverse Clinicopathological Features.

TP53-mutated "multiple-classifier" endometrial carcinomas represent a diagnostically challenging subgroup within current molecular classification algorithms. Although these tumors are assigned to POLE-mutated or mismatch repair-deficient categories according to current ESGO/FIGO-based algorithms, their biological heterogeneity remains incompletely characterized. Herein, we retrospectively analyzed TP53-mutated multiple-classifier endometrial carcinomas identified through routine molecular profiling at our institution between 2022 and 2025 using an integrated histopathological, immunohistochemical, targeted sequencing, and shallow whole-genome sequencing approach. Copy-number alteration-high (CNA-high) status was defined as ≥5 large-scale genomic alterations, corresponding to copy-number gains or losses ≥3 Mb within a single chromosomal arm excluding whole-arm alterations. Among 33 analyzable TP53-mutated multiple-classifier endometrial carcinomas, sWGS identified 12 CNA-high tumors (36.4%) and 21 CNA-low tumors (63.6%). CNA-high tumors were more frequently non-endometrioid, high-grade, and advanced-stage according to FIGO 2023. They showed higher TP53 variant allele frequencies (VAF) and higher TP53 VAF-to-tumor-cellularity ratios. After a median follow-up of 12.8 months, recurrences (6/33; 18.2%) and disease-related deaths (3/33; 9.1%) were observed in the CNA-high subgroup, whereas no recurrence or disease-related death was observed among CNA-low patients. These findings indicate that TP53-mutated multiple-classifier endometrial carcinomas comprise biologically distinct subsets that are not fully captured by current 4-tier TCGA-based molecular classification and ESGO-based risk stratification. In this cohort, sWGS identified a CNA-high group with adverse clinicopathological features and clinical events suggesting a potentially more aggressive clinical course. Integration of genome-wide copy-number profiling may therefore refine the biological interpretation of TP53 alterations in multiple-classifier endometrial carcinomas and warrants validation in larger multicenter cohorts.

TP53↗

PubMed2026

PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework.

MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

DNA Copy Number Variations↗

PubMed2025

Absolute Quantification of Cellular and Cell-Free Mitochondrial DNA Copy Number from Human Blood and Urinary Samples Using Real Time Quantitative PCR.

Mitochondrial DNA copy number (mtDNA-CN) in human body fluids is widely used as a biomarker of mitochondrial dysfunction in common metabolic diseases. Here we describe protocols to measure cellular and/or cell free (cf)-mtDNA-CN in human peripheral blood and urine. Cellular mtDNA is located inside the mitochondria where it encodes key subunits of the respiratory complexes in mitochondria and is usually normalized with reference to the nuclear genome as the mitochondrial genome to nuclear genome ratio (Mt/N) in either whole blood, peripheral blood mononuclear cells (PBMCs), or whole urine. Cf -mtDNA is usually found outside of the mitochondria, often released following mitochondrial damage, can trigger inflammatory pathways, and is usually measured as mtDNA-CN per volume of the starting material. Here we describe how to (1) separate whole blood into PBMCs, plasma, and serum fractions and whole urine into urinary supernatant and pellet, (2) prepare DNA from each of these fractions, (3) prepare reference standards for absolute quantification, (4) carry out qPCR for either relative or absolute quantification from test samples, (5) analyze qPCR data, and (6) calculate the sample size to adequately power studies. The protocol presented here is suitable for high throughput use and can be modified to quantify mtDNA from other body fluids, human cells, and tissues.

Humans↗

PubMed2026

Assessing the de novo paradigm in sporadic early-onset Alzheimer disease trios.

The genetic architecture of sporadic Early-Onset Alzheimer Disease (sEOAD, onset ≤65 years) remains largely unknown. To assess the de novo mutation (DNM) hypothesis, we performed a nationwide recruitment of 37 novel sEOAD patients-unaffected parents trios. After assessing known monogenic genes, we performed trio-based exome sequencing and jointly analyzed novel trios with 12 previously reported ones. Of these, we selected 16 trios for genome sequencing. We identified three patients with a pathogenic DNM in APP or PSEN1. Then, from the 46 remaining trios, we identified 38 non-synonymous coding DNM and 4 de novo copy number variants (CNVs) in exome data. Four DNM (2 novel, in SPHK2 and DDR1) and bi-allelic inherited variants in two genes affected Alzheimer disease-related genes. No significant burden of rare coding variants in exome/genome data from 5643 EOAD cases and 16097 controls was identified using nested windows centered on each DNM position, at the transcript level. From genome data, one non-coding DNM was predicted to affect splicing in an AD-associated gene, PINX1. Overall, 48% probands carried ≥1 inherited risk factor with odds ratio (OR) > 1.5 and GWAS-defined Genetic Risk Scores (GRS) distribution was more consistent with random distribution than enrichment in higher scores in probands. We confirm that DNMs in known monogenic genes explain sEOAD in a minority of cases, while candidate DNMs in other genes might account for a small proportion of additional cases. The majority of sEOAD patients may have a complex etiology including multiple inherited variants, however, GRS might not explain most of its genetic component.

Humans↗

PubMed2025

Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.

BACKGROUND: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms. METHOD: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas. RESULTS: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN. Conversely, no meaningful difference was found in pathogenic single-nucleotide variant frequencies, reinforcing the notion that they are early events in tumorigenesis. CDKN2A homozygous deletion was linked to poor clinical outcome in patients with early tumours (overall survival hazard ratio 2.17, 95% CI: 1.43-3.28, corrected p-value = 0.01). Furthermore, we found that KRAS mutant allele specific imbalance, i.e. focal amplification of the mutant allele, is more prevalent in locoregional or distant samples of metastatic patients than in early lesions (8.4%, 13% and 2.8% respectively). This observation was replicated in three public cohorts. Tumours with KRAS mutant allele specific imbalance show specific patterns of co-occurrence and mutual exclusion with alterations in key cancer genes like CDKN2A, TP53, STK11 and NKX2-1, often in a tumour type dependent manner. CONCLUSION: Advanced LUAD tumours exhibit higher copy-number alteration burden, with distinct alterations associated with tumour progression and metastasis. CDKN2A homozygous deletions predict poor prognosis in early disease, while KRAS mutant allele-specific imbalance is enriched in advanced tumours.

Humans↗

PubMed2025

CNV-Finder: Streamlining Copy Number Variation Discovery.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), and alpha-Synuclein (SNCA)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Copy Number Variation (CNV)↗

PubMed2026

Tumor genomic landscape of older patients with metastatic breast cancer☆.

BACKGROUND: Metastatic breast cancer (MBC) in older patients has distinct clinical and histologic characteristics. Elucidating the genomic basis of MBC helps identify potential therapeutic targets to improve outcomes for older patients with MBC. PATIENTS AND METHODS: Using a prospective database and targeted DNA sequencing (OncoPanel), we examined MBC's genomic landscape in older patients (age ≥70 years at MBC diagnosis) and compared findings with those in younger (aged <50 years) and middle-aged (aged 50-69 years) patients. After classifying single nucleotide variants (SNVs) and copy number variations (CNVs) as oncogenic (via OncoKB), the frequencies of SNVs and CNVs, tumor mutational burden (TMB), and oncogenic signaling pathways were compared by age group using Fisher's exact tests. We estimated the association between continuous age at MBC diagnosis and mutations via multivariate logistic regression analysis, adjusting for race, stage at initial diagnosis, subtype, histology, and sample tested (primary versus metastatic). RESULTS: Our study included 2379 patients [853 (35%) younger, 1311 (55%) middle-aged, and 215 (9%) older] who underwent OncoPanel testing between 2013 and 2020. The most frequent tumor alterations in older patients were SNVs in PIK3CA (44%), TP53 (33%), CDH1 (22%) and amplifications in CCND1 (18%). After adjustment, older age was associated with higher frequency of SNVs in CDH1 [odds ratio (OR) = 1.43, 95% confidence interval (CI) 1.21-1.68, q < 0.001], MAP3K1 [OR = 1.30, 95% CI 1.10-1.54, q = 0.008], and PIK3CA [OR = 1.21, 95% CI 1.11-1.31, q < 0.001] and fewer SNVs in TP53 [OR = 0.84, 95% CI 0.77-0.91, q < 0.001]. Patients in the older group were more likely to have tumors with ≥10 mutations/megabase than the youngest patients (26% versus 17%, P = 0.003). CONCLUSIONS: In this large cohort of patients with MBC, the tumor genomic landscape differed between older and younger patients even after accounting for tumor subtype. Older patients were more likely to have high-TMB and PIK3CA-mutated tumors, highlighting the importance of genomic testing for treatment applications in this population.

NGS↗

PubMed2026

Clinical and molecular prognostic factors in newly diagnosed pediatric T-cell lymphoblastic lymphoma: a prospective, multicenter, single-arm phase 2 clinical trial.

BACKGROUND: Poor early treatment response in T-cell lymphoblastic lymphoma (T-LBL) is associated with an unfavorable prognosis. This multicenter prospective study evaluated the efficacy of the response-adjusted Chinese Children's Cancer Group (CCCG-LBL-2016) protocol for pediatric T-LBL and examined clinical and molecular prognostic factors. METHODS: Clinical and laboratory data from seven pediatric oncology centers were analyzed. A sub-cohort of 23 patients underwent exploratory integrated genomic analysis, including targeted next-generation sequencing, RNA sequencing, and copy-number array analysis. Survival was evaluated using the Kaplan-Meier method, and prognostic factors were analyzed using multivariable Cox proportional hazards regression. RESULTS: A total of 163 patients (median age: 108 months; 116 males, 47 females) were enrolled, most with advanced disease (stage III: 81.0%; stage IV: 17.8%). Patients were stratified into the low-risk (R1, n = 2) and intermediate-risk groups (R2, n = 161); thirty one patients in the R2 group were escalated to the high-risk intensified regimen (R3) due to poor early response. The 3-year overall survival (OS) was 78.6% ± 3.3% and event-free survival (EFS) was 73.9% ± 3.5%. Outcomes differed by risk group (P < 0.05), with 3-year OS and EFS of 100% and 100% in R1, 82.6% ± 3.4% and 79.5% ± 3.4% in R2, and 58.6% ± 9.1% and 48.3% ± 9.1% in R3. Progression or recurrence occurred in 42 patients (median: 7 months; 3-year OS: 17.1% ± 6.3%). Clinical risk factors included R3 assignment and elevated lactate dehydrogenase. In the exploratory molecular sub-cohort, recurrent alterations included CDKN2A (39.1%), NOTCH1 (26.1%), FBXW7 (21.7%), and MTAP/PIK3R1/NRAS (13.0%). Exploratory multivariable Cox regression analysis identified that CDKN2A alteration was associated with an increased risk of progression or recurrence (hazard ratio = 35.89, 95% confidence interval: 3.07-419, P = 0.004). CONCLUSIONS: Adjusting the risk stratification based on treatment response significantly improved the overall prognosis of T-LBL. However, survival rates remain very low among patients who experience disease progression or recurrence. The preliminarily explored molecular genetic risk factors might contribute to further risk stratification and provide potential therapeutic targets.

Humans↗

PubMed2026

Non-invasive strategy for gastric cancer detection: Integration of cell-free DNA fragmentomics and protein biomarkers.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide, however, accurate and non-invasive diagnostic modalities for GC remain limited. Cell-free DNA (cfDNA) fragmentomics has emerged as a promising tool for cancer cell detection. Here we develop a gastric cancer detection model, named GaFraD model. The GaFraD model uses four cfDNA fragmentomics features, including fragment size ratio (FSR), copy number variation (CNV), 9-bp end motif (Motif), and fragment size at transcription start sites (TF). This model achieves an area under the receiver-operating characteristic curve (AUC) of 0.970 (95% CI: 0.944 - 0.990), a sensitivity of 95.0% and a specificity of 80.9%. By combining the GaFraD model and conventional protein biomarkers CA19-9 and PG-I/PG-II, the CONFIRM model was generated. The CONFIRM model attained an AUC of 0.986 (95% CI: 0.966 - 1.000), a sensitivity of 95.0% and a specificity of 95.6% in detecting GC. Moreover, the CONFIRM model achieved remarkable performance (AUC = 0.983, sensitivity 95.6%, specificity 94.2%) in distinguishing patients with early-stage GC from controls. Our work showed the high discriminatory power in distinguishing GC patients from controls, indicating the clinical potential of using cfDNA fragmentomics combined with protein biomarkers for non-invasive GC detection. The results of the study provide a new avenue for early, accurate, and non-invasive clinical diagnosis of GC.

Cell-free DNA↗

PubMed2025

Structural variation detection and association analysis of whole-genome-sequence data from 16,543 Alzheimer's disease sequencing project subjects.

INTRODUCTION: The role of structural variations (SVs) in Alzheimer's disease (AD) remains understudied. METHODS: We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,543) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). RESULTS: We found a burden of singletons (odds ratio [OR] = 1.07, p = 0.0017) and homozygous deletions (OR = 1.14, p < 0.0001) in cases. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in LD (R2 = 0.99) with rs143080277 in NCK2. We identified a rare deletion near RNA5SP293 associated with AD (OR = 1.99, p = 1.3 × 10-5), which was replicated using an independent dataset. DISCUSSION: This study highlights the pivotal role of SVs in AD genetics. HIGHLIGHTS: Observed a significant burden of singletons and homozygous deletions in Alzheimer's disease (AD) patients. Identified rare protein-altering structural variations (SVs) in ABCA7, APP, PLCG2, and SORL1. Established linkages between SVs and AD risk-associated single nucleotide variants (SNVs). Discovered a novel deletion near RNA5SP293 linked to AD, replicated independently. Uncovered over-representation of SVs in neuronal function pathways.

Humans↗