Human Mutation最新文献

Purpose: The purpose of this study is to identify genes and transcription factors underlying functional differences in neonatal versus adult peripheral blood monocytes, elucidating mechanisms of severe Group B streptococcus (GBS) infection in neonates.

Methods: Differentially expressed genes (DEGs) in neonatal and adult peripheral blood monocytes were detected via RNA sequencing (RNA-seq), followed by assay for transposase-accessible chromatin sequencing (ATAC-seq) to characterize differentially accessible region (DAR)–associated genes. Integrated analyses of RNA-seq and ATAC-seq pinpointed candidate genes and transcription factors. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) validated the mRNA expression of common genes and transcription factors.

Results: RNA-seq profiling of neonatal and adult peripheral monocytes identified 669 overexpressed and 440 underexpressed genes in neonates, with overexpressed genes enriched in bacterial response pathways and underexpressed genes in cytokine production and cell killing pathways. Chromatin accessibility analysis revealed 36,782 differential peaks (21,192 gained, 15,590 lost) in neonatal peripheral monocytes. Integrated RNA-seq and ATAC-seq analysis pinpointed 30 overlapping genes among DEGs, DAR-associated genes, and immunologically relevant genes (IRGs). qRT-PCR validated higher expression of CEBPB, JUN, BATF, PTK2B, and ITGAV and lower ADA2 and RORA expression in neonatal peripheral monocytes compared to that in adults.

Conclusions: The study revealed distinct differences in the transcriptome and chromatin accessibility between neonatal and adult peripheral monocytes, identifying potential genes linked to GBS infection vulnerability of neonates. These findings advance our understanding of neonatal immune dysfunction in severe GBS disease, informing future therapeutic targets.

Background: The high morbidity and mortality of lung adenocarcinoma (LUAD) are partly caused by a lack of sensitive and reliable molecular markers for early diagnosis. Programmed cell death (PCD) is a crucial process involved in tumorigenesis and immune regulation, and identifying PCD-correlated genes may contribute to the precision diagnosis and targeted therapy of LUAD.

Methods: LUAD samples were acquired from UCSC Xena and Gene Expression Omnibus (GEO) database. PCD-correlated module genes were identified by WGCNA. “Limma” package was employed for screening differentially expressed genes (DEGs) between LUAD and control samples, followed by conducting functional enrichment analysis with “ClusterProfiler” package. Hub genes were selected through machine learning algorithms. Biomarkers for LUAD were screened and further validated by receiver operating characteristic (ROC) analysis. The robustness of the diagnostic model was verified by the confusion matrix. Immune cell infiltration was assessed employing “ESTIMATE” and “GSVA” packages. HALLMARK pathway score was calculated with the “GSVA” package. Transcription factor (TF)–biomarker–chemical network was established using NetworkAnalyst and Cytoscape software. The expressions of the biomarkers in LUAD cells were detected by in vitro experiments. The viability, migration, and invasion of the LUAD cells were measured by CCK-8, wound healing, and Transwell assays.

Results: We identified 160 module genes and 5934 DEGs. Then, eight hub genes were selected applying LASSO and support vector machine–recursive feature elimination (SVM-RFE) analyses. Further, FGR, TLR4, and NLRC4, which showed an area under the ROC curve (AUC) > 0.7, were determined as the diagnostic biomarkers for LUAD. Interestingly, they were all low expressed in LUAD samples. We developed a diagnostic model that demonstrated robust performance in distinguishing LUAD samples from normal controls. The three biomarkers showed positive correlation to the infiltration of most immune cells and enrichment in HALLMARK pathways associated with inflammation, immune regulation, and cytokine signaling. Moreover, nine TFs and nine small-molecule compounds targeting the three biomarkers were predicted to construct a TF–biomarker–chemical network. Functional validation revealed that all the three biomarkers were significantly downregulated in LUAD cells. Notably, FGR overexpression markedly suppressed LUAD cell proliferation, migration, and invasion.

Conclusion: This study identified three PCD-related biomarkers for LUAD diagnosis, providing new potential therapeutic targets.

Peutz–Jeghers syndrome (PJS) is a rare autosomal dominant disorder hallmarked by mucocutaneous melanocytic macules and gastrointestinal hamartomatous polyposis associated with germline/somatic pathogenic variants in the tumor suppressor STK11. PJS is clinically heterogeneous; however, the relationship between clinical phenotype and genotype remains elusive. Here, we report a family with PJS that harbors a heterozygous STK11 whole gene deletion combined with a heterozygous variant in TP53AIP1 that segregates with mucocutaneous pigmentation in the family. RNA-seq analysis followed by qRT-PCR confirmed that the expression of STK11, TP53, and TP53AIP1 and a large fraction of p53 signaling pathway components are significantly reduced, while Wnt signaling pathway effectors are upregulated in cells from an affected individual. Our findings shed light on transcriptome-level pathway dysregulation in PJS with germline deletion of STK11. Further evaluation of mutational burden across relevant signaling pathways can likely inform disease prognosis.

Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. Despite the efficacy of oxaliplatin-based chemotherapy (CT) in CRC treatment, CT resistance remains a major obstacle to successful patient outcomes. Epithelial–mesenchymal transition (EMT), a key cellular process in cancer metastasis, plays a pivotal role in resistance to CT. The tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), is known to contribute to EMT and therapy resistance. Here, we employ single-cell RNA sequencing (scRNA-seq) to analyze primary CRC tumor samples from patients undergoing CT and nonchemotherapy (nCT) treatments. Our study identifies specific epithelial cell clusters resistant to oxaliplatin, elucidating the molecular pathways involved in EMT and resistance. Furthermore, we explore the role of CAF subpopulations in promoting resistance within the TME. Our findings highlight the importance of functional immune profiling and genomic analyses in identifying potential biomarkers for predicting CT responses and improving personalized treatment strategies. This work provides new insights into the molecular mechanisms of oxaliplatin resistance in CRC and supports the development of novel immune-based therapeutic approaches to enhance patient outcomes.

Pancreatitis is a severe and increasingly prevalent disease that affects the digestive system. Early detection and accurate diagnosis of this condition are crucial for reducing mortality rates and improving patient outcomes. Therefore, the development of novel diagnostic markers is essential for enhancing clinical management and advancing the understanding of pancreatitis. The initial phase involved applying the ssGSEA method to extract hypoxia scores from these samples. Subsequently, a thorough differential expression analysis was performed, complemented by functional assessments and various machine learning techniques designed to pinpoint critical diagnostic genes relevant to pancreatitis. From this, a robust diagnostic model was constructed and validated using a series of machine learning strategies. To further validate our results, molecular docking studies were conducted to determine the binding affinities between the identified markers and standard medications such as omeprazole and lansoprazole. Additionally, the ssGSEA methodology was leveraged to compute immune cell scores within the pancreatitis samples, thus enriching the analysis of the relationships between significant diagnostic genes and various immune cell types. Finally, the experiments of ELISA and qRT-PCR were used to verify the expression of key target genes. Through WGCNA, we identified a total of 50 genes associated with hypoxic conditions within the pancreatitis samples. Further investigations, including differential expression analysis and machine learning techniques, revealed six significant diagnostic markers for pancreatitis: RAP1GDS1, TOP2A, ADK, POLL, CD44, and CD4. The diagnostic model we developed exhibited a high accuracy level in predicting pancreatitis onset, while molecular docking analyses indicated that these six key diagnostic genes hold promise as drug targets. Moreover, the ssGSEA algorithm confirmed the relationships between these diagnostic markers and a range of immune cell populations. Ultimately, the expression levels of the identified key genes were rigorously validated through experimental techniques, reinforcing the credibility of our findings.

Three nonsynonymous single nucleotide variations (SNVs), rs1042602 in TYR (p.S192Y), rs1426654 in SLC24A5 (p.A111T), and rs16891982 in SLC45A2 (p.L374F), were associated with human skin pigmentation variation and may have recently undergone positive natural selection. Furthermore, these three SNVs have been reported to correlate with the risk and prognosis of melanoma. To simultaneously determine these three SNVs, a triplex fluorescent probe-based melting curve assay (FMCA) was developed. The method was validated by analyzing genomic DNA from subjects with known genotypes. For rs16891982, triplex FMCA did not allow good separation of genotypes with the initial polymerase enzyme mix used, but by changing the enzyme mix used, the three genotypes could be clearly distinguished. Using this method, we definitively genotyped these three SNVs in 93 European, 58 Tamil, 54 Sinhalese, and 52 Bangladeshi subjects. This method allows genotyping of rs1042602, rs1426654, and rs16891982 in a relatively large number of samples to perform association studies on skin pigmentation variation or melanoma risk.

Amelogenesis is the process of tooth enamel formation, and genetic variants disrupting it cause the Mendelian inherited disorder amelogenesis imperfecta (AI). AI patients have weak, discoloured or brittle enamel, caused by reduced enamel quantity or mineralisation. AI can occur in isolation or, less commonly, as part of a syndrome. Pathogenic variants in at least 38 genes have been shown to cause AI. Current genetic screening studies typically use exome sequencing, but this is expensive and involves complex data analysis workflows. Target enrichment using smMIPs (single molecule molecular inversion probes) provides a flexible alternative, allowing the creation of a disease-specific reagent for low cost, robust, high-throughput screening. Here, we describe the development of an smMIP reagent targeting 19 genes implicated in isolated AI and assess its use in screening a cohort of 181 UK probands with nonsyndromic AI. While this was intended only as a prescreen to prioritise exome sequencing more efficiently, it nevertheless led to molecular diagnoses for 63 probands (35%). Cost per sample screened was approximately £40. Variants in three genes, COL17A1, FAM83H (both dominant) and MMP20 (recessive), accounted for approximately half of solved cases. There is scope to further improve the smMIP reagent by adding additional probes targeting regions of low coverage or additional genes, including those involved in syndromic AI, as well as accommodating new information about the genetic basis of AI. The smMIP reagent provides a robust, flexible, high-throughput, low-cost approach to AI screening, and it is available as a resource to the international AI research community.

Numerous studies have emphasized the importance of the ubiquitin–proteasome system (UPS) in the malignant progression of ovarian cancer (OC). However, whether ubiquitination-related genes (UbRGs) can be used to predict the prognosis of OC remains to be revealed. Patients with OC were divided into two clusters based on the expression of UbRGs, and prognosis was compared between the two clusters. A prognostic model was established based on UbRGs, and its predictive efficiency was validated using Kaplan–Meier (K–M) curves, receiver operating characteristic (ROC) curves, and a nomogram. Immune infiltration and gene mutation analyses were used to examine the effects of UbRGs on the prognosis of OC. The prognostic model served as a valid and independent predictor of OC prognosis. Immune infiltration revealed that the unique immune microenvironment of OC was regulated by UbRGs. Gene mutation analysis indicates that UbRGs likely influence OC malignant behavior by modulating gene mutation patterns. In addition, Ube2j1 was found to play an important role in regulating the malignant progression of OC. Furthermore, the mechanism by which Ube2j1 modulates the OC phenotype and reshapes its immune microenvironment via the JAK2/STAT3/PD-L1 pathway was elucidated, providing novel insights into the potential for ubiquitination-based immunotherapy in OC. This study provides novel insights into precision immunotherapy based on UbRGs in OC. The UbRGs-based prognostic model may help to provide novel insights for the application of ubiquitination-based immunotherapy in OC.

Background: Heterogenized sphingolipid metabolism (SM) drives osteosarcoma tumorigenesis and its tumor-promoting microenvironment. State-of-the-art bioinformatic tools, such as machine learning, are essential for dissecting the prognostic value of SM by investigating its molecular and cellular mechanisms.

Methods: A tailored machine learning pipeline was established by integrating Cox regression, 5-fold cross-validation, Elastic Net, eXtreme Gradient Boosting (XGBoost), and Bayesian optimization (for hyperparameters tuning) to foster an SM Elastic Net-XGBoost (SNEX) prognostic model, interpreted by the Shapley additive explanations (SHAP) algorithm. The alterations in molecular pathways and immune microenvironment–driven unfavorable prognosis of SNEX-identified high-risk osteosarcoma were further investigated. The SNEX predicted results have also been clinically and experimentally validated.

Results: We identified 22 critical SM prognostic genes for Bayesian-optimized SNEX. This model provided outstanding estimates of the prognoses of osteosarcoma patients (C-index of 1.000). Its robustness was confirmed in the independent test set with a high area under the curve (AUC) of 0.875 at 1 year, 0.930 at 3 years, and 0.930 at 5 years. SNEX also significantly outperformed all previous genetic prognostic signatures with a significantly higher net benefit of decision curves and higher AUCs. ACTA2 was the most pivotal gene critical to the negative prediction of SNEX, while BNIP3 was for positive prediction. Mechanistically, SNEX-identified high-risk osteosarcoma suffered unfavorable prognoses due to dysregulation of many critical metabolic/inflammatory/immune biologic processes and immunosuppressive microenvironment, with reduced infiltration of 14 types of immune cells (macrophages, CD8+ T cells, NK cells, etc.). Notably, SNEX highlighted TERT as the most remarkable SM prognostic gene. Clinical osteosarcomas with high expression of TERT exhibited more significant malignant characteristics than others, as evidenced by their higher proliferation efficiency. In addition, all the experiments in vitro and in vivo validated that inhibiting TERT abundance reduces the proliferation, invasion, and migration capabilities of osteosarcoma cells.

Conclusions: This study is a first-hand report employing a tailored machine-learning pipeline for dissecting the prognostic value and roles of SM in osteosarcoma. The present study fostered a SNEX for risk-stratification with outstanding accuracy and offered deep insights into SM-mediated pathways and microenvironment dysregulation in osteosarcoma.