A genomic association study revealing subphenotypes of childhood steroid-sensitive nephrotic syndrome in a larger genomic sequencing cohort.

Dissecting the genetic components that contribute to the two main subphenotypes of steroid-sensitive nephrotic syndrome (SSNS) using genome-wide association studies (GWAS) strategy is important for understanding the disease. We conducted a multicenter cohort study (360 patients and 1835 controls) combined with a GWAS strategy to identify susceptibility variants associated with the following two subphenotypes of SSNS: steroid-sensitive nephrotic syndrome without relapse (SSNSWR, 181 patients) and steroid-dependent/frequent relapse nephrotic syndrome (SDNS/FRNS, 179 patients). The distribution of two single-nucleotide polymorphisms (SNPs) in ANKRD36 and ALPG was significant between SSNSWR and healthy controls, and that of two SNPs in GAD1 and HLA-DQA1 was significant between SDNS/FRNS and healthy controls. Interestingly, rs1047989 in HLA-DQA1 was a candidate locus for SDNS/FRNS but not for SSNSWR. No significant SNPs were observed between SSNSWR and SDNS/FRNS. Meanwhile, chromosome 2:171713702 in GAD1 was associated with a greater steroid dose (>0.75 mg/kg/d) upon relapse to first remission in patients with SDNS/FRNS (odds ratio = 3.14; 95% confidence interval, 0.97-9.87; P = 0.034). rs117014418 in APOL4 was significantly associated with a decrease in eGFR of greater than 20% compared with the baseline in SDNS/FRNS patients (P = 0.0001). Protein-protein intersection network construction suggested that HLA-DQA1 and HLA-DQB1 function together through GSDMA. Thus, SSNSWR belongs to non-HLA region-dependent nephropathy, and the HLA-DQA/DQB region is likely strongly associated with disease relapse, especially in SDNS/FRNS. The study provides a novel approach for the GWAS strategy of SSNS and contributes to our understanding of the pathological mechanisms of SSNSWR and SDNS/FRNS.

Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity.

Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.

Multi-omics analysis reveals the evolutionary origin of diterpenoid alkaloid biosynthesis pathways in Aconitum.

Diterpenoid alkaloids (DAs) have been often utilized in clinical practice due to their analgesic and anti-inflammatory properties. Natural DAs are prevalent in the family Ranunculaceae, notably in the Aconitum genus. Nevertheless, the evolutionary origin of the biosynthesis pathway responsible for DA production remains unknown. In this study, we successfully assembled a high-quality, pseudochromosome-level genome of the DA-rich species Aconitum vilmorinianum (A. vilmorinianum) (5.76 Gb). An A. vilmorinianum-specific whole-genome duplication event was discovered using comparative genomic analysis, which may aid in the evolution of the DA biosynthesis pathway. We identified several genes involved in DA biosynthesis via integrated genomic, transcriptomic, and metabolomic analyses. These genes included enzymes encoding target ent-kaurene oxidases and aminotransferases, which facilitated the activation of diterpenes and insertion of nitrogen atoms into diterpene skeletons, thereby mediating the transformation of diterpenes into DAs. The divergence periods of these genes in A. vilmorinianum were further assessed, and it was shown that two major types of genes were involved in the establishment of the DA biosynthesis pathway. Our integrated analysis offers fresh insights into the evolutionary origin of DAs in A. vilmorinianum as well as suggestions for engineering the biosynthetic pathways to obtain desired DAs.

Targeting ASIC1a Promotes Neural Progenitor Cell Migration and Neurogenesis in Ischemic Stroke.

Cell replacement therapy using neural progenitor cells (NPCs) has been shown to be an effective treatment for ischemic stroke. However, the therapeutic effect is unsatisfactory due to the imbalanced homeostasis of the local microenvironment after ischemia. Microenvironmental acidosis is a common imbalanced homeostasis in the penumbra and could activate acid-sensing ion channels 1a (ASIC1a), a subunit of proton-gated cation channels following ischemic stroke. However, the role of ASIC1a in NPCs post-ischemia remains elusive. Here, our results indicated that ASIC1a was expressed in NPCs with channel functionality, which could be activated by extracellular acidification. Further evidence revealed that ASIC1a activation inhibited NPC migration and neurogenesis through RhoA signaling-mediated reorganization of filopodia formation, which could be primarily reversed by pharmacological or genetic disruption of ASIC1a. In vivo data showed that the knockout of the ASIC1a gene facilitated NPC migration and neurogenesis in the penumbra to improve behavioral recovery after stroke. Subsequently, ASIC1a gain of function partially abrogated this effect. Moreover, the administration of ASIC1a antagonists (amiloride or Psalmotoxin 1) promoted functional recovery by enhancing NPC migration and neurogenesis. Together, these results demonstrate targeting ASIC1a is a novel strategy potentiating NPC migration toward penumbra to repair lesions following ischemic stroke and even for other neurological diseases with the presence of niche acidosis.

Transcriptomic analysis identified SLC40A1 as a key iron metabolism-related gene in airway macrophages in childhood allergic asthma.

Introduction: Asthma is the most common chronic condition in children, with allergic asthma being the most common phenotype, accounting for approximately 80% of cases. Growing evidence suggests that disruption of iron homeostasis and iron regulatory molecules may be associated with childhood allergic asthma. However, the underlying molecular mechanism remains unclear. Methods: Three childhood asthma gene expression datasets were analyzed to detect aberrant expression profiles of iron metabolism-related genes in the airways of children with allergic asthma. Common iron metabolism-related differentially expressed genes (DEGs) across the three datasets were identified and were subjected to functional enrichment analysis. Possible correlations between key iron metabolism-related DEGs and type 2 airway inflammatory genes were investigated. Single-cell transcriptome analysis further identified major airway cell subpopulations driving key gene expression. Key iron metabolism-related gene SLC40A1 was validated in bronchoalveolar lavage (BAL) cells from childhood asthmatics with control individuals by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunofluorescence. The intracellular iron content in BAL cells was assessed by Perls iron staining and the iron levels in BAL supernatant was measured by iron assay to assess airway iron metabolism status in childhood asthmatics. Results: Five common iron metabolism-related DEGs were identified, which were functionally related to iron homeostasis. Among these genes, downregulated SLC40A1 was strongly correlated with type 2 airway inflammatory markers and the gene signature of SLC40A1 could potentially be used to determine type 2-high and type 2-low subsets in childhood allergic asthmatics. Further single-cell transcriptomic analysis identified airway macrophages driving SLC40A1 expression. Immunofluorescence staining revealed colocalization of FPN (encoded by SLC40A1) and macrophage marker CD68. Down-regulation of SLC40A1 (FPN) was validated by qRT-PCR and immunofluorescence analysis. Results further indicated reduced iron levels in the BAL fluid, but increased iron accumulation in BAL cells in childhood allergic asthma patients. Furthermore, decreased expression of SLC40A1 was closely correlated with reduced iron levels in the airways of children with allergic asthma. Discussion: Overall, these findings reveal the potential role of the iron metabolism-related gene SLC40A1 in the pathogenesis of childhood allergic asthma.

Long-term effects of vitamin D on exacerbation rate, health care utilization and lung function in children with asthma.

Background: Asthma exacerbations lead to unplanned health care utilization and reduced lung function in children. Sufficient vitamin D level has been found to have a short-term protective effect against asthma exacerbation in children. However, it is unclear whether this effect remains in the long term. We evaluated the long-term effects of vitamin D levels on the occurrence of asthma exacerbations, emergency department visits or hospitalizations, and lung function among children with asthma, and further investigated the temporal trends of the effects. Methods: In this retrospective cohort study, children with asthma who were admitted to the Children's Hospital of Chongqing Medical University from 2017 to 2021 were enrolled. Negative binomial, Poisson, or logistic regression model was used for the multivariable analysis, adjusting for age, sex, body mass index z-score, and severity of asthma exacerbation. Results: Of the 370 children with asthma, 87.8% had vitamin D level less than or equal to 30 ng/mL. After adjustment for confounding factors, higher baseline vitamin D levels in asthma children were significantly associated with reduced occurrence of asthma exacerbations during the first [odds ratio 0.842, 95% confidence interval (CI): 0.805-0.881; P<0.001], second (odds ratio 0.848, 95% CI: 0.793-0.907; P<0.001) and third years (odds ratio 0.865, 95% CI: 0.811-0.922; P<0.001) of follow-up. Higher vitamin D levels in asthmatic children were also strongly associated with a reduced number of emergency department visits or hospitalizations during the first (odds ratio 0.880, 95% CI: 0.842-0.920; P<0.001), second (odds ratio 0.885, 95% CI: 0.832-0.941; P<0.001), and third years (odds ratio 0.922, 95% CI: 0.851-0.998; P=0.044) of follow-up. In addition, the vitamin D levels in asthmatic children were found to be negatively associated with the odds of large airway dysfunction (odds ratio 0.865, 95% CI: 0.771-0.970; P=0.013) and small airway dysfunction (odds ratio 0.922, 95% CI: 0.855-0.996; P=0.038) during the first year of follow-up. Conclusions: Sufficient vitamin D level is associated with lower risk of asthma exacerbations and health care utilization over a 3-year period, and improved lung function over 1 year. The protective effects of vitamin D on asthmatic children decreased over time.

Identification of potential key genes for immune infiltration in childhood asthma by data mining and biological validation.

Asthma is the most common chronic condition among children; however, the underlying molecular mechanism remains unclear. Dysregulated immune response and different infiltration states of immune cells are critical for asthma pathogenesis. Here, three childhood asthma gene expression datasets were used to detect key genes, immune cells, and pathways involved in childhood asthma. From these datasets, 33 common differentially expressed genes (DEGs) were identified, which showed enrichment in the T helper 1 (Th1) and T helper 2 (Th2) cell differentiation pathway and the T helper 17 (Th17) cell differentiation pathway. Using the weighted gene co-expression network analysis (WGCNA), CD3D and CD3G were identified as key genes closely correlated with childhood asthma. Upregulation of CD3D and CD3G was further validated in bronchoalveolar lavage cells from childhood asthmatics with control individuals by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The immune cell infiltration analysis indicated that CD3D and CD3G were negatively correlated with increased resting mast cells and eosinophils, and highly correlated with several cell markers of Th1, Th2, and Th17 cells. In addition, we found that CD3D and CD3G were closely related to the Th1 and Th2 cell differentiation pathway and the Th17 cell differentiation pathway. Our results reveal the important roles of two key genes and immune infiltration in the pathogenesis of childhood asthma. Thus, this study provides a new perspective for exploring potential molecular targets for childhood asthma treatment.

Regressive Autism Spectrum Disorder: High Levels of Total Secreted Amyloid Precursor Protein and Secreted Amyloid Precursor Protein-α in Plasma.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, repetitive behaviors, and parochial interests. Individuals with regressive ASD (RA), a unique subtype, have poor outcomes. Moreover, there are currently no validated blood-based biomarkers for ASD, hindering early diagnosis and treatment. This study was the first to examine plasma levels of total secreted amyloid precursor protein (sAPPtotal), secreted amyloid precursor protein-α (sAPPα), and secreted amyloid precursor protein-ß (sAPPß) in children diagnosed with RA (n = 23) and compare them with the levels in age-matched children with non-regressive ASD (NRA) (n = 23) and typically developing (TD) controls (n = 23). We found that sAPPtotal and sAPPα levels were significantly higher in children with RA than in children with NRA or in TD controls. In contrast, no difference was observed in sAPPß levels. In conclusion, increased plasma levels of sAPPtotal and sAPPα may be valuable biomarkers for the early identification of ASD regression. Prospective studies will be conducted using a larger sample to further investigate these differences.

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain. Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing (NGS) data. In addition, as somatic SNV detection can be impacted by multiple mutations, with germline mutations and co-occurrence of copy number variations (CNVs) common in organisms, we used the novel approach to distinguish somatic and germline mutations based on the NGS data from individual tumor samples. In summary, svmSomatic: (1) considers the influence of CNV co-occurrence in detecting somatic mutations; and (2) trains a support vector machine algorithm to distinguish between somatic and germline mutations, without requiring normal matched samples. We further tested and compared svmSomatic with other common methods. Results showed that svmSomatic performance, as measured by F1-score, was significantly better than that of others using both simulation and real NGS data.

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors.

Pulmonary large-cell neuroendocrine carcinomas (LCNECs) have similarities with other lung cancers, but their precise relationship has remained unclear. Here we perform a comprehensive genomic (n = 60) and transcriptomic (n = 69) analysis of 75 LCNECs and identify two molecular subgroups: "type I LCNECs" with bi-allelic TP53 and STK11/KEAP1 alterations (37%), and "type II LCNECs" enriched for bi-allelic inactivation of TP53 and RB1 (42%). Despite sharing genomic alterations with adenocarcinomas and squamous cell carcinomas, no transcriptional relationship was found; instead LCNECs form distinct transcriptional subgroups with closest similarity to SCLC. While type I LCNECs and SCLCs exhibit a neuroendocrine profile with ASCL1high/DLL3high/NOTCHlow, type II LCNECs bear TP53 and RB1 alterations and differ from most SCLC tumors with reduced neuroendocrine markers, a pattern of ASCL1low/DLL3low/NOTCHhigh, and an upregulation of immune-related pathways. In conclusion, LCNECs comprise two molecularly defined subgroups, and distinguishing them from SCLC may allow stratified targeted treatment of high-grade neuroendocrine lung tumors.

Transcriptomic profile of tobacco in response to Tomato zonate spot orthotospovirus infection.

BACKGROUND: Tomato zonate spot virus (TZSV), a dominant species of thrips-transmitted orthotospoviruses in Yunnan and Guangxi provinces in China, causes significant loss of yield in lots of crops and is a major threat to incomes of rural families. However, the detailed molecular mechanism of crop disease caused by TZSV remains obscure. METHODS: Next-generation sequencing (NGS)-based transcriptome analysis (RNA-seq) was performed to investigate and compare the gene expression changes in systemic leaves of tobacco upon infection with TZSV and mock-inoculated plants as a control. RESULTS: De novo assembly and analysis of tobacco transcriptome data by RNA-Seq identified 135,395 unigenes. 2102 differentially expressed genes (DEGs) were obtained in tobacco with TZSV infection, among which 1518 DEGs were induced and 584 were repressed. Gene Ontology enrichment analysis revealed that these DEGs were associated with multiple biological functions, including metabolic process, oxidation-reduction process, photosynthesis process, protein kinase activity. The KEGG pathway analysis of these DEGs indicated that pathogenesis caused by TZSV may affect multiple processes including primary and secondary metabolism, photosynthesis and plant-pathogen interactions. CONCLUSION: Our global survey of transcriptional changes in TZSV infected tobacco provides crucial information into the precise molecular mechanisms underlying pathogenesis and symptom development. This is the first report on the relationships in the TZSV-plant interaction using transcriptome analysis. Findings of present study will significantly help enhance our understanding of the complicated mechanisms of plant responses to orthotospoviral infection.

Two mouse models reveal an actionable PARP1 dependence in aggressive chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) remains an incurable disease. Two recurrent cytogenetic aberrations, namely del(17p), affecting TP53, and del(11q), affecting ATM, are associated with resistance against genotoxic chemotherapy (del17p) and poor outcome (del11q and del17p). Both del(17p) and del(11q) are also associated with inferior outcome to the novel targeted agents, such as the BTK inhibitor ibrutinib. Thus, even in the era of targeted therapies, CLL with alterations in the ATM/p53 pathway remains a clinical challenge. Here we generated two mouse models of Atm- and Trp53-deficient CLL. These animals display a significantly earlier disease onset and reduced overall survival, compared to controls. We employed these models in conjunction with transcriptome analyses following cyclophosphamide treatment to reveal that Atm deficiency is associated with an exquisite and genotype-specific sensitivity against PARP inhibition. Thus, we generate two aggressive CLL models and provide a preclinical rational for the use of PARP inhibitors in ATM-affected human CLL.ATM and TP53 mutations are associated with poor prognosis in chronic lymphocytic leukaemia (CLL). Here the authors generate mouse models of Tp53- and Atm-defective CLL mimicking the high-risk form of human disease and show that Atm-deficient CLL is sensitive to PARP1 inhibition.

Comprehensive genomic profiles of small cell lung cancer.

We have sequenced the genomes of 110 small cell lung cancers (SCLC), one of the deadliest human cancers. In nearly all the tumours analysed we found bi-allelic inactivation of TP53 and RB1, sometimes by complex genomic rearrangements. Two tumours with wild-type RB1 had evidence of chromothripsis leading to overexpression of cyclin D1 (encoded by the CCND1 gene), revealing an alternative mechanism of Rb1 deregulation. Thus, loss of the tumour suppressors TP53 and RB1 is obligatory in SCLC. We discovered somatic genomic rearrangements of TP73 that create an oncogenic version of this gene, TP73Δex2/3. In rare cases, SCLC tumours exhibited kinase gene mutations, providing a possible therapeutic opportunity for individual patients. Finally, we observed inactivating mutations in NOTCH family genes in 25% of human SCLC. Accordingly, activation of Notch signalling in a pre-clinical SCLC mouse model strikingly reduced the number of tumours and extended the survival of the mutant mice. Furthermore, neuroendocrine gene expression was abrogated by Notch activity in SCLC cells. This first comprehensive study of somatic genome alterations in SCLC uncovers several key biological processes and identifies candidate therapeutic targets in this highly lethal form of cancer.

netClass: an R-package for network based, integrative biomarker signature discovery.

In the past years, there has been a growing interest in methods that incorporate network information into classification algorithms for biomarker signature discovery in personalized medicine. The general hope is that this way the typical low reproducibility of signatures, together with the difficulty to link them to biological knowledge, can be addressed. Complementary to these efforts, there is an increasing interest in integrating different data entities (e.g. gene and miRNA expressions) into comprehensive models. To our knowledge, R-package netClass is the first software that addresses both, network and data integration. Besides several published approaches for network integration, it specifically contains our recently published STSVM method, which allows for additional integration of gene and miRNA expression data into one predictive classifier.

Network and data integration for biomarker signature discovery via network smoothed T-statistics.

Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN (http://cran.r-project.org).

Prognostic gene signatures for patient stratification in breast cancer: accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions.

BACKGROUND: Stratification of patients according to their clinical prognosis is a desirable goal in cancer treatment in order to achieve a better personalized medicine. Reliable predictions on the basis of gene signatures could support medical doctors on selecting the right therapeutic strategy. However, during the last years the low reproducibility of many published gene signatures has been criticized. It has been suggested that incorporation of network or pathway information into prognostic biomarker discovery could improve prediction performance. In the meanwhile a large number of different approaches have been suggested for the same purpose. METHODS: We found that on average incorporation of pathway information or protein interaction data did not significantly enhance prediction performance, but indeed greatly interpretability of gene signatures. Some methods (specifically network-based SVMs) could greatly enhance gene selection stability, but revealed only a comparably low prediction accuracy, whereas Reweighted Recursive Feature Elimination (RRFE) and average pathway expression led to very clearly interpretable signatures. In addition, average pathway expression, together with elastic net SVMs, showed the highest prediction performance here. RESULTS: The results indicated that no single algorithm to perform best with respect to all three categories in our study. Incorporating network of prior knowledge into gene selection methods in general did not significantly improve classification accuracy, but greatly interpretability of gene signatures compared to classical algorithms.

Biomarker gene signature discovery integrating network knowledge.

Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.