In Situ Filling of the Oxygen Vacancies with Dual Heteroatoms in Co3O4 for Efficient Overall Water Splitting.

Electrocatalytic water splitting is a crucial area in sustainable energy development, and the development of highly efficient bifunctional catalysts that exhibit activity toward both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is of paramount importance. Co3O4 is a promising candidate catalyst, owing to the variable valence of Co, which can be exploited to enhance the bifunctional catalytic activity of HER and OER through rational adjustments of the electronic structure of Co atoms. In this study, we employed a plasma-etching strategy in combination with an in situ filling of heteroatoms to etch the surface of Co3O4, creating abundant oxygen vacancies, while simultaneously filling them with nitrogen and sulfur heteroatoms. The resulting N/S-VO-Co3O4 exhibited favorable bifunctional activity for alkaline electrocatalytic water splitting, with significantly enhanced HER and OER catalytic activity compared to pristine Co3O4. In an alkaline overall water-splitting simulated electrolytic cell, N/S-VO-Co3O4 || N/S-VO-Co3O4 showed excellent overall water splitting catalytic activity, comparable to noble metal benchmark catalysts Pt/C || IrO2, and demonstrated superior long-term catalytic stability. Additionally, the combination of in situ Raman spectroscopy with other ex situ characterizations provided further insight into the reasons behind the enhanced catalyst performance achieved through the in situ incorporation of N and S heteroatoms. This study presents a facile strategy for fabricating highly efficient cobalt-based spinel electrocatalysts incorporated with double heteroatoms for alkaline electrocatalytic monolithic water splitting.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC. METHODS: We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis. RESULTS: We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients. CONCLUSIONS: Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

Detecting pathway relationship in the context of human protein-protein interaction network and its application to Parkinson's disease.

In human physiological conditions like complex diseases, a large number of genes/proteins, as well as their interactions, are involved. Thus, detecting the biochemical pathways enriched in these genes/proteins and identifying the pathway relationships is critical to understand the molecular mechanisms underlying a disease and can also be valuable in selecting the potential molecular targets for further exploration. In this study, we proposed a method to measure the relationship between pathways based on their distribution in the human PPI network. By representing each pathway as a gene module in the PPI network, a distance was calculated to measure the closeness of two pathways. For the pathways in the KEGG database, a total of 2143 pathway pairs with close connections were identified. Additional evaluations indicated the pathway relationship built via such approach was consistent with available evidence. Further, based on the genes and pathways potentially associated with the pathogenesis of Parkinson's disease (PD), we analyzed the pathway relationship and identified the major pathways related to this disorder via the new method. Also, by analyzing the pathway interaction network constructed by the identified major pathways, we explored the potential pathway targets that may be important in the etiology and development of PD. In summary, we proposed an approach to measure the relationship between pathways, which could provide a more systematic profile on pathways involved in a phenotype, and may also help to improve the result of pathway enrichment analysis.

PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary.

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method. Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method. Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

Test development, optimization and validation of a WGS pipeline for genetic disorders.

BACKGROUND: With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. METHODS: In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. RESULTS: In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7-8 deletion of SMN1 gene) validated the effectiveness of our pipeline. CONCLUSIONS: This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.

Next generation sequencing is a highly reliable method to analyze exon 7 deletion of survival motor neuron 1 (SMN1) gene.

Spinal muscular atrophy (SMA) is one of the most common and severe genetic diseases. SMA carrier screening is an effective way to identify couples at risk of having affected children. Next-generation sequencing (NGS)-based expanded carrier screening could detect SMN1 gene copy number without extra experiment and with high cost performance. However, its performance has not been fully evaluated. Here we conducted a systematic comparative study to evaluate the performance of three common methods. 478 samples were analyzed with multiplex ligation probe amplification (MLPA), real-time quantitative polymerase chain reaction (qPCR) and NGS, simultaneously. Taking MLPA-based results as the reference, for 0 copy, 1 copy and ≥ 2 copy SMN1 analysis with NGS, the sensitivity, specificity and precision were all 100%. Using qPCR method, the sensitivity was 100%, 97.52% and 94.30%, respectively; 98.63%, 95.48% and 100% for specificity; and 72.72%, 88.72% and 100% for precision. NGS repeatability was higher than that of qPCR. Moreover, among three methods, NGS had the lowest retest rate. Thus, NGS is a relatively more reliable method for SMN1 gene copy number detection. In expanded carrier screening, compared with the combination of multiple methods, NGS method could reduce the test cost and simplify the screening process.

Performance characterization of PCR-free whole genome sequencing for clinical diagnosis.

ABSTRACT: To evaluate the performance of polymerase chain reaction (PCR)-free whole genome sequencing (WGS) for clinical diagnosis, and thereby revealing how experimental parameters affect variant detection.Five NA12878 samples were sequenced using MGISEQ-2000. NA12878 samples underwent WGS with differing deoxyribonucleic acid (DNA) input and library preparation protocol (PCR-based vs PCR-free protocols for library preparation). The depth of coverage and genotype quality of each sample were compared. The performance of each sample was measured for sensitivity, coverage of depth and breadth of coverage of disease-related genes, and copy number variants. We also developed a systematic WGS pipeline (PCR-free) for the analysis of 11 clinical cases.In general, NA12878-2 (PCR-free WGS) showed better depth of coverage and genotype quality distribution than NA12878-1 (PCR-based WGS). With a mean depth of ∼40×, the sensitivity of homozygous and heterozygous single nucleotide polymorphisms (SNPs) of NA12878-2 showed higher sensitivity (>99.77% and >99.82%) than NA12878-1, and positive predictive value exceeded 99.98% and 99.07%. The sensitivity and positive predictive value of homozygous and heterozygous indels for NA12878-2 (PCR-free WGS) showed great improvement than NA128878-1. The breadths of coverage for disease-related genes and copy number variants are slightly better for samples with PCR-free library preparation protocol than the sample with PCR-based library preparation protocol. DNA input also influences the performance of variant detection in samples with PCR-free WGS. All the 19 previously confirmed variants in 11 clinical cases were successfully detected by our WGS pipeline (PCR free).Different experimental parameters may affect variant detection for clinical WGS. Clinical scientists should know the range of sensitivity of variants for different methods of WGS, which would be useful when interpreting and delivering clinical reports.

Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders.

BACKGROUND: Due to its reduced cost and incomparable advantages, WGS is likely to lead to changes in clinical diagnosis of rare and undiagnosed diseases. However, the sensitivity and breadth of coverage of clinical WGS as a diagnostic test for genetic disorders has not been fully evaluated. METHODS: Here, the performance of WGS in NA12878, the YH cell line, and the Chinese trios were measured by assessing their sensitivity, PPV, depth and breadth of coverage using MGISEQ-2000. We also compared the performance of WES and WGS using NA12878. The sensitivity and PPV were tested using the family-based trio design for the Chinese trios. We further developed a systematic WGS pipeline for the analysis of 8 clinical cases. RESULTS: In general, the sensitivity and PPV for SNV/indel detection increased with mean depth and reached a plateau at an ~ 40X mean depth using down-sampling samples of NA12878. With a mean depth of 40X, the sensitivity of homozygous and heterozygous SNPs of NA12878 was > 99.25% and > 99.50%, respectively, and the PPV was 99.97% and 98.96%. Homozygous and heterozygous indels showed lower sensitivity and PPV. The sensitivity and PPV were still not 100% even with a mean depth of ~ 150X. We also observed a substantial variation in the sensitivity of CNV detection across different tools, especially in CNVs with a size less than 1 kb. In general, the breadth of coverage for disease-associated genes and CNVs increased with mean depth. The sensitivity and coverage of WGS (~ 40X) was better than WES (~ 120X). Among the Chinese trios with an ~ 40X mean depth, the sensitivity among offspring was > 99.48% and > 96.36% for SNP and indel detection, and the PPVs were 99.86% and 97.93%. All 12 previously validated variants in the 8 clinical cases were successfully detected using our WGS pipeline. CONCLUSIONS: The current standard of a mean depth of 40X may be sufficient for SNV/indel detection and identification of most CNVs. It would be advisable for clinical scientists to determine the range of sensitivity and PPV for different classes of variants for a particular WGS pipeline, which would be useful when interpreting and delivering clinical reports.

Characterization of genomic clones by targeted deep sequencing of ctDNA to monitor liver cancer.

BACKGROUND: In recent years, the morbidity and mortality of cancer patients have continued to increase in China, and there is an urgent need to develop an effective method to monitor tumor dynamics and measure tumor burden. Derived from the cell-free fraction of blood in cancer patients, circulating tumor DNA (ctDNA) has been regarded as a promising surrogate for tumor tissue biopsies. With the development of sequencing technology, ctDNA has been recognized as a specific and highly sensitive biomarker, and it has become a hot research spot in recent years. METHODS: In this paper, we investigated clonal changes before and after surgery in liver cancer patients using ctDNA. RESULTS: First, we evaluated the accuracy and stability of the method in ctDNA detection using virtual tumor samples with known mutations. The results showed that our method detected variants with an allelic frequency of at least 0.5%. We then applied this method to 34 liver cancer patients. A total of 266 clinically relevant mutations were identified in the pretreatment plasma samples. Through the analysis of plasma DNA samples at different treatment time points, we also investigated the possibility of using ctDNA as a prognostic factor to reflect tumor dynamics and to evaluate clinical responses. CONCLUSIONS: The results demonstrated that targeted high-depth next-generation sequencing can be used in ctDNA detection. Compared to traditional biopsy, the detection of ctDNA provides more information for human liver cancer, which is essential to guide the selection of therapy and predict prognosis.

Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein-protein interaction network.

Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene's propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein-protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.

Analyzing the genes related to nicotine addiction or schizophrenia via a pathway and network based approach.

The prevalence of tobacco use in people with schizophrenia is much higher than in general population, which indicates a close relationship between nicotine addiction and schizophrenia. However, the molecular mechanism underlying the high comorbidity of tobacco smoking and schizophrenia remains largely unclear. In this study, we conducted a pathway and network analysis on the genes potentially associated with nicotine addiction or schizophrenia to reveal the functional feature of these genes and their interactions. Of the 276 genes associated with nicotine addiction and 331 genes associated with schizophrenia, 52 genes were shared. From these genes, 12 significantly enriched pathways associated with both diseases were identified. These pathways included those related to synapse function and signaling transduction, and drug addiction. Further, we constructed a nicotine addiction-specific and schizophrenia-specific sub-network, identifying 11 novel candidate genes potentially associated with the two diseases. Finally, we built a schematic molecular network for nicotine addiction and schizophrenia based on the results of pathway and network analysis, providing a systematic view to understand the relationship between these two disorders. Our results illustrated that the biological processes underlying the comorbidity of nicotine addiction and schizophrenia was complex, and was likely induced by the dysfunction of multiple molecules and pathways.

GRONS: a comprehensive genetic resource of nicotine and smoking.

Nicotine, the primary psychoactive component in tobacco, can exert a broad impact on both the central and peripheral nervous systems. During the past years, a tremendous amount of efforts has been put to exploring the molecular mechanisms underlying tobacco smoking related behaviors and diseases, and many susceptibility genes have been identified via various genomic approaches. For many human complex diseases, there is a trend towards collecting and integrating the data from genetic studies and the biological information related to them into a comprehensive resource for further investigation, but we have not found such an effort for nicotine addiction or smoking-related phenotypes yet. To collect, curate, and integrate cross-platform genetic data so as to make them interpretable and easily accessible, we developed Genetic Resources Of Nicotine and Smoking (GRONS), a comprehensive database for genes related to biological response to nicotine exposure, tobacco smoking related behaviors or diseases. GRONS deposits genes from nicotine addiction studies in the following four categories, i.e. association study, genome-wide linkage scan, expression analysis on genes/proteins via high-throughput technologies, as well as single gene/protein-based experimental studies via literature search. Moreover, GRONS not only provides tools for data browse, search and graphical presentation of gene prioritization, but also presents the results from comprehensive bioinformatics analyses for the prioritized genes associated with nicotine addiction. With more and more genetic data and analysis tools integrated, GRONS will become a useful resource for studies focusing on nicotine addiction or tobacco smoking. Database URL: http://bioinfo.tmu.edu.cn/GRONS/.