Background: Although the application of mesenchymal stem cells (MSCs) in engineered medicine, such as tissue regeneration, is well known, new evidence is emerging that shows that MSCs can also promote cancer progression, metastasis, and drug resistance. However, no large-scale cohort analysis of MSCs has been conducted to reveal their impact on the prognosis of cancer patients. Objective: We propose the MSC score as a novel surrogate for poor prognosis in pan-cancer Methods: We used single sample gene set enrichment analysis to quantify MSC-related genes into a signature score and identify the signature score as a potential independent prognostic marker for cancer using multivariate Cox regression analysis. TIDE algorithm and neural network were utilized to assess the predictive accuracy of MSC-related genes for immunotherapy. Results: MSC-related gene expression significantly differed between normal and tumor samples across the 33 cancer types. Cox regression analysis suggested the MSC score as an independent prognostic marker for kidney renal papillary cell carcinoma, mesothelioma, glioma, and stomach adenocarcinoma. The abundance of fibroblasts was also more representative of the MSC score than the stromal score. Our findings supported the combined use of the TIDE algorithm and neural network to predict the accuracy of MSC-related genes for immunotherapy. Conclusion: We comprehensively characterized the transcriptome, genome, and epigenetics of MSCs in pan-cancer and revealed the crosstalk of MSCs in the tumor microenvironment, especially with cancer-related fibroblasts. It is suggested that this may be one of the key sources of resistance to cancer immunotherapy.
{"title":"Integrated Analysis of Clinical Outcome of Mesenchymal Stem Cellrelated Genes in Pan-cancer","authors":"Mingzhe Jiang, Dantong Zhu, Dong Zhao, Yongye Liu, Jia Li, Zhendong Zheng","doi":"10.2174/0113892029291247240422060811","DOIUrl":"https://doi.org/10.2174/0113892029291247240422060811","url":null,"abstract":"Background: Although the application of mesenchymal stem cells (MSCs) in engineered medicine, such as tissue regeneration, is well known, new evidence is emerging that shows that MSCs can also promote cancer progression, metastasis, and drug resistance. However, no large-scale cohort analysis of MSCs has been conducted to reveal their impact on the prognosis of cancer patients. Objective: We propose the MSC score as a novel surrogate for poor prognosis in pan-cancer Methods: We used single sample gene set enrichment analysis to quantify MSC-related genes into a signature score and identify the signature score as a potential independent prognostic marker for cancer using multivariate Cox regression analysis. TIDE algorithm and neural network were utilized to assess the predictive accuracy of MSC-related genes for immunotherapy. Results: MSC-related gene expression significantly differed between normal and tumor samples across the 33 cancer types. Cox regression analysis suggested the MSC score as an independent prognostic marker for kidney renal papillary cell carcinoma, mesothelioma, glioma, and stomach adenocarcinoma. The abundance of fibroblasts was also more representative of the MSC score than the stromal score. Our findings supported the combined use of the TIDE algorithm and neural network to predict the accuracy of MSC-related genes for immunotherapy. Conclusion: We comprehensively characterized the transcriptome, genome, and epigenetics of MSCs in pan-cancer and revealed the crosstalk of MSCs in the tumor microenvironment, especially with cancer-related fibroblasts. It is suggested that this may be one of the key sources of resistance to cancer immunotherapy.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data. Method: Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers. Result: Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/Nano ML-5moU). Conclusion: NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.
{"title":"Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data","authors":"Jiayi Li, Feiyang Sun, Kunyang He, Lin Zhang, Jia Meng, Daiyun Huang, Yuxin Zhang","doi":"10.2174/0113892029288843240402042529","DOIUrl":"https://doi.org/10.2174/0113892029288843240402042529","url":null,"abstract":"Background: Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data. Method: Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers. Result: Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the \"AGTTC\" reference 5-mer dataset and a minimum AUROC of 0.8113 in the \"TGTGC\" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/Nano ML-5moU). Conclusion: NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.2174/0113892029296712240405053201
Rashid Mehmood
:: N6-methyladenosine (m6A) is an RNA modification wherein the N6-position of adenosine is methylated. It is one of the most prevalent internal modifications of RNA and regulates various aspects of RNA metabolism. M6A is deposited by m6A methyltransferases, removed by m6A demethylases, and recognized by reader proteins, which modulate splicing, export, translation, and stability of the modified mRNA. Recent evidence suggests that various classes of non-- coding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long con-coding RNAs (lncRNAs), are also targeted by this modification. Depending on the ncRNA species, m6A may affect the processing, stability, or localization of these molecules. The m6A-- modified ncRNAs are implicated in a number of diseases, including cancer. In this review, the author summarizes the role of m6A modification in the regulation and functions of ncRNAs in tumor development. Moreover, the potential applications in cancer prognosis and therapeutics are discussed.
{"title":"Ramifications of m6A Modification on ncRNAs in Cancer","authors":"Rashid Mehmood","doi":"10.2174/0113892029296712240405053201","DOIUrl":"https://doi.org/10.2174/0113892029296712240405053201","url":null,"abstract":":: N6-methyladenosine (m6A) is an RNA modification wherein the N6-position of adenosine is methylated. It is one of the most prevalent internal modifications of RNA and regulates various aspects of RNA metabolism. M6A is deposited by m6A methyltransferases, removed by m6A demethylases, and recognized by reader proteins, which modulate splicing, export, translation, and stability of the modified mRNA. Recent evidence suggests that various classes of non-- coding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long con-coding RNAs (lncRNAs), are also targeted by this modification. Depending on the ncRNA species, m6A may affect the processing, stability, or localization of these molecules. The m6A-- modified ncRNAs are implicated in a number of diseases, including cancer. In this review, the author summarizes the role of m6A modification in the regulation and functions of ncRNAs in tumor development. Moreover, the potential applications in cancer prognosis and therapeutics are discussed.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.2174/0113892029273121240401060228
Dan Li, Xulian Wan, Yu Yun, Yongkun Li, Weigang Duan
Background: Understanding organic functions at a molecular level is important for scientists to unveil the disease mechanism and to develop diagnostic or therapeutic methods. background: Understanding organic functions at a molecular level are important for scientists to unveil the mechanism of disease and to develop diagnostic or therapeutic methods. Aim: The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. objective: Understanding organic functions at a molecular level are important for scientists to unveil the mechanism of disease and to develop diagnostic or therapeutic methods. The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. Method: Three normal male Sprague-Dawley (SD) rats were anesthetized, their organs mentioned above were harvested, and RNA in the fresh organs was extracted. Purified RNA was reversely transcribed and sequenced using the Solexa high-throughput sequencing technique. The abundance of a gene was measured by the expected value of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM). Genes in organs with the highest expression level were sought out and compared with their median value in organs. If a gene in the highest expressed organ was significantly different (p < 0.05) from that in the medianly expressed organ, accompanied by q value < 0.05, and accounted for more than 70% of the total abundance, the gene was assumed as the selective gene in the organ. Results & Discussion: The Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) pathways were enriched by the highest expressed genes. Based on the criterion, 1,406 selective genes were screened out, 1,283 of which were described in the gene bank and 123 of which were waiting to be described. KEGG and GO pathways in the organs were partly confirmed by the known understandings and a good portion of the pathways needed further investigation. Conclusion: The novel selective genes and organic functional pathways are useful for scientists to unveil the mechanisms of the organs at the molecular level, and the selective genes’ products are candidate disease markers for organs.
{"title":"Genes Selectively Expressed in Rat Organs","authors":"Dan Li, Xulian Wan, Yu Yun, Yongkun Li, Weigang Duan","doi":"10.2174/0113892029273121240401060228","DOIUrl":"https://doi.org/10.2174/0113892029273121240401060228","url":null,"abstract":"Background: Understanding organic functions at a molecular level is important for scientists to unveil the disease mechanism and to develop diagnostic or therapeutic methods. background: Understanding organic functions at a molecular level are important for scientists to unveil the mechanism of disease and to develop diagnostic or therapeutic methods. Aim: The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. objective: Understanding organic functions at a molecular level are important for scientists to unveil the mechanism of disease and to develop diagnostic or therapeutic methods. The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. Method: Three normal male Sprague-Dawley (SD) rats were anesthetized, their organs mentioned above were harvested, and RNA in the fresh organs was extracted. Purified RNA was reversely transcribed and sequenced using the Solexa high-throughput sequencing technique. The abundance of a gene was measured by the expected value of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM). Genes in organs with the highest expression level were sought out and compared with their median value in organs. If a gene in the highest expressed organ was significantly different (p < 0.05) from that in the medianly expressed organ, accompanied by q value < 0.05, and accounted for more than 70% of the total abundance, the gene was assumed as the selective gene in the organ. Results & Discussion: The Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) pathways were enriched by the highest expressed genes. Based on the criterion, 1,406 selective genes were screened out, 1,283 of which were described in the gene bank and 123 of which were waiting to be described. KEGG and GO pathways in the organs were partly confirmed by the known understandings and a good portion of the pathways needed further investigation. Conclusion: The novel selective genes and organic functional pathways are useful for scientists to unveil the mechanisms of the organs at the molecular level, and the selective genes’ products are candidate disease markers for organs.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.2174/0113892029300247240325080421
Stefanos Roumeliotis, Maria Divani, Eleni Stamellou, Vassilios Liakopoulos
: Diabetic Kidney Disease (DKD) remains the leading cause of Chronic and End Stage Kidney Disease (ESKD) worldwide, with an increasing epidemiological burden. However, still, the disease awareness remains low, early diagnosis is difficult, and therapeutic management is ineffective. These might be attributed to the fact that DKD is a highly heterogeneous disease, with disparities and variability in clinical presentation and progression patterns. Besides environmental risk factors, genetic studies have emerged as a novel and promising tool in the field of DKD. Three decades ago, family studies first reported that inherited genetic factors might confer significant risk to DKD development and progression. During the past decade, genome-wide association studies (GWASs) screening the whole genome in large and multi-ethnic population-based cohorts identified genetic risk variants associated with traits defining DKD in both type 1 and 2 diabetes. Herein, we aim to summarize the existing data regarding the progress in the field of genomics in DKD, present how the revolution of GWAS expanded our understanding of pathophysiologic disease mechanisms and finally, suggest potential future directions.
{"title":"Genomics in Diabetic Kidney Disease: A 2024 Update","authors":"Stefanos Roumeliotis, Maria Divani, Eleni Stamellou, Vassilios Liakopoulos","doi":"10.2174/0113892029300247240325080421","DOIUrl":"https://doi.org/10.2174/0113892029300247240325080421","url":null,"abstract":": Diabetic Kidney Disease (DKD) remains the leading cause of Chronic and End Stage Kidney Disease (ESKD) worldwide, with an increasing epidemiological burden. However, still, the disease awareness remains low, early diagnosis is difficult, and therapeutic management is ineffective. These might be attributed to the fact that DKD is a highly heterogeneous disease, with disparities and variability in clinical presentation and progression patterns. Besides environmental risk factors, genetic studies have emerged as a novel and promising tool in the field of DKD. Three decades ago, family studies first reported that inherited genetic factors might confer significant risk to DKD development and progression. During the past decade, genome-wide association studies (GWASs) screening the whole genome in large and multi-ethnic population-based cohorts identified genetic risk variants associated with traits defining DKD in both type 1 and 2 diabetes. Herein, we aim to summarize the existing data regarding the progress in the field of genomics in DKD, present how the revolution of GWAS expanded our understanding of pathophysiologic disease mechanisms and finally, suggest potential future directions.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Nicotine degradation is a new strategy to block nicotine-induced pathology. The potential of human microbiota to degrade nicotine has not been explored. Aims: This study aimed to uncover the genomic potentials of human microbiota to degrade nicotine. Method: To address this issue, we performed a systematic annotation of Nicotine-Degrading Enzymes (NDEs) from genomes and metagenomes of human microbiota. A total of 26,295 genomes and 1,596 metagenomes for human microbiota were downloaded from public databases and five types of NDEs were annotated with a custom pipeline. We found 959 NdhB, 785 NdhL, 987 NicX, three NicA1, and three NicA2 homologs. Results: Genomic classification revealed that six phylum-level taxa, including Proteobacteria, Firmicutes, Firmicutes_A, Bacteroidota, Actinobacteriota, and Chloroflexota, can produce NDEs, with Proteobacteria encoding all five types of NDEs studied. Analysis of NicX prevalence revealed differences among body sites. NicX homologs were found in gut and oral samples with a high prevalence but not found in lung samples. NicX was found in samples from both smokers and non-smokers, though the prevalence might be different.
{"title":"Genomic and Metagenomic Insights into the Distribution of Nicotine-Degrading Enzymes in Human Microbiota","authors":"Ying Guan, Zhouhai Zhu, Qiyuan Peng, Meng Li, Xuan Li, Jia-Wei Yang, Yan-Hong Lu, Meng Wang, Bin-Bin Xie","doi":"10.2174/0113892029302230240319042208","DOIUrl":"https://doi.org/10.2174/0113892029302230240319042208","url":null,"abstract":"Introduction: Nicotine degradation is a new strategy to block nicotine-induced pathology. The potential of human microbiota to degrade nicotine has not been explored. Aims: This study aimed to uncover the genomic potentials of human microbiota to degrade nicotine. Method: To address this issue, we performed a systematic annotation of Nicotine-Degrading Enzymes (NDEs) from genomes and metagenomes of human microbiota. A total of 26,295 genomes and 1,596 metagenomes for human microbiota were downloaded from public databases and five types of NDEs were annotated with a custom pipeline. We found 959 NdhB, 785 NdhL, 987 NicX, three NicA1, and three NicA2 homologs. Results: Genomic classification revealed that six phylum-level taxa, including Proteobacteria, Firmicutes, Firmicutes_A, Bacteroidota, Actinobacteriota, and Chloroflexota, can produce NDEs, with Proteobacteria encoding all five types of NDEs studied. Analysis of NicX prevalence revealed differences among body sites. NicX homologs were found in gut and oral samples with a high prevalence but not found in lung samples. NicX was found in samples from both smokers and non-smokers, though the prevalence might be different.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.2174/0113892029236347240308054538
Óscar Álvarez-Machancoses, Eshel Faraggi, Enrique deAndrés-Galiana, Juan Fernández-Martínez, Andrzej Kloczkowski
Background: Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Methods: Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. objective: Roughly a half of known disease-related mutations are due to non-synonymous variants [8-9], expressed as amino-acid mutations. Therefore, it is important to unravel the links between nonsynonymous Single Nucleotide Variants and associated diseases to discriminate between pathogenic and neutral substitutions. It has been found that these substitutions could be directly related to pathological effects such as Parkinson’s or Alzheimer’s diseases, or to the involvement in complex diseases, such as cancer development. Results: We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Conclusion: Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.
背景：单氨基酸多态性（SAP）或非同义单核苷酸变异（nsSNV）是最常见的基因变异。它们是由错义突变引起的，在错义突变中，单碱基对置换改变了遗传密码，使特定位置的三联碱基（密码子）编码不同的氨基酸。由于基因突变有时会导致遗传疾病，因此理解和预见哪些变异是有害的，哪些变异是中性的（不会导致表型变化）就显得尤为重要。这可以看作是一个分类问题。方法：使用机器智能的计算方法正逐渐取代重复性的、价格过高的诱变试验。总的来说，nsSNVs 数据集的质量参差不齐、缺陷和不规则性削弱了基于人工智能的方法的便利性。因此，需要更强大、更精确的方法来解决这些问题。在本论文中，我们展示了一种建立在holdout采样器基础上的共识分类器，它显得强大而精确，超越了所有其他流行方法：已知的与疾病相关的突变中大约有一半是由非同义变异引起的[8-9]，表现为氨基酸突变。因此，揭示非同义单核苷酸变异与相关疾病之间的联系，以区分致病性和中性变异非常重要。研究发现，这些变异可能与帕金森病或阿尔茨海默病等病理效应直接相关，也可能与癌症发展等复杂疾病有关。结果在训练阶段，我们制作了 100 个暂存模型来测试不同分类器的结构和各种分类变量。我们选取了表现最出色的候选者来开发共识分类器，并使用 k 倍（1 ≤ k ≤5）交叉验证法进行测试。我们还研究了哪些蛋白质特性对精确预测 nsSNV 的影响影响最大。结论我们的 "共识保持采样器 "超越了其他流行的算法，取得了出色的结果，准确度高，标准偏差小。我们的方法的优势来自于使用一棵保留树，在这棵树上，基于 LM/AI 的不同程序以不同的方式进行采样。
{"title":"Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler","authors":"Óscar Álvarez-Machancoses, Eshel Faraggi, Enrique deAndrés-Galiana, Juan Fernández-Martínez, Andrzej Kloczkowski","doi":"10.2174/0113892029236347240308054538","DOIUrl":"https://doi.org/10.2174/0113892029236347240308054538","url":null,"abstract":"Background: Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Methods: Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. objective: Roughly a half of known disease-related mutations are due to non-synonymous variants [8-9], expressed as amino-acid mutations. Therefore, it is important to unravel the links between nonsynonymous Single Nucleotide Variants and associated diseases to discriminate between pathogenic and neutral substitutions. It has been found that these substitutions could be directly related to pathological effects such as Parkinson’s or Alzheimer’s diseases, or to the involvement in complex diseases, such as cancer development. Results: We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Conclusion: Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. Methods: A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Results: Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. Conclusions: The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.
{"title":"Testing the Significance of Ranked Gene Sets in Genome-Wide Transcriptome Profiling Data Using Weighted Rank Correlation Statistics","authors":"Min Yao, Hao He, Binyu Wang, Xinmiao Huang, Sunli Zheng, Jianwu Wang, Xuejun Gao, Tinghua Huang","doi":"10.2174/0113892029280470240306044159","DOIUrl":"https://doi.org/10.2174/0113892029280470240306044159","url":null,"abstract":"Objective: Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. Methods: A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called \"Flaver\" was developed. Results: Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. Conclusions: The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140147457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.2174/0113892029280454240214072212
Ali Mahmoudi, Mohammad Mahdi Hajihasani, Muhammed Majeed, Tannaz Jamialahmadi, Amirhossein Sahebkar
Background:: Calebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools, the present study explored the intersection of possible targets that interacted with Calebin-A and the essential genes involved in NAFLD. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique. Methods:: We first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study. Results:: We identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD. Conclusion:: Here, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.
{"title":"Effect of Calebin-A on Critical Genes Related to NAFLD: A Protein-Protein Interaction Network and Molecular Docking Study","authors":"Ali Mahmoudi, Mohammad Mahdi Hajihasani, Muhammed Majeed, Tannaz Jamialahmadi, Amirhossein Sahebkar","doi":"10.2174/0113892029280454240214072212","DOIUrl":"https://doi.org/10.2174/0113892029280454240214072212","url":null,"abstract":"Background:: Calebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools, the present study explored the intersection of possible targets that interacted with Calebin-A and the essential genes involved in NAFLD. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique. Methods:: We first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study. Results:: We identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD. Conclusion:: Here, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140004337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.2174/138920292501240205235837
Fabio Coppedè
{"title":"Preface 25 Years of Current Genomics.","authors":"Fabio Coppedè","doi":"10.2174/138920292501240205235837","DOIUrl":"10.2174/138920292501240205235837","url":null,"abstract":"","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140305146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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