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Quantitative analysis of disease-related metabolic dysregulation of human microbiota

ISCIENCE 2023年第1期26卷 105868页

作者： Fumagalli, Maria Rita Saro, Stella Maria Tajana, Matteo Zapperi, Stefano Porta, Caterina A. M. La Univ Milan Ctr Complex & Biosyst Dept Environm Sci & Policy Via Celoria 26 I-20133 Milan Italy CNR Ist Biofis Via Marini 6 I-16149 Genoa Italy Univ Milan Ctr Complex & Biosyst Dept Phys Via Celoria 16 I-20133 Milan Italy CNR Ist Chim Mat Condensata & Tecnol Energia Via R Cozzi 53 I-20125 Milan Italy

The metabolic activity of all the micro-organism composing the human micro -biome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on meta-bolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.

关键词： computational bioinformatics Microbiome

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A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia

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ISCIENCE 2023年第9期26卷 107591页

作者： Moysiadis, Theodoros Koparanis, Dimitris Liapis, Konstantinos Ganopoulou, Maria Vrachiolias, George Katakis, Ioannis Moyssiadis, Chronis Vizirianakis, Ioannis S. Angelis, Lefteris Fokianos, Konstantinos Kotsianidis, Ioannis Democritus Univ Univ Hosp Alexandroupolis Dept Hematol Thrace Med Sch Alexandroupolis 68100 Greece Aristotle Univ Thessaloniki Sch Informat Thessaloniki 54124 Greece Univ Nicosia Sch Sci & Engn Dept Comp Sci CY-2417 Nicosia Cyprus Aristotle Univ Thessaloniki Sch Math Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Sch Pharm Thessaloniki 54124 Greece Univ Nicosia Sch Life & Hlth Sci Dept Life Sci CY-2417 Nicosia Cyprus Univ Cyprus Dept Math & Stat CY-1678 Nicosia Cyprus

Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

关键词： Health sciences Mathematical biosciences computational bioinformatics Cancer systems biology

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Predicting Human Clinical Outcomes Using Mouse Multi-Organ Transcriptome

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ISCIENCE 2020年第2期23卷 100791-100791页

作者： Kozawa, Satoshi Sagawa, Fumihiko Endo, Satsuki De Almeida, Glicia Maria Mitsuishi, Yuto Sato, Thomas N. Karydo TherapeutiX Inc Kyoto Japan ERATO Sato Live Bioforecasting Project Kyoto Japan Adv Telecommun Res Inst Int Thomas N Sato BioMEC X Labs 2-2-2 HikaridaiSeika Cho Kyoto 6190288 Japan Cornell Univ Biomed Engn Ithaca NY 14850 USA Centenary Inst Sydney NSW Australia Nara Med Univ V iClinix Lab Nara Japan

Approximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database "humanized" by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.

关键词： Biocomputational Method bioinformatics Biological Sciences computational bioinformatics Pharmacoinformatics

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DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding

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ISCIENCE 2021年第6期24卷 102455页

作者： Ji, Bo-Ya You, Zhu-Hong Wang, Yi Li, Zheng-Wei Wong, Leon Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xinjiang Lab Minor Speech & Language Informat Pro Urumqi 830011 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database.

关键词： computational bioinformatics Systems biology Cancer

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Systematic analysis reveals a pan-cancer SNHG family signature predicting prognosis and immunotherapy response

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ISCIENCE 2023年第10期26卷 108055页

作者： Zheng, Haotian Wang, Guanghui Wang, Yadong Liu, Jichang Ma, Guoyuan Du, Jiajun Shandong Univ Shandong Prov Hosp Inst Oncol Jinan Peoples R China Shandong First Med Univ Shandong Prov Hosp Dept Thorac Surg Jinan Peoples R China Shandong Univ Shandong Prov Hosp Dept Thorac Surg Jinan Peoples R China

Small nucleolar RNA host genes (SNHGs) are a special family of long non-coding RNAs (lncRNAs), which not only function in a way similar to other lncRNAs but also influence the intracellular level of small nucleolar RNAs to modulate cancers. However, the features of SNHGs and their role in the prognosis and immunotherapeutic response of human cancer have not been explored. We found that SNHGs were commonly deregulated and correlated with patient survival in various cancers. The critical role of DNA methylation and somatic alterations on deregulation was also identified. SNHG family score was significantly associated with survival, multiple tumor characteristics, and tumor microenvironment. SNHG-related risk score could serve as a prognostic and immunotherapeutic response biomarker based on multiple databases. This study emphasizes the potential of SNHGs as biomarkers for prognosis and immunotherapeutic response, enabling further research into the immune regulatory mechanism and therapeutic potentials of SNHGs in cancer.

关键词： Immune response computational bioinformatics Expression study Cancer

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Understanding locomotion in trilobites by means of three-dimensional models

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ISCIENCE 2023年第9期26卷 107512页

作者： Esteve, Jorge Rubio, Pedro UCM Fac CC Geol Dept Geodinam Estratig & Paleontol Area Paleontol Jose Antonio Novais 12 Madrid 28040 Spain Burashi SL Avda Ma Zambrano 24-6B Zaragoza 50018 Spain

Trilobites were one of the first animals on Earth to leave their imprints on the sea floor. Such imprints represent behavioral traces related to feeding or protection, in both cases implying different types of locomotion. Modeling how trilobites moved is essential to understand their evolutionary history and ecological impact on marine substrates. Herein, locomotion in trilobites is approached by means of three-dimensional models, which yielded two main gait types. These two gaits reflect basic behaviors: burrowing and walking. This model reveals that trilobites could change their gait and consequently increase rapidly their speed varying the amplitude of the metachronal wave, a change independent from their biological structure. Fast increases in speed enhanced the protection of trilobites against predators and sudden environmental crises. The trilobite body pattern constrained their gaits, controlled by the distance between the pair of legs and between legs in a same segment.

关键词： Wildlife behavior Paleontology computational bioinformatics

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Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

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ISCIENCE 2023年第7期26卷 107013页

作者： Cai, Qidong He, Boxue Tu, Guangxu Peng, Weilin Shi, Shuai Qian, Banglun Liang, Qingchun Peng, Shaoliang Tao, Yongguang Wang, Xiang Cent South Univ Xiangya Hosp 2 Dept Thorac Surg Changsha 410011 Peoples R China Cent South Univ Xiangya Hosp 2 Hunan Key Lab Early Diag & Precise Treatment Lung Changsha 410011 Peoples R China Cent South Univ Xiangya Hosp 2 Dept Pathol Changsha 410011 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Cent South Univ Xiangya Hosp Dept Pathol Key Lab Carcinogenesis & Canc Invas Changsha 410078 Hunan Peoples R China Cent South Univ Canc Res Inst NHC Key Lab Carcinogenesis Changsha 410078 Hunan Peoples R China Cent South Univ Sch Basic Med Changsha 410078 Hunan Peoples R China

Exploring early detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

关键词： bioinformatics Biological sciences Cancer computational bioinformatics Genomic analysis

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An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

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ISCIENCE 2023年第5期26卷 106679页

作者： Chen, Zhiqing Xuan, Ping Heidari, Ali Asghar Liu, Lei Wu, Chengwen Chen, Huiling Escorcia-Gutierrez, Jose Mansour, Romany F. Wenzhou Polytech Sch Intelligent Mfg Wenzhou 325035 Peoples R China Shantou Univ Sch Engn Dept Comp Sci Shantou 515063 Peoples R China Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Univ Costa Dept Computat Sci & Elect CUC Barranquilla 080002 Colombia New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.

关键词： Genetics computational bioinformatics Algorithms

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Application of a deep learning algorithm in the detection of hip fractures

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ISCIENCE 2023年第8期26卷 107350页

作者： Gao, Yan Soh, Nicholas Yock Teck Liu, Nan Lim, Gilbert Ting, Daniel Cheng, Lionel Tim-Ee Min, Kang Liew, Charlene Oh, Hong Choon Tan, Jin Rong Venkataraman, Narayan Goh, Siang Hiong Yan, Yet Yen Singapore Hlth Serv SingHlth Changi Gen Hosp Hlth Serv Res Singapore Singapore Singapore Hlth Serv SingHlth Changi Gen Hosp Dept Diagnost Radiol Singapore Singapore Duke NUS Med Sch Ctr Quantitat Med Singapore Singapore Singapore Hlth Serv SingHlth Duke NUS Med Sch Singapore Singapore Singapore Gen Hosp Dept Diagnost Radiol Singapore Singapore Duke NUS Med Sch Radiol Sci ACP Singapore Singapore Singapore Hlth Serv SingHlth Changi Gen Hosp Dept Med Informat Singapore Singapore Singapore Hlth Serv SingHlth Changi Gen Hosp Dept Emergency Med Singapore Singapore

This paper describes the development of a deep learning model for prediction of hip fractures on pelvic radiographs (X-rays). Developed using over 40,000 pelvic radiographs from a single institution, the model demonstrated high sensitivity and specificity when applied to a test set of emergency department radiographs. This study approximates the real-world application of a deep learning fracture detection model by including radiographs with sub-optimal image quality, other non-hip fractures, and metallic implants, which were excluded from prior published work. The study also explores the effect of ethnicity on model performance, as well as the accuracy of visualization algorithm for fracture localization.

关键词： computational bioinformatics Imaging anatomy Musculoskeletal anatomy

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Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

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ISCIENCE 2023年第12期26卷 108473页

作者： Baron, Cantin Cherkaoui, Sarah Therrien-Laperriere, Sandra Ilboudo, Yann Poujol, Raphael Mehanna, Pamela Garrett, Melanie E. Telen, Marilyn J. Ashley-Koch, Allison E. Bartolucci, Pablo Rioux, John D. Lettre, Guillaume Rosiers, Christine Des Ruiz, Matthieu Hussin, Julie G. Univ Montreal Dept Biochim & Med Mol Montreal PQ Canada Montreal Heart Inst Montreal PQ Canada Univ Childrens Hosp Zurich Univ Zurich Childrens Res Ctr Div Oncol Zurich Switzerland Univ Paris Saclay Gustave Roussy Canc Ctr Dept Pediat & Adolescent Oncol Villejuif France Duke Mol Inst Duke Med Ctr Durham NC USA Duke Univ Med Ctr Dept Med Div Hematol Durham NC USA Univ Paris Est Creteil Hop Univ Henri Mondor Sickle Cell Referral Ctr AP HPUMGGR Creteil France Univ Paris Est Creteil Lab Excellence LABEX IMRB Creteil France Univ Montreal Dept Med Montreal PQ Canada Univ Montreal Dept Nutr Montreal PQ Canada

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehen-sive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

关键词： Association analysis computational bioinformatics Quantitative genetics

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