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Bayesian multivariate genetic analysis improves translational insights

ISCIENCE 2023年第10期26卷 107854页

作者： Urbut, Sarah M. Koyama, Satoshi Hornsby, Whitney Bhukar, Rohan Kheterpal, Sumeet Truong, Buu Selvaraj, Margaret S. Neale, Benjamin O'Donnell, Christopher J. Peloso, Gina M. Natarajan, Pradeep Massachusetts Gen Hosp Cardiovasc Res Ctr Boston MA 02114 USA Broad Inst Program Med & Populat Genet Cambridge MA 02142 USA Harvard Med Sch Dept Med Boston MA 02115 USA Massachusetts Gen Hosp Analyt & Translat Genet Unit Boston MA 02114 USA VA Boston Healthcare Syst Dept Vet Affairs Boston MA 02130 USA Boston Univ Sch Publ Hlth Dept Biostat Boston MA 02218 USA

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

关键词： Human genetics Biocomputational method computational bioinformatics Genomic analysis Association analysis

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Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial

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ISCIENCE 2023年第9期26卷 107634页

作者： Chu, Xianjing Niu, Lishui Yang, Xianghui He, Shiqi Li, Aixin Chen, Liu Liang, Zhan Jing, Di Zhou, Rongrong Cent South Univ Xiangya Hosp Dept Oncol Changsha 410008 Peoples R China Changsha Cent Hosp Dept Oncol Changsha 410004 Peoples R China Univ British Columbia Dept Comp Sci 2329 West Mall Vancouver BC Canada Univ South Affiliated Hosp 1 Hengyang Med Sch Dept Radiotherapy Hengyang 421001 Peoples R China Cent South Univ Xiangya Hosp Xiangya Lung Canc Ctr Changsha 410008 Peoples R China Cent South Univ Xiangya Hosp Natl Clin Res Ctr Geriatr Disorders Changsha 410008 Peoples R China

Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.

关键词： Health sciences Health informatics Oncology computational bioinformatics Cancer

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Gene presence/absence variation in Mytilus galloprovincialis and its implications in gene expression and adaptation

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ISCIENCE 2023年第10期26卷 107827页

作者： Saco, Amaro Rey-Campos, Magali Gallardo-Escarate, Cristian Gerdol, Marco Novoa, Beatriz Figueras, Antonio Spanish Natl Res Council Inst Marine Res Vigo Spain Univ Concepcion Ctr Aquaculture Res Concepcion Chile Univ Trieste Dept Life Sci Trieste Italy

Presence/absence variation (PAV) is a well-known phenomenon in prokaryotes that was described for the first time in bivalves in 2020 in Mytilus galloprovincialis. The objective of the present study was to further our understanding of the PAV phenomenon in mussel biology. The distribution of PAV was studied in a mussel chromosome-level genome assembly, revealing a widespread distribution but with hotspots of dispensability. Special attention was given to the effect of PAV in gene expression, since dispensable genes were found to be inherently subject to distortions due to their sparse distribution among individuals. Furthermore, the high expression and strong tissue specificity of some dispensable genes, such as myticins, strongly supported their biological relevance. The significant differences in the repertoire of dispensable genes associated with two geographically distinct populations suggest that PAV is involved in local adaptation. Overall, the PAV phenomenon would provide a key selective advantage at the population level.

关键词： Genetics bioinformatics computational bioinformatics Genomic analysis Omics

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Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

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ISCIENCE 2023年第8期26卷 107331页

作者： Roemer, Margaretha G. M. van de Brug, Tim Bosch, Erik Berry, Daniella Hijmering, Nathalie Stathi, Phylicia Weijers, Karin Doorduijn, Jeannette Bromberg, Jacoline van de Wiel, Mark Ylstra, Bauke de Jong, Daphne Kim, Yongsoo Vrije Univ Amsterdam Canc Ctr Amsterdam Dept Pathol Amsterdam UMC Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Publ Hlth Res Inst Dept Epidemiol & Data Sci Amsterdam UMC Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands Univ Amsterdam Dept Pathol HOVON Pathol Facil & Biobank HOP Med Ctr Amsterdam Netherlands Univ Med Ctr Rotterdam Erasmus MC Canc Inst Dept Hematol Rotterdam Netherlands Univ Med Ctr Rotterdam Erasmus MC Canc Inst Brain Tumor Ctr Dept Neurooncol Rotterdam Netherlands

To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.

关键词： Oncology Neuroscience Cell biology Mathematical biosciences computational bioinformatics

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Quantitative analysis of disease-related metabolic dysregulation of human microbiota

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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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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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