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Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

ISCIENCE 2023年第9期26卷 107587页

作者： Zhang, Ge Cui, Xiaolin Qin, Zhen Wang, Zeyu Lu, Yongzheng Xu, Yanyan Xu, Shuai Tang, Laiyi Zhang, Li Liu, Gangqiong Wang, Xiaofang Zhang, Jinying Tang, Junnan Zhengzhou Univ Affiliated Hosp 1 Dept Orthoped Zhengzhou 450052 Henan Peoples R China Henan Prov Key Lab Cardiac Injury & Repair Zhengzhou 450052 Henan Peoples R China Henan Prov Clin Res Ctr Cardiovasc Dis Zhengzhou 450052 Henan Peoples R China Chinese Univ Hong Kong Shenzhen Sch Med Shenzhen 518172 Peoples R China

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for pla-que destabilization and rupture is important for preventing the transition from coronary stability to insta-bility and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presen-tation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quan-tification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, em-powering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

关键词： Cardiovascular medicine Risk factor computational bioinformatics Complex system biology

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Visualizing gene expression changes in time, space, and single cells with expressyouRcell

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ISCIENCE 2023年第6期26卷

作者： Paganin, Martina Tebaldi, Toma Lauria, Fabio Viero, Gabriella Inst Biophys CNR Unit Trento Trento Italy Yale Univ Sch Med Yale Comprehens Canc Ctr New Haven CT USA Univ Trento Dept CIBIO Trento Italy

The last decade has witnessed massive advancements in high-throughput techniques capable of producing increasingly complex gene expression datasets across time and space and at the resolution of single cells. Yet, the large volume of big data available and the complexity of experimental designs hamper an easy understanding and effective communication of the results. We present expressyouRcell, an easy-to-use R package to map the multi-dimensional variations of transcript and protein levels in dynamic cell pictographs. expressyouRcell visualizes gene expression variations as pictographic representations of cell-type thematic maps. expressyouRcell visually reduces the complexity of displaying gene expression and protein level changes across multiple measurements (time points or single-cell trajectories) by generating dynamic representations of cellular pictographs. We applied expressyouRcell to single cell, bulk RNA sequencing (RNA-seq), and proteomics datasets, demonstrating its flexibility and usability in the visualization of complex variations in gene expression. Our approach improves the standard quantitative interpretation and communication of relevant results.

关键词： computational bioinformatics

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An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients

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ISCIENCE 2023年第10期26卷 108042页

作者： Leventhal, Emily L. Daamen, Andrea R. Grammer, Amrie C. Lipsky, Peter E. AMPEL BioSolut LLC Charlottesville VA 22902 USA RILITE Res Inst Charlottesville VA 22902 USA

Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.

关键词： Immunology Clinical genetics Immune system disorder computational bioinformatics

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Analysis of age-related left ventricular collagen remodeling in living donors: Implications in arrhythmogenesis

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ISCIENCE 2022年第2期25卷 103822页

作者： Garcia-Mendivil, Laura Perez-Zabalza, Maria Mountris, Konstantinos Duwe, Sam Smisdom, Nick Perez, Marta Lujan, Lluis Wolfs, Esther Driesen, Ronald B. Maria Vallejo-Gil, Jose Carlos Fresneda-Roldan, Pedro Fananas-Mastral, Javier Vazquez-Sancho, Manuel Matamala-Adell, Marta Fernando Sorribas-Berjon, Juan Andre Bellido-Morales, Javier Javier Mancebon-Sierra, Francisco Sebastian Vaca-Nunez, Alexander Ballester-Cuenca, Carlos Olivan-Viguera, Aida Diez, Emiliano Ordovas, Laura Pueyo, Esther Univ Zaragoza Aragon Inst Engn Res Biomed Signal Interpretat & Computat Simulat Grp Zaragoza 50018 Spain IIS Aragon BSICoS Zaragoza 50018 Spain Hasselt Univ Adv Opt Microscopy Ctr Biomed Res Inst B-3500 Diepenbeek Belgium Hasselt Univ Biomed Res Inst B-3500 Diepenbeek Belgium Univ Zaragoza Dept Anat Embryol & Anim Genet Zaragoza 50013 Spain Univ Zaragoza Inst Univ Invest Mixto Agroalimentario Aragon IA2 Zaragoza 50013 Spain Univ Zaragoza Dept Anim Pathol Zaragoza 50013 Spain Univ Hosp Miguel Servet Dept Cardiovasc Surg Zaragoza 50009 Spain Biomed Res Networking Ctr Bioengn Biomat & Nanome Zaragoza 50018 Spain Consejo Nacl Invest Cient & Tecn Inst Expt Med & Biol Cuyo IMBECU RA-5500 Mendoza Argentina ARAID Fdn Zaragoza 50018 Spain

Age-related fibrosis in the left ventricle (LV) has been mainly studied in animals by assessing collagen content. Using second-harmonic generation microscopy and image processing, we evaluated amount, aggregation and spatial distribution of LV collagen in young to old pigs, and middle-age and elder living donors. All collagen features increased when comparing adult and old pigs with young ones, but not when comparing adult with old pigs or middle-age with elder individuals. Remarkably, all collagen parameters strongly correlated with lipofuscin, a biological age marker, in humans. By building patient-specific models of human ventricular tissue electrophysiology, we confirmed that amount and organization of fibrosis modulated arrhythmia vulnerability, and that distribution should be accounted for arrhythmia risk assessment. In conclusion, we characterize the age associated changes in LV collagen and its potential implications for ventricular arrhythmia development. Consistency between pig and human results substantiate the pig as a relevant model of age-related LV collagen dynamics.

关键词： Disease Pathophysiology computational bioinformatics

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Polyvalent guide RNAs for CRISPR antivirals

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ISCIENCE 2022年第11期25卷 105333页

作者： Bagchi, Rammyani Tinker-Kulberg, Rachel Salehin, Mohammad Supakar, Tinku Chamberlain, Sydney Ligaba-Osena, Ayalew Josephs, Eric A. Univ N Carolina Dept Nanosci Greensboro NC 27401 USA Univ N Carolina Dept Biol Greensboro NC 27401 USA

CRISPR effector Cas13 recognizes and degrades RNA molecules that are complementary to its guide RNA (gRNA) and possesses potential as an antiviral biotechnology because it can degrade viralmRNAandRNAgenomes. Because multiplexed targeting is a critical strategy to improve viral suppression, we developed a strategy to design of gRNAs where individual gRNAs have maximized activity at multiple viral targets, simultaneously, by exploiting the molecular biophysics of promiscuous target recognition by Cas13. These ``polyvalent'' gRNA sequences (``pgRNAs'') provide superior antiviral elimination across tissue/organ scales in a higher organism (Nicotiana benthamiana) compared to conventionally-designed gRNAs-reducing detectable viral RNA by >30-fold, despite lacking perfect complementarity with either of their targets and, when multiplexed, reducing viral RNA by >99.5%. Pairs of pgRNA-targetable sequences are abundant in the genomes of RNA viruses, and this work highlights the need for specific approaches to the challenges of targeting viruses in eukaryotes using CRISPR.

关键词： Biocomputational method computational bioinformatics Biological sciences tools

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Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE

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ISCIENCE 2022年第11期25卷 105299页

作者： Cao, Buwen Li, Renfa Xiao, Sainan Deng, Shuguang Zhou, Xiangjun Zhou, Lang Hunan City Univ Coll Informat & Elect Engn Yiyang 413000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Key Lab Embedded & Cyber Phys Syst Changsha 410082 Peoples R China

Predicting associations between microRNAs (miRNAs) and diseases from the viewpoint of function modules has become increasingly popular However, existing methods obtained the relations between diseases and miRNAs only through the construction of similarity networks and neglected the complex network characteristic. In this paper, a new method named combining mirNA function similarities and network topology similarities based on module identification in networks (ComSim-MINE) was developed. Combined similarity is calculated from the harmonic mean between miRNA function similarities aid network topology similarities. Experimental results showed that ComSim-MINE can compete with several state-of-the-art weighted function module algorithms, such as ClusterONE, MCODE, NEMO, and SPICi, and achieved the satisfactory results in terms of the composite score of F-measure, sensitivity, and accuracy based on the generated miRNA function interaction network. From the analysis of case studies, some new findings obtained from our proposed method provide clinicians new clues for epidemic diseases, such as COVID-19.

关键词： Biological sciences bioinformatics computational bioinformatics

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Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting

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ISCIENCE 2022年第1期25卷 103668页

作者： Tang, Catherine Krantsevich, Artem MacCarthy, Thomas SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA SUNY Stony Brook Laufer Ctr Phys & Quantitat Biol Stony Brook NY 11794 USA Merck & Co Inc Exploratory Sci Ctr Cambridge MA USA

B cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase eta hotspots contribute further mutations. computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (k-mers) failed to explain divergent mutability for the same k-mer. We developed the DeepSHM (Deep learning on SHM) model using k-mers of size 5-21, improving accuracy over previous models. Interpretation of DeepSHM identified an extended WWRCT motif with particularly high mutability. Increased mutability was further associated with lower surrounding G content. Our model also discovered a conserved AGYCTGGGGG (Y=C/T) motif within FW1 of IGHV3 family genes with unusually high T>G substitution rates. Thus, a wider sequence context increases predictive power and identifies features that drive mutational targeting.

关键词： Biological sciences computational bioinformatics Immunology

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Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis

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ISCIENCE 2022年第6期25卷 104385页

作者： Cohen, Alan A. Leung, Diana L. Legault, Veronique Gravel, Dominique Blanchet, F. Guillaume Cote, Anne-Marie Fulop, Tamas Lee, Juhong Dufour, Frederik Liu, Mingxin Nakazato, Yuichi Univ Sherbrooke PRIMUS Res Grp Dept Family Med Sherbrooke PQ J1H 5N4 Canada Res Ctr Aging Sherbrooke PQ J1H 4C4 Canada Ctr Hosp Univ Sherbrooke Res Ctr Sherbrooke PQ J1H 5N4 Canada Univ Sherbrooke Dept Biol Sherbrooke PQ J1K 2R1 Canada Univ Sherbrooke Dept Math Sherbrooke PQ J1K 2R1 Canada Univ Sherbrooke Dept Sci Sante Communautaires Sherbrooke PQ J1H 5N4 Canada Univ Sherbrooke Dept Med Nephrol Div Sherbrooke PQ J1H 5N4 Canada Univ Sherbrooke Dept Med Geriatr Div Sherbrooke PQ J1H 5N4 Canada Ctr Hosp Univ Sherbrooke InfoCtr Ctr Integth Univ Sante & Serv Sociaux Estrie Sherbrooke PQ J1H 5N4 Canada Hakuyukai Med Corp Yuai Nisshin Clin Div Nephrol Kita Ku 2-1914-6 Nisshin Cho Saitama Saitama 3310823 Japan Yale Univ Dept Pathol New Haven CT 06519 USA

Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability-measured by coefficients of variation (CVs)-increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio;AUC up to 0.82) and starts to increase markedly similar to 3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.

关键词： health sciences medicine health informatics biological sciences bioinformatics computational bioinformatics

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Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

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ISCIENCE 2022年第12期25卷 105499页

作者： Pillai, Maalavika Chen, Zihao Jolly, Mohit Kumar Li, Chunhe Indian Inst Sci Ctr Biosyst Sci & Engn Bangalore India Indian Inst Sci Undergrad Programme Bangalore India Fudan Univ Shanghai Ctr Math Sci Shanghai Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai Peoples R China Fudan Univ Sch Math Sci Shanghai Peoples R China Fudan Univ MOE Frontiers Ctr Brain Sci Shanghai Peoples R China

Drug resistance and tumor relapse in patients with melanoma is attributed to a combination of genetic and non-genetic mechanisms. Dedifferentiation, a common mechanism of non-genetic resistance in melanoma is characterized by the loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remain poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switching in melanoma based on its underlying regulatory network. These landscapes reveal the existence of multiple alternative paths to resistance-de-differentiation and transition to a hyper-pigmented phenotype. Finally, by visualizig the changes in the landscape during in silico molecular perturbations, we identify combinatorial strategies that can lead to the most optimal outcome-a landscape with the minimum occupancy of the two drug-resistant states. Therefore, we present these landscapes as platforms to screen possible therapeutic interventions in terms of their ability to lead to the most favorable patient outcomes.

关键词： computational bioinformatics Cancer systems biology Cancer

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Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization

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ISCIENCE 2022年第10期25卷 105237页

作者： Ikeda, Kei Nakada, Taka-Aki Kageyama, Takahiro Tanaka, Shigeru Yoshida, Naoki Ishikawa, Tetsuo Goshima, Yuki Otaki, Natsuko Iwami, Shingo Shimamura, Teppei Taniguchi, Toshibumi Igari, Hidetoshi Hanaoka, Hideki Yokote, Koutaro Tsuyuzaki, Koki Nakajima, Hiroshi Kawakami, Eiryo Chiba Univ Grad Sch Med Dept Allergy & Clin Immunol Chiba 2608670 Japan Chiba Univ Grad Sch Med Dept Emergency & Crit Care Med Chiba 2608670 Japan Chiba Univ Grad Sch Med Artificial Intelligence Med Chiba 2608670 Japan RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project ADSP Yokohama Kanagawa 2300045 Japan Nagoya Univ Grad Sch Sci Div Biol Sci Interdisciplinary Biol Lab iBLab Nagoya Aichi 4648602 Japan Kyushu Univ Inst Math Ind Fukuoka 8190395 Japan Kyoto Univ Inst Adv Study Human Biol ASHBi Kyoto 6068501 Japan RIKEN Interdisciplinary Theoret & Math Sci Program iTHEM Wako Saitama 3510198 Japan Japanese Fdn Canc Res JFCR NEXT Ganken Program Tokyo 1358550 Japan Sci Groove Inc Fukuoka 8100041 Japan Nagoya Univ Grad Sch Med Div Syst Biol Nagoya Aichi 4668550 Japan Chiba Univ Chiba Univ Hosp Dept Infect Dis Chiba 2608670 Japan Chiba Univ Chiba Univ Hosp COVID Vaccine Ctr 19 Chiba 2608670 Japan Chiba Univ Chiba Univ Hosp Clin Res Ctr Chiba 2608670 Japan Chiba Univ Grad Sch Med Dept Endocrinol Hematol & Gerontol Chiba 2608670 Japan RIKEN Ctr Biosyst Dynam Res Lab Bioinformat Res Wako Saitama 3510198 Japan Chiba Univ Inst Adv Acad Res IAAR Chiba 2608670 Japan

Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based plat-form. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

关键词： Immunology computational bioinformatics machine learning

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