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Systems Biology of Streptophyte Cell Evolution

Annual review of plant biology 2025年 2025 Jan 16页

作者： Elisa S Goldbecker Jan de Vries 1Department of Applied Bioinformatics Institute for Microbiology and Genetics University of Göttingen Göttingen Germany email: elisa.goldbecker@uni-goettingen.de devries.jan@uni-goettingen.de. 2Campus Institute Data Science (CIDAS) University of Göttingen Göttingen Germany. 3Department of Applied Bioinformatics Goettingen Center for Molecular Biosciences (GZMB) University of Göttingen Göttingen Germany.

More than 500 million years ago, a streptophyte algal population established a foothold on land and started terraforming Earth through an unprecedented radiation. This event is called plant terrestrialization and yielded the Embryophyta. Recent advancements in the field of plant evolutionary developmental biology (evo-devo) have propelled our knowledge of the closest algal relatives of land plants, the zygnematophytes, highlighting that several aspects of plant cell biology are shared between embryophytes and their sister lineage. High-throughput exploration determined that routes of signaling cascades, biosynthetic pathways, and molecular physiology predate plant terrestrialization. But how do they assemble into biological programs, and what do these programs tell us about the principal functions of the streptophyte cell? Here, we make the case that streptophyte algae are unique organisms for understanding the systems biology of the streptophyte cell, informing on not only the origin of embryophytes but also their fundamental biology.

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science Gateway canvas: A business reference model for science Gateways 1

Science Gateway canvas: A business reference model for Scien...

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1st Workshop on the science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM 2015

作者： Shahand, Shayan Van Kampen, Antoine H. C. Olabarriaga, Sílvia D. e-Science Group Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Bioinformatics Laboratory Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Biosystems Data Analysis Group Swammerdam Institute for Life Sciences University of Amsterdam Netherlands

ISBN: (纸本)9781450335669

science Gateways (SGs) have emerged as systems that facilitate access to cyberinfrastructures. There is a growing interest in the exploitation and development of SGs. However, it remains challenging to understand and design SG with the required properties because of the complex nature of SGs. Additionally, it is difficult to decide upon frameworks that can be used to build them. In this paper we propose the science Gateway Canvas, a business reference model for SGs that embodies the common SG functions and their organization into groups and categories. We used the science Gateway Canvas for a systematic analysis and comparison of the functions offered by a selection of available frameworks that are used to build SGs. This illustrated the applicability of the science Gateway Canvas as a comprehensive and generic reference model for understanding SGs. The canvas can also be used for systematic analysis by scientists who are searching for an existing SG that supports their research goal or developers who want to determine the functional requirements for a new SG. Copyright © 2015 ACM.

关键词： Business reference model Collaboratory Cyberinfrastructure e-Infrastructure e-Research environment e-science Problem Solving Environment (PSE) science Gateway (SG) science Gateway Canvas (SGC) science Gateway development science Gateway frameworks science Gateway technologies science Portal Virtual Laboratory (VL) Virtual Research Environment (VRE)

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Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from A data-driven Perspective

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Genomics, Proteomics & bioinformatics 2023年第1期21卷 164-176页

作者： Jianlei Gu Jiawei Dai Hui Lu Hongyu Zhao SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision MedicineShanghai Children’s HospitalShanghai 200040China Department of Biostatistics Yale UniversityNew HavenCT 06511USA

Comprehensive characterization of spatial and temporal gene expression patterns in humans is critical for uncovering the regulatory codes of the human genome and understanding the molecular mechanisms of human *** expressed genes(UEGs)refer to the genes expressed across a majority of,if not all,phenotypic and physiological conditions of an *** is known that many human genes are broadly expressed across ***,most previous UEG studies have only focused on providing a list of UEGs without capturing their global expression patterns,thus limiting the potential use of UEG *** this study,we proposed a novel data-driven framework to leverage the extensive collection of40,000 human transcriptomes to derive a list of UEGs and their corresponding global expression patterns,which offers a valuable resource to further characterize human *** results suggest that about half(12,234;49.01%)of the human genes are expressed in at least 80%of human transcriptomes,and the median size of the human transcriptome is 16,342 genes(65.44%).Through gene clustering,we identified a set of UEGs,named LoVarUEGs,which have stable expression across human transcriptomes and can be used as internal reference genes for expression *** further demonstrate the usefulness of this resource,we evaluated the global expression patterns for 16 previously predicted disallowed genes in islet beta cells and found that seven of these genes showed relatively more varied expression patterns,suggesting that the repression of these genes may not be unique to islet beta cells.

关键词： Ubiquitous expression Housekeeping gene Disallowed gene Expression specificity Expression variability

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Parallel Genome-Wide Analysis With Central And Graphic Processing Units

Parallel Genome-Wide Analysis With Central And Graphic Proce...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Muhamad Fitra Kacamarga James W.Baurley Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University

The Indonesia Colorectal Cancer Consortium(IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the Map Reduce and Compute Unified Device Architecture(CUDA) frameworks and evaluate performance(speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data(1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620(6 cores) and NVIDIA Tesla K20 over sequential processing.

关键词： Genome-wide Analysis Parallel Programming MapReduce CUDA

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CanSeer: a translational methodology for developing personalized cancer models and therapeutics

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Scientific Reports 2025年第1期15卷 1-22页

作者： Butt, Rida Nasir Amina, Bibi Sultan, Muhammad Umer Tanveer, Zain Bin Gondal, Mahnoor Naseer Hussain, Risham Khan, Salaar Akbar, Rida Nasir, Zainab Khalid, Muhammad Farhan Channan-Khan, Asher Alban Faisal, Amir Shoaib, Muhammad Chaudhary, Safee Ullah Biomedical Informatics and Engineering Research Laboratory (BIRL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor 48109 MI United States Michigan Center for Translational Pathology University of Michigan Ann Arbor 48109 MI United States Data Science Institute Lancaster University Lancaster LA1 4YW United Kingdom Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology Penn State University University Park 16802 PA United States Mayo Clinic School of Medicine New York University NY United States Cancer Therapeutics Lab Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Epigenome and Genome Integrity Lab (EaGIL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan

Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “CanSeer”—a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc. © The Author(s) 2025.

关键词： Biomolecular network modeling Dynamical analysis of in silico models Integrative omics Personalized mono- and combinatorial therapies Therapeutic screening Translational cancer systems biology

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CLASS: A general approach to classifying categorical sequences

CLASS: A general approach to classifying categorical sequenc...

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作者： Kelil, Abdellali Nordell-Markovits, Alexei Ousman Yassine Zaralahy, Parakh Wang, Shengrui ProspectUS Data Mining and Bioinformatics Laboratory Department of Computer Science Université de Sherbrooke Sherbrooke QC J1K 2R1 Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Signifi cant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches. © 2009 IEEE.

关键词： Semantics

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Short-Term Effects of Ambient Air Pollutants on Outpatient Visits for Childhood Allergic Diseases in Shanghai,China

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Journal of Shanghai Jiaotong university(science) 2024年第6期29卷 979-994页

作者： HU Yi GU Jianlei WU Dan WANG Xiaolei LU Hui YU Guangjun Center for Biomedical Informatics Shanghai Children’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200040China SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsShanghai Jiao Tong UniversityShanghai 200240China Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai 200040China

This study aims to investigate the short-term effects of ambient air pollutants on outpatient visits for childhood allergic *** data on ambient air pollutants(NO2,SO2,CO and PM2.5)and outpatient visits for childhood allergic diseases(asthma,atopic dermatitis and allergic rhinitis)were obtained in Shanghai,China from 2013 to *** effects of ambient air pollutants were estimated for total outpatient visits for childhood allergic diseases,gender and age stratification and disease classification by using distributed lag non-linear model(DLNM).We found positive associations between short-term exposure to air pollutants and childhood allergic *** and children aged 7 years old were more likely to be sensitive to ambient air ***2 and SO2 showed stronger effects on asthma and atopic dermatitis,*** study provides evidence that short-term exposure to ambient air pollutants can increase the risk of childhood allergic diseases.

关键词： childhood allergic diseases ambient air pollutant nitrogen dioxide(NO2) sulfur dioxide(SO2) particulate matter<2.5μm in diameter(PM2.5)

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YAP/TAZ activity in PDGFRα-expressing alveolar fibroblasts modulates AT2 proliferation through Wnt4

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Cell Reports 2025年第5期44卷 115645页

作者： Song, Jane Y. Wehbe, Fabien Wong, Aaron K. Hall, Ben M. Vander Heiden, Jason A. Brightbill, Hans D. Arron, Joseph R. Garfield, David A. Dey, Anwesha Rock, Jason R. Department of Immunology Discovery Genentech Inc. 1 DNA Way South San Francisco 94080 CA United States Data & Analytics Chapter-Computational Science Genentech Inc. 1 DNA Way South San Francisco 94080 CA United States Department of Immunology and Infectious Diseases Genentech Inc. 1 DNA Way South San Francisco 94080 CA United States Department of Bioinformatics Genentech Inc. 1 DNA Way South San Francisco 94080 CA United States Department of Discovery Oncology Genentech Inc. 1 DNA Way South San Francisco 94080 CA United States

The Hippo pathway, mediated by its transcriptional effectors Yes-associated protein 1 (YAP) and WW-domain-containing transcription regulator 1 (TAZ), is crucial in maintaining lung homeostasis and facilitating injury repair. While its roles in epithelial cells are well established, its regulatory effects on lung fibroblasts remain less understood. We engineered a mouse model for the inducible knockdown of YAP/TAZ and showed that fibroblast-specific knockdown enhances PDGFRα+ alveolar fibroblasts’ support for alveolar-epithelial-stem-cell-derived organoids in vitro. Single-cell profiling revealed changes in fibroblast subpopulations, including the emergence of a Wnt4+ enriched subpopulation. Epigenomic analyses revealed shifts in transcription factor motif enrichment in both fibroblasts and epithelial cells due to fibroblast YAP/TAZ suppression. Further computational and in vivo analyses confirmed increased Wnt signaling and Wnt4 expression in PDGFRα-lineage+ fibroblasts, which enhanced SPC+ alveolar type 2 (AT2) cell proliferation. These findings highlight a mechanistic role of YAP/TAZ in PDGFRα+ alveolar fibroblasts in supporting AT2 cell maintenance and proliferation via Wnt4 secretion. © 2025 The Authors

关键词： alveolar niche alveolar type 2 proliferation CP: Stem cell research lung regeneration PDGFRα+ alveolar fibroblasts Wnt Wnt4 YAP/TAZ

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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EPS inhibitor treatment of Salmonella impacts evolution without selecting for resistance to biofilm inhibition

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npj Biofilms and Microbiomes 2025年第1期11卷 1-15页

作者： Joos, Mathieu Van Ginneken, Sybren Villanueva, Xabier Dijkmans, Marie Coppola, Guglielmo A. Pérez-Romero, Camilo Andres Vackier, Thijs Van der Eycken, Erik Marchal, Kathleen Lories, Bram Steenackers, Hans P. KU Leuven - MiCA Lab Centre of Microbial and Plant Genetics Leuven Belgium Department of Chemistry KU Leuven - Laboratory for Organic & Microwave-Assisted Chemistry (LOMAC) Leuven Belgium Department of Plant Biotechnology and Bioinformatics UGent – Internet Technology and Data Science Lab (IDLab) Gent Belgium People’s Friendship University of Russia (RUDN University) Moscow Russian Federation

Virulence factors of pathogens, such as toxin production and biofilm formation, often exhibit a public character, providing benefits to nearby non-producers. Consequently, anti-virulence drugs targeting these public traits may not select for resistance, as resistant mutants that resume production of the virulence factor share the benefits of their resistance with surrounding sensitive cells. In agreement with this, we show that even after long-term treatment with a 2-amino-imidazole (2-AI) biofilm inhibitor, Salmonella populations remained as susceptible to biofilm inhibition as the ancestral populations. Nonetheless, further genotypic and phenotypic analysis revealed that the Salmonella populations did adapt to the treatment and accumulated mutations in efflux pump regulators and alternative sigma factors. These mutations resulted in a reduced biofilm-forming capacity and increased efflux activity. Their selection was due to a growth delaying side effect of the biofilm inhibitor. Enhanced efflux activity helped overcome this growth delay, providing a fitness advantage over the ancestor. Finally, we demonstrate that chemical modification of the inhibitor enhances its specificity by partially alleviating the unintended growth delay while retaining the anti-biofilm activity, which in turn eliminated the selection pressure for increased efflux. Overall, our findings highlight that while unintended side effects can complicate anti-virulence strategies, adaptation to these effects does not necessarily restore the inhibited virulence trait. Moreover, chemical modification can mitigate these unintended side effects and enhance drug specificity. © The Author(s) 2025.

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