Molecular Systems Biology最新文献_第4页

Author Correction: From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions. 作者更正：从粗到细：不同生长条件下大肠杆菌蛋白质组的绝对值。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-11-01 DOI: 10.1038/s44320-024-00062-5

Matteo Mori, Zhongge Zhang, Amir Banaei-Esfahani, Jean-Benoît Lalanne, Hiroyuki Okano, Ben C Collins, Alexander Schmidt, Olga T Schubert, Deok-Sun Lee, Gene-Wei Li, Ruedi Aebersold, Terence Hwa, Christina Ludwig

引用次数: 0

Correction of a widespread bias in pooled chemical genomics screens improves their interpretability. 纠正集合化学基因组学筛选中的普遍偏差，提高其可解释性。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-11-01 Epub Date: 2024-09-30 DOI: 10.1038/s44320-024-00069-y

Lili M Kim, Horia Todor, Carol A Gross

引用次数: 0

Author Correction: Predictive evolution of metabolic phenotypes using model-designed environments. 作者更正：利用模型设计的环境预测代谢表型的进化。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-11-01 DOI: 10.1038/s44320-024-00066-1

Paula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Sandra Castillo, Payam Ghiaci, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramon Gonzalez, Pilar Morales, Kiran R Patil

引用次数: 0

Prediction of the 3D cancer genome from whole-genome sequencing using InfoHiC. 利用 InfoHiC 从全基因组测序预测三维癌症基因组。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-11-01 Epub Date: 2024-09-25 DOI: 10.1038/s44320-024-00065-2

Yeonghun Lee, Sung-Hye Park, Hyunju Lee

{"title":"Prediction of the 3D cancer genome from whole-genome sequencing using InfoHiC.","authors":"Yeonghun Lee, Sung-Hye Park, Hyunju Lee","doi":"10.1038/s44320-024-00065-2","DOIUrl":"10.1038/s44320-024-00065-2","url":null,"abstract":"The 3D genome prediction in cancer is crucial for uncovering the impact of structural variations (SVs) on tumorigenesis, especially when they are present in noncoding regions. We present InfoHiC, a systemic framework for predicting the 3D cancer genome directly from whole-genome sequencing (WGS). InfoHiC utilizes contig-specific copy number encoding on the SV contig assembly, and performs a contig-to-total Hi-C conversion for the cancer Hi-C prediction from multiple SV contigs. We showed that InfoHiC can predict 3D genome folding from all types of SVs using breast cancer cell line data. We applied it to WGS data of patients with breast cancer and pediatric patients with medulloblastoma, and identified neo topologically associating domains. For breast cancer, we discovered super-enhancer hijacking events associated with oncogenic overexpression and poor survival outcomes. For medulloblastoma, we found SVs in noncoding regions that caused super-enhancer hijacking events of medulloblastoma driver genes (GFI1, GFI1B, and PRDM6). In addition, we provide trained models for cancer Hi-C prediction from WGS at https://github.com/dmcb-gist/InfoHiC , uncovering the impacts of SVs in cancer patients and revealing novel therapeutic targets.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1156-1172"},"PeriodicalIF":8.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proteome-wide copy-number estimation from transcriptomics. 从转录组学估算整个蛋白质组的拷贝数

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-11-01 Epub Date: 2024-09-27 DOI: 10.1038/s44320-024-00064-3

Andrew J Sweatt, Cameron D Griffiths, Sarah M Groves, B Bishal Paudel, Lixin Wang, David F Kashatus, Kevin A Janes

{"title":"Proteome-wide copy-number estimation from transcriptomics.","authors":"Andrew J Sweatt, Cameron D Griffiths, Sarah M Groves, B Bishal Paudel, Lixin Wang, David F Kashatus, Kevin A Janes","doi":"10.1038/s44320-024-00064-3","DOIUrl":"10.1038/s44320-024-00064-3","url":null,"abstract":"Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1230-1256"},"PeriodicalIF":8.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community. Yeast9：由社区编辑的 S. cerevisiae 共识基因组尺度代谢模型。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-10-01 Epub Date: 2024-08-12 DOI: 10.1038/s44320-024-00060-7

Chengyu Zhang, Benjamín J Sánchez, Feiran Li, Cheng Wei Quan Eiden, William T Scott, Ulf W Liebal, Lars M Blank, Hendrik G Mengers, Mihail Anton, Albert Tafur Rangel, Sebastián N Mendoza, Lixin Zhang, Jens Nielsen, Hongzhong Lu, Eduard J Kerkhoven

{"title":"Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.","authors":"Chengyu Zhang, Benjamín J Sánchez, Feiran Li, Cheng Wei Quan Eiden, William T Scott, Ulf W Liebal, Lars M Blank, Hendrik G Mengers, Mihail Anton, Albert Tafur Rangel, Sebastián N Mendoza, Lixin Zhang, Jens Nielsen, Hongzhong Lu, Eduard J Kerkhoven","doi":"10.1038/s44320-024-00060-7","DOIUrl":"10.1038/s44320-024-00060-7","url":null,"abstract":"Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1134-1150"},"PeriodicalIF":8.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dengue virus preferentially uses human and mosquito non-optimal codons. 登革热病毒优先使用人类和蚊子的非最佳密码子。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-10-01 Epub Date: 2024-07-22 DOI: 10.1038/s44320-024-00052-7

Luciana A Castellano, Ryan J McNamara, Horacio M Pallarés, Andrea V Gamarnik, Diego E Alvarez, Ariel A Bazzini

{"title":"Dengue virus preferentially uses human and mosquito non-optimal codons.","authors":"Luciana A Castellano, Ryan J McNamara, Horacio M Pallarés, Andrea V Gamarnik, Diego E Alvarez, Ariel A Bazzini","doi":"10.1038/s44320-024-00052-7","DOIUrl":"10.1038/s44320-024-00052-7","url":null,"abstract":"Codon optimality refers to the effect that codon composition has on messenger RNA (mRNA) stability and translation level and implies that synonymous codons are not silent from a regulatory point of view. Here, we investigated the adaptation of virus genomes to the host optimality code using mosquito-borne dengue virus (DENV) as a model. We demonstrated that codon optimality exists in mosquito cells and showed that DENV preferentially uses nonoptimal (destabilizing) codons and avoids codons that are defined as optimal (stabilizing) in either human or mosquito cells. Human genes enriched in the codons preferentially and frequently used by DENV are upregulated during infection, and so is the tRNA decoding the nonoptimal and DENV preferentially used codon for arginine. We found that adaptation during single-host passaging in human or mosquito cells results in the selection of synonymous mutations towards DENV's preferred nonoptimal codons that increase virus fitness. Finally, our analyses revealed that hundreds of viruses preferentially use nonoptimal codons, with those infecting a single host displaying an even stronger bias, suggesting that host-pathogen interaction shapes virus-synonymous codon choice.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1085-1108"},"PeriodicalIF":8.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes. 葡萄酒酵母的高度并行实验室进化，以提高代谢表型。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-10-01 Epub Date: 2024-08-22 DOI: 10.1038/s44320-024-00059-0

Payam Ghiaci, Paula Jouhten, Nikolay Martyushenko, Helena Roca-Mesa, Jennifer Vázquez, Dimitrios Konstantinidis, Simon Stenberg, Sergej Andrejev, Kristina Grkovska, Albert Mas, Gemma Beltran, Eivind Almaas, Kiran R Patil, Jonas Warringer

{"title":"Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes.","authors":"Payam Ghiaci, Paula Jouhten, Nikolay Martyushenko, Helena Roca-Mesa, Jennifer Vázquez, Dimitrios Konstantinidis, Simon Stenberg, Sergej Andrejev, Kristina Grkovska, Albert Mas, Gemma Beltran, Eivind Almaas, Kiran R Patil, Jonas Warringer","doi":"10.1038/s44320-024-00059-0","DOIUrl":"10.1038/s44320-024-00059-0","url":null,"abstract":"Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1109-1133"},"PeriodicalIF":8.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases. 对基因变异的组织感知解释推进了罕见病的病因学研究。

IF 9.9 1区生物学

Molecular Systems Biology Pub Date : 2024-09-16 DOI: 10.1038/s44320-024-00061-6

Chanan M Argov,Ariel Shneyour,Juman Jubran,Eric Sabag,Avigdor Mansbach,Yair Sepunaru,Emmi Filtzer,Gil Gruber,Miri Volozhinsky,Yuval Yogev,Ohad Birk,Vered Chalifa-Caspi,Lior Rokach,Esti Yeger-Lotem

{"title":"Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.","authors":"Chanan M Argov,Ariel Shneyour,Juman Jubran,Eric Sabag,Avigdor Mansbach,Yair Sepunaru,Emmi Filtzer,Gil Gruber,Miri Volozhinsky,Yuval Yogev,Ohad Birk,Vered Chalifa-Caspi,Lior Rokach,Esti Yeger-Lotem","doi":"10.1038/s44320-024-00061-6","DOIUrl":"https://doi.org/10.1038/s44320-024-00061-6","url":null,"abstract":"Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted \"Tissue Risk Assessment of Causality by Expression for variants\" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":"1 1","pages":""},"PeriodicalIF":9.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rescuing error control in crosslinking mass spectrometry. 挽救交联质谱中的误差控制。

IF 8.5 1区生物学

Molecular Systems Biology Pub Date : 2024-09-01 Epub Date: 2024-08-02 DOI: 10.1038/s44320-024-00057-2

Lutz Fischer, Juri Rappsilber

引用次数: 0