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Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition. 热蛋白质组分析与多组学数据的网络整合解码了 PARP 抑制作用。
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-01 Epub Date: 2024-03-07 DOI: 10.1038/s44320-024-00025-w
Mira L Burtscher, Stephan Gade, Martin Garrido-Rodriguez, Anna Rutkowska, Thilo Werner, H Christian Eberl, Massimo Petretich, Natascha Knopf, Katharina Zirngibl, Paola Grandi, Giovanna Bergamini, Marcus Bantscheff, Maria Fälth-Savitski, Julio Saez-Rodriguez

Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.

复杂的疾病表型往往跨越多个分子过程。对这些过程进行功能表征可以揭示疾病机制和药物作用。热蛋白质组分析(TPP)是一种基于质谱(MS)的技术,可评估蛋白质热稳定性的变化,这些变化可作为蛋白质功能变化的代用指标。TPP 的这些独特见解可以补充其他全息技术的不足。在这里,我们展示了如何利用多组学整合框架 COSMOS,通过基于网络的方法将 TPP 与磷酸化蛋白质组学和转录组学整合在一起,从而提供转录因子、激酶和热稳定性发生变化的蛋白质的综合视图。这使我们能够恢复卵巢癌细胞中聚合(ADP-核糖)聚合酶(PARP)抑制对细胞周期和DNA损伤反应以及干扰素和希波信号转导的影响。我们发现,TPP 提供了一个与其他全息数据模式互补的视角,它的整合使我们能够获得有关 PARP 抑制的更完整的分子概况。我们预计这种策略可用于整合功能蛋白质组学和其他全局数据,以研究分子过程。
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引用次数: 0
Hats off to 20S proteasome substrate discovery. 向 20S 蛋白酶体底物的发现致敬。
IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-01 Epub Date: 2024-03-12 DOI: 10.1038/s44320-024-00028-7
Taylor R Church, Anna Brennan, Seth S Margolis
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引用次数: 0
Dissecting the spatiotemporal diversity of adult neural stem cells. 剖析成体神经干细胞的时空多样性
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-01 Epub Date: 2024-02-16 DOI: 10.1038/s44320-024-00022-z
Nina Mitic, Anika Neuschulz, Bastiaan Spanjaard, Julia Schneider, Nora Fresmann, Klara Tereza Novoselc, Taraneh Strunk, Lisa Münster, Pedro Olivares-Chauvet, Jovica Ninkovic, Jan Philipp Junker

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

成体干细胞对组织更替和再生非常重要。然而,在大多数成体系统中,干细胞如何呈现不同的功能状态并支持空间模式化的组织结构仍是一个谜。在这里,我们剖析了成年斑马鱼大脑中神经干细胞的多样性,该器官的特点是明显的分区和高再生能力。我们将解剖脑区的单细胞转录组学与大规模平行谱系追踪和体内 RNA 代谢标记相结合,分析了神经干细胞在空间和时间上的调控。我们检测到了神经干细胞的巨大多样性,其中一些亚型局限于单一脑区,而另一些亚型则遍布整个大脑。全局干细胞状态与神经源分化有关,不同状态的干细胞参与增殖和非增殖分化。我们的工作揭示了成体干细胞组织的原理,并为神经干细胞亚型的功能操作建立了资源。
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引用次数: 0
AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. 人工智能引导的蛋白质-蛋白质相互作用药物发现管道确定了一种 SARS-CoV-2 抑制剂。
IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-01 Epub Date: 2024-03-11 DOI: 10.1038/s44320-024-00019-8
Philipp Trepte, Christopher Secker, Julien Olivet, Jeremy Blavier, Simona Kostova, Sibusiso B Maseko, Igor Minia, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Mara J Liebich, Nadine Scharek, Anja Schütz, Marcel Sperling, Michael Lisurek, Yang Wang, Kerstin Spirohn, Tong Hao, Michael A Calderwood, David E Hill, Markus Landthaler, Soon Gang Choi, Jean-Claude Twizere, Marc Vidal, Erich E Wanker

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

蛋白质-蛋白质相互作用(PPIs)为扩大可药用蛋白质组和治疗各种疾病提供了巨大的机会,但仍然是药物发现的挑战性靶点。在这里,我们提供了一个结合实验和计算工具的综合管道,用于识别和验证 PPI 靶点并进行早期药物发现。我们开发了一种机器学习方法,通过分析来自二元 PPI 检测或 AlphaFold-Multimer 预测的定量数据来确定相互作用的优先次序。利用定量检测 LuTHy 和我们的机器学习算法,我们确定了 SARS-CoV-2 蛋白质之间的高置信度相互作用,我们使用 AlphaFold-Multimer 预测了这些蛋白质的三维结构。我们利用 VirtualFlow,通过超大规模虚拟药物筛选,锁定了 NSP10-NSP16 SARS-CoV-2 甲基转移酶复合物的接触界面。因此,我们找到了一种化合物,它能与 NSP10 结合并抑制其与 NSP16 的相互作用,同时还能破坏复合物的甲基转移酶活性以及 SARS-CoV-2 的复制。总之,这条研究路线将有助于确定 PPI 靶点的优先次序,从而加速发现以蛋白质复合物和通路为靶点的早期候选药物。
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引用次数: 0
Estrogen receptor activation remodels TEAD1 gene expression to alleviate hepatic steatosis. 雌激素受体激活可重塑 TEAD1 基因表达,从而缓解肝脏脂肪变性。
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-01 Epub Date: 2024-03-08 DOI: 10.1038/s44320-024-00024-x
Christian Sommerauer, Carlos J Gallardo-Dodd, Christina Savva, Linnea Hases, Madeleine Birgersson, Rajitha Indukuri, Joanne X Shen, Pablo Carravilla, Keyi Geng, Jonas Nørskov Søndergaard, Clàudia Ferrer-Aumatell, Grégoire Mercier, Erdinc Sezgin, Marion Korach-André, Carl Petersson, Hannes Hagström, Volker M Lauschke, Amena Archer, Cecilia Williams, Claudia Kutter

Sex-based differences in obesity-related hepatic malignancies suggest the protective roles of estrogen. Using a preclinical model, we dissected estrogen receptor (ER) isoform-driven molecular responses in high-fat diet (HFD)-induced liver diseases of male and female mice treated with or without an estrogen agonist by integrating liver multi-omics data. We found that selective ER activation recovers HFD-induced molecular and physiological liver phenotypes. HFD and systemic ER activation altered core liver pathways, beyond lipid metabolism, that are consistent between mice and primates. By including patient cohort data, we uncovered that ER-regulated enhancers govern central regulatory and metabolic genes with clinical significance in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, including the transcription factor TEAD1. TEAD1 expression increased in MASLD patients, and its downregulation by short interfering RNA reduced intracellular lipid content. Subsequent TEAD small molecule inhibition improved steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Thus, TEAD1 emerged as a new therapeutic candidate whose inhibition ameliorates hepatic steatosis.

肥胖相关肝脏恶性肿瘤的性别差异表明雌激素具有保护作用。我们利用临床前模型,通过整合肝脏多组学数据,剖析了雌激素受体(ER)同工酶在雌雄小鼠接受或不接受雌激素激动剂治疗的高脂饮食(HFD)诱导的肝脏疾病中的分子反应。我们发现,选择性ER激活可恢复HFD诱导的肝脏分子和生理表型。HFD和全身性ER激活改变了核心肝脏通路,而不仅仅是脂质代谢,这在小鼠和灵长类动物之间是一致的。通过纳入患者队列数据,我们发现ER调节的增强子控制着代谢功能障碍相关性脂肪性肝病(MASLD)患者中具有临床意义的中心调控和代谢基因,包括转录因子TEAD1。TEAD1在MASLD患者中的表达增加,通过短干扰RNA对其进行下调可降低细胞内脂质含量。随后的 TEAD 小分子抑制剂通过抑制脂肪生成途径,改善了原代人类肝细胞球体的脂肪变性。因此,TEAD1 成为一种新的候选疗法,其抑制作用可改善肝脂肪变性。
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引用次数: 0
Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease. 基础 MET 磷酸化是肝病中肝细胞失调的一个指标。
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-03-01 Epub Date: 2024-01-12 DOI: 10.1038/s44320-023-00007-4
Sebastian Burbano de Lara, Svenja Kemmer, Ina Biermayer, Svenja Feiler, Artyom Vlasov, Lorenza A D'Alessandro, Barbara Helm, Christina Mölders, Yannik Dieter, Ahmed Ghallab, Jan G Hengstler, Christiane Körner, Madlen Matz-Soja, Christina Götz, Georg Damm, Katrin Hoffmann, Daniel Seehofer, Thomas Berg, Marcel Schilling, Jens Timmer, Ursula Klingmüller

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

慢性肝病在全球范围内呈上升趋势。由于发病率迅速上升,特别是在西方国家,代谢功能障碍相关性脂肪性肝病(MASLD)正变得越来越重要,因为该病可发展为肝细胞癌。肝细胞中的脂质积累已被确定为 MASLD 发病过程中的特征性结构变化,但导致疾病进展的分子机制仍未解决。在这里,我们发现在以西式饮食(WD)喂养的临床前模型的原代肝细胞中,基础 MET 磷酸化增加,PI3K-AKT 通路强烈下调。肝细胞生长因子(HGF)信号转导动态通路建模与全局蛋白质组学相结合,发现基础 MET 磷酸化率升高是导致 WD 肝细胞增殖的信号改变的主要驱动因素。对患者来源肝细胞的模型适应显示,基础 MET 磷酸化的患者特异性差异与肝脏手术后的患者预后相关。因此,失调的基础 MET 磷酸化可作为肝脏健康状况的指标,从而告知患者术后出现肝功能衰竭的风险。
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引用次数: 0
A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X. 无处不在的 GC 含量特征是 DDX3X 多模式 mRNA 调控的基础。
IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-03-01 Epub Date: 2024-01-25 DOI: 10.1038/s44320-024-00013-0
Ziad Jowhar, Albert Xu, Srivats Venkataramanan, Francesco Dossena, Mariah L Hoye, Debra L Silver, Stephen N Floor, Lorenzo Calviello

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

从转录到蛋白质合成的过程障碍重重,因此转录后基因表达调控有多种模式。DDX3X 是 mRNA 生物学中的一个基本角色,它是一种 RNA 结合蛋白,能规范地调节 mRNA 翻译。通过监测 DDX3X 缺失后 mRNA 丰度和翻译的动态,我们观察到翻译受抑制的 mRNA 趋于稳定。我们利用可解释的统计学习模型发现,编码序列中的 GC 含量是 RNA 稳定的主要特征。这一结果证实了其他研究中检测到的与 GC 含量相关的 mRNA 调控,包括数百个 ENCODE 数据集和最近关注细胞周期中 mRNA 动态的研究。我们通过详细分析数百个样本(包括表现出细胞周期和神经发生缺陷的 Ddx3x 条件性基因敲除小鼠模型)的 RNA-seq 图谱,进一步证明了 mRNA 的稳定性。我们的研究确定了 mRNA 调控的一个普遍特征,并强调了量化基因表达级联多个步骤的重要性,因为 RNA 丰度和蛋白质的产生往往是不耦合的。
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引用次数: 0
Canalizing cell fate by transcriptional repression. 通过转录抑制改变细胞命运
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-03-01 Epub Date: 2024-02-01 DOI: 10.1038/s44320-024-00014-z
Bryce Lim, Katrin Domsch, Moritz Mall, Ingrid Lohmann

Precision in the establishment and maintenance of cellular identities is crucial for the development of multicellular organisms and requires tight regulation of gene expression. While extensive research has focused on understanding cell type-specific gene activation, the complex mechanisms underlying the transcriptional repression of alternative fates are not fully understood. Here, we provide an overview of the repressive mechanisms involved in cell fate regulation. We discuss the molecular machinery responsible for suppressing alternative fates and highlight the crucial role of sequence-specific transcription factors (TFs) in this process. Depletion of these TFs can result in unwanted gene expression and increased cellular plasticity. We suggest that these TFs recruit cell type-specific repressive complexes to their cis-regulatory elements, enabling them to modulate chromatin accessibility in a context-dependent manner. This modulation effectively suppresses master regulators of alternative fate programs and their downstream targets. The modularity and dynamic behavior of these repressive complexes enables a limited number of repressors to canalize and maintain major and minor cell fate decisions at different stages of development.

精确建立和维持细胞特性对于多细胞生物体的发育至关重要,需要对基因表达进行严格调控。虽然大量研究集中于了解细胞类型特异性基因的激活,但对替代性命运转录抑制的复杂机制还不完全了解。在此,我们概述了细胞命运调控所涉及的抑制机制。我们讨论了负责抑制替代命运的分子机制,并强调了序列特异性转录因子(TFs)在这一过程中的关键作用。这些转录因子的耗竭会导致不需要的基因表达和细胞可塑性的增加。我们认为,这些转录因子将细胞类型特异性抑制复合物招募到它们的顺式调控元件上,使它们能够以依赖于上下文的方式调节染色质的可及性。这种调节有效地抑制了替代命运程序的主调节因子及其下游靶标。这些抑制复合体的模块化和动态行为使数量有限的抑制因子能够在发育的不同阶段控制和维持主要和次要细胞命运的决定。
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引用次数: 0
Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion. 肌母细胞分化和融合过程中连续单细胞状态转换的机器学习推断。
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-03-01 Epub Date: 2024-01-18 DOI: 10.1038/s44320-024-00010-3
Amit Shakarchy, Giulia Zarfati, Adi Hazak, Reut Mealem, Karina Huk, Tamar Ziv, Ori Avinoam, Assaf Zaritsky

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

细胞在连续的状态转换过程中改变其内部组织,支持从细胞分裂到分化的功能。然而,目前还缺乏测量单个过渡细胞动态生理状态的工具。我们将活体细胞成像与机器学习相结合,对ERK1/2抑制的原代小鼠骨骼肌前体细胞进行了监测,这些细胞从增殖的肌母细胞快速而稳健地过渡到有丝分裂后的肌细胞,然后融合形成多核肌管。我们的模型是利用单细胞追踪数据中的运动或肌动蛋白强度特征训练的,能有效追踪实时连续分化,揭示了分化发生在诱导后 7.5-14.5 小时,然后在约 3 小时后融合。同时抑制ERK1/2和p38会导致分化而不发生融合。我们的模型推断共同抑制会导致末期分化,这表明从末期分化过渡到融合特别需要 p38。我们的模型还预测,共同抑制会导致肌动蛋白动力学发生变化。质谱分析支持了这些硅学预测,并提出了分化下游的新型融合和成熟调节因子。总之,这种方法可适用于各种生物过程,以发现单细胞动态状态与其功能结果之间的新联系。
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引用次数: 0
Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources. 机器学习识别细菌在不同碳源上生长的关键代谢反应。
IF 9.9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-03-01 Epub Date: 2024-01-30 DOI: 10.1038/s44320-024-00017-w
Hyunjae Woo, Youngshin Kim, Dohyeon Kim, Sung Ho Yoon

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

依赖碳源控制细菌生长是细菌生理和生存的基础。然而,由于细胞网络的复杂性,精确定位对细胞生长重要的代谢步骤具有挑战性。在这里,我们构建了弹性网模型和多层感知模型,它们整合了全基因组基因缺失数据和模拟通量分布,用于识别对生长在 30 种不同碳源上的大肠杆菌有利或有害的代谢反应。这两个模型不仅能识别出促进生长的基本反应,还能识别出促进生长的非基本反应,其表现优于传统的硅学方法。它们成功地预测了有利于细胞生长的代谢反应,而且模型之间的趋同性很高。模型显示,生物合成途径通常能促进各种碳源的生长,而能量生成途径的影响则因碳源而异。对实验训练数据以外的发现以及各种碳源对乙醛酸分流、丙酮酸脱氢酶反应和冗余嘌呤生物合成反应的影响进行了实验验证,得出了耐人寻味的预测结果。这些都凸显了模型在理解和工程微生物代谢方面的实际意义和预测能力。
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引用次数: 0
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