Top 8 Biorxiv Papers Today in Systems Biology


2.043 Mikeys
#1. Quantitative analysis of the physiological contributions of glucose to the TCA cycle
Shiyu Liu, Ziwei Dai, Daniel Cooper, David G. Kirsch, Jason Locasale
The carbon source for catabolism in vivo is a fundamental question in metabolic physiology. Limited by data and rigorous mathematical analysis, many controversial statements exist on the metabolic sources for carbon usage in the tricarboxylic acid (TCA) cycle under physiological settings. Using isotope-labeling data in vivo across several experimental conditions, we construct multiple models of central carbon metabolism and develop methods based on metabolic flux analysis (MFA) to solve for the preferences of glucose, lactate, and other nutrients used in the TCA cycle across many tissues. We show that in nearly all circumstances, glucose contributes more than lactate as a nutrient source for the TCA cycle. This conclusion is verified in different animal strains, different administrations of 13C glucose, and is extended to multiple tissue types after considering multiple nutrient sources. Thus, this quantitative analysis of organismal metabolism defines the relative contributions of nutrient fluxes in physiology, provides a...
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biorxivpreprint: Quantitative analysis of the physiological contributions of glucose to the TCA cycle https://t.co/NJxYYO1hpK #bioRxiv
biorxiv_sysbio: Quantitative analysis of the physiological contributions of glucose to the TCA cycle https://t.co/0i81je6S21 #biorxiv_sysbio
ngraham: RT @biorxiv_sysbio: Quantitative analysis of the physiological contributions of glucose to the TCA cycle https://t.co/0i81je6S21 #biorxiv_…
Github

Data and code for the MFA model for lactate and glucose utilization in tissues

Repository: Lactate_MFA
User: LocasaleLab
Language: Python
Stargazers: 1
Subscribers: 1
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Authors: 5
Total Words: 8612
Unqiue Words: 2138

2.018 Mikeys
#2. Electrical propagation of vasodilatory signals in capillary networks
Pilhwa Lee
A computational model is developed to study electrical propagation of vasodilatory signals and arteriolar regulation of blood flow depending on the oxygen tension and agonist distribution in capillary network. The involving key parameters of endothelial cell-to-cell electrical conductivity and plasma membrane area per unit volume were calibrated with the experimental data on an isolated endothelial tube of mouse skeletal feeding arteries. The oxygen saturation parameters in terms of ATP release from erythrocytes are estimated from the data of a left anterior descending coronary blood perfusion of dog. In regard to the acetylcholine induced upstream conduction, our model shows that spatially uniform superfusion of acetylcholine attenuates the electrical signal propagation, and blocking calcium activated potassium channels suppresses that attenuation. On the other hand, local infusion of acetylcholine induces enhanced electrical propagation that corresponds to physiological relevance. In the integration of the endothelial tube and...
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biorxivpreprint: Electrical propagation of vasodilatory signals in capillary networks https://t.co/4rHG9urvvt #bioRxiv
biorxiv_sysbio: Electrical propagation of vasodilatory signals in capillary networks https://t.co/KSvk7iFycb #biorxiv_sysbio
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2.004 Mikeys
#3. EXoO-Tn: Tag-n-Map the Tn Antigen in the Human Proteome
Weiming Yang, Minghui Ao, Angellina Song, Yuanwei Xu, Hui Zhang
Tn antigen (Tn), a single N-acetylgalactosamine (GalNAc) monosaccharide attached to protein Ser/Thr residues, is found on most solid tumors yet rarely detected in adult tissues, featuring it one of the most distinctive signatures of cancers. Although it is prevalent in cancers, Tn-glycosylation sites are not entirely clear owing to the lack of suitable technology. Knowing the Tn-glycosylation sites will spur the development of new vaccines, diagnostics, and therapeutics of cancers. Here, we report a novel technology named EXoO-Tn for large-scale mapping of Tn-glycosylation sites. EXoO-Tn utilizes glycosyltransferase C1GalT1 and isotopically-labeled UDP-Gal(13C6) to tag and convert Tn to Gal(13C6)-Tn, which has a unique mass being distinguishable to other glycans. This exquisite Gal(13C6)-Tn structure is recognized by OpeRATOR that specifically cleaves N-termini of the Gal(13C6)-Tn-occupied Ser/Thr residues to yield site-containing glycopeptides. The use of EXoO-Tn mapped 947 Tn-glycosylation sites from 480 glycoproteins in Jurkat...
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InnoglyA: Important tool for our WG1 on cancer: EXoO-Tn: Tag-n-Map the Tn Antigen in the Human Proteome https://t.co/RHS4KP1mTz, #glycotime @biorxiv_sysbio - via Researcher https://t.co/HdY9Gp4dou (@ResearcherApp)
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2.0 Mikeys
#4. Deep functional synthesis: a machine learning approach to gene functional enrichment
Sheng Wang, Jianzhu Ma, Samson Fong, Stefano Rensi, Jiawei Han, Jian Peng, Dexter Pratt, Russ Altman, Trey Ideker
Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment. Using a knowledge graph with 3,048,803 associations between genes, diseases, drugs, and functions, DeepSyn obtained accurate performance (range 0.74 AUC to 0.96 AUC) on a variety of biological applications including drug target identification, gene set functional enrichment, and disease gene prediction.
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AkosNyerges: RT @biorxiv_sysbio: Deep functional synthesis: a machine learning approach to gene functional enrichment https://t.co/RskpeVWhM9 #biorxiv_…
ruihan_zhang: RT @biorxivpreprint: Deep functional synthesis: a machine learning approach to gene functional enrichment https://t.co/1l9Nl4rM1l #bioRxiv
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Authors: 9
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1.998 Mikeys
#5. Division of labor in metabolic regulation by transcription, translation, acetylation and phosphorylation
Sriram Chandrasekaran
The metabolism of most organisms is controlled by a diverse cast of regulatory processes, including transcriptional regulation and post-translational modifications (PTMs). Yet how metabolic control is distributed between these regulatory processes is unknown. Here we present Comparative Analysis of Regulators of Metabolism (CAROM), an approach that compares regulators based on network connectivity, flux, and essentiality of their reaction targets. Using CAROM, we analyze transcriptome, proteome, acetylome and phospho-proteome dynamics during transition to stationary phase in E. coli and S. cerevisiae . CAROM uncovered that the targets of each regulatory process shared unique metabolic properties: growth-limiting reactions were regulated by acetylation, while isozymes and futile-cycles were preferentially regulated by phosphorylation. Reversibility, essentiality, and molecular-weight further distinguished reactions controlled through diverse mechanisms. While every enzyme can be potentially regulated by multiple mechanisms,...
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biorxivpreprint: Division of labor in metabolic regulation by transcription, translation, acetylation and phosphorylation https://t.co/mcYMjvANGP #bioRxiv
biorxiv_sysbio: Division of labor in metabolic regulation by transcription, translation, acetylation and phosphorylation https://t.co/xaaOZY9IoJ #biorxiv_sysbio
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1.997 Mikeys
#6. Classification of clear cell renal cell carcinoma based on PKM alternative splicing
Xiangyu Li, Beste Turanli, Kajetan Juszczak, Woonghee Kim, Muhammad Arif, Yusuke Sato, Seishi Ogawa, Hasan Turkez, Jens Nielsen, Jan Boren, Mathias Uhlen, Cheng Zhang, Adil Mardinoglu
Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and...
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biorxivpreprint: Classification of clear cell renal cell carcinoma based on PKM alternative splicing https://t.co/lLtc9brAVm #bioRxiv
biorxiv_sysbio: Classification of clear cell renal cell carcinoma based on PKM alternative splicing https://t.co/IXQjrakCuC #biorxiv_sysbio
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Sample Sizes : [1135, 2694]
Authors: 13
Total Words: 7139
Unqiue Words: 2202

1.997 Mikeys
#7. BowSaw: inferring higher-order trait interactions associated with complex biological phenotypes
Demetrius Dimucci, Mark Kon, Daniel Segre
Machine learning is helping the interpretation of biological complexity by enabling the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e.g. from genomic, transcriptomic and metagenomic analyses. A number of available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box is sufficient, it is interesting to pursue an enhanced understanding of how system variables end up contributing to a specific output, as an avenue towards new mechanistic insight. Here we address this challenge through a suite of algorithms, named BowSaw, which takes advantage of the structure of a trained random forest algorithm to identify combinations of variables ("rules") frequently used for classification. We first apply BowSaw to a simulated dataset, and show that the algorithm can accurately recover the sets of variables used to generate the phenotypes through...
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1.994 Mikeys
#8. Computational Model Predicts Paracrine and Intracellular Drivers of Fibroblast Phenotype After Myocardial Infarction
Angela C. Zeigler, Anders R. Nelson, Anirudha S. Chandrabhatla, Olga Brazhkina, Jeffrey W. Holmes, Jeffrey J Saucerman
The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual overexpression screens with static cytokine pairs and after myocardial infarction predicted phase-specific regulators of collagen expression. Several...
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Authors: 6
Total Words: 11732
Unqiue Words: 3451

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