Top 5 Biorxiv Papers Today in Systems Biology


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#1. Mechanochemical modelling of dorsal closure reveals emergent cell behaviour in tissue morphogenesis
Francesco Atzeni, Laurynas Pasakarnis, Gabriella Moscsa, Richard Smith, Christof Aegerter, Damian Brunner
Tissue morphogenesis integrates cell type-specific biochemistry and architecture, cellular force generation and mechanisms coordinating forces amongst neighbouring cells and tissues. We use finite element-based modelling to explore the interconnections at these multiple biological scales in embryonic dorsal closure, where pulsed actomyosin contractility in adjacent Amnioserosa (AS) cells powers the closure of an epidermis opening. Based on our in vivo observations, the model implements F-actin nucleation periodicity that is independent of MyoII activity. Our model reveals conditions, where depleting MyoII activity nevertheless indirectly affects oscillatory F-actin behaviour, without the need for biochemical feedback. In addition, it questions the previously proposed role of Dpp-mediated regulation of the patterned actomyosin dynamics in the AS tissue, suggesting them to be emergent. Tissue-specific Dpp interference supports the model's prediction. The model further predicts that the mechanical properties of the surrounding...
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biorxivpreprint: Mechanochemical modelling of dorsal closure reveals emergent cell behaviour in tissue morphogenesis https://t.co/qb5sn3qfKF #bioRxiv
biorxiv_sysbio: Mechanochemical modelling of dorsal closure reveals emergent cell behaviour in tissue morphogenesis https://t.co/TN3LfqpPgR #biorxiv_sysbio
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#2. Loss of Tet2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions
Leanna S Morinishi, Karl Kochanowski, Ross Levine, Lani Wu, Steven Altschuler
A persistent puzzle in cancer biology is how mutations, which neither alter canonical growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One notable example is the loss-of-function mutation of the DNA demethylase Tet2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse, but whose fitness advantage in the setting of anti-leukemic chemotherapy is unclear. Here we use paired isogenic human AML cell lines to show that Tet2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. Mathematical modeling and experimental validation reveal that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a Tet2 loss-of-function in AML, illustrate how a single gene mutation can alter...
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#3. Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction
Marco Tognetti, Attila Gabor, Mi Yang, Valentina Cappelletti, Jonas Windhager, Konstantina Charmpi, Natalie de Souza, Andreas Beyer, Paola Picotti, Julio Saez-Rodriguez, Bernd Bodenmiller
Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data - on more than 80 million single cells from 4,000 conditions - were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with...
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biorxivpreprint: Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction https://t.co/YsMmHgB7Yt #bioRxiv
biorxiv_sysbio: Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction https://t.co/QDhJNxPB6p #biorxiv_sysbio
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#4. PathExt: a general framework for path-based mining of omics-integrated biological networks
Nagasuma Chandra, Sridhar Hannenhalli, Narmada Sambaturu, Vaidehi Pusadkar
Motivation: Large scale transcriptomic data are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. Results: We provide a computational tool - PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract...
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e_j_duncan: RT @biorxivpreprint: PathExt: a general framework for path-based mining of omics-integrated biological networks https://t.co/pu62m4U6yz #b…
willigo09: RT @biorxivpreprint: PathExt: a general framework for path-based mining of omics-integrated biological networks https://t.co/pu62m4U6yz #b…
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#5. Multi-faceted deregulation of gene expression and protein synthesis with age
Aleksandra S Anisimova, Mark B Meerson, Maxim Gerashchenko, Ivan V Kulakovskiy, Sergey E Dmitriev, Vadim N Gladyshev
Protein synthesis represents a major metabolic activity of the cell. However, how it is affected by aging and how this in turn impacts cell function remains largely unexplored. To address this question, herein we characterized age-related changes in both the transcriptome and translatome of mouse tissues over the entire lifespan. Expression of the majority of differentially expressed genes followed a U-shaped curve with the turning point around 3-months-old. We showed that transcriptome changes govern changes in the translatome and are associated with altered expression of genes involved in inflammation, extracellular matrix and lipid metabolism. We also identified genes that may serve as candidate biomarkers of aging. At the translational level, we uncovered sustained down-regulation of a set of 5' terminal oligopyrimidine (5'TOP) transcripts encoding protein synthesis and ribosome biogenesis machinery and regulated by the mTOR pathway. For many of them, ribosome occupancy dropped 3-fold or even more. Moreover, with age, ribosome...
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bibourokuabc: RT @biorxiv_sysbio: Multi-faceted deregulation of gene expression and protein synthesis with age https://t.co/IHC9xxmjiD #biorxiv_sysbio
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