Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551411v1?rss=1 Authors: Ocana-Tienda, B., Leon-Triana, O., Perez-Beteta, J., Perez-Garcia, V. M. Abstract: Metastasis is the process through which cancer cells break away from a primary tumor, travel through the blood or lymph system, and form new tumors in distant tissues. One of the preferred sites for metastatic dissemination is the brain, affecting more than 20% of all cancer patients. This figure is increasing steadily due to improvements in treatments of primary tumors. Stereotactic radiosurgery (SRS) is one of the main treatment options for patients with a small or moderate number of brain metastases (BMs). A frequent adverse event of SRS is radiation necrosis (RN), an inflammatory condition caused by late normal tissue cell death. A major diagnostic problem is that RNs are difficult to distinguish from BM recurrences, due to their similarities on standard magnetic resonance images (MRIs). However, this distinction is key to choosing the best therapeutic approach since RNs resolve often without further interventions, while relapsing BMs may require open brain surgery. Recent research has shown that RNs have a faster growth dynamics than recurrent BMs, providing a way to differentiate the two entities, but no mechanistic explanation has been provided for those observations. In this study, computational frameworks were developed based on mathematical models of increasing complexity, providing mechanistic explanations for the differential growth dynamics of BMs relapse versus RN events and explaining the observed clinical phenomenology. Simulated tumor relapses were found to have growth exponents substantially smaller than the group in which there was inflammation due to damage induced by SRS to normal brain tissue adjacent to the BMs, thus leading to RN. ROC curves with the synthetic data had an optimal threshold that maximized the sensitivity and specificity values for a growth exponent beta* = 1.05, very close to that observed in patient datasets. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551833v1?rss=1 Authors: Liu, X., Husby, M., Stahelin, R. V., Pienaar, E. Abstract: Ebola virus (EBOV) infection is threatening human health, especially in Central and West Africa. Limited clinical trials and requirement of biosafety level-4 (BSL-4) laboratories hinders experimental work to advance our understanding of EBOV and evaluation of treatment. In this work, we use a computational model to study the assembly and budding process of EBOV and evaluate the effect of fendiline on these processes. Our results indicate that the assembly of VP40 filaments may follow the nucleation-elongation theory, as it is critical to maintain a pool of VP40 dimer for the maturation and production of virus-like particles (VLPs). We further find that the nucleation-elongation process can also be influenced by phosphatidylserine (PS), which can complicate the efficacy of fendiline, a drug that lowers cellular PS levels. We observe that fendiline may increase VLP production at earlier time points (24 h) and under low concentrations ( less than or equal to 2 M). But this effect is transient and does not change the conclusion that fendiline generally decreases VLP production. We also conclude that fendiline can be more efficient at the stage of VLP budding relative to earlier phases. Combination therapy with a VLP budding step-targeted drug may further increase the treatment efficiency of fendiline. Finally, we also show that fendiline has higher efficacy when VP40 expression is high. While these are single-cell level results based on the VP40 system, it points out a potential way of fendiline application affecting EBOV assembly, which can be further tested in experimental studies with multiple EBOV proteins or live virus. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551346v1?rss=1 Authors: Joisa, C. U., Chen, K. A., Berginski, M., Beville, S., Stuhlmiller, T., Okumu, D., Golitz, B. T., Johnson, G. L., Gomez, S. M. Abstract: Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551714v1?rss=1 Authors: Feng, S., Sanford, J. A., Weber, T. J., Hutchinson-Bunch, C. M., Dakup, P. P., Paurus, V. L., Attah, K., Sauro, H. A., Qian, W.-J., Wiley, H. S. Abstract: Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of less than 12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify greater than 46,000 phosphorylation sites on greater than 6600 proteins, of which greater than 4500 sites from 2110 proteins displayed a greater than 2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at less than 3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a greater than 2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551525v1?rss=1 Authors: Zhu, L., Wang, J. Abstract: Recent developments of single-cell sequencing technology enabling acquisition of the whole transcriptome data. To uncover the underlying mechanism of cell cycle function from such data, we reconstruct a continuous vector field based on the discrete single-cell RNA velocity to quantify the global non-equilibrium dynamical landscape and flux. We reveal that biological noise can make the global landscape more complex and less predictable. Genetic perturbations alter landscape-flux, thus identify key genes in maintaining cell cycle dynamics and predict the associated effects on cell cycle behaviour. Cell cycle initiation costs energy and sustaining cell cycle requires dissipation to increase oscillatory phase coherence. This approach enables the inference of cell cycle gene regulatory networks directly from single-cell transcriptomic data, including feedback mechanisms. Our study provides a new framework with insights into cell cycle regulation from single-cell transcriptome data and can be extended to other biological processes, such as differentiation-development and disease pathogenesis Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551537v1?rss=1 Authors: Wordsworth, J., Yde Nielsen, P., Fielder, E., Chandrasegaran, S., Shanley, D. Abstract: Ageing results from the gradual loss of homeostasis, and there are currently many hypotheses for the underlying initial causes, such as molecular damage accumulation. However, few if any theories directly connect comprehensive, underlying biological mechanisms to specific age-related diseases. We recently demonstrated how a specific maintenance system impeding overactivity disorders such as cancer might undergo positive selection while still resulting in a gradual homeostatic shift toward slower metabolism. Here we connect this metabolic slowdown, via a series of unavoidable homeostatic shifts, to the hallmarks of ageing, including mitochondrial dysfunction, insulin resistance (IR), weight gain, basal inflammation, and age-related diseases such as atherosclerosis. We constructed the fuel and energy model (FEM) based on these shifts and found that ageing via metabolic slowdown could explain not only the effects of anti-ageing interventions such as rapamycin and calorie restriction, but many of the paradoxes of ageing that currently defy alternative theories. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551433v1?rss=1 Authors: Rrustemi, T., Meyer, K., Roske, Y., Uyar, B., Akalin, A., Imami, K., Ishihama, Y., Daumke, O., Selbach, M. Abstract: Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employed peptide-based interaction proteomics to investigate 36 disease-causing mutations affecting phosphorylation sites. Our results unveiled significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We found that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments revealed the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering fresh insights into potential molecular mechanisms underlying pathogenesis. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.550998v1?rss=1 Authors: Graves, J. L., McKenzie, B., Koch, Z., Naka, A. S., Spofford, N., Morrison, J. Abstract: OBJECTIVE To evaluate age, sex, body weight, breed, neuter status, and age at neutering as risk factors for diagnosis of osteoarthritis in companion dogs ANIMALS Dogs seen as patients at Banfield Pet Hospital in the United States from 1998-2019 with a date of death in 2019. The final cohort consisted of 131,140 dogs. METHODS In this retrospective cohort study, Cox proportional hazard models were used to test for associations between osteoarthritis incidence and age at baseline, sex, maximum body weight, maximum body condition score, neuter status, and age at neutering. The same model was used to test these associations in 12 representative breeds, chosen based on breed weight and sample size. RESULTS Older age, higher adult body weight, gonadectomy, and younger age at gonadectomy were significantly associated with higher risks of osteoarthritis in the total cohort and in all 12 breeds evaluated. Higher body condition score and sex were also significantly associated with osteoarthritis but with minimal effect sizes in the overall cohort, and these risk factors were not consistently significant in all breeds tested. CLINICAL RELEVANCE These results will assist veterinarians in identifying dogs at higher risk for osteoarthritis and applying appropriate diagnostic, preventative, and treatment interventions. An understanding of potentially modifiable risk factors, such as body condition, and neutering, will support evidence-based discussions with dog owners about risk management in individual patients. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551263v1?rss=1 Authors: Doria-Belenguer, S., Xenos, A., Ceddia, G., Malod-Dognin, N., Przulj, N. Abstract: Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. Thus, we propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551708v1?rss=1 Authors: Ward, R., Tran, J. S., Banta, A. B., Bacon, E. E., Rose, W. E., Peters, J. M. Abstract: The emergence of multidrug-resistant Gram-negative bacteria underscores the need to define genetic vulnerabilities that can be therapeutically exploited. The Gram-negative pathogen, Acinetobacter baumannii, is considered an urgent threat due to its propensity to evade antibiotic treatments. Essential cellular processes are the target of existing antibiotics and a likely source of new vulnerabilities. Although A. baumannii essential genes have been identified by transposon sequencing (Tn-seq), they have not been prioritized by sensitivity to knockdown or antibiotics. Here, we take a systems biology approach to comprehensively characterize A. baumannii essential genes using CRISPR interference (CRISPRi). We show that certain essential genes and pathways are acutely sensitive to knockdown, providing a set of vulnerable targets for future therapeutic investigation. Screening our CRISPRi library against last-resort antibiotics uncovered genes and pathways that modulate beta-lactam sensitivity, an unexpected link between NADH dehydrogenase activity and growth inhibition by polymyxins, and anticorrelated phenotypes that underpin synergy between polymyxins and rifamycins. Our study demonstrates the power of systematic genetic approaches to identify vulnerabilities in Gram-negative pathogens and uncovers antibiotic-essential gene interactions that better inform combination therapies. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551393v1?rss=1 Authors: Jo, H., Hong, H., Hwang, H. J., Chang, W., Kim, J. K. Abstract: The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multimodality in transduction-time distribution informs that the response is regulated by multiple pathways with different transduction speeds. Here, we developed Density physics-informed neural network (Density-PINN) to infer the transduction-time distribution, challenging to measure, from measurable final stress response time traces. We applied Density-PINN to single-cell gene expression data from 16 promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINN can also be applied to understand various time delay systems, including infectious diseases. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551408v1?rss=1 Authors: Gupta, R., Adhikary, S., Dalpatraj, N., Laxman, S. Abstract: Cells require disparate amounts of distinct amino acids, which themselves have discrete biosynthetic costs. However, it remains unclear if and how cells respond differently to their scarcity. To explore this, we re-organized amino acids into distinct groups based on their metabolic origins. Subsequently, using yeast we assessed responses to transient disruptions in amino acid supply, and uncover diverse restoration responses for distinct amino acids. Cells hierarchically prioritize restoring glutamate-, sulfur-, pentose-phosphate- and pyruvate-derived amino acids. Particularly, the strongest response is to the glutamate-derived amino acid arginine. We find that the extent and priority of the restoration response is determined by the composite demand for an amino acid, coupled with low individual biosynthetic costs of that amino acid. We propose that cells employ a conserved strategy guided by the law of demand, to prioritize amino acids restoration upon transient limitation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551116v1?rss=1 Authors: Taye, B., Thuenauer, R., Sugrue, R. J., Maurer-Stroh, S., Kosinski, J. Abstract: Influenza A virus (IAV) causes up to five million cases of severe illness and half a million deaths worldwide each year. While there are a few clinically approved drugs for treating IAV, they are challenged by the rapid evolution of the virus leading to emergence of drug resistance and the adverse effects of the drugs. Targeting host cellular factors that support virus replication could limit resistance, increase the broad-spectrum antiviral properties of drugs, and benefit from repurposing drugs already existing against those factors. However, selecting the right drug-target pairs with low toxicity and minimal adverse effects has been challenging, even though hundreds of cellular host factors have been identified. In this study, we applied a computational and knowledge-based drug-target prioritization approach to identify promising drug-target pairs. We selected five pairs for experimental validation: telmisartan-Angiotensin II receptor, type 1 (AGTR1), metoclopramide hydrochloride-Cholinergic receptor muscarinic 1 (CHRM1), cefepime hydrochloride-phosphogluconate dehydrogenase (PGD), ranolazine dihydrochloride-sodium channel voltage-gated type v alpha subunit (SCN5A), and ofloxacin-topoisomerase II alpha 170kDa (TOP2A). Except for cefepime hydrochloride, all four drugs showed significant plaque reduction in Madin Darby canine kidney (MDCK) cells. In the immunofluorescence assay, metoclopramide hydrochloride, ranolazine dihydrochloride, and telmisartan showed antiviral activity in MDCK and/or adenocarcinoma human alveolar basal epithelial (A549) cell lines. In conclusion, our approach can prioritize and identify drugs with antiviral activity against influenza virus. Refining and strengthening such approaches could be valuable for rapid antiviral discovery and pandemic preparedness. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551282v1?rss=1 Authors: Llambrich, S., Tielemans, B., Saliën, E., Atzori, M., Wouters, K., Van Bulck, V., Platt, M., Vanherp, L., Gallego Fernandez, N., Grau de la Fuente, L., Poptani, H., Verlinden, L., Himmelreich, U., Croitor, A., Attanasio, C., Callaerts-Vegh, Z., Gsell, W., Martinez-Abadias, N., Vande Velde, G. Abstract: Down syndrome (DS) is characterized by skeletal and brain structural malformations, cognitive impairment, altered hippocampal metabolite concentration and gene expression imbalance. These alterations were usually investigated separately, and the potential rescuing effects of green tea extracts enriched in epigallocatechin-3-gallate (GTE-EGCG) provided disparate results due to different experimental conditions. We overcame these limitations by conducting the first longitudinal controlled experiment evaluating genotype and GTE-EGCG prenatal chronic treatment effects before and after treatment discontinuation. Our findings revealed that the Ts65Dn mouse model reflected the pleiotropic nature of DS, exhibiting brachycephalic skull, ventriculomegaly, reduced bone mineral density, neurodevelopmental delay, hyperactivity, and impaired long-term memory with altered hippocampal metabolite concentration and gene expression. However, Ts65Dn mice showed milder phenotypes than previously described, suggesting a drift of the mouse model. GTE-EGCG treatment modulated most systems simultaneously but did not rescue DS phenotypes. On the contrary, the treatment exacerbated trisomic phenotypes including body weight, tibia microarchitecture, neurodevelopment, adult cognition, and metabolite concentration, not supporting the therapeutic use of a prenatal chronic treatment. Our results highlight the importance of longitudinal experiments assessing the co-modulation of multiple systems throughout development when characterizing preclinical models in complex disorders and evaluating the pleiotropic effects and general safety of pharmacological treatments. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.29.551111v1?rss=1 Authors: Katebi, A. R., Chen, X., Li, S., Lu, M. Abstract: Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550388v1?rss=1 Authors: Xia, J., Phan, H. V., Vistain, L., Chen, M., Khan, A. A., Tay, S. Abstract: Proximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naive T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550891v1?rss=1 Authors: Häusler, S. Abstract: Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be uniquely determined from the statistical moments of observable network components alone, as long as the mean of each latent variable maps onto a scaled expectation of a binary variable and the functional relevance of the network components lies in their mean values. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the approach to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.30.551059v1?rss=1 Authors: Taylor, B. C., Steinthal, L. H., Dias, M., Yalamanchili, H. K., Ochsner, S. A., Zapata, G. E., Mehta, N. R., McKenna, N. J., Young, N. L., Nuotio-Antar, A. M. Abstract: Regulation of the thermogenic response by brown adipose tissue (BAT) is an important component of energy homeostasis with implications for the treatment of obesity and diabetes. Motivated to understand how BAT function is regulated epigenetically, we reanalyzed publicly available RNA-seq data and uncovered many nodes representing epigenetic modifiers that are altered in BAT in response to chronic thermogenic activation. Thus, we hypothesized that chronic thermogenic activation broadly alters epigenetic modifications of DNA and histones in BAT. To test our hypothesis, wildtype male C57BL/6J mice were housed under chronic conditions of thermoneutral temperature (TN, 28.8{degrees}C), mild cold/room temperature (RT, 22{degrees}C), or severe cold (SC, 8{degrees}C). BAT and reference tissue liver were subsequently dissected from each mouse. Reduced representation bisulfite sequencing (RRBS) reveals decreased methylation of promoters and intragenic regions in BAT genomic DNA in response to varying degrees of chronic cold exposure. Integration of our RRBS and the RNA-Seq dataset suggests a role for epigenetic modification of DNA in gene regulation in response to cold. To analyze histone modifications in BAT, we develop a robust method for the isolation of histones and report the first quantitation of histone H3.2 and H4 proteoforms. We initially observe that BAT and liver exhibit different histone proteoforms. Next, we report housing temperature-dependent changes in histone proteoforms in BAT. We observe differences in bivalent proteoforms such as H3{K9me2,K23ac,K36me1}, H3{K9me3,K23ac,K36me1} and H4. Taken together, our results provide novel findings supporting global epigenetic modification in murine BAT in response to varying degrees of chronic cold stimuli and establish a methodology to quantitatively study histones in BAT, allowing for direct comparisons to decipher mechanistic changes during the thermogenic response. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.30.549949v1?rss=1 Authors: Bourgeois, N. M., Wei, L., Ho, N. N. T., Neal, M. L., Seferos, D., Tongogara, T., Mast, F. D., Aitchison, J. D., Kaushansky, A. Abstract: Dengue is an arboviral disease causing severe illness in over 500,000 people each year. Currently, there is no way to constrain dengue in the clinic. Host kinase regulators of dengue virus (DENV) infection have the potential to be disrupted by existing therapeutics to prevent infection and/or disease progression. To evaluate kinase regulation of DENV infection, we performed kinase regression (KiR), a machine learning approach that predicts kinase regulators of infection using existing drug-target information and a small drug screen. We infected hepatocytes with DENV in vitro in the presence of a panel of 38 kinase inhibitors then quantified the effect of each inhibitor on infection rate. We employed elastic net regularization on these data to obtain predictions of which of 300 kinases are regulating DENV infection. Thirty-six kinases were predicted to have a functional role. Intriguingly, seven of the predicted kinases - EPH receptor A4 (EPHA4), EPH receptor B3 (EPHB3), EPH receptor B4 (EPHB4), erb-b2 receptor tyrosine kinase 2 (ERBB2), fibroblast growth factor receptor 2 (FGFR2), Insulin like growth factor 1 receptor (IGF1R), and ret proto-oncogene (RET) - belong to the receptor tyrosine kinase (RTK) family, which are already therapeutic targets in the clinic. We demonstrate that predicted RTKs are expressed at higher levels in DENV infected cells. Knockdown of ERBB2, FGFR2 and IGF1R reduces DENV infection in hepatocytes. Finally, we observe differential temporal induction of ERBB2 and IGF1R following DENV infection, highlighting their unique roles in regulating DENV. Collectively, our findings underscore the significance of multiple RTKs in DENV infection and advocate further exploration of RTK-oriented interventions against dengue. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.550938v1?rss=1 Authors: Ryan, M. J., Raby, E., Masuda, R., Lodge, S., Nitschke, P., Maker, G. L., Wist, J., Fear, M. W., Holmes, E., Nicholson, J., Gray, N., Whiley, L., Wood, F. M. Abstract: Impaired wound healing in burn injuries can lead to complications such as skin graft loss, infection, and increased risk of scarring, which impacts long-term patient outcomes and quality of life. While wound repair in severe burns has received substantial research attention, poor wound outcomes in cases of non-severe burns (classified as less than 20% of the total body surface area (TBSA)) remain relatively understudied despite also having considerable physiological impact and constituting the majority of hospital admissions for burns. Predicting outcomes in the early stages of healing would decrease financial and patient burden, and aid in preventing long-term complications from poor wound healing. Lipids have been implicated in inflammation and tissue repair processes and may play essential roles in burn wound healing. Longitudinal plasma samples were collected from patients (n=20) with non-severe ( less than 15% TBSA) flame or scald burns over a 6-week period including timepoints pre- and post-surgical intervention. Samples were analysed using liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance spectroscopy to detect 850 lipid species and 112 lipoproteins. Statistical analyses, including orthogonal projection to latent structures-discriminant analysis was performed to identify changes associated with either re-epithelialisation or delayed wound re-epithelisation. The results demonstrated that the plasma lipid and lipoprotein profiles at admission could predict wound re-epithelisation outcomes at two weeks post-surgery, and that these discriminatory profiles were maintained over a 6-week period. Triacylglycerides, diacylglycerides (DAG) and low density lipoprotein (LDL) subfractions were associated with delayed wound closure, with DAG(18:2_18:3) and LDL/High density lipoprotein (HDL) ratio having the most influence (p-value less than 0.02, Cliff's delta greater than 0.7), while HDL subfractions, phosphatidylinositols, phosphatidylcholines (PC), and phosphatidylserines were associated with re-epithelisation at two weeks post-surgery, with PC(16:0_18:1) and HDL-2 apolipoprotein-A1 showing the greatest influence on the model (p-value less than 0.01 , Cliff's delta less than -0.7). We demonstrate clinical prediction of wound re-epithelisation in non-severe burn patients using lipid and lipoprotein profiling. Further validation of the models will potentially lead to personalised intervention strategies to enhance injury outcomes, reducing the risk of chronic complications post-burn injury. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC