Integrative Biology最新文献

{"title":"Contracting scars from fibrin drops.","authors":"Stephen Robinson,&nbsp;Eric Parigoris,&nbsp;Jonathan Chang,&nbsp;Louise Hecker,&nbsp;Shuichi Takayama","doi":"10.1093/intbio/zyac001","DOIUrl":"https://doi.org/10.1093/intbio/zyac001","url":null,"abstract":"<p><p>This paper describes a microscale fibroplasia and contraction model that is based on fibrin-embedded lung fibroblasts and provides a convenient visual readout of fibrosis. Cell-laden fibrin microgel drops are formed by aqueous two-phase microprinting. The cells deposit extracellular matrix (ECM) molecules such as collagen while fibrin is gradually degraded. Ultimately, the cells contract the collagen-rich matrix to form a compact cell-ECM spheroid. The size of the spheroid provides the visual readout of the extent of fibroplasia. Stimulation of this wound-healing model with the profibrotic cytokine TGF-β1 leads to an excessive scar formation response that manifests as increased collagen production and larger cell-ECM spheroids. Addition of drugs also shifted the scarring profile: the FDA-approved fibrosis drugs (nintedanib and pirfenidone) and a PAI-1 inhibitor (TM5275) significantly reduced cell-ECM spheroid size. Not only is the assay useful for evaluation of antifibrotic drug effects, it is relatively sensitive; one of the few in vitro fibroplasia assays that can detect pirfenidone effects at submillimolar concentrations. Although this paper focuses on lung fibrosis, the approach opens opportunities for studying a broad range of fibrotic diseases and for evaluating antifibrotic therapeutics.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"14 1","pages":"1-12"},"PeriodicalIF":2.5,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8934703/pdf/zyac001.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10799553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phosphatases are predicted to govern prolactin-mediated JAK–STAT signaling in pancreatic beta cells","authors":"Ariella D Simoni,Holly A Huber,Senta K Georgia,Stacey D Finley","doi":"10.1093/intbio/zyac004","DOIUrl":"https://doi.org/10.1093/intbio/zyac004","url":null,"abstract":"Abstract Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin (PRL). Leveraging this proliferative response to PRL may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the PRL-mediated JAK–STAT signaling pathway in pancreatic beta cells. Here, we applied the model to identify the importance of particular signaling proteins in shaping the response of a population of beta cells. We simulated a population of 10 000 heterogeneous cells with varying initial protein concentrations responding to PRL stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing PRL receptor strengthens negative feedback mediated by the inhibitor suppressor of cytokine signaling. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"174 6","pages":"37-48"},"PeriodicalIF":2.5,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From random to predictive: a context-specific interaction framework improves selection of drug protein–protein interactions for unknown drug pathways","authors":"Jennifer L Wilson,Alessio Gravina,Kevin Grimes","doi":"10.1093/intbio/zyac002","DOIUrl":"https://doi.org/10.1093/intbio/zyac002","url":null,"abstract":"Abstract With high drug attrition, protein–protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches—statistical and distance-based—and our novel per-phenotype approach, ‘context-specific interaction’ (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76–95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"28 5","pages":"13-24"},"PeriodicalIF":2.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanofiber curvature with Rho GTPase activity increases mouse embryonic fibroblast random migration velocity.","authors":"Daniel T Bowers,&nbsp;Justin L Brown","doi":"10.1093/intbio/zyab022","DOIUrl":"https://doi.org/10.1093/intbio/zyab022","url":null,"abstract":"<p><p>Mechanotransduction arises from information encoded in the shape of materials such as curvature. It induces activation of small GTPase signaling affecting cell phenotypes including differentiation. We carried out a set of preliminary experiments to test the hypothesis that curvature (1/radius) would also affect cell motility due to signal pathway crosstalk. High molecular weight poly (methyl methacrylate) straight nanofibers were electrospun with curvature ranging from 41 to 1 μm-1 and collected on a passivated glass substrate. The fiber curvature increased mouse mesenchymal stem cell aspect ratio (P < 0.02) and decreased cell area (P < 0.01). Despite little effect on some motility patterns such as polarity and persistence, we found selected fiber curvatures can increase normalized random fibroblastic mouse embryonic cell (MEF) migration velocity close to 2.5 times compared with a flat surface (P < 0.001). A maximum in the velocity curve occurred near 2.5 μm-1 and may vary with the time since initiation of attachment to the surface (range of 0-20 h). In the middle range of fiber curvatures, the relative relationship to curvature was similar regardless of treatment with Rho-kinase inhibitor (Y27632) or cdc42 inhibitor (ML141), although it was decreased on most curvatures (P < 0.05). However, below a critical curvature threshold MEFs may not be able to distinguish shallow curvature from a flat surface, while still being affected by contact guidance. The preliminary data in this manuscript suggested the large low curvature fibers were interpreted in a manner similar to a non-curved surface. Thus, curvature is a biomaterial construct design parameter that should be considered when specific biological responses are desired. Statement of integration, innovation, and insight  Replacement of damaged or diseased tissues that cannot otherwise regenerate is transforming modern medicine. However, the extent to which we can rationally design materials to affect cellular outcomes remains low. Knowing the effect of material stiffness and diameter on stem cell differentiation, we investigated cell migration and signaling on fibrous scaffolds. By investigating diameters across orders of magnitude (50-2000 nm), we identified a velocity maximum of ~800 nm. Furthermore, the results suggest large fibers may not be interpreted by single cells as a curved surface. This work presents insight into the design of constructs for engineering tissues.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 12","pages":"295-308"},"PeriodicalIF":2.5,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759537/pdf/zyab022.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10512187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A quantitative view of strategies to engineer cell-selective ligand binding.","authors":"Zhixin Cyrillus Tan,&nbsp;Brian T Orcutt-Jahns,&nbsp;Aaron S Meyer","doi":"10.1093/intbio/zyab019","DOIUrl":"https://doi.org/10.1093/intbio/zyab019","url":null,"abstract":"<p><p>A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 11","pages":"269-282"},"PeriodicalIF":2.5,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730367/pdf/zyab019.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10412705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to: A novel two-layer-integrated microfluidic device for high-throughput yeast proteomic dynamics analysis at the single-cell level.","authors":"Kaiyue Chen,&nbsp;Nan Rong,&nbsp;Shujing Wang,&nbsp;Chunxiong Luo","doi":"10.1093/intbio/zyab021","DOIUrl":"https://doi.org/10.1093/intbio/zyab021","url":null,"abstract":"","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 10","pages":"258"},"PeriodicalIF":2.5,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39699687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translatable pathways classification (TransPath-C) for inferring processes germane to human biology from animal studies data: example application in neurobiology.","authors":"Molly J Carroll,&nbsp;Natàlia Garcia-Reyero,&nbsp;Edward J Perkins,&nbsp;Douglas A Lauffenburger","doi":"10.1093/intbio/zyab016","DOIUrl":"https://doi.org/10.1093/intbio/zyab016","url":null,"abstract":"<p><p>How to translate insights gained from studies in one organismal species for what is most likely to be germane in another species, such as from mice to humans, is a ubiquitous challenge in basic biology as well as biomedicine. This is an especially difficult problem when there are few molecular features that are obviously important in both species for a given phenotype of interest. Neuropathologies are a prominent realm of this complication. Schizophrenia is complex psychiatric disorder that affects 1% of the population. Many genetic factors have been proposed to drive the development of schizophrenia, and the 22q11 microdeletion (MD) syndrome has been shown to dramatically increase this risk. Due to heterogeneity of presentation of symptoms, diagnosis and formulation of treatment options for patients can often be delayed, and there is an urgent need for novel therapeutics directed toward the treatment of schizophrenia. Here, we present a novel computational approach, Translational Pathways Classification (TransPath-C), that can be used to identify shared pathway dysregulation between mouse models and human schizophrenia cohorts. This method uses variation of pathway activation in the mouse model to predict both mouse and human disease phenotype. Analysis of shared dysregulated pathways called out by both the mouse and human classifiers of TransPath-C can identify pathways that can be targeted in both preclinical and human cohorts of schizophrenia. In application to the 22q11 MD mouse model, our findings suggest that PAR1 pathway activation found upregulated in this mouse phenotype is germane for the corresponding human schizophrenia cohort such that inhibition of PAR1 may offer a novel therapeutic target.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 10","pages":"237-245"},"PeriodicalIF":2.5,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39793694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pervasive cytoquakes in the actomyosin cortex across cell types and substrate stiffness.","authors":"Yu Shi,&nbsp;Shankar Sivarajan,&nbsp;Katherine M Xiang,&nbsp;Geran M Kostecki,&nbsp;Leslie Tung,&nbsp;John C Crocker,&nbsp;Daniel H Reich","doi":"10.1093/intbio/zyab017","DOIUrl":"https://doi.org/10.1093/intbio/zyab017","url":null,"abstract":"<p><p>The actomyosin cytoskeleton enables cells to resist deformation, crawl, change their shape and sense their surroundings. Despite decades of study, how its molecular constituents can assemble together to form a network with the observed mechanics of cells remains poorly understood. Recently, it has been shown that the actomyosin cortex of quiescent cells can undergo frequent, abrupt reconfigurations and displacements, called cytoquakes. Notably, such fluctuations are not predicted by current physical models of actomyosin networks, and their prevalence across cell types and mechanical environments has not previously been studied. Using micropost array detectors, we have performed high-resolution measurements of the dynamic mechanical fluctuations of cells' actomyosin cortex and stress fiber networks. This reveals cortical dynamics dominated by cytoquakes-intermittent events with a fat-tailed distribution of displacements, sometimes spanning microposts separated by 4 μm, in all cell types studied. These included 3T3 fibroblasts, where cytoquakes persisted over substrate stiffnesses spanning the tissue-relevant range of 4.3 kPa-17 kPa, and primary neonatal rat cardiac fibroblasts and myofibroblasts, human embryonic kidney cells and human bone osteosarcoma epithelial (U2OS) cells, where cytoquakes were observed on substrates in the same stiffness range. Overall, these findings suggest that the cortex self-organizes into a marginally stable mechanical state whose physics may contribute to cell mechanical properties, active behavior and mechanosensing.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 10","pages":"246-257"},"PeriodicalIF":2.5,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39953453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-assisted imaging analysis of a human epiblast model.","authors":"Agnes M Resto Irizarry, Sajedeh Nasr Esfahani, Yi Zheng, Robin Zhexuan Yan, Patrick Kinnunen, Jianping Fu","doi":"10.1093/intbio/zyab014","DOIUrl":"10.1093/intbio/zyab014","url":null,"abstract":"<p><p>The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell-cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell-cell and cell-environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 9","pages":"221-229"},"PeriodicalIF":2.5,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521036/pdf/zyab014.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9601856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae.","authors":"John McCarthy","doi":"10.1093/intbio/zyab013","DOIUrl":"https://doi.org/10.1093/intbio/zyab013","url":null,"abstract":"<p><p>This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.</p>","PeriodicalId":80,"journal":{"name":"Integrative Biology","volume":"13 8","pages":"210-220"},"PeriodicalIF":2.5,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39191436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}