Integrative Biology最新文献

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.

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.

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.

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.

How cells sense and respond to mechanical stimuli remains an open question. Recent advances have identified the translocation of Yes-associated protein (YAP) between nucleus and cytoplasm as a central mechanism for sensing mechanical forces and regulating mechanotransduction. We formulate a spatiotemporal model of the mechanotransduction signalling pathway that includes coupling of YAP with the cell force-generation machinery through the Rho family of GTPases. Considering the active and inactive forms of a single Rho protein (GTP/GDP-bound) and of YAP (non-phosphorylated/phosphorylated), we study the cross-talk between cell polarization due to active Rho and YAP activation through its nuclear localization. For fixed mechanical stimuli, our model predicts stationary nuclear-to-cytoplasmic YAP ratios consistent with experimental data at varying adhesive cell area. We further predict damped and even sustained oscillations in the YAP nuclear-to-cytoplasmic ratio by accounting for recently reported positive and negative YAP-Rho feedback. Extending the framework to time-varying mechanical stimuli that simulate cyclic stretching and compression, we show that the YAP nuclear-to-cytoplasmic ratio's time dependence follows that of the cyclic mechanical stimulus. The model presents one of the first frameworks for understanding spatiotemporal YAP mechanotransduction, providing several predictions of possible YAP localization dynamics, and suggesting new directions for experimental and theoretical studies.

Alcohol injury induces hepatic fibrosis which gradually progresses to cirrhosis, sometimes may lead to liver cancer. Animal models are less efficient in mimicking responses of human liver cells, whereas in vitro models discussed so far are majorly based on rodent cells. In this work, a coculture of primary human hepatocytes (PHHs) with LX-2 cells was established on the unmodified (C:F_0:0), collagen-I modified (C:F_1:0), fibronectin modified (C:F_0:1) and 3:1 collagen-I to fibronectin modified (C:F_3:1) 3D electrospun fibrous scaffolds. The effect of alcohol injury was evaluated on this cell-scaffold model at 0-40 μl/ml alcohol concentrations over 14 days of culture period by using the gold standard sandwich culture as the control. Among all the culture groups, C:F_3:1 scaffold was able to maintain translational and transcriptional properties of human liver cells at all concentrations of alcohol treatment. The study reveals that, PHHs on C:F_3:1 were able to maintain ~4-fold and ~1.6-fold higher secretion of albumin than the gold standard sandwich culture on Day 3 and Day 7, respectively. When treated with alcohol, at concentrations of 20 and 40 μl/ml, albumin secretion was also observed to be higher (~2-fold) when compared to the gold standard sandwich culture. Again as expected, in C:F_3:1 culture group on 40 μl/ml alcohol treatment, albumin gene expression decreased by ~2-fold due to alcohol toxicity, whereas CYP2C9, CYP3A4, CYP2E1 and CYP1A2 gene expressions upregulated by ~3.5, ~~4, ~5 and ~15-fold, respectively in response to the alcohol injury. LX-2 cells also acquire more quiescent phenotype on C:F_3:1 scaffolds when compared to the gold standard sandwich culture upon alcohol treatment. Thus, C:F_3:1 scaffold with human liver cells was established as the potential platform to scan alcohol toxicity at varied alcohol concentrations. Thus, it can pave a promising path not only to support functional healthy human liver cells for liver tissue engineering but also to examine potential drugs to study the progression or inhibition of alcoholic liver fibrosis in vitro.

Purpose: To develop and validate a mechanism-based, mathematical model that characterizes 9L and C6 glioma cells' temporal response to single-dose radiation therapy in vitro by explicitly incorporating time-dependent biological interactions with radiation.

Methods: We employed time-resolved microscopy to track the confluence of 9L and C6 glioma cells receiving radiation doses of 0, 2, 4, 6, 8, 10, 12, 14 or 16 Gy. DNA repair kinetics are measured by γH2AX expression via flow cytometry. The microscopy data (814 replicates for 9L, 540 replicates for C6 at various seeding densities receiving doses above) were divided into training (75%) and validation (25%) sets. A mechanistic model was developed, and model parameters were calibrated to the training data. The model was then used to predict the temporal dynamics of the validation set given the known initial confluences and doses. The predictions were compared to the corresponding dynamic microscopy data.

Results: For 9L, we obtained an average (± standard deviation, SD) Pearson correlation coefficient between the predicted and measured confluence of 0.87 ± 0.16, and an average (±SD) concordance correlation coefficient of 0.72 ± 0.28. For C6, we obtained an average (±SD) Pearson correlation coefficient of 0.90 ± 0.17, and an average (±SD) concordance correlation coefficient of 0.71 ± 0.24.

Conclusion: The proposed model can effectively predict the temporal development of 9L and C6 glioma cells in response to a range of single-fraction radiation doses. By developing a mechanism-based, mathematical model that can be populated with time-resolved data, we provide an experimental-mathematical framework that allows for quantitative investigation of cells' temporal response to radiation. Our approach provides two key advances: (i) a time-resolved, dynamic death rate with a clear biological interpretation, and (ii) accurate predictions over a wide range of cell seeding densities and radiation doses.

As a key process within the tissue microenvironment, integrin signaling can influence cell functional responses to growth factor stimuli. We show here that clustering of integrin α5ß1 at the plasma membrane of colorectal cancer-derived epithelial cells modulates their ability to respond to stimulation by receptor tyrosine kinase (RTK)-activating growth factors EGF, NRG and HGF, through GSK3-mediated suppression of Akt pathway. We observed that integrin α5ß1 is lost from the membrane of poorly organized human colorectal tumors and that treatment with the integrin-clustering antibody P4G11 is sufficient to induce polarity in a mouse tumor xenograft model. While adding RTK growth factors (EGF, NRG and HGF) to polarized colorectal cancer cells induced invasion and loss of monolayer formation in 2D and 3D, this pathological behavior could be blocked by P4G11. Phosphorylation of ErbB family members as well as MET following EGF, NRG and HGF treatment was diminished in cells pretreated with P4G11. Focusing on EGFR, we found that blockade of integrin α5ß1 increased EGFR phosphorylation. Since activity of multiple downstream kinase pathways were altered by these various treatments, we employed computational machine learning techniques to ascertain the most important effects. Partial least-squares discriminant analysis identified GSK3 as a major regulator of EGFR pathway activities influenced by integrin α5ß1. Moreover, we used partial correlation analysis to examine signaling pathway crosstalk downstream of EGF stimulation and found that integrin α5ß1 acts as a negative regulator of the AKT signaling cascade downstream of EGFR, with GSK3 acting as a key mediator. We experimentally validated these computational inferences by confirming that blockade of GSK3 activity is sufficient to induce loss of polarity and increase of oncogenic signaling in the colonic epithelial cells.

Hyperglycemia is thought to increase production of inflammatory cytokines and permeability of the large intestine. Resulting intestinal inflammation is then often characterized by excess secretion of tumor necrosis factor alpha (TNFα). Thus, hyperglycemia in hospitalized patients suffering from severe trauma or disease is frequently accompanied by TNFα secretion, and the combined impact of these insults on the intestinal epithelium is poorly understood. This study utilized a simple yet elegant model of the intestinal epithelium, comprised of primary human intestinal stem cells and their differentiated progeny, to investigate the impact of hyperglycemia and inflammatory factors on the colonic epithelium. When compared to epithelium cultured under conditions of physiologic glucose, cells under hyperglycemic conditions displayed decreased mucin-2 (MUC2), as well as diminished alkaline phosphatase (ALP) activity. Conditions of 60 mM glucose potentiated secretion of the cytokine IL-8 suggesting that cytokine secretion during hyperglycemia may be a source of tissue inflammation. TNFα measurably increased secretion of IL-8 and IL-1β, which was enhanced at 60 mM glucose. Surprisingly, intestinal permeability and paracellular transport were not altered by even extreme levels of hyperglycemia. The presence of TNFα increased MUC2 presence, decreased ALP activity, and negatively impacted monolayer barrier function. When TNFα hyperglycemia and ≤30 mM glucose and were combined, MUC2 and ALP activity remained similar to that of TNFα alone, although synergistic effects were seen at 60 mM glucose. An automated image analysis pipeline was developed to assay changes in properties of the zonula occludens-1 (ZO-1)-demarcated cell boundaries. While hyperglycemia alone had little impact on cell shape and size, cell morphologic properties were extraordinarily sensitive to soluble TNFα. These results suggest that TNFα acted as the dominant modulator of the epithelium relative to glucose, and that control of inflammation rather than glucose may be key to maintaining intestinal homeostasis.

At present, heart failure (HF) treatment only targets the symptoms based on the left ventricle dysfunction severity; however, the lack of systemic 'omics' studies and available biological data to uncover the heterogeneous underlying mechanisms signifies the need to shift the analytical paradigm towards network-centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover HF-specific networks and potential therapeutic targets or biomarkers. We also aimed to address the issue of dealing with a limited number of samples and to show how appropriate statistical models, enrichment with other datasets as well as machine learning-guided analysis can aid in such cases. Furthermore, we elucidated specific gene expression profiles using transcriptomic and mined data from public databases. This was achieved using the two-step machine learning algorithm to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system, which has also been introduced in this study. The described methodology could be very useful for the target or biomarker selection and evaluation during the pre-clinical therapeutics development stage as well as disease progression monitoring. In addition, the present study sheds new light into the complex aetiology of HF, differentiating between subtle changes in dilated cardiomyopathies (DCs) and ischemic cardiomyopathies (ICs) on the single cell, proteome and whole transcriptome level, demonstrating that HF might be dependent on the involvement of not only the cardiomyocytes but also on other cell populations. Identified tissue remodelling and inflammatory processes can be beneficial when selecting targeted pharmacological management for DCs or ICs, respectively.