skip to main content


Title: Systems and synthetic biology approaches in understanding biological oscillators
Background

Self‐sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single‐cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators.

Results

Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest,de novooscillators in both live cells and cell‐free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations.

Conclusion

With the recent development of biological and computational tools, both approaches have made significant achievements.

 
more » « less
Award ID(s):
1553031
NSF-PAR ID:
10478007
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Quantitative Biology
Volume:
6
Issue:
1
ISSN:
2095-4689
Format(s):
Medium: X Size: p. 1-14
Size(s):
["p. 1-14"]
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract STUDY QUESTION

    Is the combined use of fluorescence lifetime imaging microscopy (FLIM)-based metabolic imaging and second harmonic generation (SHG) spindle imaging a feasible and safe approach for noninvasive embryo assessment?

    SUMMARY ANSWER

    Metabolic imaging can sensitively detect meaningful metabolic changes in embryos, SHG produces high-quality images of spindles and the methods do not significantly impair embryo viability.

    WHAT IS KNOWN ALREADY

    Proper metabolism is essential for embryo viability. Metabolic imaging is a well-tested method for measuring metabolism of cells and tissues, but it is unclear if it is sensitive enough and safe enough for use in embryo assessment.

    STUDY DESIGN, SIZE, DURATION

    This study consisted of time-course experiments and control versus treatment experiments. We monitored the metabolism of 25 mouse oocytes with a noninvasive metabolic imaging system while exposing them to oxamate (cytoplasmic lactate dehydrogenase inhibitor) and rotenone (mitochondrial oxidative phosphorylation inhibitor) in series. Mouse embryos (n = 39) were measured every 2 h from the one-cell stage to blastocyst in order to characterize metabolic changes occurring during pre-implantation development. To assess the safety of FLIM illumination, n = 144 illuminated embryos were implanted into n = 12 mice, and n = 108 nonilluminated embryos were implanted into n = 9 mice.

    PARTICIPANTS/MATERIALS, SETTING, METHODS

    Experiments were performed in mouse embryos and oocytes. Samples were monitored with noninvasive, FLIM-based metabolic imaging of nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Between NADH cytoplasm, NADH mitochondria and FAD mitochondria, a single metabolic measurement produces up to 12 quantitative parameters for characterizing the metabolic state of an embryo. For safety experiments, live birth rates and pup weights (mean ± SEM) were used as endpoints. For all test conditions, the level of significance was set at P < 0.05.

    MAIN RESULTS AND THE ROLE OF CHANCE

    Measured FLIM parameters were highly sensitive to metabolic changes due to both metabolic perturbations and embryo development. For oocytes, metabolic parameter values were compared before and after exposure to oxamate and rotenone. The metabolic measurements provided a basis for complete separation of the data sets. For embryos, metabolic parameter values were compared between the first division and morula stages, morula and blastocyst and first division and blastocyst. The metabolic measurements again completely separated the data sets. Exposure of embryos to excessive illumination dosages (24 measurements) had no significant effect on live birth rate (5.1 ± 0.94 pups/mouse for illuminated group; 5.7 ± 1.74 pups/mouse for control group) or pup weights (1.88 ± 0.10 g for illuminated group; 1.89 ± 0.11 g for control group).

    LIMITATIONS, REASONS FOR CAUTION

    The study was performed using a mouse model, so conclusions concerning sensitivity and safety may not generalize to human embryos. A limitation of the live birth data is also that although cages were routinely monitored, we could not preclude that some runt pups may have been eaten.

    WIDER IMPLICATIONS OF THE FINDINGS

    Promising proof-of-concept results demonstrate that FLIM with SHG provide detailed biological information that may be valuable for the assessment of embryo and oocyte quality. Live birth experiments support the method’s safety, arguing for further studies of the clinical utility of these techniques.

    STUDY FUNDING/COMPETING INTEREST(S)

    Supported by the Blavatnik Biomedical Accelerator Grant at Harvard University and by the Harvard Catalyst/The Harvard Clinical and Translational Science Center (National Institutes of Health Award UL1 TR001102), by NSF grants DMR-0820484 and PFI-TT-1827309 and by NIH grant R01HD092550-01. T.S. was supported by a National Science Foundation Postdoctoral Research Fellowship in Biology grant (1308878). S.F. and S.A. were supported by NSF MRSEC DMR-1420382. Becker and Hickl GmbH sponsored the research with the loaning of equipment for FLIM. T.S. and D.N. are cofounders and shareholders of LuminOva, Inc., and co-hold patents (US20150346100A1 and US20170039415A1) for metabolic imaging methods. D.S. is on the scientific advisory board for Cooper Surgical and has stock options with LuminOva, Inc.

     
    more » « less
  2. Abstract Background

    TheBIN1locus contains the second-most significant genetic risk factor for late-onset Alzheimer’s disease.BIN1undergoes alternate splicing to generate tissue- and cell-type-specific BIN1 isoforms, which regulate membrane dynamics in a range of crucial cellular processes. Whilst the expression of BIN1 in the brain has been characterized in neurons and oligodendrocytes in detail, information regarding microglial BIN1 expression is mainly limited to large-scale transcriptomic and proteomic data. Notably, BIN1 protein expression and its functional roles in microglia, a cell type most relevant to Alzheimer’s disease, have not been examined in depth.

    Methods

    Microglial BIN1 expression was analyzed by immunostaining mouse and human brain, as well as by immunoblot and RT-PCR assays of isolated microglia or human iPSC-derived microglial cells.Bin1expression was ablated by siRNA knockdown in primary microglial cultures in vitro and Cre-lox mediated conditional deletion in adult mouse brain microglia in vivo. Regulation of neuroinflammatory microglial signatures by BIN1 in vitro and in vivo was characterized using NanoString gene panels and flow cytometry methods. The transcriptome data was explored by in silico pathway analysis and validated by complementary molecular approaches.

    Results

    Here, we characterized microglial BIN1 expression in vitro and in vivo and ascertained microglia expressed BIN1 isoforms. By silencingBin1expression in primary microglial cultures, we demonstrate that BIN1 regulates the activation of proinflammatory and disease-associated responses in microglia as measured by gene expression and cytokine production. Our transcriptomic profiling revealed key homeostatic and lipopolysaccharide (LPS)-induced inflammatory response pathways, as well as transcription factors PU.1 and IRF1 that are regulated by BIN1. Microglia-specificBin1conditional knockout in vivo revealed novel roles of BIN1 in regulating the expression of disease-associated genes while counteracting CX3CR1 signaling. The consensus from in vitro and in vivo findings showed that loss ofBin1impaired the ability of microglia to mount type 1 interferon responses to proinflammatory challenge, particularly the upregulation of a critical type 1 immune response gene,Ifitm3.

    Conclusions

    Our convergent findings provide novel insights into microglial BIN1 function and demonstrate an essential role of microglial BIN1 in regulating brain inflammatory response and microglial phenotypic changes. Moreover, for the first time, our study shows a regulatory relationship betweenBin1andIfitm3, two Alzheimer’s disease-related genes in microglia. The requirement for BIN1 to regulateIfitm3upregulation during inflammation has important implications for inflammatory responses during the pathogenesis and progression of many neurodegenerative diseases.

    Graphical Abstract 
    more » « less
  3. Abstract Motivation

    Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets.

    Results

    To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets.

    Availability and implementation

    The scSGL code and analysis scripts are available on https://github.com/Single-Cell-Graph-Learning/scSGL.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  4. Abstract Background

    Double-strand break repair (DSBR) is a highly regulated process involving dozens of proteins acting in a defined order to repair a DNA lesion that is fatal for any living cell. Model organisms such asSaccharomyces cerevisiaehave been used to study the mechanisms underlying DSBR, including factors influencing its efficiency such as the presence of distinct combinations of microsatellites and endonucleases, mainly by bulk analysis of millions of cells undergoing repair of a broken chromosome. Here, we use a microfluidic device to demonstrate in yeast that DSBR may be studied at a single-cell level in a time-resolved manner, on a large number of independent lineages undergoing repair.

    Results

    We used engineeredS. cerevisiaecells in which GFP is expressed following the successful repair of a DSB induced by Cas9 or Cpf1 endonucleases, and different genetic backgrounds were screened to detect key events leading to the DSBR efficiency. Per condition, the progenies of 80–150 individual cells were analyzed over 24 h. The observed DSBR dynamics, which revealed heterogeneity of individual cell fates and their contributions to global repair efficacy, was confronted with a coupled differential equation model to obtain repair process rates. Good agreement was found between the mathematical model and experimental results at different scales, and quantitative comparisons of the different experimental conditions with image analysis of cell shape enabled the identification of three types of DSB repair events previously not recognized: high-efficacy error-free, low-efficacy error-free, and low-efficacy error-prone repair.

    Conclusions

    Our analysis paves the way to a significant advance in understanding the complex molecular mechanism of DSB repair, with potential implications beyond yeast cell biology. This multiscale and multidisciplinary approach more generally allows unique insights into the relation between in vivo microscopic processes within each cell and their impact on the population dynamics, which were inaccessible by previous approaches using molecular genetics tools alone.

     
    more » « less
  5. Background

    Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects.

    Methods

    We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria. Using this method, we compared performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U‐Net. We further compared the two networks against the adaptive active‐mask (AAM) algorithm, a representative of high‐performance conventional segmentation algorithms.

    Results

    The FCN and the U‐Net consistently outperformed the AAM in accuracy, robustness, and sensitivity, often by a significant margin. The U‐Net provided overall the best performance.

    Conclusions

    Our study demonstrates superior performance of the U‐Net and the FCN in segmentation of mitochondria. It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology.

     
    more » « less