Identification and characterization of functional modules reflecting transcriptome transition during human neuron maturation

Synaptic Functions

AbstractBackgroundNeuron maturation is a critical process in neurogenesis, during which neurons gain their morphological, electrophysiological and molecular characteristics for their functions as the central components of the nervous system.ResultsTo better understand the molecular changes during this process, we combined the protein-protein interaction network and public single cell RNA-seq data of mature and immature neurons to identify functional modules relevant to the neuron maturation process in humans. The analysis resulted in 33 discriminable modules which participate in varied functions including energy consumption, synaptic functions and housekeeping functions such as translation and splicing. Based on the identified modules, we trained a neuron maturity index (NMI) model for the quantification of maturation states of single neurons or purified bulk neurons. Applied to multiple single neuron transcriptome data sets of neuron development in humans and mice, the NMI model made estimation of neuron maturity states which were significantly correlated with the neuron maturation trajectories in both species, implying the reproducibility and conservation of the identified transcriptome transition.ConclusionWe identified 33 functional modules whose activities were significantly correlated with single neuron maturity states, which may play important roles in the neuron maturation process.

Synchronization of human retinal pigment epithelial-1 cells in mitosis

Journal of Cell Science ◽

10.1242/jcs.247940 ◽

2020 ◽

Vol 133 (18) ◽

pp. jcs247940

Author(s):

Stacey J. Scott ◽

Kethan S. Suvarna ◽

Pier Paolo D'Avino

Keyword(s):

Cell Lines ◽

Large Scale ◽

Retinal Pigment Epithelial ◽

Retinal Pigment ◽

Interaction Network ◽

Pigment Epithelial ◽

Adenocarcinoma Cells ◽

Sequential Treatments

ABSTRACTHuman retinal pigment epithelial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here, we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4 and Cdk6 inhibitor PD 0332991 (palbociclib) and the microtubule-depolymerizing drug nocodazole. With this method, the vast majority (80–90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Moreover, the cells fully recovered and re-entered the cell cycle after the palbociclib–nocodazole block. Finally, we show that this protocol could be successfully employed for the characterization of the protein–protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

Synchronization of human retinal pigment ephitilial-1 (RPE-1) cells in mitosis

10.1101/2020.04.21.052803 ◽

2020 ◽

Author(s):

Stacey J. Scott ◽

Kethan Suvarna ◽

Pier Paolo D’Avino

Keyword(s):

Cell Lines ◽

Large Scale ◽

Retinal Pigment ◽

Interaction Network ◽

Adenocarcinoma Cells ◽

Sequential Treatments ◽

Dna Synthesis Inhibitors ◽

ABSTRACTHuman retinal pigment ephitilial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4/6 inhibitor PD 0332991 (palbociclib) and the microtubule depolymerizing drug nocodazole. With this method, the vast majority (80-90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Furthermore, we show that this protocol could be successfully employed for the characterization of the protein-protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

Advances in Medical Technologies and Clinical Practice - Computer Applications in Drug Discovery and Development ◽

Construction and Analysis of Protein-Protein Interaction Network

10.4018/978-1-5225-7326-5.ch009 ◽

2019 ◽

pp. 204-220

Author(s):

Divya Dasagrandhi ◽

Arul Salomee Kamalabai Ravindran ◽

Anusuyadevi Muthuswamy ◽

Jayachandran K. S.

Keyword(s):

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Data Sets ◽

Huge Data ◽

Associated Proteins ◽

High Throughput Experiments ◽

Generation Sequencing ◽

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

Topological Analysis and Sub-Network Mining of Protein-Protein Interactions

Research and Trends in Data Mining Technologies and Applications ◽

10.4018/978-1-59904-271-8.ch008 ◽

2007 ◽

pp. 209-240 ◽

Cited By ~ 4

Author(s):

Daniel Wu ◽

Xiaohua Hu

Keyword(s):

Power Law ◽

Protein Interaction ◽

Topological Analysis ◽

Interaction Network ◽

Data Sets ◽

Protein Protein Interactions ◽

Confidence Levels ◽

Experimental Systems ◽

Average Vertex

In this chapter, we report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks, by mining and analyzing graphs constructed from the popular data sets publicly available to the bioinformatics research community. We compare the topology of these networks across different species, different confidence levels, and different experimental systems used to obtain the interaction data. Our results confirm the well-accepted claim that the degree distribution follows a power law. However, further statistical analysis shows that residues are not independent on the fit values, indicating that the power law model may be inadequate. Our results also show that the dependence of the average clustering coefficient on the vertices degree is far from a power law, contradicting many published results. For the first time, we report that the average vertex density exhibits a strong powder law dependence on the vertices degree for the networks studied, regardless of species, confidence levels, and experimental systems. We also present an efficient and accurate approach to detecting a community in a protein-protein interaction network from a given seed protein. Our experimental results show strong structural and functional relationships among member proteins within each of the communities identified by our approach, as verified by MIPS complex catalog database and annotations.

Construction and analysis of an interologous protein–protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA–Seq data sets

Plant Cell Reports ◽

10.1007/s00299-019-02440-y ◽

2019 ◽

Vol 38 (10) ◽

pp. 1249-1262 ◽

Cited By ~ 4

Author(s):

Gagandeep Singh ◽

Vikram Singh ◽

Vikram Singh

Keyword(s):

Camellia Sinensis ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Data Sets ◽

Rna Seq ◽

Mining Functional Modules in Uncertain Protein-Protein Interaction Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.135-136.602 ◽

2011 ◽

Vol 135-136 ◽

pp. 602-608

Author(s):

Ya Meng ◽

Xue Qun Shang ◽

Miao Miao ◽

Miao Wang

Keyword(s):

Protein Interaction ◽

Protein Complexes ◽

Biological Significance ◽

Interaction Network ◽

Biological Data ◽

Experimental Results ◽

Functional Modules ◽

Go Annotation ◽

Mining functional modules with biological significance has attracted lots of attention recently. However, protein-protein interaction (PPI) network and other biological data generally bear uncertainties attributed to noise, incompleteness and inaccuracy in practice. In this paper, we focus on received PPI data with uncertainties to explore interesting protein complexes. Moreover, some novel conceptions extended from known graph conceptions are used to develop a depth-first algorithm to mine protein complexes in a simple uncertain graph. Our experiments take protein complexes from MIPS database as standard of accessing experimental results. Experiment results indicate that our algorithm has good performance in terms of coverage and precision. Experimental results are also assessed on Gene Ontology (GO) annotation, and the evaluation demonstrates proteins of our most acquired protein complexes show a high similarity. Finally, several experiments are taken to test the scalability of our algorithm. The result is also observed.