Curated gene expression dataset of differentiating 3T3-L1 adipocytes under pharmacological and genetic perturbations

Maximizing the utility of public data

The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be re-analyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse.

Screening and identification of hub gene and differential gene and mutation sequence analysis of related genes in colorectal cancer based on bioinformatics analysis

Background

At present, the research of genomics is in ascendency, and using bioinformatics analysis methods to systematically explore the pathogenic genes and their regulatory mechanisms will play a great role in promoting the research of cancer. This study was to search The Cancer Genome Atlas (TCGA) database and extract inflammation-related non-coding RNA to construct a prognosis model of colon cancer and search for new immunotherapeutic targets.

Methods

The transcriptome sequencing data and clinical data of 396 colon cancer patients were downloaded from TCGA database, and the inflammation-related non-coding RNA was obtained from the non-coding RNAs in Inflammation (ncRI) database. The prognostic model was constructed by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression, and the optimal grouping threshold of risk score was determined by X-Tile software. The patients were risk stratified to further explore the differences in immune cell infiltration and biological function between the high- and low-risk groups.

Results

The TCGA dataset of colon cancer was included to screen out 120 differentially expressed genes (DEGs) that overlapped in the 2 datasets, among which 29 genes were up-regulated and 91 genes were down-regulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the above 120 DEGs showed that proximal tubule sodium bicarbonate recovery, nitrogen metabolism, pancreatic fluid secretion, and PPAR signaling pathways were closely related to the occurrence of colon cancer. The expression of copper death-related genes was significantly correlated with the correlation coefficient of colon cancer (P<0.01). Gene Ontology analysis showed that the DEGs were mainly enriched in messenger RNA processing, RNA splicing, small G protein-mediated signal transduction, adhesion junction, mitochondrial matrix, mitochondrial protein complex, chromatin binding, small G protein binding, and Ras G protein binding, among others. KEGG analysis showed that the DEGs were enriched in the following pathways: herpes simplex virus type 1 infection, pathways of neurodegenerative diseases, Huntington's disease, prion disease, Parkinson's disease, the Ras signaling pathway, and so on.

Conclusions

The key genes closely related to colon cancer were effectively screened by the bioinformatics method, which provided a theoretical basis for further study of its mechanism.

Hierarchical regulation of autophagy during adipocyte differentiation

We previously showed that some adipogenic transcription factors such as CEBPB and PPARG directly and indirectly regulate autophagy gene expression in adipogenesis. The order and effect of these events are undetermined. In this study, we modeled the gene expression, DNA-binding of transcriptional regulators, and histone modifications during adipocyte differentiation and evaluated the effect of the regulators on gene expression in terms of direction and magnitude. Then, we identified the overlap of the transcription factors and co-factors binding sites and targets. Finally, we built a chromatin state model based on the histone marks and studied their relation to the factors’ binding. Adipogenic factors differentially regulated autophagy genes as part of the differentiation program. Co-regulators associated with specific transcription factors and preceded them to the regulatory regions. Transcription factors differed in the binding time and location, and their effect on expression was either localized or long-lasting. Adipogenic factors disproportionately targeted genes coding for autophagy-specific transcription factors. In sum, a hierarchical arrangement between adipogenic transcription factors and co-factors drives the regulation of autophagy during adipocyte differentiation.

A Small Fraction of Progenitors Differentiate Into Mature Adipocytes by Escaping the Constraints on the Cell Structure

Differentiating 3T3-L1 pre-adipocytes are a mixture of non-identical culture cells. It is vital to identify the cell types that respond to the induction stimulus to understand the pre-adipocyte potential and the mature adipocyte behavior. To test this hypothesis, we deconvoluted the gene expression profiles of the cell culture of MDI-induced 3T3-L1 cells. Then we estimated the fractions of the sub-populations and their changes in time. We characterized the sub-populations based on their specific expression profiles. Initial cell cultures comprised three distinct phenotypes. A small fraction of the starting cells responded to the induction and developed into mature adipocytes. Unresponsive cells were probably under structural constraints or were committed to differentiating into alternative phenotypes. Using the same population gene markers, similar proportions were found in induced human primary adipocyte cell cultures. The three sub-populations had diverse responses to treatment with various drugs and compounds. Only the response of the maturating sub-population resembled that estimated from the profiles of the mixture. We then showed that even at a low division rate, a small fraction of cells could increase its share in a dynamic two-populations model. Finally, we used a cell cycle expression index to validate that model. To sum, pre-adipocytes are a mixture of different cells of which a limited fraction become mature adipocytes.