Prediction of novel pluripotent proteins involved in reprogramming of male Germline stem cells (GSCs) into multipotent adult Germline stem cells (maGSCs) by network analysis

DPPA2/4 Promote the Pluripotency and Proliferation of Bovine Extended Pluripotent Stem Cells by Upregulating the PI3K/AKT/GSK3β/β-Catenin Signaling Pathway

Developmental pluripotency-associated 2 (DPPA2) and DPPA4 are crucial transcription factors involved in maintaining pluripotency in humans and mice. However, the role of DPPA2/4 in bovine extended pluripotent stem cells (bEPSCs) has not been investigated. In this study, a subset of bEPSC-related differentially expressed genes (DEGs), including DPPA2 and DPPA4, was identified based on multiomics data (ATAC-seq and RNA-seq). Subsequent investigations revealed that double overexpression of DPPA2/4 facilitates the reprogramming of bovine fetal fibroblasts (BFFs) into bEPSCs, whereas knockout of DPPA2/4 in BFFs leads to inefficient reprogramming. DPPA2/4 overexpression and knockdown experiments revealed that the pluripotency and proliferation capability of bEPSCs were maintained by promoting the transition from the G1 phase to the S phase of the cell cycle. By activating the PI3K/AKT/GSK3β/β-catenin pathway in bEPSCs, DPPA2/4 can increase the nuclear accumulation of β-catenin, which further upregulates lymphoid enhancer binding factor 1 (LEF1) transcription factor activity. Moreover, DPPA2/4 can also regulate the expression of LEF1 by directly binding to its promoter region. Overall, our results demonstrate that DPPA2/4 promote the reprogramming of BFFs into bEPSCs while also maintaining the pluripotency and proliferation capability of bEPSCs by regulating the PI3K/AKT/GSK3β/β-catenin pathway and subsequently activating LEF1. These findings expand our understanding of the gene regulatory network involved in bEPSC pluripotency.

Prediction and Boolean logical modelling of synergistic microRNA regulatory networks during reprogramming of male germline pluripotent stem cells

Unipotent male germline stem (GS) cells can undergo spontaneous reprogramming to germline pluripotent stem (GPS) cells during in vitro culture. In our previous study, we proposed a Boolean logical model of gene regulatory network (GRN) during reprogramming of GS cells to GPS cells. This study was designed to predict and model synergistic microRNA (miRNA) regulatory network during reprogramming of GS cells into GPS cells. The miRNAs targeting differentially expressed genes (DEGs) among GS and GPS cells were predicted by a novel Gene Ontology (GO) enrichment analysis to construct miRNA synergistic networks (MSN) and identify regulatory miRNA modules. Qualitative Boolean logical model of synergistic miRNAs and its regulated genes was then constructed by considering discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tools. Topology, functional and community overlap studies revealed that mmu-miR-200b-3p, mmu-miR-429-3p and mmu-miR-141-3p, mmu-miR-200a-3p and mmu-miR-200c-3p in MSN belongs to the family of miR-200/429/141 and conjectured to control the pluripotency and reprogramming by promoting Mesenchymal to Epithelial Transition (MET). Synergistic network involving mmu-miR-20b-5p, mmu-miR-20a-5p, mmu-miR-106a-5p, mmu-miR-106b-5p, and mmu-miR-17-5p were found to be essential for the maintenance of GS cells. Logical miRNA regulatory network modelling showed that synergistic miRNAs regulates the gene dynamics of MET during GS-GPS reprogramming, as confirmed by perturbation analysis. Taken together, our study predicted novel synergistic miRNAs involved in the regulation of reprogramming and pluripotency in GPS cells. The Boolean logical model of synergistic miRNAs regulatory network further confirms our previous study that gene dynamics of MET regulates GS-GPS reprogramming.

Comparison of spermatozoal RNA extraction methods in goats

RNA sequencing (RNAseq) has divulged newer role of spermatozoal RNA in male fertility. This study aimed to evaluate different sperm purification and RNA extraction methods for long-read RNA sequencing of poly(A) transcriptome in goat spermatozoa. Sperm samples were purified by swim-up separation using different purification medium. Spermatozoal RNA was extracted by seven different methods with additional supplementation of reducing agents in lysis buffer. poly(A) selected RNA was used for cDNA library preparation and long-read RNAseq in Nanopore sequencer. Sperm purification by 1 h swim-up resulted in higher recovery (89.20 ± 1.15%), motility (82.33 ± 1.53%), viability (88.10 ± 5.03%) and plasma membrane integrity (71.33 ± 4.51%) in sperm TALP (sp-TL) medium. A monophasic solution of GITC with phenol and DTT resulted in the highest yield of large sized RNAs (3.89 ± 0.46 ng/million cells) suitable for long-read RNAseq of poly(A) transcripts. RNAseq resulted in reads of length, ranging from 500bp to 2 Kb. A total of 123 transcripts were identified in goat spermatozoa by de novo assembly and included sperm-specific transcripts such as CATSPERG, PRM2, CYLC2, SPATA6, PLCZ1 etc. This study is the first report of long-read RNAseq of poly(A) transcriptome in goat spermatozoa.

Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases

Purpose

Retinal pigment epithelial cell autophagy dysfunction, cellular senescence, and the retinal inflammatory response are key pathogenic factors in age-related macular degeneration (AMD), which has been reviewed in our previously work in 2019. This study aims to identify genes collectively involved in these three biological processes and target drugs in AMD.

Methods

The pubmed2ensembl database was used to perform text mining. The GeneCodis database was applied to analyze gene ontology biological process and the KEGG pathway. The STRING database was used to analyze protein–protein interaction analysis and hub genes were identified by the Cytoscape software. The Drug Gene Interaction Database was used to perform drug–gene interactions.

Results

We identified 62 genes collectively involved in AMD, autophagy, cellular senescence, and inflammatory response, 19 biological processes including 42 genes, 11 enriched KEGG pathways including 37 genes, and 12 hub genes step by step via the above biomedical databases. Finally, five hub genes (IL-6, VEGF-A, TP53, IL-1β, and transforming growth factor [TGF]-β1) and their specific interaction modes were identified, corresponding with 24 target drugs with therapeutic potential for AMD.

Conclusions

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 are pivotal in autophagy, cellular senescence, and the inflammatory response in AMD, corresponding with 24 drugs with therapeutic potential for AMD, providing definite molecular mechanisms for further research and new possibilities for AMD treatment in the future.

Translational Relevance

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 may be new targets for AMD gene therapy and drug development.

A Boolean Logical model for Reprogramming of Testes-derived male Germline Stem Cells into Germline pluripotent stem cells

BACKGROUND AND OBJECTIVE

Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available. However, the mechanism of reprogramming of GS cells into GPS cells remains elusive. This study was designed to develop a Boolean logical model of gene regulatory network (GRN) that might be involved in the reprogramming of GS cells into GPS cells.

METHODS

The gene expression profile of GS and GPS cells (GSE ID: GSE11274 and GSE74151) were analyzed using R Bioconductor to identify differentially expressed genes (DEGs) and were functionally annotated with DAVID server. Potential pluripotent genes among the DEGs were then predicted using a combination of machine learning [Support Vector Machine (SVM)] and BLAST search. Protein isoforms were identified by pattern matching with UniProt database with in-house scripts written in C++. Both linear and non-linear interaction maps were generated using the STRING server. CellNet is used to study the relationship of GRNs between the GS and GPS cells. Finally, the GRNs involving all the genes from integrated methods and literature was constructed and qualitative modelling for reprogramming of GS to GPS cells were done by considering the discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tool.

RESULTS

Through the use of machine learning and logical modeling, the present study identified 3585 DEGs and 221 novel pluripotent genes including Tet1, Cdh1, Tfap2c, Etv4, Etv5, Prdm14, and Prdm10 in GPS cells. Pathway analysis revealed that important signaling pathways such as core pluripotency network, PI3K-Akt, WNT, GDNF and BMP4 signalling pathways were important for the reprogramming of GS cells to GPS cells. On the other hand, CellNet analysis of GRNs of GS and GPS cells revealed that GS cells were similar to gonads whereas GPS cells were similar to ESCs in gene expression profile. A logical regulatory model was developed, which showed that TGFβ negatively regulated the reprogramming of the GS to GPS cells, as confirmed by perturbations studies.

CONCLUSION

The study identified novel pluripotent genes involved in the reprogramming of GS cells into GPS cells. A multivalued logical model of cellular reprogramming is proposed, which suggests that reprogramming of GS cells to GPS cells involves signalling pathways namely LIF, GDNF, BMP4, and TGFβ along with some novel pluripotency genes.

Mdig promotes oncogenic gene expression through antagonizing repressive histone methylation markers

The mineral dust-induced gene (mdig) is overexpressed in a number of human cancers, suggesting critical roles of this gene played on the pathogenesis of cancers. Unlike several other JmjC-domain containing proteins that exhibit histone demethylase activity, it remains enigmatic whether mdig is involved in the demethylation processes of the histone proteins. Methods: To provide direct evidence suggesting contribution of mdig to the demethylation of histone proteins, we recently examined the histone methylation profiles in human bronchial epithelial cells as well as two cancer cell lines with mdig knockout through CRISPR-Cas9 gene editing. Results: Global histone methylation analysis revealed a pronounced increase of the repressive histone trimethylation in three different cell types with mdig depletion, including trimethylation of lysines 9 and 27 on histone H3 (H3K9me3, H3K27me3) and trimethylation of lysine 20 of histone H4 (H4K20me3). Importantly, data from both ChIP-seq and RNA-seq suggested that genetic disruption of mdig enriches repressive histone trimethylation and inhibits expression of target genes in the oncogenic pathways of cell growth, stemness of the cells, tissue fibrosis, and cell motility. Conclusion: Taken together, our study provides the first insight into the molecular effects of mdig as an antagonist for repressive histone methylation markers and suggests that targeting mdig may represent a new area to explore in cancer therapy.