Establishment of a human cell line stably overexpressing mouse Nip45 and characterization of Nip45 subcellular localization

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC⁺ and LSC^- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC⁺ and LSC^- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

LncRNA AK077216 promotes RANKL-induced osteoclastogenesis and bone resorption via NFATc1 by inhibition of NIP45

Osteoclasts derived from the monocyte/macrophage hematopoietic lineage regulate bone resorption, a process balanced by bone formation in the continual renewal of the skeletal system. As dysfunctions of these cells result in bone metabolic diseases such as osteoporosis and osteopetrosis, the exploration of the mechanisms regulating their differentiation is a priority. A potential mechanism may involve long noncoding RNAs (lncRNAs), which are known to regulate various cell biology activities, including proliferation, differentiation, and apoptosis. The expression of the lncRNA AK077216 (Lnc-AK077216) is significantly upregulated during osteoclastogenesis identified by microarray and verified by qPCR. Up- and downregulation of Lnc-AK077216, respectively promotes and inhibits osteoclast differentiation, bone resorption, and the expression of related genes on the basis of tartrate-resistant acid phosphatase staining, qPCR, and western blot results. In addition, Lnc-AK077216 suppresses NIP45 expression and promotes the expression of NFATc1, an essential transcription factor during osteoclastogenesis. Besides, it was found that the expression of Lnc-AK077216 and Nfatc1 is upregulated, whereas Nip45 expression is downregulated in bone marrow and spleen tissues of ovariectomized mice. The results suggest that Lnc-AK077216 regulates NFATc1 expression and promotes osteoclast formation and function, providing a novel mechanism of osteoclastogenesis and a potential biomarker or a new drug target for osteoporosis.

SUMO conjugation - a mechanistic view

The regulation of protein fate by modification with the small ubiquitin-related modifier (SUMO) plays an essential and crucial role in most cellular pathways. Sumoylation is highly dynamic due to the opposing activities of SUMO conjugation and SUMO deconjugation. SUMO conjugation is performed by the hierarchical action of E1, E2 and E3 enzymes, while its deconjugation involves SUMO-specific proteases. In this review, we summarize and compare the mechanistic principles of how SUMO gets conjugated to its substrate. We focus on the interplay of the E1, E2 and E3 enzymes and discuss how specificity could be achieved given the limited number of conjugating enzymes and the thousands of substrates.

Spatiotemporal distribution of SUMOylation components during mouse brain development

Posttranslational modification of proteins might play an important role in brain cellular dynamics via the rapid turnover or functional change of critical proteins controlling neuronal differentiation or synaptic transmission. Small ubiquitin-like modifier protein (SUMO) is a family of ubiquitin-like small proteins that are covalently attached to target proteins to modify their function posttranslationally. Many cellular processes, such as transcription and protein trafficking, are regulated by SUMOylation, but its functional significance in the brain remains unclear. Although developmental regulation of SUMOylation levels in rat brain was recently demonstrated, no comparative immunohistochemical analysis of the cellular distribution profiles of SUMOylation components, including SUMO1, SUMO2/3, and Ubc9, has been undertaken so far. The present study used immunohistochemical and immunoblot analysis with the different developmental stages of mice and demonstrated the developmentally regulated distribution of SUMO1, SUMO2/3, and Ubc9 in the brain. During embryonic development, SUMOylation by SUMO1 and SUMO2/3 occurred in the nucleoplasm of nestin-positive neural stem cells. Although the total amount of SUMO-modified proteins decreased during postnatal brain development, intense and persistent accumulation of SUMO2/3 was detected throughout life in neural progenitor populations in neurogenic regions, including the subventricular zone and the hippocampal subgranular zone. In contrast, many neurons in the adult brain accumulated SUMO1 rather than SUMO2/3. Heavy immunoreactivity of SUMO1 was found in large projection neurons in the brainstem, whereas SUMO2/3 was almost absent from these areas. This heterogeneous distribution implies that both proteins play a specific and unique role in the brain.