NDUFA1 p.Gly32Arg variant in early-onset dementia

The alternative enzymes-bearing tunicates lack multiple widely distributed genes coding for peripheral OXPHOS subunits

The respiratory chain alternative enzymes (AEs) NDX and AOX from the tunicate Ciona intestinalis (Ascidiacea) have been xenotopically expressed and characterized in human cells in culture and in the model organisms Drosophila melanogaster and mouse, with the purpose of developing bypass therapies to combat mitochondrial diseases in human patients with defective complexes I and III/IV, respectively. The fact that the genes coding for NDX and AOX have been lost from genomes of evolutionarily successful animal groups, such as vertebrates and insects, led us to investigate if the composition of the respiratory chain of Ciona and other tunicates differs significantly from that of humans and Drosophila, to accommodate the natural presence of AEs. We have failed to identify in tunicate genomes fifteen orthologous genes that code for subunits of the respiratory chain complexes; all of these putatively missing subunits are peripheral to complexes I, III and IV in mammals, and many are important for complex-complex interaction in supercomplexes (SCs), such as NDUFA11, UQCR11 and COX7A. Modeling of all respiratory chain subunit polypeptides of Ciona indicates significant structural divergence that is consistent with the lack of these fifteen clear orthologous subunits. We also provide evidence using Ciona AOX expressed in Drosophila that this AE cannot access the coenzyme Q pool reduced by complex I, but it is readily available to oxidize coenzyme Q molecules reduced by glycerophosphate oxidase, a mitochondrial inner membrane-bound dehydrogenase that is not involved in SCs. Altogether, our results suggest that Ciona AEs might have evolved in a mitochondrial inner membrane environment much different from that of mammals and insects, possibly without SCs; this correlates with the preferential functional interaction between these AEs and non-SC dehydrogenases in heterologous mammalian and insect systems. We discuss the implications of these findings for the applicability of Ciona AEs in human bypass therapies and for our understanding of the evolution of animal respiratory chain.

Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts

Background

Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer's disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient.

Objective

This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD.

Methods

AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology.

Results

We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction.

Conclusions

We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.

Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment

Background

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence.

Objective

Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods.

Methods

Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established.

Results

LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN.

Conclusion

Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.

MyD88 dimerization inhibitor ST2825 targets the aggressiveness of synovial fibroblasts in rheumatoid arthritis patients

BACKGROUND

Dimerization of the myeloid differentiation primary response 88 protein (MyD88) plays a pivotal role in the exacerbated response to innate immunity-dependent signaling in rheumatoid arthritis (RA). ST2825 is a highly specific inhibitor of MyD88 dimerization, previously shown to inhibit the pro-inflammatory gene expression in peripheral blood mononuclear cells from RA patients (RA PBMC). In this study, we elucidated the effect of disrupting MyD88 dimerization by ST2825 on the pathological properties of synovial fibroblasts from RA patients (RA SFs).

METHODS

RA SFs were treated with varying concentrations of ST2825 in the presence or absence of bacterial lipopolysaccharides (LPS) to activate innate immunity-dependent TLR signaling. The DNA content of the RA SFs was quantified by imaging cytometry to investigate the effect of ST2825 on different phases of the cell cycle and apoptosis. RNA-seq was used to assess the global response of the RA SF toward ST2825. The invasiveness of RA SFs in Matrigel matrices was measured in organoid cultures. SFs from osteoarthritis (OA SFs) patients and healthy dermal fibroblasts were used as controls.

RESULTS

ST2825 reduced the proliferation of SFs by arresting the cells in the G0/G1 phase of the cell cycle. In support of this finding, transcriptomic analysis by RNA-seq showed that ST2825 may have induced cell cycle arrest by primarily inhibiting the expression of critical cell cycle regulators Cyclin E2 and members of the E2F family transcription factors. Concurrently, ST2825 also downregulated the genes encoding for pain, inflammation, and joint catabolism mediators while upregulating the genes required for the translocation of nuclear proteins into the mitochondria and members of the mitochondrial respiratory complex 1. Finally, we demonstrated that ST2825 inhibited the invasiveness of RA SFs, by showing decreased migration of LPS-treated RA SFs in spheroid cultures.

CONCLUSIONS

The pathological properties of the RA SFs, in terms of their aberrant proliferation, increased invasiveness, upregulation of pain and inflammation mediators, and disruption of mitochondrial homeostasis, were attenuated by ST2825 treatment. Taken together with the previously reported anti-inflammatory effects of ST2825 in RA PBMC, this study strongly suggests that targeting MyD88 dimerization could mitigate both systemic and synovial pathologies in a variety of inflammatory arthritic diseases.

Development of a novel immune infiltration-related diagnostic model for Alzheimer's disease using bioinformatic strategies

Background

The pathogenesis of Alzheimer's disease (AD) is complex and multi-factorial. Increasing evidence has shown the important role of immune infiltration in AD. Thus the current study was designed to identify immune infiltration-related genes and to explore their diagnostic value in AD.

Methods

The expression data of AD patients were downloaded from the GEO database. The limma R package identified differentially expressed genes (DEGs) between AD and controls. The CIBERSORT algorithm identified differentially infiltrated immune cells (DIICs) between AD and controls. DIIC-correlated DEGs were obtained by Pearson correlation analysis. WGCNA was employed to identify DIIC-related modules. Next, LASSO, RFE, and RF machine learning methods were applied to screen robust DIIC-related gene signatures in AD, followed by the construction and validation of a diagnostic nomogram. Detection of the expression of related genes in the peripheral blood of Alzheimer's disease and healthy volunteers by RT-PCR. In addition, the CTD database predicted chemicals targeting DIIC-related gene signatures in the treatment of AD.

Results

NK cells, M0 macrophages, activated myeloid dendritic cells, resting mast cells, CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, and M2 macrophages were differentially infiltrated between AD and controls. Pearson analysis identified a total of 277 DIIC-correlated DEGs between AD and controls. Thereafter, 177 DIIC-related genes were further obtained by WGCNA analysis. By LASSO, RFE and RF algorithms, CMTM2, DDIT4, LDHB, NDUFA1, NDUFB2, NDUFS5, RPL17, RPL21, RPL26 and NDUFAF2 were identified as robust gene signature in AD. The results of RT-PCR detection of peripheral blood samples from Alzheimer's disease and healthy volunteers showed that the expression trend of ten genes screened was consistent with the detection results; among them, the expression levels of CMTM2, DDIT4, LDHB, NDUFS5, and RPL21 are significantly different among groups. Thus, a diagnostic nomogram based on a DIIC-related signature was constructed and validated. Moreover, candidate chemicals targeting those biomarkers in the treatment of AD, such as 4-hydroxy-2-nonenal, rosiglitazone, and resveratrol, were identified in the CTD database.

Conclusion

For the first time, we identified 10 immune infiltration-related biomarkers in AD, which may be helpful for the diagnosis of AD and provide guidance in the treatment of AD.