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Abstract Background The selenocysteine (Sec) containing proteins, selenoproteins, are an important group of proteins present throughout all 3 kingdoms of life. With the rapid progression of selenoprotein research in the post-genomic era, application of bioinformatics methods to the identification of selenoproteins in newly sequenced species has become increasingly important. Although selenoproteins in human and other vertebrates have been investigated, studies of primitive invertebrate selenoproteomes are rarely reported outside of insects and nematodes. Result A more integrated view of selenoprotein evolution was constructed using several representative species from different evolutionary eras. Using a SelGenAmic-based selenoprotein identification method, 178 selenoprotein genes were identified in 6 invertebrates: Amphimedon queenslandica, Trichoplax adhaerens, Nematostella vectensis, Lottia gigantean, Capitella teleta, and Branchiostoma floridae. Amphioxus was found to have the most abundant and variant selenoproteins of any animal currently characterized, including a special selenoprotein P (SelP) possessing 3 repeated Trx-like domains and Sec residues in the N-terminal and 2 Sec residues in the C-terminal. This gene structure suggests the existence of two different strategies for extension of Sec numbers in SelP for the preservation and transportation of selenium. In addition, novel eukaryotic AphC-like selenoproteins were identified in sponges. Conclusion Comparison of various animal species suggests that even the most primitive animals possess a selenoproteome range and variety similar to humans. During evolutionary history, only a few new selenoproteins have emerged and few were lost. Furthermore, the massive loss of selenoproteins in nematodes and insects likely occurred independently in isolated partial evolutionary branches.

Abstract Background Computational methods for identifying selenoproteins have been developed rapidly in recent years. However, it is still difficult to identify the open reading frame (ORF) of eukaryotic selenoprotein gene, because the TGA codon for a selenocysteine (Sec) residue in the active centre of selenoprotein is traditionally a terminal signal of protein translation. Although the identification of selenoproteins from genomes through bioinformatics methods has been conducted in bacteria, unicellular eukaryotes, insects and several vertebrates, only a few results have been reported on the ancient chordate selenoproteins. Results A gene assembly algorithm SelGenAmic has been constructed and presented in this study for identifying selenoprotein genes from eukaryotic genomes. A method based on this algorithm was developed to build an optimal TGA-containing-ORF for each TGA in a genome, followed by protein similarity analysis through conserved sequence alignments to screen out selenoprotein genes form these ORFs. This method improved the sensitivity of detecting selenoproteins from a genome due to the design that all TGAs in the genome were investigated for its possibility of decoding as a Sec residue. Using this method, eighteen selenoprotein genes were identified from the genome of Ciona intestinalis, leading to its member of selenoproteome up to 19. Among them a selenoprotein W gene was found to have two SECIS elements in the 3'-untranslated region. Additionally, the disulfide bond formation protein A (DsbA) was firstly identified as a selenoprotein in the ancient chordates of Ciona intestinalis, Ciona savignyi and Branchiostoma floridae, while selenoprotein DsbAs had only been found in bacteria and green algae before. Conclusion The method based on SelGenAmic algorithm is capable of identifying eukaryotic selenoprotein genes from their genomes. Application of this method to Ciona intestinalis proves its successes in finding Sec-decoding TGA from large-scale eukaryotic genome sequences, which fills the gap in our knowledge on the ancient chordate selenoproteins.

The reduction of the cerebral glucose metabolism is closely related to the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome in Alzheimer's disease (AD); however, its underlying mechanism remains unclear. In this paper, 18F-flurodeoxyglucose positron emission tomography was used to trace cerebral glucose metabolism in vivo, along with Western blotting and immunofluorescence assays to examine the expression and distribution of associated proteins. Glucose and insulin tolerance tests were carried out to detect insulin resistance, and the Morris water maze was used to test the spatial learning and memory ability of the mice. The results show increased NLRP3 inflammasome activation, elevated insulin resistance, and decreased glucose metabolism in 3×Tg-AD mice. Inhibiting NLRP3 inflammasome activation using CY-09, a specific inhibitor for NLRP3, may restore cerebral glucose metabolism by increasing the expression and distribution of glucose transporters and enzymes and attenuating insulin resistance in AD mice. Moreover, CY-09 helps to improve AD pathology and relieve cognitive impairment in these mice. Although CY-09 has no significant effect on ferroptosis, it can effectively reduce fatty acid synthesis and lipid peroxidation. These findings provide new evidence for NLRP3 inflammasome as a therapeutic target for AD, suggesting that CY-09 may be a potential drug for the treatment of this disease. [ABSTRACT FROM AUTHOR]

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r =.67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r =.76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters. [ABSTRACT FROM AUTHOR]