Short nuclear regulatory RNAs play a key role in the main stages of maturation of the precursors of the major RNA species. Small nuclear RNAs (snRNAs) form the core of the spliceosome and are responsible for the splicing of pre-mRNA molecules. Small nucleolar RNAs (snoRNAs) direct post-transcriptional modification of pre-rRNAs. A promising strategy for the development of non-coding RNA (ncRNAs) mimicking molecules is the introduction of modified nucleotides, which are normally present in natural ncRNAs, into the structure of synthetic RNAs. We have created a set of snoRNAs and snRNA analogs and studied the effect of base modifications, specifically, pseudouridine (Ψ) and 5-methylcytidine (m⁵C), on the immune-stimulating and cytotoxic properties of these RNAs. Here, we performed a whole-transcriptome study of the influence of synthetic snoRNA analogs with various modifications on gene expression in human cells. Moreover, we confirmed the role of PKR in the recognition of snoRNA and snRNA analogs using the short hairpin RNA (shRNA) technique. We believe that the data obtained will contribute to the understanding of the role of nucleotide modification in ncRNA functions, and can be useful for creating the agents for gene regulation based on the structure of natural snoRNAs and snRNAs.

Multiple sclerosis (MS) is an inflammatory autoimmune disease that causes demyelination of nerve cell axons. This paper is devoted to the study of relapsing–remitting multiple sclerosis (RRMS) biomarkers using an LC-MS/MS-based targeted metabolomics approach and the assessment of changes in the profile of 13 amino acids and 29 acylcarnitines in plasma during the relapse of the disease. A significant increase (p < 0.05) in the concentration of glutamate in plasma in patients with RRMS was detected, while the sum of leucine and isoleucine was reduced. A decrease in the concentration of decenoylcarnitine (C10:1, p < 0.05) was observed among acylcarnitines, and this metabolite was detected as a biomarker for the disease for the first time. Several models based on a single marker or multiple pre-selected markers and multivariate analysis with a dimension reduction technique were compared in their effectiveness for the classification of RRMS and healthy controls. The best results for cross-validation showed models of general linear regression (GLM, AUC = 0.783) and random forest model (RF, AUC = 0.769) based on pre-selected biomarkers. Validation of the models on the test set showed that the RF model based on selected metabolites was the most effective (AUC = 0.72). The results obtained are promising for further development of the system of clinical decision support for the diagnosis of RRMS based on metabolic data.

Amino acids and acylcarnitines play an important role as substrates and intermediate products in most of pathways involved in schizophrenia development such as mitochondrial dysfunction, inflammation, lipid oxidation, DNA damage, oxidative stress, and apoptosis. It seems relevant to use an integrated approach with ‘omics’ technology to study their contribution. The aim of our study was to investigate serum amino acid and acylcarnitine levels in antipsychotics-treated patients with chronic schizophrenia compared with healthy donors. We measured serum levels of 15 amino acids and 30 acylcarnitines in 37 patients with schizophrenia and 36 healthy donors by means of tandem mass spectrometry. In summary, patients with chronic schizophrenia had an altered concentration of a few amino acids and acylcarnitines in comparison to the healthy probands. Further research is needed to assess and understand the identified changes.

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n=16) and control (n=12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.