Gene therapy is a promising approach to the treatment of various inherited diseases, but its development is complicated by a number of limitations of the natural promoters used. The currently used strong ubiquitous natural promoters do not allow for the specificity of expression, while natural tissue-specific promoters have lowactivity. These limitations of natural promoters can be addressed by creating new synthetic promoters that achieve high levels of tissue-specific target gene expression. This review discusses recent advances in the development of synthetic promoters that provide a more precise regulation of gene expression. Approaches to the design of synthetic promoters are reviewed, including manual design and bioinformatic methods using machine learning. Examples of successful applications of synthetic promoters in the therapy of hereditary diseases and cancer are presented, as well as prospects for their clinical use.

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

The liver is a primary metabolic hub and a pivotal target for gene therapy, owing to its capacity for protein secretion, role in metabolic homeostasis and immune tolerance. Liver-directed gene therapies are used to treat numerous inherited metabolic disorders and coagulation factor deficiencies including hemophilia (A and B), Crigler–Najjar syndrome, mucopolysaccharidoses, phenylketonuria, Fabry, Gaucher, Wilson and Pompe diseases. The efficacy and safety of liver-directed gene therapy rely on the use of strong tissue-specific promoters. To date, there are many different liver-specific promoters used in preclinical and clinical studies, including novel completely synthetic promoters. This review provides a comprehensive analysis of the design, engineering and application of liver-specific promoters. Furthermore, we discuss fundamental principles of gene expression regulation in the liver and the physiological and immunological characteristics that make it a suitable target organ for gene therapy delivery.

Recently, high-throughput sequencing of influenza A viruses has become a routine test. It should be noted that the extremely high diversity of the influenza A virus complicates the task of determining the sequences of all eight genome segments. For a fast and accurate analysis, it is necessary to select the most suitable reference for each segment. At the same time, there is no standardized method in the field of decoding sequencing results that allows the user to update the sequence databases to which the reads obtained by virus sequencing are compared. The IAVCP (influenza A virus consensus and phylogeny) was developed with the goal of automatically analyzing high-throughput sequencing data of influenza A viruses. Its goals include the extraction of a consensus genome directly from paired raw reads. In addition, the pipeline enables the identification of potential reassortment events in the evolutionary history of the virus of interest by analyzing the topological structure of phylogenetic trees that are automatically reconstructed.