Nafisa N. Nazipova

Bio: Nafisa N. Nazipova is an academic researcher from Keldysh Institute of Applied Mathematics. The author has contributed to research in topics: Tiling array & Hybridization probe. The author has an hindex of 4, co-authored 8 publications receiving 48 citations. Previous affiliations of Nafisa N. Nazipova include Russian Academy of Sciences.

Optimized models for design of efficient miR30-based shRNAs.

Abstract: Small hairpin RNAs (shRNAs) became an important research tool in cell biology. Reliable design of these molecules is essential for the needs of large functional genomics projects. To optimize the design of efficient shRNAs, we performed comparative, thermodynamic and correlation analyses of ~18,000 miR30-based shRNAs with known functional efficiencies, derived from the Sensor Assay project (Fellmann et al., 2011). We identified features of the shRNA guide strand that significantly correlate with the silencing efficiency and performed multiple regression analysis, using 4/5 of the data for training purposes and 1/5 for cross validation. A model that included the position-dependent nucleotide preferences was predictive in the cross-validation data subset (R = 0.39). However, a model, which in addition to the nucleotide preferences included thermodynamic shRNA features such as a thermodynamic duplex stability and position-dependent thermodynamic profile (dinucleotide free energy) was performing better (R = 0.53). Software “miR_Scan” was developed based upon the optimized models. Calculated mRNA target secondary structure stability showed correlation with shRNA silencing efficiency but failed to improve the model. Correlation analysis demonstrates that our algorithm for identification of efficient miR30 based shRNA molecules performs better than approaches that were developed for design of chemically synthesized siRNAs (Rmax = 0.36).

Optimization of signal-to-noise ratio for efficient microarray probe design

Abstract: Motivation: Target-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis. Results: In this study, we defined hybridization specificity as a ratio between oligo target-specific hybridization and oligo genome-wide cross-hybridization. A microarray database, derived from the Genomic Comparison Hybridization (GCH) experiment and performed using the Affymetrix platform, contains two different types of probes. The first type of oligo-probes does not have a specific target on the genome and their hybridization signals are derived from genome-wide cross-hybridization alone. The second type includes oligonucleotides that have a specific target on the genomic DNA and their signals are derived from specific and cross-hybridization components combined together in a total signal. A comparative analysis of hybridization specificity of oligo-probes, as well as their nucleotide sequences and thermodynamic features was performed on the database. The comparison has revealed that hybridization specificity was negatively affected by low stability of the fully-paired oligo-target duplex, stable probe self-folding, G-rich content, including GGG motifs, low sequence complexity and nucleotide composition symmetry. Conclusion: Filtering out the probes with defined 'negative' characteristics significantly increases specific hybridization and dramatically decreasing genome-wide cross-hybridization. Selected oligo-probes have two times higher hybridization specificity on average, compared to the probes that were filtered from the analysis by applying suggested cutoff thresholds to the described parameters. A new approach for efficient oligo-probe design is described in our study.

SAMSON: a software package for the biopolymer primary structure analysis.

Abstract: The SAMSON package is a tool for advanced analysis of primary DNA, RNA and protein structures. The package consists of 16 programs performing statistical analysis and comparison of biopolymer sequences, search for homologies, translation of DNA and RNA sequences into amino acid sequences, splicing of RNA sequences and restriction map construction, recognition of functionally related sites in biopolymer molecules, textual analysis of DNA and RNA regulatory sites and prediction of intermolecular hybridization sites in DNA and RNA molecules.

Understanding off-target effects through hybridization kinetics and thermodynamics.

Abstract: In modern biotechnological and medical research, RNA-guided nucleases (RGNs) continue to be highly effective in targeted modification of genomes and the manipulation of gene expression (Sander and Joung 2014; Wang and Wang 2017). In RNA interference (RNAi) and CRISPR (clustered regularly interspaced short palindromic repeats) -Cas (CRISPR-associated protein) systems, RGNs regulate or modify genes through sequence-specific base-pairing between a short interference or single guide RNAs (siRNAs or sgRNAs) and DNA/RNA targets (Bisaria et al. 2017; Shabalina and Koonin 2008). The patterns of base-pairing interactions may modulate RGN binding affinity and reduce off-targeting (Bisaria et al. 2017; Shabalina et al. 2006). RGNs exhibit off-target behavior when interactions and modifications are made not only in the intended location (on-target) but also elsewhere in the genome where sequences are similar to the intended target (off-target) (Klein et al. 2018; Kempton and Qi, 2019). Nucleotide sequence preferences that improve sgRNA efficiency are substantially different for variable CRISPR-based systems (Kim et al., 2019; Slaymaker et al. 2016; Xu et al. 2015), which is adapted from diverse bacterial defense systems (Koonin et al. 2017; Makarova et al. 2006). Thus, due in part to growing interest in CRISPRCas variants, this editorial primarily focuses on offtargeting in CRISPR-Cas systems and comparison with RNAi. CRISPR-Cas proteins are non-specific endonucleases that bind a protospacer adjacent motif (PAM) located in the proximity of the genomic target (Bollen et al. 2018). sgRNA enables the recognition of the target region of interest through complementary base pairing and directs the Cas nuclease there for specific editing. The sgRNA contains a “seed” region, which is especially responsive to mismatches in duplexes with PAMproximal nucleotides, but variants or mutations of the target distal to the PAM also modulate off rates (Boyle et al. 2017). The off-target behavior is not surprising due to the divergence of sgRNA targeting systems where different selective pressures result in optimizations of specificities and other important features, such as turnover rates (Kim et al. 2019). Preliminary screening of potential candidates and prediction of off-target activities were conducted using not only computational and theoretical approaches but also experimental off-target validation (Zhang et al. 2019; Wienert et al. 2019). Improvement in specificity, on-target cleavage activity, and reduction of off-targetcleavage can be achieved through changes in CRISPRderived nuclease, engineering of sgRNA, and/or CassgRNA delivery modifications. Driving improvements to these parameters is a crucial enabler for RGN-based technologies and the realization of its currently Cell Biol Toxicol (2020) 36:11–15 https://doi.org/10.1007/s10565-019-09505-4

Sequence characteristics define trade-offs between on-target and genome-wide off-target hybridization of oligoprobes.

Abstract: Off-target oligoprobe’s interaction with partially complementary nucleotide sequences represents a problem for many bio-techniques. The goal of the study was to identify oligoprobe sequence characteristics that control the ratio between on-target and off-target hybridization. To understand the complex interplay between specific and genome-wide off-target (cross-hybridization) signals, we analyzed a database derived from genomic comparison hybridization experiments performed with an Affymetrix tiling array. The database included two types of probes with signals derived from (i) a combination of specific signal and cross-hybridization and (ii) genomic cross-hybridization only. All probes from the database were grouped into bins according to their sequence characteristics, where both hybridization signals were averaged separately. For selection of specific probes, we analyzed the following sequence characteristics: vulnerability to self-folding, nucleotide composition bias, numbers of G nucleotides and GGG-blocks, and occurrence of probe’s k-mers in the human genome. Increases in bin ranges for these characteristics are simultaneously accompanied by a decrease in hybridization specificity—the ratio between specific and cross-hybridization signals. However, both averaged hybridization signals exhibit growing trends along with an increase of probes’ binding energy, where the hybridization specific signal increases significantly faster in comparison to the cross-hybridization. The same trend is evident for the S function, which serves as a combined evaluation of probe binding energy and occurrence of probe’s k-mers in the genome. Application of S allows extracting a larger number of specific probes, as compared to using only binding energy. Thus, we showed that high values of specific and cross-hybridization signals are not mutually exclusive for probes with high values of binding energy and S. In this study, the application of a new set of sequence characteristics allows detection of probes that are highly specific to their targets for array design and other bio-techniques that require selection of specific probes.

MicroRNAs: Target Recognition and Regulatory Functions

Abstract: MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

Stable RNA interference rules for silencing.

Abstract: RNA interference has become an indispensable tool for loss-of-function studies across eukaryotes. By enabling stable and reversible gene silencing, shRNAs provide a means to study long-term phenotypes, perform pool-based forward genetic screens and examine the consequences of temporary target inhibition in vivo. However, efficient implementation in vertebrate systems has been hindered by technical difficulties affecting potency and specificity. Focusing on these issues, we analyse current strategies to obtain maximal knockdown with minimal off-target effects.

Advances in CRISPR-Cas9 genome engineering: lessons learned from RNA interference

Abstract: The discovery that the machinery of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 bacterial immune system can be re-purposed to easily create deletions, insertions and replacements in the mammalian genome has revolutionized the field of genome engineering and re-invigorated the field of gene therapy. Many parallels have been drawn between the newly discovered CRISPR-Cas9 system and the RNA interference (RNAi) pathway in terms of their utility for understanding and interrogating gene function in mammalian cells. Given this similarity, the CRISPR-Cas9 field stands to benefit immensely from lessons learned during the development of RNAi technology. We examine how the history of RNAi can inform today's challenges in CRISPR-Cas9 genome engineering such as efficiency, specificity, high-throughput screening and delivery for in vivo and therapeutic applications.

Prediction of potent shRNAs with a sequential classification algorithm

Abstract: We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.