*2.1. Profiling Polysomes*

The translational status of the mRNA pool on a genome-wide scale can be estimated using polysome profiling. The basic protocols for the polysome profiling in plants are described in several publications [5]. Simple protocols have been additionally designed and verified for individual plant species, including *Arabidopsis thaliana*, *Nicotiana benthamiana*, *Solanum lycopersicum*, and *Oryza sativa*, as well as for individual plant tissues [5]. This method is based on the separation of the polysomal mRNAs, monosomal mRNAs, and steady-state mRNAs using sucrose density gradient centrifugation, referred to as polysome fractionation assays (Figure 1). Then, the transcripts (mRNAs) associated with each mRNA pool are analyzed by hybridization on microarrays or undergo RNA sequencing. Assembly, mapping, and in silico analysis of the sequencing data for different pools (polysomes, monosomes, and steady-state mRNAs) provide the researcher with the initial lists of the transcripts with different translation activities [6,7].

According to the experimental data, the results of polysome profiling can be used for a quantitative estimation of mRNA translation efficiency both at different plant growth and developmental stages and under the effect of adverse environmental factors [6,8] or for assessment of quantitative changes in the translational status of individual mRNAs [9]. As a rule, the polysome score (PS) or polysome ratio (PR) are used for this purpose; they are computed as the relative abundances of RNAs in polysomes versus RNAs in nonpolysomes or versus total mRNA. Where total mRNA is the total mRNA level in polysomal and nonpolysomal fractions, respectively [6,8,9].

The polysome profiling appeared rather efficient in the studies on differential translation regulation of specific plant mRNAs under the influence of several abiotic environmental factors [2,6,10]. For example, it has been convincingly demonstrated that the main part of the transcripts under stress displays different degrees of translation repression; moreover, a specific set of transcripts that avoids such repression and retain their transcriptional activity was detected. Below are several examples that in our view, illustrate the abilities of this method in clarifying the mechanisms of translation control in plants. In particular, it is shown that the shares of individual mRNA species in *A. thaliana* polysomal fractions under controlled growth upon a moderate dehydration stress vary from 5% to 95% and that this stress causes a decrease in the ribosome load for over 60% of all mRNAs [2]. The results of genome-wide assay of the relative amounts of individual mRNAs in polysomal versus nonpolysomal fractions under heat shock in the *A. thaliana* cell culture gave the set of genes with different translational responses, i.e., the genes that either considerably increased or considerably decreased the amounts of their mRNAs in polysomal fractions [10]. These results formed the background for further identification of the new cis-regulatory elements in 5'UTRs that influenced differential translation in response to heat shock in *A. thaliana* [8].

In another study, polysome profiling was used for a global assessment of the translation efficiency of mRNA pools during the growth and development of *A. thaliana* leaves. It was demonstrated that the degree of association of each mRNA with the polysomal fraction was different and considerably (from a strong repression to activation at a constant level) changed throughout these processes. Analysis of the functional categories of the mRNAs associated with polysomal fraction showed that

the translation control, being of physiological significance during plant growth and development, was especially pronounced in the mRNAs associated with signaling and protein synthesis. In general, these results emphasize the importance of the dynamic changes in mRNA translation during plant growth and development and suggest that mRNA translation may be controlled via complex mechanisms underlying the response to each factor [6]. throughout these processes. Analysis of the functional categories of the mRNAs associated with polysomal fraction showed that the translation control, being of physiological significance during plant growth and development, was especially pronounced in the mRNAs associated with signaling and protein synthesis. In general, these results emphasize the importance of the dynamic changes in mRNA translation during plant growth and development and suggest that mRNA translation may be controlled via complex mechanisms underlying the response to each factor [6].

Although polysome profiling has been successfully used for a global study of plant mRNA translation efficiency, this method still has some limitations [11]. One of these, it cannot precisely determine the ribosome density, i.e., the number of ribosomes per mRNA, because the mRNA–ribosome complexes from the same differential centrifugation fractions may contain a different number of ribosomes. Moreover, polysome profiling fails to determine the actual ribosome distribution along the transcript, i.e., it is impossible to determine a mRNA region (5'UTR, CDS, or 3'UTR) in which reside the arrested ribosomes. This is very important since it allows for assessing of the translation stage (initiation, elongation, or termination) associated with differential translation of an individual transcript. As a consequence, this makes it not possible to specifically search for the regulatory determinants in particular mRNA regions important for an efficient translation. Although polysome profiling has been successfully used for a global study of plant mRNA translation efficiency, this method still has some limitations [11]. One of these, it cannot precisely determine the ribosome density, i.e., the number of ribosomes per mRNA, because the mRNA– ribosome complexes from the same differential centrifugation fractions may contain a different number of ribosomes. Moreover, polysome profiling fails to determine the actual ribosome distribution along the transcript, i.e., it is impossible to determine a mRNA region (5'UTR, CDS, or 3'UTR) in which reside the arrested ribosomes. This is very important since it allows for assessing of the translation stage (initiation, elongation, or termination) associated with differential translation of an individual transcript. As a consequence, this makes it not possible to specifically search for the regulatory determinants in particular mRNA regions important for an efficient translation.

Nonetheless, these limitations of the polysome profiling technique do not diminish its tremendous potential for the study of the fine mechanisms of translation in plants on a global scale. This method not only makes it possible to determine the correlations between the observed translational and transcriptional fluctuations under normal conditions and under stress factors, but also provides researchers with general information useful for further insights into the rules of mRNA decoding, i.e., allows defining the pools of transcripts with different translation efficiency and to find regulatory contexts of mRNAs or their combinations important for translation efficiency using computational analysis (this will be considered below in more detail). According to the available experimental data, polysome profiling is, as a rule, applicable to the search for actively-translated mRNAs and the subsequent analysis, although the understanding of the mechanisms associated with the repression of translation in a certain pool of transcripts is of the same importance; perhaps, researchers will focus on this area in future. It should be also emphasized that most studies utilizing polysome profiling performed so far, involve the plants with annotated genomes. However, the use of this method is not limited to the plant species with annotated genomes and can be extended to other plant species, including those genomes that have not been yet determined or those already sequenced but poorly annotated. Nonetheless, these limitations of the polysome profiling technique do not diminish its tremendous potential for the study of the fine mechanisms of translation in plants on a global scale. This method not only makes it possible to determine the correlations between the observed translational and transcriptional fluctuations under normal conditions and under stress factors, but also provides researchers with general information useful for further insights into the rules of mRNA decoding, i.e., allows defining the pools of transcripts with different translation efficiency and to find regulatory contexts of mRNAs or their combinations important for translation efficiency using computational analysis (this will be considered below in more detail). According to the available experimental data, polysome profiling is, as a rule, applicable to the search for actively-translated mRNAs and the subsequent analysis, although the understanding of the mechanisms associated with the repression of translation in a certain pool of transcripts is of the same importance; perhaps, researchers will focus on this area in future. It should be also emphasized that most studies utilizing polysome profiling performed so far, involve the plants with annotated genomes. However, the use of this method is not limited to the plant species with annotated genomes and can be extended to other plant species, including those genomes that have not been yet determined or those already sequenced but poorly annotated.

**Figure 1.** Polysome profiling in a sucrose density gradient. Separation of the transcripts depending on the ribosome loading: the first peak corresponds to the mRNAs unbound to ribosomes; second and third peaks, to the ribosome small and large subunits, respectively; and the fourth and subsequent peaks, to the mRNAs with different ribosome loadings. **Figure 1.** Polysome profiling in a sucrose density gradient. Separation of the transcripts depending on the ribosome loading: the first peak corresponds to the mRNAs unbound to ribosomes; second and third peaks, to the ribosome small and large subunits, respectively; and the fourth and subsequent peaks, to the mRNAs with different ribosome loadings.

#### *2.2. Translating Ribosome Affinity Purification (TRAP) and TRAP-Seq 2.2. Translating Ribosome Affinity Purification (TRAP) and TRAP-Seq*

The experimental approach referred to as translating ribosome affinity purification (TRAP) is a modification of the traditional polysome profiling procedure and was for the first time described for *A. thaliana* [12]. This method utilizes the plant transgenic lines that express an epitope-tagged variant The experimental approach referred to as translating ribosome affinity purification (TRAP) is a modification of the traditional polysome profiling procedure and was for the first time described for *A. thaliana* [12]. This method utilizes the plant transgenic lines that express an epitope-tagged

of ribosomal protein L18 (usually referred to as RPL18). As a rule, these plant transgenic lines

variant of ribosomal protein L18 (usually referred to as RPL18). As a rule, these plant transgenic lines express FLAG epitope-tagged RPL18 in the N-terminal region [12,13]. The cell lysates of transgenic plants are produced under the conditions that stabilize the ribosomes on RNA and block translation. The transcripts bound to the ribosomes that carry the labeled RPL18 are selectively separated using the absorption on anti-FLAG-M2 agarose. This enables ribosome capture from crude cell extracts by a single-stage immune precipitation (Figure 2) and, as a rule, allows the pool of RNAs (designated as TRAP RNA) that are actively translated to be obtained.

This method is described and discussed in detail in several papers [3,13–15]. Note that both the traditional polysome profiling approach and TRAP give analogous proportions of the small and large polysomes (i.e., ribosome profiles) [13]. Both approaches also have similar limitations on their application, namely, in the assessment of the number of ribosomes per mRNA length and the distribution of ribosomes along the transcript (see above). However, note that a wide use of the experimental TRAP approach is also limited by the available plant transgenic lines but, nonetheless, the use of transgenic lines gives certain advantages as compared with the traditional polysome profiling. This advantage consists in the possibility of not only constitutive, but also tissue-specific RPL18 expression by using different tissue-specific promoters [14]. Thanks to the tissue-specific RPL18 expression, TRAP is applicable to profiling of actively-translated RNAs in different populations of plant cells, namely, in (i) different root cells (epidermis, cortex, or endodermis); (ii) companion phloem cells, meristem cells, and leaf mesophyll cells; and (iii) microspores, pollen, and other plant tissues and cell types [14]. For example, the use of APETALA1, APETALA3, and AGAMOUS for expression of FLAG-RPL18 in early flower development allowed for the discovery of new levels of the expression control in developing flowers associated with differential translation [16]. A systemic analysis of the mRNAs in different specimens relative to the pollen grains within buds and in vitro-germinated pollen tubes has been performed with the help of the *A. thaliana* transgenic lines expressing epitope-tagged RPL18 under the control of ProLAT52 promoter, which allowed for the identification of a cohort of the transcripts that regulate late stages of pollination in flowering plants; this paves the way for better understanding of the pollen-based mechanisms that promote fertilization [15]. It should be emphasized that the in vivo proteomic studies of pollen tubes are extremely complicated because of the difficulties with pollen collection; the selective immune purification of the transcripts associated with the polysomes in pollen tubes in this case assisted in identification of the genes important for the in vivo pollen biology. Thus, the TRAP approach has an important advantage for efficient isolation of the population of mRNA complexes from particular cell types.

The sensitive moment when using TRAP approach is during the selection of the transgenic line that expresses FLAG-RPL18, which is extremely important for a successful analysis of the tissue-specific responses. A position effect associated with the T-DNA integration site in the genome of transgenic plants is known. In this regard, the new transgenic lines intended for this research should be selected bearing in mind the presence of known tissue-specific genes in the corresponding tissues or cell types. This will ensure selection of the most appropriate line for further analysis.

According to the current opinion, not only stable plant transformants, but also a transient expression of FLAG epitope-tagged RPL18 can be used for identification of the differentially-translated mRNA pools in plant genomes, for example, utilizing the agroinfiltration of *Medicago truncatula* hairy root cultures or of *N. benthamiana* leaves by *Agrobacterium rhizogenes*.

The FLAG tag may be also added to other proteins in order to determine their role in translation. For example, the expression of tagged oligouridylate binding protein 1 (UBP1) with subsequent immune purification of the mRNA–protein complexes (mRNPs) clarified the role of this protein in the dynamic and reversible aggregation of translationally repressed mRNAs in hypoxia [17]. In particular, UBP1 constitutively binds a subpopulation of the mRNAs with the 3'UTRs enriched for uracil under normoxic conditions. In hypoxia, UBP1 is associated with non-uracil-rich mRNAs, which increases its aggregation in microscopically-visible cytoplasmic foci, referred to as UBP1 stress granules (SGs). This UBP1–mRNA association leads to a global decrease in the protein synthesis. The translation

limitation for the transcripts associated into SGs reduces the energy spending, thereby determining the priority in synthesis of the proteins that enhance plant survival in stress. The UBP1 SGs rapidly disaggregate during reoxygenation, which coincides with the mRNA return to polysomes. In this process, the mRNAs that are significantly induced and translated in hypoxia to a considerable degree manage to avoid UBP1 sequestration. Thus, it has been shown that the SG-nucleating RNA-binding UBP1 is a component of the mechanism that post-translationally reprograms plant gene expression, thereby enhancing plant survival in hypoxia [17]. *Int. J. Mol. Sci.* **2018**, *19*, x 6 of 26 stress. The UBP1 SGs rapidly disaggregate during reoxygenation, which coincides with the mRNA return to polysomes. In this process, the mRNAs that are significantly induced and translated in hypoxia to a considerable degree manage to avoid UBP1 sequestration. Thus, it has been shown that the SG-nucleating RNA-binding UBP1 is a component of the mechanism that post-translationally reprograms plant gene expression, thereby enhancing plant survival in hypoxia [17].

**Figure 2.** Polysome profiling using translating ribosome affinity purification (TRAP). General principle of selective separation on anti-FLAG-M2 agarose of the transcripts bound to ribosomes carrying the epitope-tagged variant of ribosomal protein. Brown arrows denote the FLAG epitope in ribosomal protein and black icons denote the anti-FLAG on agarose beads. **Figure 2.** Polysome profiling using translating ribosome affinity purification (TRAP). General principle of selective separation on anti-FLAG-M2 agarose of the transcripts bound to ribosomes carrying the epitope-tagged variant of ribosomal protein. Brown arrows denote the FLAG epitope in ribosomal protein and black icons denote the anti-FLAG on agarose beads.

#### *2.3. Ribosome Profiling, or Ribo-Seq 2.3. Ribosome Profiling, or Ribo-Seq*

Ribosome Profiling (RP), or Ribo-Seq, elaborated by Ingolia, Newman, and Weissman in 2009 [18], is based on the isolation and sequencing of the mRNA fragments protected by ribosome. This gives a "snapshot" of the ribosome positions along mRNA on a genome-wide scale, i.e., gives the possibility to determine both the number and positions of the ribosomes in the mRNA coding region in vivo (Figure 3). Ribosome Profiling (RP), or Ribo-Seq, elaborated by Ingolia, Newman, and Weissman in 2009 [18], is based on the isolation and sequencing of the mRNA fragments protected by ribosome. This gives a "snapshot" of the ribosome positions along mRNA on a genome-wide scale, i.e., gives the possibility to determine both the number and positions of the ribosomes in the mRNA coding region in vivo (Figure 3).

As a rule, many studies use the RP experimental protocol, which comprises five interrelated stages: (i) preparation of RNA specimens with the arrested ribosomes; (ii) controlled hydrolysis of these specimens by RNase to generate small RNA fragments associated with a ribosome (referred to as footprints); (iii) their subsequent isolation; (iv) preparation of purified footprints with a size of 28– 30 nucleotides; and (v) construction of the library and its high-throughput sequencing, as a rule, with the help of short-read sequencers. The deep sequencing reads of the footprints are analyzed using bioinformatics methods and the translation efficiency is derived by normalizing the number of reads of the footprints to the number of reads of the total transcriptome by RNA-Seq. As is mentioned above, the first experimental protocol for ribosome profiling was described in As a rule, many studies use the RP experimental protocol, which comprises five interrelated stages: (i) preparation of RNA specimens with the arrested ribosomes; (ii) controlled hydrolysis of these specimens by RNase to generate small RNA fragments associated with a ribosome (referred to as footprints); (iii) their subsequent isolation; (iv) preparation of purified footprints with a size of 28–30 nucleotides; and (v) construction of the library and its high-throughput sequencing, as a rule, with the help of short-read sequencers. The deep sequencing reads of the footprints are analyzed using bioinformatics methods and the translation efficiency is derived by normalizing the number of reads of the footprints to the number of reads of the total transcriptome by RNA-Seq.

2009 [18] and has been constantly developed, in particular, for its application to different organisms [18,19], including plants [20,21] and plant organelles—chloroplasts [20] and mitochondria [22]. The individual protocols differ in the particular details providing optimization of each of the five interrelated stages, including the differences in tissue and cell processing; pH and composition of the buffer for cell lysis; prepurification of polysomes before RNase hydrolysis (done or omitted); type of RNase used for generating monosomes [23]; and the methods used to purify the monosome fractions and construct sequencing libraries. Ribosome affinity purification (TRAP method), including the tissue-specific purification, can be also used as the starting point for ribosome profiling [20]. In general, the RP results allow for determination of the precise positions of the translating ribosomes on mRNA with an unprecedented resolution, to a single nucleotide. The specialized software for analysis, interpretation, and visualization of RP data is currently available (for detailed As is mentioned above, the first experimental protocol for ribosome profiling was described in 2009 [18] and has been constantly developed, in particular, for its application to different organisms [18,19], including plants [20,21] and plant organelles—chloroplasts [20] and mitochondria [22]. The individual protocols differ in the particular details providing optimization of each of the five interrelated stages, including the differences in tissue and cell processing; pH and composition of the buffer for cell lysis; prepurification of polysomes before RNase hydrolysis (done or omitted); type of RNase used for generating monosomes [23]; and the methods used to purify the monosome fractions and construct sequencing libraries. Ribosome affinity purification (TRAP method), including the tissue-specific purification, can be also used as the starting point for ribosome profiling [20].

review, see [24]). By assessing the relative number and location of ribosomes on mRNA, the researcher can estimate the general translation pattern i.e., to assess the translation efficiency, which is calculated as the ratio of translation (the data on the number of footprint reads in individual mRNA) to transcription (RNA-Seq data at the level of individual mRNA) (Figure 4). Note that it is In general, the RP results allow for determination of the precise positions of the translating ribosomes on mRNA with an unprecedented resolution, to a single nucleotide. The specialized software for analysis, interpretation, and visualization of RP data is currently available (for detailed review, see [24]). By assessing the relative number and location of ribosomes on mRNA, the researcher can estimate the general translation pattern i.e., to assess the translation efficiency, which is calculated as the ratio of translation (the data on the number of footprint reads in individual mRNA) to transcription (RNA-Seq data at the level of individual mRNA) (Figure 4). Note that it is possible not only to directly quantify the mRNAs that will be translated into proteins, but also to detect the new types of contexts in the plant mRNAs associated with translation, for example, uORFs (upstream ORFs) and frameshifts; to precisely determine the translation initiation site (TIS) of the main ORF; and to find new translated ORFs, including those residing in intergenic RNAs or putative noncoding short RNAs (ncRNAs) (Figure 4) [3,24,25]. The researcher gets these additional options thanks to the fact that the 80S ribosomes associate only with the portion of the transcript that will be most likely decoded into the protein product. The 80S ribosome and transcript will associate not only in CDS, but also in 5'UTRs if they contain an uORF, i.e., short translated reading frame located upstream of the main ORF (CDS), which may have an important role in translation regulation. Another most important aspect that can be studied in terms of the RP experimental data is assessment of the dynamics of ribosome movement along individual mRNAs and the rate at which certain codons are translated. This is possible because three nucleotide bases in the sequenced footprints are reflected in a periodic mode as a consequence of the ribosome movement along the mRNA coding region, since the ribosome moves along the overall coding sequence in a codon-wise manner, the 5' region of ribosome footprints tend to be mapped at the same position of each codon.

Find below several examples which in our view illustrate the distinctive capabilities of RP in clarification of the fine mechanisms underlying the translational control in plants, such as the detection of new ORFs, including those annotated as noncoding RNAs and pseudogenes. In particular, the study of translation regulation under normoxic and sublethal hypoxic conditions (hypoxia) in *A. thaliana* shoots with the help of RP not only detected an inhibitory effect of the uORF on the translation of downstream protein coding regions in normoxia, which was further modulated by hypoxia, but also determined the alternatively spliced mRNAs as well as the fact that ribosomes were associated with certain noncoding RNAs [21]. An RP study of the maize shoots under drought showed a statistically significant change in the translation efficiency of 931 genes, which according to further analysis of the transcripts was associated with the nucleotide composition of the sequence, including GC content, length of coding sequences, and normalized minimum free energy. In addition, potential translation of 3036 open reading frames (uORFs) in 2558 genes was detected; the authors believe that these uORFs are able to influence the translation efficiency of the downstream main open reading frames (ORFs) [26]. In another study, the Ribo-Seq data detected 27 and 37 translated sORFs (short ORFs) among the annotated noncoding ncRNAs and pseudogenes of *A. thaliana*, respectively [27]. Moreover, 187 translated uORFs were identified with a high degree of reliability. In addition, the events of translation from the start codons other than AUG were identified in the dataset among both annotated genes and uORFs. They also demonstrated that 15 of the 19 detected single-exon sORFs had homologs in various flowering plants, which suggests their functional significance [27].

Lukoszek et al. [28] used RNA-Seq and Ribo-Seq to assess reprogramming of the *A. thaliana* global gene expression during a long-term heat shock (3 h at 37 ◦C) at both the transcriptional and translational levels. They have shown that translation is globally impaired in the early period of the heat impact (15 to 45 min), while the stress response appears mainly at the expense of transcriptional programs. In this process, a long-term stress impact (3 h) activated translational programs, which eventually form the adaptive response. The transcripts regulated via translation display a number of common characteristics, namely, the presence of relatively conserved A/G-rich motifs in their 5'UTRs or 3'UTRs that are similar to the sequences identified as protein-binding nucleotide motifs. Another specific feature widespread among the genes upregulated in heat stress is that they are less inclined to form secondary structures, which is likely to ensure their binding with ribosomes and to enhance translation. In addition, several transcripts prevalently induced by heat contain a putative G2 quadruplex in their 5'UTRs. Note that an increased number of reads for RP footprints in quadruplex structures correlates with an expanded expression of the downstream CDSs. This suggests

an important role of these structures in translation activation of the downstream ORF according to yet unknown mechanism [28]. Ribosome profiling has been used to analyze translation of the chloroplast transcripts in maize shoots in response to changes in light conditions. According to the experimental data, all chloroplast mRNAs except for psbA maintain similar numbers of ribosomes after short-term changes in light conditions but nonetheless are more efficiently translated in the light. On the other hand, the psbA mRNA displays a sharp increase in the ribosomes over several minutes after the plants are transferred to light and restores a low ribosome loading during 1 h in the dark, which correlates with the need to replace the damaged psbA in photosystem II. These results emphasize the unique translational response of psbA in mature chloroplasts, indicate the particular light-regulated steps in the context of photosystem II activity maintenance, and provide the background for the study into the mechanisms underlying both the psbA-specific and genome-wide effects of the light on the translation in chloroplasts [29].

The RP technology was also used to study several aspects in the translation of *A. thaliana* mitochondrial genome in a dynamic mode. As has been shown, the mitochondrial mRNAs are differentially-translated; in this process, the translational levels of the transcripts encoding the subunits of mitochondrial protein complexes, in particular, complex V, proportionally correlate with the stoichiometry of respiratory chain subunits. In general, the mitochondrial translation is shown to be controlled at the level of individual mRNAs and is directly involved in the activity regulation of plant mitochondria [22].

Note that Ribo-Seq technology is currently at a relatively early stage of its development, which leads to some experimental difficulties and technical artifacts influencing the Ribo-Seq data interpretation [18]. In particular, the RP results may display statistically significant differences associated with the modifications of one of the five stages in the basic protocol, such as the conditions of cell lysis, composition of buffer solutions, selection of nucleases and the absence of pronounced specificity to the sequences to be cleaved, and construction of the library; even more so as these details in many cases are not analyzed in a systematic manner [19,23]. This suggests the need to systematically study the effects of the corresponding experimental parameters of the used RP protocols [19].

The RP technique also has its limitations. According to the current scientific consensus, the basic limitation of RP approach is a static position of ribosomes along the mRNA. This prevents distinguishing between the ribosomes involved in translation from the ribosome in a steady-state [19]. Thus, the methods used in the majority of studies involving RP can overestimate the translation efficiency because of the data related to monosomes, in which mRNA is also protected by a ribosome (Figure 5A) [18,23,26]. Underrepresentation of the transcript regions with ribosome stacking is also possible; this is associated with the stacked polysomes and may prevent hydrolysis in monosomes, because of inaccessibility to RNases (Figure 5B) [4,30]. Correspondingly, the recommendation for an additional direct measurement of the polysome-protected mRNA looks most reasonable to overcome this limitation of the RP approach [26].

Note that the Ribo-Seq technique now is mainly applicable to study translation of the organisms with annotated genomes since the deep sequencing data for footprints are represented by very short reads (28–30 nucleotides), which, as a rule, are analyzed by mapping onto the genome data (Figure 4).

However, the current limitations of this method can be bypassed and this experimental technology will remain a useful tool in the omics [30]. RP data with high resolution is a priceless resource for studying noncanonical start codons and alternative start sites and can be useful for characterizing translation of different isoforms of transcripts, identifying new translated ORFs and their quantifying, and, in general, for improving the genome annotation of poorer characterized organisms. Additional ribosome profiling can also be a proxy for the proteome or assist in proteomics studies [27,30].

Completing this section, note that the genome-wide profiling of the transcripts associated with ribosomes utilizing one of the above experimental approaches may highlight the new aspects in gene expression unvisualizable by an ordinary profiling of the total cellular mRNA. In Table 1, we attempted to consolidate the advantages, limitations, and areas of applicability of the discussed experimental approaches to the study of differential translation on a genome-wide scale. Undoubtedly, selection of the appropriate experimental approach depends on the particular aims set by a researcher. *Int. J. Mol. Sci.* **2018**, *19*, x 9 of 26 *Int. J. Mol. Sci.* **2018**, *19*, x 9 of 26

**Figure 3.** Scheme of application of ribosome profiling to functional characterization of mRNA regions. (**a**) Scheme of an mRNA with unknown ribosome positions. (**b**) The mRNA with arrested ribosomes in the transcript regions potentially important for efficient translation. (**c**) Formation of the ribosome footprints by RNase hydrolysis. The resulting footprints characterize the translational functionality of a certain mRNA region. The footprints shown with different colors correspond to different mRNA regions. (**d**) Result of analysis of the precise positions of translating ribosomes along mRNA, where A is the identified alternative open reading frames in 5'UTRs or 3'UTRs and CDS (coding sequence) is the main reading frame of the transcript. **Figure 3.** Scheme of application of ribosome profiling to functional characterization of mRNA regions. (**a**) Scheme of an mRNA with unknown ribosome positions. (**b**) The mRNA with arrested ribosomes in the transcript regions potentially important for efficient translation. (**c**) Formation of the ribosome footprints by RNase hydrolysis. The resulting footprints characterize the translational functionality of a certain mRNA region. The footprints shown with different colors correspond to different mRNA regions. (**d**) Result of analysis of the precise positions of translating ribosomes along mRNA, where A is the identified alternative open reading frames in 5'UTRs or 3'UTRs and CDS (coding sequence) is the main reading frame of the transcript. **Figure 3.** Scheme of application of ribosome profiling to functional characterization of mRNA regions. (**a**) Scheme of an mRNA with unknown ribosome positions. (**b**) The mRNA with arrested ribosomes in the transcript regions potentially important for efficient translation. (**c**) Formation of the ribosome footprints by RNase hydrolysis. The resulting footprints characterize the translational functionality of a certain mRNA region. The footprints shown with different colors correspond to different mRNA regions. (**d**) Result of analysis of the precise positions of translating ribosomes along mRNA, where A is the identified alternative open reading frames in 5'UTRs or 3'UTRs and CDS (coding sequence) is the main reading frame of the transcript.

**Figure 4.** Principle of analysis, interpretation, and visualization of the ribosome profiling data. (**a**) The ribosomes arrested on transcripts (**b**) form ribosome footprints after RNase hydrolysis. (**c**) The footprints mapped onto genome can be associated with particular sequences to assess the relative amount and positions of ribosomes on the transcripts on a genome-wide scale. **Figure 4.** Principle of analysis, interpretation, and visualization of the ribosome profiling data. (**a**) The ribosomes arrested on transcripts (**b**) form ribosome footprints after RNase hydrolysis. (**c**) The footprints mapped onto genome can be associated with particular sequences to assess the relative amount and positions of ribosomes on the transcripts on a genome-wide scale. **Figure 4.** Principle of analysis, interpretation, and visualization of the ribosome profiling data. (**a**) The ribosomes arrested on transcripts (**b**) form ribosome footprints after RNase hydrolysis. (**c**) The footprints mapped onto genome can be associated with particular sequences to assess the relative amount and positions of ribosomes on the transcripts on a genome-wide scale.



Note: The key advantages and limitations are shown for each approach; see the text for a comprehensive description.

**Table 1.** Comparative characterization of the experimental approaches producing pools of differentially-translated mRNAs.

**Figure 5.** Limitations in the use of ribosome profiling: (**a**) overestimation of translation efficiency because of the footprints of monosomes, where mRNA is also protected by ribosome and (**b**) underrepresentation of the transcript region with stacked ribosomes; carefully stacked polysomes are inaccessible to RNases, thus cannot be digested into ribosome footprints of the tested size (28–30 **Figure 5.** Limitations in the use of ribosome profiling: (**a**) overestimation of translation efficiency because of the footprints of monosomes, where mRNA is also protected by ribosome and (**b**) underrepresentation of the transcript region with stacked ribosomes; carefully stacked polysomes are inaccessible to RNases, thus cannot be digested into ribosome footprints of the tested size (28–30 nucleotides). Red spots denote the region attacked by RNase.

#### nucleotides). Red spots denote the region attacked by RNase. **3. Computational Algorithms for Predicting the Features of Plant mRNAs Important for 3. Computational Algorithms for Predicting the Features of Plant mRNAs Important for Differential Translation**

**Differential Translation**  The above described experimental approaches make it possible to detect the specific pools of transcripts with characteristic differential translation. Several computational resources are useful for The above described experimental approaches make it possible to detect the specific pools of transcripts with characteristic differential translation. Several computational resources are useful for identification of regions of specific structural features in mRNA nucleotide composition that can mediate differential translational control.

identification of regions of specific structural features in mRNA nucleotide composition that can mediate differential translational control. In this section, we summarized the resources and some computational algorithms that have been used to form the samples of target plant transcript sequences and to predict their peculiar characteristics, as well as their main functions and domains of application. Note that the resources and the corresponding software are rather numerous and, in fact, require a separate review. Here, we consider only those that have given the data on and predictions of regulatory contexts in In this section, we summarized the resources and some computational algorithms that have been used to form the samples of target plant transcript sequences and to predict their peculiar characteristics, as well as their main functions and domains of application. Note that the resources and the corresponding software are rather numerous and, in fact, require a separate review. Here, we consider only those that have given the data on and predictions of regulatory contexts in transcripts with further experimental confirmation.

#### transcripts with further experimental confirmation. *3.1. Preparing Datasets for Analysis*

*3.1. Preparing Datasets for Analysis*  The key preparatory stage in the in silico predictions is a construction of the most representative sets of sequences for the transcript pools differing in their translation efficiency. Note that the researcher needs not only full-sized transcript sequences (cDNA), but also the sequences of individual regions of these transcripts, namely, coding (CDS) and untranslated (5'UTR and 3'UTR) regions, which, as mentioned above, can also contribute to translation efficiency. Currently, many The key preparatory stage in the in silico predictions is a construction of the most representative sets of sequences for the transcript pools differing in their translation efficiency. Note that the researcher needs not only full-sized transcript sequences (cDNA), but also the sequences of individual regions of these transcripts, namely, coding (CDS) and untranslated (5'UTR and 3'UTR) regions, which, as mentioned above, can also contribute to translation efficiency. Currently, many internet resources have been elaborated that allow sets of such sequences to be downloaded,

internet resources have been elaborated that allow sets of such sequences to be downloaded,

including the sequences for plants. In particular, TAIR is the information source for the model plant *A. thaliana* (https://www.arabidopsis.org/download/index-auto.jsp?dir=%2Fdownload\_files% 2FSequences%2FTAIR10\_blastsets) [31], which is widely used for loading 5'UTR, CDS, 3'UTR, and cDNA sequences using the tools "Download", "Sequences", and TAIR10 blastsets [32–34]. Another information resource containing CDS, cDNA, 5'UTR, and 3'UTR sequences of the representatives of six key kingdoms of the living organisms, including plants, is JetGene. JetGene is publicly available at http://jetgene.bioset.org/; its data are stored and updated at the Ensembl server [35]. The intuitively clear and friendly JetGene interface allows the cDNA, 5'UTR, CDS, and 3'UTR sequences to be extracted in a FASTA format, including the specific samples on user request. Note that only the sequences with complete information about the full-sized transcripts are in most cases selected for further analysis. *A. thaliana* (https://www.arabidopsis.org/download/index-auto.jsp?dir=%2Fdownload\_files%2FSequences%2F TAIR10\_blastsets) [31], which is widely used for loading 5'UTR, CDS, 3'UTR, and cDNA sequences using the tools "Download", "Sequences", and TAIR10 blastsets [32–34]. Another information resource containing CDS, cDNA, 5'UTR, and 3'UTR sequences of the representatives of six key kingdoms of the living organisms, including plants, is JetGene. JetGene is publicly available at http://jetgene.bioset.org/; its data are stored and updated at the Ensembl server [35]. The intuitively clear and friendly JetGene interface allows the cDNA, 5'UTR, CDS, and 3'UTR sequences to be extracted in a FASTA format, including the specific samples on user request. Note that only the sequences with complete information about the full-sized transcripts are in most cases selected for further analysis.

including the sequences for plants. In particular, TAIR is the information source for the model plant

Once the sets of sequences (5'UTR, CDS, and 3'UTR) for the pools of differentially-translated transcripts are obtained, the researcher has to select for analysis the regions of transcripts and regulatory sequences that may be potentially involved in translation modulation. According to the current opinion, the complex multilevel information is encoded in the full-sized mRNA sequence (transcript) in general and in its individual parts—5'UTR, CDS, and 3'UTR (Figure 6). This gives researchers the grounds to include all these regions into in silico analysis to characterize the differentially-translated plant transcripts. Note that translation initiation is, as a rule, the stage limiting the translation rate and 5'UTR plays here the decisive role. The length, nucleotide composition, secondary structures, and regulatory elements of a smaller size, such as upstream start codons (uAUGs), uORFs, nucleotide motifs, and several other features in the 5'UTRs of transcripts, are closely examined in terms of their contributions to the translation efficiency. In this process, the probability to find the potential regulatory regions and contexts and to clarify how their properties influence the translation efficiency will be higher if more traits of this kind are involved in the initial in silico analysis. Once the sets of sequences (5'UTR, CDS, and 3'UTR) for the pools of differentially-translated transcripts are obtained, the researcher has to select for analysis the regions of transcripts and regulatory sequences that may be potentially involved in translation modulation. According to the current opinion, the complex multilevel information is encoded in the full-sized mRNA sequence (transcript) in general and in its individual parts—5'UTR, CDS, and 3'UTR (Figure 6). This gives researchers the grounds to include all these regions into in silico analysis to characterize the differentially-translated plant transcripts. Note that translation initiation is, as a rule, the stage limiting the translation rate and 5'UTR plays here the decisive role. The length, nucleotide composition, secondary structures, and regulatory elements of a smaller size, such as upstream start codons (uAUGs), uORFs, nucleotide motifs, and several other features in the 5'UTRs of transcripts, are closely examined in terms of their contributions to the translation efficiency. In this process, the probability to find the potential regulatory regions and contexts and to clarify how their properties influence the translation efficiency will be higher if more traits of this kind are involved in the initial

The further aims of the researcher could be (i) to assess the variations in distribution of individual traits in the sequences from the examined transcript pools and to figure out the statistically significant differences that are positively correlated with the translation efficiency; (ii) to find and determine the statistically significant representation of the potential regulatory contexts in the transcripts with different translation efficiencies; and (iii) to identify the specific regulatory sequences if they are present in the examined pools. in silico analysis. The further aims of the researcher could be (i) to assess the variations in distribution of individual traits in the sequences from the examined transcript pools and to figure out the statistically significant differences that are positively correlated with the translation efficiency; (ii) to find and determine the statistically significant representation of the potential regulatory contexts in the transcripts with different translation efficiencies; and (iii) to identify the specific regulatory sequences if they are present in the examined pools.

**Figure 6.** Examples of some mRNA cis-regulatory elements: (**a**) riboswitches; (**b**) internal ribosome entry sites (IRESs); and (**c**) alternative open reading frames. **Figure 6.** Examples of some mRNA cis-regulatory elements: (**a**) riboswitches; (**b**) internal ribosome entry sites (IRESs); and (**c**) alternative open reading frames.

#### *3.2. Statistical Methods 3.2. Statistical Methods*

The methods of mathematical statistics have been rather efficiently used for solution of the first task. As a rule, basic and extended statistical analyses are performable with the help of the available standard programs, such as Excel, STATA, and IBM SPSS Statistics 20 [26,32–34]. For example, the genome-wide monitoring of the changes in the translation efficiency of individual mRNAs in *A. thaliana* shoots after heat shock have demonstrated translation repression for the majority of mRNAs; however, some mRNAs still followed the differential translation pattern. Analysis of the The methods of mathematical statistics have been rather efficiently used for solution of the first task. As a rule, basic and extended statistical analyses are performable with the help of the available standard programs, such as Excel, STATA, and IBM SPSS Statistics 20 [26,32–34]. For example, the genome-wide monitoring of the changes in the translation efficiency of individual mRNAs in *A. thaliana* shoots after heat shock have demonstrated translation repression for the majority of mRNAs; however, some mRNAs still followed the differential translation pattern. Analysis of the differentially-translated mRNA sequences demonstrated that only some characteristics, such as the

G + C content in 5'UTR and cDNA length, are putatively involved in the mechanisms providing discrimination of the mRNA loading with ribosomes and are associated with differential translation of a certain transcript cohort in response to a high temperature. In particular, the translationally active mRNAs have a low G + C content (on the average, 36%) versus the transcripts with repressed translation (42%). This selection mechanism also influences the differential polysomal loading of the transcripts associated with stress and, as a consequence, the efficiency of their translation [32].

In general, the methods of mathematical statistics have made it possible to (i) find the characteristics that are representative for the analyzed mRNA, (ii) discard the characteristics the effect of which can result from a bias to the group of particular genes, and (iii) determine the statistically significant differences displaying a positive correlation with the relative translation efficiency.
