Journal of biological and health sciences http://biotecnia.unison.mx

Universidad de Sonora

ISSN: 1665-1456

Original Article

Whole-Genome Characterization and Analysis of Microsatellites in Domestic Turkey (Meleagris gallopavo) through in silico Approach

Eman Gobily1 , Shimaa Kandel1 , Hadeer Auoda1 , Radwa Ahmed1 , Mostafa Helal2*

1 Programa de Biotecnología, Facultad de Agricultura, Universidad de El Cairo, Giza, Egipto

2 Departamento de Producción Animal, Facultad de Agricultura, Universidad de El Cairo, Giza, Egipto

Caracterización del genoma completo y análisis de microsatélites mediante un enfoque in silico en pavos domésticos (Meleagris gallopavo)

ABSTRACT

The domestic Turkey (Meleagris gallopavo) is the second largest contributor to poultry meat production, following chickens. The study of microsatellite organization and dis- tribution is highly important in genomics and evolutionary studies. The in silico mining for microsatellites leverages the power of computational biology to streamline, enhance the discovery of microsatellite markers and reduce the cost of microsatellite detection. The present study aimed to evaluate in silico mining for microsatellite loci in the ge- nome of domestic turkey. Reference sequences of several chromosomes were obtained from NBCI and analyzed using Krait software. Chromosome 4 had the highest number of perfect microsatellites, while chromosome 18 had the lowest number. However, chromosome 27 had the highest relative abundance, followed by chromosome 13. Chromosome 18 again had the lowest relative abundance. Chromosome 4 had the most imperfect microsatellites and chromosome 18 had the least. A total of 121,248 microsatellite primers were designed. These microsatellite loci and markers will play important roles as instrumental in linkage mapping and will significantly enhance research on turkey genetics.

Keywords: In silico, Meleagris gallopavo, Microsatellites, Mo- tifs, Turkeys

RESUMEN

El pavo doméstico (Meleagris gallopavo) es el segundo mayor contribuyente a la producción de carne de aves de corral, después de los pollos. El estudio de la organización y distri- bución de microsatélites es muy importante en los estudios genómicos y evolutivos. La minería in silico de microsatélites aprovecha el poder de la biología computacional para agilizar, mejorar el descubrimiento de marcadores de microsatélites y reducir el costo de la detección de microsatélites. El presen- te estudio tuvo como objetivo evaluar la minería in silico de loci de microsatélites en el genoma del pavo doméstico. Se obtuvieron secuencias de referencia de varios cromosomas de NBCI y se analizaron utilizando el software Krait. El cromo- soma 4 tuvo el mayor número de microsatélites perfectos, mientras que el cromosoma 18 tuvo el menor número. Sin embargo, el cromosoma 27 tuvo la mayor abundancia rela- tiva, seguido por el cromosoma 13. El cromosoma 18 nue- vamente tuvo la menor abundancia relativa. El cromosoma

*Author for correspondence: Mostafa Helal e-mail: mostafa.helal@agr.cu.edu.eg Received: October 1, 2024

Accepted: February 4, 2025

Published: February 28, 2025

4 tuvo la mayor cantidad de microsatélites imperfectos y el cromosoma 18 tuvo la menor cantidad. Se diseñaron un total de 121.248 cebadores de microsatélites. Estos loci y marca- dores microsatélites desempeñarán un papel importante en el mapeo de ligamiento y mejorarán significativamente la investigación sobre la genética del pavo.

Palabras clave: In silico, Meleagris gallopavo, microsatélites, motivos, pavos.

INTRODUCTION

Since the domestication of turkey (Meleagris gallopavo) in the Southwestern United States and Mexico (Thornton et al., 2012; Vergara et al., 2019), it has been considered one of the major important poultry species that contributes to meat production worldwide (Aslam et al., 2011). The United States is the leading country in turkey’s meat intake, followed by Brazil and Germany, which accounted for 41, 8.1, and 8 % of the total intake of turkey meat (Hristakieva, 2021). Neverthe- less, turkey meat still shares a small proportion of global meat demand. According to FAO, turkey meat production ranked second (5 %), after chicken (90 %), of the global poultry meat production. The environmental and ethical concerns surrounding industrial animal agriculture have become increasingly evident. Therefore, the intake pattern of meat- based proteins is projected to be reshaped significantly by 2030. While ruminants are a major contributor to greenhouse gas emissions (Giamouri et al., 2023), turkey production, once viewed as a promising alternative to traditional livestock, is also expected to decrease emissions in the coming decade (Kheiralipour et al., 2024; Clauss et al., 2020).

1

Generally, avian genomes are interesting because they tend to be compact, with less DNA overall, yet packed into more chromosomes compared to mammals (Axelsson et al., 2005). Turkey’s genome is quite larger than chicken and consists of 1,115,474,681 bp, with 16,226 coding genes, and 30,708 gene transcripts (Dalloul et al., 2010). The genome of the turkey is not fully uncovered, and massive efforts are needed to be fully understood (Barros et al., 2023). For many decades, microsatellites (also called Short Sequence Repeats, SSRs) were the markers of choice for breeders and geneticists, as they were used for many purposes including the conser- vation of genetic resources (Olubunmi, 2019). Microsatellite loci are scattered throughout the genome in both coding

Volume XXVII

DOI:10.18633/biotecnia.v27.2455

and non-coding regions. Certain repeats are preferred and are often predominant in certain genomic locations. Howe- ver, the significance of this observation is unclear (Vieira et al., 2016). Microsatellite loci are among the variable types of DNA within the genome, and the changes in their polymor- phisms derive mainly from changeability in length instead of within the essential arrangement (Abdul-Muneer, 2014). A more profound understanding of the developmental and mutational properties of microsatellites is in this manner re- quired, not as it were to get it how the genome is organized, but moreover to accurately utilize microsatellites information in populace inheritance of important traits (Wöhrmann and Weising, 2011).

In the past few decades, there has been significant effort focused on the development of microsatellites in the genome of turkeys. In 1999, several turkey genomic libraries were constructed, and 50 microsatellite loci were characte- rized (Huang et al., 1999), followed by the construction of a linkage map contacting 74 markers (Burt et al., 2003), and arranging 314 microsatellite loci in 29 linkage groups. The latter resulted in the identified of ~800 microsatellite mar- kers (Reed et al., 2007). Recently, a set of 34 microsatellites was identified (Canales Vergara et al., 2020), and successfully used to estimate genetic diversity parameters in 10 domestic turkey populations.

Given the high cost, labor-intensive nature, and limited scalability of developing microsatellite markers, in silico ap- proaches present a valuable alternative, offering faster and more comprehensible insights into target genomes. There- fore, this study aimed to perform an in silico analysis of the whole-genome sequence of the turkey (Meleagris gallopavo) mining it to identify a panel of microsatellite loci, and explore the distribution and density of microsatellites within the turkey genomes.

‌MATHEMATICAL MODEL

Data source. Sequence data of the domestic turkey chro- mosomes were obtained from the National Center for Biotechnology Information (NCBI). The analyzed reference sequences were uploaded to NCBI in 2019, with a reference of Turkey_5.1 (GCA_000146605.4).

In silico mining of whole-genome-wide SSRs. The sequen- ce data were downloaded in FASTA format. The Krait software

v.1.1.0 (Du et al., 2018) was used for microsatellite mining. Krait software is based on several data mining algorithms for microsatellite detection. It uses pattern recognition to identify repeat sequences within genomic data, sequence alignment to compare these sequences against databases for accuracy, and also conducts statistical analysis to calculate frequency and distribution of microsatellites across chromosomes. The authors selected to use Krait for the analysis because it is an ultrafast tool with a user-friendly graphical interface, making it ideal for genome-wide microsatellite analysis. Additionally, Krait is a powerful tool that not only detects various types of microsatellites (both perfect and imperfect) but also assists in designing primers for them. This makes it ideal for efficiently

identifying and defining valuable microsatellite markers. The analysis was carried out based on to the following criteria: mono-nucleotide repeat motifs were required to have at least of 10 repeats, di-nucleotide repeat motifs were at least 7 repeats, tri-nucleotide repeat motifs at least 5 repeats, and tetra-, penta-, and hexa-nucleotide repeat motifs at least 4 repeats.

The Primer3 tool (Rozen and Skaletsky, 1999) integrated within the Krait software package was used to design pri- mers for the identified microsatellite markers. Primer3 uses empirical formulas to calculate the melting temperature of potential primers to select suitable Tm ranges. Additio- nally, Primer3 checks the primer specificities by aligning the primers against target sequences in order to minimize non-specific binding. The program also assesses the primer lengths and GC contents regarding optimal annealing and stability (Untergasser et al., 2012).

The total numbers obtained were normalized either as a percentage or as the number of SSRs per megabase (Mb) of sequence, enabling comparison across genome sequences of different sizes such as relative abundance. The estimated repeat density (base pairs per Mb) was obtained by dividing the total number of base pairs occupied by SSRs by the total genome size. Correlation coefficients between different SSR- related parameters were estimated using the software SPSS (Morgan et al., 2019).

‌RESULTS

The domestic Turkey is one of the most important poultry species, with a large genome consisting of 1,061,817,103 base pairs. A total of 30 autosomal and two sexual chromosomes were analyzed. The total sequence length was 1,115,474,681, and the total unmapped length was 1,080,180,254, with scaffolds of 187,695. As shown in Figure 1, the largest chro- mosomes of the turkey genome are chromosomes 1, 2, and 3, followed by the Z chromosome. In contrast, chromosome 18 is the smallest autosomal chromosome measuring 244,177 bp.

Perfect microsatellites. Table 1 presents the number of perfect microsatellites detected in different chromosomes. Interestingly, the number of perfect SSRs did not correlate with chromosome size. The highest number of perfect micro- satellites (16743) was found in chromosome 4, despite not being a large chromosome (74,864,452 bp, and it ranked 5th in size within the turkey genome). The second highest numbers were detected on chromosomes 8, 1, 15, and Z, respectively. However, significant positive correlation coefficients were observed between chromosome size and both total number (0.44) and total length (0.457) of perfect microsatellites as shown in Table 2. Conversely, the lowest number of perfect microsatellite was detected in chromosome 18, where only one microsatellite was found. No microsatellites were detec- ted in chromosome W nor mitochondrial DNA.

Table 1 also shows the total length of perfect SSRs with the highest value for chromosome 4, due to the large number of microsatellites detected. This was followed by

Chr01 Chr02

Chr03

Chr04 Chr05

Chr06 Chr07

Chr08 Chr09

Chr10 Chr11 Chr12

Chr13 Chr14 Chr15 Chr16 Chr17 Chr18

Chr19 Chr20 Chr21 Chr22 Chr23 Chr24

Chr25 Chr26 Chr27 Chr28 Chr29 Chr30

Z

W MT

Fig. 1. Ideogram of the turkey genome

Fig. 1. Ideograma del genoma del pavo.

chromosomes 8 and 1, respectively. Figure 2 illustrate the relative abundance of perfect microsatellites across all chro- mosomes. Chromosome 27 exhibited the highest relative abundance with a value of 285.5, followed by chromosome 13 at 262.4. In contrast, chromsome 18 showed the lowest re- lative abundance, primarily due to the low number of micro- satellites detected on that chromosome. This was followed by chromosome 24 with a relative abundance of 102.68.

The estimated repeat density values (bp/Mb) in each chromosome are shown in Figure 3. The repeat density pattern closely mirrors the pattern of relative abundance, with chromosome 27 and 13 exhibiting the highest repeat densities. In contrast, chromosome 18 had the lowest repeat density.

The overall distribution of SSR repeat types is presented in Figure 4, mononucleotide repeats were highly frequent, accounting for 63% of the total detected SRRs, followed by tetranucleotide repeats (14%), and dinucleotide (11%). Trinucleotide, pentanucleotide, and hexanucleotide repeats were less common, each making up less than 10% of the total. The number of imperfect microsatellite repeats de- tected per chromosome is presented in Table (3). Notably, only one mononucleotide microsatellite was detected in chromosome 18. Excluding chromosome 18, the highest number of mononucleotide microsatellite was detected in chromosomes 4, while the lowest was detected on chromo- some 27. Chromosome 4 also had the highest dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexa- nucleotide repeats. In contrast, chromosomes 28, 29, and 27 had the lowest numbers of dinucleotide, trinucleotide, and tetranucleotide repeats, respectively.

Imperfect microsatellites. Table 1 presents the number of imperfect microsatellites detected across different chromo- somes. The highest number (68202) was detected in chromo- some 4 (Figure 2). The second highest counts were detected on chromosomes 8, 1, 15, and Z, respectively. On the other hand, chromosome 18 had the lowest count with only 15 im- perfect microsatellites detected. These results have a similar trend to those obtained for perfect microsatellites.

Table 1 also presents the total length of imperfect SSRs with the highest value observed on chromosome 4, due to the large number of microsatellites detected. This was followed by chromosomes 8 and 1, respectively. Figure 2 depicts the relative abundance of imperfect microsatellites across all chromosomes. Chromosome 27 exhibited the highest relative abundance reaching 1,113.54, followed by chromosome 13 at 1,030.26. In contrast, chromosome 29 had the lowest relative density. Significant positive correlation coefficients were obtained between chromosome size and both total number (0.432) and total length (0.441) of imper- fect microsatellites as shown in Table 2. Similar to perfect microsatellites, no imperfect microsatellites were obtained in chromosome W or in the mtDNA.

The estimated repeat density values (bp/Mb) of each chromosome are shown in Figure 3. The pattern of repeat density closely mirrors that of relative abundance, with chro- mosomes 27 and 13 exhibiting the highest repeat densities. In contrast, chromosome 18 had the lowest repeat density.

The overall distribution of the type of detected imper- fect SSR repeats is presented in Figure 4. Mononucleotide repeats were the most frequent and accounting for 42 % of the total SSRs detected, followed by trinucleotide repeat (29

Table 1. The summary information of different microsatellite types.

Tabla 1. Resumen de información de diferentes tipos de microsatélites.

Table 2. Correlation coefficient of chromosome size with total number and total length of perfect and imperfect SSRs

Tabla 2. Coeficiente de correlación del tamaño de los cromosomas con el número total y longitud de SSRs perfectos e imperfectos.

%), dinucleotide (15 %), and tetranucleotide (10 %). Penta- nucleotide and hexanucleotide repeats were less frequent, each accounting for less than 5 %. The number of imperfect microsatellite repeats detected per chromosome is shown in Table 3. The highest numbers of mononucleotide microsate- llites was detected on chromosomes 8, while the lowest was found on chromosome. Similar to the results obtained for perfect microsatellites, the highest numbers of dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanu- cleotide repeats were observed in chromosome 4. However,

Fig. 2. The relative abundance of perfect and imperfect microsatellites detected in different chromosomes of turkey genome.

Fig. 2. Abundancia relativa de los microsatélites perfectos e imperfetos detectados en diferentes cromosomas del genoma del pavo.

Fig. 3. The repeat density of perfect and imperfect microsatellites detected in different chromosomes of turkey genome.

Fig. 3. Densidad de repetidos perfectos e imperfectos de microsatélites detectados en diferentes cromosomas del genoma del pavo.

chromosome 18 had the lowest numbers for dinucleotide, trinucleotide, and tetranucleotide repeats.

Designed Primers. A total of 121248 SSR primers were desig- ned. A list of these primers has been deposited in a public re- pository and can be accessed via the following link: (https:// github.com/mosthamed/SSR-primers-Meleagris-gallopavo-. git).

DISCUSSION

Genome-wide studies offer valuable insights into the evo- lutionary forces that shape the distribution and diversity

of microsatellites (Pannebakker et al., 2010), enhancing our understanding of genome architecture. Microsatellites are a significant component of the genome in all organisms, which their abundance closely correlating to genome size (Akemi et al., 2012). However, the biological significance of this geno- mic regions remains poorly understood . A thorough analysis of microsatellites is essential for uncovering their functional roles (Gochi et al., 2023). Variations in their abundance, varia- tion and repeat types are key factors that contribute to their functions. This study presents a genome-wide analysis of microsatellite distribution in the turkey genome.

Table 3. The distribution of microsatellite repeats on the different chromosomes in turkey genome.

Tabla 3. Distribución de repetidos de microsatélites en los diferentes cromosomas del genoma del pavo.

Compared to traditional methods of microsatellite identification, in silico genome mining offers several advan- tages, making it a preferred approach in modern genomics research. The in silico approach is highly efficient and cost-effective, allowing for large genomes to be scanned for potential microsatellite regions without the need for extensive wet lab experiments. By leveraging computational tools and databases, vast amounts of data can be generated

quickly. This approach is particularly valuable in fields such as biodiversity studies, conservation genetics, and breeding programs. Additionally, the precision of computational algo- rithms ensures high accuracy in marker identification, redu- cing the risk of errors that can occur with manual methods (Safaa et al., 2023).

In the current study, we examined the distribution of perfect microsatellites across different chromosomes. The

Fig. 4. The frequency of the detected perfect and imperfect SSRs in turkey genome.

Fig. 4. Frecuencia detectada de los SSRs perfectos e imperfectos en el genoma del pavo.

data revealed no correlation between the number of perfect SSRs and chromosome size. Notably, chromosome 4, which ranks fifth in size in the turkey genome, has the highest num- ber of perfect microsatellites. Chromosomes 8, 1, 15, and Z also showed high numbers of SSRs. Similarly, chromosome 4 also exhibited the highest number of imperfect microsate- llites. Previous studies (Zhao et al., 2011; Duhan et al., 2023) have generally found that microsatellite density increases with genome size.

To better understand this trend, we calculated the co- rrelation coefficients between chromosome size and both the total number and total length of detected microsatellites for the two types. Moderate positive correlations were obser- ved, suggesting that the abundance of SSRs can vary widely across animal species, of which mammals tend to have more SSRs than avian species due to the differences in chromo- some size. However, further studies should investigate the relationships between the number of chromosomes and the SSR number.

Previous research has reported different levels of corre- lations between genome size and the number of detected SSRs. For example in insects, the number of SSRs is positively correlated with genome size, with a correlation coefficient of 0.499, similar to our findings. However, the correlation bet- ween SSR density and genome size in insects was negative at −0.228 (Ding et al., 2017). In contrast, bovid species show a very high positive correlation (0.980) between SSR number and chromosome size (Qi et al., 2015). Similarly, in macaque species, the correlation between chromosome size and SSR number was positive, while the correlation with SSR density was negative (Liu et al., 2017).

In most vertebrates, mono- and di-nucleotide motifs are the most abundant microsatellite motifs (Zhao et al., 2011; Wattanadilokchatkun et al., 2022; Kumpatla and Mukhopad- hyay, 2005). In the present study, mono-nucleotide motifs

were the most prevalent for both perfect and imperfect SSRs. However, the di-nucleotide motifs ranked 3rd, following tetra- nucleotide and trinucleotide motifs for perfect and imperfect SSRs, respectively. In ducks, dinucleotide motifs were found to be the most abundant, accounting for over 50% of the total SSR motifs.

This finding contradicts the previously observed positi- ve relationship between microsatellite density and genome size. The results of the current study suggest that the factors influencing microsatellite distribution may be more complex than a simple linear correlation with genome size. Further research is required to fully elucidate the evolutionary pro- cesses shaping microsatellite characteristics in avian species and across boarder range of taxa.

‌CONCLUSIONS

In the current study, we conducted a genome-wide analysis of the distribution and density of microsatellites in the turkey genome. While the findings provides a foundation for future studies into the role of microsatellites in gene regulation, fur- ther investigation is needed to understand how these SSRs are distributed across different regions of the genome, inclu- ding both coding and non-coding areas. A large set of SSR markers was identified across the entire genome, which will be instrumental for linkage mapping and will significantly improve research in turkey genetics. This extensive characte- rization of SSR markers not only enhances our understanding of turkey genetics but also creates a foundation for further in- vestigations into their functional role in genomic regulation.

ETHICS APPROVAL

This work was approved (CU/I/F/32/23) by the Institutional Animal Care and Use Committee at Cairo University (CU- IACUC).

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

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