Rice Genomics and Genetics 2025, Vol.16, No.2, 86-95 http://cropscipublisher.com/index.php/rgg 90 4.3.2 Involvement of other environmental factors In addition to temperature, light and water supply also affect OsTms6. For example, in PTGMS materials such as PA64S, male sterility is only exhibited under specific temperatures and photoperiods (Sun et al., 2021), indicating that photoperiods also have a place in regulating fertility genes. As for water, there is not much direct evidence to show that it has a significant effect on OsTms6, but existing studies show that water stress may work together with other factors such as temperature and light to affect its expression (Campo et al., 2013). 5 Association of OsTms6 with other Genetic Pathways 5.1 Cross-influence with other sterility genes Not all studies on OsTms6 treat it as a "single-soldier" gene. Instead, it often plays a role in a larger genetic network, especially in male sterility. Transcription factors such as OsAL5 regulate OsTMS5, linking drought stress signals to thermosensitive male sterility (Wen et al., 2021). Of course, similar regulation also occurs in pollen wall development - OsMS188 transcription factors regulate OsTMS18, which directly affects the stability of pollen at different temperatures (Ni et al., 2021; Zhang et al., 2022). In other words, OsTms6 may not directly control male sterility, but indirectly affect rice fertility and response to the environment by collaborating with other genes. 5.2 Involvement with hormone pathways When it comes to regulating male sterility, hormone pathways should not be ignored. miRNAs such as miR156, miR5488, and miR399 have been found to affect genes related to fatty acid synthesis or secondary metabolism, which are critical for anther development and pollen activity (Sun et al., 2021). In this context, SPL transcription factors are particularly noteworthy, including OsSPL2, SPL4, SPL16, SPL17, etc., which affect programmed cell death (PCD) and male fertility by regulating the synthesis of flavonoids and controlling the level of ROS in the tapetum (Sun et al., 2022). Although OsTms6 itself may not be directly involved in these pathways, it is likely to be "linked" to them. The studies of Ding et al. (2012) and Wan et al. (2019) also support this idea of interactive regulation. 5.3 Genetic modifiers affecting the action of OsTms6 The regulatory effect of OsTms6 is not entirely "determined by itself". For example, studies on TGMS lines have found that the control of temperature responsiveness often involves the influence of multiple QTLs or specific loci (Reddy et al., 2000). Genes in PGMS lines, such as rpms1 and rpms2, are also considered to be involved in regulating the function of OsTms6 (Peng et al., 2008). Although these "modifying factors" may not be the protagonists, they do "pull the strings" behind the scenes to regulate the expression of OsTms6 under different conditions. Therefore, when studying this gene, it is difficult not to consider its genetic background. Overall, the relationship between OsTms6 and other genes, hormones, and even larger genetic regulatory networks is far more complicated than imagined. If you want to really make good use of it to improve rice fertility or stress resistance, it is not enough to study it alone, but you have to put it into the whole picture. 6 Application of OsTms6 in Rice Breeding 6.1 Formation process of TMS line In rice hybrid breeding, in order to establish an efficient breeding system, we must first have a suitable sterile material. For example, the temperature-sensitive male sterile line (TGMS line) is a key tool. In lines like G20S, people found a gene called OsTms6, which is located on chromosome 10 of rice and is a recessive inheritance (Liu et al., 2010). One of the special features of this gene is that it only causes male sterility when the temperature is below 29.5 °C, which makes it more flexible than the traditional TGMS line and applicable to a wider area (Liu et al., 2010). In the process of locating such genes, researchers also used many molecular markers such as SSR and InDel. With the help of these tools, the development of new TGMS lines has become more precise (Kadirimangalam et al., 2019; Khlaimongkhon et al., 2019).
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