*4.10. Cloning and Sequence Analysis of OsACT Gene in Rice*

Coding region as well as upstream region of *OsACT* was extracted from Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/) using LOC\_Os03g47860 as the query. Six overlapped primer pairs (Table S4) were designed and used to amplify *OsACT* from genomic DNA of SDWG005, 9311 and N22. DNA was extracted using Plant DNA extraction kit according to the manufacture's instruction (Tiangen, Beijing, China). Specific PCR product with expected size was subjected to Sanger sequencing (Tianyi, Wuhan, China). Sequences of *OsACT* in other 11 rice core collections were downloaded from http://www.rmbreeding.cn. Alignment of gene sequence was conducted using DNAMAN software (version 8.0, Lynnon Biosoft, San Ramon, CA, USA).

**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/3/1155/ s1. Figure S1. Cluster analysis of all samples based on lgFPKM of 3559 DEGs; Figure S2. Heatmap clustering of the global patterns of DEGs expressed at one or more time points in the heat treatment. The color scale represents the lgFPKM (Mean values of FKPM of three replicates); Figure S3. Expression levels of ten randomly selected DEGs by RNA-seq and qRT-PCR. RNA-seq: expression change of these genes in anthers of SDWG005 under heat treatments as compared to control by RNA-seq. WD-qPCR: expression change of these genes in anthers of SDWG005 under heat treatments as compared to control by real-time PCR. 9311-qPCR: expression change of these genes in anthers of 9311 under heat treatments as compared to control by real-time PCR; Table S1. Correlation of all the samples based on FKAM of all transcripts; Table S2. Differentially expressed genes (DEGs) at a minimum of time points during the heat treatment; Table S3. Differentially expressed genes (DEGs) compared with the transcriptional profiles of the pistils and anthers. Table S4. Primers for qRT-PCR and cloning of *OsACT*; File S1. *R* script for expression pattern of genes in different clusters.

**Author Contributions:** Conceptualization, C.J. and G.L.; validation, D.Q. (Dandan Qin), H.J., and C.L.; formal analysis, G.L., Z.Z. (Zhongping Zha); investigation, G.L., Z.Z. (Zhongping Zha), H.C., D.Q. (Dandan Qin), H.J., C.L., D.Q. (Dongfeng Qiu), Z.W., and Y.Y.; resources, C.J., Z.Z. (Zaijun Zhang); data curation, H.C., B.W., A.Y.; writing—original draft preparation, G.L., Z.Z. (Zhongping Zha); writing—review and editing, C.J., Z.Z. (Zaijun Zhang); visualization, G.L., Z.Z. (Zhongping Zha); supervision, C.J.; project administration, C.J.; funding acquisition, C.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by grants from China Postdoctoral Science Foundation (Grant No. 2019M652607), Natural Science Foundation of Hubei Province of China (Grant No. 2019CFB579).

**Acknowledgments:** The authors would like to thank Zhongfu Ni and Huiru Peng in China Agricultural University for their valuable advice on this study and the manuscript. The authors would like to thank Jihua Cheng at Yuan Longping Hi-Tech Agriculture Co., LTD. for reading the manuscript and providing suggestions for its improvement.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
