Data from: Conditional requirement for dimerization of the membrane-binding module for BTK signaling in lymphocyte cell lines
Data files
Jun 03, 2025 version files 766.29 KB
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fasta_files.zip
36.43 KB
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Jurkat_read_data.zip
507.23 KB
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Ramos_read_data.zip
219.80 KB
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README.md
2.83 KB
Abstract
Bruton’s tyrosine kinase (BTK) is a major drug target in immune cells. The membrane-binding pleckstrin homology and tec homology (PH-TH) domains of BTK are required for signaling. Dimerization of the PH-TH module strongly stimulates the kinase activity of BTK in vitro. Here, we investigated whether BTK dimerizes in cells using the PH-TH module and whether this dimerization is necessary for signaling. To address this question, we developed high-throughput mutagenesis assays for BTK function in Ramos B cells and Jurkat T cells. We measured the fitness costs for thousands of point mutations in the PH-TH module and kinase domain to assess whether dimerization of the PH-TH module and BTK kinase activity were necessary for function. In Ramos cells, we found that neither PH-TH dimerization nor kinase activity was required for BTK signaling. Instead, in Ramos cells, BTK signaling was enhanced by PH-TH module mutations that increased membrane adsorption, even at the cost of reduced PH-TH dimerization. In contrast, in Jurkat cells, we found that BTK signaling depended on both PH-TH dimerization and kinase activity. Evolutionary analysis indicated that BTK proteins in organisms that evolved before the divergence of ray-finned fishes lacked PH-TH dimerization but had active kinase domains, similar to other Tec family kinases. Thus, PH-TH dimerization is a distinct feature of BTK that evolved to exert stricter regulatory control on kinase activity as adaptive immune systems gained increased complexity.
https://doi.org/10.5061/dryad.sbcc2frd9
The counts of each variant in each library are uploaded here as tab-delimited text files. Fasta files that were used to generate these counts are also uploaded here.
Description of the data and file structure
There are 106 data files deposited here. 6 of these files (with suffix "fasta") are the files that contain the nucleotide sequence of all BTK variants used in this study.
Note: Jurkat T cells and Ramos B cells, which are human cancer cell lines.
The remaining 100 files (with suffix "_P.tsv", for "processed") contain the read counts for each sequence in each experiment. Each of these files has four fields in the file name, separated by an underscore.
The naming scheme of these files are as follows:
(1) The first field is the cell line from which it was derived ("Jurkat" or "Ramos").
(2) The second field contains the region of BTK analyzed in that experiment. Region codes are as follows. R1: S1 loop; R2: S2 loop; R3: activation loop; R9: canonical site; R10: peripheral site; R18: HRD motif.
(3) The third field denotes the fraction ("input" or "sort"), corresponding to whether the dataset is derived from the unselected/unsorted cell fraction or the sorted/selected cell fraction.
(4) The fourth field is the replicate number. All experiments have either 3 or 4 biological replicates.
All files contain the name or sequence of the variant and the numbers of counts associated with each variant. Note that Kallisto allows non-integer counts.
Each tsv file, which is the standard output from Kallisto, has five fields:
(1) "target_id" is the name of the BTK variant nucleotide sequence that is used for the alignment. These names correspond to the sequences in the fasta files.
(2) "length" is the length of the of the BTK variant nucleotide sequence.
(3) "eff_length" is the effective length of the sequence: the gene length minus the insert size. Please see the Kallisto study for a complete definition.
(4) "est_counts" is the number of mapped reads associated with each BTK variant sequence.
(5) "tpm" is a measure of the abundance of each sequence and it is used in RNA-seq mapping. It normalizes the est_counts to the total number of reads and the length of each mapped sequence. In our study, all sequences are the same length, tpm is est_counts multiplied by a scaler.
Code/Software
Count tables were analyzed used custom jupyter notebooks written in R. Code for normalizing the count tables and generating standard figures, such as barplots, is available on github: https://github.com/timeisen/MutagenesisPlotCode
RNA-seq libraries from BTK-transduced and CD69 selected or input cells were prepared from TRI reagent-extracted samples as follows, beginning with the reverse-transcription. Following precipitation, RNA was resuspended in water containing 5 µM RT primer. The mixture was heated to 65ºC for 5 min, then snap cooled on ice. The RNA–oligo mixture was then mixed with 1x first-strand buffer (Thermo Fisher), 0.5 mM deoxynucleotide triphosphates, 10 mM DTT, 1 µL SuperaseIn (Thermo Fisher), and 0.5 µL Superscript III (Thermo Fisher). The final reaction mixture was incubated at 50ºC for 1 h. Followed this incubation, RNA was hydrolyzed by addition of 5 µL of 1 M NaOH and incubation at 90ºC for 10 min. The mixture was then neutralized with 25 µL of 1 M HEPES, pH 7.4, and desalted using a Micro Bio-Spin P-30 column, in tris (BioRad), eluting the cDNA in 60 µL. cDNA was amplified using PCR, which also attached the Miseq hybridizing sequences. Fastq files from MiSeq runs were aligned to the Fasta files containing the full sequences of each variant using Kallisto (Bray et al., 2016) to generate read counts for each variant.
- Bray, Nicolas L; Pimentel, Harold; Melsted, Páll; Pachter, Lior (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology. https://doi.org/10.1038/nbt.3519
- Eisen, Timothy J.; Ghaffari-Kashani, Sam; Groves, Jay T. et al. (2023). Conditional Requirement for Dimerization of the Membrane-Binding Module of BTK [Preprint]. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2023.10.05.561114
- Eisen, Timothy J.; Ghaffari-Kashani, Sam; Hung, Chien-Lun et al. (2025). Conditional requirement for dimerization of the membrane-binding module for BTK signaling in lymphocyte cell lines. Science Signaling. https://doi.org/10.1126/scisignal.ado1252
