Exploring Obscurin and SPEG Kinase Biology

Three members of the obscurin protein family that contain tandem kinase domains with important signaling functions for cardiac and striated muscles are the giant protein obscurin, its obscurin-associated kinase splice isoform, and the striated muscle enriched protein kinase (SPEG). While there is increasing evidence for the specific roles that each individual kinase domain plays in cross-striated muscles, their biology and regulation remains enigmatic. Our present study focuses on kinase domain 1 and the adjacent low sequence complexity inter-kinase domain linker in obscurin and SPEG. Using Phos-tag gels, we show that the linker in obscurin contains several phosphorylation sites, while the same region in SPEG remained unphosphorylated. Our homology modeling, mutational analysis and molecular docking demonstrate that kinase 1 in obscurin harbors all key amino acids important for its catalytic function and that actions of this domain result in autophosphorylation of the protein. Our bioinformatics analyses also assign a list of putative substrates for kinase domain 1 in obscurin and SPEG, based on the known and our newly proposed phosphorylation sites in muscle proteins, including obscurin itself.publishe

Dataset for "Transcription factors operate on a limited vocabulary of binding motifs in Arabidopsis thaliana"

Zenker S, Wulf D, Meierhenrich A, et al. Dataset for "Transcription factors operate on a limited vocabulary of binding motifs in Arabidopsis thaliana". Bielefeld University; 2023.Supplemental dataset for the publication "Transcription factors operate on a limited vocabulary of binding motifs in *Arabidopsis thaliana*". Contains binding data from the publication "Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape" (O'Malley et al. 2016, 10.1016/j.cell.2016.04.038) mapped onto promoters of all nuclear encoded genes in *A. thaliana*. Visualizations available as interactive htmls

Transcription factors operate on a limited vocabulary of binding motifs in Arabidopsis thaliana

Zenker S, Wulf D, Meierhenrich A, et al. Transcription factors operate on a limited vocabulary of binding motifs in Arabidopsis thaliana. bioRxiv. 2023.Predicting gene expression from promoter sequence requires understanding of the different signal integration points within a promoter. Sequence-specific transcription factors (TFs) binding to their cognate TF binding motifs control gene expression in eukaryotes by activating and repressing transcription. Their interplay generates complex expression patterns in reaction to environmental conditions and developmental cues. We hypothesized that signals are not only integrated by different TFs binding various positions in a promoter, but also by single TF binding motifs onto which multiple TFs can bind. Analyzing 2,190 binding motifs, we identified only 76 core TF binding motifs in plants. Twenty-one TF protein families act highly specific and bind a single conserved motif. Four TF families are classified as semi-conserved as they bind up to four motifs within a family, with divisions along phylogenetic groups. Five TF families bind diverse motifs. Expression analyses revealed high competition within TF families for the same binding motif. The results show that singular binding motifs act as signal integrators in plants where a combination of binding affinity and TF abundance likely determine the output

Transcription factors mediating regulation of photosynthesis

Halpape W, Wulf D, Verwaaijen B, et al. Transcription factors mediating regulation of photosynthesis. bioRxiv. 2023.Photosynthesis by which plants convert carbon dioxide to sugars using the energy of light is fundamental to life as it forms the basis of nearly all food chains. Surprisingly, our knowledge about its transcriptional regulation remains incomplete. Effort for its agricultural optimization have mostly focused on post-translational regulatory processes1–3but photosynthesis is regulated at the post-transcriptional4and the transcriptional level5. Stacked transcription factor mutations remain photosynthetically active5,6and additional transcription factors have been difficult to identify possibly due to redundancy6or lethality. Using a random forest decision tree-based machine learning approach for gene regulatory network calculation7we determined ranked candidate transcription factors and validated five out of five tested transcription factors as controlling photosynthesisin vivo. The detailed analyses of previously published and newly identified transcription factors suggest that photosynthesis is transcriptionally regulated in a partitioned, non-hierarchical, interlooped network