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1. Short tandem repeats bind transcription factors to tune eukaryotic gene expression

   https://doi.org/10.1126/science.add1250  

   Horton, Connor A.; Alexandari, Amr M.; Hayes, Michael G.; Marklund, Emil; Schaepe, Julia M.; Aditham, Arjun K.; Shah, Nilay; Suzuki, Peter H.; Shrikumar, Avanti; Afek, Ariel; et al (September 2023, Science)

   Short tandem repeats (STRs) are enriched in eukaryoticcis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)–DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites. 

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2. De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

   https://doi.org/10.1101/2023.05.11.540401  

   Alexandari, Amr M; Horton, Connor A; Shrikumar, Avanti; Shah, Nilay; Li, Eileen; Weilert, Melanie; Pufall, Miles A; Zeitlinger, Julia; Fordyce, Polly M; Kundaje, Anshul (May 2023, bioRxiv)

   Abstract Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences,in vivogenomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained onin vitroTF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific,in vivobinding profiles. Conversely, deep learning models, trained onin vivoTF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models ofin vitroandin vivoTF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinitiesde-novofrom deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diversein vitroassays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant ofin vivobinding, suggest that deep learning models ofin vivobinding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughputin silicoexperiments to explore the influence of sequence context and variation on both intrinsic affinity andin vivooccupancy. 

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3. ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery

   https://doi.org/10.1093/bioinformatics/btz290  

   Li, Yang; Ni, Pengyu; Zhang, Shaoqiang; Li, Guojun; Su, Zhengchang; Berger, ed., Bonnie (May 2019, Bioinformatics)

   Abstract MotivationThe availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets. ResultsWe herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes. Availability and implementationSource code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms. Supplementary informationSupplementary materials are available at Bioinformatics online. 

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4. Determination and Dissection of DNA-Binding Specificity for the Thermus thermophilus HB8 Transcriptional Regulator TTHB099

   https://doi.org/10.3390/ijms21217929  

   Moncja, Kristi; Van Dyke, Michael W. (November 2020, International Journal of Molecular Sciences)

   null (Ed.)

   Transcription factors (TFs) have been extensively researched in certain well-studied organisms, but far less so in others. Following the whole-genome sequencing of a new organism, TFs are typically identified through their homology with related proteins in other organisms. However, recent findings demonstrate that structurally similar TFs from distantly related bacteria are not usually evolutionary orthologs. Here we explore TTHB099, a cAMP receptor protein (CRP)-family TF from the extremophile Thermus thermophilus HB8. Using the in vitro iterative selection method Restriction Endonuclease Protection, Selection and Amplification (REPSA), we identified the preferred DNA-binding motif for TTHB099, 5′–TGT(A/g)NBSYRSVN(T/c)ACA–3′, and mapped potential binding sites and regulated genes within the T. thermophilus HB8 genome. Comparisons with expression profile data in TTHB099-deficient and wild type strains suggested that, unlike E. coli CRP (CRPEc), TTHB099 does not have a simple regulatory mechanism. However, we hypothesize that TTHB099 can be a dual-regulator similar to CRPEc. 

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5. Predicting transcription factor binding in single cells through deep learning

   https://doi.org/10.1126/sciadv.aba9031  

   Fu, Laiyi; Zhang, Lihua; Dollinger, Emmanuel; Peng, Qinke; Nie, Qing; Xie, Xiaohui (December 2020, Science Advances)

   null (Ed.)

   Characterizing genome-wide binding profiles of transcription factors (TFs) is essential for understanding biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining them at a single-cell level remains elusive. Here, we report scFAN (single-cell factor analysis network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pretrained on genome-wide bulk assay for transposase-accessible chromatin sequencing (ATAC-seq), DNA sequence, and chromatin immunoprecipitation sequencing (ChIP-seq) data and uses single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by both studying sequence motifs enriched within predicted binding peaks and using predicted TFs for discovering cell types. We develop a new metric “TF activity score” to characterize each cell and show that activity scores can reliably capture cell identities. scFAN allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles. 

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