More Like this

1. Identification of condition-specific biomarker systems in uterine cancer

   https://doi.org/10.1093/g3journal/jkab392  

   Hickman, Allison_R; Hang, Yuqing; Pauly, Rini; Feltus, Frank_A; Andrews, ed., B. (November 2021, G3 Genes|Genomes|Genetics)

   Abstract Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment. 

   more » « less
2. Using Restriction Endonuclease, Protection, Selection, and Amplification to Identify Preferred DNA-Binding Sequences of Microbial Transcription Factors

   https://doi.org/10.1128/spectrum.04397-22  

   Barrows, John K.; Van Dyke, Michael W. (February 2023, Microbiology Spectrum)

   Polen, Tino (Ed.)

   ABSTRACT Regulation of gene expression is a vital component of cellular biology. Transcription factor proteins often bind regulatory DNA sequences upstream of transcription start sites to facilitate the activation or repression of RNA polymerase. Research laboratories have devoted many projects to understanding the transcription regulatory networks for transcription factors, as these regulated genes provide critical insight into the biology of the host organism. Various in vivo and in vitro assays have been developed to elucidate transcription regulatory networks. Several assays, including SELEX-seq and ChIP-seq, capture DNA-bound transcription factors to determine the preferred DNA-binding sequences, which can then be mapped to the host organism’s genome to identify candidate regulatory genes. In this protocol, we describe an alternative in vitro , iterative selection approach to ascertaining DNA-binding sequences of a transcription factor of interest using restriction endonuclease, protection, selection, and amplification (REPSA). Contrary to traditional antibody-based capture methods, REPSA selects for transcription factor-bound DNA sequences by challenging binding reactions with a type IIS restriction endonuclease. Cleavage-resistant DNA species are amplified by PCR and then used as inputs for the next round of REPSA. This process is repeated until a protected DNA species is observed by gel electrophoresis, which is an indication of a successful REPSA experiment. Subsequent high-throughput sequencing of REPSA-selected DNAs accompanied by motif discovery and scanning analyses can be used for determining transcription factor consensus binding sequences and potential regulated genes, providing critical first steps in determining organisms’ transcription regulatory networks. IMPORTANCE Transcription regulatory proteins are an essential class of proteins that help maintain cellular homeostasis by adapting the transcriptome based on environmental cues. Dysregulation of transcription factors can lead to diseases such as cancer, and many eukaryotic and prokaryotic transcription factors have become enticing therapeutic targets. Additionally, in many understudied organisms, the transcription regulatory networks for uncharacterized transcription factors remain unknown. As such, the need for experimental techniques to establish transcription regulatory networks is paramount. Here, we describe a step-by-step protocol for REPSA, an inexpensive, iterative selection technique to identify transcription factor-binding sequences without the need for antibody-based capture methods. 

   more » « less
3. A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix Factorization

   https://doi.org/10.1101/2022.09.09.507305  

   Mahmood, Omar; Skok Gibbs, Claudia; Bonneau, Richard; Cho, Kyunghyun (March 2023, ICML 2023)

   Inferring gene regulatory networks (GRNs) from single-cell gene expression datasets is a challenging task. Existing methods are often designed heuristically for specific datasets and lack the flexibility to incorporate additional information or compare against other algorithms. Further, current GRN inference methods do not provide uncertainty estimates with respect to the interactions that they predict, making inferred networks challenging to interpret. To overcome these challenges, we introduce Probabilistic Matrix Factorization for Gene Regulatory Network inference (PMF-GRN). PMF-GRN uses single-cell gene expression data to learn latent factors representing transcription factor activity as well as regulatory relationships between transcription factors and their target genes. This approach incorporates available experimental evidence into prior distributions over latent factors and scales well to single-cell gene expression datasets. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. To assess the accuracy of our method, we evaluate PMF-GRN using the model organisms Saccharomyces cerevisiae and Bacillus subtilis, benchmarking against database-derived gold standard interactions. We discover that, on average, PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods. Moreover, our PMF-GRN approach offers well-calibrated uncertainty estimates, as it performs gene regulatory network (GRN) inference in a probabilistic setting. These estimates are valuable for validation purposes, particularly when validated interactions are limited or a gold standard is incomplete. 

   more » « less
4. Biocuration of a Transcription Factors Network Involved in Submergence Tolerance during Seed Germination and Coleoptile Elongation in Rice (Oryza sativa)

   https://doi.org/10.3390/plants12112146  

   Naithani, Sushma; Mohanty, Bijayalaxmi; Elser, Justin; D’Eustachio, Peter; Jaiswal, Pankaj (June 2023, Plants)

   Modeling biological processes and genetic-regulatory networks using in silico approaches provides a valuable framework for understanding how genes and associated allelic and genotypic differences result in specific traits. Submergence tolerance is a significant agronomic trait in rice; however, the gene–gene interactions linked with this polygenic trait remain largely unknown. In this study, we constructed a network of 57 transcription factors involved in seed germination and coleoptile elongation under submergence. The gene–gene interactions were based on the co-expression profiles of genes and the presence of transcription factor binding sites in the promoter region of target genes. We also incorporated published experimental evidence, wherever available, to support gene–gene, gene–protein, and protein–protein interactions. The co-expression data were obtained by re-analyzing publicly available transcriptome data from rice. Notably, this network includes OSH1, OSH15, OSH71, Sub1B, ERFs, WRKYs, NACs, ZFP36, TCPs, etc., which play key regulatory roles in seed germination, coleoptile elongation and submergence response, and mediate gravitropic signaling by regulating OsLAZY1 and/or IL2. The network of transcription factors was manually biocurated and submitted to the Plant Reactome Knowledgebase to make it publicly accessible. We expect this work will facilitate the re-analysis/re-use of OMICs data and aid genomics research to accelerate crop improvement. 

   more » « less
5. Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information

   https://doi.org/10.1093/plcell/koab267  

   Zhou, Peng; Enders, Tara A; Myers, Zachary A; Magnusson, Erika; Crisp, Peter A; Noshay, Jaclyn M; Gomez-Cano, Fabio; Liang, Zhikai; Grotewold, Erich; Greenham, Kathleen; et al (November 2021, The Plant Cell)

   Abstract Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes. 

   more » « less