The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Raccomandazioni di consenso SIBioC-SIMeL per la rilevazione e gestione dei campioni emolisati e utilizzo dell’indice di emolisi

The presence of hemolysis in a biological blood sample is mainly caused by hemolytic anemia or hemolysis in vitro. The latter is caused by inappropriate collection and processing of biological samples, which may affect the reliability of test results. Hemolysis is assessed by free hemoglobin quantification, whose limit is 0.02 g/L in plasma and 0.05 g/L in serum, and visually observed when the concentration of free hemoglobin exceeds 0.30 g/L. Since hemolysis is the most frequent cause of unsuitable biological samples in clinical laboratories, with a prevalence approaching 3% of all received samples, these recommendations have been drafted specifically to assist laboratory professionals in detection and management of hemolysed specimens. In summary, the recommended approach is based on: (i) systematic detection and quantification of hemolysis, by visual inspection and subsequent quantification of the hemolysis index on all samples with visually detectable hemolysis; (ii) immediate notification to the referring department of the presence of hemolysis in the sample, as locally determined; (iii) suppression of all results affected by the presence and/or degree of hemolysis; and (iv) timely request of a second sample, on which the previously deleted tests can be performed

iTAK: A Program for Genome-wide Prediction and Classification of Plant Transcription Factors, Transcriptional Regulators, and Protein Kinases : Letter to the Editor

Transcription factors (TFs) are proteins that regulate the expression of target genes by binding to specific cis-elements in promoter regions. Transcriptional regulators (TRs) also regulate the expression of target genes; however, they operate indirectly via interaction with the basal transcription apparatus (e.g., TFs), or by altering the accessibility of DNA to TFs via chromatin remodeling. Another type of regulatory proteins, protein kinases (PKs), function in signal transduction pathways and alter the activity of target proteins by phosphorylating them. These three important classes of regulatory proteins have been associated with numerous aspects of plant growth and development (Gapper et al., 2014; Xu and Zhang, 2015), and response to biotic and abiotic stimuli (Zhang et al., 2013; Mickelbart et al., 2015). Effective and accurate identification and classification of these genes is important for understanding their evolution, biological functions, and regulatory networks. Currently, more than 100 plant genomes have been sequenced and regulatory proteins have been systematically identified from several of these plant genomes.Instituto de Fisiología Vegeta

Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize

Background: Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroidresponsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches. Results: Here, we use a hybrid allele-specific chromatin binding sequencing (HASChseq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits. Conclusion: Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize.This article is published as Hartwig, T., Banf, M., Prietsch, G.P. et al. Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize. Genome Biol 24, 108 (2023). https://doi.org/10.1186/s13059-023-02909-w. Posted with permission.This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Hybrid allele‑specific ChIP‑seq analysis identifies variation in brassinosteroid‑responsive transcription factor binding linked to traits in maize

Background: Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroidresponsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches. Results: Here, we use a hybrid allele-specific chromatin binding sequencing (HASChseq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits. Conclusion: Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize

Genome-wide prediction of metabolic enzymes, pathways, and gene clusters in plants

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters

iTAK: A Program for Genome-wide Prediction and Classification of Plant Transcription Factors, Transcriptional Regulators, and Protein Kinases : Letter to the Editor

Transcription factors (TFs) are proteins that regulate the expression of target genes by binding to specific cis-elements in promoter regions. Transcriptional regulators (TRs) also regulate the expression of target genes; however, they operate indirectly via interaction with the basal transcription apparatus (e.g., TFs), or by altering the accessibility of DNA to TFs via chromatin remodeling. Another type of regulatory proteins, protein kinases (PKs), function in signal transduction pathways and alter the activity of target proteins by phosphorylating them. These three important classes of regulatory proteins have been associated with numerous aspects of plant growth and development (Gapper et al., 2014; Xu and Zhang, 2015), and response to biotic and abiotic stimuli (Zhang et al., 2013; Mickelbart et al., 2015). Effective and accurate identification and classification of these genes is important for understanding their evolution, biological functions, and regulatory networks. Currently, more than 100 plant genomes have been sequenced and regulatory proteins have been systematically identified from several of these plant genomes.Instituto de Fisiología Vegeta