Metagenomic projects provide a unique window into the genetic composition of microbial communities. To date, metagenomic analyses have focused primarily on studying the composition of microbial populations and inferring shared metabolic pathways. In this work we analyze how high-quality metagenomic data can be leveraged to infer the composition of transcriptional regulatory networks through a combination of in silico and in vitro methods. Using the SOS response as a case example, we analyze human gut microbiome data to determine the composition of the SOS meta-regulon in a natural context. Our analysis provides proof of concept that the existing knowledgebase on regulatory networks and reference genomes can be effectively leveraged to mine meta-genomic data and reconstruct multi-species regulatory networks. This approach allows us to identify de novo the core elements of the human gut SOS meta-regulon, highlighting the relevance of error-prone polymerases in this stress response, and identifies putative novel SOS protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification. The methodology implemented in this work can be applied to other metagenomic datasets and transcriptional systems, potentially providing the means to compare regulatory networks across metagenomes. The use of metagenomic data to analyze transcriptional regulatory networks provides a realistic snapshot of these systems in their natural context and allows probing at their extended composition in non-culturable organisms, yielding insights into their interconnection and into the overall structure of transcriptional systems in microbiomes.