Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Human regulatory T (T reg ) cells are essential for immune homeostasis. The transcription factor (TF) FOXP3 maintains T reg cell identity, yet the complete set of key TFs that control T reg cell gene expression remains unknown. Here, we used pooled and arrayed Cas9 ribonucleoprotein (RNP) screens to identify TFs that regulate critical proteins in primary human T reg cells under basal and pro-inflammatory conditions. We then generated 54,424 single-cell transcriptomes from T reg cells subjected to genetic perturbations and cytokine stimulation, which revealed distinct gene networks individually regulated by FOXP3 and PRDM1, in addition to a network co-regulated by FOXO1 and IRF4. We also discovered that HIVEP2, not previously implicated in T reg cell function, co-regulates another gene network with SATB1 and is important for T reg cell-mediated immunosuppression. By integrating CRISPR screens and scRNA-seq profiling, we have uncovered novel transcriptional regulators and downstream gene networks in human T reg cells that could be targeted for immunotherapies.

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package’s potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.