The concept of "housekeeping gene" has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as "essential for cellular existence regardless of their specific function in the tissue or organism", and "stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal". However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.

Gene activity defines cell identity, drives intercellular communication, and underlies the functioning of multicellular organisms. We present the single-cell resolution atlas of gene activity of a fertile adult metazoan: Caenorhabditis elegans. This compendium comprises 180 distinct cell types and 19,657 expressed genes. We predict 7541 transcription factor expression profile associations likely responsible for defining cellular identity. We predict thousands of intercellular interactions across the C. elegans body and the ligand-receptor pairs that mediate them, some of which we experimentally validate. We identify 172 genes that show consistent expression across cell types, are involved in basic and essential functions, and are conserved across phyla; therefore, we present them as experimentally validated housekeeping genes. We developed the WormSeq application to explore these data. In addition to the integrated gene-to-systems biology, we present genome-scale single-cell resolution testable hypotheses that we anticipate will advance our understanding of the molecular mechanisms, underlying the functioning of a multicellular organism and the perturbations that lead to its malfunction.

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.

The gut microbiota metabolizes drugs and alters their efficacy and toxicity. Diet alters drugs, the metabolism of the microbiota, and the host. However, whether diet-triggered metabolic changes in the microbiota can alter drug responses in the host has been largely unexplored. Here we show that dietary thymidine and serine enhance 5-fluoro 2'deoxyuridine (FUdR) toxicity in C. elegans through different microbial mechanisms. Thymidine promotes microbial conversion of the prodrug FUdR into toxic 5-fluorouridine-5'-monophosphate (FUMP), leading to enhanced host death associated with mitochondrial RNA and DNA depletion, and lethal activation of autophagy. By contrast, serine does not alter FUdR metabolism. Instead, serine alters E. coli's 1C-metabolism, reduces the provision of nucleotides to the host, and exacerbates DNA toxicity and host death without mitochondrial RNA or DNA depletion; moreover, autophagy promotes survival in this condition. This work implies that diet-microbe interactions can alter the host response to drugs without altering the drug or the host.

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.