Faculty Bibliography

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome¹. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits^2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells⁴. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription-replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.

Pattern formation originates during embryogenesis by a series of symmetry-breaking steps throughout an expanding cell lineage. In Drosophila, classic work has shown that segmentation in the embryo is established by morphogens within a syncytium, and the subsequent action of the gap, pair-rule, and segment polarity genes. This classic model however does not translate directly to species that lack a syncytium - such as Caenorhabditis elegans - where cell fate is specified by cell-autonomous cell lineage programs and their inter-signaling. Previous single-cell RNA-Seq studies in C. elegans have analyzed cells from a mixed suspension of cells from many embryos to study late differentiation stages, or individual early stage embryos to study early gene expression in the embryo. To study the intermediate stages of early and late gastrulation (28- to 102-cells stages) missed by these approaches, here we determine the transcriptomes of the 1- to 102-cell stage to identify 119 embryonic cell states during cell fate specification, including 'equivalence-group' cell identities. We find that gene expression programs are modular according to the sub-cell lineages, each establishing a set of stripes by combinations of transcription factor gene expression across the anterior-posterior axis. In particular, expression of the homeodomain genes establishes a comprehensive lineage-specific positioning system throughout the embryo beginning at the 28-cell stage. Moreover, we find that genes that segment the entire embryo in Drosophila have orthologs in C. elegans that exhibit sub-lineage-specific expression. These results suggest that the C. elegans embryo is patterned by a juxtaposition of distinct lineage-specific gene regulatory programs each with a unique encoding of cell location and fate. This use of homologous gene regulatory patterning codes suggests a deep homology of cell fate specification programs across diverse modes of development.

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome¹. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits^2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells⁴. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription"“replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.

A philosopher reflects on his influential interrogations of free will, consciousness, and artificial intelligence.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

Group B Streptococcus (GBS) is a pathobiont that can ascend to the placenta and cause adverse pregnancy outcomes, in part through production of the toxin β-hemolysin/cytolysin (β-h/c). Innate immune cells have been implicated in the response to GBS infection, but the impact of β-h/c on their response is poorly defined. We show that GBS modulates innate immune cell states by subversion of host inflammation through β-h/c, allowing worse outcomes. We used an ascending mouse model of GBS infection to measure placental cell state changes over time following infection with a β-h/c-deficient and isogenic wild type GBS strain. Transcriptomic analysis suggests that β-h/c-producing GBS elicit a worse phenotype through suppression of host inflammatory signaling in placental macrophages and neutrophils, and comparison of human placental macrophages infected with the same strains recapitulates these results. Our findings have implications for identification of new targets in GBS disease to support host defense against pathogenic challenge.

As tumors grow, they interact with cells neighboring the tumor, but it is unclear how these interactions influence tumor progression. To investigate this, we applied spatial transcriptomics and scRNA-seq to a zebrafish model of BRAF[V600E]-driven melanoma. Using spatial transcriptomics, we identified a unique "interface" cell state localized to the tumor boundary. We used scRNA-seq to find that the interface is composed of specialized tumor and microenvironment cells that upregulate a common gene set. We found evidence of an "interface" population in patient samples, suggesting it is a conserved feature of human melanoma. In both fish and humans, interface cells are characterized by significant upregulation of the chromatin modifier HMGB2, which is prognostic in human melanoma. Loss of HMGB2 impairs invasion of human melanoma cells in vitro. HMGB2 is preferentially enriched in confined tumor cells subjected to high levels of mechanical force as they invade into neighbouring tissues. When we subjected human melanoma cells to confinement in vitro, we discovered that confinement causes HMGB2 to become cytoplasmic, where previous work suggests it may be secreted to be taken up by neighbouring cells. We are currently investigating how mechanical force signals through HMGB2 at the tumor border to induce changes in gene expression, chromatin accessibility, and cell state in both the tumor and surrounding cells. Together, our work suggests that HMGB2 may be a novel mechanosensor of the mechanical microenvironment across tumors, and demonstrates the power of spatial and single-cell transcriptomics in uncovering the biology underlying tumor cell behavior in vivo

Innate immune recognition of bacterial pathogens is a key determinant of the ensuing systemic response, and host or pathogen heterogeneity in this early interaction can impact the course of infection. To gain insight into host response heterogeneity, we investigate macrophage inflammatory dynamics using primary human macrophages infected with Group B Streptococcus. Transcriptomic analysis reveals discrete cellular states within responding macrophages, one of which consists of four sub-states, reflecting inflammatory activation. Infection with six additional bacterial species-Staphylococcus aureus, Listeria monocytogenes, Enterococcus faecalis, Yersinia pseudotuberculosis, Shigella flexneri, and Salmonella enterica-recapitulates these states, though at different frequencies. We show that modulating the duration of infection and the presence of a toxin impacts inflammatory trajectory dynamics. We provide evidence for this trajectory in infected macrophages in an in vivo model of Staphylococcus aureus infection. Our cell-state analysis defines a framework for understanding inflammatory activation dynamics in response to bacterial infection.