A single-cell perspective sheds new light on Salmonella infections

Microbiota-derived metabolites reduce virulence gene expression by Salmonella cells. By combining theory and experiments, we found these metabolites slow the growth of the virulent cell-type, implicating the gut environment as a modulator of disease severity.

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As Salmonella cells grow, they develop into two distinct cell-types distinguished by expression of Salmonella pathogenicity island-1 (SPI-1) genes.  Both are necessary for infection.  SPI-1 expressing cells inflame the host’s gut, clearing a niche for the fast-growing SPI-1 non-expressing cells.  The cooperation between the two cell-types creates a feedback loop, sustaining colonization. 

Epidemiological studies show Salmonella colonization presents along a clinical spectrum.  Acute disease followed by clearance, chronic relapsing symptoms, and asymptomatic carriage are all common outcomes of Salmonella colonization.  An out-of-whack proportion of SPI-1 expressing cells could drive this.  Too few and there’s too little inflammation for a sufficient niche to be opened leading to sub-clinical carriage; too many and a large niche opens with few non-expressing cells around to capitalize on it causing unsustainable, symptomatic colonization.  It seems, therefore, that tweaking the cooperative interactions between the two cell types could feasibly shift clinical presentation of this microbe. 

We know the gut environment is incredibly dynamic, shaped by eating patterns, resident microbes, and host physiology.  Could environmental conditions impact the cooperation between SPI-1 expressing and non-expressing cells?  Previous studies showed highly abundant microbial metabolites, Short-chain fatty acids (SCFAs), limit SPI-1 expression by populations of Salmonella cells.  But what does that mean exactly for the two cell-types?

Population-level vs single-cell observations.  A given condition may change gene expression levels by a population of cells on average (left panel).  Changes in population-level expression manifest from single-cell behaviors, with two possible mechanisms out of many, uniform and heterogenous expression, presented here (right panel).



When we started this study, it was unclear how individual cells were impacted by SCFAs:  did each expressing cell express less SPI-1 or were there fewer SPI-1 expressing cells in the population?  Flow cytometric experiments quickly resolved that question.  SCFAs don’t impact per cell expression but they do decrease the number of cells expressing in the population. 

These data support the idea SCFAs somehow limit the development of the SPI-1 expressing cell-type.  Our null hypothesis was that SCFAs were somehow acting on gene expression networks. Perhaps SPI-1 expression was initiated less frequently? Or that once initiated, SPI-1 expression was unstable and quickly repressed?  Each of these would lead to different conclusions as to how SCFAs act on SPI-1 regulation.  We turned to time-lapse microscopy in microfluidic devices to examine SPI-1 expression dynamics and this is what we saw:


SCFAs didn’t turn off SPI-1 expression.  In fact, we observed almost the opposite.  SPI-1 rarely turned off in the presence of SCFAs, remaining expressed over many hours and cell divisions.  This was unexpected and puzzling.  How could we see fewer cells in the population if SPI-1 expression was never really inhibited by SCFAs? 

We dug deeper into our dataset since our first hypothesis seemed to be a bust.  Along with quantifying per-cell expression levels and phenotypic switching events, our image analysis software also provides single-cell growth rates as an output.  We saw that while SCFAs slow the growth of both cell types, they have an even stronger growth inhibitory effect on SPI-1 expressing cells.  It was then we realized that cell-type proportion is not only determined by the phenotypic switching rates, but also by the single-cell growth rates of each cell-type. 

Parameters underlying population frequency.  The frequency of SPI-1+ cells in the population depends on the growth rate of SPI-1- cells (µ-), the rate of phenotypic switching to SPI-1+ (δ-), the growth rate of SPI-1+ cells (µ+), and the rate of phenotypic switching to SPI-1- (δ+).  A decrease in the proportion of SPI-1+ cells could arise from these non-mutually exclusive parameter shifts:  ↓ µ+, ↓ δ-, ↑ µ-, or ↑ δ+.

I turned to my physicist friend, Gabriele Micali, to see if mathematical modeling would help us gain intuition on the interplay of these parameters.  Consistent with what we observed experimentally, changes in cell-type growth rates are a strong determinant of cell-type proportion in analytical models.  And when we plugged our experimentally measured values into stochastic simulations of Salmonella populations, we saw something very similar:  SCFAs decrease the proportion of SPI-1 expressing cells by selectively slowing their growth.

But why are SPI-1 expressing and non-expressing cells impacted differently by SCFAs?  This is something we do not yet have a definitive answer for.  We found SPI-1 expressing cells have a higher proton motive force (PMF) that is more susceptible to perturbation by SCFAs, explaining their growth defect, however we still don’t know the mechanism of higher PMF nor why SPI-1 expressing cells are more sensitive. 

My pet hypothesis is that SPI-1 expressing and non-expressing cells differ in ways other than only SPI-1 expression.  In this model, each type expresses a different complement of metabolic genes to best fulfill their function in the population:  SPI-1 non-expressing cells optimized for fast growth and SPI-1 expressing cells primed for host interaction and intracellular survival. 

We are currently testing this idea with the hope of identifying gene expression patterns unique to each cell-type.  Fundamentally, understanding the mechanisms and impact of distinct cell-types of pathogens will help us to better understand infectious processes in general.  The end-game, though, is to identify pathways underlying Salmonella differentiation, hopefully providing novel therapeutic targets which can circumvent traditional antibiotic resistance mechanisms. 

Reference article:

Hockenberry AM, Micali GM, Takacs G, Weng J, Hardt W-D, Ackermann, M.  Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors.  Proceedings of the National Academy of Sciences. 2021; 118(31): e2103027118


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Alyson Hockenberry

Postdoc, ETH Zürich