Biology subjects

Farquhar, I.

Publications and source records attributed to Farquhar, I..

3 recordsLinked to original sources

bioRxiv2024

A co-transcriptional mechanism for tightly controlling RNA homeostasis in yeast

Transcription termination by the Nrd1-Nab3-Sen1 (NNS) complex is key in repressing pervasive transcription in Saccharomyces cerevisiae. Counterintuitively, during starvation, multiple mRNAs that are upregulated are also increasingly bound and prematurely terminated and degraded via NNS. Here we demonstrate that this NNS-mediated attenuation is important for controlling the expression and protein concentration of an evolutionarily conserved mitochondrial transporter, Pic2. Strikingly, we find that even a modest increase in Pic2 protein levels caused by defective NNS regulation has major phenotypical consequences, increasing cell volume and intracellular stress, prolonging cell cycle and decreasing growth rate. Disrupting Nab3 binding to PIC2 globally redistributed Nrd1 binding, changing the levels of other NNS-regulated transcripts. We propose that imbalances in the availability of the subunits constituting the NNS complex underlie the cell volume and cycle anomalies. Collectively our results illustrate that even subtle changes in how RNA-binding proteins interact with a single RNA substrate can cause global defects and they emphasise the crucial role of the NNS complex in preserving microbial fitness during stress. HighlightsO_LINNS regulates the expression and protein concentration of a stress-response protein-coding gene (PIC2), improving cell fitness and adaptability to environmental challenges. C_LIO_LICreating an imbalance in RNA binding of Nab3 and Nrd1 for PIC2 mRNA disturbs the homeostasis of co-regulated transcripts. C_LIO_LIEven a modest defect in NNS regulation of PIC2 elicits severe defects in cell growth, increases cell size and intracellular stress, and prolongs the cell cycle. C_LI

molecular biology↗

bioRxiv2023

The type of carbon source not the growth rate it supports can determine diauxie

How cells choose between potential carbon sources is a classic example of cellular decision-making, and we know that many organisms prioritise glucose. Yet there has been little investigation of whether other sugars are also preferred, blinkering our view of carbon sensing. Here we study eukaryotic budding yeast and its growth on mixtures of palatinose, an isomer of sucrose, with other sugars. We find that yeast prioritise galactose over palatinose, but not sucrose or fructose, despite all three of these sugars being able to support faster growth than palatinose. Our results therefore disfavour carbon flux-sensing as the sole mechanism. By using genetic perturbations and transcriptomics, we show that repression is active and through Gal4, the master regulator of the GAL regulon. Cells enforce their preference for galactose over palatinose by preventing runaway positive feedback in the MAL regulon, whose genes enable palatinose catabolism. They do so both by repressing MAL11, the gene encoding the palatinose transporter, and by first expressing the isomaltases, IMA1 and IMA5, which cleave palatinose and so prevent its intracellular concentration becoming enough to induce further MAL expression. Our results demonstrate that budding yeast actively maintain a preference for carbon sources other than glucose and that such preferences have been selected by more than differences in growth rates. They imply that carbon-sensing strategies even in unicellular organisms are more complex than previously thought.

systems biology↗

bioRxiv2022

A label-free method to track individuals and lineages of budding cells

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging because cells often overlap in images, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae. Here we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages, estimates growth rates as the rate of change of volumes, and identifies cytokinesis by how growth varies. Using BABY and a microfluidic device, we show that bud growth is first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. Growth rate and fitness are strongly correlated, and BABY should therefore generate much biological insight.

systems biology↗