Experiments and Analysis of Mitochondrial DNA Segregation, Yeast Polarization and RNA Polymerase Dynamics

This thesis presents work done in three different topics in biology with a combination of experiments, mathematical modeling and quantitative data analysis. All cellular materials are partitioned between daughters at cell division, but by different mechanisms and with different accuracy. Many macromolecules partition in proportion to cytoplasmic volume with significant errors at low numbers, while chromosomes accurately push half the copies to each cell regardless of volume. Little is known about organelles, but in Schizosaccharomyces pombe the mitochondria are pushed to the cell poles by the spindle, along with the chromosomes. Here we find that mitochondria spatially re-equilibrate just before division, and that the mitochondrial volume and DNA-containing nucleoids instead segregate in proportion to the cytoplasm inherited by each daughter. However, in contrast to other macromolecules, nucleoid partitioning errors are still strongly suppressed. This is ensured by control at two levels: mitochondrial volume is actively distributed throughout a cell in a process involving the microtubule associated protein Mmb1p, and nucleoids are spaced out in semi-regular arrays within mitochondria. The data also suggest that nucleoid replication control is passive and Poisson, and that low concentration noise is achieved by accurate segregation rather than corrective feedback control. Patterning in organisms is both beautiful and functional, but we have limited understanding of the general principles that cause patterns to emerge from isotropic initial conditions. Alan Turing proposed a minimal set of conditions that would allow reaction-diffusion systems to spontaneously form stable patterns. Since then many biological systems have been hypothesized to be of the Turing type, but with limited evidence that they actually rely on this mechanism. One of the conditions of Turing's theory is that reactions must have non-linear sensitivities (cooperativity) in their positive feedback cycles. We use a minimal model of yeast bud site selection to show that the amount of cooperativity achievable is naturally limited by the kinetic parameters achievable by proteins and the type of reaction mechanisms commonly used to achieve cooperativity. Further, higher levels of cooperativity necessarily cause pattern formation to slow down, likely causing a fitness defect for the organism. Then, because cooperativity is also used in many other biology systems, we show examples in TF-promoter binding and cooperative ligand binding where the trade-off between cooperativity speed of response is also present. These considerations bring into question how biological systems respond to these trade-offs. We then explore a conceptual model for yeast budding where an additional actin-based positive feedback loop uses a time-delay, not cooperativity, to ensure that yeast picks a single bud site. RNA polymerase II (RNAP) pauses and backtracks during transcription elongation to regulate gene expression, control transcriptional efficiency and ensure transcriptional accuracy. After a pause, RNAP often backtracks and, in the presence of TFIIS, cleaves off the 3’ end of nascent RNA, allowing productive transcription to restart. However, the precise determinants of RNAP pausing, backtracking, and restarting remain unclear. Native elongating transcript sequencing (NET-seq) visualizes RNA polymerase pausing and backtracking with nucleotide resolution. Applying NET-seq to wild-type S. cerevisiae gives locations where cleavage occurs after backtracking. In contrast, NET-seq data from a strain where TFIIS is deleted reveal the primary points of pausing. Using these data, we show that RNAP pausing is largely controlled by the relative strength of the RNA:DNA and DNA:DNA hybrids in and downstream of the transcription bubble. In addition, backtracking is likely determined by the stacking interactions between the 3’-end of nascent RNA and Tyr769 of the Rpb2 subunit of RNAP. Though other factors beyond structural energetics of the transcription elongation complex certainly play a role in RNAP backtracking, this work suggests that the ubiquity of RNAP backtracking is largely controlled by sequence elements.Systems Biolog

Structure and Evolution of Streptomyces Interaction Networks in Soil and In Silico

Soil grains harbor an astonishing diversity of Streptomyces strains producing diverse secondary metabolites. However, it is not understood how this genotypic and chemical diversity is ecologically maintained. While secondary metabolites are known to mediate signaling and warfare among strains, no systematic measurement of the resulting interaction networks has been available. We developed a high-throughput platform to measure all pairwise interactions among 64 Streptomyces strains isolated from several individual grains of soil. We acquired more than 10,000 time-lapse movies of colony development of each isolate on media containing compounds produced by each of the other isolates. We observed a rich set of such sender-receiver interactions, including inhibition and promotion of growth and aerial mycelium formation. The probability that two random isolates interact is balanced; it is neither close to zero nor one. The interactions are not random: the distribution of the number of interactions per sender is bimodal and there is enrichment for reciprocity—if strain A inhibits or promotes B, it is likely that B also inhibits or promotes A. Such reciprocity is further enriched in strains derived from the same soil grain, suggesting that it may be a property of coexisting communities. Interactions appear to evolve rapidly: isolates with identical 16S rRNA sequences can have very different interaction patterns. A simple eco-evolutionary model of bacteria interacting through antibiotic production shows how fast evolution of production and resistance can lead to the observed statistical properties of the network. In the model, communities are evolutionarily unstable—they are constantly being invaded by strains with new sets of interactions. This combination of experimental and theoretical observations suggests that diverse Streptomyces communities do not represent a stable ecological state but an intrinsically dynamic eco-evolutionary phenomenon

MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast'

MATLAB code and processed datasets available for reproducing the results in: Lukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast. *equal contribution

Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast

RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates—in a genome wide and quantitative manner—previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking

Energy barrier from basepairing positively correlates with long backtracks.

<p><b>A.</b> After an initial pause, RNAP often backtracks. Pause sites from <i>dst1Δ</i> data were considered as leading to “no/short backtracking” if there was a corresponding pause site in the WT NET-seq dataset 0 or 1 bases upstream and as leading to “long backtracking” if the closest upstream WT pause site was 2 to 15 bases away. If there was no pause in the WT dataset in the region 0–15 bases upstream from the <i>dst1Δ</i> pause site, the <i>dst1Δ</i> pause was not included in the analysis. <b>B.</b> The fraction of <i>dst1Δ</i> pauses that lead to long backtracks (2 to 15bp) is plotted against the energy barrier as defined for <i>dst1Δ</i> in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174066#pone.0174066.g003" target="_blank">Fig 3C</a>. <b>C.</b> Receiver operating characteristic curves and AUC values of predicting long backtracking in WT data from <i>dst1Δ</i> pause sites is shown for the energy model and for nucleosomes.</p

Change in the difference between RNA:DNA and DNA:DNA basepairing strength is a good predictor of RNAP pausing.

<p><b>A.</b> The transcription elongation complex (TEC) contains an RNA:DNA hybrid that takes the place of a DNA:DNA duplex as the polymerase transcribes; we define the TEC stability as the difference between the energy required to melt these two structures. <b>B.</b> Assuming an 8 bp long RNA:DNA hybrid, the average TEC stability is plotted around <i>dst1Δ</i> and WT pause site as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174066#pone.0174066.g002" target="_blank">Fig 2C and 2E</a>. Note that WT data is plotted on an inverted and shifted secondary axis. This shows the similarity between the TEC stability profiles if we assume that RNAP is moving upstream when captured by WT NET-seq and moving downstream in <i>dst1Δ</i> NET-seq. <b>C.</b> The fraction of sites with a pause for the highly expressed genes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174066#sec010" target="_blank">Materials and Methods</a>) as a function of TEC stability energy difference at those sites (ΔTEC, as defined in B). Inset: Same data with a logarithmic axis to show that the odds of pausing increase exponentially with ΔTEC. <b>D.</b> Receiver operating characteristic curves and AUC values for transcription pause sites using different models. The curves for position weight matrix (PWM) models are those for nearest-neighbor PWMs to ensure fair comparison with the energy model which also considers nearest neighbor interactions.</p

Mapped nucleosome positions explain only a small portion of RNAP pausing.

<p><b>A.</b> Fraction of sites with a pause as detected via NET-seq in <i>dst1Δ</i> yeast strains as a function of distance from the nearest annotated nucleosome. The trendline represent a 61 base-pair running average. <b>B.</b> Receiver operating characteristic curves and AUC values for predictions based on nucleosome positions, a single-base position weight matrix and a nearest-neighbor position weight matrix.</p

RNA:DNA and DNA:DNA basepairing energy change near pause sites.

<p><b>A.</b> The stability of transcriptional elongation complex is influenced by sequence-dependent RNA:DNA and DNA:DNA basepairing energies. <b>B.</b> Average basepairing energy for RNA:DNA and DNA:DNA duplexes, around initial transcriptional arrest sites in a <i>dst1Δ</i> strain. Each pause site is aligned and the basepairing energy around it is averaged for each position separately over all pause sites considered. <b>C.</b> The difference between RNA:DNA and DNA:DNA basepairing around <i>dst1Δ</i> pause sites plotted as a running sum over the presumed length of RNA:DNA hybrid (8 bp). Each pause site is aligned and the basepairing energy for a stretch of 60 bases around it is summed with a centered sliding window of 8 bases and averaged over all pause sites considered. The expectation for random sites in the transcriptome is plotted at the p = 10⁻³ level (gray area). <b>D.-E.</b> Same as (B) and (C), respectively, but for sites of transcriptional arrest as determined from NET-seq in WT strains where RNAP is likely to be pausing while moving upstream. The dashed line marks the front end of the RNA:DNA hybrid in the direction of RNAP movement (arrow).</p