Kinesin and dynein motors transport intracellular cargos bidirectionally by pulling them in opposite directions along microtubules, through a process frequently described as a ‘tug of war’. While kinesin produces a 6 pN force, mammalian dynein was found to be a surprisingly weak motor (0.5–1.5 pN) in vitro, suggesting many dyneins are required to counteract the pull of a single kinesin. Mammalian dynein’s association with dynactin and Bicaudal-D2 (BICD2) activates its processive motility, but how this affects dynein’s force output remained unknown. Here, we show that formation of the dynein-dynactin-BICD2 (DDB) complex increases human dynein’s force production to 4.3 pN. An in vitro tug-of-war assay revealed that a single DDB successfully resists a single kinesin. Contrary to previous reports, the clustering of many dyneins is not required to win the tug-of-war. Our work reveals the key role of dynactin and a cargo adaptor protein in shifting the balance of forces between dynein and kinesin motors during intracellular transport.

Morphogen gradients direct the spatial patterning of developing embryos; however, the mechanisms by which these gradients are interpreted remain elusive. Here we used lattice light-sheet microscopy to perform in vivo singlemolecule imaging in early Drosophila melanogaster embryos of the transcription factor Bicoid that forms a gradient and initiates patterning along the anteroposterior axis. In contrast to canonical models, we observed that Bicoid binds to DNA with a rapid off rate throughout the embryo such that its average occupancy at target loci is on-ratedependent. We further observed Bicoid forming transient "hubs" of locally high density that facilitate binding as factor levels drop, including in the posterior, where we observed Bicoid binding despite vanishingly low protein levels. We propose that localized modulation of transcription factor on rates via clustering provides a general mechanism to facilitate binding to low-affinity targets and that this may be a prevalent feature of other developmental transcription factors.

Predicting developmental outcomes from regulatory DNA sequence and transcription 17 factor patterns remains an open challenge in physical biology. Using stripe 2 of the even-skipped 18 gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key 19 step along the developmental decision-making cascade: the generation of cytoplasmic mRNA 20 patterns via the control of transcription in individual cells. Using live imaging and computational 21 approaches, we found that the transcriptional burst frequency is modulated across the stripe to 22 control the mRNA production rate. However, we discovered that bursting alone cannot 23 quantitatively recapitulate the formation of the stripe, and that control of the window of time over 24 which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical 25 modeling revealed that these regulatory strategies-bursting and the time window-obey different 26 kinds of regulatory logic, suggesting that the stripe is shaped by the interplay of two distinct 27 underlying molecular processes. 28 29 35the regulatory logic of the enhancer elements that dictate the behavior of these networks, the 36 precise prediction of how gene expression patterns and developmental outcomes are driven by 37 transcription factor concentrations remains a central challenge in the field (Vincent et al., 2016). 38 Predicting developmental outcomes demands a quantitative understanding of the flow of 39 information along the central dogma: how input transcription factors dictate the output rate of 40 1 of 69 Manuscript submitted to bioRxiv mRNA production, how this rate of mRNA production dictates cytoplasmic patterns of mRNA, 41 and how these mRNA patterns lead to protein patterns that feed back into the gene regulatory 42 network. While the connection between transcription factor concentration and output mRNA 43 production rate has been the subject of active research over the last three decades (Lawrence 44 et al.connection between this output rate and 47 In order to uncover the quantitative contribution of these three regulatory strategies to pattern 62 formation, and to determine whether other regulatory strategies are at play, it is necessary to 63 measure the rate of RNA polymerase loading in individual nuclei, in real time, in a living embryo. 64 2 of 69 Manuscript submitted to bioRxiv However, to date, most studies have relied on fixed-tissue techniques such as mRNA FISH and 65 immunofluorescence in order to obtain snapshots of the cytoplasmic distributions of mRNA and 66 protein as development progresses (Jaeger et al., 2004; Fakhouri et al., 2010; Parker et al., 2011; 67 Estrada et al., 2016; Crocker et al., 2016; Verd et al., 2017; Park et al., 2018). Such techniques 68 are virtually silent regarding the regulation of single-cell gene expression over time, and are thus 69 ill-suited to the study of how spatiotemporal variations in transcriptional dynamics give rise to 70 patterns of cytoplasmic mRNA. 71...

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.

The responses of plants to their environment often hinge on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, lack of implementation of these technologies in plants has limited concomitant quantitative studies. Here, we applied the PP7 and MS2 RNA-labeling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we count nascent RNA transcripts in real-time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana (Arabidopsis). Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the number of cells engaged in transcription rather than the transcription rate of active cells. This switch-like behavior, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from the stochasticity intrinsic to individual alleles. Taken together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.