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Recent studies have suggested that transcriptional protein condensates (or clusters) may play key roles in gene regulation and cell fate determination. However, it remains largely unclear how the gene regulation function is quantitatively tuned by transcription factor (TF) clustering and whether TF clustering may confer emergent behaviors as in cell fate control systems. Here, to address this, we construct synthetic TFs whose clustering behavior can be chemically controlled. Through single-parameter tuning of the system (i.e., TF clustering propensity), we provide lines of evidence supporting the direct transcriptional activation and amplification of target genes by TF clustering. Single-gene imaging suggests that such amplification results from the modulation of transcriptional dynamics. Importantly, TF clustering propensity modulates the gene regulation function by significantly tuning the effective TF binding affinity and to a lesser extent the ultrasensitivity, contributing to bimodality and sustained response behavior that are reminiscent of canonical cell fate control systems. Collectively, these results demonstrate that TF clustering can modulate the gene regulation function to enable emergent behaviors, and highlight the potential applications of chemically controlled protein clustering. Transcription factor (TF) condensates appear to be pervasive, yet their roles remain debated. Here, the authors use a synthetic biology approach to show that TF clusters causally amplify transcription and can confer bimodality and “memory”.

This paper investigates the decomposition of the λ-fold complete 3-uniform hypergraph λKν(3) into 4-cycles, denoted as Sλ(3,Γ5,1,v). Using the Γ5,1-structure as a model, we develop recursive construction techniques that exploit symmetric properties and provide explicit designs for small orders. These recursive frameworks enable the systematic generation of large-order hypergraph designs from smaller building blocks, illustrating the symmetric inheritance of structural properties. We establish that the necessary conditions for such a decomposition are also sufficient: an Sλ(3,Γ5,1,v) exists if and only if 24∣λv(v−1)(v−2),2∣λ(v−1)(v−2),andv≥5. This result highlights the deep interplay between combinatorial design theory and symmetry in hypergraph decompositions.

Recent single-cell studies have revealed that yeast stress response involves transcription factors that are activated in pulses. However, it remains unclear whether and how these dynamic transcription factors temporally interact to regulate stress survival. Here we show that budding yeast cells can exploit the temporal relationship between paralogous general stress regulators, Msn2 and Msn4, during stress response. We find that individual pulses of Msn2 and Msn4 are largely redundant, and cells can enhance the expression of their shared targets by increasing their temporal divergence. Thus, functional redundancy between these two paralogs is modulated in a dynamic manner to confer fitness advantages for yeast cells, which might feed back to promote the preservation of their redundancy. This evolutionary implication is supported by evidence from Msn2/Msn4 orthologs and analyses of other transcription factor paralogs. Together, we show a cell fate control mechanism through temporal redundancy modulation in yeast, which may represent an evolutionarily important strategy for maintaining functional redundancy between gene duplicates. How dynamic transcription factors temporally interact to regulate stress survival in yeast is currently unclear. Here the authors integrate single-cell imaging, RNA-seq, and modeling to identify a new cell fate control mechanism mediated by temporal redundancy modulation during yeast stress response.

A fundamental problem in biology is to understand how genetic circuits implement core cellular functions. Time-lapse microscopy techniques are beginning to provide a direct view of circuit dynamics in individual living cells. Unexpectedly, we are discovering that key transcription and regulatory factors pulse on and off repeatedly, and often stochastically, even when cells are maintained in constant conditions. This type of spontaneous dynamic behavior is pervasive, appearing in diverse cell types from microbes to mammalian cells. Here, we review recent work snowing how pulsing is generated and controlled by underlying regulatory circuits and how it provides critical capabilities to cells in stress response, signaling, and development. A major theme is the ability of pulsing to enable time-based regulation analogous to strategies used in engineered systems. Thus, pulsatile dynamics is emerging as a central, and still largely unexplored, layer of temporal organization in the cell.

With event-driven algorithms, especially spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream dataset is urgently needed. However, it is well-known that creating an ES-dataset is a time-consuming and costly task with neuromorphic cameras like dynamic vision sensors (DVS). In this work, we propose a fast and effective algorithm termed Omnidirectional Discrete Gradient (ODG) to convert the popular computer vision dataset ILSVRC2012 into its event-stream (ES) version, generating about 1,300,000 frame-based images into ES-samples in 1,000 categories. In this way, we propose an ES-dataset called ES-ImageNet, which is dozens of times larger than other neuromorphic classification datasets at present and completely generated by the software. The ODG algorithm implements image motion to generate local value changes with discrete gradient information in different directions, providing a low-cost and high-speed method for converting frame-based images into event streams, along with Edge-Integral to reconstruct the high-quality images from event streams. Furthermore, we analyze the statistics of ES-ImageNet in multiple ways, and a performance benchmark of the dataset is also provided using both famous deep neural network algorithms and spiking neural network algorithms. We believe that this work shall provide a new large-scale benchmark dataset for SNNs and neuromorphic vision.

Studies of individual living cells have revealed that many transcription factors activate in dynamic, and often stochastic, pulses within the same cell. However, it has remained unclear whether cells might exploit the dynamic interaction of these pulses to control gene expression. Here, using quantitative single-cell time-lapse imaging of Saccharomyces cerevisiae , we show that the pulsatile transcription factors Msn2 and Mig1 combinatorially regulate their target genes through modulation of their relative pulse timing. The activator Msn2 and repressor Mig1 showed pulsed activation in either a temporally overlapping or non-overlapping manner during their transient response to different inputs, with only the non-overlapping dynamics efficiently activating target gene expression. Similarly, under constant environmental conditions, where Msn2 and Mig1 exhibit sporadic pulsing, glucose concentration modulated the temporal overlap between pulses of the two factors. Together, these results reveal a time-based mode of combinatorial gene regulation. Regulation through relative signal timing is common in engineering and neurobiology, and these results suggest that it could also function broadly within the signalling and regulatory systems of the cell. Many gene-regulatory proteins have been shown to activate in pulses, but whether cells exploit the dynamic interaction between pulses of different regulatory proteins has remained unexplored; here single-cell videos show that yeast cells modulate the relative timing between the pulsatile transcription factors Msn2 and Mig1—a gene activator and a repressor, respectively—to control the expression of target genes in response to diverse environmental conditions. Phase modulation in gene control Many gene-regulatory proteins have been shown to be activated in pulses, but whether cells exploit the relative timing of pulses for different transcription factors has remained unexplored. Now Michael Elowitz and colleagues use single-cell videos to show that yeast cells modulate the relative timing between the pulsatile transcription factors Msn2 and Mig1 — a gene activator and a repressor, respectively — to control the expression of target genes in response to diverse environmental conditions. They also demonstrate that yeast cells respond to various concentrations of glucose by actively modulating the fraction of overlapping Msn2 and Mig1 pulses and regulate target gene expression accordingly.

Regulation of cellular ATP level is critical for diverse biological processes and may be defective in diseases such as cancer and mitochondrial disorders. While mitochondria play critical roles in ATP level regulation, we still lack a systematic and quantitative picture of how individual mitochondrial-related genes contribute to cellular ATP level and how dysregulated ATP levels may affect downstream cellular processes. Advances in genetically encoded ATP biosensors have provided new opportunities for addressing these issues. ATP biosensors allow researchers to quantify the changes of ATP levels in real time at the single-cell level and characterize corresponding effects at the cellular, tissue, and organismal level. Along this direction, several recent single-cell studies using ATP biosensors, including the work by Mendelsohn and colleagues, have started to uncover the principles for how genetic and nongenetic parameters may modulate ATP levels to affect cellular functions and human health.

Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple (≈1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions. Significance Growth and division of individual cells are the fundamental events underlying many biological processes, including the development of organisms, the growth of tumors, and pathogen–host interactions. Quantitative studies of bacteria can provide insights into single-cell growth and division but are challenging owing to the intrinsic noise in these processes. Now, by using a unique combination of measurement and analysis technologies, together with mathematical modeling, we discover quantitative features that are conserved across physiological conditions. These universal behaviors reflect the physical principle that a single timescale governs noisy bacterial growth and division despite the complexity of underlying molecular mechanisms.

Access to surgical care in low- and middle-income countries (LMICs) remains deficient without an adequate workforce. There is limited understanding of the gender gap in surgical trainees in LMICs. In Rwanda, females fill only one of 20 positions available. Understanding surgeons' experiences and perceptions of surgical careers may help facilitate support for females to contribute to the global surgical workforce. We performed qualitative analysis on perceptions of surgical careers through semi-structured interviews of all female surgeons (n = 6) and corresponding male surgeons (n = 6) who are training or have trained at University of Rwanda. Transcripts were analyzed with code structure formed through an integrated approach. Question categories formed the deductive framework, while theoretical saturation was reached through inductive grounded theory. Themes were organized within two key points of the career timeline. First, for developing interest in surgery, three main themes were identified: role models, patient case encounters, and exposure to surgery. Second, for selecting and sustaining surgical careers, four main themes emerged: social expectations about roles within the family, physical and mental challenges, professional and personal support, and finances. All female surgeons emphasized gender assumptions and surgical working culture as obstacles, with a corresponding strong sense of self-confidence and internal motivation that drew them to select and maintain careers in surgery. Family, time, and physical endurance were cited as persistent challenges for female participants. Our study reveals concepts for further exploration about gendered perceptions of surgical careers. Efforts to improve support for female surgical careers as a strategy for shaping surgical work culture and professional development in Rwanda should be considered. Such strategies may be beneficial for improving the global surgical workforce.

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.