Dual-role transcription factors stabilize intermediate expression levels.

Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.

A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement.

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.

The Crystal Structure of a Biological Insulated Transmembrane Molecular Wire.

A growing number of bacteria are recognized to conduct electrons across their cell envelope, and yet molecular details of the mechanisms supporting this process remain unknown. Here, we report the atomic structure of an outer membrane spanning protein complex, MtrAB, that is representative of a protein family known to transport electrons between the interior and exterior environments of phylogenetically and metabolically diverse microorganisms. The structure is revealed as a naturally insulated biomolecular wire possessing a 10-heme cytochrome, MtrA, insulated from the membrane lipidic environment by embedding within a 26 strand ß-barrel formed by MtrB. MtrAB forms an intimate connection with an extracellular 10-heme cytochrome, MtrC, which presents its hemes across a large surface area for electrical contact with extracellular redox partners, including transition metals and electrodes.

Structural basis of transcription reduction by a promoter-proximal +1 nucleosome.

At active human genes, the +1 nucleosome is located downstream of the RNA polymerase II (RNA Pol II) pre-initiation complex (PIC). However, at inactive genes, the +1 nucleosome is found further upstream, at a promoter-proximal location. Here, we establish a model system to show that a promoter-proximal +1 nucleosome can reduce RNA synthesis in vivo and in vitro, and we analyze its structural basis. We find that the PIC assembles normally when the edge of the +1 nucleosome is located 18 base pairs (bp) downstream of the transcription start site (TSS). However, when the nucleosome edge is located further upstream, only 10 bp downstream of the TSS, the PIC adopts an inhibited state. The transcription factor IIH (TFIIH) shows a closed conformation and its subunit XPB contacts DNA with only one of its two ATPase lobes, inconsistent with DNA opening. These results provide a mechanism for nucleosome-dependent regulation of transcription initiation.

Neural Algorithms and Circuits for Motor Planning.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

Metabolic and nontranscriptional circadian clocks: eukaryotes.

Circadian clocks are cellular timekeeping mechanisms that coordinate behavior and physiology around the 24-h day in most living organisms. Misalignment of an organism's clock with its environment is associated with long-term adverse fitness consequences, as exemplified by the link between circadian disruption and various age-related diseases in humans. Current eukaryotic models of the circadian oscillator rely on transcription/translation feedback loop mechanisms, supplemented with accessory cytosolic loops that connect them to cellular physiology. However, mounting evidence is questioning the absolute necessity of transcription-based oscillators for circadian rhythmicity, supported by the recent discovery of oxidation-reduction cycles of peroxiredoxin proteins, which persist even in the absence of transcription. A more fundamental mechanism based on metabolic cycles could thus underlie circadian transcriptional and cytosolic rhythms, thereby promoting circadian oscillations to integral properties of cellular metabolism.

Highly selective photoelectrochemical CO2 reduction by crystal phase-modulated nanocrystals without parasitic absorption.

Photoelectrochemical (PEC) carbon dioxide (CO2) reduction (CO2R) holds the potential to reduce the costs of solar fuel production by integrating CO2 utilization and light harvesting within one integrated device. However, the CO2R selectivity on the photocathode is limited by the lack of catalytic active sites and competition with the hydrogen evolution reaction. On the other hand, serious parasitic light absorption occurs on the front-side-illuminated photocathode due to the poor light transmittance of CO2R cocatalyst films, resulting in extremely low photocurrent density at the CO2R equilibrium potential. This paper describes the design and fabrication of a photocathode consisting of crystal phase-modulated Ag nanocrystal cocatalysts integrated on illumination-reaction decoupled heterojunction silicon (Si) substrate for the selective and efficient conversion of CO2. Ag nanocrystals containing unconventional hexagonal close-packed phases accelerate the charge transfer process in CO2R reaction, exhibiting excellent catalytic performance. Heterojunction Si substrate decouples light absorption from the CO2R catalyst layer, preventing the parasitic light absorption. The obtained photocathode exhibits a carbon monoxide (CO) Faradaic efficiency (FE) higher than 90% in a wide potential range, with the maximum FE reaching up to 97.4% at -0.2 V vs. reversible hydrogen electrode. At the CO2/CO equilibrium potential, a CO partial photocurrent density of -2.7 mA cm-2 with a CO FE of 96.5% is achieved in 0.1 M KHCO3 electrolyte on this photocathode, surpassing the expensive benchmark Au-based PEC CO2R system.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts.

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

High-level cognition is supported by information-rich but compressible brain activity patterns.

To efficiently yet reliably represent and process information, our brains need to produce information-rich signals that differentiate between moments or cognitive states, while also being robust to noise or corruption. For many, though not all, natural systems, these two properties are often inversely related: More information-rich signals are less robust, and vice versa. Here, we examined how these properties change with ongoing cognitive demands. To this end, we applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. We considered two primary aspects of the neural data recorded in these different experimental conditions. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower engagement.

Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods.

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants.