搜索结果：bioRxiv : the preprint server for biology

Bacterial RNA polymerases (RNAPs) have two flexibly tethered α subunit C-terminal domains (α-CTDs) that bind DNA. Interaction between α-CTDs and some promoter DNA motifs is known to accelerate transcription initiation, but the physical mechanism by which it does so is unclear. We used single-molecule multiwavelength fluorescence microscopy to test how the diffusion-limited binding kinetics of core RNAP to non-promoter DNA differ from those of mutant RNAPs that lack one or both α-CTDs. We find that even though α-CTDs and their tethers are small compared to the complete RNAP molecule, the presence of two α-CTDs accelerates DNA binding by ∼10-fold and ∼55-fold respectively relative to RNAP constructs in which one or both α-CTDs are deleted. In contrast, the presence of α-CTDs did not have a detectable effect on RNAP-DNA complex lifetimes in the absence of RNA synthesis. We explain how α-CTDs achieve the dramatic acceleration of RNAP binding to DNA using a quantitative three-state kinetic model that includes a transient binding intermediate where only the α-CTD(s) are bound to DNA, tethering the rest of the RNAP in the vicinity of DNA. The model and assumed parameters are validated using Brownian dynamics simulations of the DNA association reactions for two-, one-, or zero-CTD RNAP constructs. The combination of single-molecule experiments, mathematical theory, and simulations suggests that adding a flexible DNA-binding tether is a general physical mechanism which can accelerate the diffusion-limited binding of a large protein like RNAP to DNA and quantitatively defines the conditions under which this acceleration can occur. Large enzymes that must associate with DNA to perform their biological functions are expected to bind DNA only slowly because of slow enzyme diffusion. However, some DNA-binding enzymes have one or more additional small DNA-binding domains that can diffuse rapidly but are attached to the enzyme through an unstructured flexible tether. Combining single-molecule experiments, theory, and computation, we present evidence for a general mechanism by which such tethered domains can accelerate enzyme binding to DNA by orders of magnitude, while not significantly changing the duration of the DNA-bound state. We demonstrate this effect for the flexibly-tethered α-subunit C-terminal domain of bacterial RNA polymerase. The mechanism may allow the polymerase to rapidly bind DNA while minimizing non-functional sequestration on the genome.

Microbes closely interact with every living organism, including meiofauna (i.e., microbial eukaryotes 38 μm - 1 mm in length), and influence the development, life cycle, and evolution of diverse metazoans. Together, meiofauna and their microbiomes, collectively referred to as the holobiont, underpin biogeochemical cycles and drive decomposition of organic matter. However, our understanding of the ecological and evolutionary dynamics of meiofauna microbiomes are limited, typically owed to low-resolution 16S rRNA surveys, which cannot accurately delineate bacterial taxa. Single-specimen holobiont sequencing can help overcome the limitations of metabarcoding approaches by 1) generating metagenome-assembled genomes (MAGs) of the host microbiome and 2) recovering host single-copy genes (SCGs) to phylogenetically confirm the identity of the host organism. However, most bioinformatics pipelines for the assembly of metagenomic datasets have been developed for the assembly of high-complexity microbial communities of bulk sediment or soil samples (and cannot be used for the assembly of host genomes), rely on co-assembly approaches (which collapses strain-level genomic information of bacterial taxa), and focus on binning either prokaryotic or eukaryotic taxa. Therefore, there is a tremendous need for a computational workflow for the dual analysis of host genomes and their microbiomes. Here, we developed MeioBIOME, a modular Snakemake pipeline for the reproducible analysis of holobiont metagenomes obtained from individually sequenced microbial metazoa. We analyze publicly available single-specimen metagenomics datasets to show the utility of MeioBIOME and recover host-associated symbiont MAGs and host SCGs. Additionally, we integrate state-of-the-art binning algorithms which generate more MAGs than the DOE Joint Genome Institute metagenomic pipeline. We anticipate that MeioBIOME will facilitate studies of phylosymbiosis by generating high-quality host genome skims (to build well-supported host phylogenetic trees) and host-associated prokaryotic MAGs obtained from single specimens.

Thermal proteome profiling (TPP) and proteome integral solubility alteration (PISA) assays measure drug-target interactions by monitoring protein thermal stability across the proteome. While detergents are routinely used in lysate-based thermal profiling, the field lacks consensus on whether detergents should be present during the melting step or only added afterward as an extraction buffer, and whether detergent identity matters for this choice. Here, we evaluate how commonly used detergents and the timing of their use in thermal stability workflows affect proteome-wide thermal stability and PISA hit calling in TF-1 lysates. We find that NP-40 and DDM produce highly correlated melting profiles when used exclusively as post-melt extraction buffers, but diverge substantially when present during the melting step. DDM in particular prevents the thermally-induced loss in solubility of large classes of proteins, such as cell surface proteins, and these effects propagate directly into PISA hit calling. Performing the PISA melt in DDM versus NP-40 results in the gain and loss of distinct drug-target interactions for both the PAK4 inhibitor PF-3758309 and the PLK1 inhibitor volasertib. Notably, DDM enables detection of a volasertib-TMEM97 interaction that was previously not detected in NP-40. However, we also find that the stabilization effects of DDM mask the identification of some known PISA hits for these drugs. We further introduce a four-parameter logistic model of protein melting to aid in modeling of these findings and a linear regression framework for PISA hit calling that outperforms pairwise t-tests in low-replicate settings. Together, these results establish detergent selection as a tunable experimental variable in thermal profiling and suggest that some drug-target engagements previously attributed exclusively to intact-cell context may be recoverable in lysates with appropriate buffer conditions.

Many endosymbioses in eukaryotes superficially appear to be beneficial to both participants. However, there is little direct evidence for this, and symbioses naturally set up conditions in which each member of the pair is under selection to extract resources from the other. Ultimately, the endosymbiont either evolves to be in conflict with the interests of the host or to act cooperatively with the host contrary to its own best interests. Focusing on obligate symbioses, we develop theory to clarify the population-genetic conditions favoring the alternative outcomes. The balance is usually tipped in favor of exploitation by the symbiont, particularly when the number of symbionts within host cells is high, selection is strong on symbionts relative to hosts, there is horizontal transfer of symbionts, and/or the symbionts have accelerated mutation rates or turnover times. If the symbiont conditions the host-cell biology to enhance within-host population sizes, selection for selfish symbionts will be further enhanced by the diminished level of within-host drift. Although the host evolves in parallel to exploit resources from the endosymbiont, the net result is often a stalemate in which the host is no better off than prior to host-symbiont coevolution. Strict vertical inheritance can result in an evolutionary alignment of interests of the endosymbiont and the host, as this minimizes the possibility of within-host selection, but even here there is a critical host population size below which the symbiont evolves to exploit the host. These results suggest that the evolutionary enslavement of a symbiont to benefit a host species requires a narrow mix of population-biological features of both participants.

Fetal hemoglobin (HbF) expression is silenced postnatally in adult erythroid cells. Sufficiently increased expression of HbF has been shown to overcome the pathophysiologic sequelae of both sickle cell disease and beta-thalassemia. As the MBD2a-NuRD chromatin remodeling complex is required for silencing of HbF, the present studies were aimed at exploring a potential therapeutic approach for disrupting this complex. AlphaFold 3 and a recent crystal structure were employed to predict the critical interaction domains linking GATAD2A in the histone deacetylase core subcomplex (HDCC) of NuRD and the CHD4 ATPase which has been shown to be required for silencing of the fetal gamma-globin ( HBG ) genes. The two predicted critical domains, the CR2 helical domain of GATAD2A and the C-terminal domains 1 and 2 (C1b and C2ab) of CHD4, were validated by in vitro biophysical studies. Mutation of two amino acids in the CR2 helical domain of the endogenous GATAD2A gene in HUDEP-2 cells resulted in dissociation of CHD4, loss of repressive chromatin over the HBG promoter and ~40% HbF levels compared to < 1% in control cells. Strikingly, enforced expression of a peptide containing the helical portion of the CR2 domain of GATAD2A in both HUDEP-2 cells and primary adult erythroid cells resulted in high levels of HbF, with up to ~75% HbF compared to mutant peptide control level of ~9% in the latter without perturbing erythroid differentiation. These results suggest that targeting the critical interaction domains of GATAD2A and CHD4 with a macrocyclic peptide or small molecule may lead to much needed small molecule therapeutics for sickle cell disease. Association of CHD4 with the HDCC core of the MBD2-NuRD chromatin remodeling complex is required for silencing of HbF expression in adult human erythroid cellsGenetic alteration or enforced peptide expression of a critical helical domain of GATAD2A results in dissociation of CHD4 from the MBD2-NuRD complex and high-level expression of HbF.

The ubiquitin-proteasome system represents the main pathway for targeted protein degradation in eukaryotic cells. The majority of substrates is recruited for degradation through ubiquitin modifications, and the underlying principles are well established. However, the requirements for ubiquitin-independent substrates are still poorly understood. Here, we reveal the mechanisms for the antizyme-mediated degradation of the yeast ornithine decarboxylase (yODC), the first reported ubiquitin-independent substrate of the 26S proteasome. Using biochemical studies and cryo-EM structure determination, we show how antizyme binding makes the yODC monomer prone for degradation by exposing an interface that is normally buried in the catalytically active ODC dimer. Together with a surface on antizyme, yODC forms a two-part interface that binds the N-terminal coiled coil of two ATPase subunits, Rpt4 and Rpt5, for delivery to the 26S proteasome motor. This positions the N-terminal unstructured region of yODC for insertion into the ATPase channel to initiate degradation, which we found does not depend on a specific sequence. Interestingly, binding of the globular yODC/antizyme complex to the Rpt4/Rpt5 coiled coil allosterically stabilizes a proteasome conformation that facilitates substrate engagement by the ATPase motor and may represent a primed pre-initiation state with a general role in ubiquitin-dependent and -independent degradation.

The immature HIV-1 virion is assembled by the Gag polyprotein using inositol hexakisphosphate (IP6) as an essential assembly co-factor. Gag binds the genomic RNA Psi packaging signal via the nucleocapsid (NC) domain and associates with the plasma membrane via the matrix (MA) domain. Previous studies revealed that Gag exists in both compact (C) and extended (E) conformational states in solution. Only E-Gag formed virus-like particles with the correct size and IP6 shifted the equilibrium of DNA-bound Gag to the E state. The influence of specific RNA elements on this conformational change is unknown. In this work, a dual dye-labeled Gag was prepared for probing the effect of RNA binding on Gag conformation using F&#xf6;rster resonance energy transfer (FRET). In low salt and in the absence of other factors, Gag was primarily in the C state. Psi RNA binding induced a more significant FRET decrease than binding to non-Psi RNAs, consistent with a shift to E-Gag. IP6 alone also promoted the E-Gag state in the absence and presence of RNA. Atomistic molecular dynamics simulations are consistent with and provide detail into the role of NC-Psi RNA binding in the conformational switch of C-Gag to assembly-competent E-Gag. Simulations also showed that this switch is driven by capsid (CA) linker domain orientational flexibility and MA-CA unbinding dynamics. Thus, the highly flexible multi-domain Gag polyprotein leverages both viral and host cell factors to sample and stabilize distinct conformations, thereby orchestrating the viral assembly process.

The Caffeinated Coli Educational Module brings inquiry-driven learning to high schools, introducing students to scientific research, synthetic biology, and genetic engineering. The module focuses on the exploration of a genetically engineered strain of E. coli modified to grow exclusively on caffeine and, as such, can be used as a measurement device to determine the amount of caffeine in a liquid or beverage. Students conduct two bioassay experiments using these bacteria. During this process, they learn to create cultures and then measure bacterial growth followed by calculating the caffeine concentrations of unknown samples using their own data. This flexible module contains five weeks of original lectures and student learning materials that follow Next Generation Science Standards (NGSS), allowing the content to be adapted for basic, intermediate, or advanced biology courses. Upon completion of the module, students and teachers expressed that the most memorable aspects of the module include collaboration with peers, hands-on learning of content, and the opportunity to interact with the professor/mentors through office hours. Already implemented in 8 Texas high schools over the past two academic years, our module inspires STEM learning while bringing 21 st century biology research to new audiences.

Scaling laws describe how model performance improves as the amount of training data increases, and recent theories such as the zeta law suggest that scaling behavior is influenced by the eigenspectrum of the model's latent representation. Here, we evaluated whether the distribution of discriminative signals across spectral modes predicts the future scaling behavior, for MRI transformers trained for disease classification. We trained three supervised 3D vision transformers (ViT3D, MINiT, and NIT) for Alzheimer's disease classification using 2,822 training scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI); we compared their encoder spectra with that of a frozen self-supervised DINO ViT-B/16 encoder adapted to 3D MRI. The supervised models learned highly concentrated representations, with 90-96% of CLS-token variance captured by a single principal component, whereas DINO distributed signal across many latent directions. Via spectral expansion of the Mahalanobis signal, we found that supervised training concentrated disease information into a single dominant mode, while self-supervised training produced a richer spectral geometry with higher effective rank and discoverability. This led to different scaling behavior: supervised models exhibited flatter AUC( N ) curves, yet DINO continued to improve as sample size increased, gaining 11.0 percentage points from N=50 to N=2,822. Overall, the spectral distribution of the discriminative signal, for these different encoder types, influenced how much performance remained discoverable as sample size increased. Distributed representations may retain signal across many latent modes and continue to improve with additional data, whereas concentrated representations tend to exhaust most of the discoverable signal at much lower sample sizes.

Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by how they evolve in time, it is a natural goal to compare their dynamics. Dynamical systems comparison is well defined, but has been intractable for nonlinear, high-dimensional, noisy, and partially observed data. As a result, standard comparison metrics have focused on the geometry or topology of data. Here we present Dynamical Similarity Analysis (DSA), a class of methods to compare systems by their temporal evolution. Its foundation is Koopman Operator theory, which recasts nonlinear systems as linear operators. We estimate these operators from data, then compare the operators across systems. The computation is fast, scalable, and robust to noise and partial observation. It is also differentiable. DSA identifies dynamical structure that geometric and topological methods miss. It matches recordings from the head direction circuit to ring attractor models. It shows that macaque motor cortex dynamics for two reaching tasks drift apart across years despite preserved behavior, and that primary motor cortex breaks from premotor cortex as movement begins. As an optimization objective, it induces neural networks to learn never-before hypothesized solutions that run counter to their inductive biases. Thus, DSA transforms the dynamics of a system into an object that can be measured, compared, and optimized.

Computational modeling and the use of simulation software tools are essential for biomedical optics research. Designing effective simulations often requires in-depth understanding of the underlying physical problems and proper configuration of the software settings, which often constitute key barriers for novice users including students. The rapid emergence of large language models (LLMs) offers new opportunities for natural-language-based interaction, but integrating them with technical software remains challenging because of their limited output reproducibility. Overcoming these limitations would allow more intuitive, efficient, and reproducible interaction between scientists and scientific software. We investigate the use of LLMs in quantitative biophotonics simulation tools, with a goal of enabling novice users to build complex photon simulations using intuitive natural-language-based problem descriptions. We have explored prompt engineering strategies that enable LLMs to bridge the gap between natural language descriptions and advanced simulation software by constraining LLM outputs using a data schema ( i.e. , format) and a modular component architecture, followed by deterministic validation to ensure correctness and reproducibility of the outputs. Using Monte Carlo eXtreme (MCX) - a widely used photon transport simulator - as an example, we showcase the capability of the proposed framework to convert user descriptions to structured simulation inputs. Benchmarked using 33 diverse natural language simulation descriptions, our LLM interface, MCX-LLM, achieves 98% accuracy and 99% repeatability, with an average processing time of 8.96 seconds per prompt. The framework also successfully handles various linguistic styles and diverse simulation settings, achieving a 100% success rate on 20 unconstrained real-world prompts. With only minor adjustments, our LLM interface also produces valid inputs for a finite-element-based diffusion solver to demonstrate generality towards other optical simulators. By combining LLMs' capability for textual data comprehension with structured constraints, this work provides a pathway to making complex scientific tools accessible while ensuring the reliability and technical correctness required for rigorous scientific research. MCX-LLM has been integrated with MCX Cloud accessible at https://mcx.space/cloud .

Primary mitochondrial diseases (PMD) have limited disease-modifying therapies, currently applicable to only 3 of over 400 discrete gene disorders. Cycloheximide (CHX) is a global cytosolic translation inhibitor we previously reported to rescue PMD preclinical models, although its toxicity precluded clinical development. To identify specific mediators underlying CHX treatment benefit in PMD, SOMAscan-based proteomics was performed in complex I deficient and genetic disease fibroblast cell line models grown in galactose. Thrombopoietin (THPO) and insulin-like growth factor binding protein 5 (IGFBP5) were the only two differentially regulated proteins, together with ERK/MAPK pathway dysregulation, identified upon CHX treatment in PMD versus healthy control cells. THPO inhibition by siRNA or pharmacologic approaches rescued stress-induced viability loss in patient fibroblasts having diverse PMD gene etiologies, and significantly improved mitochondrial stress, linear growth, and neuromuscular function in a classical ndufs2 -/- C. elegans model. IGFBP5 overexpression by lentiviral or mRNA approaches rescued cell viability across distinct PMD gene etiologies, as did IGF1 pharmacologic inhibition across both PMD mutant and C. elegans models. MAPK pharmacologic inhibition rescued multiple distinct complex I disease cells' survival, as well as mitochondrial stress in SLC25A46 -/- C. elegans . Combination therapies targeting multiple of these glucose signaling pathway proteins, together with glucose and N-acetylcysteine, yielded superior therapeutic benefit in complex I disease cell and C. elegans models. Additionally, single or combined pharmacologic inhibition of THPO or IGF1 significantly enhanced primary and metastatic osteosarcoma cell death. Collectively, targeted small molecule and genetic modulation of THPO, IGF1, or MAPK recapitulated the significant therapeutic benefit of CHX in PMD, while avoiding global translation inhibition. These novel PMD therapies likely confer benefit by attenuating MAPK-driven autophagy and potentially promoting noncanonical glucose uptake, improving cellular energy balance. Overall, these glucose signaling cellular pathway targets hold broad therapeutic promise for PMD patients, warranting further clinical research development.

Current &#x3b3;&#x3b4; T-cell expansion protocols often sacrifice functionality for yield and largely ignore the context of activation. Here we utilize a tunable alginate microgel system functionalized with anti-CD3 and co-stimulatory antibodies (&#x3b1;CD28 or &#x3b1;CD2) to investigate the impact of biochemical signaling and substrate mechanics on &#x3b3;&#x3b4; T-cell activation. Microgel-mediated expansion was compared to conventional soluble antibodies and TransAct beads. The microgels enhanced &#x3b3;&#x3b4; T-cell expansion compared to soluble antibodies, allowed for controlled tuning of differentiation state, and promoted higher NKG2D, IFN-&#x3b3; and TNF-&#x3b1; expression levels. Functionally, microgel-expanded &#x3b3;&#x3b4; T-cells exhibited superior cytotoxicity against both solid and liquid tumor targets. This system also allowed elucidation of the differences in stimulation requirements for various donors, based on the starting phenotype. These findings establish a tunable platform for engineering &#x3b3;&#x3b4; T-cells with improved therapeutic potential. &#x3b3;&#x3b4; T-cells have shown promising therapeutic effects when used for T cell-based immunotherapy to treat solid tumor. However, achieving rapid expansion of &#x3b3;&#x3b4; T-cells while maintaining their functionality remains a major challenge, especially given the heterogeneous responses from donors. We demonstrate that a tunable microgel system with flexible presentation of stimulatory cues improves &#x3b3;&#x3b4; T-cell expansion while preserving cytotoxic function and reveal how starting phenotypes influence responses to activation. These understandings will provide design rationale to enable patient-specific treatment for optimal therapeutic outcomes.

Left-handed (Z-form) double-stranded nucleic acid conformers (Z-DNA and Z-RNA, collectively called Z-NA) are molecular patterns recognized by ADAR1 and ZBP1, which are sensor proteins involved in innate immunity. Monoclonal antibodies raised against Z-DNA, such as Z22 and Z-D11, are employed as probes for the study of Z-NAs, but their substrate specificity and functional equivalence remain unclear. Here, we used biochemical, biophysical, and structural approaches to compare the binding modes and target specificies of Z22 and Z-D11, using Z-prone CG-rich dsDNA, dsRNA, and DNA-RNA hybrids as substrates. Both antibodies failed to form stable complexes with short CG-rich dsRNA under conditions compatible with antibody stability, which suggests that they are unable to induce A-to-Z transitions in dsRNA. In contrast, both antibodies robustly complexed with dsDNA and DNA-RNA hybrid substrates. Cryo-EM structural analysis confirmed that both antibodies interact with Z-NA through a conserved interaction network, and demonstrated that complexes between the antibodies and DNA-RNA hybrids adopted distinct higher-order organizations. Z22 retained binding to both Z-DNA and Z-RNA segments, whereas Z-D11 displayed substrate-dependent organization and was restricted to Z-DNA segments Molecular modelling provided mechanistic explanation for the differences in Z-RNA binding ability between the two antibodies, which was confirmed in situ with ADAR1-depleted, IAV- infected and JTE607-treated cells. We also demonstrate that Z-D11, in contrast to previously described Z22, is unable to induce B-to-Z-transitions in short d(CG) 6 oligos under physiological conditions. Together, these results show that anti Z-NA monoclonal antibodies are not functionally interchangeable: whereas Z22 recognizes pre-formed Z-RNA, Z-DNA- RNA hybrids, and Z-DNA, Z-D11 recognizes Z-DNA, and DNA segments in Z-DNA-RNA hybrids. These differences are dictated by substrate composition, local geometry, and conformational accessibility, which also likely impact their ability to induce NA structural transitions. These findings have important implications for interpreting antibody-based detection of left-handed nucleic acids in biological systems.

High annual honey bee colony losses are associated with environmental and biological stressors, including virus infections. In insects, the octopamine pathway orchestrates the "fight-or-flight" response, regulating energy mobilization, temperature, and flight. We determined that sacbrood virus (SBV) infections induce expression of an octopamine receptor and enhance honey flight performance, whereas deformed wing virus (DWV) infections reduce flight performance, but how viruses interface with this pathway remained unknown. To elucidate the relationships between the octopamine response, virus infection, and flight, honey bees were infected with SBV or DWV and exposed to octopamine (OA), epinastine (EP)-an OA receptor antagonist, or both OA and EP; flight and gene expression were assessed. Pharmacologic manipulation revealed that octopamine supplementation rescued flight deficits in DWV-infected bees, but diminished performance in SBV-infected bees, while blocking octopamine receptors altered these effects. Transcriptome analyses indicated that SBV infections, and DWV infection with OA treatment, activated honey bee metabolic pathways, and that SBV infected bees had greater expression of genes involved in OA synthesis, unless treated with OA. These results provide mechanistic insight for virus-specific impacts on honey bee flight, which may have consequences on foraging efficiency, colony health and virus transmission. Differential virus-specific impacts on honey bee flight performance are regulated by octopamine signaling and chemical stressors affecting this pathway may impact colony health.

Plastids house the biology of eukaryotic photosynthesis. While 1000s of plastid genomes have been sequenced, the availability of less than ten proteomes and only two species with full 70S plastid ribosomal structures limit our understanding of plant evolution. We optimized a protocol for the rapid isolation of Marchantia polymorpha plastids that provides a highly enriched and intact organelle fraction from gradient volumes as little as 2 mL. The approach was successfully applied to six other species. Focusing on M. polymorpha , we determined the proteome of the plastid fraction, identifying 1337 nuclear-encoded proteins with a high confidence, where 83% belong to orthologs shared with angiosperms. We further isolated protein complexes by RNA affinity purification using poly-lysine and provide the high-resolution structures of the 50S subunit of the chloroplast ribosome and RuBisCO using cryogenic EM and image reconstruction to 2.23 and 2.12 &#xc5; resolution, respectively. For plastids, our data show that the genome reduction event experienced by the bryophyte common ancestor has had little impact on the organelle's complexity and they underscore a high level of structural conservation of key components. Our data provide novel resources to explore the functional evolution of plastid proteomes and major macromolecular complexes of cyanobacterial origin.

Our modern environment - with its artificial lighting, irregular work hours, and frequent travel - often disrupts our circadian rhythms, which can lead to health problems, particularly in learning and memory. This is especially concerning given the aging population and the rising prevalence of dementia. Yet, the biological mechanisms linking circadian disruption to cognitive impairment remain poorly understood. At the molecular level, genetic techniques have been used to attenuate or abolish expression of key genes involved in circadian rhythms and these manipulations have detrimental effects on memory function. However, whether environmentally induced circadian disruption, impairs memory via changes in overall gene expression levels in the hippocampus or rather via changes in the coordinated rhythmic patterns of circadian expression across groups of genes is less known. Here, we examined how environmental circadian disruption affects the expression of genes involved in the circadian clock and memory in the hippocampus of rats using a forced desynchrony model. Circadian disruption changed the rhythmic properties of gene expression in most genes assessed but had no measurable effect on average expression levels across the day. These findings suggest that the inability to maintain circadian synchrony rather than overall expression may underlie the cognitive deficits observed in circadian-related disorders.

Polysaccharides remain the least understood biomacromolecules, particularly in terms of the relationship between their chemical structure and physical properties. On the other hand, polysaccharides often serve as the main structural components in biofilms: surface-attached aggregates of bacterial cells encased within a mechanically resilient extracellular matrix. The large chemical space explored by bacteria within biofilms provides excellent opportunities to establish the structure-function relationship for polysaccharides. In this paper, we systematically characterize various polymer properties of V ibrio p oly s accharide (VPS), the major exopolysaccharide in biofilms formed by Vibrio cholerae , the causative agent of pandemic cholera. Using a combination of shear rheology, dynamic and static light scattering, and small-angle X-ray scattering, we measure the viscosity, molecular weight, persistence length, radius of gyration, and hydrodynamic radius of this chemically unique biopolymer. Combining all-atom and coarse-grained simulations, we show how the conformational flexibility of a single glycosidic linkage within each VPS monomer can lead to dramatic compaction of the entire polymer chain and nonclassical entanglement behavior. Our comprehensive quantification represents a rare endeavor for bacterial biofilms, whose matrix composition and physical properties remain largely nebulous; it also represents a significant step towards a detailed understanding of the molecular origins of biofilm mechanics.

Inter-subunit communication and allosteric regulation are central to the function of oligomeric enzymes, yet these features remain difficult to characterize. Conventional kinetic and structural methods typically yield ensemble averages or static snapshots, thus making it difficult to uncover the dynamic cross-subunit cooperation obligatory for multi-site catalysis by oligomeric enzymes. Here, we investigate Salmonella FraB-a homodimeric deglycase and a potential drug target-to showcase the value of an integrated approach combining native mass spectrometry (nMS), surface-induced dissociation (SID), and kinetic studies to gain insights into catalytic intermediates and inter-subunit communication. By resolving substrate-, product-, and mixed-occupancy species, nMS revealed that both inter-subunit active sites in FraB bind substrate even though only one catalytic center generates the product at any given time. To characterize each active site independently, we designed heterodimers with a mutation that changes the general base or acid in only one active site. Kinetic studies with these mutants indicate that although the two active sites are likely coupled, they do not concomitantly perform cleavage. Consistent with the conformational asymmetry observed in apo -FraB crystal structures, our findings establish a half-site reactivity mechanism in which post-binding conformational changes across the dimer interface restrict substrate cleavage to one active site even though both protomers are able to bind substrate. Importantly, this nMS-based workflow offers a broadly applicable framework for resolving the catalytic states and inter-site communication of oligomeric enzymes that are otherwise difficult to uncover by conventional structural methods.