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Familial dilated cardiomyopathy (DCM) is frequently caused by autosomal dominant point mutations in genes involved in diverse cellular processes, including sarcomeric contraction. While patient studies have defined the genetic landscape of DCM, genetics are not currently used in patient care, and patients receive similar treatments regardless of the underlying mutation. It has been suggested that a precision medicine approach based on the molecular mechanism of the underlying mutation could improve outcomes; however, realizing this approach has been challenging due to difficulties linking genotype and phenotype and then leveraging this information to identify therapeutic approaches. Here, we used multiscale experimental and computational approaches to test whether knowledge of molecular mechanism could be harnessed to connect genotype, phenotype, and drug response for a DCM mutation in troponin T, deletion of K210. Previously, we showed that at the molecular scale, the mutation reduces thin filament activation. Here, we used computational modeling of this molecular defect to predict that the mutant will reduce cellular and tissue contractility, and we validated this prediction in human cardiomyocytes and engineered heart tissues. We then used our knowledge of molecular mechanism to computationally model the effects of a small molecule that can activate the thin filament. We demonstrate experimentally that the modeling correctly predicts that the small molecule can partially rescue systolic dysfunction at the expense of diastolic function. Taken together, our results demonstrate how molecular mechanism can be harnessed to connect genotype and phenotype and inspire strategies to optimize mechanism-based therapeutics for DCM., Competing Interests: Conflict of interest statement: All experiments were conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Skeletal muscle actin (ACTA1) mutations are a prevalent cause of skeletal myopathies consistent with ACTA1's high expression in skeletal muscle. Rare de novo mutations in ACTA1 associated with combined cardiac and skeletal myopathies have been reported, but ACTA1 represents only ~20% of the total actin pool in cardiomyocytes, making its role in cardiomyopathy controversial. Here we demonstrate how a mutation in an actin isoform expressed at low levels in cardiomyocytes can cause cardiomyopathy by focusing on a unique ACTA1 mutation, R256H. We previously identified this mutation in multiple family members with dilated cardiomyopathy (DCM), who had reduced systolic function without clinical skeletal myopathy. Using a battery of multiscale biophysical tools, we show that R256H has potent functional effects on ACTA1 function at the molecular scale and in human cardiomyocytes. Importantly, we demonstrate that R256H acts in a dominant manner, where the incorporation of small amounts of mutant protein into thin filaments is sufficient to disrupt molecular contractility, and that this effect is dependent on the presence of troponin and tropomyosin. To understand the structural basis of this change in regulation, we resolved a structure of R256H filaments using Cryo-EM, and we see alterations in actin's structure that have the potential to disrupt interactions with tropomyosin. Finally, we show that ACTA1 R256H/+ human induced pluripotent stem cell cardiomyocytes demonstrate reduced contractility and sarcomeric disorganization. Taken together, we demonstrate that R256H has multiple effects on ACTA1 function that are sufficient to cause reduced contractility and establish a likely causative relationship between ACTA1 R256H and clinical cardiomyopathy., Competing Interests: Competing Interest Statement: AG reports no disclosures. SJ reports no disclosures. RZ reports no disclosures. KJL reports consulting agreements from Implicit Biosciences, Cytokinetics, Medtronic, Kiniksa Pharmaceuticals, and SUN Pharmaceuticals. MJG reports no disclosures.

Understanding the mechanisms of cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short lifespan and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. In this study, by analyzing experimental data it was shown that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional chemical-mechanical model was developed and used to suggest and test hypothesized mechanisms of bud morphogenesis during aging. Experimentally calibrated simulations showed that tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip guided by the polarized Cdc42 signal during the early stage of budding. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage, as observed in experiments, through a reduction on the new cell surface material insertion or an expansion of the polarization site. Thus model simulations suggest the maintenance of new cell surface material insertion or chemical signal polarization could be weakened due to cellular aging in yeast and other cell types., Competing Interests: Competing Interests Statement: The authors declare no conflict of interest.

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is K V 11.1 (hERG), comprising the primary cardiac repolarizing current, I Kr . hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with myriad available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions aligning well with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as a novel method to predict discrete protein conformations and novel linkages from structure to function., Competing Interests: Ethics declarations Competing interests: The authors declare no competing interests.

In mammalian cells, the cohesin protein complex is believed to translocate along chromatin during interphase to form dynamic loops through a process called active loop extrusion. Chromosome conformation capture and imaging experiments have suggested that chromatin adopts a compact structure with limited interpenetration between chromosomes and between chromosomal sections. We developed a theory demonstrating that active loop extrusion causes the apparent fractal dimension of chromatin to cross over between two and four at contour lengths on the order of 30 kilo-base pairs (kbp). The anomalously high fractal dimension D = 4 is due to the inability of extruded loops to fully relax during active extrusion. Compaction on longer contour length scales extends within topologically associated domains (TADs), facilitating gene regulation by distal elements. Extrusion-induced compaction segregates TADs such that overlaps between TADs are reduced to less than 35% and increases the entanglement strand of chromatin by up to a factor of 50 to several Mega-base pairs. Furthermore, active loop extrusion couples cohesin motion to chromatin conformations formed by previously extruding cohesins and causes the mean square displacement of chromatin loci during lag times ( Δ t ) longer than tens of minutes to be proportional to Δ t 1 / 3 . We validate our results with hybrid molecular dynamics - Monte Carlo simulations and show that our theory is consistent with experimental data. This work provides a theoretical basis for the compact organization of interphase chromatin, explaining the physical reason for TAD segregation and suppression of chromatin entanglements which contribute to efficient gene regulation., Competing Interests: Competing Interest Statement: The authors declare no competing interests.

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically-determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus SARS-CoV-2, resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4-6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across the studied human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9-8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4-9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities, with implications for potential protein-binding modes and therapeutic targets., Competing Interests: Conflicts of interest: Stanford University is filing patent applications based on concepts described in this paper.

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances.

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 μm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae , confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine., Competing Interests: Competing Interest Statement: The authors declare no competing interests.

Replication fork reversal is a fundamental process required for resolution of encounters with DNA damage. A key step in the stabilization and eventual resolution of reversed forks is formation of RAD51 nucleoprotein filaments on exposed ssDNA. To avoid genome instability, RAD51 filaments are tightly controlled by a variety of positive and negative regulators. RADX is a recently discovered negative regulator that binds tightly to ssDNA, directly interacts with RAD51, and regulates replication fork reversal and stabilization in a context-dependent manner. Here we present a structure-based investigation of RADX's mechanism of action. Mass photometry experiments showed that RADX forms multiple oligomeric states in a concentration dependent manner, with a predominance of trimers in the presence of ssDNA. The structure of RADX, which has no structurally characterized orthologs, was determined ab initio by cryo-electron microscopy (EM) from maps in the 2-3 Å range. The structure reveals the molecular basis for RADX oligomerization and binding of ssDNA binding. The binding of RADX to RAD51 filaments was imaged by negative stain EM, which showed a RADX oligomer at the end of filaments. Based on these results, we propose a model in which RADX functions by capping and restricting the growing end of RAD51 filaments., Competing Interests: Competing interest statement: The authors declare that they have no conflicts of interest.

Cryptococcus neoformans is a fungal pathogen responsible for cryptococcosis and cryptococcal meningitis. The C. neoformans capsular polysaccharide and shed exopolysaccharide functions both as a key virulence factor and to protect the fungal cell from phagocytosis. Currently, a glycoconjugate of these polysaccharides is being explored as a vaccine to protect against C. neoformans infection. In this combined NMR and MD study, experimentally determined NOEs and J -couplings support a structure of the synthetic decasaccharide, GXM10-Ac 3 , obtained by MD. GXM10-Ac 3 was designed as an extension of glucuronoxylomannan (GXM) polysaccharide motif (M2) which is common in the clinically predominant serotype A strains and is recognized by protective forms of GXM-specific monoclonal antibodies. The M2 motif is characterized by a 6-residue α-mannan backbone repeating unit, consisting of a triad of α-(1→3)-mannoses, modified by β-(1→2)-xyloses on the first two mannoses and a β-(1→2)-glucuronic acid on the third mannose. The combined NMR and MD analyses reveal that GXM10-Ac 3 adopts an extended structure, with xylose/glucuronic acid branches alternating sides along the α-mannan backbone. O -acetyl esters also alternate sides and are grouped in pairs. MD analysis of a twelve M2-repeating unit polymer supports the notion that the GXM10-Ac 3 structure is uniformly represented throughout the polysaccharide. This experimentally consistent GXM model displays high flexibility while maintaining a structural identity, yielding new insights to further explore intermolecular interactions between polysaccharides, interactions with anti-GXM mAbs, and the cryptococcal polysaccharide architecture., Competing Interests: Competing Interest Statement: The authors declare no competing interest.

Significance Hematopoietic cell transplantation is the gold standard treatment for many blood disorders, including blood cancers. Nonetheless, frequent post-transplant complications limit the long-term benefit of HCT. Here, we find that circulating cell-free DNA is a highly versatile analyte for monitoring of the most important complications of HCT: Graft-Versus-Host Disease, infection, graft failure and disease relapse. We show that these different therapeutic complications can be informed from a single cell-free DNA sequencing assay followed by disease-specific bioinformatic analyses. This test requires only low coverage DNA sequencing and is compatible with small volumes of blood. Cell-free DNA may improve the care of allogeneic HCT recipients by enabling earlier detection and better prediction of the complex array of complications that occur after HCT., Allogeneic hematopoietic cell transplantation (HCT) provides effective treatment for hematologic malignancies and immune disorders. Monitoring of posttransplant complications is critical, yet current diagnostic options are limited. Here, we show that cell-free DNA (cfDNA) in blood is a versatile analyte for monitoring of the most important complications that occur after HCT: graft-versus-host disease (GVHD), a frequent immune complication of HCT, infection, relapse of underlying disease, and graft failure. We demonstrate that these therapeutic complications are informed from a single assay, low-coverage bisulfite sequencing of cfDNA, followed by disease-specific bioinformatic analyses. To inform GVHD, we profile cfDNA methylation marks to trace the cfDNA tissues-of-origin and to quantify tissue-specific injury. To inform infection, we implement metagenomic cfDNA profiling. To inform cancer relapse, we implement analyses of tumor-specific genomic aberrations. Finally, to detect graft failure, we quantify the proportion of donor- and recipient-specific cfDNA. We applied this assay to 170 plasma samples collected from 27 HCT recipients at predetermined timepoints before and after allogeneic HCT. We found that the abundance of solid-organ–derived cfDNA in the blood at 1 mo after HCT is predictive of acute GVHD (area under the curve, 0.88). Metagenomic profiling of cfDNA revealed the frequent occurrence of viral reactivation in this patient population. The fraction of donor-specific cfDNA was indicative of relapse and remission, and the fraction of tumor-specific cfDNA was informative of cancer relapse. This proof-of-principle study shows that cfDNA has the potential to improve the care of allogeneic HCT recipients by enabling earlier detection and better prediction of the complex array of complications that occur after HCT.

Significance A major mechanism of evolution involves fusing genes that encode single-domain proteins to create multidomain structures that achieve new functions. Here, we develop methods to design multidomain proteins entirely from scratch and achieve the premier de novo design of an allosterically regulated phenol oxidase that responds to the binding of a synthetic porphyrin., We describe the de novo design of an allosterically regulated protein, which comprises two tightly coupled domains. One domain is based on the DF (Due Ferri in Italian or two-iron in English) family of de novo proteins, which have a diiron cofactor that catalyzes a phenol oxidase reaction, while the second domain is based on PS1 (Porphyrin-binding Sequence), which binds a synthetic Zn-porphyrin (ZnP). The binding of ZnP to the original PS1 protein induces changes in structure and dynamics, which we expected to influence the catalytic rate of a fused DF domain when appropriately coupled. Both DF and PS1 are four-helix bundles, but they have distinct bundle architectures. To achieve tight coupling between the domains, they were connected by four helical linkers using a computational method to discover the most designable connections capable of spanning the two architectures. The resulting protein, DFP1 (Due Ferri Porphyrin), bound the two cofactors in the expected manner. The crystal structure of fully reconstituted DFP1 was also in excellent agreement with the design, and it showed the ZnP cofactor bound over 12 Å from the dimetal center. Next, a substrate-binding cleft leading to the diiron center was introduced into DFP1. The resulting protein acts as an allosterically modulated phenol oxidase. Its Michaelis–Menten parameters were strongly affected by the binding of ZnP, resulting in a fourfold tighter Km and a 7-fold decrease in kcat. These studies establish the feasibility of designing allosterically regulated catalytic proteins, entirely from scratch.

Biomolecular hydration is fundamental to biological functions. Using phase-resolved chiral sum-frequency generation spectroscopy (SFG), we probe molecular architectures and interactions of water molecules around a self-assembling antiparallel β-sheet protein. We find that the phase of the chiroptical response from the O-H stretching vibrational modes of water flips with the absolute chirality of the (l-) or (d-) antiparallel β-sheet. Therefore, we can conclude that the (d-) antiparallel β-sheet organizes water solvent into a chiral supermolecular structure with opposite handedness relative to that of the (l-) antiparallel β-sheet. We use molecular dynamics to characterize the chiral water superstructure at atomic resolution. The results show that the macroscopic chirality of antiparallel β-sheets breaks the symmetry of assemblies of surrounding water molecules. We also calculate the chiral SFG response of water surrounding (l-) and (d-) LK(7)β to confirm the presence of chiral water structures. Our results offer a different perspective as well as introduce experimental and computational methodologies for elucidating hydration of biomacromolecules. The findings imply potentially important but largely unexplored roles of water solvent in chiral selectivity of biomolecular interactions and the molecular origins of homochirality in the biological world.

Significance Necroptosis is an emergency controlled cell-death mechanism, which only may be activated in case of impaired apoptosis. Necroptosis leads to unchecked release of proinflammatory cytoplasmatic content of the cells and is thereby implicated in disease. Two covalent inhibitor classes targeting the same cysteine of the necroptosis effector protein mixed-lineage kinase domain-like protein (MLKL) are reported. Here, we unravel the mode of action of the xanthine class of inhibitors that work by stabilizing the inactive state of MLKL by an essential π–π stacking interaction. The widely used covalent tool compound Necrosulfonamide (NSA) employs a distinct mode of action., As an alternative pathway of controlled cell death, necroptosis can be triggered by tumor necrosis factor via the kinases RIPK1/RIPK3 and the effector protein mixed-lineage kinase domain-like protein (MLKL). Upon activation, MLKL oligomerizes and integrates into the plasma membrane via its executioner domain. Here, we present the X-ray and NMR costructures of the human MLKL executioner domain covalently bound via Cys86 to a xanthine class inhibitor. The structures reveal that the compound stabilizes the interaction between the auto-inhibitory brace helix α6 and the four-helix bundle by stacking to Phe148. An NMR-based functional assay observing the conformation of this helix showed that the F148A mutant is unresponsive to the compound, providing further evidence for the importance of this interaction. Real-time and diffusion NMR studies demonstrate that xanthine derivatives inhibit MLKL oligomerization. Finally, we show that the other well-known MLKL inhibitor Necrosulfonamide, which also covalently modifies Cys86, must employ a different mode of action.

Significance Liquid–liquid phase separation of proteins results in biomolecular condensates, which contribute to the organization of cellular matter into membraneless organelles. It is still unclear, however, whether these condensates represent a common state of proteins. Here, based on biophysical principles driving phase separation, we report a proteome-wide ranking of proteins according to their propensity to condensate into a droplet state. We analyze two mechanisms for droplet formation—driver proteins can spontaneously phase separate, while client proteins require additional components. We conclude that the droplet state, as the native and amyloid states, is a fundamental state of proteins, with most proteins expected to be capable of undergoing liquid–liquid phase separation via either of these two mechanisms., A wide range of proteins have been reported to condensate into a dense liquid phase, forming a reversible droplet state. Failure in the control of the droplet state can lead to the formation of the more stable amyloid state, which is often disease-related. These observations prompt the question of how many proteins can undergo liquid–liquid phase separation. Here, in order to address this problem, we discuss the biophysical principles underlying the droplet state of proteins by analyzing current evidence for droplet-driver and droplet-client proteins. Based on the concept that the droplet state is stabilized by the large conformational entropy associated with nonspecific side-chain interactions, we develop the FuzDrop method to predict droplet-promoting regions and proteins, which can spontaneously phase separate. We use this approach to carry out a proteome-level study to rank proteins according to their propensity to form the droplet state, spontaneously or via partner interactions. Our results lead to the conclusion that the droplet state could be, at least transiently, accessible to most proteins under conditions found in the cellular environment.

Significance Propelled agents that align their orientations upon collisions can give rise to macroscopic order. Depending on the details of the agents’ collisions, this ordering process can lead to spatiotemporal patterns, including traveling polar waves and nematic lanes. Recent experiments suggest that such patterns can also coexist and dynamically interconvert. Here we propose a general mechanism for such a phenomenology, which is based on patterned-induced local symmetry breaking. We show that macroscopic order in active-matter systems is linked to spatiotemporal density patterns, which in turn can lead to local symmetry breaking if the agent density exceeds the relevant threshold values. Our study sheds light on the principles of pattern formation and the emergence of symmetry in biological active-matter systems., The emergence of macroscopic order and patterns is a central paradigm in systems of (self-)propelled agents and a key component in the structuring of many biological systems. The relationships between the ordering process and the underlying microscopic interactions have been extensively explored both experimentally and theoretically. While emerging patterns often show one specific symmetry (e.g., nematic lane patterns or polarized traveling flocks), depending on the symmetry of the alignment interactions patterns with different symmetries can apparently coexist. Indeed, recent experiments with an actomysin motility assay suggest that polar and nematic patterns of actin filaments can interact and dynamically transform into each other. However, theoretical understanding of the mechanism responsible remains elusive. Here, we present a kinetic approach complemented by a hydrodynamic theory for agents with mixed alignment symmetries, which captures the experimentally observed phenomenology and provides a theoretical explanation for the coexistence and interaction of patterns with different symmetries. We show that local, pattern-induced symmetry breaking can account for dynamically coexisting patterns with different symmetries. Specifically, in a regime with moderate densities and a weak polar bias in the alignment interaction, nematic bands show a local symmetry-breaking instability within their high-density core region, which induces the formation of polar waves along the bands. These instabilities eventually result in a self-organized system of nematic bands and polar waves that dynamically transform into each other. Our study reveals a mutual feedback mechanism between pattern formation and local symmetry breaking in active matter that has interesting consequences for structure formation in biological systems.

Significance We have developed a fluorescent protein, mGreenLantern, that features exceptionally high brightness in mouse, bacterial, and human cells (up to sixfold brighter than EGFP) and have demonstrated its superior ability to highlight neuronal morphology compared to EGFP and EYFP. Screening fluorescent protein mutants based on whole-cell brightness while evaluating expression kinetics in lysate enabled us to identify variants exhibiting striking divergences between their computed spectroscopic brightness and actual performance in cells. mGreenLantern additionally features unusually high chemical and thermodynamic stability and is compatible with existing GFP filter sets, excitation sources, commercial EGFP antibodies, expansion microscopy, and whole-brain tissue clearing. Our hypothesis-driven engineering strategy represents a generalizable method with great potential to enhance the performance of constitutive reporters and GFP-based biosensors., Although ubiquitous in biological studies, the enhanced green and yellow fluorescent proteins (EGFP and EYFP) were not specifically optimized for neuroscience, and their underwhelming brightness and slow expression in brain tissue limits the fidelity of dendritic spine analysis and other indispensable techniques for studying neurodevelopment and plasticity. We hypothesized that EGFP’s low solubility in mammalian systems must limit the total fluorescence output of whole cells, and that improving folding efficiency could therefore translate into greater brightness of expressing neurons. By introducing rationally selected combinations of folding-enhancing mutations into GFP templates and screening for brightness and expression rate in human cells, we developed mGreenLantern, a fluorescent protein having up to sixfold greater brightness in cells than EGFP. mGreenLantern illuminates neurons in the mouse brain within 72 h, dramatically reducing lag time between viral transduction and imaging, while its high brightness improves detection of neuronal morphology using widefield, confocal, and two-photon microscopy. When virally expressed to projection neurons in vivo, mGreenLantern fluorescence developed four times faster than EYFP and highlighted long-range processes that were poorly detectable in EYFP-labeled cells. Additionally, mGreenLantern retains strong fluorescence after tissue clearing and expansion microscopy, thereby facilitating superresolution and whole-brain imaging without immunohistochemistry. mGreenLantern can directly replace EGFP/EYFP in diverse systems due to its compatibility with GFP filter sets, recognition by EGFP antibodies, and excellent performance in mouse, human, and bacterial cells. Our screening and rational engineering approach is broadly applicable and suggests that greater potential of fluorescent proteins, including biosensors, could be unlocked using a similar strategy.

Significance Membrane-embedded cation/proton antiporters (CPAs) exchange monovalent cations with protons by alternating between two extreme states: inward- and outward-facing. Although atomic-resolution structures are available, there is still an ongoing debate regarding these antiporters’ transport mechanism. Here, we use computer simulations to explore the dynamics of two bacterial antiporters along two axes of motion: a tilting movement and a vertical translation of the antiporters’ two functional domains. By analyzing these dynamic changes, rather than comparing the two extreme states that CPAs adopt, we determine that it is the tilting movement of the antiporters’ mobile domain that drives the relevant conformational changes. Finally, by applying the knowledge gained from these simulations, we predict an unknown outward-facing conformation and verify it experimentally., There is ongoing debate regarding the mechanism through which cation/proton antiporters (CPAs), like Thermus thermophilus NapA (TtNapA) and Escherichia coli NapA (EcNhaA), alternate between their outward- and inward-facing conformations in the membrane. CPAs comprise two domains, and it is unclear whether the transition is driven by their rocking-bundle or elevator motion with respect to each other. Here we address this question using metadynamics simulations of TtNapA, where we bias conformational sampling along two axes characterizing the two proposed mechanisms: angular and translational motions, respectively. By applying the bias potential for the two axes simultaneously, as well as to the angular, but not the translational, axis alone, we manage to reproduce each of the two known states of TtNapA when starting from the opposite state, in support of the rocking-bundle mechanism as the driver of conformational change. Next, starting from the inward-facing conformation of EcNhaA, we sample what could be its long-sought-after outward-facing conformation and verify it using cross-linking experiments.

Significance The replisome machine that duplicates the DNA genome encounters a variety of blocks to replication, such as DNA bound proteins, R-loops, and DNA lesions. Studies in bacterial systems demonstrate that replisome advance through blocks is facilitated by an accessory monomeric helicase that acts on the opposite strand from the replicative hexameric helicase. This report examines this issue in a eukaryotic system, using Pif1, a monomeric helicase that acts on the opposite strand from the replicative CMG helicase. The report shows that an inactive “dead” Cas9 (dCas9) protein R-loop block arrests the replisome, but Pif1 enables replisome bypass of the dCas9 R-loop block. Hence, use of monomeric helicases may have evolved to aid replisome bypass of protein-DNA and protein-bound R-loop blocks., Efficient and faithful replication of the genome is essential to maintain genome stability. Replication is carried out by a multiprotein complex called the replisome, which encounters numerous obstacles to its progression. Failure to bypass these obstacles results in genome instability and may facilitate errors leading to disease. Cells use accessory helicases that help the replisome bypass difficult barriers. All eukaryotes contain the accessory helicase Pif1, which tracks in a 5′–3′ direction on single-stranded DNA and plays a role in genome maintenance processes. Here, we reveal a previously unknown role for Pif1 in replication barrier bypass. We use an in vitro reconstituted Saccharomyces cerevisiae replisome to demonstrate that Pif1 enables the replisome to bypass an inactive (i.e., dead) Cas9 (dCas9) R-loop barrier. Interestingly, dCas9 R-loops targeted to either strand are bypassed with similar efficiency. Furthermore, we employed a single-molecule fluorescence visualization technique to show that Pif1 facilitates this bypass by enabling the simultaneous removal of the dCas9 protein and the R-loop. We propose that Pif1 is a general displacement helicase for replication bypass of both R-loops and protein blocks.

Significance Polycomb repressive complex 2 (PRC2) is a major epigenetic machinery that maintains transcriptionally silent heterochromatin in the nucleus and plays critical roles in embryonic development and oncogenesis. It is generally thought that PRC2 propagates repressive histone marks by modifying neighboring nucleosomes in strictly linear progression. However, the behavior of PRC2 on native-like chromatin substrates remains incompletely characterized, making the precise mechanism of PRC2-mediated heterochromatin maintenance elusive. Here we use single-molecule force spectroscopy and computational modeling to dissect the interactions between PRC2 and polynucleosome arrays. Our results provide direct evidence that PRC2 can simultaneously engage nonadjacent nucleosome pairs. The demonstration of PRC2's ability to bridge noncontiguous chromosomal segments furthers our understanding of how Polycomb complexes spread epigenetic modifications and compact chromatin., Polycomb repressive complex 2 (PRC2) installs and spreads repressive histone methylation marks on eukaryotic chromosomes. Because of the key roles that PRC2 plays in development and disease, how this epigenetic machinery interacts with DNA and nucleosomes is of major interest. Nonetheless, the mechanism by which PRC2 engages with native-like chromatin remains incompletely understood. In this work, we employ single-molecule force spectroscopy and molecular dynamics simulations to dissect the behavior of PRC2 on polynucleosome arrays. Our results reveal an unexpectedly diverse repertoire of PRC2 binding configurations on chromatin. Besides reproducing known binding modes in which PRC2 interacts with bare DNA, mononucleosomes, and adjacent nucleosome pairs, our data also provide direct evidence that PRC2 can bridge pairs of distal nucleosomes. In particular, the “1–3” bridging mode, in which PRC2 engages two nucleosomes separated by one spacer nucleosome, is a preferred low-energy configuration. Moreover, we show that the distribution and stability of different PRC2–chromatin interaction modes are modulated by accessory subunits, oncogenic histone mutations, and the methylation state of chromatin. Overall, these findings have implications for the mechanism by which PRC2 spreads histone modifications and compacts chromatin. The experimental and computational platforms developed here provide a framework for understanding the molecular basis of epigenetic maintenance mediated by Polycomb-group proteins.

Significance A central question in neuroscience concerns how humans and animals trade off multiple decision-making strategies. Another question pertains to the use of egocentric and allocentric strategies during navigation. We introduce reinforcement-learning models based on learning to predict future reward directly from states and actions or via learning to predict future “successor” states, choosing actions from either system based on the reliability of its predictions. We show that this model explains behavior on both spatial and nonspatial decision tasks, and we map the two model components onto the function of the dorsal hippocampus and the dorsolateral striatum, thereby unifying findings from the spatial-navigation and decision-making fields., Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus–response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In “response learning,” associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In “place learning,” associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a “successor representation” of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to “blocking” by prior state–reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal–striatal system as a general system for decision making via adaptive combination of stimulus–response learning and the use of a cognitive map.

Rie Koga, Mami Yamamoto, Takahiro Kosugi, Naohiro Kobayashi, Toshihiko Sugiki, Toshimichi Fujiwara, Nobuyasu Koga, Robust folding of a de novo designed ideal protein even with most of the core mutated to valine. Proceedings of the National Academy of Sciences. 117 (49), 31149-31156 (2020). DOI: 10.1073/pnas.2002120117., Protein design provides a stringent test for our understanding of protein folding. We previously described principles for designing ideal protein structures stabilized by consistent local and nonlocal interactions, based on a set of rules relating local backbone structures to tertiary packing motifs. The principles have made possible the design of protein structures having various topologies with high thermal stability. Whereas nonlocal interactions such as tight hydrophobic core packing have traditionally been considered to be crucial for protein folding and stability, the rules proposed by our previous studies suggest the importance of local backbone structures to protein folding. In this study, we investigated the robustness of folding of de novo designed proteins to the reduction of the hydrophobic core, by extensive mutation of large hydrophobic residues (Leu, Ile) to smaller ones (Val) for one of the designs. Surprisingly, even after 10 Leu and Ile residues were mutated to Val, this mutant with the core mostly filled with Val was found to not be in a molten globule state and fold into the same backbone structure as the original design, with high stability. These results indicate the importance of local backbone structures to the folding ability and high thermal stability of designed proteins and suggest a method for engineering thermally stabilized natural proteins.

Significance Circadian rhythms including wake-sleep cycles are driven by molecular time cues generated by a self-sustaining transcriptional negative feedback loop. Among all clock proteins, PERIOD (PER) is considered the pacemaker protein because its rhythm of accumulation and nuclear entry generates the timing and duration of feedback inhibition. Here we provide a new understanding of how robust PER rhythms are generated: the collective action of interacting PER molecules, not a random mass action of individual molecules, allows compensation of spatial and temporal differences (or “noise”) of individual molecules. We also show that the collective PER rhythm requires healthy cytoplasmic trafficking, and that circadian sleep disorders can arise in such conditions as obesity, aging, and neurodegenerative disorders in which the cytoplasm becomes congested., The circadian clock is based on a transcriptional feedback loop with an essential time delay before feedback inhibition. Previous work has shown that PERIOD (PER) proteins generate circadian time cues through rhythmic nuclear accumulation of the inhibitor complex and subsequent interaction with the activator complex in the feedback loop. Although this temporal manifestation of the feedback inhibition is the direct consequence of PER’s cytoplasmic trafficking before nuclear entry, how this spatial regulation of the pacemaker affects circadian timing has been largely unexplored. Here we show that circadian rhythms, including wake-sleep cycles, are lengthened and severely unstable if the cytoplasmic trafficking of PER is disrupted by any disease condition that leads to increased congestion in the cytoplasm. Furthermore, we found that the time delay and robustness in the circadian clock are seamlessly generated by delayed and collective phosphorylation of PER molecules, followed by synchronous nuclear entry. These results provide clear mechanistic insight into why circadian and sleep disorders arise in such clinical conditions as metabolic and neurodegenerative diseases and aging, in which the cytoplasm is congested.

Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia, Berlinia, and Anthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies.

Significance Natural systems (e.g., cells and ecosystems) generally consist of reaction networks (e.g., metabolic networks or food webs) with nonlinear flux functions (e.g., Michaelis–Menten kinetics and density-dependent selection). Despite their complex nonlinearities, these systems often exhibit simple exponential growth in the long term. How exponential growth emerges from nonlinear networks remains elusive. Our work demonstrates mathematically how two principles, multivariate scalability of flux functions and ergodicity of the rescaled system, guarantee a well-defined growth rate. By connecting ergodic theory, a powerful branch of mathematics, to the study of growth in biology, our theoretical framework can recapitulate various growth modalities (from balanced growth to periodic, quasi-periodic, or even chaotic behaviors), greatly expanding the types of growing systems that can be studied., Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. Here, we describe a general theoretical framework that reveals underlying principles of long-term growth: scalability of flux functions and ergodicity of the rescaled systems. Our theory shows that nonlinear fluxes can generate not only balanced growth but also oscillatory or chaotic growth modalities, explaining nonequilibrium dynamics observed in cell cycles and ecosystems. Our mathematical framework is broadly useful in predicting long-term growth rates from natural and synthetic networks, analyzing the effects of system noise and perturbations, validating empirical and phenomenological laws on growth rate, and studying autocatalysis and network evolution.

Significance Liquid–liquid phase separation causes formation of membraneless condensates containing concentrated biomolecules, which play essential roles in diverse cellular processes. Currently, micrometer-scale phase-separated condensates are examined routinely to elucidate their functions and mechanisms in details. However, limited by current commonly used methods, the transition process from miscible individual molecules to micrometer-scale condensates is mostly unknown. Herein, we captured formation of nanoscale condensates, whose size, growth rate, molecular stoichiometry, and binding affinity to recruit client molecules were all quantified. To achieve this, we developed a dual-color fluorescence cross-correlation spectroscopy (dcFCCS) method, and we expect that dcFCCS can be widely applied to investigate phase separation of other biomolecules at the nanoscale., Liquid–liquid phase separation, driven by multivalent macromolecular interactions, causes formation of membraneless compartments, which are biomolecular condensates containing concentrated macromolecules. These condensates are essential in diverse cellular processes. Formation and dynamics of micrometer-scale phase-separated condensates are examined routinely. However, limited by commonly used methods which cannot capture small-sized free-diffusing condensates, the transition process from miscible individual molecules to micrometer-scale condensates is mostly unknown. Herein, with a dual-color fluorescence cross-correlation spectroscopy (dcFCCS) method, we captured formation of nanoscale condensates beyond the detection limit of conventional fluorescence microscopy. In addition, dcFCCS is able to quantify size and growth rate of condensates as well as molecular stoichiometry and binding affinity of client molecules within condensates. The critical concentration to form nanoscale condensates, identified by our experimental measurements and Monte Carlo simulations, is at least several fold lower than the detection limit of conventional fluorescence microscopy. Our results emphasize that, in addition to micrometer-scale condensates, nanoscale condensates are likely to play important roles in various cellular processes and dcFCCS is a simple and powerful quantitative tool to examine them in detail.

Significance The desmosome is a major cell–cell junction connecting cells in tissues under high mechanical load. Currently, while structures of the constituent cadherins are known, the desmosome architecture has remained elusive. The primary reason is the high plasticity of the cadherins. As many other cellular structures, their high flexibility cannot be easily addressed by conventional structural techniques that rely on averaging many identical structures. For this, we combine high-end cryo-electron tomography with large-scale molecular dynamics simulations to produce a molecular model of the desmosome that integrates new with decades-old observations, accounts for the remarkable biophysical properties, and maps the intermolecular interactions., Desmosomes are cell–cell junctions that link tissue cells experiencing intense mechanical stress. Although the structure of the desmosomal cadherins is known, the desmosome architecture—which is essential for mediating numerous functions—remains elusive. Here, we recorded cryo-electron tomograms (cryo-ET) in which individual cadherins can be discerned; they appear variable in shape, spacing, and tilt with respect to the membrane. The resulting sub-tomogram average reaches a resolution of ∼26 Å, limited by the inherent flexibility of desmosomes. To address this challenge typical of dynamic biological assemblies, we combine sub-tomogram averaging with atomistic molecular dynamics (MD) simulations. We generate models of possible cadherin arrangements and perform an in silico screening according to biophysical and structural properties extracted from MD simulation trajectories. We find a truss-like arrangement of cadherins that resembles the characteristic footprint seen in the electron micrograph. The resulting model of the desmosomal architecture explains their unique biophysical properties and strength.

Significance Spatial patterns are a prominent and interesting feature of both biological and physical systems. We have discovered that mammalian mitotic chromosomes, which comprise two closely associated sister chromatids, exhibit two interesting spatial patterns. In one pattern, the structural axis of each chromatid acquires sequential partial helices of alternating handedness. In the other, an array of evenly spaced bridges links these two axes. Development of any spatial pattern requires communication within the system. We present a constellation of observations suggesting that these patterns are related and are promoted by a mechanical mechanism in which the communication required for development of patterns arises from redistribution of mechanical stress. Models involving bonded chromatin/axis bilayers and Kirchhoff–Love theory for elastic rods are discussed., Spatial patterns are ubiquitous in both physical and biological systems. We have recently discovered that mitotic chromosomes sequentially acquire two interesting morphological patterns along their structural axes [L. Chu et al., Mol. Cell, 10.1016/j.molcel.2020.07.002 (2020)]. First, axes of closely conjoined sister chromosomes acquire regular undulations comprising nearly planar arrays of sequential half-helices of similar size and alternating handedness, accompanied by periodic kinks. This pattern, which persists through all later stages, provides a case of the geometric form known as a “perversion.” Next, as sister chromosomes become distinct parallel units, their individual axes become linked by bridges, which are themselves miniature axes. These bridges are dramatically evenly spaced. Together, these effects comprise a unique instance of spatial patterning in a subcellular biological system. We present evidence that axis undulations and bridge arrays arise by a single continuous mechanically promoted progression, driven by stress within the chromosome axes. We further suggest that, after sister individualization, this same stress also promotes chromosome compaction by rendering the axes susceptible to the requisite molecular remodeling. Thus, by this scenario, the continuous presence of mechanical stress within the chromosome axes could potentially underlie the entire morphogenetic chromosomal program. Direct analogies with meiotic chromosomes suggest that the same effects could underlie interactions between homologous chromosomes as required for gametogenesis. Possible mechanical bases for generation of axis stress and resultant deformations are discussed. Together, these findings provide a perspective on the macroscopic changes of organized chromosomes.

Significance Animals frequently need to choose the best alternative from a set of possibilities, whether it is which direction to swim in or which food source to favor. How long should a network of neurons take to choose the best of N options? Theoretical results suggest that the optimal time grows as log(N), if the values of each option are imperfectly perceived. However, standard self-terminating neural network models of decision-making cannot achieve this optimal behavior. We show how using certain additional nonlinear response properties in neurons, which are ignored in standard models, results in a decision-making architecture that both achieves the optimal scaling of decision time and accounts for multiple experimentally observed features of neural decision-making., An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes ∼N⁡log(N) time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

Significance In breast cancer, the chronically stiffening mechanical microenvironment promotes tumor growth/invasion. However, the molecular details and integration of cancer mechanotransduction signaling pathways are not well understood. We find that nontumorigenic breast epithelial cells are mechanically sensitive and respond to acute mechanical stimuli with the generation of ROS-stimulated calcium signaling in a microtubule-dependent manner. These results establish that epithelial cells conserve a mechanotransduction pathway from muscle and bone (X-ROS). Using common breast cancer genetic mutations, we further report that oncogene activation reduces X-ROS pathway mechanoresponsiveness at the level of ROS sensitivity. This definition of how oncogene activation disrupts highly conserved mechanotransduction signaling mechanisms could improve the understanding of altered tumor cell responses to the mechanical microenvironment and reveal new therapeutic opportunities., Changes in the mechanical microenvironment and mechanical signals are observed during tumor progression, malignant transformation, and metastasis. In this context, understanding the molecular details of mechanotransduction signaling may provide unique therapeutic targets. Here, we report that normal breast epithelial cells are mechanically sensitive, responding to transient mechanical stimuli through a two-part calcium signaling mechanism. We observed an immediate, robust rise in intracellular calcium (within seconds) followed by a persistent extracellular calcium influx (up to 30 min). This persistent calcium was sustained via microtubule-dependent mechanoactivation of NADPH oxidase 2 (NOX2)-generated reactive oxygen species (ROS), which acted on transient receptor potential cation channel subfamily M member 8 (TRPM8) channels to prolong calcium signaling. In contrast, the introduction of a constitutively active oncogenic KRas mutation inhibited the magnitude of initial calcium signaling and severely blunted persistent calcium influx. The identification that oncogenic KRas suppresses mechanically-induced calcium at the level of ROS provides a mechanism for how KRas could alter cell responses to tumor microenvironment mechanics and may reveal chemotherapeutic targets for cancer. Moreover, we find that expression changes in both NOX2 and TRPM8 mRNA predict poor clinical outcome in estrogen receptor (ER)-negative breast cancer patients, a population with limited available treatment options. The clinical and mechanistic data demonstrating disruption of this mechanically-activated calcium pathway in breast cancer patients and by KRas activation reveal signaling alterations that could influence cancer cell responses to the tumor mechanical microenvironment and impact patient survival.

Significance The World Health Organization has declared antimicrobial resistance one of the greatest threats to human health. Understanding the molecular mechanisms by which bacteria resist the effects of antibiotic therapies is central to understanding antimicrobial resistance and to informing the development of new treatments that can circumvent these resistance mechanisms. This study reveals the molecular details of the mechanism of FusB-mediated resistance to fusidic acid, an important clinical treatment for Staphylococcus aureus infections, including methicillin-resistant Staphylococcus aureus. Here we elucidate an antibiotic resistance mechanism driven by an allosteric effect on the conformational flexibility of the drug target., Antibiotic resistance in clinically important bacteria can be mediated by target protection mechanisms, whereby a protein binds to the drug target and protects it from the inhibitory effects of the antibiotic. The most prevalent source of clinical resistance to the antibiotic fusidic acid (FA) is expression of the FusB family of proteins that bind to the drug target (Elongation factor G [EF-G]) and promote dissociation of EF-G from FA-stalled ribosome complexes. FusB binding causes changes in both the structure and conformational flexibility of EF-G, but which of these changes drives FA resistance was not understood. We present here detailed characterization of changes in the conformational flexibility of EF-G in response to FusB binding and show that these changes are responsible for conferring FA resistance. Binding of FusB to EF-G causes a significant change in the dynamics of domain III of EF-GC3 that leads to an increase in a minor, more disordered state of EF-G domain III. This is sufficient to overcome the steric block of transmission of conformational changes within EF-G by which FA prevents release of EF-G from the ribosome. This study has identified an antibiotic resistance mechanism mediated by allosteric effects on the dynamics of the drug target.

Significance When sound vibrations reach the inner ear, fine protein filaments called “tip links” stretch and open cochlear hair-cell mechanosensitive channels that trigger sensory perception. Similarly, vestibular hair cells use tip links to sense mechanical stimuli produced by head motions. Tip links are formed by cadherin-23 and protocadherin-15, two large proteins involved in hearing loss and balance disorders. Here we present multiple structures, models, and simulations that depict the lower end of the tip link, including the complete protocadherin-15 ectodomain. These models show an essential connection between cadherin-23 and protocadherin-15 with dual molecular “handshakes” and various protein sites that are mutated in inherited deafness. The simulations also reveal how the tip link responds to force to mediate hearing and balance sensing., The vertebrate inner ear, responsible for hearing and balance, is able to sense minute mechanical stimuli originating from an extraordinarily broad range of sound frequencies and intensities or from head movements. Integral to these processes is the tip-link protein complex, which conveys force to open the inner-ear transduction channels that mediate sensory perception. Protocadherin-15 and cadherin-23, two atypically large cadherins with 11 and 27 extracellular cadherin (EC) repeats, are involved in deafness and balance disorders and assemble as parallel homodimers that interact to form the tip link. Here we report the X-ray crystal structure of a protocadherin-15 + cadherin-23 heterotetrameric complex at 2.9-Å resolution, depicting a parallel homodimer of protocadherin-15 EC1-3 molecules forming an antiparallel complex with two cadherin-23 EC1-2 molecules. In addition, we report structures for 10 protocadherin-15 fragments used to build complete high-resolution models of the monomeric protocadherin-15 ectodomain. Molecular dynamics simulations and validated crystal contacts are used to propose models for the complete extracellular protocadherin-15 parallel homodimer and the tip-link bond. Steered molecular dynamics simulations of these models suggest conditions in which a structurally diverse and multimodal protocadherin-15 ectodomain can act as a stiff or soft gating spring. These results reveal the structural determinants of tip-link–mediated inner-ear sensory perception and elucidate protocadherin-15’s structural and adhesive properties relevant in disease.

Significance We present a theory of neural circuits’ design and function, inspired by the random connectivity of real neural circuits and the mathematical power of random projections. Specifically, we introduce a family of statistical models for large neural population codes, a straightforward neural circuit architecture that would implement these models, and a biologically plausible learning rule for such circuits. The resulting neural architecture suggests a design principle for neural circuit—namely, that they learn to compute the mathematical surprise of their inputs, given past inputs, without an explicit teaching signal. We applied these models to recordings from large neural populations in monkeys’ visual and prefrontal cortices and show them to be highly accurate, efficient, and scalable., The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

Significance A hyperinflammatory syndrome reminiscent of toxic shock syndrome (TSS) is observed in severe COVID-19 patients, including children with Multisystem Inflammatory Syndrome in Children (MIS-C). TSS is typically caused by pathogenic superantigens stimulating excessive activation of the adaptive immune system. We show that SARS-CoV-2 spike contains sequence and structure motifs highly similar to those of a bacterial superantigen and may directly bind T cell receptors. We further report a skewed T cell receptor repertoire in COVID-19 patients with severe hyperinflammation, in support of such a superantigenic effect. Notably, the superantigen-like motif is not present in other SARS family coronaviruses, which may explain the unique potential for SARS-CoV-2 to cause both MIS-C and the cytokine storm observed in adult COVID-19., Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 is a newly recognized condition in children with recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. These children and adult patients with severe hyperinflammation present with a constellation of symptoms that strongly resemble toxic shock syndrome, an escalation of the cytotoxic adaptive immune response triggered upon the binding of pathogenic superantigens to T cell receptors (TCRs) and/or major histocompatibility complex class II (MHCII) molecules. Here, using structure-based computational models, we demonstrate that the SARS-CoV-2 spike (S) glycoprotein exhibits a high-affinity motif for binding TCRs, and may form a ternary complex with MHCII. The binding epitope on S harbors a sequence motif unique to SARS-CoV-2 (not present in other SARS-related coronaviruses), which is highly similar in both sequence and structure to the bacterial superantigen staphylococcal enterotoxin B. This interaction between the virus and human T cells could be strengthened by a rare mutation (D839Y/N/E) from a European strain of SARS-CoV-2. Furthermore, the interfacial region includes selected residues from an intercellular adhesion molecule (ICAM)-like motif shared between the SARS viruses from the 2003 and 2019 pandemics. A neurotoxin-like sequence motif on the receptor-binding domain also exhibits a high tendency to bind TCRs. Analysis of the TCR repertoire in adult COVID-19 patients demonstrates that those with severe hyperinflammatory disease exhibit TCR skewing consistent with superantigen activation. These data suggest that SARS-CoV-2 S may act as a superantigen to trigger the development of MIS-C as well as cytokine storm in adult COVID-19 patients, with important implications for the development of therapeutic approaches.

Significance Nanotechnology and neurobiology combined efforts might succeed in the design of hybrid microsystems that, once functionally integrated into the nervous tissue, might help in healing the injured spinal cord. A substantial challenge in this area is the development of structural scaffolds favoring spinal cord reconstruction. The future success of such smart devices resides also in the use of nanomaterials exploiting spinal microenvironment physical properties, such as mechanical and electrical ones, and their potential in promoting axonal regeneration. We synthesized an artificial scaffold based on nanomaterials with the necessary characteristics to guide axonal rewiring. The translational potential of introducing physics rules to neural tissue repair strategies was tested by implanting such a scaffold in spinal cord injury animal models., The regrowth of severed axons is fundamental to reestablish motor control after spinal-cord injury (SCI). Ongoing efforts to promote axonal regeneration after SCI have involved multiple strategies that have been only partially successful. Our study introduces an artificial carbon-nanotube based scaffold that, once implanted in SCI rats, improves motor function recovery. Confocal microscopy analysis plus fiber tracking by magnetic resonance imaging and neurotracer labeling of long-distance corticospinal axons suggest that recovery might be partly attributable to successful crossing of the lesion site by regenerating fibers. Since manipulating SCI microenvironment properties, such as mechanical and electrical ones, may promote biological responses, we propose this artificial scaffold as a prototype to exploit the physics governing spinal regenerative plasticity.

Significance Many biochemical processes in living systems take place in compartmentalized environments, where individual compartments can interact with each other and undergo dynamic remodeling. Studying such processes through mathematical models poses formidable challenges because the underlying dynamics involve a large number of states, which evolve stochastically with time. Here we propose a mathematical framework to study stochastic biochemical networks in compartmentalized environments. We develop a generic population model, which tracks individual compartments and their molecular composition. We then show how the time evolution of this system can be studied effectively through a small number of differential equations, which track the statistics of the population. Our approach is versatile and renders an important class of biological systems computationally accessible., Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.

Significance The selectivity of binding of colloidal particles is an important research topic for the field of targeted drug delivery. Extensive theoretical work has shown that high selectivity can be obtained by using multivalent weak interactions. Here we provide comprehensive experimental proof using DNA-coated particles. The ligand–receptor affinity is varied by changing the number of complementary bases, showing that fewer complementary bases yield a higher binding selectivity. The experimental data and corresponding numerical model simulations demonstrate the scaling behavior between molecular density, molecular affinity, and resulting density selectivity of interparticle binding. These results are important for the design of novel systems for targeted nanoparticle drug delivery., Targeted drug delivery critically depends on the binding selectivity of cargo-transporting colloidal particles. Extensive theoretical work has shown that two factors are necessary to achieve high selectivity for a threshold receptor density: multivalency and weak interactions. Here, we study a model system of DNA-coated particles with multivalent and weak interactions that mimics ligand–receptor interactions between particles and cells. Using an optomagnetic cluster experiment, particle aggregation rates are measured as a function of ligand and receptor densities. The measured aggregation rates show that the binding becomes more selective for shorter DNA ligand–receptor pairs, proving that multivalent weak interactions lead to enhanced selectivity in interparticle binding. Simulations confirm the experimental findings and show the role of ligand–receptor dissociation in the selectivity of the weak multivalent binding.

Significance Mammalian hearing requires outer hair cells for amplification and tuning in the cochlea. The amplification process works at frequencies at least 10 times higher than might be expected based on electrical properties of the cells. The present report demonstrates how protein-dependent membrane piezoelectricity underlies high-frequency function, and why power output is maximum at frequencies much higher than would be predicted based on traditional experimental measurements. The interplay between electrical charge displacement and mechanical strain in the membrane motor is key. The same biophysical principles identify the origins of infrared laser-induced capacitive currents reported previously in hair cells, HEK cells, and neurons., Cochlear outer hair cells (OHCs) are among the fastest known biological motors and are essential for high-frequency hearing in mammals. It is commonly hypothesized that OHCs amplify vibrations in the cochlea through cycle-by-cycle changes in length, but recent data suggest OHCs are low-pass filtered and unable to follow high-frequency signals. The fact that OHCs are required for high-frequency hearing but appear to be throttled by slow electromotility is the “OHC speed paradox.” The present report resolves this paradox and reveals origins of ultrafast OHC function and power output in the context of the cochlear load. Results demonstrate that the speed of electromotility reflects how fast the cell can extend against the load, and does not reflect the intrinsic speed of the motor element itself or the nearly instantaneous speed at which the coulomb force is transmitted. OHC power output at auditory frequencies is revealed by emergence of an imaginary nonlinear capacitance reflecting the phase of electrical charge displacement required for the motor to overcome the viscous cochlear load.

Significance We developed a strategy to avoid human subjectivity by assessing the quality of red blood cells using imaging flow cytometry and deep learning. We successfully automated traditional expert assessment by training a computer with example images of healthy and unhealthy morphologies. However, we noticed that experts disagree on ∼18% of cells, so instead of relying on experts’ visual assessment, we taught a deep-learning network the degradation phenotypes objectively from images of red blood cells sampled over time. Although training with diverse samples is needed to create and validate a clinical-grade model, doing so would eliminate subjective assessment and facilitate research. The time-based deep-learning strategy may also prove useful for other biological progressions, such as development and disease progression., Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

The early steps of DNA double-strand break (DSB) repair in human cells involve the MRE11-RAD50-NBS1 (MRN) complex and its cofactor, phosphorylated CtIP. The roles of these proteins in nucleolytic DSB resection are well characterized, but their role in bridging the DNA ends for efficient and correct repair is much less explored. Here we study the binding of phosphorylated CtIP, which promotes the endonuclease activity of MRN, to single long (∼50 kb) DNA molecules using nanofluidic channels and compare it to the yeast homolog Sae2. CtIP bridges DNA in a manner that depends on the oligomeric state of the protein, and truncated mutants demonstrate that the bridging depends on CtIP regions distinct from those that stimulate the nuclease activity of MRN. Sae2 is a much smaller protein than CtIP, and its bridging is significantly less efficient. Our results demonstrate that the nuclease cofactor and structural functions of CtIP may depend on the same protein population, which may be crucial for CtIP functions in both homologous recombination and microhomology-mediated end-joining., Proceedings of the National Academy of Sciences of the United States of America, 117 (35), ISSN:0027-8424, ISSN:1091-6490

Significance Here we use the expression and accessibility data from a diverse set of cell types to learn a model for the dependence of the accessibility of a regulatory element on its DNA sequence and TF expression. Using GTEx samples with WGS data, we show that the noncoding variants predicted to affect accessibility are more strongly associated with the expression of nearby genes. To interpret a personal genome, we combine the sequence information with context-specific TF expression to prioritize variants and regulatory elements in any genomic region of interest. This approach should be helpful in the study of risk loci previously identified by GWAS. Results from analysis of height and WGS data from the GTEx project support this hypothesis., A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person’s phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference context-specific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others.

Significance Cytometry technologies are able to profile immune cells at single-cell resolution. They are widely used for both clinical diagnosis and biological research. We developed a deep learning model for analyzing cytometry data. We demonstrated that the deep learning model accurately diagnoses the latent cytomegalovirus (CMV) in healthy individuals. In addition, we developed a method for interpreting the deep learning model, allowing us to identify biomarkers associated with latent CMV infection. The deep learning model is widely applicable to other cytometry data related to human diseases., Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).

Significance Reengineering naturally occurring proteins to have new functions has had considerable impact on industrial and biomedical applications, but is limited by the finite number of known proteins. A promise of de novo protein design is to generate a larger and more diverse set of protein structures than is currently available. This vision has not yet been realized for small-molecule binder or enzyme design due to the complexity of pocket-containing structures. Here we present an algorithm that systematically generates NTF2-like protein structures with diverse pocket geometries. The scaffold sets, the insights gained from detailed structural characterization, and the computational method for generating unlimited numbers of structures should contribute to a new generation of de novo small-molecule binding proteins and catalysts., To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

Significance Creation of a chromosomal double-strand break (DSB) is rapidly accompanied by extensive phosphorylation of yeast histone H2A isoform (H2AX in mammals) by Mec1 (ATR) and Tel1 (ATM) protein kinases. This phosphorylation, termed γ-H2AX, spreads 50 kb on either of the DSB, but how this phosphorylation is propagated is poorly understood. We have monitored the kinetics and extent of spreading individually for Mec1 and Tel1 and find that the patterns of spreading are significantly different for the two kinases. By comparing these experimental data to mathematical models of spreading, either by one-dimensional (1D) or 3D diffusion, we report that Bayesian model selections suggest Tel1 acts by directed motion along a chromatin fiber, whereas Mec1 primarily uses a 3D mode of propagation., One of the hallmarks of DNA damage is the rapid spreading of phosphorylated histone H2A (γ-H2AX) around a DNA double-strand break (DSB). In the budding yeast Saccharomyces cerevisiae, nearly all H2A isoforms can be phosphorylated, either by Mec1ATR or Tel1ATM checkpoint kinases. We induced a site-specific DSB with HO endonuclease at the MAT locus on chromosome III and monitored the formation of γ-H2AX by chromatin immunoprecipitation (ChIP)-qPCR in order to uncover the mechanisms by which Mec1ATR and Tel1ATM propagate histone modifications across chromatin. With either kinase, γ-H2AX spreads as far as ∼50 kb on both sides of the lesion within 1 h; but the kinetics and distribution of modification around the DSB are significantly different. The total accumulation of phosphorylation is reduced by about half when either of the two H2A genes is mutated to the nonphosphorylatable S129A allele. Mec1 activity is limited by the abundance of its ATRIP partner, Ddc2. Moreover, Mec1 is more efficient than Tel1 at phosphorylating chromatin in trans—at distant undamaged sites that are brought into physical proximity to the DSB. We compared experimental data to mathematical models of spreading mechanisms to determine whether the kinases search for target nucleosomes by primarily moving in three dimensions through the nucleoplasm or in one dimension along the chromatin. Bayesian model selection indicates that Mec1 primarily uses a three-dimensional diffusive mechanism, whereas Tel1 undergoes directed motion along the chromatin.

Significance For many countries a plain-eye inspection of the COVID-19 infection curves reveals a remarkable linear growth over extended time periods. This observation is practically impossible to understand with traditional epidemiological models. These, to make them expressible in compact mathematical form, typically ignore the structure of real contact networks that are essential in the characteristic spreading dynamics of COVID-19. Here we show that by properly taking some relevant network features into account, linear growth can be naturally explained. Further, the effect of nonpharmaceutical interventions (NPIs), like national lockdowns, can be modeled with a remarkable degree of precision without fitting or fine-tuning of parameters., Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, Dc, below which linear growth and low infection prevalence must occur. Above Dc traditional epidemiological dynamics take place, e.g., as in susceptible–infected–recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find Dc∼7.2. Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.

Significance A specific ATP-binding cassette (ABC) transporter is generally viewed to function as either an exporter or an importer, but in principle ABC transporters can transport substrates in both directions across the membrane. Structural studies of the prokaryotic ABC exporter NaAtm1 demonstrate that progression through the transport cycle is accompanied by changes in transmembrane helix 6 (TM6) that modulate the binding cavity for transported substrate. Significantly, kinking of TM6 in a post-ATP hydrolysis state stabilized by MgADPVO4 eliminates the substrate-binding cavity. The presence of this cavity during the transition from the inward-facing to outward-facing conformational states, and its absence in the reverse direction, thereby provide an elegant and conceptually simple mechanism for enforcing the export directionality of transport., The ATP-binding cassette (ABC) transporter of mitochondria (Atm1) mediates iron homeostasis in eukaryotes, while the prokaryotic homolog from Novosphingobium aromaticivorans (NaAtm1) can export glutathione derivatives and confer protection against heavy-metal toxicity. To establish the structural framework underlying the NaAtm1 transport mechanism, we determined eight structures by X-ray crystallography and single-particle cryo-electron microscopy in distinct conformational states, stabilized by individual disulfide crosslinks and nucleotides. As NaAtm1 progresses through the transport cycle, conformational changes in transmembrane helix 6 (TM6) alter the glutathione-binding site and the associated substrate-binding cavity. Significantly, kinking of TM6 in the post-ATP hydrolysis state stabilized by MgADPVO4 eliminates this cavity, precluding uptake of glutathione derivatives. The presence of this cavity during the transition from the inward-facing to outward-facing conformational states, and its absence in the reverse direction, thereby provide an elegant and conceptually simple mechanism for enforcing the export directionality of transport by NaAtm1. One of the disulfide crosslinked NaAtm1 variants characterized in this work retains significant glutathione transport activity, suggesting that ATP hydrolysis and substrate transport by Atm1 may involve a limited set of conformational states with minimal separation of the nucleotide-binding domains in the inward-facing conformation.