Intracellular Ebola virus nucleocapsid assembly revealed by in situ cryo-electron tomography.

Filoviruses, including the Ebola and Marburg viruses, cause hemorrhagic fevers with up to 90% lethality. The viral nucleocapsid is assembled by polymerization of the nucleoprotein (NP) along the viral genome, together with the viral proteins VP24 and VP35. We employed cryo-electron tomography of cells transfected with viral proteins and infected with model Ebola virus to illuminate assembly intermediates, as well as a 9 Å map of the complete intracellular assembly. This structure reveals a previously unresolved third and outer layer of NP complexed with VP35. The intrinsically disordered region, together with the C-terminal domain of this outer layer of NP, provides the constant width between intracellular nucleocapsid bundles and likely functions as a flexible tether to the viral matrix protein in the virion. A comparison of intracellular nucleocapsids with prior in-virion nucleocapsid structures reveals that the nucleocapsid further condenses vertically in the virion. The interfaces responsible for nucleocapsid assembly are highly conserved and offer targets for broadly effective antivirals.

Cryo-ET of Env on intact HIV virions reveals structural variation and positioning on the Gag lattice.

HIV-1 Env mediates viral entry into host cells and is the sole target for neutralizing antibodies. However, Env structure and organization in its native virion context has eluded detailed characterization. Here, we used cryo-electron tomography to analyze Env in mature and immature HIV-1 particles. Immature particles showed distinct Env positioning relative to the underlying Gag lattice, providing insights into long-standing questions about Env incorporation. A 9.1-Å sub-tomogram-averaged reconstruction of virion-bound Env in conjunction with structural mass spectrometry revealed unexpected features, including a variable central core of the gp41 subunit, heterogeneous glycosylation between protomers, and a flexible stalk that allows Env tilting and variable exposure of neutralizing epitopes. Together, our results provide an integrative understanding of HIV assembly and structural variation in Env antigen presentation.

In Situ Structure of an Intact Lipopolysaccharide-Bound Bacterial Surface Layer.

Most bacterial and all archaeal cells are encapsulated by a paracrystalline, protective, and cell-shape-determining proteinaceous surface layer (S-layer). On Gram-negative bacteria, S-layers are anchored to cells via lipopolysaccharide. Here, we report an electron cryomicroscopy structure of the Caulobacter crescentus S-layer bound to the O-antigen of lipopolysaccharide. Using native mass spectrometry and molecular dynamics simulations, we deduce the length of the O-antigen on cells and show how lipopolysaccharide binding and S-layer assembly is regulated by calcium. Finally, we present a near-atomic resolution in situ structure of the complete S-layer using cellular electron cryotomography, showing S-layer arrangement at the tip of the O-antigen. A complete atomic structure of the S-layer shows the power of cellular tomography for in situ structural biology and sheds light on a very abundant class of self-assembling molecules with important roles in prokaryotic physiology with marked potential for synthetic biology and surface-display applications.

Molecular Architecture of the SARS-CoV-2 Virus.

SARS-CoV-2 is an enveloped virus responsible for the COVID-19 pandemic. Despite recent advances in the structural elucidation of SARS-CoV-2 proteins, the detailed architecture of the intact virus remains to be unveiled. Here we report the molecular assembly of the authentic SARS-CoV-2 virus using cryoelectron tomography (cryo-ET) and subtomogram averaging (STA). Native structures of the S proteins in pre- and postfusion conformations were determined to average resolutions of 8.7-11 Å. Compositions of the N-linked glycans from the native spikes were analyzed by mass spectrometry, which revealed overall processing states of the native glycans highly similar to that of the recombinant glycoprotein glycans. The native conformation of the ribonucleoproteins (RNPs) and their higher-order assemblies were revealed. Overall, these characterizations revealed the architecture of the SARS-CoV-2 virus in exceptional detail and shed light on how the virus packs its ∼30-kb-long single-segmented RNA in the ∼80-nm-diameter lumen.

Structure and Function of a Bacterial Gap Junction Analog.

Multicellular lifestyle requires cell-cell connections. In multicellular cyanobacteria, septal junctions enable molecular exchange between sister cells and are required for cellular differentiation. The structure of septal junctions is poorly understood, and it is unknown whether they are capable of controlling intercellular communication. Here, we resolved the in situ architecture of septal junctions by electron cryotomography of cryo-focused ion beam-milled cyanobacterial filaments. Septal junctions consisted of a tube traversing the septal peptidoglycan. Each tube end comprised a FraD-containing plug, which was covered by a cytoplasmic cap. Fluorescence recovery after photobleaching showed that intercellular communication was blocked upon stress. Gating was accompanied by a reversible conformational change of the septal junction cap. We provide the mechanistic framework for a cell junction that predates eukaryotic gap junctions by a billion years. The conservation of a gated dynamic mechanism across different domains of life emphasizes the importance of controlling molecular exchange in multicellular organisms.

Robust organ size in Arabidopsis is primarily governed by cell growth rather than cell division patterns.

Organ sizes and shapes are highly reproducible, or robust, within a species and individuals. Arabidopsis thaliana sepals, which are the leaf-like organs that enclose flower buds, have consistent size and shape, indicating robust development. Cell growth is locally heterogeneous due to intrinsic and extrinsic noise. To achieve robust organ shape, fluctuations in cell growth must average to an even growth rate, which requires that fluctuations are uncorrelated or anti-correlated in time and space. Here, we live image and quantify the development of sepals with an increased or decreased number of cell divisions (lgo mutant and LGO overexpression, respectively), a mutant with altered cell growth variability (ftsh4), and double mutants combining these. Changes in the number of cell divisions do not change the overall growth pattern. By contrast, in ftsh4 mutants, cell growth accumulates in patches of over- and undergrowth owing to correlations that impair averaging, resulting in increased organ shape variability. Thus, we demonstrate in vivo that the number of cell divisions does not affect averaging of cell growth, preserving robust organ morphogenesis, whereas correlated growth fluctuations impair averaging.

Cryo-EM structures of perforin-2 in isolation and assembled on a membrane suggest a mechanism for pore formation.

Perforin-2 (PFN2, MPEG1) is a key pore-forming protein in mammalian innate immunity restricting intracellular bacteria proliferation. It forms a membrane-bound pre-pore complex that converts to a pore-forming structure upon acidification; but its mechanism of conformational transition has been debated. Here we used cryo-electron microscopy, tomography and subtomogram averaging to determine structures of PFN2 in pre-pore and pore conformations in isolation and bound to liposomes. In isolation and upon acidification, the pre-assembled complete pre-pore rings convert to pores in both flat ring and twisted conformations. On membranes, in situ assembled PFN2 pre-pores display various degrees of completeness; whereas PFN2 pores are mainly incomplete arc structures that follow the same subunit packing arrangements as found in isolation. Both assemblies on membranes use their P2 ß-hairpin for binding to the lipid membrane surface. Overall, these structural snapshots suggest a molecular mechanism for PFN2 pre-pore to pore transition on a targeted membrane, potentially using the twisted pore as an intermediate or alternative state to the flat conformation, with the capacity to cause bilayer distortion during membrane insertion.

In situ fate of Chikungunya virus replication organelles.

Chikungunya virus (CHIKV) is a mosquito-borne pathogen responsible for an acute musculoskeletal disease in humans. Replication of the viral RNA genome occurs in specialized membranous replication organelles (ROs) or spherules, which contain the viral replication complex. Initially generated by RNA synthesis-associated plasma membrane deformation, alphavirus ROs are generally rapidly endocytosed to produce type I cytopathic vacuoles (CPV-I), from which nascent RNAs are extruded for cytoplasmic translation. By contrast, CHIKV ROs are poorly internalized, raising the question of their fate and functionality at the late stage of infection. Here, using in situ cryogenic-electron microscopy approaches, we investigate the outcome of CHIKV ROs and associated replication machinery in infected human cells. We evidence the late persistence of CHIKV ROs at the plasma membrane with a crowned protein complex at the spherule neck similar to the recently resolved replication complex. The unexpectedly heterogeneous and large diameter of these compartments suggests a continuous, dynamic growth of these organelles beyond the replication of a single RNA genome. Ultrastructural analysis of surrounding cytoplasmic regions supports that outgrown CHIKV ROs remain dynamically active in viral RNA synthesis and export to the cell cytosol for protein translation. Interestingly, rare ROs with a homogeneous diameter are also marginally internalized in CPV-I near honeycomb-like arrangements of unknown function, which are absent in uninfected controls, thereby suggesting a temporal regulation of this internalization. Altogether, this study sheds new light on the dynamic pattern of CHIKV ROs and associated viral replication at the interface with cell membranes in infected cells.IMPORTANCEThe Chikungunya virus (CHIKV) is a positive-stranded RNA virus that requires specialized membranous replication organelles (ROs) for its genome replication. Our knowledge of this viral cycle stage is still incomplete, notably regarding the fate and functional dynamics of CHIKV ROs in infected cells. Here, we show that CHIKV ROs are maintained at the plasma membrane beyond the first viral cycle, continuing to grow and be dynamically active both in viral RNA replication and in its export to the cell cytosol, where translation occurs in proximity to ROs. This contrasts with the homogeneous diameter of ROs during internalization in cytoplasmic vacuoles, which are often associated with honeycomb-like arrangements of unknown function, suggesting a regulated mechanism. This study sheds new light on the dynamics and fate of CHIKV ROs in human cells and, consequently, on our understanding of the Chikungunya viral cycle.

The consequence of ATP synthase dimer angle on mitochondrial morphology studied by cryo-electron tomography.

Mitochondrial ATP synthases form rows of dimers, which induce membrane curvature to give cristae their characteristic lamellar or tubular morphology. The angle formed between the central stalks of ATP synthase dimers varies between species. Using cryo-electron tomography and sub-tomogram averaging, we determined the structure of the ATP synthase dimer from the nematode worm C. elegans and show that the dimer angle differs from previously determined structures. The consequences of this species-specific difference at the dimer interface were investigated by comparing C. elegans and S. cerevisiae mitochondrial morphology. We reveal that C. elegans has a larger ATP synthase dimer angle with more lamellar (flatter) cristae when compared to yeast. The underlying cause of this difference was investigated by generating an atomic model of the C. elegans ATP synthase dimer by homology modelling. A comparison of our C. elegans model to an existing S. cerevisiae structure reveals the presence of extensions and rearrangements in C. elegans subunits associated with maintaining the dimer interface. We speculate that increasing dimer angles could provide an advantage for species that inhabit variable-oxygen environments by forming flatter more energetically efficient cristae.

Cryo-ET of Toxoplasma parasites gives subnanometer insight into tubulin-based structures.

Tubulin is a conserved protein that polymerizes into different forms of filamentous structures in Toxoplasma gondii, an obligate intracellular parasite in the phylum Apicomplexa. Two key tubulin-containing cytoskeletal components are subpellicular microtubules (SPMTs) and conoid fibrils (CFs). The SPMTs help maintain shape and gliding motility, while the CFs are implicated in invasion. Here, we use cryogenic electron tomography to determine the molecular structures of the SPMTs and CFs in vitrified intact and detergent-extracted parasites. Subvolume densities from detergent-extracted parasites yielded averaged density maps at subnanometer resolutions, and these were related back to their architecture in situ. An intralumenal spiral lines the interior of the 13-protofilament SPMTs, revealing a preferred orientation of these microtubules relative to the parasite's long axis. Each CF is composed of nine tubulin protofilaments that display a comma-shaped cross-section, plus additional associated components. Conoid protrusion, a crucial step in invasion, is associated with an altered pitch of each CF. The use of basic building blocks of protofilaments and different accessory proteins in one organism illustrates the versatility of tubulin to form two distinct types of assemblies, SPMTs and CFs.

Comparing methods for statistical inference with model uncertainty.

Probability models are used for many statistical tasks, notably parameter estimation, interval estimation, inference about model parameters, point prediction, and interval prediction. Thus, choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process. Here we focus on one such choice, that of variables to include in a linear regression model. Many methods have been proposed, including Bayesian and penalized likelihood methods, and it is unclear which one to use. We compared 21 of the most popular methods by carrying out an extensive set of simulation studies based closely on real datasets that span a range of situations encountered in practical data analysis. Three adaptive Bayesian model averaging (BMA) methods performed best across all statistical tasks. These used adaptive versions of Zellner's g-prior for the parameters, where the prior variance parameter g is a function of sample size or is estimated from the data. We found that for BMA methods implemented with Markov chain Monte Carlo, 10,000 iterations were enough. Computationally, we found two of the three best methods (BMA with g=√n and empirical Bayes-local) to be competitive with the least absolute shrinkage and selection operator (LASSO), which is often preferred as a variable selection technique because of its computational efficiency. BMA performed better than Bayesian model selection (in which just one model is selected).

Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics.

The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.

Abundance-diversity relationship as a unique signature of temporal scaling in the fossil record.

Species diversity increases with the temporal grain of samples according to the species-time relationship (STR), impacting palaeoecological analyses because the temporal grain (time averaging) of fossil assemblages varies by several orders of magnitude. We predict a positive relation between total abundance and sample size-independent diversity (ADR) in fossil assemblages because an increase in time averaging, determined by a decreasing sediment accumulation, should increase abundance and depress species dominance. We demonstrate that, in contrast to negative ADR of non-averaged living assemblages, the ADR of Holocene fossil assemblages is positive, unconditionally or when conditioned on the energy availability gradient. However, the positive fossil ADR disappears when conditioned on sediment accumulation, demonstrating that ADR is a signature of diversity scaling induced by variable time averaging. Conditioning ADR on sediment accumulation can identify and remove the scaling effect caused by time averaging, providing an avenue for unbiased biodiversity comparisons across space and time.

Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials.

The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these approaches assume that external data are from previously conducted trials, leaving a rich source of untapped real-world data (RWD) that cannot yet be effectively leveraged. We propose a semi-supervised mixture (SS-MIX) multisource exchangeability model (MEM); a flexible, two-step Bayesian approach for incorporating RWD into randomized controlled trial analyses. The first step is a SS-MIX model on a modified propensity score and the second step is a MEM. The first step targets a representative subgroup of individuals from the trial population and the second step avoids borrowing when there are substantial differences in outcomes among the trial sample and the representative observational sample. When comparing the proposed approach to competing borrowing approaches in a simulation study, we find that our approach borrows efficiently when the trial and RWD are consistent, while mitigating bias when the trial and external data differ on either measured or unmeasured covariates. We illustrate the proposed approach with an application to a randomized controlled trial investigating intravenous hyperimmune immunoglobulin in hospitalized patients with influenza, while leveraging data from an external observational study to supplement a subgroup analysis by influenza subtype.

A flexible Bayesian framework for individualized inference via adaptive borrowing.

The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide substantial data on a single individual, how best to use multisource population data to improve individualized inference remains an open research question. One possible approach, the multisource exchangeability model (MEM), is a Bayesian method for integrating data from supplementary sources into the analysis of a primary source. MEM was originally developed to improve inference for a single study by asymmetrically borrowing information from a set of similar previous studies and was further developed to apply a more computationally intensive symmetric borrowing in the context of basket trial; however, even for asymmetric borrowing, its computational burden grows exponentially with the number of supplementary sources, making it unsuitable for applications where hundreds or thousands of supplementary sources (i.e., individuals) could contribute to inference on a given individual. In this article, we propose the data-driven MEM (dMEM), a two-stage approach that includes both source selection and clustering to enable the inclusion of an arbitrary number of sources to contribute to individualized inference in a computationally tractable and data-efficient way. We illustrate the application of dMEM to individual-level human behavior and mental well-being data collected via smartphones, where our approach increases individual-level estimation precision by 84% compared with a standard no-borrowing method and outperforms recently proposed competing methods in 80% of individuals.

Bayesian joint models for multi-regional clinical trials.

In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace's method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT.

Bayesian multiregional clinical trials using model averaging.

Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference.

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models.

Optimal foraging theory suggests that animals make decisions which maximize their food intake per unit time when foraging, but the mechanisms animals use to track the value of behavioral alternatives and choose between them remain unclear. Several models for how animals integrate past experience have been suggested. However, these models make differential predictions for the occurrence of spontaneous recovery of choice: a behavioral phenomenon in which a hiatus from the experimental environment results in animals reverting to a behavioral allocation consistent with a reward distribution from the more distant past, rather than one consistent with their most recently experienced distribution. To explore this phenomenon and compare these models, three free-operant experiments with rats were conducted using a serial reversal design. In Phase 1, two responses (A and B) were baited with pellets on concurrent variable interval schedules, favoring option A. In Phase 2, lever baiting was reversed to favor option B. Rats then entered a delay period, where they were maintained at weight in their home cages and no experimental sessions took place. Following this delay, preference was assessed using initial responding in test sessions where levers were presented, but not baited. Models were compared in performance, including an exponentially weighted moving average, the Temporal Weighting Rule, and variants of these models. While the data provided strong evidence of spontaneous recovery of choice, the form and extent of recovery was inconsistent with the models under investigation. Potential interpretations are discussed in relation to both the decision rule and valuation functions employed.

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim.

This discussion provides commentary on the paper by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim entitled "LEAP: the latent exchangeability prior for borrowing information from historical data". The authors propose a novel method to bridge the incorporation of supplemental information into a study while also identifying potentially exchangeable subgroups to better facilitate information sharing. In this discussion, we highlight the potential relationship with other Bayesian model averaging approaches, such as multisource exchangeability modeling, and provide a brief numeric case study to illustrate how the concepts behind latent exchangeability prior may also improve the performance of existing methods. The results provided by Alt et al. are exciting, and we believe that the method represents a meaningful approach to more efficient information sharing.