ESX type VII secretion systems are complex secretion machineries spanning across the mycobacterial membrane and play an important role in pathogenicity, nutrient uptake and conjugation. We previously reported the role of ESX-4 in modulating Mycobacterium abscessus intracellular survival. The loss of EccB4 was associated with limited secretion of two effector proteins belonging to the WXG-100 family, EsxU and EsxT, and encoded by the esx-4 locus. This prompted us to investigate the function of M. abscessus EsxU and EsxT in vitro and in vivo. Herein, we show that EsxU and EsxT are substrates of ESX-4 and form a stable 1:1 heterodimer that permeabilizes artificial membranes. While expression of esxU and esxT was up-regulated in M. abscessus-infected macrophages, their absence in an esxUT deletion mutant prevented phagosomal membrane disruption while maintaining M. abscessus in an unacidified phagosome. Unexpectedly, the esxUT deletion was associated with a hyper-virulent phenotype, characterised by increased bacterial loads and mortality in mouse and zebrafish infection models. Collectively, these results demonstrate that the presence of EsxU and EsxT dampens survival and persistence of M. abscessus during infection. Author summary: The evolution of bacterial secretion systems is strongly associated with the intracellular lifestyle and interactions of bacteria with their hosts. Here, we show that an ancestral type VII secretion system, ESX-4, modulates the pathophysiology of Mycobacterium abscessus infection, which is directly related to its major effectors, EsxU and EsxT. While EsxU/EsxT were found to permeabilize lipid bilayers, deletion of the esxUT genes increased the virulence of M. abscessus in animal models. In support of these observations, strictly pathogenic mycobacteria in humans and animals, such as Mycobacterium tuberculosis or Mycobacterium marinum have evolved without evidence of secretion of their respective EsxU/EsxT substrates. Together, these results underscore the possible in vivo advantage procured by the absence of EsxU and EsxT in strictly pathogenic mycobacterial species.

Selective vulnerability is an enigmatic feature of neurodegenerative diseases (NDs), whereby a widely expressed protein causes lesions in specific cell types and brain regions. Using the RiboTag method in mice, translational responses of five neural subtypes to acquired prion disease (PrD) were measured. Pre-onset and disease onset timepoints were chosen based on longitudinal electroencephalography (EEG) that revealed a gradual increase in theta power between 10- and 18-weeks after prion injection, resembling a clinical feature of human PrD. At disease onset, marked by significantly increased theta power and histopathological lesions, mice had pronounced translatome changes in all five cell types despite appearing normal. Remarkably, at a pre-onset stage, prior to EEG and neuropathological changes, we found that 1) translatomes of astrocytes indicated reduced synthesis of ribosomal and mitochondrial components, 2) glutamatergic neurons showed increased expression of cytoskeletal genes, and 3) GABAergic neurons revealed reduced expression of circadian rhythm genes. These data demonstrate that early translatome responses to neurodegeneration emerge prior to conventional markers of disease and are cell type-specific. Therapeutic strategies may need to target multiple pathways in specific populations of cells, early in disease. Author summary: Prions are infectious agents composed of a misfolded protein. When isolated from a mammalian brain and transferred to the same host species, prions will cause the same neurodegenerative disease affecting the same brain regions and cell types. This concept of selective vulnerability is also a feature of more common types of neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Huntington’s. To better understand the mechanisms behind selective vulnerability, we studied disease responses of five cell types with different vulnerabilities in prion-infected mice at two different disease stages. Responses were measured as changes to mRNAs undergoing translation, referred to as the translatome. Before prion-infected mice demonstrated typical disease signs, electroencephalography (a method used clinically to characterize neurodegeneration in humans) revealed brain changes resembling those in human prion diseases, and surprisingly, the translatomes of all cells were drastically changed. Furthermore, before electroencephalography changes emerged, three cell types made unique responses while the most vulnerable cell type did not. These results suggests that mechanisms causing selective vulnerability will be difficult to dissect and that therapies will likely need to be provided before clinical signs emerge and individually engage multiple cell types and their distinct molecular pathways.

Children are particularly susceptible to typhoid fever caused by the bacterial pathogen Salmonella Typhi. Typhoid fever is prevalent in developing countries where diets can be less well-balanced. Here, using a murine model, we investigated the role of the macronutrient composition of the diet in maternal vaccination efficacies of two subunit vaccines targeting typhoid toxin: ToxoidVac and PltBVac. We found that maternal vaccinations protected all offspring against a lethal-dose typhoid toxin challenge in a balanced, normal diet (ND) condition, but the declined protection in a malnourished diet (MD) condition was observed in the PltBVac group. Despite the comparable antibody titers in both MD and ND mothers, MD offspring had a significantly lower level of typhoid toxin neutralizing antibodies than their ND counterparts. We observed a lower expression of the neonatal Fc receptor on the yolk sac of MD mothers than in ND mothers, agreeing with the observed lower antibody titers in MD offspring. Protein supplementation to MD diets, but not fat supplementation, increased FcRn expression and protected all MD offspring from the toxin challenge. Similarly, providing additional typhoid toxin-neutralizing antibodies to MD offspring was sufficient to protect all MD offspring from the toxin challenge. These results emphasize the significance of balanced/normal diets for a more effective maternal vaccination transfer to their offspring. Author summary: Typhoid fever is a life-threatening systemic infectious disease caused by Salmonella Typhi, which is prevalent in developing countries where diets can be less well-balanced. Here, we used mice to study the role of nutrition in maternal vaccination efficacies of two subunit vaccines targeting Salmonella’s typhoid toxin. We found maternal vaccinations protected all offspring from a lethal-dose typhoid toxin challenge in a balanced/normal diet (ND) condition, but the lack of protection in a malnourished diet (MD) condition was observed in the PltBVac group. Our data indicate that the difference in maternal vaccination outcomes between ND and MD offspring was due to the less effective maternal antibody transfer from MD mothers to their offspring. Providing additional proteins to MD mothers or additional toxin-neutralizing antibodies to MD offspring saved all malnourished offspring from a lethal-dose typhoid toxin challenge, highlighting the importance of balanced/normal diets for effective maternal vaccination outcomes.

Host genetics and the environment influence which fungal microbes colonize a plant. A new study in PLOS Biology finds that the relative influence of these factors changes throughout the development of the biofuel crop switchgrass growing in field settings. Host genetics and the environment influence which fungal microbes colonize a plant. This Primer explores a new study in PLOS Biology which finds that the relative influence of these factors changes throughout the development of the biofuel crop switchgrass growing in the field.

Epithelial cells secrete apical extracellular matrices to form protruding structures such as denticles, ridges, scales, or teeth. The mechanisms that shape these structures remain poorly understood. Here, we show how the actin cytoskeleton and a provisional matrix work together to sculpt acellular longitudinal alae ridges in the cuticle of adult C. elegans. Transient assembly of longitudinal actomyosin filaments in the underlying lateral epidermis accompanies deposition of the provisional matrix at the earliest stages of alae formation. Actin is required to pattern the provisional matrix into longitudinal bands that are initially offset from the pattern of longitudinal actin filaments. These bands appear ultrastructurally as alternating regions of adhesion and separation within laminated provisional matrix layers. The provisional matrix is required to establish these demarcated zones of adhesion and separation, which ultimately give rise to alae ridges and their intervening valleys, respectively. Provisional matrix proteins shape the alae ridges and valleys but are not present within the final structure. We propose a morphogenetic mechanism wherein cortical actin patterns are relayed to the laminated provisional matrix to set up distinct zones of matrix layer separation and accretion that shape a permanent and acellular matrix structure. Author summary: Animal surfaces are often decorated with intricately shaped structures such as denticles, ridges, or scales that are composed of extracellular matrix materials. We don’t understand how those extracellular materials get sculpted into appropriate shapes, but most models propose that cellular protrusions initiate the process. Here we investigated the formation of nematode adult alae, which are three racing stripe-like cuticle ridges that run along the left and right sides of the body. We found that alae development requires the actin cytoskeleton and a set of temporary matrix components, both of which organize into longitudinal stripes that presage the final structure. Using electron microscopy, we saw no evidence for cell membrane protrusion or folding that would explain a ridged matrix pattern. Instead, we observed that the first sign of alae formation is the appearance of four small separations between different layers of the temporary matrix. We propose that cytoskeletal patterns are relayed to the to the temporary matrix to trigger delamination and subsequent zonal differences in matrix accumulation that establish the alternating valley and ridge pattern of the alae.

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficacy of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework. Author summary: Understanding the brain’s function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanity’s great undertakings. In experimental and clinical work probing the brain’s activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution. Author summary: With the boom of spatially-resolved transcriptomics methods, a set of big opportunities appear for researchers to understand better developmental processes without relying on existing single cell RNA sequencing datasets. In context, we applied spage2vec, a graph convolutional neural network model, to explore the cellular diversity present during the human heart development from week 4.5 to week 9. We showed that this type of approaches, which do not rely on cellular segmentation, can be used to identify molecular signatures better than existing cell typing tools that can be used to explore the spatial component of processes such as cellular differentiation. Among the signatures identified, we focused on exploring the molecular diversity within cardiomyocytes and we described the differentiation process of epicardial cells in the context of space. All the molecular signatures detected and their gene expression profiles were also integrated in a useful online tool that can be used by the community for further exploration.

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms—BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit—on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial. Author summary: Reconstructing the evolutionary history from the genome sequences of single cells can provide a detailed understanding of evolutionary events and changes on a very fine-grained scale, for instance in the development of organs and cancer. Due to the increasing sizes of single-cell datasets, scalable and accurate methods are required. In this work we describe Scelestial, a software implementing an adapted Steiner tree approximation algorithm for evolutionary tree reconstruction from the analysis of single-cell datasets. The Steiner tree approximation algorithm, unlike other heuristics and sampling-based methods (e. g. Markov chain Monte Carlo), provides guarantees of its performance. A comparison of Scelestial with state of the art methods showed that it performed favourably in terms of quality of the inferred trees as well as speed across a large number of simulated data sets, and produced the most plausible evolutionary scenarios on single cell data sets from cancer patients. Taken together, our results show that Scelestial provides a valuable addition to current single cell lineage inference techniques.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike. Author summary: We used a new method called genomic structural equation modeling to analyse data from 165,717 men and 193,073 women from the UKBiobank. Our results revealed that genetic influences on BMI were very stable between ages 40 and 73. In other words, there did not appear to be any age-dependent genetic influences on BMI during this period. The same results were seen in men and women.

Eukaryotic circadian clocks are based on self-sustaining, cell-autonomous oscillatory feedback loops that can synchronize with the environment via recurrent stimuli (zeitgebers) such as light. The components of biological clocks and their network interactions are becoming increasingly known, calling for a quantitative understanding of their role for clock function. However, the development of data-driven mathematical clock models has remained limited by the lack of sufficiently accurate data. Here we present a comprehensive model of the circadian clock of Neurospora crassa that describe free-running oscillations in constant darkness and entrainment in light-dark cycles. To parameterize the model, we measured high-resolution time courses of luciferase reporters of morning and evening specific clock genes in WT and a mutant strain. Fitting the model to such comprehensive data allowed estimating parameters governing circadian phase, period length and amplitude, and the response of genes to light cues. Our model suggests that functional maturation of the core clock protein Frequency causes a delay in negative feedback that is critical for generating circadian rhythms. Author summary: Circadian rhythms are endogenous autonomous clocks that emancipate daily rhythms in physiology and behavior. Lately, a large body of research has contributed to our understanding of clocks’ genetic and mechanistic basis across kingdoms of life, i.e., mammals, fungi, plants, and bacteria. Several mathematical models have made key contributions to our current understanding of the design principles of the Neurospora crassa circadian clock and conditions for self-sustained oscillations. However, previous models uncovered and described the principle properties of the clock in generic manner due to a lack of experimental data. In this study, we developed a mathematical model based on systems of differential equations to describe the core clock components and estimated model parameters from luciferase data that capture experimental observations. We demonstrate the model predictive control simulation emphasizing the importance of functional maturation of the core clock protein Frequency in generating circadian rhythms.