Adrenergic Signaling in Muscularis Macrophages Limits Infection-Induced Neuronal Loss.

Enteric-associated neurons (EANs) are closely associated with immune cells and continuously monitor and modulate homeostatic intestinal functions, including motility and nutrient sensing. Bidirectional interactions between neuronal and immune cells are altered during disease processes such as neurodegeneration or irritable bowel syndrome. We investigated the effects of infection-induced inflammation on intrinsic EANs (iEANs) and the role of intestinal muscularis macrophages (MMs) in this context. Using murine models of enteric infections, we observed long-term gastrointestinal symptoms, including reduced motility and loss of excitatory iEANs, which was mediated by a Nlrp6- and Casp11-dependent mechanism, depended on infection history, and could be reversed by manipulation of the microbiota. MMs responded to luminal infection by upregulating a neuroprotective program via ß2-adrenergic receptor (ß2-AR) signaling and mediated neuronal protection through an arginase 1-polyamine axis. Our results identify a mechanism of neuronal death post-infection and point to a role for tissue-resident MMs in limiting neuronal damage.

Can U Turn ILC2s Up?

Innate lymphoid cells (ILCs) are tuned to quickly respond to and amplify tissue-specific signals. Work of three independent groups in Nature uncovers a novel mode of inflammatory communication between ILC2s and neurons at mucosal surfaces.

Neuro-immune Interactions Drive Tissue Programming in Intestinal Macrophages.

Proper adaptation to environmental perturbations is essential for tissue homeostasis. In the intestine, diverse environmental cues can be sensed by immune cells, which must balance resistance to microorganisms with tolerance, avoiding excess tissue damage. By applying imaging and transcriptional profiling tools, we interrogated how distinct microenvironments in the gut regulate resident macrophages. We discovered that macrophages exhibit a high degree of gene-expression specialization dependent on their proximity to the gut lumen. Lamina propria macrophages (LpMs) preferentially expressed a pro-inflammatory phenotype when compared to muscularis macrophages (MMs), which displayed a tissue-protective phenotype. Upon luminal bacterial infection, MMs further enhanced tissue-protective programs, and this was attributed to swift activation of extrinsic sympathetic neurons innervating the gut muscularis and norepinephrine signaling to ß2 adrenergic receptors on MMs. Our results reveal unique intra-tissue macrophage specialization and identify neuro-immune communication between enteric neurons and macrophages that induces rapid tissue-protective responses to distal perturbations.

Crosstalk between muscularis macrophages and enteric neurons regulates gastrointestinal motility.

Intestinal peristalsis is a dynamic physiologic process influenced by dietary and microbial changes. It is tightly regulated by complex cellular interactions; however, our understanding of these controls is incomplete. A distinct population of macrophages is distributed in the intestinal muscularis externa. We demonstrate that, in the steady state, muscularis macrophages regulate peristaltic activity of the colon. They change the pattern of smooth muscle contractions by secreting bone morphogenetic protein 2 (BMP2), which activates BMP receptor (BMPR) expressed by enteric neurons. Enteric neurons, in turn, secrete colony stimulatory factor 1 (CSF1), a growth factor required for macrophage development. Finally, stimuli from microbial commensals regulate BMP2 expression by macrophages and CSF1 expression by enteric neurons. Our findings identify a plastic, microbiota-driven crosstalk between muscularis macrophages and enteric neurons that controls gastrointestinal motility. PAPERFLICK:

Major impacts of widespread structural variation on sorghum.

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

Spineless Behavior of CX3CR1+ Monocytes in Response to Infection.

Sickness in mammals can lead to cognition deficits, although the underlying mechanisms remain elusive. In a recent Nature Medicine article, Garré et al. (2017) report that sickness-induced cortical dendritic spine loss and impaired memory formation is mediated by CX3CR1+ monocyte-derived TNF-α.

Author Correction: Microbiota modulate sympathetic neurons via a gut-brain circuit.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Microbiota modulate sympathetic neurons via a gut-brain circuit.

Connections between the gut and brain monitor the intestinal tissue and its microbial and dietary content1, regulating both physiological intestinal functions such as nutrient absorption and motility2,3, and brain-wired feeding behaviour2. It is therefore plausible that circuits exist to detect gut microorganisms and relay this information to areas of the central nervous system that, in turn, regulate gut physiology4. Here we characterize the influence of the microbiota on enteric-associated neurons by combining gnotobiotic mouse models with transcriptomics, circuit-tracing methods and functional manipulations. We find that the gut microbiome modulates gut-extrinsic sympathetic neurons: microbiota depletion leads to increased expression of the neuronal transcription factor cFos, and colonization of germ-free mice with bacteria that produce short-chain fatty acids suppresses cFos expression in the gut sympathetic ganglia. Chemogenetic manipulations, translational profiling and anterograde tracing identify a subset of distal intestine-projecting vagal neurons that are positioned to have an afferent role in microbiota-mediated modulation of gut sympathetic neurons. Retrograde polysynaptic neuronal tracing from the intestinal wall identifies brainstem sensory nuclei that are activated during microbial depletion, as well as efferent sympathetic premotor glutamatergic neurons that regulate gastrointestinal transit. These results reveal microbiota-dependent control of gut-extrinsic sympathetic activation through a gut-brain circuit.

RecA levels modulate biofilm development in Acinetobacter baumannii.

Infections caused by Acinetobacter baumannii, a Gram-negative opportunistic pathogen, are difficult to eradicate due to the bacterium's propensity to quickly gain antibiotic resistances and form biofilms, a protective bacterial multicellular community. The A. baumannii DNA damage response (DDR) mediates the antibiotic resistance acquisition and regulates RecA in an atypical fashion; both RecALow and RecAHigh cell types are formed in response to DNA damage. The findings of this study demonstrate that the levels of RecA can influence formation and dispersal of biofilms. RecA loss results in surface attachment and prominent biofilms, while elevated RecA leads to diminished attachment and dispersal. These findings suggest that the challenge to treat A. baumannii infections may be explained by the induction of the DDR, common during infection, as well as the delicate balance between maintaining biofilms in low RecA cells and promoting mutagenesis and dispersal in high RecA cells. This study underscores the importance of understanding the fundamental biology of bacteria to develop more effective treatments for infections.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy.

OBJECTIVE: In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS: TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS: A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION: A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

Compartmentalized gut lymph node drainage dictates adaptive immune responses.

The intestinal immune system has the challenging task of tolerating foreign nutrients and the commensal microbiome, while excluding or eliminating ingested pathogens. Failure of this balance leads to conditions such as inflammatory bowel diseases, food allergies and invasive gastrointestinal infections1. Multiple immune mechanisms are therefore in place to maintain tissue integrity, including balanced generation of effector T (TH) cells and FOXP3+ regulatory T (pTreg) cells, which mediate resistance to pathogens and regulate excessive immune activation, respectively1-4. The gut-draining lymph nodes (gLNs) are key sites for orchestrating adaptive immunity to luminal perturbations5-7. However, it is unclear how they simultaneously support tolerogenic and inflammatory reactions. Here we show that gLNs are immunologically specific to the functional gut segment that they drain. Stromal and dendritic cell gene signatures and polarization of T cells against the same luminal antigen differ between gLNs, with the proximal small intestine-draining gLNs preferentially giving rise to tolerogenic responses and the distal gLNs to pro-inflammatory T cell responses. This segregation permitted the targeting of distal gLNs for vaccination and the maintenance of duodenal pTreg cell induction during colonic infection. Conversely, the compartmentalized dichotomy was perturbed by surgical removal of select distal gLNs and duodenal infection, with effects on both lymphoid organ and tissue immune responses. Our findings reveal that the conflict between tolerogenic and inflammatory intestinal responses is in part resolved by discrete gLN drainage, and encourage antigen targeting to specific gut segments for therapeutic immune modulation.

Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.

The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities.

Correction: Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.

[This corrects the article DOI: 10.1371/journal.pcbi.1010919.].

Intelligent image-based deformation-assisted cell sorting with molecular specificity.

Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels.

A new hyperelastic lookup table for RT-DC.

Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real-time. A lookup table (LUT) disentangles the experimentally derived steady-state cell deformation and the projected area to extract the cell stiffness in the form of the Young's modulus. So far, two lookup tables exist but are limited to simple linear material models and cylindrical channel geometries. Here, we present two new lookup tables for RT-DC based on a neo-Hookean hyperelastic material numerically derived by simulations based on the finite element method in square and cylindrical channel geometries. At the same time, we quantify the influence of the shear-thinning behavior of the surrounding medium on the stationary deformation of cells in RT-DC and discuss the applicability and impact of the proposed LUTs regarding past and future RT-DC data analysis. Additionally, we provide insights about the cell strain and stresses, as well as the influence resulting from the rotational symmetric assumption on the cell deformation and volume estimation. The new lookup tables and the numerical cell shapes are made freely available.

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation.

BACKGROUND: CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. RESULTS: We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CONCLUSIONS: CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

DryMass: handling and analyzing quantitative phase microscopy images of spherical, cell-sized objects.

BACKGROUND: Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics. RESULTS: We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes. CONCLUSION: DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.

Single-cell mechanical phenotype is an intrinsic marker of reprogramming and differentiation along the mouse neural lineage.

Cellular reprogramming is a dedifferentiation process during which cells continuously undergo phenotypical remodeling. Although the genetic and biochemical details of this remodeling are fairly well understood, little is known about the change in cell mechanical properties during the process. In this study, we investigated changes in the mechanical phenotype of murine fetal neural progenitor cells (fNPCs) during reprogramming to induced pluripotent stem cells (iPSCs). We find that fNPCs become progressively stiffer en route to pluripotency, and that this stiffening is mirrored by iPSCs becoming more compliant during differentiation towards the neural lineage. Furthermore, we show that the mechanical phenotype of iPSCs is comparable with that of embryonic stem cells. These results suggest that mechanical properties of cells are inherent to their developmental stage. They also reveal that pluripotent cells can differentiate towards a more compliant phenotype, which challenges the view that pluripotent stem cells are less stiff than any cells more advanced developmentally. Finally, our study indicates that the cell mechanical phenotype might be utilized as an inherent biophysical marker of pluripotent stem cells.

nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data.

BACKGROUND: Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps. RESULTS: We introduce the Python package nanite, which automates all basic aspects of FD data analysis, including data import, tip-sample separation, base line correction, contact point retrieval, and model fitting. In addition, nanite enables the automation of the sorting step using supervised learning. This learning approach relates subjective ratings to predefined features extracted from FD curves. For ratings ranging from 0 to 10, our approach achieves a mean squared error below 1.0 rating points and a classification accuracy between good and poor curves that is above 87%. We showcase our approach by quantifying Young's moduli of the zebrafish spinal cord at different classification thresholds and by introducing data quality as a new dimension for quantitative AFM image analysis. CONCLUSION: The addition of quality-based sorting using supervised learning enables a fully automated and reproducible FD data analysis pipeline for biological samples in AFM.