Human inherited CCR2 deficiency underlies progressive polycystic lung disease.

We describe a human lung disease caused by autosomal recessive, complete deficiency of the monocyte chemokine receptor C-C motif chemokine receptor 2 (CCR2). Nine children from five independent kindreds have pulmonary alveolar proteinosis (PAP), progressive polycystic lung disease, and recurrent infections, including bacillus Calmette Guérin (BCG) disease. The CCR2 variants are homozygous in six patients and compound heterozygous in three, and all are loss-of-expression and loss-of-function. They abolish CCR2-agonist chemokine C-C motif ligand 2 (CCL-2)-stimulated Ca2+ signaling in and migration of monocytic cells. All patients have high blood CCL-2 levels, providing a diagnostic test for screening children with unexplained lung or mycobacterial disease. Blood myeloid and lymphoid subsets and interferon (IFN)-γ- and granulocyte-macrophage colony-stimulating factor (GM-CSF)-mediated immunity are unaffected. CCR2-deficient monocytes and alveolar macrophage-like cells have normal gene expression profiles and functions. By contrast, alveolar macrophage counts are about half. Human complete CCR2 deficiency is a genetic etiology of PAP, polycystic lung disease, and recurrent infections caused by impaired CCL2-dependent monocyte migration to the lungs and infected tissues.

Spatiotemporal insight into early pregnancy governed by immune-featured stromal cells.

Endometrial decidualization connecting embryo implantation and placentation is transient but essential for successful pregnancy, which, however, is not systematically investigated. Here, we use a scStereo-seq technology to spatially visualize and define the dynamic functional decidual hubs assembled by distinct immune, endothelial, trophoblast, and decidual stromal cells (DSCs) in early pregnant mice. We unravel the DSC transdifferentiation trajectory and surprisingly discover a dual-featured type of immune-featured DSCs (iDSCs). We find that immature DSCs attract immune cells and induce decidual angiogenesis at the mesenchymal-epithelial transition hub during decidualization initiation. iDSCs enable immune cell recruitment and suppression, govern vascularization, and promote cytolysis at immune cell assembling and vascular hubs, respectively, to establish decidual homeostasis at a later stage. Interestingly, dysfunctional and spatially disordered iDSCs cause abnormal accumulation of immune cells in the vascular hub, which disrupts decidual hub specification and eventually leads to pregnancy complications in DBA/2-mated CBA/J mice.

Recurrent inversion polymorphisms in humans associate with genetic instability and genomic disorders.

Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversions <2 kbp form by twin-priming during L1 retrotransposition; 80% of the larger inversions are balanced and affect twice as many nucleotides as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or retrotransposons. Since flanking repeats promote non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7 × 10-4 per locus per generation. Recurrent inversions exhibit a sex-chromosomal bias and co-localize with genomic disorder critical regions. We propose that inversion recurrence results in an elevated number of heterozygous carriers and structural SD diversity, which increases mutability in the population and predisposes specific haplotypes to disease-causing CNVs.

The emergence and ongoing convergent evolution of the SARS-CoV-2 N501Y lineages.

The independent emergence late in 2020 of the B.1.1.7, B.1.351, and P.1 lineages of SARS-CoV-2 prompted renewed concerns about the evolutionary capacity of this virus to overcome public health interventions and rising population immunity. Here, by examining patterns of synonymous and non-synonymous mutations that have accumulated in SARS-CoV-2 genomes since the pandemic began, we find that the emergence of these three "501Y lineages" coincided with a major global shift in the selective forces acting on various SARS-CoV-2 genes. Following their emergence, the adaptive evolution of 501Y lineage viruses has involved repeated selectively favored convergent mutations at 35 genome sites, mutations we refer to as the 501Y meta-signature. The ongoing convergence of viruses in many other lineages on this meta-signature suggests that it includes multiple mutation combinations capable of promoting the persistence of diverse SARS-CoV-2 lineages in the face of mounting host immune recognition.

Modularity and robustness of frontal cortical networks.

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Competitive Disinhibition Mediates Behavioral Choice and Sequences in Drosophila.

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

Identification of a DNA methylation episignature for recurrent constellations of embryonic malformations.

The term "recurrent constellations of embryonic malformations" (RCEM) is used to describe a number of multiple malformation associations that affect three or more body structures. The causes of these disorders are currently unknown, and no diagnostic marker has been identified. Consequently, providing a definitive diagnosis in suspected individuals is challenging. In this study, genome-wide DNA methylation analysis was conducted on DNA samples obtained from the peripheral blood of 53 individuals with RCEM characterized by clinical features recognized as VACTERL and/or oculoauriculovertebral spectrum association. We identified a common DNA methylation episignature in 40 out of the 53 individuals. Subsequently, a sensitive and specific binary classifier was developed based on the DNA methylation episignature. This classifier can facilitate the use of RCEM episignature as a diagnostic biomarker in a clinical setting. The study also investigated the functional correlation of RCEM DNA methylation relative to other genetic disorders with known episignatures, highlighting the common genomic regulatory pathways involved in the pathophysiology of RCEM.

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.

Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.

Neural heterogeneity controls computations in spiking neural networks.

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Chaotic neural dynamics facilitate probabilistic computations through sampling.

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

A neural pathway for social modulation of spontaneous locomotor activity (SoMo-SLA) in Drosophila.

Social enrichment or social isolation affects a range of innate behaviors, such as sex, aggression, and sleep, but whether there is a shared mechanism is not clear. Here, we report a neural mechanism underlying social modulation of spontaneous locomotor activity (SoMo-SLA), an internal-driven behavior indicative of internal states. We find that social enrichment specifically reduces spontaneous locomotor activity in male flies. We identify neuropeptides Diuretic hormone 44 (DH44) and Tachykinin (TK) to be up- and down-regulated by social enrichment and necessary for SoMo-SLA. We further demonstrate a sexually dimorphic neural circuit, in which the male-specific P1 neurons encoding internal states form positive feedback with interneurons coexpressing doublesex (dsx) and Tk to promote locomotion, while P1 neurons also form negative feedback with interneurons coexpressing dsx and DH44 to inhibit locomotion. These two opposing neuromodulatory recurrent circuits represent a potentially common mechanism that underlies the social regulation of multiple innate behaviors.

Meiotic Chromosome Structure, the Synaptonemal Complex, and Infertility.

In meiosis, homologous chromosome synapsis is mediated by a supramolecular protein structure, the synaptonemal complex (SC), that assembles between homologous chromosome axes. The mammalian SC comprises at least eight largely coiled-coil proteins that interact and self-assemble to generate a long, zipper-like structure that holds homologous chromosomes in close proximity and promotes the formation of genetic crossovers and accurate meiotic chromosome segregation. In recent years, numerous mutations in human SC genes have been associated with different types of male and female infertility. Here, we integrate structural information on the human SC with mouse and human genetics to describe the molecular mechanisms by which SC mutations can result in human infertility. We outline certain themes in which different SC proteins are susceptible to different types of disease mutation and how genetic variants with seemingly minor effects on SC proteins may act as dominant-negative mutations in which the heterozygous state is pathogenic.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

Culture-acquired genetic variation in human pluripotent stem cells: Twenty years on.

Genetic changes arising in human pluripotent stem cells (hPSC) upon culture may bestow unwanted or detrimental phenotypes to cells, thus potentially impacting on the applications of hPSCs for clinical use and basic research. In the 20 years since the first report of culture-acquired genetic aberrations in hPSCs, a characteristic spectrum of recurrent aberrations has emerged. The preponderance of such aberrations implies that they provide a selective growth advantage to hPSCs upon expansion. However, understanding the consequences of culture-acquired variants for specific applications in cell therapy or research has been more elusive. The rapid progress of hPSC-based therapies to clinics is galvanizing the field to address this uncertainty and provide definitive ways both for risk assessment of variants and reducing their prevalence in culture. Here, we aim to provide a timely update on almost 20 years of research on this fascinating, but a still unresolved and concerning, phenomenon.

Trophoblast PR-SET7 dysfunction induces viral mimicry response and necroptosis associated with recurrent miscarriage.

Recurrent miscarriage (RM) is a distressing pregnancy complication. While the etiology of RM remains unclear, growing evidence has indicated the relevance of trophoblast impairment to the pathogenesis of RM. PR-SET7 is the sole enzyme catalyzing monomethylation of H4K20 (H4K20me1) and has been implicated in many pathophysiological processes. However, how PR-SET7 functions in trophoblasts and its relevance to RM remain unknown. Here, we found that trophoblast-specific loss of Pr-set7 in mice led to defective trophoblasts, resulting in early embryonic loss. Mechanistic analysis revealed that PR-SET7 deficiency in trophoblasts derepressed endogenous retroviruses (ERVs), leading to double-stranded RNA stress and subsequent viral mimicry, which drove overwhelming interferon response and necroptosis. Further examination discovered that H4K20me1 and H4K20me3 mediated the inhibition of cell-intrinsic expression of ERVs. Importantly, dysregulation of PR-SET7 expression and the corresponding aberrant epigenetic modifications were observed in the placentas of RM. Collectively, our results demonstrate that PR-SET7 acts as an epigenetic transcriptional modulator essential for repressing ERVs in trophoblasts, ensuring normal pregnancy and fetal survival, which sheds new light on potential epigenetic causes contributing to RM.

Multitasking via baseline control in recurrent neural networks.

Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a recurrent neural network with random couplings. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncovered a number of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. We identified several phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across a phase boundary retain the memory of the preceding phase. In each bistable phase, the reservoir performs a different binary decision-making task. Fast switching between different tasks can be controlled by adjusting the baseline input mean and variance. Moreover, we found that the reservoir network achieves optimal memory performance at any first-order phase boundary. In summary, baseline control enables multitasking without any optimization of the network couplings, opening directions for brain-inspired artificial intelligence and providing an interpretation for the ubiquitously observed behavioral modulations of cortical activity.

Optimal information loading into working memory explains dynamic coding in the prefrontal cortex.

Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.

Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning.

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Dorsoventral Heterogeneity of Synaptic Connectivity in Hippocampal CA3 Pyramidal Neurons.

The hippocampal CA3 region plays an important role in learning and memory. CA3 pyramidal neurons (PNs) receive two prominent excitatory inputs-mossy fibers (MFs) from dentate gyrus (DG) and recurrent collaterals (RCs) from CA3 PNs-that play opposing roles in pattern separation and pattern completion, respectively. Although the dorsoventral heterogeneity of the hippocampal anatomy, physiology, and behavior has been well established, nothing is known about the dorsoventral heterogeneity of synaptic connectivity in CA3 PNs. In this study, we performed Timm's sulfide silver staining, dendritic and spine morphological analyses, and ex vivo electrophysiology in mice of both sexes to investigate the heterogeneity of MF and RC pathways along the CA3 dorsoventral axis. Our morphological analyses demonstrate that ventral CA3 (vCA3) PNs possess greater dendritic lengths and more complex dendritic arborization, compared with dorsal CA3 (dCA3) PNs. Moreover, using ChannelRhodopsin2 (ChR2)-assisted patch-clamp recording, we found that the ratio of the RC-to-MF excitatory drive onto CA3 PNs increases substantially from dCA3 to vCA3, with vCA3 PNs receiving significantly weaker MFs, but stronger RCs, excitation than dCA3 PNs. Given the distinct roles of MF versus RC inputs in pattern separation versus completion, our findings of the significant dorsoventral variations of MF and RC excitation in CA3 PNs may have important functional implications for the contribution of CA3 circuit to the dorsoventral difference in hippocampal function.