2025-04-06 | | Total: 132
The cerebral cortex is a multi-layered structure generated through the migration of neural precursors from their birthplace in the ventricular zone to their destination within the cortical plate. Neuronal migration defects are responsible for many human pathologies collectively called neuronal migration disorders, which include subcortical band heterotopia and cobblestone brain (COB) malformation. One example of a protein involved in a neuronal migration disorder is the echinoderm microtubule-associated protein-like 1 (EML1) protein, one of six members of the mammalian EML family. Absence of EML1 protein results in subcortical band heterotopia in mice and humans. Here, we report that absence of the paralogous protein EML3 leads to delayed embryonic development and small size, and a COB-like phenotype with neuronal ectopias in the dorsal telencephalon. We found that EML3 is expressed in the neuroepithelium and meningeal mesenchyme when those tissues participate in pial basement membrane (PBM) formation. Transmission electron microscopy demonstrated that the extracellular matrix of the PBM is structurally abnormal in Eml3 null mice when the first radially migrating neurons arrive. The reduced structural integrity of the PBM leads to focal over-migration of neurons into the subarachnoid space. These findings strengthen the link between the EML protein family and cortical neuronal migration defects by identifying Eml3 as the first EML family member whose absence leads to over-migration of neuroblasts. Moreover, we report the first COB-like phenotype with PBM structural defects when a single microtubule-associated protein is deleted.
Hair cells and their apically located stereocilia bundle are responsible for detecting sound and balance, by converting mechanical stimuli into electrical signals through the mechano-electric transduction (MET) channels, located at the lower end of the tip link connecting adjacent stereocilia. A long-studied regulation of the MET process is slow adaptation, which is hypothesized to contribute to the auditory systems remarkable dynamic range. Recent studies challenged the old model of slow adaptation which centered around myosin motors. We support a new model of slow adaptation that relies on phosphatidylinositol 4,5-bisphosphate (PIP2) interactions with the MET complex protein Tmie. First, we further support the hypothesized location of the slow adaptation mechanism at the lower end of the tip link by showing that slow adaptation is independent of myosin VIIa, located at the upper end of the tip link. Next, in both cochlear and vestibular hair cells, we demonstrate the reliance of slow adaptation on PIP2. Most strikingly, slow adaptation was rescued with exogenous PIP2 when myosin motors were inhibited, indicating the primary importance of PIP2. Finally, we suggest the importance of Tmie that binds PIP2 in the slow adaptation mechanism. These data support a new model of slow adaptation where PIP2 interactions with Tmie mediate slow adaptation in mammalian hair cells with myosin motors having a classic cargo transport role.
Allelic variations of Sperm antigen with calponin homology and Coiled-Coil domains 1 Like (SPECC1L) have been associated with a spectrum of cranial-facial pathologies including Teebi hypertelorism and Opitz G/BBB syndrome which manifest as clefting of the palate, wide-eyes, and incomplete closure of the esophagus among others. These pathologies may be indicative of improper cranial neural crest cell (CNCC) delamination and migration. SPECC1L is hypothesized to be an actin-microtubule cross-linking protein as it co-localizes with both microtubules and actin in tissue culture cells. Further, it has been shown to immunoprecipitate with a protein phosphatase complex-1β (PP1β) member, MYPT1, as well as being involved in the PI3K-AKT signaling axis. In this study we sought to investigate the SPECC1L Drosophila homolog Spdi and despite sharing close homology with its mammalian counterpart we found that Spdi is associated with both non-muscle myosin-II and actin. RNAi depletion of Spdi led to an increase in focal adhesion dynamics and when we introduced conserved point mutations to Spdi that are analogous to those associated with human disease we observed a further increase in focal adhesion dynamics above that of depletion alone. Collectively, our findings suggest that Spdi is a non-muscle myosin II (NMII) binding protein that likely affects focal adhesion dynamics through this association. Our results also suggest that some of the pathologies associated with allelic variants of SPECC1L may be the result of aberrant cell-matrix adhesion.
Chromosome territories (CTs) are intricately organized and regulated within the nucleus. Despite remarkable advances in our understanding of genome packaging and gene expression, the interplay among CTs, pairing of parental homologous chromosomes, and genome function during development remains elusive. Here, we employ an Oligopaints-based high-resolution imaging approach to examine variable CT organization in single nuclei during the developmental process of zygotic genome activation. We reveal large-scale chromosome changes with extensive homolog pairing at the whole-chromosome level that decreases locally due to spatial variability in chromosome conformations. In the absence of one homolog copy, the dynamics of CT compaction and RNA polymerase II recruitment are supported by transcriptional changes in haploid embryos. Finally, global inhibition of transcription results in decreased CT opening and no significant impact on CT pairing levels. These findings enhance our understanding of parental genome folding and regulation, which may inform strategies for chromosome-based diseases.
Purpose: We aimed to compare the effects of atelocollagen (AC) and individual growth factors on the expression of key molecular markers associated with tendon healing. Methods: C2C12 myoblasts were cultured in Dulbecco’s Modified Eagle Medium (DMEM) containing 5% fetal bovine serum (FBS) and treated with 1 nM or 10 nM of Atelocollagen (AC), bone morphogenetic protein-2 (BMP-2), transforming growth factor-beta 1 (TGF-β1), insulin-like growth factor-1 (IGF-1), or vascular endothelial growth factor (VEGF) for 5 days. After 5 days of treatment, cells were harvested from the culture medium, and Western blot analysis was performed to quantify the expression of phosphorylated extracellular signal-regulated kinase (p-ERK), Collagen type I (Col I), Collagen type III (Col III), and Tenascin C (TnC). Additionally, immunofluorescence staining was conducted to qualitatively assess cytoskeletal organization and cell adhesion, which are key factors in tendon healing. Results: In the AC-treated groups, the expression levels of p-ERK, Col I/Col III, and TnC were significantly higher compared to the groups treated with individual growth factors (BMP-2, IGF, VEGF, and TGF-β1) (p < 0.05). These changes were dose-dependent, as there was no significant difference in protein expression between AC and the growth factors at 1 nM, whereas at 10 nM, AC treatment resulted in a significant increase (p < 0.05). In the cell proliferation assay, C2C12 myoblasts treated with AC at 10 nM exhibited significantly higher proliferation rates compared to those treated with individual growth factors (p < 0.05). Additionally, immunofluorescence analysis revealed greater cytoskeletal alignment in AC-treated cells, suggesting enhanced cell adhesion, structural organization, and mechanical stability. Conclusions: AC significantly upregulated key molecular markers involved in cell proliferation, extracellular matrix remodeling, and tendon structural integrity more effectively than individual growth factors. The increased expression of these genes in myoblasts suggests AC’s potential role in promoting tendon healing. Clinical relevance: Through the modulation of key molecular pathways critical to tendon healing, AC presents strong potential as an effective biological augmentation strategy for improving tendon-to-bone interface healing after surgical repair.
Cellular membranes orchestrate critical processes such as molecular transport and signal transduction, both regulated by the lateral mobility of lipids and proteins. However, resolving nanoscale diffusional heterogeneities and elucidating their underlying mechanisms remains a formidable challenge due to the membrane's intricate architecture and compositional diversity. Here, we present point-cloud single-molecule diffusivity mapping (pc-SMdM), a cutting-edge super-resolution technique that offers a point-cloud data format with enhanced spatial resolution for diffusivity mapping. Using pc-SMdM, we visualize nanoscale diffusion slowdown clusters with ~50 nm in diameter on plasma membranes. These clusters are predominantly governed by cholesterol content, alongside contributions from membrane protein assemblies and topographical features. Leveraging two-color pc-SMdM, we concurrently imaged multiple lipids and membrane probes, revealing distinct "fingerprint" diffusivity maps shaped by their interactions within lipid bilayers. Our findings position pc-SMdM as a transformative tool for spatially resolving molecular interactions and membrane dynamics in live cells, offering new insights into the underlying mechanisms that govern membrane mobility at the nanoscale.
Deciphering the cellular history of different cancers is critical for understanding tumor development and improving diagnostic and therapeutic strategies in oncology. Previous studies have shown that chromatin accessibility of the normal cell of transformation (COT) is a major determinant of a cancer's mutational landscape. We leveraged single-cell chromatin accessibility data from 559 healthy cell subsets to predict the COT across 36 cancer subtypes, providing unprecedented scale and resolution into the cellular beginnings of cancers. Our machine learning model predicts the COT with high robustness and accuracy, confirming both the known anatomical and cellular origins of numerous cancers, often at cell subset resolution. Unexpectedly, our work challenges traditional views that small cell lung cancer arises from neuroendocrine cells, opening new avenues for research. Our study also highlights different cellular trajectories for histological subtypes and a metaplastic state during tumorigenesis for multiple gastrointestinal cancers, which have important implications for cancer prevention, early detection, and treatment stratification.
Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is critical for comprehensive genomic analysis. While traditional short-read sequencing performs well for SNP and INDEL detection, it struggles to resolve SVs, especially in complex genomic regions, due to inherent read length limitations. Linked-read sequencing technologies, such as single-tube Long Fragment Read sequencing (stLFR), overcome these challenges by employing molecular barcodes, providing crucial long-range information. This study investigates traditional pair-end linked-reads and a conceptual extension of linked-read technology: barcoded single-end reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR), generated using the single-tube Long Fragment Read (stLFR) platform. Unlike conventional paired-end (PE100_stLFR) linked reads, these longer single-end reads could offer improved resolution for variant detection by leveraging extended read lengths per barcode. We simulated a diverse set of datasets for the HG002 sample using T2T-based realistic genome simulation. Variant detection performance was then systematically assessed across three stLFR configurations: standard PE100_stLFR, SE500_stLFR, and SE1000_stLFR. Benchmarking against the Genome in a Bottle (GIAB) gold standard reveals distinct strengths of each configuration. Extended single-end reads (SE500_stLFR and SE1000_stLFR) significantly enhance SV detection, with SE1000_stLFR providing the best balance between precision and recall. In contrast, the shorter PE100_stLFR reads exhibit higher precision for SNP and INDEL calling, particularly within high-confidence regions, though with reduced performance in low-mappability contexts. To explore optimization strategies, we constructed hybrid libraries combining paired-end and single-end barcoded reads. These hybrid approaches integrate the complementary advantages of different read types, consistently outperforming single libraries across small variant types and genomic contexts. Collectively, our findings offer a robust comparative framework for evaluating stLFR sequencing strategies, highlight the promise of barcoded single-end reads for improving SV detection, and provide practical guidance for tailoring sequencing designs to the complexities of the genome.
G protein-coupled receptors (GPCRs) are the largest class of receptors in the genome and control many signaling cascades essential for survival. GPCR signaling is regulated by β-arrestins, multifunctional adapter proteins that direct receptor desensitization, internalization, and signaling. While at many GPCRs, β-arrestins interact with a wide array of signaling effectors, it is unclear how β-arrestins promote such varied functions. Here we show that β-arrestins undergo liquid-liquid phase separation (LLPS) to form condensates that regulate GPCR function. We demonstrate that β-arrestin oligomerization occurs in proximity to the GPCR and regulates GPCR functions such as internalization and signaling. This model is supported by a cryoEM structure of the adhesion receptor ADGRE1 in a 2:2 complex with β-arrestin 1, with a β-arrestin orientation that can promote oligomerization. Our work provides a paradigm for β-arrestin condensates as regulators of GPCR function, with LLPS serving as an important promoter of signaling compartmentalization at GPCRs.
T cells are one of the most powerful weapons to fight cancer; however, T cell exhaustion and dysfunction restrict their long-lasting function in anti-tumor immunity. B-cell lymphoma 6 (BCL6) has many functions in CD8 T cells but it is unclear how it regulates the effector function and exhaustion of CD8 cells. Overall, a low level of BCL6 mRNA in human cancer samples is associated with better outcomes. We found that BCL6 deficiency in activated CD8 T cells enhanced tumor repression in multiple mouse models. More IL-2-expressing CD8 T cells and reduced proportions of exhausted or dysfunctional CD8 T cells were detected within tumors when Bcl6 was knocked out upon T cell activation. Glycolysis was promoted in BCL6-deficient CD8 T cells owing to derepression of glucose transporter GLUT3 (encoded by Slc2a3). The BCL6 inhibitor Fx1 promoted anti-tumor immunity in a T cell-dependent manner. These findings suggest a novel pathway to restore effector function of CD8 T cells by changing their energy utilization pathways to facilitate long-term tumor resistance.
In insects, conspicuous larval pigmentation patterns serve critical ecological roles such as warning signals and mimicry, yet their underlying genetic regulation remains poorly understood. In this study, I investigated the molecular mechanisms underlying black and yellow pigmentation patterns in three distinct larval spot types of the silkworm Bombyx mori: large, diffuse L-spots of the Multilunar (L) mutant; small, sharply defined +p-spots of the Normal strain; and oval pM-hybrid spots of an interspecific hybrid with Bombyx mandarina. Each spot type comprises a yellowish center surrounded by a black periphery, forming crescent-shaped pigmentation patterns. Chemical treatments confirmed that both colors are melanin-based. Using quantitative PCR and RNA interference (RNAi), I analyzed six melanin synthesis genes (Tyrosine Hydroxylase, Dopa Decarboxylase, laccase2, yellow, tan, and ebony) and discovered that black pigmentation involves both dopa/dopamine- and NBAD-melanin synthesis, whereas yellow pigmentation primarily reflects only the latter. I further examined Wnt1 and apontic-like (apt-like) using qPCR, RNAi, and TALEN-mediated mosaic analysis. Wnt1 expression localized to presumptive spot areas, functioning dose-dependently to regulate both spot size and pigment composition: high Wnt1 levels induced larger spots with yellow centers, while reduced Wnt1 expression resulted in black pigmentation and smaller spots. Wnt1-activated transcription factor apt-like was required for pigmentation in all spot types without influencing spot size. Taken together, the results of this study reveal a morphogen-driven gene regulatory network in which Wnt1 dosage and downstream transcriptional cascades orchestrate pigment placement and patterning, offering new insights into the modular genetic control of insect pigmentation.
As a crucial plateau freshwater lake in Yunnan Province, China, Erhai Lake exhibits distinct environmental heterogeneity driven by its unique watershed characteristics and human activities, significantly influencing sediment microbial communities. This study investigated the spatial relationships between environmental factors and microbial community structures in surface sediments from the eastern, western, and northern shores using redundancy analysis (RDA) and Spearman correlation analysis. Results revealed that pH, total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), and redox potential (Eh) were key drivers of microbial community divergence. The western shore, with the highest TP, TOC, and nitrogen levels, displayed elevated microbial diversity dominated by Proteobacteria and Bacteroidetes, reflecting higher pollution loads. The northern shore exhibited severe nitrogen pollution, marked by the highest TN content and enrichment of Thiobacillus, potentially enhancing water self-purification. The eastern shore, with minimal anthropogenic disturbance, showed the highest bacterial diversity but the lowest nutrient concentrations. Fungal community structure was significantly influenced by pH, Eh, and TOC, while ecological restoration measures on the western shore enhanced fungal community stability. This study highlights how spatial heterogeneity in environmental factors regulates microbial community structure and function, ultimately affecting the stability of lake ecosystems. These findings provide a scientific basis for ecological restoration and sustainable management of plateau lakes.
Canonical prokaryotic two-component signal transduction systems (TCSs) are widely utilized by bacteria to respond to their environment and are typically composed of a transmembrane sensor His kinase (HK) and a cytosolic DNA-binding response regulator (RR) that work together to respond to environmental stimuli. An important TCS that regulates the expression of genes involved in biofilm formation and antibiotic resistance in many pathogens is the BqsRS/CarRS system, originally identified in Pseudomonas aeruginosa. Transcriptomics data suggested that the cognate PaBqsRS stimulus is Fe2+, but PaBqsS has not been characterized at the protein level, and a direct interaction between Fe2+ and PaBqsS has not been demonstrated. In this work, we biochemically and functionally characterize intact PaBqsS, an iron-sensing membrane HK, for the first time. Using bioinformatics, protein modeling, metal analyses, site-directed mutagenesis, and X-ray absorption spectroscopy (XAS), we show that PaBqsS binds a single Fe2+ ion per protein dimer within the periplasmic sensor domain by using a unique ligation motif comprising Glu48 and Asn49. Using activity assays, we show that both intact PaBqsS and its truncated cytosolic domain have competent ATPase activity, consistent with predicted function. Importantly, we show that the ATP hydrolysis of intact PaBqsS is stimulated exclusively by Fe2+, revealing metal-based activation of a functional, intact membrane HK for the first time. Taken together, this work uncovers important structural and biochemical properties of an intact, metal-sensing membrane HK that could be leveraged to target the BqsRS system for future therapeutic developments.
Campylobacter and non-typhoidal Salmonella (NTS) are among the most common foodborne pathogens found in chickens at any production stage and cause gastroenteritis in humans. This study aimed to estimate the prevalence of Campylobacter spp. (C. coli and C. jejuni) and NTS in broiler production and distribution networks (PDN) using a Bayesian approach. A cross-sectional study was conducted in four provinces in northern Vietnam between March 2021 and March 2022. A total of 102 sites, including live bird markets, slaughter facilities (slaughterhouses and slaughter points), and their supplying farms, were randomly selected for sampling. Cecal and environmental samples were cultured for isolation of Campylobacter and NTS, with serotypes of NTS determined by targeted analysis of whole genome sequences. Bayesian models were developed to estimate the prevalence of Campylobacter at two levels (bird-level and site-level) and NTS at site-level. The selected best-fitted models indicated that C. jejuni prevalence was primarily influenced by site type, while C. coli was affected by both province and site types. For NTS, only site type was included. The highest overall prevalence of infected broilers was estimated on farms for C. coli (26.2% [95% High Density Interval (HDI): 19.0-36.0%]) and C. jejuni (19.9% [95%HDI 13.0-27.0%]). Slaughter points (97.6% [95%HDI 63.3-99.9%]) and wholesale markets (91.7% [95%HDI 28.2-99.9%]) had the highest probability of C. coli and C. jejuni contamination, respectively, but retail markets had the highest proportion of infected broilers at contaminated sites. NTS contamination was more frequent in markets and slaughter facilities (42.8% [95%HDI 30.8-57.1%]) than on farms (18.6% [95%HDI 9.5-30.1%]). Among 16 detected NTS serotypes, S. Infantis and S. Kentucky were the most common. These findings highlight the widespread contamination of broiler PDNs with Campylobacter and NTS in northern Vietnam, emphasizing the need for enhanced surveillance and control measures in PDNs to mitigate the risk of foodborne transmission.
It is a common observation that individuals within a species age at different rates. Variation in both genetics and environmental interaction are generally thought responsible. Surprisingly, even genetically identical organisms cultured under environmentally homogeneous conditions age at different rates, implying a more fundamental cause of aging. Here we have examined the basis for lifespan variance in haploid, single-celled yeast of Saccharomyces cerevisiae. The probabilistic nature of metabolism means metabolites often, but not always, follow the same route through the metabolic network. We speculate redundancy in metabolic pathway choice is sufficient to explain lifespan variance. To interrogate the reaction flux space of S. cerevisiae we used a model of its intermediary metabolism, comprising 1,150 genes, 4,058 reactions, and 2,742 metabolites (yeast GEM_v8.5.0). We restricted traffic through the metabolic network by knocking out each of the 1,150 genes, then generated a total of 406,500 flux distributions spanning the solution space of the resulting 812 viable mutants. We collected replicative life span (RLS) data for the 812 viable mutants, corresponding to 66,400 individual cells. Four approaches were then employed to test whether reaction flux configuration could be used to predict lifespan: Principal Component Analysis (PCA) in conjunction with non-linear modeling of RLS; deep learning of RLS using either a Regression Neural Network (RNN) or a Classification Neural Network (CfNN); and deep learning using a convolutional neural network (CNN) following conversion of flux distributions to pixelated images. The four approaches reveal a core network of highly correlated reactions controlling aging rate that is sufficient to explain all lifespan variance. It includes biosynthetic pathways encompassing ceramides, monolysocardiolipins, phosphoinositides, porphyrin and glycerolipids. Our data lead to two novel conclusions. First, variance in the replicative lifespan of S. cerevisiae is an emergent property of its metabolic network. Second, there is convergence among metabolic configurations toward three meta-stable flux states – one associated with extended life, another with shortened life, and a third with wild type life span. One Sentence Summary Traffic routes and rates through the metabolic network of S. cerevisiae fully account for variance in replicative lifespan.
Autotaxin (ATX) is a lysophospholipase D (lysoPLD) serving as both a lysophosphatidic acid (LPA)-producing enzyme and a LPA docking molecule. ATX binds to the cell surface via interaction with adhesive molecules, including [beta]1-integrin, potentially facilitating LPA access to its specific G protein-coupled receptors. However, the precise protein-protein interaction sequences and their biological implications remain unknown. Here, we identify the interaction domains between ATX and [beta]1-integrin and generate specific blocking antibodies allowing to demonstrate that ATX-[beta]1 binding domain involves a cryptic epitope unmasked by LPA docking. In addition, whereas anti-ATX antibodies do not inhibit the lysoPLD activity, immunological neutralization of the ATX-[beta]1 integrin binding site reduces arthritis development in a collagen-induced arthritis model. These findings offer novel insights into the molecular mechanisms governing ATX functions, which, in addition to its enzymatic activity, requires cell surface binding. These findings suggest that ATX binding domains could be targeted for novel therapeutic approaches.
Influenza poses a persistent global health challenge, necessitating innovative approaches to predict host-virus interactions and inform antiviral strategies. Despite advancements, machine-learning-based computational methods typically struggle with limited high-quality negative samples and inadequate modeling of complex host-virus interactions, which hinder predictive accuracy and generalization. To address these challenges, we present SEHI-PPI, a novel end-to-end protein-protein interaction (PPI) prediction for human-influenza. SEHI-PPI proposes a double-view deep learning approach to extract global and local sequence features with a novel adaptive negative sampling strategy for high-quality negative sample generation. SEHI-PPI outperforms various benchmarks, including the state-of-the-art large language models, with superior performance in sensitivity (0.986) and AUROC (0.987). In a test where both human and influenza protein families are new from the training data, our model reached an AUROC of 0.837. We further validate its generalizability by applying it to other human-virus PPI predictions, and on average, we achieved 0.929 in sensitivity and 0.928 in AUROC. Combined with the structural predictions from AlphaFold3, our case studies show that viral proteins predicted to bind the same human protein have similar structures and functions based on the clustering results. These discoveries demonstrate the reliability of our SEHI-PPI framework in uncovering biologically meaningful host-virus interactions and potential therapeutic targets.
Recent studies have shown that Escherichia coli in highly confined porous media exhibit extended periods of ‘trapping’ punctuated by forward ‘hops’, a significant restructuring of the classical run- and-tumble model of motility. However, bacterial species must navigate a diverse range of complex habitats, such as biological tissues, soil, and sediments. These natural environments display varying levels of both (1) packing density (i.e., confinement) and (2) packing structure (i.e., disorder). Here, we introduce a microfluidic device that enables precise tuning of these environmental parameters, allowing for a more systematic exploration of bacterial motility bridging the extremes of unconfined and highly confined conditions. We observe that motility patterns characteristic of both hop-and-trap and run-and-tumble models coexist in nearly all environments tested, with ensemble dynamics transitioning between these behaviors as both confinement and disorder increase. We demonstrate that dynamics expected from the hop-and-trap model emerge naturally from a modified run-and- tumble model under specific environmental constraints. Our results suggest that bacterial motility patterns lie along a continuum, rather than being confined to a small set of discrete locomotive modes.
We can rapidly learn recurring patterns that occur within our sensory environments. This knowledge allows us to form expectations about future sensory events. Several influential predictive coding models posit that, when a stimulus matches our expectations, the activity of feature-selective neurons in visual cortex will be suppressed relative to when that stimulus is unexpected. However, after accounting for known critical confounds, there is currently scant evidence for these hypothesised effects from studies recording electrophysiological neural activity. To provide a strong test for expectation effects on stimulus-evoked responses in visual cortex, we performed a probabilistic cueing experiment while recording electroencephalographic (EEG) data. Participants (n=488) learned associations between visual cues and subsequently presented gratings. A given cue predicted the appearance of a certain grating orientation with 10%, 25%, 50%, 75%, or 90% validity. We did not observe any stimulus expectancy effects on grating-evoked event-related potentials. Bayes factors generally favoured the null hypothesis throughout the time-courses of the grating-evoked responses. Multivariate classifiers trained to discriminate between grating orientations performed better when classifying 10% compared to 90% probability gratings. However, classification performance did not substantively differ across any other stimulus expectancy conditions. Our findings provide very limited evidence for modulations of prediction error signalling by probabilistic expectations as specified in contemporary predictive coding models.
Despite over a decade of effort, microbial single-cell genomics and transcriptomics remain a challenge, especially for complex communities such as the human microbiome. Here we report a solution based on the Laser-Induced Forward Transfer (LIFT) technology, which circumvent the need for droplet microfluidics and complex barcoding, and allow selective amplification of the genome and the transcriptome of commensal bacteria from complex samples. We capture the diverse oral microbiome, and elucidate the single-cell transcriptome in sporulation. This single-cell method is both scalable and precise, and enables investigation into tissue samples. The approach shows great promise for elucidating the single-cell fate of microbial populations and the native environment of the host-associated microbiome.
Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded with-out further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for an opto-tagged visual cortex dataset and brain region classification for the public International Brain Laboratory brain-wide map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings.
Alpine Upper Palaeolithic contexts exhibit specialised subsistence strategies, heavily dependent on Capra ibex. Among them, the rock shelter Riparo Dalmeri stands out, with C. Ibex dominating faunal remains across all occupation phases, spanning the Pleistocene/Holocene transition. This evidence positions Riparo Dalmeri as a key site for exploring the interdependence between human groups and C. ibex during one of the most critical climatic and cultural shifts in human evolution. Here, we present the first multidisciplinary study on Late Palaeolithic C. ibex teeth from Riparo Dalmeri, integrating direct radiocarbon dating, isotope (87Sr/86Sr, δ13C, δ18O), proteomic, and aDNA analyses. We generated the earliest aDNA sequences for C. ibex and contextual evidence on mobility, seasonality, and sex ratios. We found that most C. ibex were local to the area despite consistent human presence. They reveal significant dietary differences between sexes as well as increased seasonality at the Pleistocene-Holocene transition. Our results identify Riparo Dalmeri as an extinct branch of the ibex mtDNA phylogeny, offering unprecedented insights into ibex ecology and evolution that resonate with present-day issues on the conservation of this species in the face of climate change.
Inositol phosphates (IPs) play important roles in nervous system development and function. One of these roles uncovered by loss-of-function studies, is that IP isomers are essential for proper neural tube formation. In this study, we show that inositol pentakisphosphate 2-kinase (IPPK-1), the kinase that phosphorylates IP5 to generate IP6, is involved in assembling the ventral nerve cord (VNC) in C. elegans. We show that mutations in ippk-1 lead to the mispositioning of motor neurons along the VNC of newly hatched larvae. These positioning defects reflect disruption of VNC assembly during embryogenesis, as ippk-1 embryos display improper alignment of VNC neuroblasts and delays in rosette-mediated convergent extension (CE). We further show that injection of exogenous IP6 into the gonads of ippk-1 mutants can rescue both embryonic and neuron positioning defects. Our findings indicate that inositol metabolism is important for regulating CE in C. elegans and suggest that IP isomers play a conserved role in the formation of central nerve cords. Highlights – ipmk-1 and ippk-1 mutants display neuron position defects in the ventral nerve cord (VNC). – ippk-1 mutants display disorganization in VNC neuronal precursors during midline convergence. – IPPK-1 is involved in convergent extension during VNC formation. – Exogenous IP6 rescues larval and embryonic defects in ippk-1 mutants.
Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environmental factors. A common approach to estimating bacterial populations involves successive dilution and plating, followed by multiplying colony counts by dilution factors. This method, however, overestimates the heterogeneity in bacterial population because it conflates the inherent uncertainty in drawing a subsample from the total population with the uncertainty in the sample arising from biological origins. In other words, this approach may obscure features that may otherwise be present in the data hinting at the presence of genuine subpopulations. For example, in plate counting applied to C. elegans gut microbiota, observed multimodality is often interpreted as large host-to-host variance, while the randomness introduced by measurement is frequently ignored. To explicitly account for the uncertainty introduced by dilution and plating randomness, we introduce REPOP, a PyTorch-based library to REconstruct POpulations from Plates within a Bayesian framework. Beyond simple cases, REPOP addresses more complex scenarios, including multimodal populations and correcting the mathematically subtle, but experimentally relevant, bias introduced by excluding plates deemed too crowded to distinguish individual colonies. We demonstrate REPOP's ability to resolve distinct population peaks otherwise obscured by standard multiplication methods. Applications to both simulated and experimental datasets, including bacterial samples of different concentrations and ones from the gut microbiota of C. elegans, show that REPOP accurately recovers the underlying multimodality by properly accounting for error propagation, where naive multiplication fails. REPOP is available on GitHub: \url{https://github.com/PessoaP/REPOP}.
Labyrinthula species are protist organisms found across a variety of marine environments whose defining characteristic is the secretion of an extracellular ectoplasmic net. Under certain conditions, colonies form a spatial network of 'tracks' through which cells move bidirectionally. We show that this network morphology depends on the presence of a liquid overlay, with air exposed colonies exhibiting instead a dense, aggregated morphology. We demonstrate dynamic restructuring between these two morphologies upon addition or removal of the liquid overlay, and investigate growth behaviour under varying nutrient conditions. Given the inter-tidal environment of certain seagrass species colonised by Labyrinthula, our results may shed light on the relationship between this organism and its seagrass host, for which it is an opportunistic pathogen associated with seagrass wasting disease.