Cardiomyocyte hypertrophy and apoptosis underlie cardiomyopathies and heart failure. While previous studies have reported both hypertrophy and apoptosis at the population level, how individual cells commit to these distinct analog and digital fates is unclear. To elucidate how individual cells decide to grow and/or die, we developed a high-content microscopy approach to track single-cell dynamics of neonatal rat cardiomyocytes. Even untreated cells exhibited substantial single-cell variability in growth and death. Uniform treatments of staurosporine or phenylephrine induced distinctive morphological programs resulting in apoptosis and hypertrophy, respectively, but only in cell subpopulations. Increasing concentrations of the β-adrenergic receptor agonist isoproterenol caused a population-level biphasic induction of hypertrophy and then apoptosis, consistent with either apoptosis in the most hypertrophic cells (a grow-and-die model) or an early decision between hypertrophy and delayed apoptosis (a grow-or-die model). By tracking single-cell fates, we found that when stressed with either isoproterenol or phenylephrine, individual cells that hypertrophy are protected from later apoptosis. Further, caspase 3 inhibition shifted the single-cell probability from apoptosis to hypertrophy fates. Machine learning models found that a cell's initial size and DNA content or condensation could predict a cell's bias for hypertrophy or apoptosis. Together, these data support a grow-or-die conceptual model for cardiomyocyte decisions. This single-cell profiling method for tracking joint analog-digital cell decisions reveals that despite hypertrophy and apoptosis co-occurring at the population level, individual cardiomyocytes decide early whether to grow or die.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI’s capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.
Species distribution models (SDMs) are widely used to gain ecological understanding and guide conservation decisions. These models are developed with a wide variety of algorithms – from statistic-based approaches to machine learning approaches – but a requirement almost all share is the use of predictor variables that strongly simplify the temporal variability of driving factors. Conversely, novel architectures of deep learning neural networks allow dealing with fully explicit spatiotemporal dynamics and thus fitting SDMs without the need to simplify the temporal and spatial dimension of predictor data. We present and demonstrate a deep learning based SDM approach that uses time series of spatial data as predictors using distribution data for 74 species from a well-established benchmark dataset. The deep learning approach provided consistently accurate models, directly using time series of predictor data and thus avoiding the use of pre-processed predictor sets that can obscure relevant aspects of environmental variation.
Primordial follicles are quiescent ovarian structures comprised of a single oocyte surrounded by a layer of somatic supporting pregranulosa cells. Primordial follicle activation is the first step towards oocyte maturation and, ultimately, ovulation. As there is a finite number of primordial follicles within the ovary, their rate of activation is a critical parameter of the female reproductive lifespan. Follicle activation is characterised by morphological and molecular changes in both pregranulosa cells and oocytes. However, the signal initiating activation, and even the cell-type responding to this signal, remain unidentified. Through single-cell transcriptomic profiling of neonatal mouse ovaries we identify a putative gene expression signature of activating pregranulosa cells. We find precocious expression of this putative pregranulosa cell signature in the Cdkn1b-/- or p27kip1-null model, which has ubiquitous follicle activation neonatally. We confirm that the protein product of one of these genes, Tnni3, is present within mouse granulosa cells, which has also been described recently for human granulosa cells. We demonstrate that expression of cardiac troponin I (TNNI3), an actin-binding protein, is upregulated upon cell-cycle resumption in activating pregranulosa cells. This indicates pregranulosa cells likely initiate primordial follicle activation and support the broader hypothesis that changes in cell tension are involved in triggering follicle activation.
To maintain CO2 fixation in the Calvin Benson-Bassham cycle, multi-step regulation of the chloroplast ATP synthase (CF1Fo) is crucial to balance the ATP output of photosynthesis with protection of the apparatus. A well-studied mechanism is thiol modulation; a light/dark regulation through reversible cleavage of a disulfide in the CF1Fo γ-subunit. The disulfide hampers ATP synthesis and hydrolysis reactions in dark-adapted CF1Fo from land plants by increasing the required transmembrane electrochemical proton gradient . Here, we show in Chlamydomonas reinhardtii that algal CF1Fo is differently regulated in vivo. A specific hairpin structure in the γ-subunit redox domain disconnects activity regulation from disulfide formation in the dark. Electrochromic shift measurements suggested that the hairpin kept wild type CF1Fo active whereas the enzyme was switched off in algal mutant cells expressing a plant-like hairpin structure. The hairpin segment swap resulted in an elevated threshold to activate plant-like CF1Fo, increased by ∼1.4 photosystem (PS) I charge separations. The resulting dark-equilibrated dropped in the mutants by ∼2.7 PSI charge separation equivalents. Photobioreactor experiments showed no phenotypes in autotrophic aerated mutant cultures. In contrast, chlorophyll fluorescence measurements under heterotrophic dark conditions point to a reduced plastoquinone pool in cells with the plant-like CF1Fo as the result of bioenergetic bottlenecks. Our results suggest that the lifestyle of Chlamydomonas reinhardtii requires a specific CF1Fo dark regulation that partakes in metabolic coupling between the chloroplast and acetate-fueled mitochondria. Significance Statement The microalga Chlamydomonas reinhardtii exhibits a non-classical thiol modulation of the chloroplast ATP synthase for the sake of metabolic flexibility. The redox switch, although established, was functionally disconnected in vivo thanks to a hairpin segment in the γ-subunit redox domain. Dark enzymatic activity was prevented by replacing the algal hairpin segment with the one from land plants, restoring a classical thiol modulation pattern. Thereby, ATP was saved at the expense of thylakoid membrane energization levels in the dark. However, metabolism was impaired upon silencing dark ATPase activity, indicating that a functional disconnect from the redox switch represents an adaptation to different ecological niches.
The nature of social interactions determines engagement or avoidance with conspecifics. Here, we explore the circuit mechanisms that guide approach or avoidance behavior in mice based on the valence of previous social interactions. We identify a novel circuit connecting D1 receptor expressing neurons of the anterior insular cortex (AIC) to D1R expressing neurons of the nucleus accumbens (NAc). These cells become active during social interactions in a valence-dependent manner. Lower frequency patterns encoded appetitive interactions, while aversive interactions led to higher activation. These activity patterns elicited distinct forms of synaptic plasticity in the accumbal target neurons, which were causal for subsequent approach or avoidance behavior. Our results unravel the synaptic mechanisms instructing behavior after the social interaction of opposite valence.
Despite the curative potential of checkpoint blockade immunotherapy, most patients remain unresponsive to existing treatments. Glyco-immune checkpoints – interactions of cell-surface glycans with lectin, or glycan-binding, immunoreceptors – have emerged as prominent mechanisms of immune evasion and therapeutic resistance in cancer. Here, we describe antibody-lectin chimeras (AbLecs), a modular platform for glyco-immune checkpoint blockade. AbLecs are bispecific antibody-like molecules comprising a cell-targeting antibody domain and a lectin “decoy receptor” domain that directly binds glycans and blocks their ability to engage inhibitory lectin receptors. AbLecs potentiate anticancer immune responses including phagocytosis and cytotoxicity, outperforming most existing therapies and combinations tested. By targeting a distinct axis of immunological regulation, AbLecs synergize with blockade of established immune checkpoints. AbLecs can be readily designed to target numerous tumor and immune cell subsets as well as glyco-immune checkpoints, and therefore represent a new modality for cancer immunotherapy.
Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet. Highlights DeePathNet integrates biological pathways for enhanced cancer analysis. DeePathNet utilizes Transformer-based deep learning for superior accuracy. DeePathNet outperforms existing models in drug response prediction. DeePathNet enables pathway-level biomarker discovery in cancer research.
Neuroblastoma is characterised by extensive inter- and intra-tumour genetic heterogeneity and varying clinical outcomes. One possible driver for this heterogeneity are extrachromosomal DNAs (ecDNA), which segregate independently to the daughter cells during cell division and can lead to rapid amplification of oncogenes. While ecDNA-mediated oncogene amplification has been shown to be associated with poor prognosis in many cancer entities, the effects of ecDNA copy number heterogeneity on intermediate phenotypes are still poorly understood. Here, we leverage DNA and RNA sequencing data from the same single cells in cell lines and neuroblastoma patients to investigate these effects. We utilise ecDNA amplicon structures to determine precise ecDNA copy numbers and reveal extensive intercellular ecDNA copy number heterogeneity. We further provide direct evidence for the effects of this heterogeneity on gene expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells. These results highlight the potential for rapid adaptability of cellular states within a tumour cell population mediated by ecDNA copy number, emphasising the need for ecDNA-specific treatment strategies to tackle tumour formation and adaptation.
Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.
The Global Panzootic Lineage (GPL) of the pathogenic fungus Batrachochytrium dendrobatidis (Bd) has caused severe amphibian population declines, yet the drivers underlying the high frequency of GPL in regions of amphibian decline are unclear. Using publicly available Bd genome sequences, we identified multiple non-GPL Bd isolates that contain a circular Rep-encoding single stranded DNA (CRESS)-like virus which we named BdDV-1. We further sequenced and constructed genome assemblies with long read sequences to find that the virus is integrated into the nuclear genome in some strains. Attempts to cure virus positive isolates were unsuccessful, however, phenotypic differences between naturally virus positive and virus negative Bd isolates suggested that BdDV-1 decreases the growth of its host in vitro but increases the virulence of its host in vivo. BdDV-1 is the first described CRESS DNA mycovirus of zoosporic true fungi with a distribution inversely associated with the emergence of the panzootic lineage.
Understanding how neurons communicate and coordinate their activity is essential for unraveling the brain’s complex functionality. To analyze the intricate spatiotemporal dynamics of neural signaling, we developed Geometric Scattering Trajectory Homology (neuro-GSTH), a novel framework that captures time-evolving neural signals and encodes them into low-dimensional representations. GSTH integrates geometric scattering transforms, which extract multiscale features from brain signals modeled on anatomical graphs, with t-PHATE, a manifold learning method that maps the temporal evolution of neural activity. Topological descriptors from computational homology are then applied to characterize the global structure of these neural trajectories, enabling the quantification and differentiation of spatiotemporal brain dynamics. We demonstrate the power of neuro-GSTH in neuroscience by applying it to both simulated and biological neural datasets. First, we used neuro-GSTH to analyze neural oscillatory behavior in the Kuramoto model, revealing its capacity to track the synchronization of neural circuits as coupling strength increases. Next, we applied neuro-GSTH to neural recordings from the visual cortex of mice, where it accurately reconstructed visual stimulus patterns such as sinusoidal gratings. Neuro-GSTH-derived neural trajectories enabled precise classification of stimulus properties like spatial frequency and orientation, significantly outperforming traditional methods in capturing the underlying neural dynamics. These findings demonstrate that neuro-GSTH effectively identifies neural motifs—distinct patterns of spatiotemporal activity—providing a powerful tool for decoding brain activity across diverse tasks, sensory inputs, and neurological disorders. Neuro-GSTH thus offers new insights into neural communication and dynamics, advancing our ability to map and understand complex brain functions.
The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.
Insulin secretion is governed by insulin-PI3K signaling. Resolving the mechanism of this feedback is necessary to understand how insulin operates. Mice lacking the insulin receptor, or AKT1 and AKT2 in adipocytes, are severely lipoatrophic. Thus, the role of adipocyte insulin-PI3K signaling in the control of insulin secretion remains unknown. Using adipocyte- specific PI3Kα knockout mice (PI3KαAdQ) and a panel of isoform-selective PI3K inhibitors, we have found that PI3Kα and PI3Kβ activities are functionally redundant in adipocyte insulin signaling. PI3Kβ-selective inhibitors had no effect on adipocyte AKT phosphorylation in control mice but blunted AKT phosphorylation specifically in adipocytes of PI3KαAdQ mice, demonstrating adipocyte-selective inhibition of PI3K signaling. Adipocyte-selective PI3K inhibition increased serum FFA and potently induced insulin secretion. We name this phenomenon the adipoincretin effect. The adipoincretin effect was dissociated from blood glucose and blood glucose counterregulatory response. The contribution of lipolysis, lipid, and amino acid metabolism, and selected adipokines to the adipoincretin effect has been investigated. We conclude that basal insulin secretion is chiefly controlled by adipocyte PI3K signaling through the adipoincretin effect. This phenomenon reveals an essential role for adipocyte insulin-PI3K signaling in linking the rates of adipose tissue lipolysis with baseline insulin secretion during fasting.
The colonization of our stomachs by Helicobacter pylori is believed to predate the oldest splits between extant human populations. We identify a “Hardy” ecospecies of H. pylori associated with indigenous groups, isolated from people in Siberia, Canada, USA and Chile. The ecospecies shares the ancestry of “Ubiquitous” H. pylori from the same geographical region in most of the genome but has nearly fixed SNP differences in 100 genes, many of which encode outer membrane proteins and host interaction factors. For these parts of the genome, the ecospecies has a separate, independently evolving gene pool with a distinct evolutionary history. H. acinonychis, found in big cats, and a newly identified primate-associated lineage both belong to the Hardy ecospecies and both represent human to animal host jumps. Most strains from the ecospecies encode an additional iron-dependent urease that is shared by Helicobacter from carnivorous hosts, as well as a tandem duplication of vacA, encoding the vacuolating toxin. We conclude that H. pylori split into two highly distinct ecospecies in Africa and that both dispersed around the globe with humans, but the Hardy ecospecies has gone extinct in most parts of the world. Our analysis also pushes back the likely length of the association between H. pylori and humans.
Protogyny, being capable of changing from female to male during their lifetime, is prevalent in 20 families of teleosts but is believed to have evolved within specific evolutionary lineages. Therefore, shared regulatory factors governing the sex change process are expected to be conserved across protogynous fishes. However, a comprehensive understanding of this mechanism remains elusive. To identify these factors, we conducted a meta-analysis using gonadal transcriptome data from seven species. We curated data pairs of ovarian tissue and transitional gonad, and employed ratios of expression level as a unified criterion for differential expression, enabling a meta-analysis across species. Our approach revealed that classical sex change-related genes exhibited differential expression levels between the ovary and transitional gonads, consistent with previous reports. These results validate our methodology’s robustness. Additionally, we identified novel genes not previously linked to gonadal sex change in fish. Notably, changes in the expression levels of acetoacetyl-CoA synthetase and apolipoprotein Eb, which are involved in cholesterol synthesis and transport, respectively, suggest that the levels of cholesterol, a precursor of steroid hormones crucial for sex change, are decreased upon sex change onset in the gonads. This implies a potential universal influence of cholesterol dynamics on gonadal transformation in protogyny.
Models of perceptual awareness often lack tractable neurobiological constraints. Inspired by recent cellular recordings in a mouse model of tactile threshold detection, we constructed a biophysical model of perceptual awareness that incorporated essential features of thalamocortical anatomy and cellular physiology. Our model reproduced, and mechanistically explains, the key in vivo neural and behavioural signatures of perceptual awareness in the mouse model, as well as the response to a set of causal perturbations. We generalised the same model (with identical parameters) to visual rivalry and found that the same thalamic-mediated mechanism of perceptual awareness determined perceptual dominance. This led to the generation of a set of novel, and directly testable, electrophysiological predictions. Analyses based on dynamical systems theory showed that perceptual awareness in simulations of both threshold detection and visual rivalry arises from the emergent systems-level dynamics of thalamocortical loops.
Pain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain’s learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signalled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants’ pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of the nociceptive flexion reflex and EEG markers of cortical nociceptive processing (N2-P2 evoked potential and gamma-band power). Additionally, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.
