Search bioRxiv⌕ Search

Biology subjects

Ebrahimi, A.

Publications and source records attributed to Ebrahimi, A..

At least 19 recordsLinked to original sources

Chromosome-scale Juglans genomes focus fungal defense gene divergence in gene presence and absence instead of copy number

Butternut (Juglans cinerea) is a North American hardwood in steep decline, driven largely by an introduced fungal pathogen that causes butternut canker disease. Tolerance exists within the genus, with the Asian Juglans ailantifolia comparatively tolerant and hybridizing readily with J. cinerea, yet the genomic basis of that difference is poorly resolved. Chromosome-scale reference genomes are presented for J. ailantifolia and for a second J. cinerea accession from the species primary range, with independent scaffolding and uniform annotation across all compared assemblies. Gene family evolution was assessed across twelve Juglandaceae genomes, one per species, and a four accession pangenome was built from J. ailantifolia and J. mandshurica representing section Cardiocaryon and two J. cinerea accessions, Brenton and New Brunswick, representing section Trachycaryon. Neither J. cinerea accession is a putative tolerant selection, so their contrast provides a null against which section-level differences are calibrated. Spanning two sections separated by approximately 30 My, the pangenome partitions many tandem arrays by accession instead of by gene, so copy number is interpreted after merging homology nodes representing the same gene. Section-level variation resides primarily in gene presence and absence, where pathogenesis-related proteins, cysteine-rich receptor-like kinases, wall-associated kinases and lectin-domain receptor kinases are over-represented in the lineage-specific complement of both sections, indicating rapid bidirectional turnover. A smaller set of families is asymmetric, with chitinases and chitin-binding proteins and NB-LRR receptors biased toward the tolerant section and dehydrins, pectin-modifying enzymes and the CBF regulon toward the susceptible one. The largest copy number difference in the dataset separates the two conspecific accessions, at a senescence-associated cysteine protease. The two butternuts are further distinguished by their demographic histories and by the functional composition of their accession-specific gene content: the northern New Brunswick accession retains lower heterozygosity, a distinct coalescent trajectory, and enrichment for calcium transport across channels, pumps and homeostasis, for salt stress response regulation, and for raffinose family oligosaccharide biosynthesis, spanning both the perception and the cryoprotective effectors of freezing tolerance. These findings support recognition of the northern population as a cryptic glacial refugium and a conservation priority.

genomics↗

Acute and cumulative light exposure differentially influence mood in healthy adults

Light influences mood in both clinical and experimental settings, yet it remains unclear how ambient light exposure relates to mood in daily life, which dimensions of mood are most sensitive to light and over what timescales these associations emerge. Here, we combined continuous personal light monitoring using wearable light sensors with repeated assessments of multiple dimensions of mood and affect, including self-reported mood ratings and cognitive measures of affective bias, alongside physiological monitoring in 49 healthy adults living under naturalistic conditions. Brighter light was consistently associated with more positive self-reported mood. Associations emerged over distinct temporal windows, with greater melanopic irradiance during the preceding 30 min associated with increased feelings of energy. Importantly, this association remained significant after accounting for alertness, physical activity and sleep-related covariates, suggesting that it is not solely explained by the established alerting effects of light. In contrast, several positive mood dimensions, including happiness, feeling accompanied, enjoyment, motivation and relaxation, were associated with light accumulated over longer periods (60-240 min). Furthermore, happiness, feeling accompanied, enjoyment and relaxation, but not energy, were also associated with greater cumulative daily exposure to light above 250 lux melanopic equivalent daylight illuminance (EDI), a threshold proposed to support healthy daytime light exposure. Behavioural measures of affective bias showed no robust associations with light exposure. Together, these findings indicate that everyday light exposure is associated with distinct dimensions of mood over different timescales, highlighting a complex relationship between light and affect in daily life. Significance StatementLight is a promising target for improving mental health, yet little is known about how everyday light exposure relates to mood outside laboratory and clinical settings. Using wearable light sensors and repeated mood assessments in healthy adults living under naturalistic conditions, we show that different dimensions of mood are linked to light exposure over distinct timescales, from recent exposure to cumulative light history. Importantly, the association between recent light exposure and feelings of energy remained independent of physical activity and sleep-related factors. These findings demonstrate that effects of light on mood are dynamic and multidimensional, highlighting the need to move beyond simple measures of total light exposure and informing future efforts to design healthier light environments that support emotional wellbeing.

neuroscience↗

A genetic network coordinated by TCP16 and LHY integrates regulation of the vegetative-reproductive phase transition in Arabidopsis thaliana

The transition to flowering in Arabidopsis thaliana is a complex process governed by many biological and environmental stimuli. Although many of the genes which regulate this process have been identified over the past 30 years, it remains unclear how these networks are integrated. In this study, we used the transcriptional responses of Col-0, Ler-1, and three mutant lines, to build a genome wide regulatory network of Arabidopsis thaliana during the flowering transition. The expression profiles of 22,810 genes across five genotypes were collected from the GEO database Series GSE57 from which we assigned flowering-time genes to different interacting modules by an adapted form of Hierarchical Complete Linkage Clustering (HCLC) after reconstruction of regulatory networks according to the Position Weight Matrix (PWM)-based method. Within these modules, we identified 77 core genes and 31 controller or driver genes. We identify two genes, LHY and, less expectedly, the transcription factor TCP16, to be topographically positioned at the regulatory hubs a nine-gene transcriptional control unit, implying they have the capacity to integrate information from across the flowering time pathways which interpret different environmental or endogenous cues during the vegetative-reproductive transition. Interrogating their behaviour across transcriptional datasets, we show that both LHY and TCP16 show transcriptional oscillations during the flowering transition, with a wavelength that varies depending on environmental conditions. We suggest that the transcriptional responses of LHY and TCP16 allow them to regulate the flow of information through the genetic networks which integrates different floral transition cues, and that genetic modelling approaches can provide new insights into the regulation of well-studied biological processes such as the flowering transition. Author summaryHow plants decide when to flower is a critical stage for completing their life cycles. It is also of key agricultural importance, as crops need to flower at the right time of year to allow efficient pollination and harvesting. Many genes are known to affect flowering time control in plants. Here, we use computational approaches to estimate how different genes interact in flowering time control in Arabidopsis, a small plant in the mustard family which is widely used for molecular studies. We use large-scale studies of how gene expression changes in different plant lines which have disrupted or adjusted flowering time to group the many genes involved in flowering into different interacting pathway, which we visualise as sets of coloured nodes controlling one another in a network. We show that two genes may have new rols in integrating information from different pathways, and discuss how their behaviour might help them to function as intregrators of biological information - including the daily oscaillations in their expression.

genetics↗

Recovering biological structure in sparse single-cell proteomics with GIRAFI

Single-cell proteomics (SCP) based on liquid-chromatography mass-spectrometry resolves protein-level cellular heterogeneity, but interpretation remains limited by detection-linked sparsity. SCP profiles continuous, peptide-derived intensities and has lower throughput than single-cell RNA sequencing, making denoising methods for large-scale, count-based transcriptomics difficult to apply. Here we present GIRAFI, a graph-informed statistical learning framework that imputes missing values and reveals reproducible cell states by constraining inference to dataset-aware, prior-knowledge-informed protein neighborhoods. We evaluated GIRAFI across SCP datasets spanning diverse biological/technical contexts. In masking-based recovery experiments and cell-type-specific protein-protein interaction inference, GIRAFI outperformed existing methods, and matched bulk proteomics comparisons corroborated recovery accuracy and ablations supported the graph-informed design. Beyond reduced replicate- and source-associated technical structure, GIRAFI recovered ground-truth cell-type annotations, improved cell state-resolved pathway analysis, and enabled trajectory inference consistent with known time courses. These results establish graph-constrained imputation as an effective strategy for improving SCP robustness, biological structure, interpretation, and cross-dataset comparability.

bioinformatics↗

Host-derived lipids license proliferation of sterol-auxotrophic tick cells and promote flavivirus output

Sterol molecules play indispensable roles in the biology of cells and organisms. Yet, arthropods, including ticks, lack the capacity for de novo sterol biosynthesis and must rely on exogenous sources. How these organisms sense and utilise exogenous lipids at the cellular level remains poorly understood. Here, we show that host cholesterol and linoleic acid act as essential external signals that license proliferation of Ixodes ricinus tick cells. In lipid-depleted conditions, cells fail to proliferate despite retaining intracellular cholesterol, indicating that exogenous lipids function as regulatory cues rather than merely structural components. We further demonstrate that tick cells exhibit a non-canonical intracellular distribution of cholesterol, with predominant accumulation in lysosome-associated compartments, and that lipid availability induces a coordinated transcriptional program involving lipid uptake and trafficking machinery. Importantly, we find that limiting host lipid availability significantly reduces extracellular RNA output of tick-borne encephalitis virus (TBEV) from infected tick cells, indicating that viral production is affected by the metabolic state of the host cell. Together, our findings establish host-derived lipids as key regulators of cellular state in a sterol-auxotrophic organism and identify lipid availability as a determinant of flavivirus output. This work provides a conceptual framework linking nutrient sensing, cell proliferation, and vector-pathogen interactions.

microbiology↗

Single-cell proteomics reveals proteome remodeling and cellular heterogeneity during NGF-induced PC12 neuronal differentiation

1Single-cell proteomics (SCP) enables direct measurement of cellular heterogeneity during dynamic biological processes. Here, we applied an SCP workflow to investigate proteome diversity during nerve growth factor (NGF)-induced differentiation of PC12 cells. Differentiated PC12 cells are highly adherent and prone to aggregation, complicating single-cell sample preparation. To address this challenge, sample handling was optimized using gentle dissociation, anti-adhesive conditions, and rapid processing immediately prior to cell isolation. Individual cells were deposited using a refined thermal inkjet (TIJ) dispensing system, enabling accurate single-cell placement with minimal sample loss. Inclusion of the mild nonionic surfactant n-dodecyl-{beta}-D-maltoside (DDM) improved recovery of membrane-associated and other low-solubility proteins. Coupled with high-sensitivity liquid chromatography-ion mobility-mass spectrometry, this workflow consistently quantified approximately 2,000-3,000 proteins per cell across differentiation stages. Single-cell proteomic profiles acquired over the differentiation time course revealed clear separation between undifferentiated and NGF-treated cells by Day 6. At later stages (Days 4-6), cells further partitioned into two distinct subpopulations with protein expression patterns not evident in bulk measurements. Dimensionality reduction and non-negative matrix factorization identified multiple proteomic states coexisting within the same differentiation stages, characterized by coordinated differences in pathways related to intracellular trafficking, protein translation, and neuronal structural organization. Together, these results show that while global proteome remodeling during PC12 differentiation is captured in both bulk and single-cell data, single-cell proteomics uniquely resolves functionally distinct cellular subpopulations that are masked in population-averaged analyses.

neuroscience↗

Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and Expanded Framework for Data Evaluation

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

biochemistry↗

Appearance of Amyloid-β Early in Life Initiates Neuronal Hyper-excitability, Mitochondrial Decay, and Loss of Dendritic Complexity in the Hippocampal CA1 Region of 5xFAD Mice

Alzheimers disease (AD) is characterized by progressive cognitive decline and stereotyped neuropathology, yet the earliest cellular events that precede overt plaque burden and measurable behavioral impairment remain incompletely defined. Here, we tested the hypothesis that synaptic hyperexcitability and subcellular metabolic dysfunction emerge early in the 5xFAD mouse model and contribute to region-specific neuronal vulnerability before substantial amyloid plaque deposition. Using the 5xFAD heterozygous mouse, we first established the onset of transgene expression and the timing of plaque accumulation. Robust transgene expression was detected by postnatal day 15 and significant plaque accumulation by 4 months of age. Ex vivo electrophysiology revealed an early hyperexcitable phenotype at 1 month of age, including both increased AMPA receptor-mediated transmission and N-methyl-D-aspartate receptor signaling associated with the GluN2B subunit. Given the tight coupling between glutamatergic hyperactivity, calcium dysregulation, and mitochondrial health, we assessed mitochondrial structure and function at this pre-plaque stage. Mitochondrial abnormalities consistent with impaired bioenergetic homeostasis were evident. Morphological analyses further demonstrated that these early changes were associated with altered dendritic architecture in the CA1 and dentate gyrus regions, revealing hippocampal subregional susceptibility. Finally, spatial transcriptomics supported this anatomical selectivity by identifying regionally enriched molecular signatures consistent with differential vulnerability. The CA1 region exhibited more reductions in mitochondria-related transcripts than CA3 or dentate gyrus and these reductions were specifically associated with CA1 pyramidal cell neurons. Together, these findings define a pre-plaque window in 5xFAD mice marked by GluN2B-linked glutamatergic hyperexcitability, early mitochondrial disruption, and selective dendritic and transcriptional vulnerability across hippocampal subregions. This integrated timeline suggests that synaptic and metabolic dysfunctions arise before substantial plaque deposition and may represent tractable early targets for intervention aimed at delaying or preventing downstream neurodegeneration in AD.

neuroscience↗

Small extracellular vesicles reflect senescence progression in human bone marrow-derived mesenchymal stem cells during hollow fiber bioreactor culture

Prolonged three-dimensional culture exposes stem cells to sustained microenvironmental and mechanical stresses that can promote aging- and senescence-associated phenotypic alterations. This study examined how long-term expansion of human bone marrow-derived mesenchymal stem cells (BMSCs) in a hollow fiber bioreactor (HFB) influences cellular senescence and the molecular composition of secreted small extracellular vesicles (sEVs). During extended HFB culture, BMSCs exhibited progressive morphological flattening and cytoskeletal disorganization, accompanied by increased senescence-associated {beta}-galactosidase activity and immunophenotypic remodeling characterized by reduced fluorescence intensity and spatial redistribution of canonical MSC markers, consistent with a stress-adapted, early senescence-associated cellular state. In parallel, sEVs were collected longitudinally over 40 days and characterized by nanoparticle tracking analysis, immunoblotting, and quantitative proteomics. While vesicle size, marker expression, and yield remained stable throughout culture, proteomic profiling revealed pronounced, phase-dependent remodeling of sEV cargo, including coordinated alterations in oxidative stress-related processes, lysosomal and extracellular matrix-associated pathways, and relative depletion of cytoskeletal and translational components. Notably, these vesicular signatures closely mirrored senescence-associated changes observed at the cellular level. The strong correspondence between cellular phenotypes and sEV proteomic profiles establishes vesicle analysis as a convergent and noninvasive readout of BMSC aging, enabling sensitive monitoring of senescence progression while reducing reliance on parallel, labor-intensive cellular assays. Collectively, these findings indicate that prolonged HFB culture promotes a controlled, stress-associated senescence program in BMSCs and position sEV proteomic profiling as a robust approach for assessing stem cell aging dynamics during long-term three-dimensional bioreactor culture.

molecular biology↗

Phylogenomic and comparative genome analysis of the Juglandaceae family reveals black walnut (Juglans nigra) as an ancient lineage of the Rhysocaryon section.

Accelerated climate change causes heatwaves, droughts, and severe freezes, posing threats to tree regeneration and to global biodiversity. Deeper genomic insights are crucial for breeding, conserving genetic resources, guiding management, and building resilience to these threats. Juglans, a globally distributed genus of timber and nut-producing trees, still has unresolved species relationships; comprehensive genomic data would clarify adaptation to rapid climate change and inform assisted migration strategies. We analyzed 20 Juglans species from North and South America and Asia using whole-genome data, chloroplast genome data, cold-hardiness gene families, and environmental variables related to freezing tolerance. Phylogenomic analyses indicate that black walnuts (J. nigra, section Rhysocaryon) may have diverged ~28.7 MYA (late Oligocene). J. nigra reveals higher nucleotide diversity with more genes than other Juglans, whereas J. hindsii clusters with Central/South American species, consistent with a more recent divergence within Rhysocaryon. As expected, Juglans species originating from mild-winter regions are distinct from those in cold-climate regions in genome-wide and cold-hardiness gene analyses, with introgression occurring mainly from temperate into tropical lineages. Juglans species from milder or warmer winters show CBF gene deletions, whereas cold-adapted species lack such gene deletions. These results support J. nigra as an ancient lineage within Rhysocaryon, consistent with subsequent colonization of Central/South America and longer evolutionary histories. Genome analyses further suggest Rhysocaryon is ancestral to Cardicaryon, Trachycaryon, and Dioscaryon. These findings illuminate the demographic history of the Juglans, particularly Rhysocaryon, and have direct implications for gene conservation, breeding, and assisted migration.

genomics↗

Artificial Intelligence-enabled Histological Analysis in Preclinical Respiratory Disease Models: A Scoping Review

Histological analysis is a cornerstone of preclinical respiratory disease research, enabling assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale due to low throughput and inter-observer variability. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows, but its use and evaluation in preclinical respiratory models have not been synthesized. We conducted a scoping review following Joanna Briggs Institute guidelines, searching MEDLINE and Embase (inception-January 2025) for preclinical studies using AI to analyze histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate. Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Hematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Preprocessing methods (e.g., stain normalization) were common, but annotation and training practices were inconsistently reported. Performance was generally high (accuracy [≥]90%; 7 studies) though validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported. AI tools are poised to transform histological analysis in preclinical respiratory research. By addressing gaps in validation, transparency, and standardization, the field can harness these technologies to deliver robust, efficient, and scalable workflows. Registration: Open Science Framework https://doi.org/10.17605/OSF.IO/NM94E

cell biology↗

Non-canonical enhancers control gene expression and cell fate in human pluripotent stem cells

Enhancers are key gene regulatory elements that ensure the precise spatiotemporal execution of developmental gene expression programmes. However recent findings indicate that approaches to identify enhancers may not capture the full repertoire of active enhancers in mammalian genomes. Here, we combine massively parallel enhancer assays with chromatin structure and transcriptome profiling to functionally annotate enhancers genome-wide in human induced pluripotent stem cells. We find that a substantial fraction ([~]40%) of accessible chromatin regions with enhancer function lack key features associated with active enhancers, including the active enhancer mark histone H3 lysine 27 acetylation and enhancer-associated RNAs. Perturbation of this class of non-canonical enhancers by CRISPR-mediated epigenome editing results in decreased levels of target gene expression and, in one instance, loss of pluripotent stem cell characteristics. Collectively, our data demonstrate enhancer activity for a class of gene regulatory elements that had until now only been associated with a neutral or inactive status, challenging current models of enhancer function.

genomics↗

sCIN: A Contrastive Learning Framework for Single-Cell Multi-omics Data Integration

The rapid advancement of single-cell omics technologies such as scRNA-seq and scATAC-seq has transformed our understanding of cellular heterogeneity and regulatory mechanisms. However, integrating these data types remains challenging due to distributional discrepancies and distinct feature spaces. To address this, we present a novel single-cell Contrastive INtegration framework (sCIN), that integrates different omics modalities into a shared low-dimensional latent space. sCIN uses modality-specific encoders and contrastive learning to generate latent representations for each modality, aligning cells across modalities and removing technology-specific biases. The framework was designed to rigorously prevent data leakage between training and testing, and was extensively evaluated on three real-world paired and unpaired datasets including SHARE-seq, 10X PBMC (10k version), and CITE-seq. Paired datasets refer to multi-omics data generated using technologies capable of capturing different omics features from the same individual cells whereas unpaired datasets are collected from different but similar cell populations within the same tissue, with each modality measured separately. Results on paired and unpaired datasets show that sCIN outperforms state-of-the-art models, including scGLUE, scBridge, sciCAN, Con-AAE, Harmony, and MOFA+, across multiple metrics: Average Silhouette Width (ASW) for clustering quality, Recall@k, Cell type@k, Cell type accuracy, and Median Rank for integration quality. Moreover, sCIN was evaluated on simulated unpaired datasets derived from paired data, demonstrating its ability to leverage available biological information for effective multimodal integration. In summary, sCIN reliably integrates omics modalities while preserving biological meaning in both paired and unpaired settings.

bioinformatics↗

Embodied processing in whisker somatosensory cortex during exploratory behaviour in freely moving mice

Sensory systems have evolved to solve computational challenges that animals face during behaviour in their natural environments. To illuminate how sensory cortex operates under such conditions, we investigated the function of neurons in whisker-related Somatosensory Cortex (wS1) of freely moving mice, engaged in tactile exploratory behaviour. By recording neural activity from wS1 whilst tracking the mouse body in 3D, we found that wS1 neurons are substantially modulated by body state (configuration of individual body-parts and their derivatives), even in the absence of whisker afferent input. Most neurons were modulated by multiple dimensions of body state, with the most prominently encoded being the angle of the head to the body and locomotion speed. Overall, our data suggest that sensory cortex functions as an embodied representation, which integrates signals from its associated sense organ within a body schema.

neuroscience↗

Enhanced restoration of visual code after targeting on bipolar cells compared to retinal ganglion cells with optogenetic therapy

Optogenetic therapy is a promising vision restoration method where light sensitive opsins are introduced to the surviving inner retina following photoreceptor degeneration. The cell type targeted for opsin expression will likely influence the quality of restored vision. However, a like-for-like pre-clinical comparison of visual responses evoked following equivalent opsin expression in the two major targets, ON bipolar (ON BCs) or retinal ganglion cells (RGCs), is absent. We address this deficit by comparing stimulus-response characteristics at single unit resolution in retina and dorsal lateral geniculate nucleus (dLGN) of retinally degenerate mice genetically engineered to express the opsin ReaChR in Grm6- or Brn3c-expressing cells (ON BC vs RGCs respectively). For both targeting strategies, we find ReaChR-evoked responses have equivalent sensitivity and can encode contrast across different background irradiances. Compared to ON BCs, targeting RGCs decreased response reproducibility and resulted in more stereotyped responses with reduced diversity in response polarity, contrast sensitivity and temporal frequency tuning. Recording ReaChR-driven responses in visually intact retinas confirmed that RGC-targeted ReaChR expression disrupts visual feature selectivity of individual RGCs. Our data show that while both approaches restore visual responses with impressive fidelity, ON BC targeting produces a richer visual code better approaching that of wildtype mice.

neuroscience↗

Functional integrity of visual coding following advanced photoreceptor degeneration

Photoreceptor degeneration sufficient to produce severe visual loss often spares the inner retina. This raises the hope that treatments using optogenetics or electrical stimulation, which generate a replacement light input signal in surviving neurons, may restore vision. The success of these approaches is dependent on the capacity of surviving circuits in the early stages of the visual system to generate and propagate an appropriate visual code in the face of neuroanatomical remodelling. To determine the capacity of surviving circuits in advanced retinal degeneration to present an appropriate visual code, we generated a transgenic mouse expressing the optogenetic actuator ReaChR in ON bipolar cells (second order neurons in the visual projection). After crossing this with the rd1 model of photoreceptor degeneration, we compared ReaChR derived responses with photoreceptor-driven responses in wildtype (WT) mice in retinal ganglion cells and visual thalamus. The ReaChR-driven responses in rd1 animals showed low photosensitivity, but in other respects generated a visual code that was very similar to WT. Furthermore, ReaChR rd1 units in the retina had high response reproducibility and showed sensitivity normalisation to code contrast stably across different background intensities. At the single unit level, ReaChR-derived responses exhibited broadly similar variation in light response polarity, contrast sensitivity and temporal frequency tuning as WT. Units from WT and ReaChR rd1 mice clustered together when subjected to unsupervised community detection based on stimulus-response properties. Our data reveal an impressive ability for surviving circuitry to recreate a rich visual code following advanced retinal degeneration and are promising for regenerative medicine in the central nervous system.

neuroscience↗

Intercellular collectivity is governed by enzyme secretion strategies in marine polysaccharide degrading bacteria

Polysaccharide breakdown by bacteria requires the activity of enzymes that degrade polymers extracellularly. This generates a localized pool of breakdown products that are accessible to the enzyme producers themselves as well as to other organisms. Marine bacterial taxa often show marked differences in the production and secretion of degradative enzymes that break down polysaccharides. These differences can have profound effects on the pool of diffusible breakdown products and hence on the ecological dynamics. However, the consequences of differences in enzymatic secretions on cellular growth dynamics and interactions are unclear. Here we combine experiments and models to study the growth dynamics of single cells within populations of marine Vibrionaceae strains that grow on the abundant marine polymer alginate, using microfluidics coupled to quantitative single-cell analysis and mathematical modelling. We find that strains that have low extracellular secretions of alginate lyases show stronger aggregative behaviors compared to strains that secrete high levels of enzymes. One plausible reason for this observation is that low secretors require a higher cellular density to achieve maximal growth rates in comparison with high secretors. Our findings indicate that increased aggregation increases intercellular synergy amongst cells of low-secreting strains. By mathematically modelling the impact of the level of degradative enzyme secretion on the rate of oligomer loss to diffusion, we find that enzymatic capability modulates the propensity of cells within clonal populations to cooperate or compete with each other. Our experiments and models demonstrate that marine bacteria display distinct aggregative behaviors and intercellular interactions based on their enzymatic secretion capabilities when growing on polysaccharides.

microbiology↗

Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles

In response to environmental stresses such as starvation, many bacteria facultatively aggregate into multicellular structures that can attain new metabolic functions and behaviors. Despite the ubiquity and relevance of this form of collective behavior, we lack an understanding of how the spatiotemporal dynamics of aggregate development emerge from cellular physiology. Here, we show that the coupling between growth and spatial gradient formation leads to the emergence of a complex lifecycle, akin to those known for multicellular bacteria. Under otherwise carbon-limited growth conditions, the marine bacterium Vibrio splendidus 12B01 forms multicellular groups to collectively harvest carbon from the brown-algal polysaccharide alginate. This is achieved during growth on dissolved alginate polymer through formation of spherical, clonal clusters of cells that grow up to 40 {micro}m in radius. Clusters develop striking spatial patterning as they grow due to phenotypic differentiation of sub-populations into a shell of static cells surrounding a motile core. Combining in situ measurements of cell physiology with transcriptomics, we show that shell cells express adhesive type IV pili, while motile core cells express carbon storage granules. The emergence of shell and core phenotypes is cued by opposing gradients of carbon and nitrogen that form within cell clusters due to local metabolic activity. Eventually, the shell ruptures, releasing the carbon-storing core, and we show that carbon-storing cells more readily propagate on alginate than non-carbon storing cells. We propose that phenotypic differentiation promotes the resilience of 12B01 groups by enabling clonal groups to grow larger and propagate more effectively. Phenotypic differentiation may be a widespread, but overlooked, strategy among bacteria to enhance resilience in the context of resource limitation.

microbiology↗