Biology subjects

Mora, A.

Publications and source records attributed to Mora, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Reframing enzyme function prediction as conditional generation

Enzymes frequently exhibit promiscuous activity beyond their native roles, providing starting-points for new functions. Finding these promiscuous enzymes, especially for non-native chemical transformations, is challenging but highly valuable, as they promise novel, sustainable solutions for chemistry and biotechnology. However, current machine learning methods are poorly suited to discovering unseen chemistry as they often frame function prediction as closed set classification or a retrieval task. Here, we present Fluxion, a generative deep learning framework that learns enzymatic catalysis by modeling dynamic electron flow trajectories across the enzyme's catalytic residues. By combining both synthetic chemistry and biochemical datasets with protein language model representations, Fluxion generates multi-step electron-flow trajectories analogous to the arrow-pushing representations used to describe enzyme reaction mechanisms. Generation is conditioned on enzyme context, including the enzyme sequence, catalytic residues, substrates, and cofactors. We show that this conditioning allows Fluxion to learn enzyme-dependent regioselectivity across cytochrome P450 enzymes with different sequences shifting the predicted reaction sites for the same substrate. We then demonstrate that Fluxion's embeddings are useful for downstream tasks, such as specificity prediction on two experimental datasets, with and without finetuning. Finally, we show that Fluxion has the potential to transfer synthetic chemical logic to biology; it can generate the observed non-native product from real-world non-native directed evolution screens. Our results establish a proof of concept that generative modeling through mechanistic representations of enzymes can shift enzyme function prediction beyond static database retrieval and closed set classification to function generation. This conceptual framework provides a stepping stone towards an in silico generative method to discover non-native biocatalysts.

biochemistry↗

bioRxiv2026

Inhibition of p107 alleviates liver steatosis by reducing de novo fatty acid synthesis.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by excessive hepatic lipid accumulation driven by increased de novo lipogenesis (DNL) and impaired lipid oxidation. p107, a member of the retinoblastoma (Rb) family, extensively studied in the context of cell cycle regulation and adipocyte differentiation recently has been identified as a metabolic regulator controlling thermogenic activity. However, its role in hepatic lipid homeostasis remains poorly understood. Here, we identify the cell cycle regulator p107 as a key modulator of hepatic lipid metabolism. p107 expression is increased in patients with MASLD and correlates with disease severity. In mouse models, global and liver-specific p107 deficiency protect against high-fat diet-induced steatosis without affecting body weight. This is associated with reduced expression of lipogenic enzymes including fatty acid synthase (FASN), and enhanced mitochondrial oxidative pathways. Conversely, hepatic restoration of p107 reversed these effects and promoted lipid accumulation and endoplasmic reticulum stress. Consistent with this in human hepatocytes, p107 silencing reduces lipid accumulation, decreases DNL and enhances mitochondrial respiration, whereas p107 overexpression induces the opposite phenotype. Notably, FASN knockdown attenuates the pro-steatotic effects of p107, indicating that it is a critical downstream mediator of p107. Together, these findings establish p107 as a physiological regulator of hepatic lipid metabolism, with its dysregulation contributing to the development of MASLD.

physiology↗

bioRxiv2026

Discovery of a phenazine thiol conjugase from sparse data using genome-informed machine learning

Machine learning has enabled powerful biological discoveries using models trained on large datasets. However, for many important biological questions, such as identifying enzymes that transform understudied substrates, sparsity of training data is often a major bottleneck. Here, using phenazine natural products as a case study, we show that integrating genome-informed data augmentation with contrastive learning in protein language space enables identification of phenazine-interacting proteins starting from only 14 known phenazine modifying sequences. Applying this framework led to the discovery of PTC (Phenazine-Thiol Conjugase), the first enzyme known to catalyze phenazine thioconjugation, a phenazine modification reaction long observed but previously presumed to occur only through non-enzymatic chemistry. In silico simulation and experimental measurements demonstrate that PTC binds to both phenazine and glutathione as substrates. Recombinant expression and biochemical characterization reveal that PTC promotes glutathione-dependent modification of phenazines, yielding distinct reaction outcomes that depend on substrate identity. Although thiol-conjugated phenazine products exhibit reduced toxicity to bacterial cells, deletion of the gene encoding PTC does not confer a strong fitness disadvantage, illustrating how direct learning of sequences can uncover relevant enzymes that might evade phenotype-based genetic screens. Together, these results demonstrate that coupling comparative genomics with protein machine learning can convert "small data" typically outside the scope of machine learning into actionable predictive power, thereby facilitating enzyme discovery. SignificanceMachine learning excels when large, well-labeled datasets are available, yet many biologically important problems lack sufficient experimental data to support such approaches to discovery. This limitation is particularly acute for identifying enzymes acting on rare or understudied substrates. Here, we show that genomic organization can be leveraged as an additional source of biological information to address data sparsity. Starting with only 14 enzymes experimentally shown to modify phenazines, we developed a model identifying phenazine-interacting enzymes by integrating genome-informed data augmentation with protein machine learning. Guided by the model, we discovered the first enzyme known to catalyze thioconjugation modifications of phenazines, demonstrating a simple yet powerful strategy for extracting predictive insight from sparse biological knowledge.

microbiology↗

bioRxiv2026

A unified pipeline for discovering previously unknown enzyme activities

Enzymes catalyze diverse chemical transformations and offer a sustainable approach to both breaking and making chemical bonds. However, finding an enzyme capable of performing a specific chemical reaction remains a challenge. We developed a new framework, Enzyme-toolkit (Enzyme-tk), that integrates 23 open-source tools to enable the discovery of enzymes that have activity toward a specific target reaction. Additionally, we introduce two new methods to facilitate enzyme discovery: (1) Func-e, an ML tool that searches large databases for enzymes that potentially catalyze a specific chemical transformation and (2) Oligopoolio, a gene assembly approach that reduces the cost of accessing protein sequences and thus the barrier to their experimental validation. We applied Enzyme-tk to find enzymes for chemical degradation of two man-made pollutants, di-(2-ethylhexyl) phthalate (DEHP) and triphenyl phosphate (TPP). We demonstrate that new, previously unannotated enzymes with favorable characteristics, such as high thermostability, can be identified using Enzyme-tk for reactions that are dissimilar to the training set.

biochemistry↗

bioRxiv2026

Sulcal Widening in Schizophrenia Maps onto Sulcal Hubs and Energy-Synaptic Genes

Schizophrenia (SZ) is increasingly framed as a disorder of large-scale brain networks emerging from atypical early neurodevelopment, yet how network architecture relates to cortical folding abnormalities remains unclear. Here, we introduce a sulcal morphological-centred network framework that integrates normative modelling of sulcal width with diffusion-derived structural connectivity and transcriptomic data in a large multisite cohort (n = 5,392; 377 SZ). Individuals with SZ showed widespread sulcal widening, affecting 30 of 40 sulci and most pronounced in frontal, temporal and occipital regions. Critically, sulci with higher degree centrality, reflecting greater embedding within the structural connectome, exhibited disproportionately greater widening in SZ (pspin = 0.02), indicating that network hubs of cortical folding are preferentially affected. Transcriptomic integration using partial least squares regression identified a single component explaining 56.5% of SZ-related sulcal widening variance (pperm = 0.041), implicating genes enriched for synaptic signalling and energy metabolism with adult cortical expression bias and genetic enrichment for cross-disorder psychiatric risk. In contrast, oppositely weighted genes showed prenatal expression bias and enrichment for rare disruptive variants in autism spectrum disorder. Together, these findings link aberrant sulcal morphology in SZ to the brains network topology and molecular architecture, positioning cortical folding as a network-embedded phenotype in SZ.

neuroscience↗

bioRxiv2025

Generating Hybrid Proteins with the MSA-Transformer

Protein superfamilies display extensive sequence and functional divergence, providing a rich landscape for engineering functionally enhanced variants. We present a stochastic, iterative framework that leverages the MSA-Transformer to generate intermediate sequences between a homologous pair of user-specified "source" and "target" proteins in sequence space. Sequence sites to "mask" is either selected by embedding-based dissimilarity or by row-attention information, while beam search concurrently explores multiple mutational pathways. Pre-trained sparse autoencoders combined with sequence and structural analyses are used to trace the inheritance and exchange of features across the "mutational pathway", revealing "hybrid" sequences that integrate properties of both source and target proteins. Applied across diverse protein families, the framework produces sequences that occupy biologically meaningful regions of sequence space and achieve higher consistency and plausibility scores than random baselines according to in silico metrics. In the B1/B2 metallo-{beta}-lactamase family, hybrids largely retain their core fold recombining structural and active-site motifs from both subclasses, demonstrating the models capacity to preserve catalytic features while exploring novel structural permutations. Code availabilityThe implementation is available via GitHub at https://github.com/santule/protmixy.

bioinformatics↗

bioRxiv2025

An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation

Cellular senescence is a primordial driver of tissue and organ aging, and the accumulation of senescent cells (SnCs) has been implicated in numerous age-related diseases. A major barrier to studying senescence is the rarity and heterogeneity of SnCs, which are not a uniform population but instead comprise diverse senotypes shaped by cell-of-origin and microenvironmental context. Such heterogeneity exceeds what classical senescence hallmarks can resolve at single-cell resolution, motivating the need for computational frameworks that can capture senotype-level diversity intrinsically. Here, we introduce DeepSAS, a deep graph representation learning framework that robustly identifies cell-type-specific SnCs and their senescence-associated genes (SnGs). DeepSAS incorporates a heterogeneous graph that integrates intracellular transcriptional states with intercellular communication cues, enabling the joint inference of senescent cells and senescence-linked genes through attention-based contrastive learning. Applied to public healthy eye and lung atlases, DeepSAS identified SnCs whose proportions positively correlate with aging. From in-house idiopathic pulmonary fibrosis (IPF) patient scRNA-seq data, DeepSAS detected 1,678 SnCs (out of 24,125 cells) and 263 SnGs across 26 cell types, including 43 SnGs that are uniquely associated with a single cell type. We generated high-resolution Xenium spatial transcriptomics data to further validate SnGs in IPF, revealing NFE2L2 as a SnG specifically enriched in CTHRC1+ fibroblasts. Notably, the ex vivo bleomycin-induced senescence in human precision-cut lung slice (hPCLS) samples similarly identified NFE2L2 as an SnG in CTHRC1+ fibroblasts, albeit with stronger transcriptional signals, suggesting mechanistic differences in senescence cells associated with chronic and acute injury. Overall, DeepSAS uncovers distinct senescence programs and infers cell-type-specific SnGs that are difficult to resolve using existing marker-based approaches. We believe it offers a generalizable and translationally relevant strategy for advancing senescence biology and therapeutic development.

bioinformatics↗

bioRxiv2025

Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

Enzymes present a sustainable alternative to traditional chemical industries, drug synthesis, and bioremediation applications. Because catalytic residues are the key amino acids that drive enzyme function, their accurate prediction facilitates enzyme function prediction. Sequence similarity-based approaches such as BLAST are fast but require previously annotated homologs. Machine learning approaches aim to overcome this limitation; however, current gold-standard machine learning (ML)-based methods require high-quality 3D structures limiting their application to large datasets. To address these challenges, we developed Squidly, a sequence-only tool that leverages contrastive representation learning with a biology-informed, rationally designed pairing scheme to distinguish catalytic from non-catalytic residues using per-token Protein Language Model embeddings. Squidly surpasses state-of-the-art ML annotation methods in catalytic residue prediction while remaining sufficiently fast to enable wide-scale screening of databases. We ensemble Squidly with BLAST to provide an efficient tool that annotates catalytic residues with high precision and recall for both in- and out-of-distribution sequences.

bioinformatics↗

bioRxiv2025

Adipocyte MKK3 Increases in Human Obesity protecting against Insulin Resistance by p38-beta activation

Obesity is a major global health concern, and a key predisposing factor for insulin resistance and type 2 diabetes mellitus. Adipocytes play a critical role in the development of obesity-induced insulin resistance, with several signaling pathways influencing insulin sensitivity. Among these, p38 MAP kinases are essential for adipose tissue physiology and the regulation of processes such as differentiation, thermogenesis, and inflammation. p38 activation is mediated by the upstream kinases MKK3 and MKK6 in response to inflammatory signals. While MKK6 inhibition promotes browning and thermogenesis and protects against obesity, the role of MKK3 remains unclear. Here, we investigated the function of MKK3 in adipose tissue. In human adipose tissue samples, MKK3 expression was positively correlated with body mass index (BMI) and negatively correlated with glycated hemoglobin, a marker of hyperglycemia. Using whole-body and adipose-specific Mkk3 and p38{beta} knockout mice, we found that Mkk3 activation in adipose tissue during obesity enhances insulin sensitivity. Mechanistically, adipose tissue from Mkk3- or p38{beta}-deficient mice exhibited elevated basal p70S6K activity compared with wild-type controls. This increased p70S6K activity was linked to higher serine phosphorylation of insulin receptor substrate 1 (IRS1) and impaired insulin-stimulated Akt phosphorylation, contributing to worsened insulin resistance. Collectively, our data suggest that activation of the MKK3/p38{beta} signaling axis in adipocytes may protect against high-fat diet-induced insulin resistance and diabetes.

molecular biology↗

bioRxiv2025

Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation

The alternative calcineurin A variant CnA{beta}1 has a unique C-terminal domain that provides it with distinct subcellular localization and mechanism of action different from other calcineurin isoforms. Here, we used mice lacking CnA{beta}1s C-terminal domain (CnA{beta}1{Delta}i12) to show that the absence of this specific isoform strongly reprograms metabolism. CnA{beta}1{Delta}i12 mice on a high-fat diet showed reduced body weight, white adipose tissue (WAT) mass, and circulating triglycerides, together with enhanced insulin sensitivity. In brown adipose tissue (BAT), CnA{beta}1 deficiency increased mitochondrial content and upregulated fatty acid oxidation and thermogenic proteins, improving cold resistance. Conversely, under starvation, CnA{beta}1{Delta}i12 mice experienced rapid fat depletion and hypothermia. Importantly, BAT-specific FoxO1 knockout in CnA{beta}1{Delta}i12 mice reduced catabolism-related gene expression and partially reversed the metabolic phenotypes, increasing body weight and WAT mass. Our findings reveal a relevant role for CnA{beta}1 in orchestrating BAT metabolism, highlighting its potential as a therapeutic target for obesity and metabolic syndrome.

physiology↗

bioRxiv2024

SenSet, a novel human lung senescence cell gene signature, identifies cell-specific senescence mechanisms

Cellular senescence is a major hallmark of aging. Senescence is defined as an irreversible growth arrest observed when cells are exposed to a variety of stressors including DNA damage, oxidative stress, or nutrient deprivation. While senescence is a well-established driver of aging and age-related diseases, it is a highly heterogeneous process with significant variations across organisms, tissues, and cell types. The relatively low abundance of senescence in healthy aged tissues represents a major challenge to studying senescence in a given organ, including the human lung. To overcome this limitation, we developed a Positive-Unlabeled (PU) learning framework to generate a comprehensive senescence marker gene list in human lungs (termed SenSet) using the largest publicly available single-cell lung dataset, the Human Lung Cell Atlas (HLCA). We validated SenSet in a highly complex ex vivo human 3D lung tissue culture model subjected to the senescence inducers bleomycin, doxorubicin, or irradiation, and established its sensitivity and accuracy in characterizing senescence. Using SenSet, we identified and validated cell-type specific senescence signatures in distinct lung cell populations upon aging and environmental exposures. Our study presents the first comprehensive analysis of senescent cells in the healthy aging lung and uncovers cell-specific gene signatures of senescence, presenting fundamental implications for our understanding of major lung diseases, including cancer, fibrosis, chronic obstructive pulmonary disease, or asthma.

genomics↗

bioRxiv2024

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Sequence-function data provides valuable information about the protein functional landscape, but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeqs ability to accurately detect variants under various experimental conditions. Finally, we show LevSeqs utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

biochemistry↗

bioRxiv2024

Leveraging remote sensing to distinguish closely related beech species in assisted gene flow scenarios

European beech (Fagus sylvatica L.) forests are suffering under increasingly severe and frequent drought. Three closely related, hybridizing beech species, ranging from Bulgaria through Asia Minor and the Caucasus to Iran, offer potential resources for assisted gene flow (AGF) with the aim of increasing the adaptive capacity of European beech forests. However, due to similar morphology and leaf color, as well as hybridization, it is challenging to track the fate of introduced beech genotypes from these related species. Traditional identification methods relying on detailed morphological characterization and genetic testing are labor-intensive and costly, making them impractical for large-scale applications. Using multispectral data from PlanetScope SuperDove, we developed a classification approach that captures phenological differences between the European beech F. sylvatica and co-planted Caucasian beech (Fagus hohenackeriana Palibin). The approach focuses on key temporal windows and spectral features to optimize classification performance. We evaluated various machine learning algorithms with stratified spatial and temporal cross-validation on data from more than 200 genetically classified individuals in two well-studied sites in France and Switzerland, where Caucasian beech was introduced over a century ago. Our approach was then tested on three different study areas in Germany, where Caucasian beech was also planted, but without specific tree coordinates. Our results reveal consistent temporal and spectral differences during spring and autumn, aligning with budbreak and senescence periods. Most algorithms achieved classification accuracies of 90% and above. The algorithms effectively identified candidate zones for Caucasian beech within or near areas indicated by local foresters. This study demonstrates the potential of high-resolution multispectral satellite imagery and machine learning for scalable classification of closely related and hybridizing species, thereby facilitating forest management in the face of global change. HighlightsO_LIRemote sensing of unique phenological differences between closely related beech species C_LIO_LIModels with optimized classification parameters differentiated highly similar, introduced trees C_LIO_LIReal-world application to assisted gene flow sites yielded high performance and testable predictions C_LI

ecology↗

bioRxiv2023

Lack of p38 activation in T cells increases IL-35 production and protects against obesity by promoting thermogenesis

Obesity is characterized with low grade inflammation, energy imbalance and impaired thermogenesis. The role of regulatory T cells (Treg) in inflammation-mediated maladaptive thermogenesis has not been well established. We discovered that p38 pathway is a key regulator of T cell-mediated adipose tissue (AT) inflammation and browning. Mice with T cells specific deletion of the p38 activators, MKK3/6, were protected against diet-induced obesity and AT inflammation improving their metabolic profile, higher browning and thermogenesis. We identified IL-35 as a driver of adipocyte thermogenic program through ATF2/UCP1/FGF21 pathway. IL-35 limits CD8+ T cell infiltration and inflammation in AT. Interestingly, we found that IL35 was reduced in visceral fat from obese patients. Mechanistically we showed that p38 controls the expression of IL-35 in human and mouse Treg cells through mTOR pathway activation. Our findings highlight p38 signaling as a molecular orchestrator of AT T cell accumulation and function and identify p38 and IL-35 as promising targets for metabolic diseases.

immunology↗

bioRxiv2022

D-mannose ameliorates age-associated cellular senescence in the bladder urothelium and NLRP3/Gasdermin/IL-1β -driven pyroptotic epithelial cell shedding

Aging is a risk factor for disease via increased susceptibility to infection, decreased ability to maintain homeostasis, inefficiency in combatting stress, and decreased regenerative capacity. Multiple diseases including urinary tract infection (UTI), are more prevalent with age; however, the mechanisms underlying how aging affects the urinary tract mucosa and the reason why aging correlates with disease are poorly understood. Here, we show that, relative to young (8-12 weeks) mice, the urothelium of aged (18-24 months) female mice accumulates large lysosomes with decreased acid phosphatase activity and shows overall decreased autophagic flux. Aged bladders exhibit basally high accumulation of reactive oxygen species (ROS) and dampened redox response. Furthermore, the aged urothelium exhibits a canonical senescence-associated secretory phenotype (SASP) at baseline with continuous NLRP3-inflammasome- and Gasdermin D (GSDMD)-dependent pyroptotic cell death. Accordingly, we find that aged mice chronically exfoliate epithelial cells. When infected with uropathogenic E. coli, infected aged mice harbor more bacterial reservoirs post-infection and are prone to spontaneous recurrent UTI. Finally, treatment of aged mice with D-Mannose, a natural bioactive monosaccharide, rescues autophagy flux, reverses SASP, and limits pyroptotic epithelial shedding. Thus, normal aging dramatically affects bladder physiology with aging alone increasing baseline cellular stress and susceptibility to infection. Additionally, our results suggest that mannose supplementation could serve as a senotherapeutic to limit age-associated urothelial dysfunction.

cell biology↗

bioRxiv2022

Studies on the interaction of three lytic bacteriophages with a wide collection of Escherichia coli strains implicated in swine enteric colibacillosis

AO_SCPLOWBSTRACTC_SCPLOWThe misuse of antibiotics in the swine industry and their on-going restriction requires alternatives to control enterotoxigenic and shiga toxin-producing Escherichia coli (ETEC and STEC, respectively). This study evaluates the potential of three coliphages, vB_EcoM_FJ1, vB_EcoM_FN and vB_EcoM_SP1 against 104 ETEC, STEC and ETEC/STEC strains isolated from pig colibacillosis in Portuguese (2018-2020) and Spanish farms (2006-2016), encompassing 71.2% mcr-positive strains (33.7% with mcr-1, 1.9% mcr-2, 35.6% mcr-4 and 2.9% mcr-5) and 18.3% positive strains for TEM (1%), SHV (6.7%), and CTX-M (11.5%) extended-spectrum beta-lactamase-encoding genes. In general, all bacteriophages presented a narrow lytic spectrum (up to 2.9%) against the 104 ETEC, STEC and ETEC/STEC. Bacteriophages shared >80% overall nucleotide identity with E. coli phage T4 (Tevenvirinae subfamily), but a particular look at the distal part of the long tail fiber (gp38) revealed no homology. All bacteriophages recognize lipopolysaccharides as receptors, and additionally, FN binds to an outer membrane protein A. Bacteriophage-insensitive mutants of vB_EcoM_FJ1 (90%) and vB_EcoM_FN (100%) were shown to be more susceptible to pig serum inactivation comparatively to the parental strain and furthermore, their adhesion capacity to porcine intestinal cells was diminished by, approximately, 90%. Contrariwise, vB_EcoM_SP1 insensitive variants did not display phenotypic differences comparing to the wild-type strain. This study demonstrates that besides being T4-like, these bacteriophages revealed a narrow lytic spectrum against diarrhoeagenic E. coli strains and that the acquisition of novel bacteriophage-encoded adhesins (gp38) seems to be determinant for such results.

microbiology↗

bioRxiv2022

The Role of EBP50 in Regulating Endothelial-To-Mesenchymal Transition in Pulmonary Hypertension

ObjectivePulmonary hypertension (PH) is a cardiopulmonary disease manifesting in increased pulmonary arterial pressure and right ventricular dysfunction. PH pathogenesis involves extensive pulmonary vascular remodeling precipitated, at least in part, by endothelial reprogramming. Mounting evidence points to endothelial-to-mesenchymal transition (EndMT) as an important potentiator of endothelial reprogramming in PH, yet progress in dissecting these processes remains limited. Approach and ResultsLung samples from pulmonary arterial hypertension (PAH) patients and two rodent models of PH were used. Expression of the scaffolding protein ezrin-radixin-moesin-binding phosphoprotein 50 (EBP50, or NHERF1) was downregulated in PAH patient pulmonary arteries and isolated pulmonary arterial endothelial cells (PAECs), and in PH animal lung tissue and mouse isolated PAECs. In human PAECs in vitro, EBP50 was downregulated by PH-relevant stimuli, hypoxia and proinflammatory cytokine interleukin-1 beta (IL-1{beta}). Phenocopy of EBP50 reduction in PAECs time-dependently increased expression and nuclear abundance of EndMT transcription factors Snail and Zeb1, and potentiated hypoxia-driven upregulation of Slug. Loss of EBP50 also drove expression of mesenchymal markers S100A4, fibronectin, N-cadherin, and transgelin (SM22), and inhibited cell proliferation and barrier function. In vivo studies on female EBP50+/- mice demonstrated that downregulation of EBP50 exacerbated the chronic hypoxia-induced rise in RV maximum pressure. ConclusionsThese data identify EBP50 as a key regulator of EndMT in PH whose expression is downregulated in PH patient pulmonary endothelium and whose partial deletion exacerbates PH disease manifestations in rodents, opening doors for future therapeutic strategies to target EBP50 restoration to reverse PH.

cell biology↗

bioRxiv2022

How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data

Accurately identifying cell populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the best-performing metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of a scRNA-seq dataset, specific to the biological system under study, has a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the system under study and provide practical guidance for researchers looking to optimise cell similarity search for the structural properties of their own data.

bioinformatics↗