Biology subjects

Pellegrini, M.

Publications and source records attributed to Pellegrini, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Age Associated Shifts Toward Anaerobic Taxa and Amino Acid Degradation Potential in the Canine Oral Microbiome

Introduction: Periodontal disease is common in dogs and is associated with changes in oral microbial community composition. How the canine oral microbiome restructures across age at both ecological and metabolic levels remains incompletely characterized. Methods: We analyzed shotgun metagenomic buccal-swab profiles from 1,125 dogs using MetaPhlAn. Functional profiles from HUMAnN were analyzed at pathway and functional-group levels. We annotated species-level taxa with oxygen preference and evaluated oxygen-tolerant and anaerobic taxa abundances across age. We also characterized community structure, metabolic potential, Shannon diversity, and age and weight prediction using nested cross-validated Elastic Net models. Results: Age was associated with substantial restructuring in the oral microbiome. Mean oxygen-tolerant taxa abundance decreased from 67.5% in puppies to 29.1% in mature dogs, whereas anaerobic abundance increased from 14.3% to 53.8%. The oxygen-tolerant: anaerobe log-ratio declined with age (Spearman {rho} = -0.616). Metabolic profiles also varied with age, with higher amino-acid-degradation potential and lower glycolysis and central-carbohydrate potential, and aerobic-respiration-I abundance in older dogs. Shannon diversity increased with age ({rho} = 0.537, p < 0.001). Elastic Net models trained on microbiome species abundance predicted age (Pearson r = 0.609; R-squared = 0.371) and weight (r = 0.498; R-squared = 0.248) with moderate accuracy. Discussion: Canine age was associated with coordinated changes in oral-community oxygen preference and metabolic pathways. These findings extend prior taxon-specific observations by examining a broader ecological shift and metabolic potential in the canine oral microbiome. This provides a reference for future longitudinal or clinical studies of oral maturation and periodontal disease.

microbiology↗

bioRxiv2026

Alignment-Free k-mer Analysis Reveals Age-Associated k-mer Signals in Canine Buccal DNA and Cell-Free DNA

Aging is associated with progressive molecular and cellular changes that alter chromatin organization, epigenetic regulation, and microbial community structure. While DNA methylation-based biomarkers have demonstrated age-prediction performance, the potential of alignment-free sequence analysis for detecting age-associated signals remains unexplored. We performed a k-mer-based analysis of whole-genome sequencing datasets from two canine cohorts: 1,187 buccal DNA samples and 100 cell-free DNA (cfDNA) samples. Age-associated k-mers were identified through correlation analysis, and sequence motifs and nucleotide composition patterns were characterized across both sample types. Ridge regression models were used to predict age from k-mer frequency profiles, and synthetic datasets were generated under biological compositional assumptions to evaluate potential drivers of the observed signals. Age-associated k-mer signals were identified in both buccal DNA and cfDNA, with substantially stronger patterns observed in buccal samples. Buccal-derived k-mers exhibited more pronounced motif patterns, stronger GC and AT enrichment trends, and improved age-prediction performance relative to cfDNA. Nucleotide enrichment trends differed between sample types, suggesting that distinct biological processes may contribute to age-associated sequence composition changes in buccal DNA and cfDNA. Simulation analyses successfully reproduced major enrichment trends observed empirically, and age-associated signals were largely preserved following restriction to read-start sequence composition. These findings demonstrate that alignment-free k-mer analysis can detect age-associated sequence composition signals in canine DNA and highlight its potential as a computationally efficient complementary approach for aging biomarker discovery.

bioinformatics↗

bioRxiv2026

Is epigenetics Lamarck's revenge? The evolutionary and ecological impacts of DNA methylation in a California oak

DNA methylation, an epigenetic mechanism, has been proposed as a basis for Lamarckian inheritance of acquired characteristics. This hypothesis raises questions about environmental induction of DNA methylation, its persistence across generations, and associations with gene expression. We investigated these processes in the California oak, Quercus lobata. Based on 40 range-wide adult trees and their progeny grown in two common gardens, we tested four predictions: (i) DNA methylation is environmentally induced; (ii) methylation shows parent-offspring correspondence; (iii) environmentally induced patterns remain stable after transmission; and (iv) DNA methylation is associated with gene expression, varying by sequence context and TE proximity. Using methylomes and transcriptomes, we found support for all predictions except the third: CHH methylation was environmentally induced, but rarely showed parent/offspring correspondence, whereas CG methylation showed the opposite pattern. DNA methylation was associated with gene expression, depending on the sequence context and proximity to TEs. We identified one stress-associated gene with environmentally responsive CHH methylation that showed parent offspring correspondence in two common gardens in both gardens. Still, overall evidence for Lamarckian like evolution is not compelling. Nevertheless, DNA methylation was environmentally responsive, showed parent offspring correspondence, and was associated with gene expression, highlighting its potential importance for the ecological and evolutionary responses of long-lived trees to environmental change.

evolutionary biology↗

bioRxiv2026

Derivation of primary fetal epithelial organoids from cryopreserved human amniotic fluid cells.

Primary fetal human epithelial organoids are canonically derived from tissue samples obtained after termination of pregnancy. Recently, we have demonstrated that these organoids can be consistently generated from amniotic fluid cells isolated prenatally during diagnostic and interventional procedures. Amniotic Fluid-derived Organoids (AFOs) are promising fetal epithelial lung, kidney and small intestine tissue models that bypass some of the ethical and legal constraints associated with obtaining primary fetal tissue. Despite this, the widespread adoption of this technology is limited by the lack of standardised and accessible pipelines for the biobanking, culturing and distribution of organoid-forming amniotic fluid cells. To enable broader AFO use for research and clinical purposes, this work investigates two cryopreservation strategies to optimise AF cell recovery for organoid derivation. In Strategy 1, viable AF cells were sorted before freezing, while in Strategy 2 unsorted AF was frozen, and viable AF cells were sorted for viability after thawing. We present here a comprehensive evaluation of 5 commercially available GMP-compliant freezing media (FM1-5) alongside a standard lab-grade control (FM CT). AFOs were assessed for formation efficiency, morphological characteristics, proliferation capacity, epithelial identity, and tissue type. Our results demonstrate that organoid-forming AF cells can be successfully cryopreserved both pre- and post-sorting. Strategy 1 yielded higher organoid formation efficiency, with AFOs derived from cryopreserved and fresh cells exhibiting comparable expansion potential. Finally, FM4 provided minimal to no decline in survival rate, making it the most effective GMP-grade freezing medium tested. In conclusion, we present two viable cryopreservation strategies adaptable to different laboratory settings and identify optimal GMP-compliant freezing media, to support safe distribution and centralised processing, thereby facilitating scalability and collaborative work on the AFO technology.

cell biology↗

bioRxiv2026

Systematic Comparison of 10X Genomics and Parse Single Cell RNA Technologies across PBMC and CD8+ TEMRA Cells

Single-cell RNA sequencing technologies provide insights into gene expression at the cellular level, enabling detailed analysis of cellular heterogeneity. In this study, we systematically compared two scRNA-seq platforms,10X Genomics and Parse Biosciences, using human peripheral blood mononuclear cells (PBMCs) and terminally differentiated effector memory CD8+ T cells (TEMRAs). We identified significant differences in gene expression variability and platform-specific biases, such as ribosomal and mitochondrial gene capture. 10X has a bias for shorter genes, while Parse exhibited enhanced detection of longer transcripts. In CD8+ TEMRAs, the expression of key genes related to antimicrobial immune responses were underrepresented in Parse cells compared to 10X cells (e.g. GNLY, PRF1 and GZMB). These findings underscore the need for careful selection of scRNA-seq platforms based on specific research objectives, as platform-specific biases can influence cell type identification and as well as mechanistic insights derived from gene expression data. Our results provide insights for selection of scRNA-seq experimental platforms in immunological studies.

immunology↗

bioRxiv2026

Physics-Informed Modeling of Biological Aging through DNA Methylation Entropy

Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are predominantly empirical models with limited explicit characterization of the underlying methylation variability, lacking a direct connection to the physical mechanisms of aging. In this work, we bridge this gap by introducing an information-theoretic framework for DNA methylation dynamics combined with nonlinear machine learning to develop a competitive and interpretable age predictor. We model the population distribution of methylation {beta}-values at each CpG site using a reparameterized three-parameter Generalized Gamma Distribution (GGD) and derive a closed-form expression for its differential Shannon entropy. The resulting CpG-level entropy is used to characterize methylation variability and as a criterion for locus filtering. We introduce the Stacy Gradient Boosting Clock (Stacy-GB), which combines this GGD-based representation with a LightGBM regressor. The model was evaluated across independent cohorts using the ComputAgeBench epigenetic clock benchmark. Stacy-GB achieved a mean absolute error (MAE) of 3.74 years and a median error (bias) of 2.41 years, significantly outperforming state-of-the-art epigenetic clock baselines. Furthermore, age acceleration estimated by Stacy-GB was associated with several clinical pathologies, including ischemic heart disease, HIV infection, multiple sclerosis, and Werner syndrome, supporting its potential as an accurate and biophysically grounded tool for clinical aging research.

genetics↗

bioRxiv2026

Conformal Uncertainty Quantification for BayesAge Epigenetic Age Predictions

Epigenetic clocks predict chronological age from DNA methylation (DNAm) profiles, yet most provide point estimates without calibrated uncertainty. This limits their use when quantified error bounds are required. We apply conformal prediction to BayesAge, a maximum-likelihood clock that models nonlinear DNAm-age relationships using a small set of CpG loci and a count-based likelihood. Split conformal prediction yields distribution-free prediction intervals with finite-sample marginal coverage guarantees under exchangeability and requires only a single model fit per split. We also evaluate a locally scaled variant that produces age-dependent interval widths using a locally estimated error scale. In our targeted bisulfite sequencing cohort, split conformalized BayesAge attains near-nominal empirical coverage while preserving BayesAge point-prediction accuracy. The locally scaled variant yields wider intervals at older ages, but its coverage is less stable in small-calibration regimes, consistent with additional uncertainty from estimating the local scale. Relative to Monte Carlo intervals that propagate read-sampling variability and to higher-dimensional linear baselines (conformalized linear quantile regression and conformalized Lasso regression), conformalized BayesAge provides calibrated uncertainty using substantially fewer CpG sites and with weaker age-dependent structure in residuals. These results support conformal prediction as a practical approach for uncertainty quantification in DNAm-based age estimation.

bioinformatics↗

bioRxiv2026

Mucus cell and ionocyte precursors migrate between epithelial cells to disperse through the zebrafish epidermis

Mucus-secreting cells and ionocytes play critical roles in many organs. Both cell types are usually distributed as scattered, solitary cells within an epithelium, a pattern presumably optimal for their function. To investigate how they attain their dispersed distributions, we imaged mucus cell and ionocyte precursors in the epidermis of developing zebrafish. Previous reports found that precursors of both cell types are first detected in the ventral epidermis (covering the yolk) before spreading dorsally, but the mechanism driving this progression was unknown. Photoconverting basal epidermal cells in the ventral embryo revealed that some cells actively migrate away from this area to populate the rest of the epidermis. These cells lose their basal cell identity when they start migrating and begin expressing markers of mature mucus cells or ionocytes during migration. These cells travel entirely between the two epithelial layers of the epidermis, occasionally pause migration to divide, and repel one another through contact inhibition of locomotion, behaviors that likely aid in their dispersal. After migrating for about a day, mucus cell and ionocyte precursors intercalate into the superficial epithelial layer of the epidermis, where they complete differentiation. These observations reveal how mucus cells and ionocytes achieve their scattered distributions in the zebrafish epidermis, suggesting that similar processes promote their distribution in other mucosal organs.

developmental biology↗

bioRxiv2026

MKMC enables reference-free transcriptomic analysis using k-mer representations

Traditional RNA-seq analysis depends heavily on genome alignment and gene annotation, limiting its utility in non-model organisms and introducing biases that can obscure regulatory complexity. We present MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer-based statistics to detect biological variation without requiring alignment. MKMC integrates fast k-mer counting, abundance matrix generation, normalization, dimensionality reduction, and differential analysis into a unified workflow. Across diverse datasets, MKMC recapitulates key biological signals--including sex differences in killifish liver--and matches alignment-based pipelines in differential expression analysis and transcriptomic age prediction. Notably, MKMC detects isoform-specific events missed by traditional methods, one of which we validated using in situ hybridization. These results reveal previously hidden isoform-level regulatory events that contribute to sex-and age-associated transcriptional programs. MKMC offers a robust, extensible alternative to alignment-based approaches, enabling transcriptomic discovery across both model and non-model systems. While we focus here on RNA-seq as a primary application, MKMC is broadly applicable to any k-mer-based analysis of next-generation sequencing data.

bioinformatics↗

bioRxiv2026

Novel Synthetic Promoter Armed Oncolytic Herpes Simplex Virus For Treatment of PAX3-FOXO1 Positive Rhabdomyosarcoma

BackgroundDue to the difficulty in targeting oncogenic transcription factors, there are no targeted treatments available for pediatric patients afflicted with PAX3-FOXO1 fusion positive alveolar rhabdomyosarcoma. Additionally, alveolar rhabdomyosarcomas are immunologically cold tumors inherently resistant to immune checkpoint blockade. Viral vectors present a unique opportunity to address this gap in treatment strategies as their presence causes immune infiltration, and a virus can be engineered to rely on tumor specific transcriptional networks for its own replication. MethodologyWe engineered a synthetic promoter which relies on PAX3-FOXO1 to drive expression of a downstream gene. We then placed the HSV-1 RL1 gene encoding ICP34.5 under this promoter within the G47{Delta} oncolytic herpes simplex virus (oHSV), generating a novel oHSV targeting cells harboring the PAX3-FOXO1 fusion gene, which we name oRP3Fus ("Orpheus;" oncolytic recombinant G47{Delta} targeting PAX3-FOXO1 virus). PAX3-FOXO1 specificity was determined by qRT-PCR, protein expression, and chromatin immunoprecipitation. Antitumor efficacy of oncolytic virus and anti-PD1 antibody was determined using immuno-competent mice harboring syngeneic PAX3-FOXO1 expressing rhabdomyosarcoma tumors. Bulk RNA sequencing was performed on infected and mock infected fusion positive and negative cells for differential gene expression analysis. ResultsoRP3Fus kills PAX3-FOXO1 positive human rhabdomyosarcoma lines, while remaining non-toxic to normal human skeletal muscle cells in vitro. In immunocompetent mouse models, both novel and parental viruses improved tumor control when combined with anti-PD1, while oRP3Fus treatment increased T cell infiltration within tumors. RNA and DNA extraction from tumors demonstrate viral expansion within oRP3Fus treated tumors up to three weeks post treatment. Meanwhile oRP3Fus treated mice displayed decreased amounts of necroptosis-associated gene expression within normal organs as compared to groups treated with parental virus. Bulk RNA sequencing of fusion positive and negative human rhabdomyosarcoma lines demonstrated PAX3-FOXO1 increases expression of genes involved in the TNFa-NF{kappa}B signaling axis, as well as innate and adaptive immune signaling. Additionally, ICP34.5 expression from oRP3Fus reduces RIPK1 activation. ConclusionsICP34.5-armed oHSV (oRP3Fus) combined with anti-PD1 provides effective antitumor activity against PAX3-FOXO1-positive alveolar rhabdomyosarcoma in vivo and should be further explored for fusion oncogene-driven tumors. What is already known on this topic?PAX3-FOXO1 fusion-positive and negative rhabdomyosarcoma (RMS) is highly resistant to immune checkpoint blockade. Previously, a PAX3-FOXO1 driven oncolytic adenovirus showed efficacy in an immuno-compromised fusion-positive RMS model. In fusion-negative, immuno-competent RMS models, oncolytic herpes viruses have been shown to improve survival when combined with anti-PD1 therapy, but combinational efficacy was only observed with sex-mismatched models and was never tested on fusion-positive RMS tumors in immunocompetent mice. What this study adds?We improved the specificity of the synthetic PAX3-FOXO1 targeted promoter. We also show, for the first time, efficacy of oncolytic herpes simplex virus-1 in immunocompetent mice harboring orthotopic PAX3-FOXO1-expressing RMS tumors. Our study also demonstrates improvement in survival efficacy in sex-matched tumors, whereas previous reports did not observe complete responses except when using sex-mismatched tumor models. How this study might effect research, practice, or policy?The synthetic promoter generated here can be used for future gene therapy or targeting efforts to study PAX3-FOXO1 or target PAX3-FOXO1 tumors. We observe anti-PD1 therapy benefits survival when paired with an oHSV, demonstrating oncolytic virus can sensitize PAX3-FOXO1 expressing RMS tumors to immunotherapy.

Cancer Biology↗

bioRxiv2026

Improving the Accuracy of Forensic Age Estimation Through Bias Reduction

Chronological age estimation can provide supporting information in forensic casework when traditional identification methods are limited. DNA methylation, a stable epigenetic mark, has emerged as a promising tool for predicting chronological age from trace samples. However, many existing age estimation models rely on linear regression approaches, which often yield biased prediction errors across the age distribution (i.e. model residuals show a significant age dependence). In this study, we compared three approaches for age estimation modeling: multivariable linear regression, random forest regression and maximum likelihood estimation. While the first two approaches are well established, for the third one we constructed and validated a DNA methylation-based LOESS regression maximum likelihood model for age estimation utilizing forensic-relevant CpG markers. In all cases, model performance was evaluated through Leave-One-Out Cross-Validation (LOOCV). We utilized three independent publicly accessible methylation datasets collected using droplet digital PCR (ddPCR) to evaluate the most effective method for accuracy and bias in age estimation. Notably, when we compare the results of the maximum likelihood approach to the other approaches, multivariable linear regression and random forest regression, we find less bias in the age associated residuals compared to the other methods. These findings highlight the utility of non-linear modeling techniques in reducing the biases of epigenetic age estimation for forensic applications.

bioinformatics↗

bioRxiv2026

Accurate estimation of canine inbreeding using ultra low-coverage whole genomesequencing

The measurement of inbreeding has gained significance across diverse fields, including population and conservation genetics, agricultural genetics, breeding programs for animals and plants, and wildlife management. This is due to the fact that inbreeding leads to increased homozygosity and results in lower genetic diversity, rendering populations more vulnerable to environmental changes, diseases, and other stressors. High or mid-coverage whole genome sequencing (WGS) has been widely used for inbreeding estimation, but it is resource-intensive. We aimed to investigate the use of ultra low-coverage whole genome sequencing (ulcWGS) as a cost-effective alternative for inbreeding analysis. Domestic dogs were used for our study as their extensive breeding histories lead to populations with a wide range of inbreeding levels. We constructed a multi-breed reference panel from high-coverage WGS samples. Inbreeding in independent ulcWGS samples was then estimated using runs of homozygosity (RoH) and inbreeding coefficients (F). We modeled the relationship between these measures and sequencing depth using nonlinear regression, to generate inbreeding estimates relative to sequencing depth. Resulting relative RoH and F measurements were significantly correlated, with purebred dogs exhibiting more runs of homozygosity and higher inbreeding coefficients compared to mixed-breed dogs. Our findings demonstrate that ulcWGS can provide reliable and economical estimations of inbreeding, expanding accessibility to genetic monitoring.

bioinformatics↗

bioRxiv2026

Inference of admixture in dogs from whole genome sequences

BackgroundUnderstanding the genetic architecture of domestic dogs provides unique insights into the processes of domestication, breed formation, and the genetic basis of complex traits and diseases. Dog populations, characterized by their diverse morphologies and behaviors, also exhibit extensive evidence of historical and ongoing admixture. This widespread mixing, driven by both natural migration and selective breeding practices, has profoundly shaped the genomic landscape of modern dog breeds. Though global admixture has been extensively estimated in human population studies, where the number of subgroups is typically limited, there has been more limited analysis in canines, where there may be dozens of ancestral groups, or breeds. ResultsHere we present a procedure for estimating global admixture in dogs from whole genome sequence data using SCOPE. We created a reference population of 65 dog breeds that included 349 individuals, from which we determined breed-informative SNPs. We demonstrate that SCOPE can accurately infer breed composition in both simulated and real admixed samples, even at low sequencing depths. We also characterized the genetic similarity between our reference dog breeds and recovered previously reported relationships. ConclusionThis approach allows us to identify the strength of the genetic signature of breeds and place error bounds on admixture estimates. It also provides evidence that admixture can be accurately inferred in subjects that may originate from multiple ancestral populations.

genomics↗

bioRxiv2026

AlphaFold-Driven Structural Proteomics Reveals Extensive Cellulosome Machinery in Human Ruminococcal Symbionts

Cellulosomes are large, surface-displayed enzyme complexes that enable anaerobic bacteria to degrade recalcitrant plant polysaccharides, yet cellulosome-expressing bacteria are thought to be rare in the human gut. Here we show that extensive sequence divergence obscures the detection of many ruminococcal cellulosomes by conventional sequence homology-based methods. Using proteome-scale AlphaFold2 structural predictions, we uncovered a substantially expanded set of cellulosome-producing Ruminococcus species, including six previously unrecognized human symbionts. Structure-based clustering identifies several novel cohesin families that retain conserved folds despite extreme sequence divergence and define distinct, phylogenetically conserved cellulosome architectures. The analysis reveals R. callidus and related human symbionts encode elaborate cellulosomes that are invisible to sequence-based annotation. Similarly, R. difficilis, a human gut symbiont, is found to produce an atypical cohesin-based assembly enriched in amylases and related starch-binding proteins that may enable this microbe to degrade resistant starches that evade digestion in the upper gastrointestinal tract. Together, these findings reveal that ruminococcal cellulosomes are far more prevalent and diverse than previously appreciated and demonstrate the power of structural proteomics to uncover deeply divergent functional systems in the gut microbiome. Significance StatementPlant cell wall polysaccharides are a major dietary carbon source, yet their degradation relies on rare, highly specialized microbial enzyme assemblies known as cellulosomes, which have long appeared uncommon in the human gut. Using proteome-scale structure prediction combined with experimental validation, we show that cellulosomes are far more widespread and structurally diverse in human-associated Ruminococcus species than previously appreciated. We identify multiple new cohesin families and reveal distinct cellulosome architectures likely adapted to degrade different dietary substrates. Together, these findings redefine the distribution and evolution of cellulosomes in gut microbes and demonstrate the power of structural proteomics to uncover deeply diverged biological systems.

microbiology↗

bioRxiv2025

Determining the age of single cells using scBayesAge

1Aging is a complex biological process marked by a gradual decline in physiological function that contributes to increased vulnerability to disease and mortality. Numerous studies have investigated the cellular and molecular aspects of aging at single-cell resolution, yet the heterogeneity of cellular aging in an individual remains poorly understood. To enhance our ability to study aging at the single cell level, we developed a statistical framework to predict the age of individual cells based on their transcriptomic profiles. Our Bayesian approach estimates the most likely age of a cell given its read counts. We applied the model to data from Tabula Muris Senis and examined organ- and cell-type-specific transcriptomic signatures of aging. Compared with standard regression-based methods, our framework achieved higher predictive accuracy. We show that scMLEAge is a powerful tool for dissecting the cellular heterogeneity of aging and age-related functional decline.

developmental biology↗

bioRxiv2025

MAGIC: Methylation Analysis with Genomic Inferred Contexts

MotivationWhole-genome bisulfite sequencing (WGBS) enables base-resolution methylation profiling but poses statistical challenges for differential methylation analysis. While existing methods have made important progress using uniform dispersion shrinkage across CpGs, there remains opportunity to better capture methylation heterogeneity across the genome. There is a need to account for context-specific methylation variability and to improve detection accuracy under realistic sequencing constraints. ResultsWe propose MAGIC (Methylation Analysis with Genomic Inferred Contexts), a beta-binomial mixture model that learns genomic contexts directly from methylation data. MAGIC models genomic heterogeneity through context-specific dispersion shrinkage and detects differential methylation via two complementary tests, including a Wald test and Bayes factor test. Simulations across various coverage and effect sizes demonstrate that MAGIC improves sensitivity and false-positive rate compared with DSS, providing a robust framework for differential methylation analysis in WGBS data. AvailabilityThe source code of MAGIC is available at https://github.com/Pickledzebra/MAGIC. ContactMatteoP@mcdb.ucla.edu Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2025

Robust software development practices improve citations of RNA-seq tools

RNA sequencing (RNA-seq) has emerged as an exemplary technology in biology and clinical applications, offering a crucial complement to other transcriptomic profiling protocols due to its high sensitivity, precision, and accuracy in characterizing transcriptomes. However, the rapid proliferation of RNA-seq tools necessitates the adoption of robust software development practices. Such development underscores the critical need to examine how RNA-seq tools are developed, maintained, and distributed; and whether the data they generate is reproducible as all of these factors are essential for ensuring software reliability, transparency, and trust in scientific findings. We conducted a comprehensive assessment of 434 RNA-seq tools developed between 2008 and 2024, categorizing them based on the type of analysis they perform. Our evaluation encompassed their software development and distribution methodologies, as well as the attributes contributing to their widespread adoption and dependability within the biomedical community, which were quantified by factors such as package manager availability, containerization, multithreading support, documentation quality, and inclusion of example datasets. Our findings establish the first documented positive association between rigorous software development practices and their adoption of published RNA-seq tools as measured by citations (Mann-Whitney U test, p-value = 4.9 x 10-26). By identifying key characteristics of widely adopted software, our findings guide developing robust and user-friendly RNA-seq tools, thereby reinforcing the call for rigorous community-wide standards.

bioinformatics↗

bioRxiv2025

Spatial profiling reveals TREM2+ macrophages as central to Mycobacterium tuberculosis pathogenesis in human pulmonary tuberculosis

Pulmonary tuberculosis (TB) exhibits marked spatial heterogeneity, with alveolar pneumonia and organized granulomas frequently coexisting within the same lung. While granulomas have long dominated conceptual models of TB pathogenesis, the immune programs operating within alveolar TB pneumonia in humans remain incompletely defined. Here, we integrate spatial transcriptomics, single-cell RNA sequencing, high-resolution imaging, and functional assays of human lung biopsies to directly compare alveolar pneumonia with adjacent granulomas from the same individuals. We demonstrate that alveolar TB pneumonia is enriched for TREM2 lipid-laden macrophages characterized by lipid metabolic reprogramming, sparse T-cell infiltration, attenuated antimicrobial gene expression, and abundant Mycobacterium tuberculosis (Mtb) transcripts and antigens. In contrast, neighboring granulomas exhibit organized lymphoid architecture and robust antimicrobial programs. Mechanistically, the mycobacterial virulence lipid phthiocerol dimycocerosate (PDIM) and free mycolic acids induce TREM2 expression and activate TREM2-DAP12 signaling, promoting lipid droplet accumulation, suppressing autophagy, and enhancing intracellular Mtb survival in human macrophages. This immunometabolic state is pharmacologically reversible: 1,25-dihydroxyvitamin D downregulates TREM2, restores autophagy, reduces lipid droplets, and limits bacterial viability. Together, these findings define a spatially localized TREM2 foamy macrophage program within alveolar pneumonia that contrasts sharply with adjacent granulomatous immunity, establishing an niche permissive for bacillary persistence and potentially transmission, as well as identifying a tractable host pathway in human TB pathogenesis.

immunology↗