Biology subjects

Zito, A.

Publications and source records attributed to Zito, A..

12 recordsLinked to original sources

bioRxiv2026

WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[≥]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

bioinformatics↗

bioRxiv2026

Predicting global biodiversity via Hubbell regression

Understanding global biodiversity patterns and their drivers is a prerequisite for countering the biodiversity crisis. In this paper, we introduce a novel generalized linear model, Hubbell regression, to estimate a key biodiversity descriptor, the fundamental biodiversity number. This can be converted into a set of biodiversity descriptors, including Shannon and Simpson indices, and more. Hence, quantifying the impact of environmental conditions on the fundamental biodiversity number allows us to predict the general properties of local biodiversity in any setting. In addition to having a strong mathematical foundation, Hubbell regression consistently outperformed current state-of-the-art models in predicting global biodiversity. We apply the method to arthropods, which account for the majority of terrestrial biodiversity. By parameterizing the models using samples of 1.78 million arthropods from 2415 samples collected at 135 sites spanning all continents, we pinpoint the drivers of arthropod biodiversity and its features at the global scale. We find that actual evapotranspiration is the single largest predictor of arthropod diversity and explains nearly 30% of the variation in richness. Moreover, we infer that high human activity has led to a 21.3 % and 29.2% decrease in potential insect richness in tropical and dry zones, respectively, but increased insect richness in polar regions. These insights bring a new foundation for biodiversity research and action.

ecology↗

bioRxiv2026

Pan-Cancer Genomic Scars of Alternative End Joining and Single-Strand Annealing

DNA double-strand breaks (DSBs) are among the most cytotoxic lesions encountered by cells and represent a major source of genome instability in cancer. The preferred pathway to repair resected DSBs is high-fidelity homologous recombination (HR), but error-prone annealing-dependent pathways such as single-strand annealing (SSA) and alternative end joining (Alt-EJ) generally act as backup repair mechanisms in HR-deficient contexts. However, the extent to which these pathways are engaged across tumor types and how their activity is coupled with HR deficiency remains unclear. In this work, we systematically characterize scars from SSA and Alt-EJ across 2,157 whole-genome-sequenced tumors spanning 17 cancer types, identifying 832 SSA-like and 37,359 Alt-EJ-like deletions in total. We find that Alt-EJ is the predominant backup repair pathway in HR-deficient tumors compared with SSA; however, prostate adenocarcinoma and hepatocellular carcinoma exhibit elevated SSA-like deletion burdens despite low HR-deficiency scores. Moreover, our genome-wide analysis reveals that SSA-like deletions preferentially occur in SINE-rich regions and exhibit pronounced enrichment near transcription start sites in HR-proficient lymphoid lineage tumors. Our results show that SSA- and Alt-EJ-associated genomic scars are not confined to HR-deficient tumors, but are shaped by local genomic and transcriptional context, capturing distinct dimensions of DSB repair activity beyond HR deficiency alone.

genomics↗

bioRxiv2026

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

cancer biology↗

bioRxiv2025

Discovering the unseen: a performance comparison of taxonomic classification methods under unknown DNA barcodes

O_LIDNA barcoding and metabarcoding have emerged as cost-efficient, standardized methods for characterizing local biodiversity. Based on the sequencing of a small targeted gene fragment, it is theoretically possible to identify a wide diversity of taxa by comparing them with reference sequence databases. However, a key challenge for accurate taxonomic classification is the incompleteness of such databases, leading to most query sequences lacking species-level matches. C_LIO_LIWhere species-level matches are missing, it may be possible to classify query sequences to a higher taxonomic level, such as genus or family, based on the similarity of related reference taxa. The challenge then lies in confidently recognizing whether the sequence belongs to an unobserved (here, "novel") taxon on a given taxonomic level. C_LIO_LIIn this study, we evaluated the performance and utility of several methods for taxonomic classification. Methods were assessed based on the classification accuracy of both observed and novel taxa, training time, space requirements, and run time. We did this for two cases: the COI barcode for arthropods, and the ITS barcode for fungi, with the latter representing an instance with substantially greater sequence similarity variation within classes. To test classification of novel taxa, we used well-curated datasets with partially distinct taxonomic distribution between the training and test set. Novel taxa occurred at all evaluated taxonomic levels, such as novel species in observed genera and novel genera in observed families. We further assessed the effect on performance when shifting from full-length barcodes to shorter sequences as generated through metabarcoding in the testing dataset. C_LIO_LIThis study sheds light on the strengths and limitations of different classification algorithms across varied ecological contexts and provides guidance for researchers in selecting suitable algorithms for DNA barcoding and metabarcoding applications. In particular, it demonstrates the supreme performance of phylogenetic placement methods such as EPA-ng for classification of arthropod COI barcodes, and composition-based classifiers such as SINTAX, RDP-NBC, and IDTAXA for fungal ITS. C_LI

bioinformatics↗

bioRxiv2025

PLAID: ultrafast single-sample gene set enrichment scoring

MotivationIn recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples, rather than across pooled samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling identification of unique molecular profiles based on gene set and pathway activity in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. ResultsTo address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. A distinctive feature of PLAID is its integration of the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. Availability and ImplementationPLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid

bioinformatics↗

bioRxiv2025

Molecular and Epigenetic Pathways Underlying Epithelial Damage and Repair in Necrotizing Enterocolitis via Multi-omics Approach

IntroductionNeonatal necrotizing enterocolitis (NEC) is a severe gastrointestinal disorder with high mortality, characterized by epithelial cell injury and compromised epithelial repair. The mechanisms underlying defective epithelial regeneration remain poorly understood despite advances in single-cell omics. Addressing these challenges is essential for elucidating the pathogenesis of NEC and identifying therapeutic targets to restore epithelial regeneration and replace the damaged epithelial layer. MethodsMulti-omics approaches were employed to investigate molecular and spatial changes in experimental NEC at epigenetic and transcriptomic levels. These included bulk RNA sequencing, single-nucleus RNA sequencing (snRNA-seq), single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), and multiplexed error-robust fluorescence in situ hybridization (MERFISH) for spatial transcriptomics. Complementary in vitro experiments and in vivo mouse models were utilized to evaluate NEC phenotypes, intestinal tissue morphology, and organoid formation. ResultsChanges in cell type composition, transcriptional network remodeling, and chromatin accessibility were observed in the small intestine of neonatal mice with NEC. Chromatin accessibility significantly changed in epithelial cells, highlighting their pivotal roles in NEC. A marked reduction in intestinal stem cells (ISCs) and transit-amplifying cells, along with an increased proportion of enteroendocrine cells, indicates disrupted epithelial regeneration and functional differentiation. These changes correlated with disrupted WNT signaling and stem cell maintenance genes (e.g., Lgr5, Smoc2, Axin2) and activation of inflammatory and hypoxia-related pathways (e.g., Il6, Tnf). The epigenetic regulator Ezh2 was identified as a critical factor in maintaining LGR5+ ISCs and epithelial homeostasis. Knockdown of Ezh2 reduced stemness and proliferation-related gene expression and exacerbated inflammation. Reactivation of WNT signaling restored Ezh2 and Lgr5 expression, improving intestinal regeneration. ConclusionThis study reveals dynamic transcriptomic, epigenetic, and spatial changes in NEC and highlights Ezh2 as a key regulator of LGR5+ intestinal stem cell function and epithelial regeneration. These findings provide insights into NEC pathogenesis and a basis for therapies targeting Ezh2 and WNT signaling to restore intestinal integrity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/647851v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1ad89eaorg.highwire.dtl.DTLVardef@396282org.highwire.dtl.DTLVardef@172596forg.highwire.dtl.DTLVardef@4daf28_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2024

NPmatch: Latent Batch Effects Correction of Omics data by Nearest-Pair Matching

MotivationBatch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPmatch, a nearest-neighbor matching-based method that adjusts BEs satisfactorily and outperforms current methods in a wide range of datasets. ResultsWe assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPmatch demonstrates overall superior performance in correcting for BEs while preserving biological differences than existing methods. Altogether, our method proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. Data availability and implementationNPmatch is freely available on Github (https://github.com/bigomics/NPmatch) and on Omics Playground (https://bigomics.ch/omics-playground). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus (GEO) repository.

bioinformatics↗

bioRxiv2023

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kolliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled [~]1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this studys translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

neuroscience↗

bioRxiv2021

The origins and functional effects of postzygotic mutations throughout the human lifespan

Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remains unclear. To study the origins and functional consequences of PZMs, we generated a multi-tissue atlas of PZMs from 948 donors using the final major release of the Genotype-Tissue Expression (GTEx) project. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, while 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we find that their type and predicted functional impact varies during prenatal development, across tissues, and the germ cell lifecycle. Remarkably, a class of prenatal mutations was predicted to be more deleterious than any other category of genetic variation investigated and under positive selection as strong as somatic mutations in cancers. In total, the data indicate that PZMs can contribute to phenotypic variation throughout the human lifespan, and, to better understand the relationship between genotype and phenotype, we must broaden the long-held assumption of one genome per individual to multiple, dynamic genomes per individual. One-Sentence SummaryThe predicted rates, functional effects and selection pressure of postzygotic mutations vary through the human lifecycle.

genomics↗

bioRxiv2021

Escape from X-inactivation in twins exhibits intra- and inter-individual variability across tissues and is heritable

X-chromosome inactivation (XCI) silences one X-chromosome in female cells to balance sex-differences in X-dosage. A subset of X-linked genes escape XCI, but the extent to which this phenomenon occurs and how it varies across tissues and in a population is as yet unclear. In order to characterize the incidence and variability of escape across individuals and tissues, we conducted a large scale transcriptomic study of XCI escape in adipose, skin, lymphoblastoid cell lines (LCLs) and immune cells in 248 twins drawn from a healthy population cohort. We identify 159 X-linked genes with detectable escape, of which 54 genes, including 19 lncRNAs, were not previously known to escape XCI. Across tissues we find a range of tissue-specificity, with 11% of genes escaping XCI constitutively across tissues and 24% demonstrating tissue-restricted escape, including genes with cell-type specific escape between immune cell types (B, T-CD4+, T-CD8+ and NK cells) of the same individual. Escape genes interact with autosomal-encoded proteins and are involved in varied biological processes such as gene regulation. We find substantial variability in escape between individuals. 49% of genes show inter-individual variability in escape, indicating escape from XCI is an under-appreciated source of gene expression differences. We utilized twin models to investigate the role of genetics in variable escape. Overall, monozygotic (MZ) twin pairs share more similar escape than dizygotic twin pairs, indicating that genetic factors underlie differences in escape across individuals. However, we also identify instances of discordant XCI within MZ co-twin pairs, suggesting that environmental factors also influence escape. Thus, XCI escape may be shaped by an interplay of genetic factors with tissue- and cell type-specificity, and environment. These results illuminate an intricate phenotype whose characterization aids understanding the basis of variable trait expressivity in females.

genetics↗

bioRxiv2019

Heritability of skewed X-inactivation in female twins is tissue-specific and dependent on age

To balance the X-linked transcriptional dosages between the sexes, one of the two X-chromosomes is randomly selected to be inactivated in somatic tissues of female placental mammals. Non-random, or skewed X-chromosome inactivation (XCI) toward one parental X has been observed in female somatic tissues, and this skewing effect has been associated with several complex human traits. However, the extent of the influence of genetic and environmental factors on XCI skewing is largely unknown. Here, we use RNA-seq and DNA-seq data taken from a large cohort of female twins to quantify the degree of skewing of XCI (DS) in multiple tissues and to study the relationship of XCI with age, genetic factors and complex traits. We show that the XCI patterns are highly tissue-specific with a higher prevalence of skewed XCI in blood-derived tissues than in fat or skin tissue. We also show that the DS in blood-derived tissues is associated with age and that the acquired DS occurs uniquely in blood-derived tissues with an inflection point at approximately 55 years of age. Heritability analysis indicates that the heritability of DS is both age and tissue specific; DS is heritable in blood tissue of females >55 years-old (h2 = 0.34) but is not heritable in blood tissues of females <55 years-old (h2 = 0), nor in skin and fat tissues at any age. We find a positive association between the DS and smoking status in blood tissues of older females (P = 0.02). The high tissue specificity of XCI patterns in human indicates the existence of tissue-specific mechanisms influencing XCI patterns, including genetic and environmental factors. We conclude that the heritability of XCI skewing in blood-derived tissues is dependent on age, representing a Gene x Age interaction that can shift the functional allelic dosage of an entire chromosome in a tissue-restricted manner.

genetics↗