Biology subjects

Salvatore, M.

Publications and source records attributed to Salvatore, M..

18 recordsLinked to original sources

bioRxiv2026

Mapping active cis-regulatory elements from transcription initiation events

Determining the activity of cis-regulatory elements (CREs) is essential for modeling gene regulation and interpreting genetic variation. Yet, current methods often lack the specificity to distinguish active regulation from permissive chromatin, the sensitivity to detect unstable enhancer RNAs, or the scalability required to profile limited input material and primary cells. Here, we introduce nucCAGE, a transcription start site (TSS) assay for profiling nuclear, capped RNAs, and PRIME, a computational framework for identifying active CREs from TSS data. Together, these methods increase sensitivity to low-abundance RNAs and enable robust detection of active regulatory elements across diverse contexts. Across multiple orthogonal functional and genetic benchmarks, including fine-mapped eQTLs, ClinVar variants, GWAS loci, and CRISPRi-tested elements, nucCAGE-derived PRIME predictions achieve superior recall compared to state-of-the-art methods while maintaining strong enrichment for phenotype-associated variation. Applying PRIME to the FANTOM5 dataset yields a comprehensive, cell-type-resolved atlas of active CREs that recapitulates known tissue-trait relationships. We demonstrate how this atlas can be used to nominate causal noncoding variants, linking immune-cell enhancer regulation of SMAD3 to asthma and NCOR2 to premature separation of placenta. Together, nucCAGE and PRIME provide a framework for high-sensitivity genome-wide discovery of active CREs and a resource for variant-to-function studies.

genomics↗

bioRxiv2024

Comparative Analysis of Stain-Free Fourier Ptychographic Microscopy and Traditional Histopathological Light Microscopy in Renal Membranous Nephropathy

BackgroundHistology remains a cornerstone in the diagnosis and prognosis of renal diseases, with histopathological analysis of kidney tissue being crucial for understanding renal pathophysiology. The availability of multiple stained sections is essential for conducting a comprehensive histopathological analysis and achieving an accurate diagnosis. Recently, Fourier Ptychographic Microscopy (FPM) earned a spot among the most promising microscopy techniques. The ability to provide high-resolution, quantitative phase-contrast images over a wide area, particularly in a stain-free mode, makes FPM highly appealing to experts in histopathology. Since renal pathologies are characterized by subtle morphological changes encoded in tissue slides, phase maps obtained using FPM are well-suited for providing detailed, high-contrast images of tissue structures. Thus, FPM provides a quantitative imaging tool that can be descriptive of the sample and/or expressive of the disease. MethodsIn this study, we explore FPM capability to image pathological kidney tissue, enabling pathologists to select regions of interest within the intricate architecture of renal tissue and zoom in to observe minute submicron structures, ranging from overall tissue organization and glomeruli distribution to individual cell membranes. Attention is focused on membranous glomerulonephritis since it is a nephropathy highly dependent on histological examination. ResultsThe comparative analysis between FPM and traditional light microscopy showed a difference in thickness of glomerular basal membranes between healthy kidney tissues and those affected by membranous glomerulonephritis (MG). Moreover, the results reported in our investigation revealed better glomerular membranes contrast in FPM images with respect to the H&E-stained images. ConclusionsOur study shows the broad potential of FPM in characterizing hallmarks of MG disease even in stain-free tissue slides.

pathology↗

bioRxiv2023

Using LLM Models and Explainable ML to Analyse Biomarkers at Single Cell Level for Improved Understanding of Diseases

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell types disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approachs efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

bioinformatics↗

bioRxiv2023

Interferon epsilon restricts Zika virus infection in the female reproductive tract

Interferon {varepsilon} (IFN{varepsilon}) is a unique type I IFN that has been implicated in host defense against sexually transmitted infections (STIs). Zika virus (ZIKV), an emerging pathogen, can infect the female reproductive tract (FRT) and cause devastating diseases, particularly in pregnant women. How IFN{varepsilon} contributes to protection against ZIKV infection in vivo is unknown. Here, we show that IFN{varepsilon} plays a critical role in host protection against vaginal ZIKV infection in mice. We found that IFN{varepsilon} was expressed not only by epithelial cells in the FRT, but also by certain immune and other cells at baseline or after exposure to viruses or specific TLR agonists. IFN{varepsilon}-deficient mice exhibited abnormalities in the epithelial border and underlying tissue in the cervicovaginal tract, and these defects were associated with increased susceptibility to vaginal, but not subcutaneous ZIKV infection. IFN{varepsilon}-deficiency resulted in an increase in magnitude, duration, and depth of ZIKV infection in the FRT. Critically, intravaginal administration of recombinant IFN{varepsilon} protected Ifn{varepsilon}-/- mice and highly susceptible Ifnar1-/-mice against vaginal ZIKV infection, indicating that IFN{varepsilon} was sufficient to provide protection even in the absence of signals from other type I IFNs and in an IFNAR1-independent manner. Our findings reveal a potentially critical role for IFN{varepsilon} in mediating protection against transmission of ZIKV in the context of sexual contact. SignificanceInterferon {varepsilon} (IFN{varepsilon}), a unique Type I IFN that is highly expressed in the epithelium of the female reproductive tract (FRT), is thought to protect the host against sexually transmitted infections (STIs) but the mechanism of action is not defined. Zika virus (ZIKV), a causative agent for preterm birth and other severe diseases in pregnant women, can be spread through vaginal transmission. Here, we show that mice lacking the Ifn{varepsilon} gene have abnormal epithelial development and tissue architecture in the cervicovaginal tract. The role of IFN{varepsilon} in protecting host against ZIKV is FRT-specific and is independent of IFNAR1 signaling. Our findings suggest potential preventive strategies based on harnessing mucosal immunity against STIs.

immunology↗

bioRxiv2022

Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning

AO_SCPLOWBSTRACTC_SCPLOWUnraveling sequence determinants which drive protein-RNA interaction is crucial for studying binding mechanisms and the impact of genomic variants. While CLIP-seq allows for transcriptome-wide profiling of in vivo protein-RNA interactions, it is limited to expressed transcripts, requiring computational imputation of missing binding information. Existing classification-based methods predict binding with low resolution and depend on prior labeling of transcriptome regions for training. We present RBPNet, a novel deep learning method, which predicts CLIP crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. CLIP-seq suffers from various technical biases, complicating downstream interpretation. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences corresponding to known binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves inference of protein-RNA interaction, as well as mechanistic interpretation of predictions.

bioinformatics↗

bioRxiv2022

Transfer learning reveals sequence determinants of regulatory element accessibility

Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.

bioinformatics↗

bioRxiv2022

Reduced inflammatory response and promoted multiciliated cell differentiation in mice protected by defective interfering influenza virus

Influenza defective interfering (DI) viruses have long been considered promising antiviral candidates because of their ability to interfere with replication-competent viruses and to induce antiviral immunity. However, the mechanisms underlying DI-mediated antiviral immunity have not been extensively explored. Here, we demonstrated interferon (IFN) independent protection conferred by influenza DI virus against homologous virus infection in mice deficient in type I and III IFN signaling. By integrating transcriptional and post-transcriptional regulatory data we identified unique host signatures in response to DI co-infection. DI-treated mice exhibited reduced viral transcription, less intense inflammatory and innate immune responses, and primed multiciliated cell differentiation in their lungs at an early stage of infection, even in the absence of type I or III IFNs. Overall, our study provides mechanistic insight into the protection mediated by DIs, implying a unifying theme involving inflammation and multiciliogenesis in maintaining respiratory homeostasis, and reveals their IFN-independent antiviral activity.

microbiology↗

bioRxiv2022

Identifying interactions in omics data for clinical biomarker discovery

The identification of predictive biomarker signatures from omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability. Here, we present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics data sets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms. Due to their high performance, simplicity and explicit functional form, these biomarker signatures can be readily explained, thereby making them attractive tools for high-stakes applications in primary care, clinical decision making and patient stratification.

bioinformatics↗

bioRxiv2021

A metagenomic DNA sequencing assay that is robust against environmental DNA contamination

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.

genomics↗

bioRxiv2021

Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

Genetic and environmental exposures cause variability in gene expression. Although most genes are affected in a population, their effect sizes vary greatly, indicating the existence of regulatory mechanisms that could amplify or attenuate expression variability. Here, we investigate the relationship between the sequence and transcription start site architectures of promoters and their expression variability across human individuals. We find that expression variability can be largely explained by a promoters DNA sequence and its binding sites for specific transcription factors. We show that promoter expression variability reflects the biological process of a gene, demonstrating a selective trade-off between stability for metabolic genes and plasticity for responsive genes and those involved in signaling. Promoters with a rigid transcription start site architecture are more prone to have variable expression and to be associated with genetic variants with large effect sizes, while a flexible usage of transcription start sites within a promoter attenuates expression variability and limits genotypic effects. Our work provides insights into the variable nature of responsive genes and reveals a novel mechanism for supplying transcriptional and mutational robustness to essential genes through multiple transcription start site regions within a promoter.

genomics↗

bioRxiv2021

Endogenous retroviruses co-opted into divergently transcribed regulatory elements shape the regulatory landscape of embryonic stem cells

Transposable elements are an abundant source of transcription factor binding sites and favorable genomic integration may lead to their recruitment by the host genome for gene regulatory functions. However, it is unclear how frequent co-option of transposable elements as regulatory elements is, to which regulatory programs they contribute and how they compare to regulatory elements devoid of transposable elements. Here, we report a transcription initiation-centric, in-depth characterization of the transposon-derived regulatory landscape of mouse embryonic stem cells. We demonstrate that a substantial number of transposable elements, in particular endogenous retroviral elements, carry open chromatin regions that are divergently transcribed into unstable RNAs in a cell-type specific manner, and that these elements contribute to a sizable proportion of active enhancers and gene promoters. We further show that transposon subfamilies contribute differently and distinctly to the pluripotency regulatory program through their repertoires of transcription factor binding sites, shedding light on the formation of regulatory programs and the origins of regulatory elements.

genomics↗

bioRxiv2021

Prophylactic protection against respiratory viruses conferred by a prototype live attenuated influenza virus vaccine.

The influenza A non-structural protein 1 (NS1) is known for its ability to hinder the synthesis of type I interferon (IFN) during viral infection. Influenza viruses lacking NS1 ({Delta}NS1) are under clinical development as live attenuated human influenza virus vaccines and induce potent influenza virus-specific humoral and cellular adaptive immune responses. Attenuation of {Delta}NS1 influenza viruses is due to their high IFN inducing properties, that limit their replication in vivo. This study demonstrates that pre-treatment with a {Delta}NS1 virus results in an immediate antiviral state which prevents subsequent replication of homologous and heterologous viruses, preventing disease from virus respiratory pathogens, including SARS-CoV-2. Our studies suggest that {Delta}NS1 influenza viruses could be used for the prophylaxis of influenza, SARS-CoV-2 and other human respiratory viral infections, and that an influenza virus vaccine based on {Delta}NS1 live attenuated viruses would confer broad protection against influenza virus infection from the moment of administration, first by non-specific innate immune induction, followed by specific adaptive immunity.

microbiology↗

bioRxiv2021

Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

microbiology↗

bioRxiv2020

Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

molecular biology↗

bioRxiv2020

Cancer PRSweb - an Online Repository with Polygenic Risk Scores (PRS) for Major Cancer Traits and Their Phenome-wide Exploration in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRS) research, we created an extensive PRS online repository for 49 common cancer traits integrating freely available genome-wide association studies (GWAS) summary statistics from three sources: published GWAS, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWAS. Our framework condenses these summary statistics into PRS using various approaches such as linkage disequilibrium pruning / p-value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRS in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance, calibration, and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRS. We expect this integrated platform to accelerate PRS-related cancer research.

genetics↗

bioRxiv2019

Microbial composition of the human nasopharynx varies according to influenza virus type and vaccination status.

Factors that contribute to enhanced susceptibility to severe bacterial disease after influenza infection are not well defined, but likely include the microbiome of the respiratory tract. Vaccination against influenza, while having variable effectiveness, could also play a role in microbial community stability. We collected nasopharyngeal samples from 215 individuals infected with influenza A/H3N2 or influenza B and profiled the microbiota by target sequencing of the 16S rRNA gene. We identified signature taxonomic groups by performing linear discriminant analysis and effective size comparisons (LEfSe) and defined bacterial community types using Dirichlet Multinomial Mixture (DMM) models. Influenza infection was shown to be significantly associated with microbial composition of the nasopharynx according to the virus type and the vaccination status of the patient. We identified 4 microbial community types across the combined cohort of influenza patients and healthy individuals with one community type most representative of the influenza-infected group. We also identified microbial taxa for which relative abundance was significantly higher in the unvaccinated elderly group; these taxa include species known to be associated with pneumonia.\n\nImportanceOur results suggest that there is a significant association between the composition of the microbiota in the nasopharynx and the influenza virus type causing the infection. We observe that vaccination status, especially in more senior individuals, also has an association with the microbial community profile. This indicates that vaccination against influenza, even when ineffective to prevent disease, could play a role in controlling secondary bacterial complications.

microbiology↗

bioRxiv2019

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state of art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria and chloroplasts or other plastids.\n\nBy examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, i.e. the one following the initial methionine, has a strong influence on the classification. When subsequently examining all targeting peptides, we observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position two, but only 20% of other plant proteins. Further highlighting the importance of the second residue, we also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide.\n\nTargetP 2.0 is available at http://www.cbs.dtu.dk/services/TargetP-2.0/index.php

bioinformatics↗

bioRxiv2018

Exploring Various Polygenic Risk Scores for Basal Cell Carcinoma, Cutaneous Squamous Cell Carcinoma and Melanoma in the Phenomes of the Michigan Genomics Initiative and the UK Biobank

Polygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.\n\nAuthor summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patients risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

genetics↗