Biology subjects

Dehghannasiri, R.

Publications and source records attributed to Dehghannasiri, R..

13 recordsLinked to original sources

bioRxiv2025

RNase H1 counteracts DNA damage and ameliorates SMN-dependent phenotypes in a Drosophila model of Spinal Muscular Atrophy

Spinal Muscular Atrophy (SMA) is caused by a deficiency of the Survival Motor Neuron (SMN) protein. Mutations in SMN disrupt mRNA splicing and translation, leading to maladaptive changes in transcriptomes, proteomes, neuroinflammation, and metabolism, which drive motor neuron degeneration in SMA patients. Using a Drosophila SMA model, we found that systemic depletion of Smn leads to accumulation of RNA:DNA hybrids (R-loops), increased DNA damage, dysregulation of amino acids and sugar metabolism and activation of the innate immune response, recapitulating key pathological features reported in mammalian models and severe SMA patients. Persistent DNA damage in Smn-deficient flies alters cell proliferation rates in larval brains and induces extensive cell death in the developing eye. Importantly here, we show that stimulating the resolution of RNA:DNA hybrids with transgenic human RNAse H1 prevents the accumulation of DNA damage and attenuates the transcriptome and amino acid alterations induced by Smn depletion, mitigating the Smn-dependent cellular and developmental abnormalities, in Smn-deficient flies. Our data suggest that depletion of Smn causes an accumulation of aberrant transcripts and chronic DNA damage, which--along with the altered metabolomic profiles associated with Smn deficiency--trigger systemic inflammatory responses, ultimately affecting neuronal function and survival.

genetics↗

bioRxiv2024

sc-SPLASH provides ultra-efficient reference-free discovery in barcoded single-cell sequencing

Typical high-throughput single-cell RNA-sequencing (scRNA-seq) analyses are primarily conducted by (pseudo)alignment, through the lens of annotated gene models, and aimed at detecting differential gene expression. This misses diversity generated by other mechanisms that diversify the transcriptome such as splicing and V(D)J recombination, and is blind to sequences missing from imperfect reference genomes. Here, we present sc-SPLASH, a highly efficient pipeline that extends our SPLASH framework for statistics-first, reference-free discovery to barcoded scRNA-seq (10x Chromium) and spatial transcriptomics (10x Visium); we also provide its optimized module for preprocessing and k-mer counting in barcoded data, BKC, as a standalone tool. sc-SPLASH rediscovers known biology including V(D)J recombination and cell-type-specific alternative splicing in human and trans-splicing in tunicate (Ciona) and when applied to spatial datasets, detects sequence variation including tumor-specific somatic mutation. In sponge (Spongilla) and tunicate (Ciona), we uncover secreted repeat proteins expressed in immune-type cells and regulated during development; the sponge genes were absent from the reference assembly. sc-SPLASH provides a powerful alternative tool for exploring transcriptomes that is applicable to the breadth of lifes diversity.

bioinformatics↗

bioRxiv2023

NOMAD2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads

SPLASH is an unsupervised, reference-free, and unifying algorithm that discovers regulated sequence variation through statistical analysis of k-mer composition, subsuming many application-specific methods. Here, we introduce SPLASH2, a fast, scalable implementation of SPLASH based on an efficient k-mer counting approach. SPLASH2 enables rapid analysis of massive datasets from a wide range of sequencing technologies and biological contexts, delivering unparalleled scale and speed. The SPLASH2 algorithm unveils new biology (without tuning) in single-cell RNA-sequencing data from human muscle cells, as well as bulk RNA-seq from the entire Cancer Cell Line Encyclopedia (CCLE), including substantial unannotated alternative splicing in cancer transcriptome. The same untuned SPLASH2 algorithm recovers the BCR-ABL gene fusion, and detects circRNA sensitively and specifically, underscoring SPLASH2s unmatched precision and scalability across diverse RNA-seq detection tasks.

bioinformatics↗

bioRxiv2022

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Myriad mechanisms diversify the sequence content of eukaryotic transcripts at both the DNA and RNA levels, leading to profound functional consequences. Examples of this diversity include RNA splicing and V(D)J recombination. Currently, these mechanisms are detected using fragmented bioinformatic tools that require predefining a form of transcript diversification and rely on alignment to an incomplete reference genome, filtering out unaligned sequences, potentially crucial for novel discoveries. Here, we present SPLASH+, significantly advancing biological discovery possible with SPLASH, our recently introduced efficient, reference-free statistical approach. Integrating a micro-assembly and biological interpretation framework, SPLASH+ enables new discoveries including broad and novel examples of transcript diversification in single cells de novo, without the need for cell type metadata, which is impossible with current algorithms. Applied to 10,326 primary human single cells across 19 tissues profiled with SmartSeq2, SPLASH+ discovers a set of splicing and histone regulators with highly conserved intronic regions that are themselves subject to complex splicing regulation. Additionally, it reveals unreported transcript diversity in the heat shock protein HSP90AA1, as well as diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansion, all missed by existing methods. SPLASH+ is highly efficient, enabling the discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new automated paradigm of unbiased transcriptomic analysis.

bioinformatics↗

bioRxiv2022

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

genomics↗

bioRxiv2022

Mouse lemur transcriptomic atlas elucidates primate genes, physiology, disease, and evolution

Mouse lemurs (Microcebus spp.) are an emerging primate model organism. However, little is known about their genetics or cellular and molecular biology. In the accompanying paper, we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types, characterize their full transcriptomic profiles, and study evolution of primate cell types. Here we use the atlas to characterize mouse lemur genes, mutations, physiology, and disease. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions that globally define lemur gene structures and reveal over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system, comparing the global expression profiles of key immune genes in health and disease, and molecular mapping of immune cell development, trafficking, and their local and global activation to infection. We characterize primate/lemur-specific physiology and disease including molecular features of the immune program, of lemur adipocytes that exhibit dramatic seasonal rhythms, and of metastatic endometrial cancer that resembles the human malignancy. We identify and describe the expression patterns of over 400 primate genes missing in mice, many with similar expression patterns in human and lemur and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally-occurring nonsense (null) mutations in three primate genes missing in mice and analyzing their transcriptional phenotypes. This work establishes mouse lemur as a tractable primate model organism for genetic and molecular analysis, and it prioritizes primate genes, splice junctions, physiology, and disease for future study.

genetics↗

bioRxiv2022

Activated interstitial macrophages are a predominant target of viral takeover and focus of inflammation in COVID-19 initiation in human lung

Early stages of deadly respiratory diseases such as COVID-19 have been challenging to elucidate due to lack of an experimental system that recapitulates the cellular and structural complexity of the human lung while allowing precise control over disease initiation and systematic interrogation of molecular events at cellular resolution. Here we show healthy human lung slices cultured ex vivo can be productively infected with SARS-CoV-2, and the cellular tropism of the virus and its distinct and dynamic effects on host cell gene expression can be determined by single cell RNA sequencing and reconstruction of "infection pseudotime" for individual lung cell types. This revealed that the prominent SARS-CoV-2 target is a population of activated interstitial macrophages (IMs), which as infection proceeds accumulate thousands of viral RNA molecules per cell, comprising up to 60% of the cellular transcriptome and including canonical and novel subgenomic RNAs. During viral takeover of IMs, there is cell-autonomous induction of a pro-fibrotic program (TGFB1, SPP1), and an inflammatory program characterized by the early interferon response, chemokines (CCL2, 7, 8, 13, CXCL10) and cytokines (IL6, IL10), along with destruction of cellular architecture and formation of dense viral genomic RNA bodies revealed by super-resolution microscopy. In contrast, alveolar macrophages (AMs) showed neither viral takeover nor induction of a substantial inflammatory response, although both purified AMs and IMs supported production of infectious virions. Spike-dependent viral entry into AMs was neutralized by blockade of ACE2 or Sialoadhesin/CD169, whereas IM entry was neutralized only by DC-SIGN/CD209 blockade. These results provide a molecular characterization of the initiation of COVID-19 in human lung tissue, identify activated IMs as a prominent site of viral takeover and focus of inflammation and fibrosis, and suggest therapeutic targeting of the DC-SIGN/CD209 entry mechanism to prevent IM infection, destruction and early pathology in COVID-19 pneumonia. Our approach can be generalized to define the initiation program and evaluate therapeutics for any human lung infection at cellular resolution.

genomics↗

bioRxiv2021

ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

RNA processing (RNAP), including splicing and alternative polyadenylation, is crucial to gene function and regulation, but methods to detect RNAP from single-cell RNA sequencing data are limited by reliance on pre-existing annotations, peak-calling heuristics, and collapsing measurements by cell type. We introduce ReadZS, the first annotation-free statistical approach to identify regulated RNAP in single cells. ReadZS discovers cell type-specific RNAP in the human lung and conserved, developmentally regulated RNAP in mammalian spermatogenesis - including global 3 UTR shortening in human spermatogenesis. ReadZS also discovers global 3 UTR lengthening in Arabidopsis root development, highlighting the usefulness of this method in under-annotated transcriptomes.

bioinformatics↗

bioRxiv2021

RNA splicing programs define tissue compartments and cell types at single cell resolution

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

bioinformatics↗

bioRxiv2020

The SZS is an efficient statistical method to identify regulated splicing events in droplet-based RNA sequencing

To date, detecting robust single-cell-regulated splicing is viewed as out of reach from droplet based technologies such as 10x Chromium. This prevents the discovery of single-cell-regulated splicing in rare cell types or those that are difficult or impossible to sequence deeply. Here, we introduce a novel, robust, and computationally efficient set of statistics, the Splicing Z Score (SpliZ) and SpliZVD, to detect regulated splicing in single cell RNA-seq including 10x Chromium. The SpliZ(VD) provides annotation-free detection of differentially regulated, complex alternative splicing events. The SpliZ generalizes and increases statistical power compared to the Percent Spliced In (PSI) and mathematically reduces to PSI for simple exon-skipping. We applied the SpliZ to primary human lung cells to discover hundreds of genes with new regulated cell-type-specific splicing. The SpliZ has wide application to enable biological discovery of genes predicted to have functionally significant splicing programs including those regulated in development.

bioinformatics↗

bioRxiv2020

TGS1 controls snRNA 3' end processing, prevents neurodegeneration and ameliorates SMN-dependent neurological phenotypes in vivo

Trimethylguanosine synthase 1 (TGS1) is a highly conserved enzyme that converts the 5 mono-methylguanosine cap of snRNAs to a trimethylguanosine cap. Here, we show that loss of TGS1 in C. elegans, D. melanogaster and D. rerio results in neurological phenotypes similar to those caused by Survival Motor Neuron (SMN) deficiency. Importantly, expression of human TGS1 ameliorates the SMN-dependent neurological phenotypes in both flies and worms, revealing that TGS1 can partly counteract the effects of SMN deficiency. TGS1 loss in HeLa cells leads to the accumulation of immature U2 and U4atac snRNAs with long 3 tails that are often uridylated. snRNAs with defective 3 terminations also accumulate in Drosophila Tgs1 mutants. Consistent with defective snRNA maturation, TGS1 and SMN mutant cells also exhibit partially overlapping transcriptome alterations that include aberrantly spliced and readthrough transcripts. Together, these results identify a neuroprotective function for TGS1 and reinforce the view that defective snRNA maturation affects neuronal viability and function.

genetics↗

bioRxiv2020

Specific splice junction detection in single cells with SICILIAN

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIANs precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, increases agreement between biological replicates, and reliably detects un-annotated splicing in single cells, enabling the discovery of novel splicing regulation.

bioinformatics↗

bioRxiv2019

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Short AbstractThe extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function. Specific protein domains are enriched in DEEPEST calls, demonstrating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. DEEPEST also reveals a high enrichment for fusions involving known and novel oncogenes in diseases including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.\n\nSignificanceGene fusions are tumor-specific genomic aberrations and are among the most powerful biomarkers and drug targets in translational cancer biology. The advent of RNA-Seq technologies over the past decade has provided a unique opportunity for detecting novel fusions via deploying computational algorithms on public sequencing databases. Yet, precise fusion detection algorithms are still out of reach. We develop DEEPEST, a highly specific and efficient statistical pipeline specially designed for mining massive sequencing databases, and apply it to all 33 tumor types and 10,500 samples in The Cancer Genome Atlas database. We systematically profile the landscape of detected fusions via employing classic statistical models and identify several signatures of selection for fusions in tumors.\n\nSoftware availabilityDEEPEST-Fusion workflow with a detailed readme file is available as a Github repository: https://github.com/salzmanlab/DEEPEST-Fusion. In addition to the main workflow, which is based on CWL, example input and batch scripts (for job submission on local clusters), and codes for building the SBT files and SBT querying are provided in the repository. All custom scripts used for systematic analysis of fusions are also available in the same repository.

bioinformatics↗