Biology subjects

Pinello, L.

Publications and source records attributed to Pinello, L..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Gene editing without ex vivo culture evades genotoxicity in human hematopoietic stem cells

Gene editing the BCL11A erythroid enhancer is a validated approach to fetal hemoglobin (HbF) induction for {beta}-hemoglobinopathy therapy, though heterogeneity in edit allele distribution and HbF response may impact its safety and efficacy. Here we compared combined CRISPR-Cas9 endonuclease editing of the BCL11A +58 and +55 enhancers with leading gene modification approaches under clinical investigation. We found that combined targeting of the BCL11A +58 and +55 enhancers with 3xNLS-SpCas9 and two sgRNAs resulted in superior HbF induction, including in engrafting erythroid cells from sickle cell disease (SCD) patient xenografts, attributable to simultaneous disruption of core half E-box/GATA motifs at both enhancers. We corroborated prior observations that double strand breaks (DSBs) could produce unintended on- target outcomes in hematopoietic stem and progenitor cells (HSPCs) such as long deletions and centromere-distal chromosome fragment loss. We show these unintended outcomes are a byproduct of cellular proliferation stimulated by ex vivo culture. Editing HSPCs without cytokine culture bypassed long deletion and micronuclei formation while preserving efficient on-target editing and engraftment function. These results indicate that nuclease editing of quiescent hematopoietic stem cells (HSCs) limits DSB genotoxicity while maintaining therapeutic potency and encourages efforts for in vivo delivery of nucleases to HSCs.

genetics↗

bioRxiv2023

Novel mechanism of MYC deregulation in Multiple Myeloma

MYC deregulation occurs in 67% of multiple myeloma (MM) cases and associates with progression and worse prognosis in MM. Enhanced MYC expression is known to be driven by translocation or amplification events, but it only occurs in 40% of MM patients. Here, we describe a new mechanism of MYC regulation, whereby epigenetic regulation of MYC by increased accessibility of a cell-type specific enhancer leads to increased MYC expression. We found enhancer activity does not associate with enhancer hijacking events. We identified specific binding of c-MAF, IRF4, and SPIB transcription factors to the enhancer can activate MYC. In addition, we discovered focal amplification of this specific enhancer in approximately 4% of MM patients. Together, our findings define a new epigenetic mechanism of MYC deregulation in MM beyond known translocations or amplifications and point to the importance of non-coding regulatory elements and their associated transcription factor networks as drivers of MM progression.

cancer biology↗

bioRxiv2023

A molecular glue approach to control the half-life of CRISPR-based technologies

Cas9 is a programmable nuclease that has furnished transformative technologies, including base editors and transcription modulators (e.g., CRISPRi/a), but several applications of these technologies, including therapeutics, mandatorily require precision control of their half-life. For example, such control can help avert any potential immunological and adverse events in clinical trials. Current genome editing technologies to control the half-life of Cas9 are slow, have lower activity, involve fusion of large response elements (> 230 amino acids), utilize expensive controllers with poor pharmacological attributes, and cannot be implemented in vivo on several CRISPR-based technologies. We report a general platform for half-life control using the molecular glue, pomalidomide, that binds to a ubiquitin ligase complex and a response-element bearing CRISPR-based technology, thereby causing the latters rapid ubiquitination and degradation. Using pomalidomide, we were able to control the half-life of large CRISPR-based technologies (e.g., base editors, CRISPRi) and small anti-CRISPRs that inhibit such technologies, allowing us to build the first examples of on-switch for base editors. The ability to switch on, fine-tune and switch-off CRISPR-based technologies with pomalidomide allowed complete control over their activity, specificity, and genome editing outcome. Importantly, the miniature size of the response element and favorable pharmacological attributes of the drug pomalidomide allowed control of activity of base editor in vivo using AAV as the delivery vehicle. These studies provide methods and reagents to precisely control the dosage and half-life of CRISPR-based technologies, propelling their therapeutic development.

bioengineering↗

bioRxiv2022

Integrative dissection of gene regulatory elements at base resolution

Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here we combine epigenetic perturbations, base editing, and deep learning models to dissect regulatory sequences within the exemplar immune locus encoding CD69. Focusing on a differentially accessible and acetylated upstream enhancer, we find that the complementary strategies converge on a [~]170 base interval as critical for CD69 induction in stimulated Jurkat T cells. We pinpoint individual cytosine to thymine base edits that markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on binding competition between the transcriptional activator GATA3 and the repressor BHLHE40. Systematic analysis of GATA and bHLH/Ebox motifs suggests that interplay between these factors plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing gene regulatory elements in their endogenous chromatin contexts and identifying operative artificial variants. HighlightsO_LIBase editing screens and deep learning pinpoint sequences and single bases affecting immune gene expression C_LIO_LIAn artificial C-to-T variant in a regulatory element suppresses CD69 expression by altering the balance of transcription factor binding C_LIO_LICompetition between GATA3 and BHLHE40 regulates inducible immune genes and T cell states C_LI

molecular biology↗

bioRxiv2022

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference: dynamic rewiring, causal inference, feedback-loop modeling, and context specificity. To address them, we develop Dictys, a dynamic GRN inference and analysis method which leverages multi-omic single-cell assays of chromatin accessibility and gene expression, context specific transcription factor (TF) footprinting, stochastic process network, and efficient probabilistic modeling of scRNA-seq read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context specific and dynamic GRNs across developmental contexts. Dictys network analyses recover unique insights in human blood and mouse skin development with cell-type specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver TFs and their regulated targets. Dictys is available as a free, open source, and user-friendly Python package.

bioinformatics↗

bioRxiv2022

Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data

Single-cell RNA Velocity has dramatically advanced our ability to model cellular differentiation and cell fate decisions. However, current preprocessing choices and model assumptions often lead to errors in assigning developmental trajectories. Here, we develop, Pyro-Velocity, a Bayesian, generative, and multivariate RNA Velocity model to estimate the uncertainty of cell future states. This approach models raw sequencing counts with the synchronized cell time across all expressed genes to provide quantifiable and improved information on cell fate choices and developmental trajectory dynamics.

bioinformatics↗

bioRxiv2021

SIMBA: SIngle-cell eMBedding Along with features

Recent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements. Currently, most single-cell analysis pipelines are cluster-centric and cannot explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities. SIMBA is implemented as an efficient, comprehensive, and extensible Python library (https://simba-bio.readthedocs.io) for the analysis of single-cell omics data using graph embedding.

bioinformatics↗

bioRxiv2021

Human genetic diversity modifies therapeutic gene editing off-target potential

CRISPR gene editing holds great promise to modify somatic genomes to ameliorate disease. In silico prediction of homologous sites coupled with biochemical evaluation of possible genomic off-targets may predict genotoxicity risk of individual gene editing reagents. However, standard computational and biochemical methods focus on reference genomes and do not consider the impact of genetic diversity on off-target potential. Here we developed a web application called CRISPRme that explicitly and efficiently integrates human genetic variant datasets with orthogonal genomic annotations to nominate and prioritize off-target sites at scale. The method considers both single-nucleotide variants (SNVs) and indels, accounts for bona fide haplotypes, accepts spacer:protospacer mismatches and bulges, and is suitable for personal genome analyses. We tested the tool with a guide RNA (gRNA) targeting the BCL11A erythroid enhancer that has shown therapeutic promise in clinical trials for sickle cell disease (SCD) and {beta}-thalassemia1. We find that the top candidate off-target site is produced by a non-reference allele common in African-ancestry populations (rs114518452, minor allele frequency (MAF)=4.5%) that introduces a protospacer adjacent motif (PAM) for SpCas9. We validate that SpCas9 generates indels ([~]9.6% frequency) and chr2 pericentric inversions in a strictly allele-specific manner in edited CD34+ hematopoietic stem/progenitor cells (HSPCs), although a high-fidelity Cas9 variant mitigates this off-target. The CRISPRme tool highlights alternative allele-specific off-target editing as a prevalent risk of gRNAs considered for therapeutic gene editing. Our report illustrates how population and private genetic variants should be considered as modifiers of genome editing outcomes. We suggest that variant-aware off-target assessment should be considered in therapeutic genome editing efforts going forward and provide a powerful approach for comprehensive off-target nomination.

genetics↗

bioRxiv2021

Global-scale CRISPR gene editor specificity profiling by ONE-seq identifies population-specific, variant off-target effects

Defining off-target profiles of gene-editing nucleases and CRISPR base editors remains an important challenge for use of these technologies, therapeutic or otherwise. Existing methods can identify off-target sites induced by these gene editors on an individual genome but are not designed to account for the broad diversity of genomic sequence variation that exists within populations of humans or other organisms. Here we describe OligoNucleotide Enrichment and sequencing (ONE-seq), a novel in vitro method that leverages customizable, high-throughput DNA synthesis technology instead of purified genomic DNA (gDNA) from individual genomes to profile gene editor off-target sites. We show that ONE-seq matches or exceeds the sensitivity of existing single-genome methods for identifying bona fide CRISPR-Cas9 off-target sites in cultured human cells and in vivo in a liver-humanized mouse model. In addition, ONE-seq outperforms existing best-in-class single-genome methods for defining off-target sites of CRISPR-Cas12a nucleases, cytosine base editors (CBEs), and adenine base editors (ABEs), unveiling previously undescribed bona fide off-target sites for all these editors in human cells. Most importantly, we leveraged ONE-seq to generate the first experimentally-derived population-scale off-target profiles for Cas9 nucleases that define the impacts of sequence variants from >2,500 individual human genome sequences in the 1000 Genomes Project database. Notably, some of the variants we identified that lead to increased mutation frequencies at off-target sites are enriched in specific human populations. We validated that novel population-specific, variant-sensitive off-target sites nominated by ONE-seq in vitro can show increased frequencies of mutations in human lymphoblastoid cells (LCLs) harboring these sequence variants. Collectively, our results demonstrate that ONE-seq is a highly sensitive off-target nomination method that can uniquely be used to identify population subgroup-linked differences in off-target profiles of gene editors. ONE-seq provides an important new pathway by which to assess the impacts of global human genetic sequence diversity on the specificities of gene editors, thereby enabling a broader and more all-inclusive approach for profiling off-target effects of these transformative therapeutic technologies.

molecular biology↗

bioRxiv2021

GRAFIMO: variant and haplotype aware motif scanning on pangenome graphs

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO. Author summaryTranscription factors (TFs) are key regulatory proteins and mutations occurring in their binding sites can alter the normal transcriptional landscape of a cell and lead to disease states. Pangenome variation graphs (VGs) efficiently encode genomes from a population of individuals and their genetic variations. GRAFIMO is an open-source tool that extends the traditional PWM scanning procedure to VGs. By scanning for potential TBFS in VGs, GRAFIMO can simultaneously search thousands of genomes while accounting for SNPs, indels, and structural variants. GRAFIMO reports motif occurrences, their statistical significance, frequency, and location within the reference or alternative haplotypes in a given VG. GRAFIMO makes it possible to study how genetic variation affects the binding landscape of known TFs within a population of individuals.

bioinformatics↗

bioRxiv2020

ZNF410 represses fetal globin by devoted control of CHD4/NuRD

Major effectors of adult-stage fetal globin silencing include the transcription factors (TFs) BCL11A and ZBTB7A/LRF and the NuRD chromatin complex, although each has potential on-target liabilities for rational {beta}-hemoglobinopathy therapeutic inhibition. Here through CRISPR screening we discover ZNF410 to be a novel fetal hemoglobin (HbF) repressing TF. ZNF410 does not bind directly to the{gamma} -globin genes but rather its chromatin occupancy is solely concentrated at CHD4, encoding the NuRD nucleosome remodeler, itself required for HbF repression. CHD4 has two ZNF410-bound regulatory elements with 27 combined ZNF410 binding motifs constituting unparalleled genomic clusters. These elements completely account for ZNF410s effects on{gamma} -globin repression. Knockout of ZNF410 reduces CHD4 by 60%, enough to substantially de-repress HbF while avoiding the cellular toxicity of complete CHD4 loss. Mice with constitutive deficiency of the homolog Zfp410 are born at expected Mendelian ratios with unremarkable hematology. ZNF410 is dispensable for human hematopoietic engraftment potential and erythroid maturation unlike known HbF repressors. These studies identify a new rational target for HbF induction for the {beta}-hemoglobin disorders with a wide therapeutic index. More broadly, ZNF410 represents a special class of gene regulator, a conserved transcription factor with singular devotion to regulation of a chromatin subcomplex.

genetics↗

bioRxiv2020

Histone demethylome map reveals combinatorial gene regulatory functions in embryonic stem cells

Epigenetic regulators and transcription factors establish distinct regulatory networks for gene regulation to maintain the embryonic stem cell (ESC) state. Although much has been learned regarding individual epigenetic regulators, their combinatorial functions remain elusive. Here, we report previously unknown combinatorial functions of histone demethylases (HDMs) in gene regulation of mouse ESCs. Generation of a histone demethylome (HDMome) map of 20 well-characterized HDMs based on their genome-wide binding revealed co-occupancy of HDMs in different combinations: KDM1A-KDM4B-KDM6A and JARID2-KDM2B-KDM4A-KDM4C-KDM5B largely co-occupy at enhancers and promoters, respectively. Mechanistic studies uncover that KDM1A-KDM6A combinatorially modulates P300/H3K27ac, H3K4me2 deposition and OCT4 recruitment that directs the OCT4/CORE regulatory network for target gene expression; while co-operative actions of JARID2-KDM2B-KDM4A-KDM4C-KDM5B control H2AK119ub1 and bivalent marks of polycomb-repressive complexes that facilitate the PRC regulatory network for target gene repression. Thus, combinatorial functions of HDMs differentially impact gene expression programs in mESCs.

genomics↗

bioRxiv2020

singlecellVR: interactive visualization of single-cell data in virtual reality

Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, [~]$10). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.

genomics↗

bioRxiv2020

PrimeDesign software for rapid and simplified design of prime editing guide RNAs

Prime editing (PE) is a versatile genome editing technology, but design of the required guide RNAs is more complex than for standard CRISPR-based nucleases or base editors. Here we describe PrimeDesign, a user-friendly, end-to-end web application and command-line tool for the design of PE experiments. PrimeDesign can be used for single and combination editing applications, as well as genome-wide and saturation mutagenesis screens. Using PrimeDesign, we also constructed PrimeVar, the first comprehensive and searchable database for prime editing guide RNA (pegRNA) and nicking sgRNA (ngRNA) combinations to install or correct >68,500 pathogenic human genetic variants from the ClinVar database.

bioinformatics↗

bioRxiv2020

Mechanism of monoallelic expression and allelic rheostat role of DNA methylation

In diploid cells, maternal and paternal copies of genes usually have similar transcriptional activity. Mammalian allele-specific epigenetic mechanisms such as X-chromosome inactivation (XCI) and imprinting were historically viewed as rare exceptions to this rule. The discovery of mitotically stable monoallelic autosomal expression (MAE) a decade ago revealed an additional allele-specific mode regulating thousands of mammalian genes. However, despite its prevalence, the mechanistic basis of MAE remains unknown. To uncover the mechanism of MAE maintenance, we devised a small-molecule screen for reactivation of silenced alleles across multiple loci using targeted RNA sequencing. Contrary to previous reports, we identified DNA methylation as a key mechanism of MAE mitotic maintenance. In contrast with the binary choice of the active allele in XCI, stringent transcriptome-wide analysis revealed MAE as a regulatory mode with tunable control of allele-specific expression, dependent on the extent of DNA methylation. In a subset of MAE genes, allelic imbalance was insensitive to changes in DNA methylation, implicating additional mechanisms in MAE maintenance in these loci. Our findings identify a key mechanism of MAE maintenance, reveal tunability of this mode of gene regulation, and provide the essential platform for probing the biological role of MAE in development and disease.

genomics↗

bioRxiv2019

A saturating mutagenesis CRISPR-Cas9 mediated functional genomic screen identifies cis- and trans-regulatory elements of Oct4 in embryonic stem cells

Regulatory elements (REs) consist of enhancers and promoters that occupy a significant portion of the non-coding genome and control gene expression programs either in -cis or in - trans. Putative REs have been identified largely based on their regulatory features (co-occupancy of ESC-specific transcription factors, enhancer histone marks and DNase hypersensitivity) in mouse embryonic stem cells (mESCs). However, less has been established regarding their regulatory functions in their native context. We deployed cis- and trans-regulatory elements scanning through saturating mutagenesis and sequencing (ctSCAN-SMS) to target elements within the [~]12kb cis-region (Cis-REs; CREs) of the Oct4 gene locus, as well as genome-wide 2,613 high-confidence trans-REs (TREs), in mESCs. ctSCAN-SMS identified 10 CREs and 12 TREs, as novel candidate REs of the Oct4 gene in mESCs. Furthermore, deletions of these candidate REs confirmed that the majority of the REs are functionally active, and CREs are more active than TREs in controlling Oct4 gene expression. A subset of active CREs and TREs physically interact with the Oct4 promoter to varying degrees; specifically, a greater number of active CREs compared to active TREs, physically interact with the Oct4 promoter. Moreover, comparative genomics analysis reveals that more number of active CREs than active TREs are evolutionary conserved between mouse and primates, including human. Taken together, our study demonstrates the reliability and robustness of ctSCAN-SMS screening to identify critical REs, and investigate their roles in the regulation of transcriptional output of a target gene (in this case Oct4) in their native context.

genomics↗

bioRxiv2019

Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

Aging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

cell biology↗

bioRxiv2019

Assessment of computational methods for the analysis of single-cell ATAC-seq data

BackgroundRecent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans) lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (20-50% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level.\n\nResultsWe present a benchmarking framework that was applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were evaluated by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed.\n\nConclusionsThis reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC was the only method able to analyze a large dataset (> 80,000 cells).

bioinformatics↗