Biology subjects

Hoffman, G.

Publications and source records attributed to Hoffman, G..

11 recordsLinked to original sources

bioRxiv2026

Cilia SubQ, a modular suite of pipelines for automated analysis of primary cilia and ciliary subdomains

Primary cilia are conserved, antenna-like organelles that protrude from the surface of most vertebrate cells. They function as specialized sensory compartments that detect extracellular cues and convert them into downstream signaling events essential for embryogenesis and tissue homeostasis. The cilium is physically separated from the cytoplasm and contains specialized subcompartments, including the basal body, the transition zone, and the ciliary tip, all of which are critical for its structure, function, and signaling output. Mutations in ciliary genes that disrupt these subcompartments can lead to a wide range of developmental disorders collectively known as ciliopathies. Analysis of these submicron ciliary domains is often time-consuming, repetitive, and prone to user bias. In addition, automated tools for subdomain analysis remain limited, requiring the development of novel, unbiased, and precise segmentation tools applicable to both healthy and pathological conditions. Here, we introduce Cilia SubQ, a versatile suite of flexible pipelines for ZEISS arivis Pro that enables segmentation of the primary cilium, pericentriolar material, basal body, transition zone, and ciliary tip, achieving an approximately eightfold reduction in analysis time with controlled manual intervention. These pipelines are built on our newly developed Cilia.AI, a machine-learning model that recognizes primary cilia with minimal manual correction. The suite also includes a validated script for generating kymographs to assess intraflagellar transport (IFT) dynamics in mammalian primary cilia. Cilia SubQ files and video tutorials are publicly available through the Open Science Framework (OSF) at https://osf.io/hm38f/. Together, the Cilia SubQ pipelines provide batch, high-throughput, and reproducible quantification of primary cilia and ciliary subdomains, delivering greater data output with reduced hand-on analysis.

cell biology↗

bioRxiv2025

Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery

MicroRNAs and RNA binding proteins are crucial elements of post-transcriptional gene regulation, which governs the fate of mRNA molecules in the cell. However, the landscape of these regulatory interactions, particularly across different mammalian cell types, remains underexplored. We describe REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation directly from RNA sequence. REPRESS was trained on AGO2-CLIP, miR-eCLIP and Degradome-Seq data profiling millions of microRNA binding and mRNA degradation sites across multiple cell types in human and mouse. It reveals biology that other state-of-the-art methods did not, such as identifying repressive non-canonical miRNA target sites and decoding the regulatory effects of sequence context and miRNA binding site multiplicity. REPRESS outperforms other advanced methods and neural architectures on a comprehensive suite of seven orthogonal tasks, including identifying genetic variants that affect microRNA binding, predicting out-of-distribution data from massively parallel reporter assays, and predicting canonical and non-canonical miRNA mediated repression. To demonstrate the general utility of REPRESS, we show that it provides insights into novel biology and the design of RNA therapeutics. Code is available at : https://github.com/deepgenomics/repress

genomics↗

bioRxiv2024

High-risk neuropsychiatric copy number variants are associated with convergent transcriptomic changes in human brain cells

Large, recurrent copy number variants (CNVs) are among the strongest risk factors for neuropsychiatric conditions, contributing to multiple phenotypes with overlapping psychiatric and cognitive symptoms. However, the molecular basis of this convergent risk remains unknown. We evaluated the human brain transcriptome in carriers of nine high-risk neuropsychiatric CNVs and matched non-carriers using single nucleus RNA-sequencing. Brain tissue from carriers displayed widespread disruptions of gene expression, with thousands of differentially expressed genes, mostly located outside of the respective CNV regions. There were greater changes in deletions compared to reciprocal duplications. Functional enrichment analysis revealed changes in mitochondrial energy metabolism and synaptic function that converged across CNVs and cell types. For mirror CNVs, the direction of effects was often reversed between deletions and duplications and showed correlation with CNV gene dosage. These findings suggest that a shared pathophysiology underlies risk for convergent brain phenotypes across CNVs and point toward promising therapeutic targets.

genetics↗

bioRxiv2024

Anchor-Enhanced Bead Design for Reduced Oligonucleotide Synthesis Errors in Single-cell sequencing

Single-cell transcriptomics, reliant on the incorporation of barcodes and unique molecular identifiers (UMIs) into captured polyA+ mRNA, faces a significant challenge due to synthesis errors in oligonucleotide capture sequences. These inaccuracies, which are especially problematic in long-read sequencing, impair the precise identification of sequences and result in inaccuracies in UMI deduplication. To mitigate this issue, we have modified the oligonucleotide capture design, which integrates an interposed anchor between the barcode and UMI, and a V base anchor adjacent to the polyA capture region. This configuration is devised to ensure compatibility with both short and long-read sequencing technologies, facilitating improved UMI recovery and enhanced feature detection, thereby improving the efficacy of droplet-based sequencing methods.

molecular biology↗

bioRxiv2023

Correcting PCR amplification errors in unique molecular identifiers to generate absolute numbers of sequencing molecules.

Unique Molecular Identifiers (UMIs) are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are the main source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing UMIs using homotrimeric nucleotide blocks provides an error correcting solution, that allows absolute counting of sequenced molecules.

genomics↗

bioRxiv2019

A genome-wide CRISPR screen identifies ZCCHC14 as a host factor required for hepatitis B surface antigen production

GRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/718940v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (45K):\norg.highwire.dtl.DTLVardef@1782e32org.highwire.dtl.DTLVardef@120f111org.highwire.dtl.DTLVardef@1b2ad50org.highwire.dtl.DTLVardef@1907bc1_HPS_FORMAT_FIGEXP M_FIG C_FIG SUMMARYA hallmark of chronic hepatitis B virus (CHB) infection is the presence of high circulating levels of non-infectious small lipid HBV surface antigen (HBsAg) vesicles. Although rare, sustained HBsAg loss is the idealized endpoint of any CHB therapy. A novel small molecule RG7834 has been previously reported to inhibit HBsAg expression by targeting terminal nucleotidyltransferase protein 4A and 4B (TENT4A and TENT4B). In this study, we describe a genome-wide CRISPR screen to identify other potential novel host factors required for HBsAg expression and to gain further insights into the mechanism of RG7834. We report more than 60 genes involved in regulating HBsAg and identified novel factors involved in RG7834 activity, including a zinc finger CCHC-type containing 14 (ZCCHC14) protein. We show that ZCCHC14, together with TENT4A/B, stabilizes HBsAg expression through HBV RNA tailing, providing a potential new therapeutic target to achieve functional cure in CHB patients.

microbiology↗

bioRxiv2019

Functional annotation of rare structural variation in the human brain

Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44x10-9) and regulatory SVs (enhancer p = 3.22x10-23, CTCF p = 3.86x10-18) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)--deletions and duplications--exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R2 = 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.

genomics↗

bioRxiv2018

Unperturbed Expression Bias of Imprinted Genes in Schizophrenia

How gene expression correlates with schizophrenia across individuals is beginning to be examined through analyses of RNA-seq from post-mortem brains of individuals with disease and control brains. Here we focus on variation in allele-specific expression, following up on the Common Mind Consortium (CMC) RNA-seq experiments of nearly 600 human dorsolateral prefrontal cortex (DLPFC) samples. Analyzing the extent of allelic expression bias--a hallmark of imprinting--we find that the number of imprinted human genes is consistent with lower estimates ({approx}0.5% of all genes) and thus contradicts much higher estimates. Moreover, the handful of putatively imprinted genes are all in close genomic proximity to known imprinted genes. Joint analysis of the imprinted genes across hundreds of individuals allowed us to establish how allelic bias depends on various factors. We find that age and genetic ancestry have gene-specific, differential effect on allelic bias. In contrast, allelic bias appears to be independent of schizophrenia.

genomics↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗

bioRxiv2017

Transcriptomic Imputation of Bipolar Disorder and Bipolar subtypes reveals 29 novel associated genes

Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to \"impute\" genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder.\n\nWe applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 individuals with subtype 1 (BD-I), 3,543 individuals with subtype 2 (BD-II), and 1,500 individuals with schizoaffective subtype (SAB).\n\nWe identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel; i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.

genetics↗

bioRxiv2017

Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissecting this signal into multiple independent eQTL for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (N=467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context specific (i.e. tissue, cell type, or developmental time point specific) regulation of gene expression. Integrating the Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC conditional eQTL data reveals forty loci with strong evidence for co-localization (posterior probability >0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes and identify novel genes for schizophrenia risk, and provide specific hypotheses for their functional follow-up.

genetics↗